From e7a4674ffe33818f830955e44bdb93c863e75076 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 14:25:20 -0500 Subject: [PATCH 001/156] update readme --- readme.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/readme.md b/readme.md index a67044d..ead99a2 100644 --- a/readme.md +++ b/readme.md @@ -2,6 +2,7 @@ This repository contains code for comparing models of human object learning against measurements of human behavior (n=371k trials). + # Installation We developed this repository using Python 3.9. Follow the steps below to use it: @@ -34,3 +35,7 @@ Once it is downloaded, unzip it (it should turn into an `images` folder). Move t To see how to view the raw behavioral data and/or score an example learning model, check out the examples in `examples/`. + +# Paper +This codebase originally accompanied the 2023 paper ["How well do rudimentary plasticity rules predict adult visual object learning?"](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011713). +Since that point, this codebase has been refactored and streamlined, introducing breaking changes to the API. However, to see the codebase that reflects its content at the time of publication, check out the `v1` tag. From 6c380b77714bff0ab3fc26fd39b7d6498480ce03 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 14:27:07 -0500 Subject: [PATCH 002/156] update readme 2 --- .idea/misc.xml | 3 +++ readme.md | 3 +-- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/.idea/misc.xml b/.idea/misc.xml index 32f1555..c7c3ad4 100644 --- a/.idea/misc.xml +++ b/.idea/misc.xml @@ -1,4 +1,7 @@ + + \ No newline at end of file diff --git a/readme.md b/readme.md index ead99a2..5532933 100644 --- a/readme.md +++ b/readme.md @@ -1,7 +1,6 @@ # Human object learning benchmarks -This repository contains code for comparing models of human object learning against measurements of human behavior (n=371k trials). - +This repository contains benchmarks for comparing models of object learning against psychometric measurements of human behavior. # Installation From 04f62aa559deb8bced77e7ddcf9b96ce721c8fef Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 14:27:37 -0500 Subject: [PATCH 003/156] Remove .idea from tracking --- .idea/hobj.iml | 13 ------------- .idea/inspectionProfiles/Project_Default.xml | 14 -------------- .idea/inspectionProfiles/profiles_settings.xml | 6 ------ .idea/misc.xml | 7 ------- .idea/modules.xml | 8 -------- .idea/other.xml | 6 ------ .idea/vcs.xml | 6 ------ 7 files changed, 60 deletions(-) delete mode 100644 .idea/hobj.iml delete mode 100644 .idea/inspectionProfiles/Project_Default.xml delete mode 100644 .idea/inspectionProfiles/profiles_settings.xml delete mode 100644 .idea/misc.xml delete mode 100644 .idea/modules.xml delete mode 100644 .idea/other.xml delete mode 100644 .idea/vcs.xml diff --git a/.idea/hobj.iml b/.idea/hobj.iml deleted file mode 100644 index f45935a..0000000 --- a/.idea/hobj.iml +++ /dev/null @@ -1,13 +0,0 @@ - - - - - - - - - - - - \ No newline at end of file diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml deleted file mode 100644 index 0c6e339..0000000 --- a/.idea/inspectionProfiles/Project_Default.xml +++ /dev/null @@ -1,14 +0,0 @@ - - - - \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml deleted file mode 100644 index 105ce2d..0000000 --- a/.idea/inspectionProfiles/profiles_settings.xml +++ /dev/null @@ -1,6 +0,0 @@ - - - - \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml deleted file mode 100644 index c7c3ad4..0000000 --- a/.idea/misc.xml +++ /dev/null @@ -1,7 +0,0 @@ - - - - - - \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml deleted file mode 100644 index d52717c..0000000 --- a/.idea/modules.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - \ No newline at end of file diff --git a/.idea/other.xml b/.idea/other.xml deleted file mode 100644 index 68993fb..0000000 --- a/.idea/other.xml +++ /dev/null @@ -1,6 +0,0 @@ - - - - - \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml deleted file mode 100644 index 94a25f7..0000000 --- a/.idea/vcs.xml +++ /dev/null @@ -1,6 +0,0 @@ - - - - - - \ No newline at end of file From 437bb9eedba6664f6bd57deec2170fd5981ff0d2 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 14:32:35 -0500 Subject: [PATCH 004/156] add CI --- .github/workflows/ci.yml | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 .github/workflows/ci.yml diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..bde7cae --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,25 @@ +name: CI + +on: + push: + paths: + # Changes to source code + - hobj/** + # Changes to workflows + - .github/workflows/ci.yml +jobs: + unit_tests: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.12' + cache: 'pip' + cache-dependency-path: setup.py # See https://github.com/actions/setup-python/blob/main/docs/advanced-usage.md#caching-packages + - name: Install hobj + run: pip3 install -e . + - name: Run pytests + run: pytest -s From b576b4591213b91d00290135cdbf163d2a442160 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 14:33:13 -0500 Subject: [PATCH 005/156] added status badge --- readme.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/readme.md b/readme.md index 5532933..6cb70fc 100644 --- a/readme.md +++ b/readme.md @@ -1,5 +1,7 @@ # Human object learning benchmarks +[![CI](https://github.com/himjl/hobj/actions/workflows/ci.yml/badge.svg)](https://github.com/himjl/hobj/actions/workflows/ci.yml) + This repository contains benchmarks for comparing models of object learning against psychometric measurements of human behavior. # Installation From ba561f8b4c69d32fe257989b1d2da9515686afdd Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 14:37:52 -0500 Subject: [PATCH 006/156] adjust requirements --- requirements.txt | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/requirements.txt b/requirements.txt index cbd745b..d3256c7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,4 @@ -pillow -url-normalize -matplotlib -requests -tqdm -scipy \ No newline at end of file +iniconfig==2.0.0 +packaging==24.2 +pluggy==1.5.0 +pytest==8.3.4 From ee2dbeba3b58191536af967da68813c4280b7348 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 14:38:12 -0500 Subject: [PATCH 007/156] adjust semver --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 830fcfe..560b317 100644 --- a/setup.py +++ b/setup.py @@ -10,7 +10,7 @@ print(find_packages()) setup( name="hobj", - version="0.1", + version="2.0.0", packages=find_packages(), install_requires=requirements, author="Michael J. Lee", From 7d55490fd9d029223a6b8f0f639c309776bfcec9 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 14:39:04 -0500 Subject: [PATCH 008/156] adjust setup.py --- setup.py | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/setup.py b/setup.py index 560b317..a5b6939 100644 --- a/setup.py +++ b/setup.py @@ -1,20 +1,22 @@ -from setuptools import setup, find_packages -import os +from setuptools import setup -if os.path.exists('requirements.txt'): - with open('requirements.txt', 'r') as fb: - requirements = fb.readlines() -else: - requirements = [] +# Function to read the requirements.txt file +def parse_requirements(filename): + with open(filename, 'r') as file: + return [line.strip() for line in file if line.strip() and not line.startswith('#')] + + +# Read dependencies from requirements.txt +requirements = parse_requirements('requirements.txt') -print(find_packages()) setup( name="hobj", version="2.0.0", - packages=find_packages(), + packages=['hobj'], install_requires=requirements, author="Michael J. Lee", author_email="mil@mit.edu", description="Human object learning benchmarks", keywords="", + python_requires='>=3.11', ) From b330b48b40a2342dc8a0e6ec3e736fd2df7b3944 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 14:40:07 -0500 Subject: [PATCH 009/156] adjust triggers --- .github/workflows/ci.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index bde7cae..ccf34cb 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -7,6 +7,9 @@ on: - hobj/** # Changes to workflows - .github/workflows/ci.yml + # Changes to setup.py and requirements.txt + - 'setup.py' + - 'requirements.txt' jobs: unit_tests: runs-on: ubuntu-latest From d2ec9a58d6217f95c0d3955013cbf69932415ccb Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 14:41:23 -0500 Subject: [PATCH 010/156] adjust readme --- hobj/config.py | 5 ----- readme.md | 4 ---- 2 files changed, 9 deletions(-) diff --git a/hobj/config.py b/hobj/config.py index 8179c15..7d73031 100644 --- a/hobj/config.py +++ b/hobj/config.py @@ -1,5 +1,4 @@ import os -import torch hlb_cachedir = os.path.join(os.path.expanduser('~'), 'hlb_cache') @@ -10,7 +9,3 @@ device = 'cpu' - -if torch.cuda.is_available(): - print("Using GPU") - device = 'cuda' diff --git a/readme.md b/readme.md index 6cb70fc..521aa37 100644 --- a/readme.md +++ b/readme.md @@ -19,10 +19,6 @@ pip install -e . ```bash conda install -c conda-forge xarray dask netCDF4 bottleneck ``` -5. Install [PyTorch](https://pytorch.org). If you are using a computer without a GPU, you can run the command: -```bash -conda install pytorch torchvision torchaudio -c pytorch -``` ### Download all images (recommended) From 60cba6091e088b5c8160c36be1e8f69a80d50928 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 14:41:53 -0500 Subject: [PATCH 011/156] add dummy test --- tests/dummy_test.py | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 tests/dummy_test.py diff --git a/tests/dummy_test.py b/tests/dummy_test.py new file mode 100644 index 0000000..603860f --- /dev/null +++ b/tests/dummy_test.py @@ -0,0 +1,2 @@ +def test_dummy(): + assert True \ No newline at end of file From 6e71170ab5aaa3c7ea595c4750b2cbd2bdd9db9e Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 14:46:26 -0500 Subject: [PATCH 012/156] numpy requirements and readme --- hobj/utils/file_io.py | 23 +++++++++++++---------- readme.md | 2 +- requirements.txt | 1 + 3 files changed, 15 insertions(+), 11 deletions(-) diff --git a/hobj/utils/file_io.py b/hobj/utils/file_io.py index 9b0e48a..2d9bbeb 100644 --- a/hobj/utils/file_io.py +++ b/hobj/utils/file_io.py @@ -11,10 +11,11 @@ import json -def get_canonical_url(url:str): - return url_normalize.url_normalize(url = url) +def get_canonical_url(url: str): + return url_normalize.url_normalize(url=url) -def get_local_save_location(url:str, cachedir:str): + +def get_local_save_location(url: str, cachedir: str): canonical_url = get_canonical_url(url) parsed = urlparse(canonical_url) @@ -22,24 +23,27 @@ def get_local_save_location(url:str, cachedir:str): if path.startswith('/'): path = path[1:] - domain = parsed.netloc # domain is the hostname + domain = parsed.netloc # domain is the hostname save_location = os.path.join(cachedir, domain, path) return save_location + def prepare_savepath(savepath): savedir = os.path.dirname(savepath) if not os.path.exists(savedir): os.makedirs(savedir, exist_ok=True) -def get_image_from_url(image_url:str): + +def get_image_from_url(image_url: str): response = requests.get(image_url) img = PIL.Image.open(BytesIO(response.content)) return img -def download_file(url:str, savepath=None,cachedir:str=None): + +def download_file(url: str, savepath=None, cachedir: str = None): if savepath is None: assert cachedir is not None - savepath = get_local_save_location(url = url, cachedir=cachedir) + savepath = get_local_save_location(url=url, cachedir=cachedir) prepare_savepath(savepath) @@ -63,8 +67,8 @@ def update_to(b=1, bsize=1, tsize=None): return update_to - with tqdm(desc = f'downloading {url}') as pbar: - urllib.request.urlretrieve(url = url, filename=savepath, reporthook=my_hook(pbar)) + with tqdm(desc=f'downloading {url}') as pbar: + urllib.request.urlretrieve(url=url, filename=savepath, reporthook=my_hook(pbar)) return savepath @@ -72,4 +76,3 @@ def load_json(json_path): with open(json_path, 'r') as fb: val = json.load(fb) return val - diff --git a/readme.md b/readme.md index 521aa37..7526238 100644 --- a/readme.md +++ b/readme.md @@ -15,7 +15,7 @@ We developed this repository using Python 3.9. Follow the steps below to use it: ```bash pip install -e . ``` -4. Then, install the following dependencies: +4. Then, install [xarray](https://docs.xarray.dev/en/latest/getting-started-guide/installing.html) and its dependencies by running the following command: ```bash conda install -c conda-forge xarray dask netCDF4 bottleneck ``` diff --git a/requirements.txt b/requirements.txt index d3256c7..ab75605 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,5 @@ iniconfig==2.0.0 +numpy==2.2.2 packaging==24.2 pluggy==1.5.0 pytest==8.3.4 From ba38bd88749185be7e2a821f640c789a3c98e80a Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 14:47:09 -0500 Subject: [PATCH 013/156] clean up setup.py file --- setup.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/setup.py b/setup.py index a5b6939..89ecb51 100644 --- a/setup.py +++ b/setup.py @@ -1,13 +1,8 @@ from setuptools import setup -# Function to read the requirements.txt file -def parse_requirements(filename): - with open(filename, 'r') as file: - return [line.strip() for line in file if line.strip() and not line.startswith('#')] - - # Read dependencies from requirements.txt -requirements = parse_requirements('requirements.txt') +with open('requirements.txt', 'r') as file: + requirements = [line.strip() for line in file if line.strip() and not line.startswith('#')] setup( name="hobj", From 8f78ce217ea6b263c09d5226da2ad750f5f112ac Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 14:47:57 -0500 Subject: [PATCH 014/156] removed unused state_space code --- hobj/utils/state_space/__init__.py | 0 hobj/utils/state_space/fit_bernoulli.py | 138 ------------------- hobj/utils/state_space/readme.md | 8 -- hobj/utils/state_space/src/__init__.py | 0 hobj/utils/state_space/src/backwardfilter.py | 33 ----- hobj/utils/state_space/src/em_bino.py | 49 ------- hobj/utils/state_space/src/forwardfilter.py | 72 ---------- hobj/utils/state_space/src/newtonsolve.py | 92 ------------- hobj/utils/state_space/src/pdistnv2.py | 40 ------ 9 files changed, 432 deletions(-) delete mode 100644 hobj/utils/state_space/__init__.py delete mode 100644 hobj/utils/state_space/fit_bernoulli.py delete mode 100644 hobj/utils/state_space/readme.md delete mode 100644 hobj/utils/state_space/src/__init__.py delete mode 100644 hobj/utils/state_space/src/backwardfilter.py delete mode 100644 hobj/utils/state_space/src/em_bino.py delete mode 100644 hobj/utils/state_space/src/forwardfilter.py delete mode 100644 hobj/utils/state_space/src/newtonsolve.py delete mode 100644 hobj/utils/state_space/src/pdistnv2.py diff --git a/hobj/utils/state_space/__init__.py b/hobj/utils/state_space/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/hobj/utils/state_space/fit_bernoulli.py b/hobj/utils/state_space/fit_bernoulli.py deleted file mode 100644 index e5ee6d3..0000000 --- a/hobj/utils/state_space/fit_bernoulli.py +++ /dev/null @@ -1,138 +0,0 @@ -# Adapted from code written by Anne Smith in 2003 from the Brown Lab at MIT -import numpy as np -from tqdm import tqdm -from PySS.src.forwardfilter import forwardfilter -from PySS.src.backwardfilter import backwardfilter -from PySS.src.em_bino import em_bino -from PySS.src.pdistnv2 import pdistnv2 - -def fit(Responses, - MaxResponse = 1, - chance_prior_type = 'fixed', - disable = True - ): - ''' - - :param Responses: sequence of 0s or 1s. dtype = list, np.array. shape = (1, N) - :param MaxResponse: number of possible correct per trial. Can be a vector. - :param chance_prior_type one of 'fixed', 'update', 'none', : dtype: str - :return: pmid, p025, p075 - ''' - - if chance_prior_type not in ['fixed', 'none', 'update']: - raise ValueError('Specify chance_prior_type of "fixed", "update", or "none". Gave: %s'%(str(chance_prior_type))) - - UpdaterFlagDict = {'fixed': 0, 'update': 1, 'none': 2, } - - - BackgroundProb = 0.5 - UpdaterFlag = UpdaterFlagDict[chance_prior_type] - - SigE = 0.5 # default variance of learning state process is sqrt(0.5) - - Responses, MaxResponse = _check_input(Responses, MaxResponse) - I = np.concatenate([Responses, MaxResponse * np.ones(Responses.shape)], axis=0) - - SigsqGuess = np.square(SigE) - - # set the value of mu from the chance of correct - mu = np.log(np.true_divide(BackgroundProb, 1 - BackgroundProb)) - - # convergence criterion for SIG_EPSILON^2 - CvgceCrit = 1e-5 - - ''' Start ''' - xguess = 0. - NumberSteps = 3000 - - newsigsq = [] - xnew1save = [] - for i in tqdm(range(NumberSteps), disable = disable): - p, x, s, xold, sold = forwardfilter(I, SigE, xguess, SigsqGuess, mu) - - # Compute the backward (smoothing algorithm) estimates of the learning - # state and its variance: x{k|K} and sigsq{k|K} - xnew, signewsq, A = backwardfilter(x, xold, s, sold) - # xnew: (1, ntrials+1) - # signewsq: (1, ntrials+1) - # A: (1, ntrials) - - if chance_prior_type == 'fixed': - # UpdaterFlag == 0 - xnew[0, 0] = 0 # fixes initial value to 50% - signewsq[0, 0] = np.square(SigE) - elif chance_prior_type == 'update': - # Dampens the initial value toward chance - xnew[0, 0] = 0.5 * xnew[0, 1] # updates the initial value of the latent process - signewsq[0, 0] = np.square(SigE) - elif chance_prior_type == 'none': - # UpdaterFlag == 2 - # Unconstrained initial value - xnew[0, 0] = xnew[0, 1] # x[0] = x[1] means no prior chance probability - signewsq[0, 0] = signewsq[0, 1] - - # Compute the EM estimate of the learning state process variance - newsigsq.append(em_bino(I, xnew, signewsq, A, UpdaterFlag)) - - xnew1save.append(xnew[0, 0]) - - # Check for convergence - if i > 0: - a1 = np.abs(newsigsq[i] - newsigsq[i-1]) - a2 = np.abs(xnew1save[i] - xnew1save[i-1]) - - if (a1 < CvgceCrit) and (a2 < CvgceCrit) and (UpdaterFlag >= 1): - if not disable: - print ('EM estimates of learning state process variance and start point converged after %d steps'%(i+1)) - break - elif (a1 < CvgceCrit) and (UpdaterFlag == 0): - if not disable: - print ('EM estimates of learning state process variance converged after %d steps'%(i+1)) - break - - SigE = np.sqrt(newsigsq[i]) - xguess = xnew[0, 0] - SigsqGuess = signewsq[0, 0] - - if i == (NumberSteps-1): - print( 'Failed to converge after %d steps; convergence criterion was %f'%(i+1, CvgceCrit)) - - # Use sampling to convert from state to a probability - - # Can used smoothed or filtered estimates - here used smoothed - - if chance_prior_type == 'fixed': - # UpdaterFlag == 0 - xnew[0, 0] = 0 # fixes initial value to 50% - - p025, p975, pmid, _, pcert = pdistnv2(xnew, signewsq, mu, BackgroundProb); - - # Remove the prior - p025 = p025[0, :-1] - p975 = p975[0, :-1] - pmid = pmid[0, :-1] - - return pmid, p025, p975 - -# Helper functions -def _check_input(Responses, MaxResponse): - - Responses = np.array(Responses) - if isinstance(MaxResponse, (np.ndarray, list)): - MaxResponse = np.array(MaxResponse) - if (MaxResponse.shape) == 1: - MaxResponse = MaxResponse[None, :] - if MaxResponse.shape[0] !=1: - MaxResponse = np.transpose(MaxResponse) - assert MaxResponse.shape[0] == 1 - - - - if len(Responses.shape) == 1: - Responses = Responses[None, :] - - if Responses.shape[0] != 1: - Responses = np.transpose(Responses) - - assert Responses.shape[0] == 1 - return Responses, MaxResponse diff --git a/hobj/utils/state_space/readme.md b/hobj/utils/state_space/readme.md deleted file mode 100644 index 0f09fcf..0000000 --- a/hobj/utils/state_space/readme.md +++ /dev/null @@ -1,8 +0,0 @@ -# PySS -Functions for computing the state space trajectory of a Bernoulli process. - -# References - -Smith, Anne C., and Emery N. Brown. "Estimating a state-space model from point process observations." Neural computation 15.5 (2003): 965-991. - -Smith, Anne C., et al. "Dynamic analysis of learning in behavioral experiments." Journal of Neuroscience 24.2 (2004): 447-461. diff --git a/hobj/utils/state_space/src/__init__.py b/hobj/utils/state_space/src/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/hobj/utils/state_space/src/backwardfilter.py b/hobj/utils/state_space/src/backwardfilter.py deleted file mode 100644 index 9e74f4c..0000000 --- a/hobj/utils/state_space/src/backwardfilter.py +++ /dev/null @@ -1,33 +0,0 @@ -import numpy as np - -def backwardfilter(x, xold, sigsq, sigsqold): - ''' - backwardfilter is a helper function that implements the backward filter - smoothing algorithm to estimate the learning state at trial k, given all - the data, as the Gaussian random variable with mean x{k|K} (xnew) and - SIG^2{k|K} (signewsq). - - :param x: (1 x (N(+1?)) - :param xold: (N+1). dtype = list - :param sigsq: (N+1). dtype = list - :param sigsqold: (N+1). dtype = list - :return: - ''' - - T = x.shape[1] # total number of posterior mode estimates (K + 1) - - # Initial conditions: use values of posterior mode and posterior variance - xnew = list(np.zeros(T-1)) + [x[0, -1]] - signewsq = list(np.zeros(T-1)) + [sigsq[-1]] - xnew = np.array(xnew)[None, :] - signewsq = np.array(signewsq)[None, :] - - A = np.zeros((1, T-1)) - for i in range(T-1, 1, -1): - A[0, i - 1] = np.true_divide(sigsq[i - 1], sigsqold[i]) - - xnew_update = x[0, i - 1] + A[0, i - 1] * (xnew[0, i] - xold[i]) - xnew[0, i - 1] = xnew_update - signewsq[0, i - 1] = sigsq[i - 1] + A[0, i - 1] * A[0, i - 1] * (signewsq[0, i] - sigsqold[i]) - - return xnew, signewsq, A diff --git a/hobj/utils/state_space/src/em_bino.py b/hobj/utils/state_space/src/em_bino.py deleted file mode 100644 index ced7526..0000000 --- a/hobj/utils/state_space/src/em_bino.py +++ /dev/null @@ -1,49 +0,0 @@ -import numpy as np - -def em_bino(I, xnew, signewsq, A, startflag): - ''' - em_bino is a helper function that computes sigma_eps squared (estimated - learning process variance). - :param I: (2 x ntrials) - :param xnew: (1 x ntrials(+1?)) - :param signewsq: (1 x ntrials(+1?)) - :param A: (1 x ntrials) - :param startflag: () - :return: - ''' - - ''' - print I.shape - print xnew.shape - print signewsq.shape - print A.shape - print startflag - raise Exception - ''' - - M = xnew.shape[1] - xnewt = xnew[0, 2:M] - xnewtm1 = xnew[0, 1:M-1] - signewsqt = signewsq[0, 2:M] - A = A[0, 1:] - - covcalc = signewsqt * A - - term1 = np.sum(np.square(xnewt)) + np.sum(signewsqt) - term2 = np.sum(covcalc) + np.sum(xnewt * xnewtm1) - - if startflag == 1: - term3 = 1.5 * xnew[0, 1] * xnew[0, 1] + 2 * signewsq[0, 1] - term4 = np.square(xnew[0, -1]) + signewsq[0, -1] - elif startflag == 0: - term3 = 2 * xnew[0, 1] * xnew[0, 1] + 2 * signewsq[0, 1] - term4 = np.square(xnew[0, -1]) + signewsq[0, -1] - elif startflag == 2: - term3 = 1 * xnew[0, 1]*xnew[0, 1] + 2*signewsq[0, 1] - term4 = np.square(xnew[0, -1]) + signewsq[0, -1] - M = M - 1 - else: - raise ValueError('startflag of %d not supported'%startflag) - newsigsq = (2 * (term1 - term2) + term3 - term4) / float(M) - - return newsigsq \ No newline at end of file diff --git a/hobj/utils/state_space/src/forwardfilter.py b/hobj/utils/state_space/src/forwardfilter.py deleted file mode 100644 index 47afe88..0000000 --- a/hobj/utils/state_space/src/forwardfilter.py +++ /dev/null @@ -1,72 +0,0 @@ -import numpy as np -from PySS.src.newtonsolve import newtonsolve - - -def forwardfilter(I, sigE, xguess, sigsqguess, mu): - ''' - forwardfilter is a helper function that implements the forward recursive - filtering algorithm to estimate the learning state (hidden process) at - trial k as the Gaussian random variable with mean x{k|k} (xhat) and - SIG^2{k|k} (sigsq). - - :param I: [2xN]. dtype = int. [num_successes_v_time, total_possible] - :param sigE: (). dtype = float. default variance of learning state process - :param xguess: (). - :param sigsqguess: (). - :param mu: (). - ''' - - ''' - :param xhatold: x{k|k-1}, one-step prediction (equation A.6)* - :param sigsqold: SIG^2{k|k-1}, one-step prediction variance (equation A.7)* - :param xhat x{k|k}, posterior mode (equation A.8)* - :param sigsq SIG^2{k|k}, posterior variance (equation A.9)* - :param p p{k|k}, observation model probability (equation 2.2)* - :param N vector of number correct at each trial - :param Nmax total number that could be correct at each trial - :param K total number of trials - :param number_fail saves the time steps if Newton's Method fails - ''' - - K = I.shape[1] # total number of trials - N = I[0, :] # vector of number correct at each trial - Nmax = I[1, :] # total number that could be correct at each trial - - # Initial conditions: use values from previous iteration - xhat = [xguess] - sigsq = [sigsqguess] - number_fail = [] - - xhatold = [0] - sigsqold = [0] - - for k in range(2, K+1+1): - # for each trial, compute estimates of the one-step prediction, the - # posterior mode (using Newton's Method), and the posterior variance - # (estimates from subject's POV) - - # Compute the one-step prediction estimate of mean and variance - xhatold.append(xhat[k-1-1]) - sigsqold.append(sigsq[k-1-1] + np.square(sigE)) - - # Use Newton's Method to compute the nonlinear posterior mode estimate - xhat_next, flagfail = newtonsolve(mu, xhatold[k-1], sigsqold[k-1], N[k-1-1], Nmax[k-1-1]) - - xhat.append(xhat_next) - - # if Newton's Method fails, number_fail saves the time step - if flagfail > 0: - number_fail.append(k-1) - - # Compute the posterior variance estimate - denom = np.true_divide(-1, sigsqold[k-1]) - Nmax[k-1-1]*np.exp(mu)*np.exp(xhat[k-1])/np.square(1 + np.exp(mu) * np.exp(xhat[k-1])) - sigsq.append(np.true_divide(-1, denom)) - - if len(number_fail) > 0: - print ('Newton convergence failed at times', number_fail) - - # Compute the observation model probability estimate - p = np.true_divide(np.exp(mu) * np.exp(xhat), 1 + np.exp(mu) * np.exp(xhat)) - - xhat = np.array(xhat)[None, :] - return p, xhat, sigsq, xhatold, sigsqold diff --git a/hobj/utils/state_space/src/newtonsolve.py b/hobj/utils/state_space/src/newtonsolve.py deleted file mode 100644 index ac65e3e..0000000 --- a/hobj/utils/state_space/src/newtonsolve.py +++ /dev/null @@ -1,92 +0,0 @@ -import numpy as np - - -def newtonsolve(mu, xold, sigoldsq, N, Nmax): - - ''' - - newtonsolve is a helper function that implements Newton's Method in order - to recursively estimate the posterior mode (x). Once the subsequent estimates - sufficiently converge, the function returns the last estimate. If, having - never met this convergence condition, the function goes through all of the - recursions, then a special flag (timefail) - indicating the convergence - failure - is returned along with the last posterior mode estimate. - - :param mu: float - :param xold: float - :param sigoldsq: float - :param N: float - :param Nmax: float - :return: - ''' - - it = xold + sigoldsq * (N - Nmax * np.exp(mu) * np.exp(xold) / (1 + np.exp(mu) * np.exp(xold))) - - it = [it] - - g = [] - gprime = [] - niter = 100 - - for i in range(niter): - gupdate = xold + sigoldsq*(N - Nmax*np.exp(mu)*np.exp(it[i])/(1+np.exp(mu)*np.exp(it[i]))) - it[i] - g.append(gupdate) - - gprime_update = -Nmax*sigoldsq*np.exp(mu)*np.exp(it[i])/np.square(1+np.exp(mu)*np.exp(it[i])) - 1 - gprime.append(gprime_update) - - it_update = it[i] - g[i]/gprime[i] - it.append(it_update) - - x = it[-1] - - if np.abs(x - it[-2]) < 1e-8: - timefail = 0 - return x, timefail - - # This tries a new initial condition if first Newtons doesn't work - if i == (niter - 1): - it = [-1] - g = [] - gprime = [] - - for i in range(niter): - gupdate = xold + sigoldsq*(N - Nmax*np.exp(mu)*np.exp(it[i])/(1+np.exp(mu)*np.exp(it[i]))) - it[i] - g.append(gupdate) - - gprime_update = -Nmax*sigoldsq*np.exp(mu)*np.exp(it[i])/np.square(1+np.exp(mu)*np.exp(it[i]))- 1 - gprime.append(gprime_update) - - it_update = it[i] - g[i] / gprime[i] - it.append(it_update) - - x = it[-1] - - if np.abs(x - it[i]) < 1e-8: - timefail = 0 - return x, timefail - - # This tries a new initial condition if second Newtons doesn't work - if i == (niter - 1): - it = [1] - g = [] - gprime = [] - - for i in range(niter): - gupdate = xold + sigoldsq*(N - Nmax*np.exp(mu)*np.exp(it[i])/(1+np.exp(mu)*np.exp(it[i]))) - it[i] - g.append(gupdate) - - gprime_update = -Nmax*sigoldsq*np.exp(mu)*np.exp(it[i])/np.square(1+np.exp(mu)*np.exp(it[i]))- 1 - gprime.append(gprime_update) - - it_update = it[i] - g[i] / gprime[i] - it.append(it_update) - - x = it[-1] - - if np.abs(x - it[i]) < 1e-8: - timefail = 0 - return x, timefail - - timefail = 1 - return x, timefail diff --git a/hobj/utils/state_space/src/pdistnv2.py b/hobj/utils/state_space/src/pdistnv2.py deleted file mode 100644 index 5f85625..0000000 --- a/hobj/utils/state_space/src/pdistnv2.py +++ /dev/null @@ -1,40 +0,0 @@ -import numpy as np - -def pdistnv2(x, s, mu, background_prob): - - ''' - pdist is a helper function that calculates the confidence limits of the EM - estimate of the learning state process. For each trial, the function - constructs the probability density for a correct response. It then builds - the cumulative density function from this and computes the p values of - the confidence limits - - :param x: (1 x ntrials) - :param s: - :param mu: () - :param background_prob: () - :return: - ''' - - num_samps = 10000 - p025 = np.zeros(x.shape) - p975 = np.zeros(x.shape) - pmid = np.zeros(x.shape) - pmodnew = np.zeros(x.shape) # not computed - pcert = np.zeros(x.shape) - - for ov in range(1, x.shape[1]+1): - - xx = x[0, ov-1] - ss = s[0, ov-1] - samps = xx + np.sqrt(ss)*np.random.randn(num_samps) - pr_samps = np.true_divide(np.exp(mu + samps), 1 + np.exp(mu + samps)) - - order_pr_samps = np.sort(pr_samps) - - p025[0, ov - 1] = float(order_pr_samps[int(np.floor(0.025 * num_samps))]) - p975[0, ov - 1] = float(order_pr_samps[int(np.ceil(0.975 * num_samps))]) - pmid[0, ov - 1] = float(order_pr_samps[int(np.round(0.5 * num_samps))]) - pcert[0, ov - 1] = np.mean(pr_samps > background_prob) - - return p025, p975, pmid, pmodnew, pcert From 9a2d8faacbeafa15e0d36c2da05c6c71735fef02 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 14:53:30 -0500 Subject: [PATCH 015/156] adjust requirements --- requirements.txt | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/requirements.txt b/requirements.txt index ab75605..6a87929 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,13 @@ iniconfig==2.0.0 numpy==2.2.2 packaging==24.2 +pandas==2.2.3 pluggy==1.5.0 pytest==8.3.4 +python-dateutil==2.9.0.post0 +pytz==2024.2 +setuptools==75.1.0 +six==1.17.0 +tzdata==2025.1 +wheel==0.44.0 +xarray==2025.1.1 From 528007bfbb7e7bc368c7ffe7df9142280dff4da1 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 14:54:43 -0500 Subject: [PATCH 016/156] removed irrelevant line --- hobj/config.py | 1 - requirements.txt | 7 +++++++ 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/hobj/config.py b/hobj/config.py index 7d73031..27ba6a6 100644 --- a/hobj/config.py +++ b/hobj/config.py @@ -7,5 +7,4 @@ simulation_cachedir = os.path.join(hlb_cachedir, 'model_simulations') features_cachedir = os.path.join(hlb_cachedir, 'image_features') -device = 'cpu' diff --git a/requirements.txt b/requirements.txt index 6a87929..1401f44 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,8 +1,15 @@ +contourpy==1.3.1 +cycler==0.12.1 +fonttools==4.55.4 iniconfig==2.0.0 +kiwisolver==1.4.8 +matplotlib==3.10.0 numpy==2.2.2 packaging==24.2 pandas==2.2.3 +pillow==11.1.0 pluggy==1.5.0 +pyparsing==3.2.1 pytest==8.3.4 python-dateutil==2.9.0.post0 pytz==2024.2 From 359fd6f713f7fdb895b8276d07622293436d9176 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 14:59:31 -0500 Subject: [PATCH 017/156] adjust requirements --- readme.md | 1 + requirements.txt | 9 --------- 2 files changed, 1 insertion(+), 9 deletions(-) diff --git a/readme.md b/readme.md index 7526238..6a63003 100644 --- a/readme.md +++ b/readme.md @@ -15,6 +15,7 @@ We developed this repository using Python 3.9. Follow the steps below to use it: ```bash pip install -e . ``` + 4. Then, install [xarray](https://docs.xarray.dev/en/latest/getting-started-guide/installing.html) and its dependencies by running the following command: ```bash conda install -c conda-forge xarray dask netCDF4 bottleneck diff --git a/requirements.txt b/requirements.txt index 1401f44..e0c2249 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,20 +1,11 @@ contourpy==1.3.1 cycler==0.12.1 fonttools==4.55.4 -iniconfig==2.0.0 kiwisolver==1.4.8 matplotlib==3.10.0 numpy==2.2.2 packaging==24.2 -pandas==2.2.3 pillow==11.1.0 -pluggy==1.5.0 pyparsing==3.2.1 -pytest==8.3.4 python-dateutil==2.9.0.post0 -pytz==2024.2 -setuptools==75.1.0 six==1.17.0 -tzdata==2025.1 -wheel==0.44.0 -xarray==2025.1.1 From 640aad60d5153210d0bdb8b890731504020dfbce Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 15:01:50 -0500 Subject: [PATCH 018/156] added tqdm --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index e0c2249..6ab2ff6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,3 +9,4 @@ pillow==11.1.0 pyparsing==3.2.1 python-dateutil==2.9.0.post0 six==1.17.0 +tqdm==4.67.1 From 910e74c943f0daf9b8c91448414406e70c426ce6 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 15:03:19 -0500 Subject: [PATCH 019/156] add pytest dep --- requirements.txt | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/requirements.txt b/requirements.txt index 6ab2ff6..35a128b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,12 +1,19 @@ contourpy==1.3.1 cycler==0.12.1 fonttools==4.55.4 +iniconfig==2.0.0 kiwisolver==1.4.8 matplotlib==3.10.0 numpy==2.2.2 packaging==24.2 +pandas==2.2.3 pillow==11.1.0 +pluggy==1.5.0 pyparsing==3.2.1 +pytest==8.3.4 python-dateutil==2.9.0.post0 +pytz==2024.2 six==1.17.0 tqdm==4.67.1 +tzdata==2025.1 +xarray==2025.1.1 From 83334b3f7dd7f91364626df09bcd130acc39eb43 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 15:04:18 -0500 Subject: [PATCH 020/156] added pydantic dep --- hobj/schema.py | 1 + requirements.txt | 4 ++++ 2 files changed, 5 insertions(+) create mode 100644 hobj/schema.py diff --git a/hobj/schema.py b/hobj/schema.py new file mode 100644 index 0000000..92aae38 --- /dev/null +++ b/hobj/schema.py @@ -0,0 +1 @@ +import pydantic diff --git a/requirements.txt b/requirements.txt index 35a128b..69997fc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,4 @@ +annotated-types==0.7.0 contourpy==1.3.1 cycler==0.12.1 fonttools==4.55.4 @@ -9,11 +10,14 @@ packaging==24.2 pandas==2.2.3 pillow==11.1.0 pluggy==1.5.0 +pydantic==2.10.5 +pydantic_core==2.27.2 pyparsing==3.2.1 pytest==8.3.4 python-dateutil==2.9.0.post0 pytz==2024.2 six==1.17.0 tqdm==4.67.1 +typing_extensions==4.12.2 tzdata==2025.1 xarray==2025.1.1 From 9de14149b52c138b14087d8acaa6918bafc3911c Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 15:06:42 -0500 Subject: [PATCH 021/156] added reqs --- requirements.txt | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/requirements.txt b/requirements.txt index 69997fc..4887f00 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,10 @@ annotated-types==0.7.0 +certifi==2024.12.14 +charset-normalizer==3.4.1 contourpy==1.3.1 cycler==0.12.1 fonttools==4.55.4 +idna==3.10 iniconfig==2.0.0 kiwisolver==1.4.8 matplotlib==3.10.0 @@ -16,8 +19,12 @@ pyparsing==3.2.1 pytest==8.3.4 python-dateutil==2.9.0.post0 pytz==2024.2 +requests==2.32.3 +scipy==1.15.1 six==1.17.0 tqdm==4.67.1 typing_extensions==4.12.2 tzdata==2025.1 +url-normalize==1.4.3 +urllib3==2.3.0 xarray==2025.1.1 From 55f928794bf15e14c6c7b4ab11083974c4d46d84 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 15:12:25 -0500 Subject: [PATCH 022/156] installing matplotlib --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 4887f00..4bb5f13 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,7 +7,7 @@ fonttools==4.55.4 idna==3.10 iniconfig==2.0.0 kiwisolver==1.4.8 -matplotlib==3.10.0 +matplotlib==3.9.2 numpy==2.2.2 packaging==24.2 pandas==2.2.3 From a938bdcb05d881fa5ce77e4115bb24f2d2676dca Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 21 Jan 2025 15:25:29 -0500 Subject: [PATCH 023/156] deprecated benchmark class --- hobj/benchmarks/benchmarks.py | 12 ------- .../experiment_simulation.py | 10 +++--- .../MutatorHighVar/mut_highvar_benchmark.py | 3 +- .../MutatorOneshot/mut_oneshot_benchmark.py | 3 +- hobj/schema.py | 22 +++++++++++++ hobj/utils/hash.py | 31 +++++++++++++------ 6 files changed, 51 insertions(+), 30 deletions(-) delete mode 100644 hobj/benchmarks/benchmarks.py diff --git a/hobj/benchmarks/benchmarks.py b/hobj/benchmarks/benchmarks.py deleted file mode 100644 index 74dccc9..0000000 --- a/hobj/benchmarks/benchmarks.py +++ /dev/null @@ -1,12 +0,0 @@ -import xarray as xr -import hobj.learning_models.learning_model as lm - -class Benchmark(object): - - def __init__(self): - return - - def evaluate_model(self, learner: lm.LearningModel, force_recompute=False): - return xr.Dataset() - - diff --git a/hobj/benchmarks/experiment_simulation/experiment_simulation.py b/hobj/benchmarks/experiment_simulation/experiment_simulation.py index 6be39d8..9c03755 100644 --- a/hobj/benchmarks/experiment_simulation/experiment_simulation.py +++ b/hobj/benchmarks/experiment_simulation/experiment_simulation.py @@ -8,6 +8,7 @@ from tqdm import tqdm from typing import Union + class ExperimentSimulation(object): """ Wraps a set of environments, and simulates them self.nreps times each @@ -32,7 +33,7 @@ def environments(self): def run(self, learner: lm.LearningModel, seed: Union[type(None), int], force_recompute=False, show_pbar=True): - savepath = os.path.join(self.cachedir, self.experiment_name, 'seed_'+str(seed), f'ds_{learner.learner_id}.nc') + savepath = os.path.join(self.cachedir, self.experiment_name, 'seed_' + str(seed), f'ds_{learner.learner_id}.nc') if os.path.exists(savepath) and not force_recompute: ds = xr.load_dataset(savepath) return ds @@ -49,12 +50,12 @@ def run(self, learner: lm.LearningModel, seed: Union[type(None), int], force_rec ds = self.postprocess_behavioral_data(ds=ds, environment=environment) dlist.append(ds) - ds = xr.concat(dlist, dim = self.environment_name_dim, ) + ds = xr.concat(dlist, dim=self.environment_name_dim, ) io.prepare_savepath(savepath) ds.to_netcdf(savepath) return ds - def postprocess_behavioral_data(self, ds:xr.Dataset, environment:env.Environment): + def postprocess_behavioral_data(self, ds: xr.Dataset, environment: env.Environment): """ Called following the simulation of a single environment. Allows for postprocessing of the behavioral data. :param ds: @@ -100,8 +101,7 @@ def simulate_environment( for k in environment.current_state_meta: d[k][1][i_trial, i_rep] = (environment.current_state_meta[k]) - ds = xr.Dataset(d) ds = ds.assign_coords(environment.meta) - ds = ds.assign_coords(image_url = ds['image_url']) + ds = ds.assign_coords(image_url=ds['image_url']) return ds diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py index 38cd8e1..a2c7715 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py @@ -4,7 +4,6 @@ import xarray as xr from hobj.behavioral_data import human_data as human_data -from hobj.benchmarks.benchmarks import Benchmark from hobj.learning_models import learning_model as lm from hobj.utils import stats as stats import hobj.benchmarks.leedicarlo_benchmarks.MutatorHighVar.mut_highvar_experiment as mutator @@ -17,7 +16,7 @@ import hobj.statistics.hypothesis_testing.approximated_null_distribution as approximated_null_distribution -class MutatorHighVarBenchmark(Benchmark): +class MutatorHighVarBenchmark: def __init__(self): super().__init__() diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py index da6ba14..8ba892d 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py @@ -9,7 +9,6 @@ import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_experiment as mutator -import hobj.benchmarks.benchmarks as benchmarks import hobj.behavioral_data.human_data as human_data from hobj.statistics.resamplers.resamplers import Resampler import hobj.statistics.score_statistics.MSEn as R2n_funcs @@ -18,7 +17,7 @@ import hobj.statistics.hypothesis_testing.approximated_null_distribution as approximated_null_distribution -class MutatorOneshotBenchmark(benchmarks.Benchmark): +class MutatorOneshotBenchmark: environment_name_dim = 'session' def __init__(self): diff --git a/hobj/schema.py b/hobj/schema.py index 92aae38..12d8c20 100644 --- a/hobj/schema.py +++ b/hobj/schema.py @@ -1 +1,23 @@ +from typing import Literal, List + +import PIL.Image import pydantic + +from hobj.utils.hash import hash_image + + +# %% Image +class ImageRef(pydantic.BaseModel): + sha256: str = pydantic.Field(pattern=r'^[a-f0-9]{64}$') + + def __lt__(self, other) -> bool: + return self.sha256 < other.sha256 + + @classmethod + def from_image(cls, image: PIL.Image): + return cls( + sha256=hash_image(image=image) + ) + + def get_image_data(self) -> PIL.Image: + raise NotImplementedError \ No newline at end of file diff --git a/hobj/utils/hash.py b/hobj/utils/hash.py index 7b33a8b..b231d77 100644 --- a/hobj/utils/hash.py +++ b/hobj/utils/hash.py @@ -1,19 +1,32 @@ import numpy as np import hashlib +import PIL.Image -def hash_ndarray(x:np.ndarray): +def hash_ndarray(x: np.ndarray): assert isinstance(x, np.ndarray) - bytes = x.tobytes(order = "C") + bytes = x.tobytes(order="C") m = hashlib.sha256(string=bytes) digest = m.hexdigest() return digest -def hash_image(image:np.ndarray): - assert isinstance(image, np.ndarray) - assert 2<= len(image.shape) <= 3 - image = image.astype(np.uint8) - return hash_ndarray(image) -def hash_string(x:str): - return hash_ndarray(np.fromstring(str(x), dtype=np.uint8)) \ No newline at end of file +def hash_image(image: PIL.Image) -> str: + """ + Hash an image based on its np.uint8 representation. + :param image: + :return: + """ + sha256_hash = hashlib.sha256() + + # Always cast the image to RGBA format + image = image.convert('RGBA') + + # Convert the image to a numpy array + image_array = np.array(image).astype(np.uint8) + + # Update the hash with the image array + sha256_hash.update(image_array.tobytes()) + + # Return the hexadecimal representation of the hash + return sha256_hash.hexdigest() From 477e4aa44be2b7935ae37d5b0ceb69d3ffdf0d32 Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 07:31:01 -0500 Subject: [PATCH 024/156] before learning model ref --- examples/score_example_model.py | 23 +- ...ation.py => experiment_simulation2_tmp.py} | 0 .../MutatorHighVar/mut_highvar_benchmark.py | 239 ++++++++---------- .../MutatorHighVar/mut_highvar_experiment.py | 109 +++++++- .../MutatorOneshot/mut_oneshot_experiment.py | 2 +- hobj/data/__init__.py | 0 hobj/{ => data}/schema.py | 0 hobj/data/store.py | 0 hobj/learning_models/learning_model.py | 3 + hobj/statistics/lapse_rate.py | 10 +- .../statistics/variance_estimates/binomial.py | 1 + 11 files changed, 242 insertions(+), 145 deletions(-) rename hobj/benchmarks/experiment_simulation/{experiment_simulation.py => experiment_simulation2_tmp.py} (100%) create mode 100644 hobj/data/__init__.py rename hobj/{ => data}/schema.py (100%) create mode 100644 hobj/data/store.py diff --git a/examples/score_example_model.py b/examples/score_example_model.py index ad53ef5..6fc95ef 100644 --- a/examples/score_example_model.py +++ b/examples/score_example_model.py @@ -3,15 +3,16 @@ import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_benchmark as oneshot_benchmark dummy_model = lm.LearningModel(learner_id='dummylearner') -benchmarks = [ - highvar_benchmark.MutatorHighVarBenchmark(), - oneshot_benchmark.MutatorOneshotBenchmark() -] -force_recompute = False -for benchmark in benchmarks: - result = benchmark.evaluate_model( - learner=dummy_model, - force_recompute=force_recompute - ) - print(result) +hv = highvar_benchmark.MutatorHighVarBenchmark() +raise +os = oneshot_benchmark.MutatorOneshotBenchmark() + +result = hv.evaluate_model( + learner=dummy_model, +) +print(result) + +result2 = os.evaluate_model( + learner=dummy_model, +) \ No newline at end of file diff --git a/hobj/benchmarks/experiment_simulation/experiment_simulation.py b/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py similarity index 100% rename from hobj/benchmarks/experiment_simulation/experiment_simulation.py rename to hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py index a2c7715..1fd3e05 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py @@ -6,7 +6,6 @@ from hobj.behavioral_data import human_data as human_data from hobj.learning_models import learning_model as lm from hobj.utils import stats as stats -import hobj.benchmarks.leedicarlo_benchmarks.MutatorHighVar.mut_highvar_experiment as mutator import hobj.statistics.lapse_rate as lapse_rate_funcs from hobj.statistics.resamplers.resamplers import LearningCurveResampler, WorkerResampler @@ -15,16 +14,29 @@ import hobj.statistics.hypothesis_testing.bootstrapped_confidence_intervals as bootstrapped_confidence_intervals import hobj.statistics.hypothesis_testing.approximated_null_distribution as approximated_null_distribution +from dataclasses import dataclass +from typing import List +import warnings + +import scipy.stats as ss + +import hobj.images.imagesets as imagesets +from pathlib import Path + +import os + +import json +import pydantic + class MutatorHighVarBenchmark: def __init__(self): - super().__init__() + self.nboot = 1000 self.tearly = slice(1, 6) self.tlate = slice(94, 100) self.dataset = human_data.MutatorHighVarDataset() - self.experiment = mutator.MutatorHighVarExperiment() self.resampler = LearningCurveResampler( ds=self.ds_worker_table, condition_dim='subtask', @@ -37,40 +49,114 @@ def __init__(self): worker_dim='worker_id' ) - def evaluate_model(self, learner: lm.LearningModel, force_recompute: bool = False, seed = None) -> xr.Dataset: + self.imageset = imagesets.MutatorHighVarImageset() + + + # Todo: move/load this manifest elsewhere – e.g. to imageset + subtask_manifest = Path(os.path.dirname(__file__)) / 'MutatorHighVarSubtasks.json' + subtask_manifest = json.loads(subtask_manifest.read_text()) + self.subtask_manifest = subtask_manifest + + + @dataclass + class SimulateExperimentResult: + """ + The result of simulating an experiment on this battery of experiments. + """ + subtasks: List[str] + k: np.ndarray # [trial, subtask] + n: np.ndarray # [trial, subtask] + + def _simulate_experiment(self, learner: lm.LearningModel, seed: int) -> SimulateExperimentResult: + # Todo + nreps = 500 + ntrials = 100 + warnings.warn("Returning dummy results.") + RS = np.random.RandomState(0) + + return self.SimulateExperimentResult( + subtasks=[], + k=RS.randint(low=0, high=n, size=(64, 100)), + n=np.ones((64, 100)) * n + ) + + @dataclass + class EvaluateModelResult: + mse_n: float + subtask_vector_spearmanr: float + perf_early: float + perf_late: float + + subtasks: List[str] + model_phat: np.ndarray # [trial, subtask] + model_varhat_phat: np.ndarray # [trial, subtask] + model_lapse_rate: float + + human_phat: np.ndarray # [trial, subtask] + human_varhat_phat: np.ndarray # [trial, subtask] + + def evaluate_model(self, learner: lm.LearningModel, seed: int = 0) -> EvaluateModelResult: """ :param learner: LearningModel :param force_recompute: bool. If True, recompute the model behavior, even if it is already cached. :return: """ - # Get behavior of this model - ds_behavior = self.experiment.run(learner=learner, seed=seed, force_recompute=force_recompute) - - k = ds_behavior.k.isel(stimulus_category=1) + (ds_behavior.n.isel(stimulus_category=0) - ds_behavior.k.isel(stimulus_category=0)) - n = ds_behavior.n.isel(stimulus_category=1) + ds_behavior.n.isel(stimulus_category=0) + # Simulate the behavior of this model + model_behavior = self._simulate_experiment(learner=learner, seed=seed) + k = model_behavior.k + n = model_behavior.n - # Fit optimal lapse rate + # Fit a lapse rate which minimizes the MSE between the model and human data lapse_rate = lapse_rate_funcs.fit_optimal_lapse_rate( - phat=k / n, - p=(self.ds_behavioral_statistics.perf), + phat=model_behavior.k / model_behavior.n, + p=self.ds_behavioral_statistics.perf.values, nway=2, - condition_dims=('subtask', 'trial') ) - # Compute scores + # Load human data target_point = self.ds_worker_table.sum('worker_id') - ds_scores = self.score_predictions( - k=k, - n=n, - ktarget=target_point.k, - ntarget=target_point.n, - lapse_rate=lapse_rate, - ).assign_coords( - lapse_rate=lapse_rate - ) + ktarget = target_point.k.values + ntarget = target_point.n.values + phat_target = ktarget / ntarget + varhat_phat_target = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=ktarget, nvec=ntarget) + + # Get estimates of performance + phat_model = k / n + varhat_phat_model = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=k, nvec=n) - return ds_scores + # Get MSEn point estimate on the lapse-rate corrected predictions + phat_model_lapsed = phat_model * (1 - lapse_rate) + 0.5 * lapse_rate + varhat_phat_model_lapsed = varhat_phat_model * (1 - lapse_rate) ** 2 + + msen_elementwise = np.square(phat_target - phat_model_lapsed) - varhat_phat_model_lapsed + MSEn_point = msen_elementwise.mean() + + # Get SpearmanR between the subtask difficulty patterns + predicted_subtask_vector = phat_model.mean(1) # [subtask] + actual_subtask_vector = phat_target.mean(1) # [subtask] + + SpearmanR_point = ss.spearmanr(predicted_subtask_vector, actual_subtask_vector).correlation + + # %% Get the (g)rand (l)earning (c)urve: the average over all subtasks, and the confidence intervals + glc = phat_model.mean(0) # [trial] + glc_lapsed = glc * (1 - lapse_rate) + 0.5 * lapse_rate + overall_perf = glc_lapsed.mean() # Prediction of lapse-rate adjusted model + + # %% Get early and late performances + perf_early = glc[self.tearly].mean() + perf_late = glc[self.tlate].mean() + + return self.EvaluateModelResult( + mse_n = MSEn_point, + subtask_vector_spearmanr = SpearmanR_point, + subtasks = subtasks, + model_phat = phat_model, + model_varhat_phat = varhat_phat_model, + model_lapse_rate = lapse_rate, + human_phat = phat_target, + human_varhat_phat = varhat_phat_target + ) @property def ds_ceilings(self): @@ -90,8 +176,8 @@ def ds_ceilings(self): # %% Get point estimate of R2 ceiling, and confidence intervals point = self.ds_worker_table.sum('worker_id') ds_ceilings['MSEn'] = binomial_funcs.estimate_variance_of_binomial_proportion( - kvec = point.k, - nvec = point.n + kvec=point.k, + nvec=point.n ).mean(['subtask', 'trial']) # Approximate the null distribution of R2n, @@ -168,106 +254,6 @@ def get_null_MSEn_functional(ds_data: xr.Dataset): return self._ds_ceilings - def score_predictions( - self, - k: xr.DataArray, - n: xr.DataArray, - ktarget: xr.DataArray, - ntarget: xr.DataArray, - lapse_rate: Union[float, int, xr.DataArray] = 0., - ): - - """ - k: (subtask, trial). The number of correct classifications in that condition. - n: (subtask, trial). The number of trials for each condition. - ktarget: (subtask, trial). The number of correct classifications in that condition, for the system aiming to be modeled. - ntarget: (subtask, trial). The number of trials for each condition, for the system aiming to be modeled - lapse_rate: a scalar. Adjusts the predictions of the model by simulating a random guess rate. Can only drive the behavior of the model to randomness. - - returns ds_scores, which contains: - - Point estimates, 95% CIs, and standard errors of: - R2n: () the noise-corrected R^2 score - glc: (trial) (g)rand (l)earning (c)urve over all subtasks - overall_perf_raw: () the average performance over all subtasks and trials (before lapse rate correction) - overall_perf: () the average performance over all subtasks and trials (after lapse rate correction) - SpearmanR: () between subtask difficulty vectors - """ - - assert np.all(lapse_rate >= 0) and np.all(lapse_rate <= 1), lapse_rate - - def check_kn(k, n): - assert isinstance(k, xr.DataArray) - assert isinstance(n, xr.DataArray) - assert np.all(k <= n) - assert np.all(n > 0) - assert 'worker_id' not in k.dims - assert 'subtask' in k.dims - assert 'trial' in k.dims - - # assert set(k.dims) == {'subtask', 'trial'}, (k.dims) - - assert set(k.dims) == set(n.dims), (k.dims, n.dims) - - check_kn(k, n) - check_kn(ktarget, ntarget) - - alpha = 0.05 - # Get estimates of performance - hat_prob = k / n - hat_var_hat_prob = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=k, nvec=n) - - # %% Get MSEn point estimate on the lapse-rate corrected predictions - hat_prob_lapse_rate = hat_prob * (1 - lapse_rate) + 0.5 * (lapse_rate) - hat_var_hat_prob_lapse_rate = hat_var_hat_prob * (1 - lapse_rate) ** 2 - - hat_prob_target = ktarget / ntarget - - MSEn_point = R2n_funcs.estimate_MSEn( - hat_theta_model=hat_prob_lapse_rate, - hat_var_hat_theta_model=hat_var_hat_prob_lapse_rate, - hat_theta_target=hat_prob_target, - condition_dims=('subtask', 'trial'), - ) - - - # %% Get SpearmanR between the subtask difficulty patterns - predicted_subtask_vector = hat_prob.mean("trial") - actual_subtask_vector = hat_prob_target.mean('trial') - - SpearmanR_point = stats.calc_correlation( - obs_dim='subtask', - y_pred=predicted_subtask_vector, - y_actual=actual_subtask_vector, - spearman=True - ) - - # %% Get the (g)rand (l)earning (c)urve: the average over all subtasks, and the confidence intervals - glc = hat_prob.mean('subtask') - glc_lapsed = glc * (1 - lapse_rate) + 0.5 * (lapse_rate) - overall_perf = glc_lapsed.mean('trial') # Prediction of lapse-rate adjusted model - - # %% Get early and late performances - perf_early = glc.isel(trial=self.tearly).mean('trial') - perf_late = glc.isel(trial=self.tlate).mean('trial') - - # %% Assemble scores - ds_scores = xr.Dataset( - data_vars=dict( - overall_perf=overall_perf, - overall_perf_raw=glc.mean('trial'), - SpearmanR=SpearmanR_point, - MSEn=MSEn_point, - glc=glc, - early_perf=perf_early, - late_perf=perf_late, - ), - coords = dict( - model_var_correction_term = float(hat_var_hat_prob_lapse_rate.mean()) - ) - ) - - return ds_scores @property def ds_worker_table(self): @@ -346,4 +332,3 @@ def ds_boot(self): self._ds_boot = self.resampler.get_ds_boot(nboot=self.nboot, seed=0) return self._ds_boot - diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py index e632486..4ba0c7c 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py @@ -4,9 +4,116 @@ import numpy as np import hobj.benchmarks.experiment_simulation.environment as env_template -from hobj.benchmarks.experiment_simulation.experiment_simulation import ExperimentSimulation import hobj.utils.file_io as io import hobj.images.imagesets as imagesets +import hobj.config as config +import hobj.learning_models.learning_model as lm +import hobj.benchmarks.experiment_simulation.environment as env +import hobj.utils.file_io as io +import os +import xarray as xr +import numpy as np +from tqdm import tqdm +from typing import Union + + +class ExperimentSimulation(object): + """ + Wraps a set of environments, and simulates them self.nreps times each + """ + nreps = None + ntrials = None + environment_name_dim = 'environment' + + def __init__(self, cachedir=config.simulation_cachedir): + self.cachedir = cachedir + return + + @property + def experiment_name(self): + raise NotImplementedError + return 'write_experiment_name_here' + + @property + def environments(self): + raise NotImplementedError + return [env.Environment()] + + def run(self, learner: lm.LearningModel, seed: Union[type(None), int], force_recompute=False, show_pbar=True): + + savepath = os.path.join(self.cachedir, self.experiment_name, 'seed_' + str(seed), f'ds_{learner.learner_id}.nc') + if os.path.exists(savepath) and not force_recompute: + ds = xr.load_dataset(savepath) + return ds + + dlist = [] + RS = np.random.RandomState(seed=seed) + if show_pbar: + pbar = tqdm(total=self.nreps * self.ntrials * len(self.environments), desc='simulation trials') + else: + pbar = None + + for environment in self.environments: + ds = self.simulate_environment(learner=learner, environment=environment, RS=RS, nreps=self.nreps, ntrials=self.ntrials, pbar=pbar) + ds = self.postprocess_behavioral_data(ds=ds, environment=environment) + dlist.append(ds) + + ds = xr.concat(dlist, dim=self.environment_name_dim, ) + io.prepare_savepath(savepath) + ds.to_netcdf(savepath) + return ds + + def postprocess_behavioral_data(self, ds: xr.Dataset, environment: env.Environment): + """ + Called following the simulation of a single environment. Allows for postprocessing of the behavioral data. + :param ds: + :param environment: + :return: + """ + return ds + + @staticmethod + def simulate_environment( + learner: lm.LearningModel, + environment: env.Environment, + RS: np.random.RandomState, + nreps: int, + ntrials: int, + pbar: tqdm = None, + ): + + d = { + 'image_url': (['trial', 'rep', ], [['' for _ in range(nreps)] for _ in range(ntrials)]), + 'action': (['trial', 'rep', ], np.zeros((ntrials, nreps))), + 'reward': (['trial', 'rep', ], np.zeros((ntrials, nreps))), + } + + for i_rep in range(nreps): + learner.reset() + environment.initialize(RS=RS) + for i_trial in range(ntrials): + if pbar is not None: + pbar.update(1) + + image_url = environment.sample_image() + action = learner.respond(image_url=image_url) + action = int(action) + reward = environment.provide_feedback(action=action) + reward = float(reward) + learner.learn(reward=reward) + + d['image_url'][1][i_trial][i_rep] = image_url + d['action'][1][i_trial, i_rep] = action + d['reward'][1][i_trial, i_rep] = reward + + for k in environment.current_state_meta: + d[k][1][i_trial, i_rep] = (environment.current_state_meta[k]) + + ds = xr.Dataset(d) + ds = ds.assign_coords(environment.meta) + ds = ds.assign_coords(image_url=ds['image_url']) + return ds + _loc = os.path.dirname(__file__) diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_experiment.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_experiment.py index 07aa448..4029d12 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_experiment.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_experiment.py @@ -5,7 +5,7 @@ import numpy as np import hobj.benchmarks.experiment_simulation.environment as env_template -from hobj.benchmarks.experiment_simulation.experiment_simulation import ExperimentSimulation +from hobj.benchmarks.experiment_simulation.experiment_simulation2_tmp import ExperimentSimulation import hobj.images.imagesets as imagesets _loc = os.path.dirname(__file__) diff --git a/hobj/data/__init__.py b/hobj/data/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/hobj/schema.py b/hobj/data/schema.py similarity index 100% rename from hobj/schema.py rename to hobj/data/schema.py diff --git a/hobj/data/store.py b/hobj/data/store.py new file mode 100644 index 0000000..e69de29 diff --git a/hobj/learning_models/learning_model.py b/hobj/learning_models/learning_model.py index 9c20b22..4a2b419 100644 --- a/hobj/learning_models/learning_model.py +++ b/hobj/learning_models/learning_model.py @@ -1,3 +1,6 @@ +from abc import ABC, abstractmethod + + class LearningModel(object): """ This class is meant to be subclassed. As long as the functions below are implemented (and returns the correct dtype), diff --git a/hobj/statistics/lapse_rate.py b/hobj/statistics/lapse_rate.py index d31f548..c557261 100644 --- a/hobj/statistics/lapse_rate.py +++ b/hobj/statistics/lapse_rate.py @@ -4,10 +4,10 @@ def fit_optimal_lapse_rate( - phat: Union[xr.DataArray, np.ndarray, float, int], - p: Union[xr.DataArray, np.ndarray, float, int], + phat: Union[np.ndarray, np.generic, float, int], + p: Union[np.ndarray, np.generic, float, int], nway: int, - condition_dims: Union[float, int, tuple, Tuple[str], List[str]] = -1): +): """ Fits a lapse rate (gamma, which is the probability of a uniform random guess) that minimizes the empirical MSE loss between the phat and p: @@ -34,8 +34,8 @@ def fit_optimal_lapse_rate( assert p.shape == phat.shape - numerator = -(2 * phat / nway - 2 * np.square(phat) + 2 * phat * p - 2 * p / nway).sum(condition_dims) - denominator = (2 / (nway ** 2) - 4 * phat / nway + 2 * (phat ** 2)).sum(condition_dims) + numerator = -(2 * phat / nway - 2 * np.square(phat) + 2 * phat * p - 2 * p / nway).sum() + denominator = (2 / (nway ** 2) - 4 * phat / nway + 2 * (phat ** 2)).sum() gamma_star = numerator / denominator gamma_star = np.clip(gamma_star, 0, 1) diff --git a/hobj/statistics/variance_estimates/binomial.py b/hobj/statistics/variance_estimates/binomial.py index 3f36a1d..e83e058 100644 --- a/hobj/statistics/variance_estimates/binomial.py +++ b/hobj/statistics/variance_estimates/binomial.py @@ -1,6 +1,7 @@ from scipy.stats import beta import numpy as np import xarray as xr +from typing import Union def estimate_variance_of_binomial_proportion(kvec, nvec): From 5b9481fb1a697c4c0822f86c4e246a47f00a3d07 Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 07:32:01 -0500 Subject: [PATCH 025/156] remove learner id --- hobj/learning_models/learning_model.py | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/hobj/learning_models/learning_model.py b/hobj/learning_models/learning_model.py index 4a2b419..406b02f 100644 --- a/hobj/learning_models/learning_model.py +++ b/hobj/learning_models/learning_model.py @@ -1,19 +1,14 @@ from abc import ABC, abstractmethod -class LearningModel(object): +class LearningModel(ABC): """ - This class is meant to be subclassed. As long as the functions below are implemented (and returns the correct dtype), + This abstract class (meant to be subclassed) is. + + As long as the functions below are implemented (and returns the correct dtype), the LearningModel can be scored on any benchmark in this repo. """ - def __init__(self, learner_id: str): - """ - :param learner_id: a string that identifies the learning model. - """ - self.learner_id = learner_id - return - def reset(self) -> None: """ This function is called at the beginning of each simulated behavioral session. From 6ebc46ebf6115455f751dfbcac3ab72a264184a0 Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 07:54:51 -0500 Subject: [PATCH 026/156] refactor learning model --- examples/score_example_model.py | 2 +- .../experiment_simulation2_tmp.py | 6 +- .../MutatorHighVar/mut_highvar_benchmark.py | 4 +- .../MutatorHighVar/mut_highvar_experiment.py | 6 +- .../MutatorOneshot/mut_oneshot_benchmark.py | 2 +- hobj/learning_models/learning_model.py | 76 +++++++++++++++---- .../linear_learners/linear_learner.py | 6 +- 7 files changed, 73 insertions(+), 29 deletions(-) diff --git a/examples/score_example_model.py b/examples/score_example_model.py index 6fc95ef..c06ccd6 100644 --- a/examples/score_example_model.py +++ b/examples/score_example_model.py @@ -2,7 +2,7 @@ import hobj.benchmarks.leedicarlo_benchmarks.MutatorHighVar.mut_highvar_benchmark as highvar_benchmark import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_benchmark as oneshot_benchmark -dummy_model = lm.LearningModel(learner_id='dummylearner') +dummy_model = lm.BinaryLearningModel(learner_id='dummylearner') hv = highvar_benchmark.MutatorHighVarBenchmark() raise diff --git a/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py b/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py index 9c03755..8879ac1 100644 --- a/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py +++ b/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py @@ -31,7 +31,7 @@ def environments(self): raise NotImplementedError return [env.Environment()] - def run(self, learner: lm.LearningModel, seed: Union[type(None), int], force_recompute=False, show_pbar=True): + def run(self, learner: lm.BinaryLearningModel, seed: Union[type(None), int], force_recompute=False, show_pbar=True): savepath = os.path.join(self.cachedir, self.experiment_name, 'seed_' + str(seed), f'ds_{learner.learner_id}.nc') if os.path.exists(savepath) and not force_recompute: @@ -66,7 +66,7 @@ def postprocess_behavioral_data(self, ds: xr.Dataset, environment: env.Environme @staticmethod def simulate_environment( - learner: lm.LearningModel, + learner: lm.BinaryLearningModel, environment: env.Environment, RS: np.random.RandomState, nreps: int, @@ -88,7 +88,7 @@ def simulate_environment( pbar.update(1) image_url = environment.sample_image() - action = learner.respond(image_url=image_url) + action = learner.respond(image=image_url) action = int(action) reward = environment.provide_feedback(action=action) reward = float(reward) diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py index 1fd3e05..1a9c601 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py @@ -67,7 +67,7 @@ class SimulateExperimentResult: k: np.ndarray # [trial, subtask] n: np.ndarray # [trial, subtask] - def _simulate_experiment(self, learner: lm.LearningModel, seed: int) -> SimulateExperimentResult: + def _simulate_experiment(self, learner: lm.BinaryLearningModel, seed: int) -> SimulateExperimentResult: # Todo nreps = 500 ntrials = 100 @@ -95,7 +95,7 @@ class EvaluateModelResult: human_phat: np.ndarray # [trial, subtask] human_varhat_phat: np.ndarray # [trial, subtask] - def evaluate_model(self, learner: lm.LearningModel, seed: int = 0) -> EvaluateModelResult: + def evaluate_model(self, learner: lm.BinaryLearningModel, seed: int = 0) -> EvaluateModelResult: """ :param learner: LearningModel :param force_recompute: bool. If True, recompute the model behavior, even if it is already cached. diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py index 4ba0c7c..37d4209 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py @@ -39,7 +39,7 @@ def environments(self): raise NotImplementedError return [env.Environment()] - def run(self, learner: lm.LearningModel, seed: Union[type(None), int], force_recompute=False, show_pbar=True): + def run(self, learner: lm.BinaryLearningModel, seed: Union[type(None), int], force_recompute=False, show_pbar=True): savepath = os.path.join(self.cachedir, self.experiment_name, 'seed_' + str(seed), f'ds_{learner.learner_id}.nc') if os.path.exists(savepath) and not force_recompute: @@ -74,7 +74,7 @@ def postprocess_behavioral_data(self, ds: xr.Dataset, environment: env.Environme @staticmethod def simulate_environment( - learner: lm.LearningModel, + learner: lm.BinaryLearningModel, environment: env.Environment, RS: np.random.RandomState, nreps: int, @@ -96,7 +96,7 @@ def simulate_environment( pbar.update(1) image_url = environment.sample_image() - action = learner.respond(image_url=image_url) + action = learner.respond(image=image_url) action = int(action) reward = environment.provide_feedback(action=action) reward = float(reward) diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py index 8ba892d..77d4a08 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py @@ -103,7 +103,7 @@ def __init__(self): 'MutatorB2000_2909,MutatorB2000_4256' ] - def evaluate_model(self, learner: lm.LearningModel, force_recompute: bool = False): + def evaluate_model(self, learner: lm.BinaryLearningModel, force_recompute: bool = False): """ :param learner: LearningModel :param force_recompute: bool. If True, recompute the results even if they are already cached. diff --git a/hobj/learning_models/learning_model.py b/hobj/learning_models/learning_model.py index 406b02f..2fc3c3b 100644 --- a/hobj/learning_models/learning_model.py +++ b/hobj/learning_models/learning_model.py @@ -1,37 +1,81 @@ from abc import ABC, abstractmethod +import hobj.data.schema as schema +import typing +import PIL.Image +import numpy as np -class LearningModel(ABC): - """ - This abstract class (meant to be subclassed) is. +class BinaryLearningModel(ABC): - As long as the functions below are implemented (and returns the correct dtype), - the LearningModel can be scored on any benchmark in this repo. - """ + @abstractmethod + def reset(self, seed: int) -> None: + """ + This function resets the LearningModel to some initial state. + :param seed: if the LearningModel has any stochastic components, this seed should be used to seed them. + :return: + """ + raise NotImplementedError - def reset(self) -> None: + @abstractmethod + def respond( + self, + image: typing.Union[schema.ImageRef, PIL.Image], + offline: bool = False + ) -> typing.Literal[0, 1]: """ - This function is called at the beginning of each simulated behavioral session. - It should reset the learning model to an initial state. + This function takes the current stimulus image (given by its image_url) and returns an action (parameterized by an integer). + :param image: A public URL to the image. + :param offline: If True, calling this function should not have any side effects. :return: """ - return + raise NotImplementedError + + @abstractmethod + def learn(self, reward: float) -> None: + """ + This function takes as input a scalar reward from the environment. The LearningModel may use this reward to update its parameters. + :return: + """ + raise NotImplementedError + + +# %% +class DummyBinaryLearner(BinaryLearningModel): + """ + A dummy model which simply returns random outputs, independent of the given image. + """ + + def __init__(self, seed: int = 0): + self.random_generator = np.random.default_rng(seed=seed) + + def reset(self, seed: int) -> None: + self.random_generator = np.random.default_rng(seed=seed) - def respond(self, image_url: str) -> int: + def respond( + self, + image: typing.Union[schema.ImageRef, PIL.Image], + offline: bool = False + ) -> typing.Literal[0, 1]: """ This function takes the current stimulus image (given by its image_url) and returns an action (parameterized by an integer). - :param image_url: A public URL to the image. + :param image: An image (or a reference to an image). + :param offline: If True, calling this function should not have any side effects. :return: """ + if offline: + initial_state = self.random_generator.bit_generator.state + action = self.random_generator.integers(2) + self.random_generator.bit_generator.state = initial_state + else: + action = self.random_generator.integers(2) + + action = int(action) - action = 0 return action def learn(self, reward: float) -> None: """ - This function takes a reward from the environment. One may use this reward to update the learning model. - This function is called by the environment, after the call to respond(). + This function takes as input a scalar reward from the environment. The LearningModel may use this reward to update its parameters. :return: """ - return diff --git a/hobj/learning_models/linear_learners/linear_learner.py b/hobj/learning_models/linear_learners/linear_learner.py index a990a69..58f2d47 100644 --- a/hobj/learning_models/linear_learners/linear_learner.py +++ b/hobj/learning_models/linear_learners/linear_learner.py @@ -5,7 +5,7 @@ import numpy as np -class LinearLearner(lm.LearningModel): +class LinearLearner(lm.BinaryLearningModel): """ A learning model based on a standard cognitive theory of learning. """ @@ -33,8 +33,8 @@ def reset(self): self.b = np.zeros((self.nactions,)) return - def respond(self, image_url: str): - f = self.representational_model.get_features(image_url=image_url) + def respond(self, image: str): + f = self.representational_model.get_features(image_url=image) self.f = f self.logits = self.f @ self.w + self.b # [action] self.action = int(_random_tiebreaking_argmax(self.logits)) From 06424db778f71dae01babc7e9bbfe5893526b87a Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 08:02:18 -0500 Subject: [PATCH 027/156] refactored / renamed learning model --- examples/score_example_model.py | 3 +- .../experiment_simulation2_tmp.py | 6 +-- .../MutatorHighVar/mut_highvar_experiment.py | 6 +-- hobj/learning_models/learning_model.py | 37 +++++-------------- .../linear_learners/linear_learner.py | 8 ++-- .../tests/test_dummy_learner.py | 23 ++++++++++++ 6 files changed, 43 insertions(+), 40 deletions(-) create mode 100644 hobj/learning_models/tests/test_dummy_learner.py diff --git a/examples/score_example_model.py b/examples/score_example_model.py index c06ccd6..7108ae2 100644 --- a/examples/score_example_model.py +++ b/examples/score_example_model.py @@ -2,10 +2,9 @@ import hobj.benchmarks.leedicarlo_benchmarks.MutatorHighVar.mut_highvar_benchmark as highvar_benchmark import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_benchmark as oneshot_benchmark -dummy_model = lm.BinaryLearningModel(learner_id='dummylearner') +dummy_model = lm.DummyBinaryLearner() hv = highvar_benchmark.MutatorHighVarBenchmark() -raise os = oneshot_benchmark.MutatorOneshotBenchmark() result = hv.evaluate_model( diff --git a/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py b/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py index 8879ac1..f29ad29 100644 --- a/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py +++ b/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py @@ -81,18 +81,18 @@ def simulate_environment( } for i_rep in range(nreps): - learner.reset() + learner.reset_state() environment.initialize(RS=RS) for i_trial in range(ntrials): if pbar is not None: pbar.update(1) image_url = environment.sample_image() - action = learner.respond(image=image_url) + action = learner.get_response(image=image_url) action = int(action) reward = environment.provide_feedback(action=action) reward = float(reward) - learner.learn(reward=reward) + learner.give_feedback(reward=reward) d['image_url'][1][i_trial][i_rep] = image_url d['action'][1][i_trial, i_rep] = action diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py index 37d4209..fb258c8 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py @@ -89,18 +89,18 @@ def simulate_environment( } for i_rep in range(nreps): - learner.reset() + learner.reset_state() environment.initialize(RS=RS) for i_trial in range(ntrials): if pbar is not None: pbar.update(1) image_url = environment.sample_image() - action = learner.respond(image=image_url) + action = learner.get_response(image=image_url) action = int(action) reward = environment.provide_feedback(action=action) reward = float(reward) - learner.learn(reward=reward) + learner.give_feedback(reward=reward) d['image_url'][1][i_trial][i_rep] = image_url d['action'][1][i_trial, i_rep] = action diff --git a/hobj/learning_models/learning_model.py b/hobj/learning_models/learning_model.py index 2fc3c3b..ee0b5ea 100644 --- a/hobj/learning_models/learning_model.py +++ b/hobj/learning_models/learning_model.py @@ -8,7 +8,7 @@ class BinaryLearningModel(ABC): @abstractmethod - def reset(self, seed: int) -> None: + def reset_state(self, seed: int) -> None: """ This function resets the LearningModel to some initial state. :param seed: if the LearningModel has any stochastic components, this seed should be used to seed them. @@ -17,21 +17,19 @@ def reset(self, seed: int) -> None: raise NotImplementedError @abstractmethod - def respond( + def get_response( self, image: typing.Union[schema.ImageRef, PIL.Image], - offline: bool = False ) -> typing.Literal[0, 1]: """ - This function takes the current stimulus image (given by its image_url) and returns an action (parameterized by an integer). - :param image: A public URL to the image. - :param offline: If True, calling this function should not have any side effects. + This function takes the current stimulus image (given either as a PIL.Image or a ImageRef) and returns one of two possible actions (parameterized by an integer). + :param image: An image (or a reference to an image). :return: """ raise NotImplementedError @abstractmethod - def learn(self, reward: float) -> None: + def give_feedback(self, reward: float) -> None: """ This function takes as input a scalar reward from the environment. The LearningModel may use this reward to update its parameters. :return: @@ -48,34 +46,17 @@ class DummyBinaryLearner(BinaryLearningModel): def __init__(self, seed: int = 0): self.random_generator = np.random.default_rng(seed=seed) - def reset(self, seed: int) -> None: + def reset_state(self, seed: int) -> None: self.random_generator = np.random.default_rng(seed=seed) - def respond( + def get_response( self, image: typing.Union[schema.ImageRef, PIL.Image], - offline: bool = False ) -> typing.Literal[0, 1]: - """ - This function takes the current stimulus image (given by its image_url) and returns an action (parameterized by an integer). - :param image: An image (or a reference to an image). - :param offline: If True, calling this function should not have any side effects. - :return: - """ - if offline: - initial_state = self.random_generator.bit_generator.state - action = self.random_generator.integers(2) - self.random_generator.bit_generator.state = initial_state - else: - action = self.random_generator.integers(2) + action = self.random_generator.integers(2) action = int(action) - return action - def learn(self, reward: float) -> None: - """ - This function takes as input a scalar reward from the environment. The LearningModel may use this reward to update its parameters. - :return: - """ + def give_feedback(self, reward: float) -> None: return diff --git a/hobj/learning_models/linear_learners/linear_learner.py b/hobj/learning_models/linear_learners/linear_learner.py index 58f2d47..7a69db7 100644 --- a/hobj/learning_models/linear_learners/linear_learner.py +++ b/hobj/learning_models/linear_learners/linear_learner.py @@ -23,24 +23,24 @@ def __init__( self.nactions = nactions self.w = np.zeros((self.representational_model.d, self.nactions)) self.b = np.zeros((self.nactions,)) - self.reset() + self.reset_state() return - def reset(self): + def reset_state(self): self.representational_model.reset() self.update_rule.reset() self.w = np.zeros((self.representational_model.d, self.nactions)) self.b = np.zeros((self.nactions,)) return - def respond(self, image: str): + def get_response(self, image: str): f = self.representational_model.get_features(image_url=image) self.f = f self.logits = self.f @ self.w + self.b # [action] self.action = int(_random_tiebreaking_argmax(self.logits)) return self.action - def learn(self, reward: float): + def give_feedback(self, reward: float): delta_w, delta_b = self.update_rule.get_update(x=self.f, w=self.w, b=self.b, logits=self.logits, action=self.action, reward=reward) # [action] self.w += delta_w self.b += delta_b diff --git a/hobj/learning_models/tests/test_dummy_learner.py b/hobj/learning_models/tests/test_dummy_learner.py new file mode 100644 index 0000000..5cc845d --- /dev/null +++ b/hobj/learning_models/tests/test_dummy_learner.py @@ -0,0 +1,23 @@ +import pytest +from hobj.learning_models.learning_model import DummyBinaryLearner +import hobj.data.schema as schema + +@pytest.fixture +def dummy_learner() -> DummyBinaryLearner: + return DummyBinaryLearner(seed=0) + +@pytest.fixture +def test_image() -> schema.ImageRef: + return schema.ImageRef(sha256='0'*64) + + +def test_dummy_learner_deterministic(dummy_learner, test_image): + + ntests = 10 + actions = [] + expected_actions = [1, 1, 1, 0, 0, 0, 0, 0, 0, 1] + for i in range(ntests): + a = dummy_learner.get_response(image=test_image) + actions.append(a) + + assert actions == expected_actions \ No newline at end of file From 725bcdde88094d8fb17495e4e37f06670e43f357 Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 08:50:52 -0500 Subject: [PATCH 028/156] added BinaryTask --- hobj/behavioral_data/human_data.py | 4 +- .../benchmark_utils/data_resampling.py | 2 +- .../{environment.py => environment_depr.py} | 0 .../experiment_simulation2_tmp.py | 2 +- hobj/benchmarks/experiment_simulation/task.py | 109 +++++++++++ .../MutatorHighVar/mut_highvar_benchmark.py | 23 ++- .../MutatorHighVar/mut_highvar_experiment.py | 6 +- .../MutatorOneshot/mut_oneshot_benchmark.py | 2 +- .../MutatorOneshot/mut_oneshot_experiment.py | 4 +- hobj/{ => data}/images/__init__.py | 0 .../images/image_meta/ds_MutatorB0_BurnIn.nc | Bin .../image_meta/ds_MutatorB2000_Oneshot64.nc | Bin ...ds_MutatorB2000_Subset128_FullVar_Train.nc | Bin hobj/{ => data}/images/imagesets.py | 0 hobj/{ => data}/images/load_image.py | 1 + hobj/{ => data}/images/readme.md | 0 hobj/data/schema.py | 5 +- hobj/data/store.py | 181 ++++++++++++++++++ 18 files changed, 316 insertions(+), 23 deletions(-) rename hobj/benchmarks/experiment_simulation/{environment.py => environment_depr.py} (100%) create mode 100644 hobj/benchmarks/experiment_simulation/task.py rename hobj/{ => data}/images/__init__.py (100%) rename hobj/{ => data}/images/image_meta/ds_MutatorB0_BurnIn.nc (100%) rename hobj/{ => data}/images/image_meta/ds_MutatorB2000_Oneshot64.nc (100%) rename hobj/{ => data}/images/image_meta/ds_MutatorB2000_Subset128_FullVar_Train.nc (100%) rename hobj/{ => data}/images/imagesets.py (100%) rename hobj/{ => data}/images/load_image.py (99%) rename hobj/{ => data}/images/readme.md (100%) diff --git a/hobj/behavioral_data/human_data.py b/hobj/behavioral_data/human_data.py index daa64f3..d31d958 100644 --- a/hobj/behavioral_data/human_data.py +++ b/hobj/behavioral_data/human_data.py @@ -19,7 +19,7 @@ def apply_experiment_specific_filtering(self, ds_flat): ds_flat = ds_flat[['perf', 'reaction_time_msec']] # Assign subtask names - import hobj.images.imagesets as imagesets + import hobj.data.images.imagesets as imagesets ds_meta = imagesets.MutatorHighVarImageset().ds_meta urls = ds_flat.stimulus_url.transpose('session', 'trial').values url_to_obj = {url: obj for url, obj in zip(ds_meta.image_url.values, ds_meta.obj.values)} @@ -104,7 +104,7 @@ class MutatorOneShotGeneralizationDataset(template.SR_Dataset): def apply_experiment_specific_filtering(self, ds_flat): import numpy as np - import hobj.images.imagesets as imagesets + import hobj.data.images.imagesets as imagesets ds_meta = imagesets.MutatorOneshotImageset().ds_meta image_url_to_transformation = {url:trans for url, trans in zip(ds_meta.image_url.values, ds_meta.transformation.values)} image_url_to_transformation_level = {url:trans_level for url, trans_level in zip(ds_meta.image_url.values, ds_meta.transformation_level.values)} diff --git a/hobj/benchmarks/benchmark_utils/data_resampling.py b/hobj/benchmarks/benchmark_utils/data_resampling.py index fb2f867..f8ceeb7 100644 --- a/hobj/benchmarks/benchmark_utils/data_resampling.py +++ b/hobj/benchmarks/benchmark_utils/data_resampling.py @@ -132,7 +132,7 @@ def get_policy_grain_splithalf_and_bootstrap_resampled_data(ds_raw, nsplits): worker_to_kn = {} nworkers = len(set(ds_raw.worker_id.values)) - import hobj.images.imagesets as imagesets + import hobj.data.images.imagesets as imagesets ds_meta = imagesets.MutatorHighVarImageset().ds_meta for worker_id, ws in tqdm(ds_raw.groupby('worker_id'), total=nworkers): diff --git a/hobj/benchmarks/experiment_simulation/environment.py b/hobj/benchmarks/experiment_simulation/environment_depr.py similarity index 100% rename from hobj/benchmarks/experiment_simulation/environment.py rename to hobj/benchmarks/experiment_simulation/environment_depr.py diff --git a/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py b/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py index f29ad29..87f93f4 100644 --- a/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py +++ b/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py @@ -1,6 +1,6 @@ import hobj.config as config import hobj.learning_models.learning_model as lm -import hobj.benchmarks.experiment_simulation.environment as env +import hobj.benchmarks.experiment_simulation.environment_depr as env import hobj.utils.file_io as io import os import xarray as xr diff --git a/hobj/benchmarks/experiment_simulation/task.py b/hobj/benchmarks/experiment_simulation/task.py new file mode 100644 index 0000000..19e902d --- /dev/null +++ b/hobj/benchmarks/experiment_simulation/task.py @@ -0,0 +1,109 @@ +from abc import ABC, abstractmethod +from typing import List, Literal, Union + +import PIL.Image +import numpy as np +import pydantic + +import hobj.data.schema as schema +from hobj.learning_models.learning_model import BinaryLearningModel + + +class BinaryTask(ABC): + class RunSessionResult(pydantic.BaseModel): + reward_sequence: List[float] + action_sequence: List[Literal[0, 1]] + image_sequence: List[schema.ImageRef] + + @abstractmethod + def run_session( + self, + learner: BinaryLearningModel, + seed: int + ) -> RunSessionResult: + """ + Runs a session of the task with the given learner. Note that the learner is not reset by this function; it runs the + session using the learner "as given". + :param learner: + :param seed: + :return: + """ + raise NotImplementedError + + +class SimpleBinaryTask(BinaryTask): + """ + A task which samples uniformly (with or without replacement) from a pool of images. + Feedback consists of +1 or -1 rewards, depending on the class of the image. + """ + + def __init__( + self, + classA: List[Union[schema.ImageRef, PIL.Image]], + classB: List[Union[schema.ImageRef, PIL.Image]], + ntrials: int, + replace: bool, + ): + + classA = self._process_images(images=classA) + classB = self._process_images(images=classB) + nclassA = len(classA) + nclassB = len(classB) + + if replace and nclassA + nclassB < ntrials: + raise ValueError(f"Specified replace=True, but only {nclassA + nclassB} images available. (len(classA)={nclassA}; len(classB)={nclassB})") + + self.image_refs = classA + classB + self.image_labels = [0] * nclassA + [1] * nclassB + self.ntrials = ntrials + self.replace = replace + + def run_session( + self, + learner: BinaryLearningModel, + seed: int + ) -> BinaryTask.RunSessionResult: + + reward_seq = [] + action_seq = [] + image_seq = [] + + gen = np.random.default_rng(seed=seed) + + if self.replace: + i_image_seq = gen.integers(len(self.image_refs), size=self.ntrials) + else: + i_image_seq = gen.permutation(len(self.image_refs))[:self.ntrials] + + # Iterate over trials + for i_image in i_image_seq: + # Get response from learner + a = learner.get_response(image=self.image_refs[i_image]) + + # Calculate feedback based on response + feedback = 1. if a == self.image_labels[i_image] else -1. + + # Deliver feedback to learner + learner.give_feedback(feedback) + + # Record results of trial + reward_seq.append(feedback) + action_seq.append(a) + image_seq.append(self.image_refs[i_image]) + + return BinaryTask.RunSessionResult( + reward_sequence=reward_seq, + action_sequence=action_seq, + image_sequence=image_seq + ) + + def _process_images(self, images: List[Union[schema.ImageRef, PIL.Image]]) -> List[schema.ImageRef]: + """ + Converts a list of images to a (sorted) list of unique ImageRefs. + :param images: + :return: + """ + + image_refs = [img if isinstance(img, schema.ImageRef) else schema.ImageRef.from_image(img) for img in images] + image_refs = sorted(set(image_refs)) + return image_refs diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py index 1a9c601..dfed583 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py @@ -20,7 +20,7 @@ import scipy.stats as ss -import hobj.images.imagesets as imagesets +import hobj.data.images.imagesets as imagesets from pathlib import Path import os @@ -28,6 +28,8 @@ import json import pydantic +from hobj.benchmarks.experiment_simulation.task import SimpleBinaryTask + class MutatorHighVarBenchmark: @@ -51,13 +53,11 @@ def __init__(self): self.imageset = imagesets.MutatorHighVarImageset() - # Todo: move/load this manifest elsewhere – e.g. to imageset subtask_manifest = Path(os.path.dirname(__file__)) / 'MutatorHighVarSubtasks.json' subtask_manifest = json.loads(subtask_manifest.read_text()) self.subtask_manifest = subtask_manifest - @dataclass class SimulateExperimentResult: """ @@ -148,14 +148,14 @@ def evaluate_model(self, learner: lm.BinaryLearningModel, seed: int = 0) -> Eval perf_late = glc[self.tlate].mean() return self.EvaluateModelResult( - mse_n = MSEn_point, - subtask_vector_spearmanr = SpearmanR_point, - subtasks = subtasks, - model_phat = phat_model, - model_varhat_phat = varhat_phat_model, - model_lapse_rate = lapse_rate, - human_phat = phat_target, - human_varhat_phat = varhat_phat_target + mse_n=MSEn_point, + subtask_vector_spearmanr=SpearmanR_point, + subtasks=subtasks, + model_phat=phat_model, + model_varhat_phat=varhat_phat_model, + model_lapse_rate=lapse_rate, + human_phat=phat_target, + human_varhat_phat=varhat_phat_target ) @property @@ -254,7 +254,6 @@ def get_null_MSEn_functional(ds_data: xr.Dataset): return self._ds_ceilings - @property def ds_worker_table(self): if not hasattr(self, '_ds_worker_table'): diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py index fb258c8..e9bd043 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py @@ -3,12 +3,12 @@ import xarray as xr import numpy as np -import hobj.benchmarks.experiment_simulation.environment as env_template +import hobj.benchmarks.experiment_simulation.environment_depr as env_template import hobj.utils.file_io as io -import hobj.images.imagesets as imagesets +import hobj.data.images.imagesets as imagesets import hobj.config as config import hobj.learning_models.learning_model as lm -import hobj.benchmarks.experiment_simulation.environment as env +import hobj.benchmarks.experiment_simulation.environment_depr as env import hobj.utils.file_io as io import os import xarray as xr diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py index 77d4a08..31991aa 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py @@ -5,7 +5,7 @@ from tqdm import trange import hobj.learning_models.learning_model as lm -import hobj.images.imagesets as imagesets +import hobj.data.images.imagesets as imagesets import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_experiment as mutator diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_experiment.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_experiment.py index 4029d12..1ec66d5 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_experiment.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_experiment.py @@ -4,9 +4,9 @@ import xarray as xr import numpy as np -import hobj.benchmarks.experiment_simulation.environment as env_template +import hobj.benchmarks.experiment_simulation.environment_depr as env_template from hobj.benchmarks.experiment_simulation.experiment_simulation2_tmp import ExperimentSimulation -import hobj.images.imagesets as imagesets +import hobj.data.images.imagesets as imagesets _loc = os.path.dirname(__file__) diff --git a/hobj/images/__init__.py b/hobj/data/images/__init__.py similarity index 100% rename from hobj/images/__init__.py rename to hobj/data/images/__init__.py diff --git a/hobj/images/image_meta/ds_MutatorB0_BurnIn.nc b/hobj/data/images/image_meta/ds_MutatorB0_BurnIn.nc similarity index 100% rename from hobj/images/image_meta/ds_MutatorB0_BurnIn.nc rename to hobj/data/images/image_meta/ds_MutatorB0_BurnIn.nc diff --git a/hobj/images/image_meta/ds_MutatorB2000_Oneshot64.nc b/hobj/data/images/image_meta/ds_MutatorB2000_Oneshot64.nc similarity index 100% rename from hobj/images/image_meta/ds_MutatorB2000_Oneshot64.nc rename to hobj/data/images/image_meta/ds_MutatorB2000_Oneshot64.nc diff --git a/hobj/images/image_meta/ds_MutatorB2000_Subset128_FullVar_Train.nc b/hobj/data/images/image_meta/ds_MutatorB2000_Subset128_FullVar_Train.nc similarity index 100% rename from hobj/images/image_meta/ds_MutatorB2000_Subset128_FullVar_Train.nc rename to hobj/data/images/image_meta/ds_MutatorB2000_Subset128_FullVar_Train.nc diff --git a/hobj/images/imagesets.py b/hobj/data/images/imagesets.py similarity index 100% rename from hobj/images/imagesets.py rename to hobj/data/images/imagesets.py diff --git a/hobj/images/load_image.py b/hobj/data/images/load_image.py similarity index 99% rename from hobj/images/load_image.py rename to hobj/data/images/load_image.py index 72f5a2e..1a51203 100644 --- a/hobj/images/load_image.py +++ b/hobj/data/images/load_image.py @@ -5,6 +5,7 @@ import hobj.utils.file_io as io import hobj.config as config + def get_image_location(image_url: str, cachedir=config.image_cachedir): """ Return the location of the image at the specified image_url on disk. If it is not present on disk yet, download it. diff --git a/hobj/images/readme.md b/hobj/data/images/readme.md similarity index 100% rename from hobj/images/readme.md rename to hobj/data/images/readme.md diff --git a/hobj/data/schema.py b/hobj/data/schema.py index 12d8c20..2bd917d 100644 --- a/hobj/data/schema.py +++ b/hobj/data/schema.py @@ -20,4 +20,7 @@ def from_image(cls, image: PIL.Image): ) def get_image_data(self) -> PIL.Image: - raise NotImplementedError \ No newline at end of file + raise NotImplementedError + + def __hash__(self): + return hash(self.sha256) diff --git a/hobj/data/store.py b/hobj/data/store.py index e69de29..c8ae731 100644 --- a/hobj/data/store.py +++ b/hobj/data/store.py @@ -0,0 +1,181 @@ +import json +import tempfile +from pathlib import Path +from typing import List, Iterator + +import PIL.Image + +import geo.data.schema as schema +import geo.utils as utils +from geo.config import Config, default_config + +__all__ = ['DataStore', 'default_data_store'] + + +class _PathManager: + def __init__(self, config: Config): + self.config = config + + # JSON caching from web + def get_json_cache_path(self, url: str) -> Path: + """ + Construct a cache path for a URL. + :param url: + :return: + """ + if not url.endswith('.json'): + raise ValueError(f"URL {url} does not point to a JSON file.") + + path = self.config.cachedir / 'json_cache' / (utils.hash_string(url) + '.json') + return path + + def get_image_cache_path(self, sha256: str) -> Path: + path = self.config.cachedir / 'image_cache' / (sha256 + '.png') + return path + + def get_triplet_cache_path(self, url:str) -> Path: + + if not url.endswith('.json.gz'): + raise ValueError(f"URL {url} does not point to a gzipped JSON file; expecting .json.gz file") + + path = self.config.cachedir / 'compressed_tripletsets' / (utils.hash_string(url) + '.json.gz') + return path + + def get_unpacked_tripletset_path(self, url: str) -> Path: + if not url.endswith('.json.gz'): + raise ValueError(f"URL {url} does not point to a gzipped JSON file; expecting .json.gz file") + + path = self.config.cachedir / 'compressed_tripletsets' / (utils.hash_string(url) + '.json') + return path + + def get_zipped_imageset_cache_path(self, imageset_url: str) -> Path: + if not imageset_url.endswith('.zip'): + raise ValueError(f"URL {imageset_url} does not point to a ZIP file") + + path = self.config.cachedir / 'compressed_imagesets' / (utils.hash_string(imageset_url) + '.zip') + return path + + +class DataStore: + + def __init__(self, config: Config): + self.config = config + self.path_manager = _PathManager(config=config) + self._image_cache_manifest = set() + self._db = None + + # Images + def register_image(self, image_data: PIL.Image) -> schema.ImageRef: + """ + Register an image in the data store. + :param image_data: + :return: + """ + sha256 = utils.hash_image(image=image_data) + path = self.path_manager.get_image_cache_path(sha256=sha256) + if not path.parent.exists(): + path.parent.mkdir(parents=True) + image_data.save(path) + return schema.ImageRef(sha256=sha256) + + def check_image_exists(self, sha256: str) -> bool: + """ + Checks whether an image is already cached in the data store. + :param sha256: + :return: + """ + if sha256 not in self._image_cache_manifest: + path = self.path_manager.get_image_cache_path(sha256=sha256) + if path.exists(): + self._image_cache_manifest.add(sha256) + return sha256 in self._image_cache_manifest + + def list_images(self) -> List[schema.ImageRef]: + """ + List all images in the data store. + :return: + """ + raise NotImplementedError() + + def load_image(self, image_ref: schema.ImageRef, as_rgb: bool = True) -> PIL.Image: + """ + Load an image from the data store. + If the image is not there, registers it. + :param image_ref: + :return: + """ + path = self.path_manager.get_image_cache_path(sha256=image_ref.sha256) + with PIL.Image.open(path) as img: + if as_rgb: + img = img.convert('RGB') + loaded_image = img.copy() + + return loaded_image + + def get_compressed_triplet_dataset(self, url:str, redownload: bool = False) -> Path: + """ + Get the path to the compressed triplet dataset file. + :param url: + :param redownload: + :return: + """ + + path = self.path_manager.get_triplet_cache_path(url=url) + if not path.exists() or redownload: + with tempfile.TemporaryDirectory() as tempdir: + tempdir = Path(tempdir) + savepath = tempdir / 'tmp.json.gz' + utils.download_file(url=url, output_path=savepath) + + if not path.parent.exists(): + path.parent.mkdir(parents=True) + + # Move the file to the correct location + savepath.rename(path) + + return path + + def get_compressed_imageset(self, imageset_url: str, redownload: bool = False) -> Path: + """ + Get the path to the compressed imageset file. + :param imageset_url: + :return: + """ + path = self.path_manager.get_zipped_imageset_cache_path(imageset_url) + if not path.exists() or redownload: + + # Save it in a tempdir + with tempfile.TemporaryDirectory() as tempdir: + tempdir = Path(tempdir) + savepath = tempdir / 'tmp.zip' + utils.download_file(url=imageset_url, output_path=savepath) + + if not path.parent.exists(): + path.parent.mkdir(parents=True) + + # Move the file to the correct location + savepath.rename(path) + + return path + + def get_json_data_from_url(self, url: str, redownload: bool = False): + """ + Loads JSON data file from a url, caching it if it is not already present. + :param url: + :param redownload: + :return: + """ + path = self.path_manager.get_json_cache_path(url=url) + + if not path.exists() or redownload: + json_data = utils.download_json(url=url) + if not path.parent.exists(): + path.parent.mkdir(parents=True) + path.write_text(json.dumps(json_data)) + print(f"Downloaded JSON data from {url} to {path}") + + return json.loads(path.read_text()) + + +# Default data store +default_data_store = DataStore(config=default_config) From 832bef36516cb5d732672d4cb89ea1420bdbbc72 Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 08:52:06 -0500 Subject: [PATCH 029/156] removed readme --- hobj/data/images/imagesets.py | 5 ++++- hobj/data/images/readme.md | 13 ------------- 2 files changed, 4 insertions(+), 14 deletions(-) delete mode 100644 hobj/data/images/readme.md diff --git a/hobj/data/images/imagesets.py b/hobj/data/images/imagesets.py index db20faf..5d2cce9 100644 --- a/hobj/data/images/imagesets.py +++ b/hobj/data/images/imagesets.py @@ -1,7 +1,7 @@ import hobj.config as config import os import xarray as xr -import hobj.images.load_image as load_image +import hobj.data.images.load_image as load_image import PIL.Image image_meta_loc = os.path.join(os.path.dirname(__file__), 'image_meta') @@ -49,3 +49,6 @@ def ds_meta(self): self._ds_meta = ds_meta return self._ds_meta + +if __name__ == '__main__': + x = WarmupImageset() \ No newline at end of file diff --git a/hobj/data/images/readme.md b/hobj/data/images/readme.md deleted file mode 100644 index 069c162..0000000 --- a/hobj/data/images/readme.md +++ /dev/null @@ -1,13 +0,0 @@ -# images - -A centralized location for all the images used in this study. - -Example: - -```python - -import hobj - -imageset = hobj.images.imagesets.MutatorImageset() -ds_meta = imageset.ds_meta -``` \ No newline at end of file From 53d65f2469de5a20dfc6e933e866f81a1da5b1e1 Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 08:52:26 -0500 Subject: [PATCH 030/156] moved behavioral data --- hobj/benchmarks/benchmark_utils/data_resampling.py | 2 +- .../MutatorHighVar/mut_highvar_benchmark.py | 7 +------ .../MutatorOneshot/mut_oneshot_benchmark.py | 2 +- hobj/{ => data}/behavioral_data/__init__.py | 0 hobj/{ => data}/behavioral_data/human_data.py | 2 +- hobj/{ => data}/behavioral_data/readme.md | 0 hobj/{ => data}/behavioral_data/template/__init__.py | 0 hobj/{ => data}/behavioral_data/template/sr_data.py | 0 8 files changed, 4 insertions(+), 9 deletions(-) rename hobj/{ => data}/behavioral_data/__init__.py (100%) rename hobj/{ => data}/behavioral_data/human_data.py (98%) rename hobj/{ => data}/behavioral_data/readme.md (100%) rename hobj/{ => data}/behavioral_data/template/__init__.py (100%) rename hobj/{ => data}/behavioral_data/template/sr_data.py (100%) diff --git a/hobj/benchmarks/benchmark_utils/data_resampling.py b/hobj/benchmarks/benchmark_utils/data_resampling.py index f8ceeb7..c4507db 100644 --- a/hobj/benchmarks/benchmark_utils/data_resampling.py +++ b/hobj/benchmarks/benchmark_utils/data_resampling.py @@ -223,6 +223,6 @@ def get_policy_grain_splithalf_and_bootstrap_resampled_data(ds_raw, nsplits): if __name__ == '__main__': - import hobj.behavioral_data.human_data as human_data + import hobj.data.behavioral_data.human_data as human_data ds_raw = human_data.MutatorHighVarDataset().load_ds_data() result = get_policy_grain_splithalf_and_bootstrap_resampled_data(ds_raw = ds_raw, nsplits = 1000) diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py index dfed583..d272610 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py @@ -1,9 +1,7 @@ -from typing import Union - import numpy as np import xarray as xr -from hobj.behavioral_data import human_data as human_data +from hobj.data.behavioral_data import human_data as human_data from hobj.learning_models import learning_model as lm from hobj.utils import stats as stats @@ -26,9 +24,6 @@ import os import json -import pydantic - -from hobj.benchmarks.experiment_simulation.task import SimpleBinaryTask class MutatorHighVarBenchmark: diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py index 31991aa..423505b 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py @@ -9,7 +9,7 @@ import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_experiment as mutator -import hobj.behavioral_data.human_data as human_data +import hobj.data.behavioral_data.human_data as human_data from hobj.statistics.resamplers.resamplers import Resampler import hobj.statistics.score_statistics.MSEn as R2n_funcs import hobj.statistics.variance_estimates.binomial as binomial_funcs diff --git a/hobj/behavioral_data/__init__.py b/hobj/data/behavioral_data/__init__.py similarity index 100% rename from hobj/behavioral_data/__init__.py rename to hobj/data/behavioral_data/__init__.py diff --git a/hobj/behavioral_data/human_data.py b/hobj/data/behavioral_data/human_data.py similarity index 98% rename from hobj/behavioral_data/human_data.py rename to hobj/data/behavioral_data/human_data.py index d31d958..e40780b 100644 --- a/hobj/behavioral_data/human_data.py +++ b/hobj/data/behavioral_data/human_data.py @@ -1,5 +1,5 @@ import xarray as xr -import hobj.behavioral_data.template.sr_data as template +import hobj.data.behavioral_data.template.sr_data as template import os import numpy as np import collections diff --git a/hobj/behavioral_data/readme.md b/hobj/data/behavioral_data/readme.md similarity index 100% rename from hobj/behavioral_data/readme.md rename to hobj/data/behavioral_data/readme.md diff --git a/hobj/behavioral_data/template/__init__.py b/hobj/data/behavioral_data/template/__init__.py similarity index 100% rename from hobj/behavioral_data/template/__init__.py rename to hobj/data/behavioral_data/template/__init__.py diff --git a/hobj/behavioral_data/template/sr_data.py b/hobj/data/behavioral_data/template/sr_data.py similarity index 100% rename from hobj/behavioral_data/template/sr_data.py rename to hobj/data/behavioral_data/template/sr_data.py From ebc37d09637d5d77aa67729a81c5301416d9ba3e Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 08:52:35 -0500 Subject: [PATCH 031/156] renamed dir --- hobj/benchmarks/benchmark_utils/data_resampling.py | 2 +- .../MutatorHighVar/mut_highvar_benchmark.py | 2 +- .../MutatorOneshot/mut_oneshot_benchmark.py | 2 +- hobj/data/{behavioral_data => behavior}/__init__.py | 0 hobj/data/{behavioral_data => behavior}/human_data.py | 2 +- hobj/data/{behavioral_data => behavior}/readme.md | 0 hobj/data/{behavioral_data => behavior}/template/__init__.py | 0 hobj/data/{behavioral_data => behavior}/template/sr_data.py | 0 8 files changed, 4 insertions(+), 4 deletions(-) rename hobj/data/{behavioral_data => behavior}/__init__.py (100%) rename hobj/data/{behavioral_data => behavior}/human_data.py (98%) rename hobj/data/{behavioral_data => behavior}/readme.md (100%) rename hobj/data/{behavioral_data => behavior}/template/__init__.py (100%) rename hobj/data/{behavioral_data => behavior}/template/sr_data.py (100%) diff --git a/hobj/benchmarks/benchmark_utils/data_resampling.py b/hobj/benchmarks/benchmark_utils/data_resampling.py index c4507db..3799434 100644 --- a/hobj/benchmarks/benchmark_utils/data_resampling.py +++ b/hobj/benchmarks/benchmark_utils/data_resampling.py @@ -223,6 +223,6 @@ def get_policy_grain_splithalf_and_bootstrap_resampled_data(ds_raw, nsplits): if __name__ == '__main__': - import hobj.data.behavioral_data.human_data as human_data + import hobj.data.behavior.human_data as human_data ds_raw = human_data.MutatorHighVarDataset().load_ds_data() result = get_policy_grain_splithalf_and_bootstrap_resampled_data(ds_raw = ds_raw, nsplits = 1000) diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py index d272610..76b4378 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py @@ -1,7 +1,7 @@ import numpy as np import xarray as xr -from hobj.data.behavioral_data import human_data as human_data +from hobj.data.behavior import human_data as human_data from hobj.learning_models import learning_model as lm from hobj.utils import stats as stats diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py index 423505b..6a79b65 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py @@ -9,7 +9,7 @@ import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_experiment as mutator -import hobj.data.behavioral_data.human_data as human_data +import hobj.data.behavior.human_data as human_data from hobj.statistics.resamplers.resamplers import Resampler import hobj.statistics.score_statistics.MSEn as R2n_funcs import hobj.statistics.variance_estimates.binomial as binomial_funcs diff --git a/hobj/data/behavioral_data/__init__.py b/hobj/data/behavior/__init__.py similarity index 100% rename from hobj/data/behavioral_data/__init__.py rename to hobj/data/behavior/__init__.py diff --git a/hobj/data/behavioral_data/human_data.py b/hobj/data/behavior/human_data.py similarity index 98% rename from hobj/data/behavioral_data/human_data.py rename to hobj/data/behavior/human_data.py index e40780b..39816d8 100644 --- a/hobj/data/behavioral_data/human_data.py +++ b/hobj/data/behavior/human_data.py @@ -1,5 +1,5 @@ import xarray as xr -import hobj.data.behavioral_data.template.sr_data as template +import hobj.data.behavior.template.sr_data as template import os import numpy as np import collections diff --git a/hobj/data/behavioral_data/readme.md b/hobj/data/behavior/readme.md similarity index 100% rename from hobj/data/behavioral_data/readme.md rename to hobj/data/behavior/readme.md diff --git a/hobj/data/behavioral_data/template/__init__.py b/hobj/data/behavior/template/__init__.py similarity index 100% rename from hobj/data/behavioral_data/template/__init__.py rename to hobj/data/behavior/template/__init__.py diff --git a/hobj/data/behavioral_data/template/sr_data.py b/hobj/data/behavior/template/sr_data.py similarity index 100% rename from hobj/data/behavioral_data/template/sr_data.py rename to hobj/data/behavior/template/sr_data.py From 7bdf58f417e8bed9767b5e1ef0f22189583cd700 Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 09:00:39 -0500 Subject: [PATCH 032/156] removed mut highvar experiment --- .../MutatorHighVar/mut_highvar_benchmark.py | 35 +-- .../MutatorHighVar/mut_highvar_experiment.py | 261 ------------------ hobj/data/schema.py | 2 - readme.md | 4 +- 4 files changed, 18 insertions(+), 284 deletions(-) delete mode 100644 hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py index 76b4378..b46b9a6 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py @@ -1,29 +1,24 @@ -import numpy as np -import xarray as xr - -from hobj.data.behavior import human_data as human_data -from hobj.learning_models import learning_model as lm -from hobj.utils import stats as stats - -import hobj.statistics.lapse_rate as lapse_rate_funcs -from hobj.statistics.resamplers.resamplers import LearningCurveResampler, WorkerResampler -import hobj.statistics.score_statistics.MSEn as R2n_funcs -import hobj.statistics.variance_estimates.binomial as binomial_funcs -import hobj.statistics.hypothesis_testing.bootstrapped_confidence_intervals as bootstrapped_confidence_intervals -import hobj.statistics.hypothesis_testing.approximated_null_distribution as approximated_null_distribution - +import json +import os +import warnings from dataclasses import dataclass +from pathlib import Path from typing import List -import warnings +import numpy as np import scipy.stats as ss +import xarray as xr import hobj.data.images.imagesets as imagesets -from pathlib import Path - -import os - -import json +import hobj.statistics.hypothesis_testing.approximated_null_distribution as approximated_null_distribution +import hobj.statistics.hypothesis_testing.bootstrapped_confidence_intervals as bootstrapped_confidence_intervals +import hobj.statistics.lapse_rate as lapse_rate_funcs +import hobj.statistics.score_statistics.MSEn as R2n_funcs +import hobj.statistics.variance_estimates.binomial as binomial_funcs +from hobj.data.behavior import human_data as human_data +from hobj.learning_models import learning_model as lm +from hobj.statistics.resamplers.resamplers import LearningCurveResampler, WorkerResampler +from hobj.utils import stats as stats class MutatorHighVarBenchmark: diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py deleted file mode 100644 index e9bd043..0000000 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py +++ /dev/null @@ -1,261 +0,0 @@ -import os - -import xarray as xr -import numpy as np - -import hobj.benchmarks.experiment_simulation.environment_depr as env_template -import hobj.utils.file_io as io -import hobj.data.images.imagesets as imagesets -import hobj.config as config -import hobj.learning_models.learning_model as lm -import hobj.benchmarks.experiment_simulation.environment_depr as env -import hobj.utils.file_io as io -import os -import xarray as xr -import numpy as np -from tqdm import tqdm -from typing import Union - - -class ExperimentSimulation(object): - """ - Wraps a set of environments, and simulates them self.nreps times each - """ - nreps = None - ntrials = None - environment_name_dim = 'environment' - - def __init__(self, cachedir=config.simulation_cachedir): - self.cachedir = cachedir - return - - @property - def experiment_name(self): - raise NotImplementedError - return 'write_experiment_name_here' - - @property - def environments(self): - raise NotImplementedError - return [env.Environment()] - - def run(self, learner: lm.BinaryLearningModel, seed: Union[type(None), int], force_recompute=False, show_pbar=True): - - savepath = os.path.join(self.cachedir, self.experiment_name, 'seed_' + str(seed), f'ds_{learner.learner_id}.nc') - if os.path.exists(savepath) and not force_recompute: - ds = xr.load_dataset(savepath) - return ds - - dlist = [] - RS = np.random.RandomState(seed=seed) - if show_pbar: - pbar = tqdm(total=self.nreps * self.ntrials * len(self.environments), desc='simulation trials') - else: - pbar = None - - for environment in self.environments: - ds = self.simulate_environment(learner=learner, environment=environment, RS=RS, nreps=self.nreps, ntrials=self.ntrials, pbar=pbar) - ds = self.postprocess_behavioral_data(ds=ds, environment=environment) - dlist.append(ds) - - ds = xr.concat(dlist, dim=self.environment_name_dim, ) - io.prepare_savepath(savepath) - ds.to_netcdf(savepath) - return ds - - def postprocess_behavioral_data(self, ds: xr.Dataset, environment: env.Environment): - """ - Called following the simulation of a single environment. Allows for postprocessing of the behavioral data. - :param ds: - :param environment: - :return: - """ - return ds - - @staticmethod - def simulate_environment( - learner: lm.BinaryLearningModel, - environment: env.Environment, - RS: np.random.RandomState, - nreps: int, - ntrials: int, - pbar: tqdm = None, - ): - - d = { - 'image_url': (['trial', 'rep', ], [['' for _ in range(nreps)] for _ in range(ntrials)]), - 'action': (['trial', 'rep', ], np.zeros((ntrials, nreps))), - 'reward': (['trial', 'rep', ], np.zeros((ntrials, nreps))), - } - - for i_rep in range(nreps): - learner.reset_state() - environment.initialize(RS=RS) - for i_trial in range(ntrials): - if pbar is not None: - pbar.update(1) - - image_url = environment.sample_image() - action = learner.get_response(image=image_url) - action = int(action) - reward = environment.provide_feedback(action=action) - reward = float(reward) - learner.give_feedback(reward=reward) - - d['image_url'][1][i_trial][i_rep] = image_url - d['action'][1][i_trial, i_rep] = action - d['reward'][1][i_trial, i_rep] = reward - - for k in environment.current_state_meta: - d[k][1][i_trial, i_rep] = (environment.current_state_meta[k]) - - ds = xr.Dataset(d) - ds = ds.assign_coords(environment.meta) - ds = ds.assign_coords(image_url=ds['image_url']) - return ds - - -_loc = os.path.dirname(__file__) - - -class BinaryClassificationEnvironment(env_template.Environment): - """ - Implements a binary classification subtask - """ - - def __init__( - self, - category0_urls: list, - category1_urls: list, - replace=False, # sample with replacement - ): - super().__init__() - - assert isinstance(category0_urls, list) - assert isinstance(category1_urls, list) - - assert len(category0_urls) == len(category1_urls) - self.labels = [0 for _ in category0_urls] + [1 for _ in category1_urls] - self.image_urls = category0_urls + category1_urls - self.i_seq = None - self.step = None - self.replace = replace - return - - @property - def current_state_meta(self): - # Returns meta information for the current environmental state, that will be logged in a simulation. Not allowed to be used by the learner. - return {} - - def initialize(self, RS: np.random.RandomState): - if not self.replace: - self.i_seq = RS.permutation(len(self.image_urls)) - else: - self.i_seq = RS.choice(len(self.image_urls), replace=True, size=len(self.image_urls)) - self.step = 0 - - def sample_image(self): - icur = self.i_seq[self.step] - image_url = self.image_urls[icur] - return image_url - - def provide_feedback(self, action: int): - assert action in [0, 1] - icur = self.i_seq[self.step] - - if action == self.labels[icur]: - reward = 1. - else: - reward = -1. - - self.step += 1 - - return reward - - -class MutatorHighVarExperiment(ExperimentSimulation): - nreps = 500 - ntrials = 100 - imageset = imagesets.MutatorHighVarImageset() - environment_name_dim = 'subtask' - - @property - def experiment_name(self): - return 'MutatorHighVarExperiment' - - def postprocess_behavioral_data(self, ds: xr.Dataset, environment: env_template.Environment): - - """ - - :param ds: - :param environment: - :return: - action: (trial, stimulus_category). Probability of taking action 1, given this trial, and this stimulus category. - """ - ds = ds.transpose('rep', 'trial') - - ds_meta = self.imageset.ds_meta - nreps = len(ds.rep) - ntrials = len(ds.trial) - k_action = np.zeros((nreps, ntrials, 2)) - n_action = np.zeros((nreps, ntrials, 2)) - for i_rep in range(len(ds.rep)): - objseq = ds_meta.sel(image_url=ds.image_url.values[i_rep]).obj.values - objects = sorted(np.unique(objseq)) - assert len(objects) == 2 - - for i_trial in range(len(ds.trial)): - i_obj = objects.index(objseq[i_trial]) - reward = ds.reward.values[i_rep, i_trial] - - if i_obj == 1: - if reward > 0: - took_action1 = True - else: - took_action1 = False - elif i_obj == 0: - if reward > 0: - took_action1 = False - else: - took_action1 = True - else: - raise Exception - - k_action[i_rep, i_trial, i_obj] = int(took_action1) - n_action[i_rep, i_trial, i_obj] = 1 - - assert np.all(k_action <= n_action) - - k_action = k_action.sum(0) - n_action = n_action.sum(0) - prob_action = k_action / n_action - del ds['action'] - del ds['reward'] - del ds['image_url'] - ds['action'] = (('trial', 'stimulus_category'), prob_action) - ds['k'] = (('trial', 'stimulus_category'), k_action) - ds['n'] = (('trial', 'stimulus_category'), n_action) - - return ds - - @property - def environments(self): - if not hasattr(self, '_environments'): - subtasks = io.load_json(json_path=os.path.join(_loc, 'MutatorHighVarSubtasks.json')) - """ - subtasks: - {subtask:obj:[image urls]} - """ - - environments = [] - for subtask in subtasks: - obj0, obj1 = subtask.split(',') - urls0, urls1 = subtasks[subtask][obj0], subtasks[subtask][obj1] - env_cur = BinaryClassificationEnvironment(category0_urls=urls0, category1_urls=urls1) - env_cur.meta = {'subtask': subtask} - environments.append(env_cur) - self._environments = environments - return self._environments - - - diff --git a/hobj/data/schema.py b/hobj/data/schema.py index 2bd917d..4add97a 100644 --- a/hobj/data/schema.py +++ b/hobj/data/schema.py @@ -1,5 +1,3 @@ -from typing import Literal, List - import PIL.Image import pydantic diff --git a/readme.md b/readme.md index 6a63003..6e7b0ae 100644 --- a/readme.md +++ b/readme.md @@ -36,4 +36,6 @@ To see how to view the raw behavioral data and/or score an example learning mode # Paper This codebase originally accompanied the 2023 paper ["How well do rudimentary plasticity rules predict adult visual object learning?"](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011713). -Since that point, this codebase has been refactored and streamlined, introducing breaking changes to the API. However, to see the codebase that reflects its content at the time of publication, check out the `v1` tag. + + +Since then, this codebase has been refactored and streamlined, introducing breaking changes to the API. To see the codebase that reflects its contents at the time of publication, check out the `v1` tag. From accc9ff7a5688c42d9bb6a9df36bf5180e40b8b8 Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 09:01:37 -0500 Subject: [PATCH 033/156] removed simulation caching --- .../experiment_simulation2_tmp.py | 12 +++--------- hobj/config.py | 1 - 2 files changed, 3 insertions(+), 10 deletions(-) diff --git a/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py b/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py index 87f93f4..dad2e1f 100644 --- a/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py +++ b/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py @@ -17,8 +17,7 @@ class ExperimentSimulation(object): ntrials = None environment_name_dim = 'environment' - def __init__(self, cachedir=config.simulation_cachedir): - self.cachedir = cachedir + def __init__(self): return @property @@ -31,12 +30,8 @@ def environments(self): raise NotImplementedError return [env.Environment()] - def run(self, learner: lm.BinaryLearningModel, seed: Union[type(None), int], force_recompute=False, show_pbar=True): + def run(self, learner: lm.BinaryLearningModel, seed: Union[type(None), int], show_pbar=True): - savepath = os.path.join(self.cachedir, self.experiment_name, 'seed_' + str(seed), f'ds_{learner.learner_id}.nc') - if os.path.exists(savepath) and not force_recompute: - ds = xr.load_dataset(savepath) - return ds dlist = [] RS = np.random.RandomState(seed=seed) @@ -51,8 +46,7 @@ def run(self, learner: lm.BinaryLearningModel, seed: Union[type(None), int], for dlist.append(ds) ds = xr.concat(dlist, dim=self.environment_name_dim, ) - io.prepare_savepath(savepath) - ds.to_netcdf(savepath) + return ds def postprocess_behavioral_data(self, ds: xr.Dataset, environment: env.Environment): diff --git a/hobj/config.py b/hobj/config.py index 27ba6a6..344ce61 100644 --- a/hobj/config.py +++ b/hobj/config.py @@ -4,7 +4,6 @@ image_cachedir = os.path.join(hlb_cachedir, 'images') behavior_cachedir = os.path.join(hlb_cachedir, 'behavior') -simulation_cachedir = os.path.join(hlb_cachedir, 'model_simulations') features_cachedir = os.path.join(hlb_cachedir, 'image_features') From b5032bdfe3ac906e37bb55b2178b685f5a664905 Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 09:02:00 -0500 Subject: [PATCH 034/156] removed image features config line --- hobj/config.py | 1 - 1 file changed, 1 deletion(-) diff --git a/hobj/config.py b/hobj/config.py index 344ce61..c2cf591 100644 --- a/hobj/config.py +++ b/hobj/config.py @@ -4,6 +4,5 @@ image_cachedir = os.path.join(hlb_cachedir, 'images') behavior_cachedir = os.path.join(hlb_cachedir, 'behavior') -features_cachedir = os.path.join(hlb_cachedir, 'image_features') From 6b781730eb9df3f3d8cc02c01516a719e5e0acfb Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 09:03:17 -0500 Subject: [PATCH 035/156] turned into path --- hobj/config.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/hobj/config.py b/hobj/config.py index c2cf591..a10922a 100644 --- a/hobj/config.py +++ b/hobj/config.py @@ -1,8 +1,7 @@ -import os +from pathlib import Path -hlb_cachedir = os.path.join(os.path.expanduser('~'), 'hlb_cache') - -image_cachedir = os.path.join(hlb_cachedir, 'images') -behavior_cachedir = os.path.join(hlb_cachedir, 'behavior') +hlb_cachedir = Path.home() / 'hlb_cache' +if not hlb_cachedir.exists(): + hlb_cachedir.mkdir() From 16d15b0a29d46be5df59e62e59f565b11034928f Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 09:04:26 -0500 Subject: [PATCH 036/156] renamed cache --- hobj/config.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/hobj/config.py b/hobj/config.py index a10922a..079dca2 100644 --- a/hobj/config.py +++ b/hobj/config.py @@ -1,6 +1,9 @@ from pathlib import Path +from dataclasses import dataclass -hlb_cachedir = Path.home() / 'hlb_cache' +@dataclass +class Config: + cachedir = Path.home() / 'hobj_cache' if not hlb_cachedir.exists(): hlb_cachedir.mkdir() From d610bf98d76663f12c759726ec8e3a0e3cb6ea8e Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 09:05:05 -0500 Subject: [PATCH 037/156] removed readme --- hobj/config.py | 6 ++---- hobj/data/behavior/readme.md | 3 --- hobj/data/store.py | 4 +--- 3 files changed, 3 insertions(+), 10 deletions(-) delete mode 100644 hobj/data/behavior/readme.md diff --git a/hobj/config.py b/hobj/config.py index 079dca2..39ee7b5 100644 --- a/hobj/config.py +++ b/hobj/config.py @@ -3,8 +3,6 @@ @dataclass class Config: - cachedir = Path.home() / 'hobj_cache' - -if not hlb_cachedir.exists(): - hlb_cachedir.mkdir() + cachedir: str = Path.home() / 'hobj_cache' +default_config = Config() diff --git a/hobj/data/behavior/readme.md b/hobj/data/behavior/readme.md deleted file mode 100644 index 82d0201..0000000 --- a/hobj/data/behavior/readme.md +++ /dev/null @@ -1,3 +0,0 @@ -# - -Worker IDs have been obfuscated. \ No newline at end of file diff --git a/hobj/data/store.py b/hobj/data/store.py index c8ae731..623cf01 100644 --- a/hobj/data/store.py +++ b/hobj/data/store.py @@ -5,9 +5,7 @@ import PIL.Image -import geo.data.schema as schema -import geo.utils as utils -from geo.config import Config, default_config +from hobj.config import Config, default_config __all__ = ['DataStore', 'default_data_store'] From a1e48a422f5a8ea48d8e29d7bff5dfe240e498e3 Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 09:18:28 -0500 Subject: [PATCH 038/156] data store --- hobj/config.py | 7 +-- hobj/data/schema.py | 16 ++++-- hobj/data/store.py | 126 ++++---------------------------------------- 3 files changed, 22 insertions(+), 127 deletions(-) diff --git a/hobj/config.py b/hobj/config.py index 39ee7b5..07bc6e0 100644 --- a/hobj/config.py +++ b/hobj/config.py @@ -1,8 +1,3 @@ from pathlib import Path -from dataclasses import dataclass -@dataclass -class Config: - cachedir: str = Path.home() / 'hobj_cache' - -default_config = Config() +cachedir: Path = Path.home() / 'hobj_cache' diff --git a/hobj/data/schema.py b/hobj/data/schema.py index 4add97a..991fa5b 100644 --- a/hobj/data/schema.py +++ b/hobj/data/schema.py @@ -2,23 +2,29 @@ import pydantic from hobj.utils.hash import hash_image +from hobj.data.store import default_data_store # %% Image class ImageRef(pydantic.BaseModel): sha256: str = pydantic.Field(pattern=r'^[a-f0-9]{64}$') - def __lt__(self, other) -> bool: - return self.sha256 < other.sha256 - @classmethod - def from_image(cls, image: PIL.Image): + def from_image(cls, image: PIL.Image, register: bool = True): + if register: + return default_data_store.register_image(image=image) + return cls( sha256=hash_image(image=image) ) def get_image_data(self) -> PIL.Image: - raise NotImplementedError + return default_data_store.load_image(image_ref=self) + + def __lt__(self, other) -> bool: + return self.sha256 < other.sha256 def __hash__(self): return hash(self.sha256) + +# %% Behavior diff --git a/hobj/data/store.py b/hobj/data/store.py index 623cf01..10310d6 100644 --- a/hobj/data/store.py +++ b/hobj/data/store.py @@ -5,71 +5,36 @@ import PIL.Image -from hobj.config import Config, default_config +import hobj.config as config +import hobj.data.schema as schema +from hobj.utils.hash import hash_image __all__ = ['DataStore', 'default_data_store'] class _PathManager: - def __init__(self, config: Config): - self.config = config - # JSON caching from web - def get_json_cache_path(self, url: str) -> Path: - """ - Construct a cache path for a URL. - :param url: - :return: - """ - if not url.endswith('.json'): - raise ValueError(f"URL {url} does not point to a JSON file.") - - path = self.config.cachedir / 'json_cache' / (utils.hash_string(url) + '.json') - return path + def __init__(self, cachedir: Path): + self.cachedir = cachedir def get_image_cache_path(self, sha256: str) -> Path: - path = self.config.cachedir / 'image_cache' / (sha256 + '.png') - return path - - def get_triplet_cache_path(self, url:str) -> Path: - - if not url.endswith('.json.gz'): - raise ValueError(f"URL {url} does not point to a gzipped JSON file; expecting .json.gz file") - - path = self.config.cachedir / 'compressed_tripletsets' / (utils.hash_string(url) + '.json.gz') - return path - - def get_unpacked_tripletset_path(self, url: str) -> Path: - if not url.endswith('.json.gz'): - raise ValueError(f"URL {url} does not point to a gzipped JSON file; expecting .json.gz file") - - path = self.config.cachedir / 'compressed_tripletsets' / (utils.hash_string(url) + '.json') - return path - - def get_zipped_imageset_cache_path(self, imageset_url: str) -> Path: - if not imageset_url.endswith('.zip'): - raise ValueError(f"URL {imageset_url} does not point to a ZIP file") - - path = self.config.cachedir / 'compressed_imagesets' / (utils.hash_string(imageset_url) + '.zip') + path = self.cachedir / 'image_cache' / (sha256 + '.png') return path class DataStore: - def __init__(self, config: Config): - self.config = config - self.path_manager = _PathManager(config=config) + def __init__(self, cachedir: Path): + self.path_manager = _PathManager(cachedir=cachedir) self._image_cache_manifest = set() - self._db = None - # Images def register_image(self, image_data: PIL.Image) -> schema.ImageRef: """ Register an image in the data store. :param image_data: :return: """ - sha256 = utils.hash_image(image=image_data) + sha256 = hash_image(image=image_data) path = self.path_manager.get_image_cache_path(sha256=sha256) if not path.parent.exists(): path.parent.mkdir(parents=True) @@ -88,13 +53,6 @@ def check_image_exists(self, sha256: str) -> bool: self._image_cache_manifest.add(sha256) return sha256 in self._image_cache_manifest - def list_images(self) -> List[schema.ImageRef]: - """ - List all images in the data store. - :return: - """ - raise NotImplementedError() - def load_image(self, image_ref: schema.ImageRef, as_rgb: bool = True) -> PIL.Image: """ Load an image from the data store. @@ -110,70 +68,6 @@ def load_image(self, image_ref: schema.ImageRef, as_rgb: bool = True) -> PIL.Ima return loaded_image - def get_compressed_triplet_dataset(self, url:str, redownload: bool = False) -> Path: - """ - Get the path to the compressed triplet dataset file. - :param url: - :param redownload: - :return: - """ - - path = self.path_manager.get_triplet_cache_path(url=url) - if not path.exists() or redownload: - with tempfile.TemporaryDirectory() as tempdir: - tempdir = Path(tempdir) - savepath = tempdir / 'tmp.json.gz' - utils.download_file(url=url, output_path=savepath) - - if not path.parent.exists(): - path.parent.mkdir(parents=True) - - # Move the file to the correct location - savepath.rename(path) - - return path - - def get_compressed_imageset(self, imageset_url: str, redownload: bool = False) -> Path: - """ - Get the path to the compressed imageset file. - :param imageset_url: - :return: - """ - path = self.path_manager.get_zipped_imageset_cache_path(imageset_url) - if not path.exists() or redownload: - - # Save it in a tempdir - with tempfile.TemporaryDirectory() as tempdir: - tempdir = Path(tempdir) - savepath = tempdir / 'tmp.zip' - utils.download_file(url=imageset_url, output_path=savepath) - - if not path.parent.exists(): - path.parent.mkdir(parents=True) - - # Move the file to the correct location - savepath.rename(path) - - return path - - def get_json_data_from_url(self, url: str, redownload: bool = False): - """ - Loads JSON data file from a url, caching it if it is not already present. - :param url: - :param redownload: - :return: - """ - path = self.path_manager.get_json_cache_path(url=url) - - if not path.exists() or redownload: - json_data = utils.download_json(url=url) - if not path.parent.exists(): - path.parent.mkdir(parents=True) - path.write_text(json.dumps(json_data)) - print(f"Downloaded JSON data from {url} to {path}") - - return json.loads(path.read_text()) - # Default data store -default_data_store = DataStore(config=default_config) +default_data_store = DataStore(cachedir=config.cachedir) From 86d4d3afe5577956f7240fe09ea5b8b81029ddc3 Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 09:23:46 -0500 Subject: [PATCH 039/156] delete load image --- hobj/data/images/load_image.py | 37 ---------------------------------- 1 file changed, 37 deletions(-) delete mode 100644 hobj/data/images/load_image.py diff --git a/hobj/data/images/load_image.py b/hobj/data/images/load_image.py deleted file mode 100644 index 1a51203..0000000 --- a/hobj/data/images/load_image.py +++ /dev/null @@ -1,37 +0,0 @@ -import os - -import PIL.Image - -import hobj.utils.file_io as io -import hobj.config as config - - -def get_image_location(image_url: str, cachedir=config.image_cachedir): - """ - Return the location of the image at the specified image_url on disk. If it is not present on disk yet, download it. - :param image_url: URL of the image to download. - :param cachedir: Directory to cache the image in. - :return: - """ - save_location = io.get_local_save_location(url=image_url, cachedir=cachedir) - - if not os.path.exists(save_location): - io.prepare_savepath(save_location) - image = io.get_image_from_url(image_url=image_url) - image.save(save_location) - print('Downloaded image to {}'.format(save_location)) - return save_location - - -def get_image(image_url: str, cachedir=config.image_cachedir) -> PIL.Image: - """ - Download an image from a URL and return it as a PIL.Image. - If cached, return the cached image. - :param image_url: URL of the image to retrieve. - :param cachedir: Directory to cache the image in. - :return: - """ - - save_location = get_image_location(image_url=image_url, cachedir=cachedir) - image = PIL.Image.open(save_location) - return image From 5d0806f21b9ca21bbf1d83256df737f39c2f16ec Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 10:24:39 -0500 Subject: [PATCH 040/156] add imageset template --- hobj/data/images/template.py | 141 +++++++++++++++++++++++++++++++++++ tests/dummy_test.py | 2 - 2 files changed, 141 insertions(+), 2 deletions(-) create mode 100644 hobj/data/images/template.py delete mode 100644 tests/dummy_test.py diff --git a/hobj/data/images/template.py b/hobj/data/images/template.py new file mode 100644 index 0000000..6797ac3 --- /dev/null +++ b/hobj/data/images/template.py @@ -0,0 +1,141 @@ +import tempfile +from abc import ABC +from pathlib import Path +from typing import Any, Dict, TypeVar, Generic, List + +import PIL.Image +import pydantic +from tqdm import tqdm + +from hobj.utils.file_io import unzip_file +from hobj.utils.hash import hash_image +from hobj.data.schema import ImageRef +from hobj.data.store import DataStore, default_data_store + + +class ImageManifestEntry(pydantic.BaseModel, ABC): + sha256: str = pydantic.Field(pattern=r'^[a-f0-9]{64}$') + relpath: Path = pydantic.Field( + description='The relative path to the image file inside of the unzipped imageset directory.' + ) + annotation: Any = pydantic.Field( + description='Arbitrary annotation data for the image, in a JSON-valid format.' + ) + + +class ImageManifest(pydantic.BaseModel): + entries: List[ImageManifestEntry] = pydantic.Field( + description='A list of image manifest entries.' + ) + + zipped_images_url: str = pydantic.Field( + description='The URL to the zipped imageset file.' + ) + + +# This is a type variable for ImageManifestEntry.annotation: +IA = TypeVar('IA') + + +class Imageset(Generic[IA], ABC): + """ + Imagesets are a combination of: + - Images + - Annotations on those images + """ + + manifest_url: str + annotation_schema: IA + + def __init__( + self, + data_store: DataStore = None, + redownload_manifest=False, + ): + """ + Unwrap the image manifest and save the images to the cache. + """ + + if not data_store: + self.data_store: DataStore = default_data_store + + # Load the manifest if it is already cached + manifest_data = self.data_store.get_json_data_from_url(url=self.manifest_url, redownload=redownload_manifest) + image_manifest = ImageManifest(**manifest_data) + + self._register_image_urls(manifest=image_manifest) + self._manifest = image_manifest + + self._image_ref_to_annotation: Dict[ImageRef, IA] = {} + self._image_refs: List[ImageRef] = [] + + for entry in image_manifest.entries: + image_ref = ImageRef(sha256=entry.sha256) + self._image_refs.append(image_ref) + if image_ref in self._image_ref_to_annotation: + raise ValueError(f"Multiple annotations for image {image_ref}: \n{entry.annotation} \n{self._image_ref_to_annotation[image_ref]}") + self._image_ref_to_annotation[image_ref] = self.annotation_schema(**entry.annotation) + + def _register_image_urls(self, manifest: ImageManifest): + """ + Ensures the entries of the manifest are registered in the data store. + """ + + num_undownloaded_images = 0 + for manifest_entry in manifest.entries: + + + if not self.data_store.check_image_exists(sha256=manifest_entry.sha256): + num_undownloaded_images += 1 + + if num_undownloaded_images == 0: + return + + print(f'Missing {num_undownloaded_images}/{len(manifest.entries)} images for this imageset.') + + # Download the images + zipped_images_path = self.data_store.get_zipfile(zipfile_url=manifest.zipped_images_url) + + # Make a tempdir to unzip the images + with tempfile.TemporaryDirectory() as tempdir: + tempdir = Path(tempdir) + unzip_file(zip_path=zipped_images_path, output_dir=tempdir) + + # Register the images + pbar = tqdm(total=len(manifest.entries)) + for manifest_entry in manifest.entries: + reported_sha256 = manifest_entry.sha256 + relpath = manifest_entry.relpath + image_path = tempdir / relpath + image_data = PIL.Image.open(image_path) + actual_sha256 = hash_image(image=image_data) + if not actual_sha256 == reported_sha256: + raise ValueError(f"SHA256 mismatch for image {manifest_entry}: {actual_sha256} != {reported_sha256}") + + self.data_store.register_image(image_data=image_data) + pbar.update(1) + + @property + def image_refs(self) -> List[ImageRef]: + """ + List of image refs in this imageset. + :return: + """ + return self._image_refs + + def get_annotation(self, image_ref: ImageRef) -> IA: + """ + Get the annotation for a given image. If an image has multiple annotations, this will throw an error. + """ + + entry = self._image_ref_to_annotation[image_ref] + return self.annotation_schema(**entry.annotation) + + def __len__(self) -> int: + return len(self.image_refs) + + def __repr__(self): + return f"{self.__class__.__name__}({len(self)})" + + def __str__(self): + return f"Imageset(n={len(self)})" diff --git a/tests/dummy_test.py b/tests/dummy_test.py deleted file mode 100644 index 603860f..0000000 --- a/tests/dummy_test.py +++ /dev/null @@ -1,2 +0,0 @@ -def test_dummy(): - assert True \ No newline at end of file From 2ac13ef9c523a8edf1cbbb5896a0538088b880db Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 10:24:48 -0500 Subject: [PATCH 041/156] tag depr --- .../benchmark_utils/data_resampling.py | 4 +- .../MutatorHighVar/mut_highvar_benchmark.py | 4 +- .../MutatorOneshot/mut_oneshot_benchmark.py | 4 +- .../MutatorOneshot/mut_oneshot_experiment.py | 6 +- hobj/data/behavior/human_data.py | 8 +- .../{imagesets.py => depr_imagesets.py} | 32 +++-- .../images/image_meta/ds_MutatorB0_BurnIn.nc | Bin 182784 -> 0 bytes .../image_meta/ds_MutatorB2000_Oneshot64.nc | Bin 2008487 -> 0 bytes ...ds_MutatorB2000_Subset128_FullVar_Train.nc | Bin 3926016 -> 0 bytes hobj/data/images/tmp_package_imagesets.py | 0 hobj/data/store.py | 87 ++++++++++-- hobj/utils/file_io.py | 128 +++++++++++++----- hobj/utils/hash.py | 10 ++ 13 files changed, 206 insertions(+), 77 deletions(-) rename hobj/data/images/{imagesets.py => depr_imagesets.py} (69%) delete mode 100644 hobj/data/images/image_meta/ds_MutatorB0_BurnIn.nc delete mode 100644 hobj/data/images/image_meta/ds_MutatorB2000_Oneshot64.nc delete mode 100644 hobj/data/images/image_meta/ds_MutatorB2000_Subset128_FullVar_Train.nc create mode 100644 hobj/data/images/tmp_package_imagesets.py diff --git a/hobj/benchmarks/benchmark_utils/data_resampling.py b/hobj/benchmarks/benchmark_utils/data_resampling.py index 3799434..723db50 100644 --- a/hobj/benchmarks/benchmark_utils/data_resampling.py +++ b/hobj/benchmarks/benchmark_utils/data_resampling.py @@ -132,8 +132,8 @@ def get_policy_grain_splithalf_and_bootstrap_resampled_data(ds_raw, nsplits): worker_to_kn = {} nworkers = len(set(ds_raw.worker_id.values)) - import hobj.data.images.imagesets as imagesets - ds_meta = imagesets.MutatorHighVarImageset().ds_meta + import hobj.data.images.depr_imagesets as imagesets + ds_meta = imagesets.MutatorHighVarDeprImageset().ds_meta for worker_id, ws in tqdm(ds_raw.groupby('worker_id'), total=nworkers): diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py index b46b9a6..690ce79 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py @@ -9,7 +9,7 @@ import scipy.stats as ss import xarray as xr -import hobj.data.images.imagesets as imagesets +import hobj.data.images.depr_imagesets as imagesets import hobj.statistics.hypothesis_testing.approximated_null_distribution as approximated_null_distribution import hobj.statistics.hypothesis_testing.bootstrapped_confidence_intervals as bootstrapped_confidence_intervals import hobj.statistics.lapse_rate as lapse_rate_funcs @@ -41,7 +41,7 @@ def __init__(self): worker_dim='worker_id' ) - self.imageset = imagesets.MutatorHighVarImageset() + self.imageset = imagesets.MutatorHighVarDeprImageset() # Todo: move/load this manifest elsewhere – e.g. to imageset subtask_manifest = Path(os.path.dirname(__file__)) / 'MutatorHighVarSubtasks.json' diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py index 6a79b65..5430699 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py @@ -5,7 +5,7 @@ from tqdm import trange import hobj.learning_models.learning_model as lm -import hobj.data.images.imagesets as imagesets +import hobj.data.images.depr_imagesets as imagesets import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_experiment as mutator @@ -27,7 +27,7 @@ def __init__(self): self.ds_raw = self.dataset.load_ds_data() # Remove sessions with less than 100% catch performance - self.ds_meta = imagesets.MutatorOneshotImageset().ds_meta + self.ds_meta = imagesets.MutatorOneshotDeprImageset().ds_meta self.url_to_trans_id = {url: trans for (url, trans) in zip(self.ds_meta.image_url.values, self.ds_meta.transformation_id.values)} self.url_to_obj = {url: obj for (url, obj) in zip(self.ds_meta.image_url.values, self.ds_meta.obj.values)} self.experiment = mutator.MutatorOneShotExperiment() diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_experiment.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_experiment.py index 1ec66d5..2f027ec 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_experiment.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_experiment.py @@ -6,7 +6,7 @@ import hobj.benchmarks.experiment_simulation.environment_depr as env_template from hobj.benchmarks.experiment_simulation.experiment_simulation2_tmp import ExperimentSimulation -import hobj.data.images.imagesets as imagesets +import hobj.data.images.depr_imagesets as imagesets _loc = os.path.dirname(__file__) @@ -111,7 +111,7 @@ def provide_feedback(self, action: int): class MutatorOneShotExperiment(ExperimentSimulation): nreps = 500 # per subtask (n=32) ntrials = 20 - imageset = imagesets.MutatorOneshotImageset() + imageset = imagesets.MutatorOneshotDeprImageset() environment_name_dim = 'subtask' @property @@ -247,7 +247,7 @@ def environments(self): # Replicate the same kinds of trials done in humans if not hasattr(self, '_environments'): - ds_meta = imagesets.MutatorOneshotImageset().ds_meta + ds_meta = imagesets.MutatorOneshotDeprImageset().ds_meta obj_to_support_url = {} obj_to_test_urls = {} diff --git a/hobj/data/behavior/human_data.py b/hobj/data/behavior/human_data.py index 39816d8..45229ce 100644 --- a/hobj/data/behavior/human_data.py +++ b/hobj/data/behavior/human_data.py @@ -19,8 +19,8 @@ def apply_experiment_specific_filtering(self, ds_flat): ds_flat = ds_flat[['perf', 'reaction_time_msec']] # Assign subtask names - import hobj.data.images.imagesets as imagesets - ds_meta = imagesets.MutatorHighVarImageset().ds_meta + import hobj.data.images.depr_imagesets as imagesets + ds_meta = imagesets.MutatorHighVarDeprImageset().ds_meta urls = ds_flat.stimulus_url.transpose('session', 'trial').values url_to_obj = {url: obj for url, obj in zip(ds_meta.image_url.values, ds_meta.obj.values)} sseq = [] @@ -104,8 +104,8 @@ class MutatorOneShotGeneralizationDataset(template.SR_Dataset): def apply_experiment_specific_filtering(self, ds_flat): import numpy as np - import hobj.data.images.imagesets as imagesets - ds_meta = imagesets.MutatorOneshotImageset().ds_meta + import hobj.data.images.depr_imagesets as imagesets + ds_meta = imagesets.MutatorOneshotDeprImageset().ds_meta image_url_to_transformation = {url:trans for url, trans in zip(ds_meta.image_url.values, ds_meta.transformation.values)} image_url_to_transformation_level = {url:trans_level for url, trans_level in zip(ds_meta.image_url.values, ds_meta.transformation_level.values)} diff --git a/hobj/data/images/imagesets.py b/hobj/data/images/depr_imagesets.py similarity index 69% rename from hobj/data/images/imagesets.py rename to hobj/data/images/depr_imagesets.py index 5d2cce9..47195fa 100644 --- a/hobj/data/images/imagesets.py +++ b/hobj/data/images/depr_imagesets.py @@ -1,24 +1,24 @@ -import hobj.config as config import os -import xarray as xr -import hobj.data.images.load_image as load_image + import PIL.Image +import xarray as xr +from typing import List +from hobj.data.store import default_data_store +from pathlib import Path image_meta_loc = os.path.join(os.path.dirname(__file__), 'image_meta') -class Imageset(object): +# %% +class DeprImageset(object): meta_path: str = None - def __init__(self, cachedir=config.image_cachedir): - self.cachedir = cachedir - return - def load_image(self, image_url: str) -> PIL.Image: - return load_image.get_image(image_url=image_url, cachedir=self.cachedir) + raise NotImplementedError() + return image_ref.get_image_data() @property - def ds_meta(self): + def ds_meta(self) -> xr.Dataset: if not hasattr(self, '_ds_meta'): ds_meta = xr.load_dataset(self.meta_path) self._ds_meta = ds_meta @@ -26,19 +26,20 @@ def ds_meta(self): return self._ds_meta @property - def image_urls(self): + def image_urls(self) -> List[str]: return list(self.ds_meta.image_url.values) -class WarmupImageset(Imageset): +# %% +class WarmupDeprImageset(DeprImageset): meta_path = os.path.join(image_meta_loc, 'ds_MutatorB0_BurnIn.nc') -class MutatorHighVarImageset(Imageset): +class MutatorHighVarDeprImageset(DeprImageset): meta_path = os.path.join(image_meta_loc, 'ds_MutatorB2000_Subset128_FullVar_Train.nc') -class MutatorOneshotImageset(Imageset): +class MutatorOneshotDeprImageset(DeprImageset): meta_path = os.path.join(image_meta_loc, 'ds_MutatorB2000_Oneshot64.nc') @property @@ -50,5 +51,6 @@ def ds_meta(self): return self._ds_meta +# %% if __name__ == '__main__': - x = WarmupImageset() \ No newline at end of file + x = WarmupDeprImageset() diff --git a/hobj/data/images/image_meta/ds_MutatorB0_BurnIn.nc b/hobj/data/images/image_meta/ds_MutatorB0_BurnIn.nc deleted file mode 100644 index d03abed7542facbea689863b15fded228e89609f..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 182784 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z>PS~tx_Z*pm#%?y4W(-&U1RB*NY_-lX3{m6u7z|hrE4W!Yw6lZ*H*fA(zTbagLECG z>m*%g>AFbQRl07{b(gM(bUmf(C0%dn`bgJTx_;92mu`S`1Em`z-C*g4NH?QrCTK3V(FGhw^X`i(k+*6g>);WTP59U>DEZM zR=Rc4t^cSC3JMO4&o@Z7QMygiZI*6}bX%p{Cf#=Fc1X8Vx?R%kmTr%9d!^ea-G1o~ zNOw@WL((0V?uc|pr8_3wap_J-cT&1j(w&y>jC5zEJ15ucZ50x^JZWR=V$``(C<7(mj^$iF8k; z`$4)NrTa;`pQU>y-7nJpD&2GGUP$*+x>wS@mhO#oze)GIbbmizZ!k>0(G1Q@U8v z#SXalo&{dJgD0q34308+s7*Jkaw(&j&pidVc5ypcjN*2zp`Y zMW7djUJQD1=p~?+gkB1IY3OC3mxW#qdU@y-pjU)m33_GdRiIaeUJZJ6=ry3%gkB4J zZRmBN*M(jWdVS~(pf`lx2zq1aO`tc0-VAzk=q;ePgx(5zYv^sDw}svgdVA;{pm&7c z33_MfU7&Y`-VJ(p=slqKgx(8!Z|HrX_x)JUn?FxK=>4G&fIblVAn1dk4}m@u`Y`Cj zp^t$63G`2)e+K{}TFG=;NS|hdu%NMCg;CPli4P`c&xCpihTB z1Nuzpv!Kt0J_q_-=<}e@hrR&%Lg%HpkIf61Nu$qx1iq+^!Fe4 z3(l7-FhB8*ba$n@CtcZ+RV#)3{~kl&0L}mW`(OV{k2h4{agz(Yy$voeaG48qfy1rn z0#6@Zc=bSb1uDP1b*QcIUcy0p@z zlP1s Path: path = self.cachedir / 'image_cache' / (sha256 + '.png') return path + # JSON caching from web + def get_json_cache_path(self, url: str) -> Path: + """ + Construct a cache path for a URL. + :param url: + :return: + """ + if not url.endswith('.json'): + raise ValueError(f"URL {url} does not point to a JSON file.") + + path = self.cachedir / 'json_cache' / (hash_string(url) + '.json') + return path + + def get_zipped_file_cache_path(self, zipfile_url: str) -> Path: + if not zipfile_url.endswith('.zip'): + raise ValueError(f"URL {zipfile_url} does not point to a ZIP file") + + path = self.cachedir / 'zipfile_cache' / (hash_string(zipfile_url) + '.zip') + return path + class DataStore: @@ -28,18 +48,18 @@ def __init__(self, cachedir: Path): self.path_manager = _PathManager(cachedir=cachedir) self._image_cache_manifest = set() - def register_image(self, image_data: PIL.Image) -> schema.ImageRef: + def register_image(self, image_data: PIL.Image) -> str: """ Register an image in the data store. :param image_data: - :return: + :return: The SHA256 hash of the image. """ sha256 = hash_image(image=image_data) path = self.path_manager.get_image_cache_path(sha256=sha256) if not path.parent.exists(): path.parent.mkdir(parents=True) image_data.save(path) - return schema.ImageRef(sha256=sha256) + return sha256 def check_image_exists(self, sha256: str) -> bool: """ @@ -53,14 +73,14 @@ def check_image_exists(self, sha256: str) -> bool: self._image_cache_manifest.add(sha256) return sha256 in self._image_cache_manifest - def load_image(self, image_ref: schema.ImageRef, as_rgb: bool = True) -> PIL.Image: + def load_image(self, sha256: str, as_rgb: bool = True) -> PIL.Image: """ Load an image from the data store. If the image is not there, registers it. :param image_ref: :return: """ - path = self.path_manager.get_image_cache_path(sha256=image_ref.sha256) + path = self.path_manager.get_image_cache_path(sha256=sha256) with PIL.Image.open(path) as img: if as_rgb: img = img.convert('RGB') @@ -68,6 +88,47 @@ def load_image(self, image_ref: schema.ImageRef, as_rgb: bool = True) -> PIL.Ima return loaded_image + def get_json_data_from_url(self, url: str, redownload: bool = False): + """ + Loads JSON data file from a url, caching it if it is not already present. + :param url: + :param redownload: + :return: + """ + path = self.path_manager.get_json_cache_path(url=url) + + if not path.exists() or redownload: + json_data = download_json(url=url) + if not path.parent.exists(): + path.parent.mkdir(parents=True) + path.write_text(json.dumps(json_data)) + print(f"Downloaded JSON data from {url} to {path}") + + return json.loads(path.read_text()) + + def get_zipfile(self, zipfile_url: str, redownload: bool = False) -> Path: + """ + Get the path to the compressed imageset file. + :param zipfile_url: + :return: + """ + path = self.path_manager.get_zipped_file_cache_path(zipfile_url) + if not path.exists() or redownload: + + # Save it in a tempdir + with tempfile.TemporaryDirectory() as tempdir: + tempdir = Path(tempdir) + savepath = tempdir / 'tmp.zip' + download_file(url=zipfile_url, output_path=savepath) + + if not path.parent.exists(): + path.parent.mkdir(parents=True) + + # Move the file to the correct location + savepath.rename(path) + + return path + # Default data store -default_data_store = DataStore(cachedir=config.cachedir) +default_data_store = DataStore(cachedir=hobj.config.cachedir) diff --git a/hobj/utils/file_io.py b/hobj/utils/file_io.py index 2d9bbeb..a8081d0 100644 --- a/hobj/utils/file_io.py +++ b/hobj/utils/file_io.py @@ -1,14 +1,35 @@ +import json +import os import urllib.request -import requests -import PIL.Image +import zipfile from io import BytesIO -import os -import url_normalize +from pathlib import Path from urllib.parse import urlparse +import PIL.Image +import requests +import url_normalize from tqdm import tqdm +from typing import Any -import json +def unzip_file(zip_path: Path, output_dir: Path) -> None: + """ + Unzip a file to a given directory. + :param zip_path: + :param output_dir: + :return: + """ + + if not zip_path.as_posix().endswith('.zip'): + raise ValueError(f"Expected a .zip file, got {zip_path}") + if not zip_path.exists(): + raise FileNotFoundError(f"Zip file {zip_path} does not exist.") + + if not output_dir.exists(): + output_dir.mkdir(parents=True) + + with zipfile.ZipFile(zip_path, 'r') as zip_ref: + zip_ref.extractall(output_dir) def get_canonical_url(url: str): @@ -40,39 +61,74 @@ def get_image_from_url(image_url: str): return img -def download_file(url: str, savepath=None, cachedir: str = None): - if savepath is None: - assert cachedir is not None - savepath = get_local_save_location(url=url, cachedir=cachedir) - - prepare_savepath(savepath) - - def my_hook(t): - - last_b = [0] - - def update_to(b=1, bsize=1, tsize=None): - """ - b : int, optional - Number of blocks transferred so far [default: 1]. - bsize : int, optional - Size of each block (in tqdm units) [default: 1]. - tsize : int, optional - Total size (in tqdm units). If [default: None] remains unchanged. - """ - if tsize is not None: - t.total = tsize - t.update((b - last_b[0]) * bsize) - last_b[0] = b - - return update_to - - with tqdm(desc=f'downloading {url}') as pbar: - urllib.request.urlretrieve(url=url, filename=savepath, reporthook=my_hook(pbar)) - return savepath - +def download_file(url: str, output_path: Path) -> None: + # Send a GET request to fetch the file + response = requests.get(url, stream=True) + total_size_in_bytes = int(response.headers.get('content-length', 0)) + + # Create the output directory if it does not exist + if not output_path.parent.exists(): + output_path.parent.mkdir(parents=True) + + size, unit = get_bytes_size(num_bytes=total_size_in_bytes) + # Create a progress bar + with tqdm(total=size, unit=unit, unit_scale=True, disable=False, desc='Download progress') as progress_bar: + with open(output_path.as_posix(), 'wb') as file: + # Iterate over the response data in chunks and write to file + for chunk in response.iter_content(chunk_size=1024): + if chunk: + file.write(chunk) + chunk_size, _ = get_bytes_size(num_bytes=len(chunk), output_units=unit) + progress_bar.update(chunk_size) + + + +def get_bytes_size(num_bytes: int, output_units: str = None) -> (float, str): + """ + + :param num_bytes: + :param output_units: If None, automatically selected + :return: + """ + + unit_conversion = { + 'B': 1, + 'KB': 1e3, + 'MB': 1e6, + 'GB': 1e9 + } + + # Automatically select the byte unit (KB, MB, etc.) + if output_units is None: + if num_bytes == 0: + output_units = 'B' + elif num_bytes < 1e6: + output_units = 'KB' + elif num_bytes < 1e9: + output_units = 'MB' + else: + output_units = 'GB' + + # Convert the size to the selected unit + size = num_bytes / unit_conversion[output_units] + return size, output_units def load_json(json_path): with open(json_path, 'r') as fb: val = json.load(fb) return val + + +def download_json(url: str) -> Any: + """ + Download a JSON file from a URL. + :param url: + :return: + """ + response = urllib.request.urlopen(url) + if response.status != 200: + raise ValueError(f"Could not load JSON data from {url}", response) + data = response.read().decode('utf-8') + json_data = json.loads(data) + + return json_data \ No newline at end of file diff --git a/hobj/utils/hash.py b/hobj/utils/hash.py index b231d77..1e17e5f 100644 --- a/hobj/utils/hash.py +++ b/hobj/utils/hash.py @@ -30,3 +30,13 @@ def hash_image(image: PIL.Image) -> str: # Return the hexadecimal representation of the hash return sha256_hash.hexdigest() + + +def hash_string(input_string: str) -> str: + sha256_hash = hashlib.sha256() + + # Convert string to bytes + sha256_hash.update(input_string.encode('utf-8')) + + # Return the hexadecimal representation of the hash + return sha256_hash.hexdigest() From 18f5dae9591829aafef309de503c02aa94d10562 Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 11:04:18 -0500 Subject: [PATCH 042/156] added warmup imageset --- hobj/data/images/imagesets/__init__.py | 0 hobj/data/images/imagesets/warmup.py | 26 ++++++++++++ hobj/data/images/template.py | 52 +++++++++++++---------- hobj/data/images/tmp_package_imagesets.py | 1 + 4 files changed, 56 insertions(+), 23 deletions(-) create mode 100644 hobj/data/images/imagesets/__init__.py create mode 100644 hobj/data/images/imagesets/warmup.py diff --git a/hobj/data/images/imagesets/__init__.py b/hobj/data/images/imagesets/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/hobj/data/images/imagesets/warmup.py b/hobj/data/images/imagesets/warmup.py new file mode 100644 index 0000000..b3a6ce4 --- /dev/null +++ b/hobj/data/images/imagesets/warmup.py @@ -0,0 +1,26 @@ +from hobj.data.images.template import Imageset, ImageManifestEntry, ImageManifest +import pydantic + +from typing import Literal +from hobj.utils.hash import hash_image + + +class WarmupAnnotation(pydantic.BaseModel): + category: str = pydantic.Field( + examples=[ + 'Mutator19', + 'Mutator30', + ], + pattern=r'^Mutator\d+$' + ) + + +class WarmupImageset(Imageset[WarmupAnnotation]): + manifest_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-warmup/mutator-warmup-manifest.json' + zipped_images_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-warmup/MutatorWarmup.zip' + annotation_schema = WarmupAnnotation + + +if __name__ == '__main__': + + imageset = WarmupImageset(redownload=True) diff --git a/hobj/data/images/template.py b/hobj/data/images/template.py index 6797ac3..941bc2f 100644 --- a/hobj/data/images/template.py +++ b/hobj/data/images/template.py @@ -11,6 +11,7 @@ from hobj.utils.hash import hash_image from hobj.data.schema import ImageRef from hobj.data.store import DataStore, default_data_store +import warnings class ImageManifestEntry(pydantic.BaseModel, ABC): @@ -24,12 +25,8 @@ class ImageManifestEntry(pydantic.BaseModel, ABC): class ImageManifest(pydantic.BaseModel): - entries: List[ImageManifestEntry] = pydantic.Field( - description='A list of image manifest entries.' - ) - - zipped_images_url: str = pydantic.Field( - description='The URL to the zipped imageset file.' + entries: Dict[str, ImageManifestEntry] = pydantic.Field( + description='A mapping from a unique image ID to image manifest entries.' ) @@ -45,12 +42,13 @@ class Imageset(Generic[IA], ABC): """ manifest_url: str + zipped_images_url: str annotation_schema: IA def __init__( self, data_store: DataStore = None, - redownload_manifest=False, + redownload=False, ): """ Unwrap the image manifest and save the images to the cache. @@ -60,30 +58,33 @@ def __init__( self.data_store: DataStore = default_data_store # Load the manifest if it is already cached - manifest_data = self.data_store.get_json_data_from_url(url=self.manifest_url, redownload=redownload_manifest) + manifest_data = self.data_store.get_json_data_from_url(url=self.manifest_url, redownload=redownload) image_manifest = ImageManifest(**manifest_data) - self._register_image_urls(manifest=image_manifest) + self._register_image_urls(manifest=image_manifest, redownload=redownload) self._manifest = image_manifest - self._image_ref_to_annotation: Dict[ImageRef, IA] = {} + self._image_id_to_annotation: Dict[str, IA] = {} + self._image_id_to_image_ref: Dict[str, ImageRef] = {} + self._image_ref_to_image_ids: Dict[ImageRef, List[str]] = {} self._image_refs: List[ImageRef] = [] - for entry in image_manifest.entries: + for image_id, entry in image_manifest.entries.items(): image_ref = ImageRef(sha256=entry.sha256) self._image_refs.append(image_ref) - if image_ref in self._image_ref_to_annotation: - raise ValueError(f"Multiple annotations for image {image_ref}: \n{entry.annotation} \n{self._image_ref_to_annotation[image_ref]}") - self._image_ref_to_annotation[image_ref] = self.annotation_schema(**entry.annotation) + self._image_id_to_image_ref[image_id] = image_ref + self._image_id_to_annotation[image_id] = self.annotation_schema(**entry.annotation) + if image_ref not in self._image_ref_to_image_ids: + self._image_ref_to_image_ids[image_ref] = [] + self._image_ref_to_image_ids[image_ref].append(image_id) - def _register_image_urls(self, manifest: ImageManifest): + def _register_image_urls(self, manifest: ImageManifest, redownload:bool = False): """ Ensures the entries of the manifest are registered in the data store. """ num_undownloaded_images = 0 - for manifest_entry in manifest.entries: - + for image_id, manifest_entry in manifest.entries.items(): if not self.data_store.check_image_exists(sha256=manifest_entry.sha256): num_undownloaded_images += 1 @@ -94,7 +95,7 @@ def _register_image_urls(self, manifest: ImageManifest): print(f'Missing {num_undownloaded_images}/{len(manifest.entries)} images for this imageset.') # Download the images - zipped_images_path = self.data_store.get_zipfile(zipfile_url=manifest.zipped_images_url) + zipped_images_path = self.data_store.get_zipfile(zipfile_url=self.zipped_images_url, redownload=redownload) # Make a tempdir to unzip the images with tempfile.TemporaryDirectory() as tempdir: @@ -103,7 +104,7 @@ def _register_image_urls(self, manifest: ImageManifest): # Register the images pbar = tqdm(total=len(manifest.entries)) - for manifest_entry in manifest.entries: + for image_id, manifest_entry in manifest.entries.items(): reported_sha256 = manifest_entry.sha256 relpath = manifest_entry.relpath image_path = tempdir / relpath @@ -123,13 +124,18 @@ def image_refs(self) -> List[ImageRef]: """ return self._image_refs - def get_annotation(self, image_ref: ImageRef) -> IA: + def get_annotation(self, image_id: str = None, image_ref: ImageRef = None) -> IA: """ Get the annotation for a given image. If an image has multiple annotations, this will throw an error. """ - - entry = self._image_ref_to_annotation[image_ref] - return self.annotation_schema(**entry.annotation) + if image_id is None: + image_ids = self._image_ref_to_image_ids[image_ref] + if len(image_ids) > 1: + warnings.warn(f"Image {image_ref} has multiple annotations: {image_ids}. Returning the first one.") + image_id = image_ids[0] + + entry = self._image_id_to_annotation[image_id] + return entry def __len__(self) -> int: return len(self.image_refs) diff --git a/hobj/data/images/tmp_package_imagesets.py b/hobj/data/images/tmp_package_imagesets.py index e69de29..bb2e1d5 100644 --- a/hobj/data/images/tmp_package_imagesets.py +++ b/hobj/data/images/tmp_package_imagesets.py @@ -0,0 +1 @@ +import xarray as xr \ No newline at end of file From 5bdb323cc6e9fb2dce0c13add728a335ffa7104c Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 11:47:40 -0500 Subject: [PATCH 043/156] added highvar and oneshot --- hobj/data/images/depr_imagesets.py | 5 +-- hobj/data/images/imagesets/highvar.py | 24 ++++++++++++++ hobj/data/images/imagesets/oneshot.py | 48 +++++++++++++++++++++++++++ hobj/data/images/imagesets/warmup.py | 3 -- 4 files changed, 73 insertions(+), 7 deletions(-) create mode 100644 hobj/data/images/imagesets/highvar.py create mode 100644 hobj/data/images/imagesets/oneshot.py diff --git a/hobj/data/images/depr_imagesets.py b/hobj/data/images/depr_imagesets.py index 47195fa..60c6e9f 100644 --- a/hobj/data/images/depr_imagesets.py +++ b/hobj/data/images/depr_imagesets.py @@ -31,9 +31,6 @@ def image_urls(self) -> List[str]: # %% -class WarmupDeprImageset(DeprImageset): - meta_path = os.path.join(image_meta_loc, 'ds_MutatorB0_BurnIn.nc') - class MutatorHighVarDeprImageset(DeprImageset): meta_path = os.path.join(image_meta_loc, 'ds_MutatorB2000_Subset128_FullVar_Train.nc') @@ -53,4 +50,4 @@ def ds_meta(self): # %% if __name__ == '__main__': - x = WarmupDeprImageset() + x = MutatorHighVarDeprImageset() diff --git a/hobj/data/images/imagesets/highvar.py b/hobj/data/images/imagesets/highvar.py new file mode 100644 index 0000000..1f6fd77 --- /dev/null +++ b/hobj/data/images/imagesets/highvar.py @@ -0,0 +1,24 @@ + +from hobj.data.images.template import Imageset, ImageManifestEntry, ImageManifest +import pydantic + + +class MutatorHighVarAnnotation(pydantic.BaseModel): + category: str = pydantic.Field( + examples=[ + 'MutatorB2000_4872', + 'MutatorB2000_419', + ], + pattern=r'^MutatorB2000_\d+$' + ) + + +class MutatorHighVarImageset(Imageset[MutatorHighVarAnnotation]): + manifest_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-highvar/mutator-highvar-manifest.json' + zipped_images_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-highvar/MutatorB2000_Subset128_FullVar_Train.zip' + annotation_schema = MutatorHighVarAnnotation + + +if __name__ == '__main__': + + imageset = MutatorHighVarImageset() diff --git a/hobj/data/images/imagesets/oneshot.py b/hobj/data/images/imagesets/oneshot.py new file mode 100644 index 0000000..fd2ee29 --- /dev/null +++ b/hobj/data/images/imagesets/oneshot.py @@ -0,0 +1,48 @@ + +from hobj.data.images.template import Imageset, ImageManifestEntry, ImageManifest +import pydantic +from typing import Literal + + +class MutatorOneShotAnnotation(pydantic.BaseModel): + category: str = pydantic.Field( + examples=[ + 'MutatorB2000_2444', + 'MutatorB2000_288', + ], + pattern=r'^MutatorB2000_\d+$', + description='The name for the object model associated with this image.' + ) + + transformation: Literal[ + 'combinednat', + 'blur', + 'inplanetranslation', + 'scale', + 'outplanerotation', + 'noise', + 'backgrounds', + 'inplanerotation', + 'delpixels', + 'contrast', + 'original' + ] = pydantic.Field(description = 'The type of transformation applied to the image. "original" indicates no transformation.') + + transformation_level: float = pydantic.Field( + description='The extent of transformation applied to the image. The interpretation of this number varies by transformation.' + ) + + base_image_id: str = pydantic.Field( + description = 'An image ID for the "original" image associated with this one.', + examples = [ + 'MutatorB2000_2444_rx0.00000_ry0.00000_rz0.00000.png', + 'MutatorB2000_4792_rx0.00000_ry0.00000_rz0.00000.png' + ] + ) + + +class MutatorOneShotImageset(Imageset[MutatorOneShotAnnotation]): + manifest_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-oneshot/mutator-oneshot-manifest.json' + zipped_images_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-oneshot/MutatorB2000_Oneshot64.zip' + annotation_schema = MutatorOneShotAnnotation + diff --git a/hobj/data/images/imagesets/warmup.py b/hobj/data/images/imagesets/warmup.py index b3a6ce4..bf8ab56 100644 --- a/hobj/data/images/imagesets/warmup.py +++ b/hobj/data/images/imagesets/warmup.py @@ -1,9 +1,6 @@ from hobj.data.images.template import Imageset, ImageManifestEntry, ImageManifest import pydantic -from typing import Literal -from hobj.utils.hash import hash_image - class WarmupAnnotation(pydantic.BaseModel): category: str = pydantic.Field( From 1836f6fe45081ee410114455fd8ed831945045da Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 11:49:36 -0500 Subject: [PATCH 044/156] delete old imageset --- hobj/data/images/depr_imagesets.py | 53 ----------------------- hobj/data/images/imagesets/oneshot.py | 2 +- hobj/data/images/tmp_package_imagesets.py | 1 - 3 files changed, 1 insertion(+), 55 deletions(-) delete mode 100644 hobj/data/images/depr_imagesets.py delete mode 100644 hobj/data/images/tmp_package_imagesets.py diff --git a/hobj/data/images/depr_imagesets.py b/hobj/data/images/depr_imagesets.py deleted file mode 100644 index 60c6e9f..0000000 --- a/hobj/data/images/depr_imagesets.py +++ /dev/null @@ -1,53 +0,0 @@ -import os - -import PIL.Image -import xarray as xr -from typing import List -from hobj.data.store import default_data_store -from pathlib import Path - -image_meta_loc = os.path.join(os.path.dirname(__file__), 'image_meta') - - -# %% -class DeprImageset(object): - meta_path: str = None - - def load_image(self, image_url: str) -> PIL.Image: - raise NotImplementedError() - return image_ref.get_image_data() - - @property - def ds_meta(self) -> xr.Dataset: - if not hasattr(self, '_ds_meta'): - ds_meta = xr.load_dataset(self.meta_path) - self._ds_meta = ds_meta - - return self._ds_meta - - @property - def image_urls(self) -> List[str]: - return list(self.ds_meta.image_url.values) - - -# %% - -class MutatorHighVarDeprImageset(DeprImageset): - meta_path = os.path.join(image_meta_loc, 'ds_MutatorB2000_Subset128_FullVar_Train.nc') - - -class MutatorOneshotDeprImageset(DeprImageset): - meta_path = os.path.join(image_meta_loc, 'ds_MutatorB2000_Oneshot64.nc') - - @property - def ds_meta(self): - if not hasattr(self, '_ds_meta'): - ds_meta = xr.load_dataset(self.meta_path) - ds_meta['transformation_id'] = (['image_url'], [f'{trans} | {tlevel}' for (trans, tlevel) in zip(ds_meta.transformation.values, ds_meta.transformation_level.values)]) - self._ds_meta = ds_meta - - return self._ds_meta - -# %% -if __name__ == '__main__': - x = MutatorHighVarDeprImageset() diff --git a/hobj/data/images/imagesets/oneshot.py b/hobj/data/images/imagesets/oneshot.py index fd2ee29..7704d22 100644 --- a/hobj/data/images/imagesets/oneshot.py +++ b/hobj/data/images/imagesets/oneshot.py @@ -2,7 +2,7 @@ from hobj.data.images.template import Imageset, ImageManifestEntry, ImageManifest import pydantic from typing import Literal - +import pandas as pd class MutatorOneShotAnnotation(pydantic.BaseModel): category: str = pydantic.Field( diff --git a/hobj/data/images/tmp_package_imagesets.py b/hobj/data/images/tmp_package_imagesets.py deleted file mode 100644 index bb2e1d5..0000000 --- a/hobj/data/images/tmp_package_imagesets.py +++ /dev/null @@ -1 +0,0 @@ -import xarray as xr \ No newline at end of file From fa2473c21db34e948e7ba293715f4de204236978 Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 11:51:39 -0500 Subject: [PATCH 045/156] clean up directory structure --- hobj/data/images/__init__.py | 3 +++ hobj/data/images/imagesets/oneshot.py | 8 +++++--- hobj/data/images/imagesets/warmup.py | 11 ++++++----- 3 files changed, 14 insertions(+), 8 deletions(-) diff --git a/hobj/data/images/__init__.py b/hobj/data/images/__init__.py index e69de29..7d80149 100644 --- a/hobj/data/images/__init__.py +++ b/hobj/data/images/__init__.py @@ -0,0 +1,3 @@ +from hobj.data.images.imagesets.highvar import MutatorHighVarImageset +from hobj.data.images.imagesets.oneshot import MutatorOneShotImageset +from hobj.data.images.imagesets.warmup import MutatorWarmupImageset diff --git a/hobj/data/images/imagesets/oneshot.py b/hobj/data/images/imagesets/oneshot.py index 7704d22..7ab54e6 100644 --- a/hobj/data/images/imagesets/oneshot.py +++ b/hobj/data/images/imagesets/oneshot.py @@ -1,8 +1,10 @@ -from hobj.data.images.template import Imageset, ImageManifestEntry, ImageManifest -import pydantic from typing import Literal -import pandas as pd + +import pydantic + +from hobj.data.images.template import Imageset + class MutatorOneShotAnnotation(pydantic.BaseModel): category: str = pydantic.Field( diff --git a/hobj/data/images/imagesets/warmup.py b/hobj/data/images/imagesets/warmup.py index bf8ab56..9ce4501 100644 --- a/hobj/data/images/imagesets/warmup.py +++ b/hobj/data/images/imagesets/warmup.py @@ -1,8 +1,9 @@ -from hobj.data.images.template import Imageset, ImageManifestEntry, ImageManifest import pydantic +from hobj.data.images.template import Imageset -class WarmupAnnotation(pydantic.BaseModel): + +class MutatorWarmupAnnotation(pydantic.BaseModel): category: str = pydantic.Field( examples=[ 'Mutator19', @@ -12,12 +13,12 @@ class WarmupAnnotation(pydantic.BaseModel): ) -class WarmupImageset(Imageset[WarmupAnnotation]): +class MutatorWarmupImageset(Imageset[MutatorWarmupAnnotation]): manifest_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-warmup/mutator-warmup-manifest.json' zipped_images_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-warmup/MutatorWarmup.zip' - annotation_schema = WarmupAnnotation + annotation_schema = MutatorWarmupAnnotation if __name__ == '__main__': - imageset = WarmupImageset(redownload=True) + imageset = MutatorWarmupImageset(redownload=True) From 175043859b8bfc9b964a8c063f262c376fb6efe8 Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 15:18:17 -0500 Subject: [PATCH 046/156] add probe imageset --- hobj/data/images/imagesets/probe.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 hobj/data/images/imagesets/probe.py diff --git a/hobj/data/images/imagesets/probe.py b/hobj/data/images/imagesets/probe.py new file mode 100644 index 0000000..350dede --- /dev/null +++ b/hobj/data/images/imagesets/probe.py @@ -0,0 +1,22 @@ + +from hobj.data.images.template import Imageset, ImageManifestEntry, ImageManifest +import pydantic +from typing import Literal + + +class ProbeAnnotation(pydantic.BaseModel): + color: Literal['blue', 'orange'] + text: Literal['press right', 'press left'] + + +class ProbeImageset(Imageset[ProbeAnnotation]): + """ + An imageset consisting of two images: a + """ + manifest_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/probe-images/probe-images-manifest.json' + zipped_images_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/probe-images/probe-images.zip' + annotation_schema = ProbeAnnotation + +# %% +if __name__ == '__main__': + imageset = ProbeImageset() From 67cae2440ba38d29a38ddae24570d73513c4be13 Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 22 Jan 2025 16:33:36 -0500 Subject: [PATCH 047/156] repackaged behavioral data --- examples/score_example_model.py | 2 +- examples/view_behavioral_data.py | 2 +- .../benchmark_utils/data_resampling.py | 2 +- .../MutatorHighVarSubtasks.json | 0 .../MutatorOneshot/mut_oneshot_benchmark.py | 2 +- .../mut_highvar_benchmark.py | 13 +++-- hobj/data/behavior.py | 50 +++++++++++++++++++ hobj/data/behavior/template/__init__.py | 0 .../depr_behavior}/__init__.py | 0 .../depr_human_data.py} | 2 +- .../depr_template}/__init__.py | 0 .../depr_template}/sr_data.py | 0 hobj/data/images/__init__.py | 1 + hobj/data/images/template.py | 24 ++++----- hobj/data/schema.py | 49 +++++++++++++++++- hobj/data/store.py | 2 +- 16 files changed, 121 insertions(+), 28 deletions(-) rename hobj/benchmarks/leedicarlo_benchmarks/{MutatorHighVar => }/MutatorHighVarSubtasks.json (100%) rename hobj/benchmarks/leedicarlo_benchmarks/{MutatorHighVar => }/mut_highvar_benchmark.py (98%) create mode 100644 hobj/data/behavior.py delete mode 100644 hobj/data/behavior/template/__init__.py rename hobj/{benchmarks/leedicarlo_benchmarks/MutatorHighVar => data/depr_behavior}/__init__.py (100%) rename hobj/data/{behavior/human_data.py => depr_behavior/depr_human_data.py} (98%) rename hobj/data/{behavior => depr_behavior/depr_template}/__init__.py (100%) rename hobj/data/{behavior/template => depr_behavior/depr_template}/sr_data.py (100%) diff --git a/examples/score_example_model.py b/examples/score_example_model.py index 7108ae2..c63825f 100644 --- a/examples/score_example_model.py +++ b/examples/score_example_model.py @@ -1,5 +1,5 @@ import hobj.learning_models.learning_model as lm -import hobj.benchmarks.leedicarlo_benchmarks.MutatorHighVar.mut_highvar_benchmark as highvar_benchmark +import hobj.benchmarks.leedicarlo_benchmarks.mut_highvar_benchmark as highvar_benchmark import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_benchmark as oneshot_benchmark dummy_model = lm.DummyBinaryLearner() diff --git a/examples/view_behavioral_data.py b/examples/view_behavioral_data.py index 9fc5dd8..bfc386d 100644 --- a/examples/view_behavioral_data.py +++ b/examples/view_behavioral_data.py @@ -1,4 +1,4 @@ -import hobj.benchmarks.leedicarlo_benchmarks.MutatorHighVar.mut_highvar_benchmark as highvar_benchmark +import hobj.benchmarks.leedicarlo_benchmarks.mut_highvar_benchmark as highvar_benchmark import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_benchmark as oneshot_benchmark import matplotlib.pyplot as plt import numpy as np diff --git a/hobj/benchmarks/benchmark_utils/data_resampling.py b/hobj/benchmarks/benchmark_utils/data_resampling.py index 723db50..fdf0750 100644 --- a/hobj/benchmarks/benchmark_utils/data_resampling.py +++ b/hobj/benchmarks/benchmark_utils/data_resampling.py @@ -223,6 +223,6 @@ def get_policy_grain_splithalf_and_bootstrap_resampled_data(ds_raw, nsplits): if __name__ == '__main__': - import hobj.data.behavior.human_data as human_data + import hobj.data.depr_behavior.human_data as human_data ds_raw = human_data.MutatorHighVarDataset().load_ds_data() result = get_policy_grain_splithalf_and_bootstrap_resampled_data(ds_raw = ds_raw, nsplits = 1000) diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/MutatorHighVarSubtasks.json b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVarSubtasks.json similarity index 100% rename from hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/MutatorHighVarSubtasks.json rename to hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVarSubtasks.json diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py index 5430699..459b0d2 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py @@ -9,7 +9,7 @@ import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_experiment as mutator -import hobj.data.behavior.human_data as human_data +import hobj.data.depr_behavior.human_data as human_data from hobj.statistics.resamplers.resamplers import Resampler import hobj.statistics.score_statistics.MSEn as R2n_funcs import hobj.statistics.variance_estimates.binomial as binomial_funcs diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/mut_highvar_benchmark.py similarity index 98% rename from hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py rename to hobj/benchmarks/leedicarlo_benchmarks/mut_highvar_benchmark.py index 690ce79..6bc5f3d 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/mut_highvar_benchmark.py @@ -9,25 +9,26 @@ import scipy.stats as ss import xarray as xr -import hobj.data.images.depr_imagesets as imagesets import hobj.statistics.hypothesis_testing.approximated_null_distribution as approximated_null_distribution import hobj.statistics.hypothesis_testing.bootstrapped_confidence_intervals as bootstrapped_confidence_intervals import hobj.statistics.lapse_rate as lapse_rate_funcs import hobj.statistics.score_statistics.MSEn as R2n_funcs import hobj.statistics.variance_estimates.binomial as binomial_funcs -from hobj.data.behavior import human_data as human_data +from hobj.data.depr_behavior import human_data as human_data from hobj.learning_models import learning_model as lm from hobj.statistics.resamplers.resamplers import LearningCurveResampler, WorkerResampler from hobj.utils import stats as stats +from hobj.data.images import MutatorHighVarImageset +from hobj.benchmarks.experiment_simulation.task import SimpleBinaryTask class MutatorHighVarBenchmark: + nboot = 1000 + tearly = slice(1, 6) + tlate = slice(94, 100) def __init__(self): - self.nboot = 1000 - self.tearly = slice(1, 6) - self.tlate = slice(94, 100) self.dataset = human_data.MutatorHighVarDataset() self.resampler = LearningCurveResampler( ds=self.ds_worker_table, @@ -41,8 +42,6 @@ def __init__(self): worker_dim='worker_id' ) - self.imageset = imagesets.MutatorHighVarDeprImageset() - # Todo: move/load this manifest elsewhere – e.g. to imageset subtask_manifest = Path(os.path.dirname(__file__)) / 'MutatorHighVarSubtasks.json' subtask_manifest = json.loads(subtask_manifest.read_text()) diff --git a/hobj/data/behavior.py b/hobj/data/behavior.py new file mode 100644 index 0000000..e8aa970 --- /dev/null +++ b/hobj/data/behavior.py @@ -0,0 +1,50 @@ +from abc import ABC +from abc import ABC +from typing import List + +import pydantic + +import hobj.data.schema as schema +from hobj.data.store import DataStore, default_data_store + + +class LearningSessionArray(pydantic.BaseModel): + sessions: List[schema.HumanLearningSession] + + +class LearningDataset(ABC): + dataset_url: str + + def __init__( + self, + data_store: DataStore = None, + redownload=False, + ): + if data_store is None: + self.data_store = default_data_store + + # Download the data + json_data = self.data_store.get_json_data_from_url( + url = self.dataset_url, + redownload=redownload + ) + + self._learning_sessions = LearningSessionArray(**json_data) + + @property + def learning_sessions(self) -> List[schema.HumanLearningSession]: + return self._learning_sessions.sessions + + +class MutatorHighVarDataset(LearningDataset): + dataset_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-highvar-human-learning-data.json' + + +class MutatorOneShotDataset(LearningDataset): + dataset_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-oneshot-human-learning-data.json' + + +if __name__ == '__main__': + dataset = MutatorHighVarDataset() + oneshot_dataset = MutatorOneShotDataset(redownload=True) + #print(dataset.learning_sessions) \ No newline at end of file diff --git a/hobj/data/behavior/template/__init__.py b/hobj/data/behavior/template/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/__init__.py b/hobj/data/depr_behavior/__init__.py similarity index 100% rename from hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/__init__.py rename to hobj/data/depr_behavior/__init__.py diff --git a/hobj/data/behavior/human_data.py b/hobj/data/depr_behavior/depr_human_data.py similarity index 98% rename from hobj/data/behavior/human_data.py rename to hobj/data/depr_behavior/depr_human_data.py index 45229ce..c8d2bca 100644 --- a/hobj/data/behavior/human_data.py +++ b/hobj/data/depr_behavior/depr_human_data.py @@ -1,5 +1,5 @@ import xarray as xr -import hobj.data.behavior.template.sr_data as template +import hobj.data.depr_behavior.depr_template.sr_data as template import os import numpy as np import collections diff --git a/hobj/data/behavior/__init__.py b/hobj/data/depr_behavior/depr_template/__init__.py similarity index 100% rename from hobj/data/behavior/__init__.py rename to hobj/data/depr_behavior/depr_template/__init__.py diff --git a/hobj/data/behavior/template/sr_data.py b/hobj/data/depr_behavior/depr_template/sr_data.py similarity index 100% rename from hobj/data/behavior/template/sr_data.py rename to hobj/data/depr_behavior/depr_template/sr_data.py diff --git a/hobj/data/images/__init__.py b/hobj/data/images/__init__.py index 7d80149..0fc51d1 100644 --- a/hobj/data/images/__init__.py +++ b/hobj/data/images/__init__.py @@ -1,3 +1,4 @@ from hobj.data.images.imagesets.highvar import MutatorHighVarImageset from hobj.data.images.imagesets.oneshot import MutatorOneShotImageset from hobj.data.images.imagesets.warmup import MutatorWarmupImageset +from hobj.data.images.imagesets.probe import ProbeImageset \ No newline at end of file diff --git a/hobj/data/images/template.py b/hobj/data/images/template.py index 941bc2f..96a93ca 100644 --- a/hobj/data/images/template.py +++ b/hobj/data/images/template.py @@ -65,18 +65,18 @@ def __init__( self._manifest = image_manifest self._image_id_to_annotation: Dict[str, IA] = {} - self._image_id_to_image_ref: Dict[str, ImageRef] = {} - self._image_ref_to_image_ids: Dict[ImageRef, List[str]] = {} + self._image_id_to_sha256: Dict[str, str] = {} + self._sha256_to_image_ids: Dict[str, List[str]] = {} self._image_refs: List[ImageRef] = [] for image_id, entry in image_manifest.entries.items(): image_ref = ImageRef(sha256=entry.sha256) self._image_refs.append(image_ref) - self._image_id_to_image_ref[image_id] = image_ref + self._image_id_to_sha256[image_id] = image_ref.sha256 self._image_id_to_annotation[image_id] = self.annotation_schema(**entry.annotation) - if image_ref not in self._image_ref_to_image_ids: - self._image_ref_to_image_ids[image_ref] = [] - self._image_ref_to_image_ids[image_ref].append(image_id) + if image_ref.sha256 not in self._sha256_to_image_ids: + self._sha256_to_image_ids[image_ref.sha256] = [] + self._sha256_to_image_ids[image_ref.sha256].append(image_id) def _register_image_urls(self, manifest: ImageManifest, redownload:bool = False): """ @@ -124,16 +124,14 @@ def image_refs(self) -> List[ImageRef]: """ return self._image_refs - def get_annotation(self, image_id: str = None, image_ref: ImageRef = None) -> IA: + def get_annotation(self, *, sha256: str) -> IA: """ Get the annotation for a given image. If an image has multiple annotations, this will throw an error. """ - if image_id is None: - image_ids = self._image_ref_to_image_ids[image_ref] - if len(image_ids) > 1: - warnings.warn(f"Image {image_ref} has multiple annotations: {image_ids}. Returning the first one.") - image_id = image_ids[0] - + image_ids = self._sha256_to_image_ids[sha256] + if len(image_ids) > 1: + warnings.warn(f"Image {sha256} has multiple annotations: {image_ids}. Returning the first one.") + image_id = image_ids[0] entry = self._image_id_to_annotation[image_id] return entry diff --git a/hobj/data/schema.py b/hobj/data/schema.py index 991fa5b..d8e1152 100644 --- a/hobj/data/schema.py +++ b/hobj/data/schema.py @@ -4,22 +4,28 @@ from hobj.utils.hash import hash_image from hobj.data.store import default_data_store +from typing import List, Literal +import datetime + # %% Image class ImageRef(pydantic.BaseModel): sha256: str = pydantic.Field(pattern=r'^[a-f0-9]{64}$') + model_config = pydantic.ConfigDict( + frozen=True + ) @classmethod def from_image(cls, image: PIL.Image, register: bool = True): if register: - return default_data_store.register_image(image=image) + default_data_store.register_image(image_data=image) return cls( sha256=hash_image(image=image) ) def get_image_data(self) -> PIL.Image: - return default_data_store.load_image(image_ref=self) + return default_data_store.load_image(sha256=self.sha256) def __lt__(self, other) -> bool: return self.sha256 < other.sha256 @@ -27,4 +33,43 @@ def __lt__(self, other) -> bool: def __hash__(self): return hash(self.sha256) + # %% Behavior +class HumanLearningSession(pydantic.BaseModel): + + worker_id: str = pydantic.Field( + description='The anonymized worker ID of the participant.' + ) + + stimulus_sha256_seq: List[str] = pydantic.Field( + description='The SHA256 hash of the image (in np.uint8 format and RGBA mode) shown to the participant.', + ) + + stimulus_duration_msec_seq: List[int] = pydantic.Field( + description='The duration of the stimulus presentation in milliseconds.', + ) + + action_seq: List[Literal[0, 1, None]] = pydantic.Field(description='The action taken by the participant. 0 maps to "F" (left) and 1 maps to "J" (right). None maps to no action taken.') + reward_seq: List[Literal[-1, 1]] = pydantic.Field(description='The reward received by the participant following their action.') + timestamp_start_seq: List[datetime.datetime] = pydantic.Field(description='The timestamp of the start of the trial.') + + @pydantic.field_serializer('timestamp_start_seq') + def serialize_timestamp_start(self, v: List[datetime.datetime], _info) -> List[float]: + # Serializes datetime objects into Unix timestamps (seconds) + return [entry.timestamp() for entry in v] + + + + @pydantic.model_validator(mode='after') + def check_passwords_match(self) -> 'HumanLearningSession': + + stimulus_len = len(self.stimulus_sha256_seq) + duration_len = len(self.stimulus_duration_msec_seq) + action_len = len(self.action_seq) + reward_len = len(self.reward_seq) + timestamp_len = len(self.timestamp_start_seq) + + if not stimulus_len == duration_len == action_len == reward_len == timestamp_len: + raise ValueError(f'All lists must be of the same length. Got: {stimulus_len, duration_len, action_len, reward_len, timestamp_len}') + + return self \ No newline at end of file diff --git a/hobj/data/store.py b/hobj/data/store.py index 74404a5..8b94282 100644 --- a/hobj/data/store.py +++ b/hobj/data/store.py @@ -8,7 +8,7 @@ import json import tempfile -__all__ = ['DataStore'] +__all__ = ['DataStore', 'default_data_store'] # %% From a8f59a19481fc4173bb3303e0b25e97edc46a649 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 23 Jan 2025 08:22:27 -0500 Subject: [PATCH 048/156] progress on behavioral template --- hobj/data/behavior.py | 50 --------------- hobj/data/behavior/__init__.py | 0 hobj/data/behavior/template.py | 110 +++++++++++++++++++++++++++++++++ hobj/data/schema.py | 43 ------------- 4 files changed, 110 insertions(+), 93 deletions(-) delete mode 100644 hobj/data/behavior.py create mode 100644 hobj/data/behavior/__init__.py create mode 100644 hobj/data/behavior/template.py diff --git a/hobj/data/behavior.py b/hobj/data/behavior.py deleted file mode 100644 index e8aa970..0000000 --- a/hobj/data/behavior.py +++ /dev/null @@ -1,50 +0,0 @@ -from abc import ABC -from abc import ABC -from typing import List - -import pydantic - -import hobj.data.schema as schema -from hobj.data.store import DataStore, default_data_store - - -class LearningSessionArray(pydantic.BaseModel): - sessions: List[schema.HumanLearningSession] - - -class LearningDataset(ABC): - dataset_url: str - - def __init__( - self, - data_store: DataStore = None, - redownload=False, - ): - if data_store is None: - self.data_store = default_data_store - - # Download the data - json_data = self.data_store.get_json_data_from_url( - url = self.dataset_url, - redownload=redownload - ) - - self._learning_sessions = LearningSessionArray(**json_data) - - @property - def learning_sessions(self) -> List[schema.HumanLearningSession]: - return self._learning_sessions.sessions - - -class MutatorHighVarDataset(LearningDataset): - dataset_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-highvar-human-learning-data.json' - - -class MutatorOneShotDataset(LearningDataset): - dataset_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-oneshot-human-learning-data.json' - - -if __name__ == '__main__': - dataset = MutatorHighVarDataset() - oneshot_dataset = MutatorOneShotDataset(redownload=True) - #print(dataset.learning_sessions) \ No newline at end of file diff --git a/hobj/data/behavior/__init__.py b/hobj/data/behavior/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/hobj/data/behavior/template.py b/hobj/data/behavior/template.py new file mode 100644 index 0000000..5a1e13f --- /dev/null +++ b/hobj/data/behavior/template.py @@ -0,0 +1,110 @@ +import datetime +from abc import ABC +from typing import List, Literal + +import pydantic + +from hobj.data.store import DataStore, default_data_store + + +# %% +class HumanLearningSession(pydantic.BaseModel): + + worker_id: str = pydantic.Field( + description='The anonymized worker ID of the participant.' + ) + + stimulus_sha256_seq: List[str] = pydantic.Field( + description='The SHA256 hash of the image (in np.uint8 format and RGBA mode) shown to the participant.', + ) + + stimulus_duration_msec_seq: List[int] = pydantic.Field( + description='The duration of the stimulus presentation in milliseconds.', + ) + + action_seq: List[Literal[0, 1, None]] = pydantic.Field(description='The action taken by the participant. 0 maps to "F" (left) and 1 maps to "J" (right). None maps to no action taken.') + reward_seq: List[Literal[-1, 1]] = pydantic.Field(description='The reward received by the participant following their action.') + timestamp_start_seq: List[datetime.datetime] = pydantic.Field(description='The timestamp of the start of the trial.') + + @pydantic.field_serializer('timestamp_start_seq') + def serialize_timestamp_start(self, v: List[datetime.datetime], _info) -> List[float]: + # Serializes datetime objects into Unix timestamps (seconds) + return [entry.timestamp() for entry in v] + + @pydantic.model_validator(mode='after') + def validate_lengths(self) -> 'HumanLearningSession': + + stimulus_len = len(self.stimulus_sha256_seq) + duration_len = len(self.stimulus_duration_msec_seq) + action_len = len(self.action_seq) + reward_len = len(self.reward_seq) + timestamp_len = len(self.timestamp_start_seq) + + if not stimulus_len == duration_len == action_len == reward_len == timestamp_len: + raise ValueError(f'All lists must be of the same length. Got: {stimulus_len, duration_len, action_len, reward_len, timestamp_len}') + + return self + + +class LearningDataset(ABC): + """ + Abstract class for a set of "raw" human learning sessions, downloaded from dataset_url. + """ + dataset_url: str + + def __init__( + self, + data_store: DataStore = None, + redownload=False, + ): + if data_store is None: + self.data_store = default_data_store + + # Download the data: + json_data = self.data_store.get_json_data_from_url( + url=self.dataset_url, + redownload=redownload + ) + + # Validate the data: + class LearningSessionArray(pydantic.BaseModel): + sessions: List[HumanLearningSession] + self._learning_sessions = LearningSessionArray(**json_data) + + all_worker_ids = set() + for session in self._learning_sessions.sessions: + all_worker_ids.add(session.worker_id) + + self._worker_ids = list(all_worker_ids) + + @property + def learning_sessions(self) -> List[HumanLearningSession]: + """ + Returns a list of "raw" learning sessions. + :return: + """ + return self._learning_sessions.sessions + + @property + def worker_ids(self) -> List[str]: + return self._worker_ids + + def __str__(self): + return f'{self.__class__.__name__}(sessions={len(self.learning_sessions)}, workers={len(self.worker_ids)})' + + def __repr__(self): + return str(self) + + + + +# %% +class MutatorOneShotDataset(LearningDataset): + dataset_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-oneshot-human-learning-data.json' + +# %% + +if __name__ == '__main__': + + oneshot_dataset = MutatorOneShotDataset() + print(oneshot_dataset) diff --git a/hobj/data/schema.py b/hobj/data/schema.py index d8e1152..dd191e2 100644 --- a/hobj/data/schema.py +++ b/hobj/data/schema.py @@ -4,9 +4,6 @@ from hobj.utils.hash import hash_image from hobj.data.store import default_data_store -from typing import List, Literal -import datetime - # %% Image class ImageRef(pydantic.BaseModel): @@ -33,43 +30,3 @@ def __lt__(self, other) -> bool: def __hash__(self): return hash(self.sha256) - -# %% Behavior -class HumanLearningSession(pydantic.BaseModel): - - worker_id: str = pydantic.Field( - description='The anonymized worker ID of the participant.' - ) - - stimulus_sha256_seq: List[str] = pydantic.Field( - description='The SHA256 hash of the image (in np.uint8 format and RGBA mode) shown to the participant.', - ) - - stimulus_duration_msec_seq: List[int] = pydantic.Field( - description='The duration of the stimulus presentation in milliseconds.', - ) - - action_seq: List[Literal[0, 1, None]] = pydantic.Field(description='The action taken by the participant. 0 maps to "F" (left) and 1 maps to "J" (right). None maps to no action taken.') - reward_seq: List[Literal[-1, 1]] = pydantic.Field(description='The reward received by the participant following their action.') - timestamp_start_seq: List[datetime.datetime] = pydantic.Field(description='The timestamp of the start of the trial.') - - @pydantic.field_serializer('timestamp_start_seq') - def serialize_timestamp_start(self, v: List[datetime.datetime], _info) -> List[float]: - # Serializes datetime objects into Unix timestamps (seconds) - return [entry.timestamp() for entry in v] - - - - @pydantic.model_validator(mode='after') - def check_passwords_match(self) -> 'HumanLearningSession': - - stimulus_len = len(self.stimulus_sha256_seq) - duration_len = len(self.stimulus_duration_msec_seq) - action_len = len(self.action_seq) - reward_len = len(self.reward_seq) - timestamp_len = len(self.timestamp_start_seq) - - if not stimulus_len == duration_len == action_len == reward_len == timestamp_len: - raise ValueError(f'All lists must be of the same length. Got: {stimulus_len, duration_len, action_len, reward_len, timestamp_len}') - - return self \ No newline at end of file From 88d68d2c168471923a8586834e46288589a78c6a Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 23 Jan 2025 08:33:23 -0500 Subject: [PATCH 049/156] to pydantic model --- hobj/data/behavior/template.py | 83 ++++++++++++++-------------------- 1 file changed, 35 insertions(+), 48 deletions(-) diff --git a/hobj/data/behavior/template.py b/hobj/data/behavior/template.py index 5a1e13f..293e459 100644 --- a/hobj/data/behavior/template.py +++ b/hobj/data/behavior/template.py @@ -46,65 +46,52 @@ def validate_lengths(self) -> 'HumanLearningSession': return self -class LearningDataset(ABC): +class LearningDataset(pydantic.BaseModel): """ - Abstract class for a set of "raw" human learning sessions, downloaded from dataset_url. + Model which wraps a set of "raw" human learning sessions. + + A .from_url method is provided to instantiate it from a URL. """ - dataset_url: str - def __init__( - self, + class Config: + frozen = True + + sessions: List[HumanLearningSession] + + @classmethod + def from_url( + cls, + dataset_url: str, + redownload:bool = False, data_store: DataStore = None, - redownload=False, - ): + ) -> 'LearningDataset': + """ + Instantiates a LearningDataset object from a given URL. + :param url: + :param redownload: + :return: + """ if data_store is None: - self.data_store = default_data_store + data_store = default_data_store # Download the data: - json_data = self.data_store.get_json_data_from_url( - url=self.dataset_url, + json_data = data_store.get_json_data_from_url( + url=dataset_url, redownload=redownload ) - # Validate the data: - class LearningSessionArray(pydantic.BaseModel): - sessions: List[HumanLearningSession] - self._learning_sessions = LearningSessionArray(**json_data) - - all_worker_ids = set() - for session in self._learning_sessions.sessions: - all_worker_ids.add(session.worker_id) - - self._worker_ids = list(all_worker_ids) - - @property - def learning_sessions(self) -> List[HumanLearningSession]: - """ - Returns a list of "raw" learning sessions. - :return: - """ - return self._learning_sessions.sessions - - @property - def worker_ids(self) -> List[str]: - return self._worker_ids + return cls( + **json_data + ) def __str__(self): - return f'{self.__class__.__name__}(sessions={len(self.learning_sessions)}, workers={len(self.worker_ids)})' + unique_workers = set() + ntrials =0 + for session in self.sessions: + unique_workers.add(session.worker_id) + ntrials += len(session.action_seq) + nworkers = len(unique_workers) + return f'{self.__class__.__name__}(sessions={len(self.sessions)}, workers={nworkers}, trials={ntrials})' def __repr__(self): - return str(self) - - - - -# %% -class MutatorOneShotDataset(LearningDataset): - dataset_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-oneshot-human-learning-data.json' - -# %% - -if __name__ == '__main__': - - oneshot_dataset = MutatorOneShotDataset() - print(oneshot_dataset) + return self.__str__() From 34a8ddae6497d39113e3f89f2fb761f189846080 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 23 Jan 2025 09:34:10 -0500 Subject: [PATCH 050/156] remove irrelevant --- .../benchmark_utils/data_resampling.py | 228 ---------------- .../mut_highvar_benchmark.py | 255 ++---------------- hobj/data/behavior/highvar_behavior.py | 115 ++++++++ hobj/data/behavior/oneshot_behavior.py | 2 + hobj/data/behavior/template.py | 5 +- hobj/data/depr_behavior/depr_human_data.py | 4 - .../bootstrapped_confidence_intervals.py | 7 +- hobj/stats_new/__init__.py | 1 + hobj/stats_new/ci.py | 40 +++ .../null_distribution.py} | 0 requirements.txt | 57 +++- 11 files changed, 241 insertions(+), 473 deletions(-) delete mode 100644 hobj/benchmarks/benchmark_utils/data_resampling.py create mode 100644 hobj/data/behavior/highvar_behavior.py create mode 100644 hobj/data/behavior/oneshot_behavior.py create mode 100644 hobj/stats_new/__init__.py create mode 100644 hobj/stats_new/ci.py rename hobj/{benchmarks/benchmark_utils/__init__.py => stats_new/null_distribution.py} (100%) diff --git a/hobj/benchmarks/benchmark_utils/data_resampling.py b/hobj/benchmarks/benchmark_utils/data_resampling.py deleted file mode 100644 index fdf0750..0000000 --- a/hobj/benchmarks/benchmark_utils/data_resampling.py +++ /dev/null @@ -1,228 +0,0 @@ -import xarray as xr -import numpy as np -from tqdm import tqdm, trange - - -def get_subtask_grain_splithalf_and_bootstrap_resampled_data(ds_raw, nsplits): - """ - Performs split-half and bootstrap resampling of the data, by worker. - - ds_raw: xr.Dataset with mandatory variables: - stimulus_url: (trial, session) - worker_id: (trial, session) - perf: (trial, session) - subtask: (session) - - ds_splits: - perf: (worker_split, split_iter, trial, subtask) - ds_boot: - perf: (boot_iter, trial, subtask) - """ - - all_subtasks = sorted(set(ds_raw.subtask.values)) - subtask_to_i_subtask = {sub: i_subtask for (i_subtask, sub) in enumerate(all_subtasks)} - ntrials = len(ds_raw.trial.values) - nsubtasks = len(all_subtasks) - - # %% Get workerwise data - make_subtask_kn_table = lambda: np.zeros((nsubtasks, ntrials, 2)).astype(int) - worker_to_kn = {} - nworkers = len(set(ds_raw.worker_id.values)) - for worker_id, ws in tqdm(ds_raw.groupby('worker_id'), total=nworkers): - - worker_to_kn[worker_id] = make_subtask_kn_table() - - perfs = ws.perf.transpose('session', 'trial').values - subtask_seq = ws.subtask.values - - for i_sess in range(perfs.shape[0]): - subtask_cur = subtask_seq[i_sess] - i_subtask = subtask_to_i_subtask[subtask_cur] - perfs_cur = perfs[i_sess] - - worker_to_kn[worker_id][i_subtask, :, 0] += perfs_cur.astype(int) - worker_to_kn[worker_id][i_subtask, :, 1] += 1 - all_workers = sorted(worker_to_kn.keys()) - - # %% Perform splitting and bootstrapping - i_split_to_k = [] - i_split_to_n = [] - - i_boot_to_k = [] - i_boot_to_n = [] - - for i_split in trange(nsplits): - RS = np.random.RandomState(i_split) - wcur = RS.permutation(all_workers) - workers0 = wcur[:len(wcur) // 2] - workers1 = wcur[len(wcur) // 2:] - workers_boot = RS.choice(all_workers, replace=True, size=nworkers) - - dat0 = make_subtask_kn_table() - dat1 = make_subtask_kn_table() - datboot = make_subtask_kn_table() - - for workers, dat in zip([workers0, workers1, workers_boot], [dat0, dat1, datboot]): - for w in workers: - dat += worker_to_kn[w] - - i_split_to_k.append((dat0[..., 0], dat1[..., 0])) - i_split_to_n.append((dat0[..., 1], dat1[..., 1])) - i_boot_to_k.append(datboot[..., 0]) - i_boot_to_n.append(datboot[..., 1]) - - ds_splits = xr.Dataset( - data_vars={ - 'k': (['split_iter', 'worker_split', 'subtask', 'trial'], i_split_to_k), - 'n': (['split_iter', 'worker_split', 'subtask', 'trial'], i_split_to_n) - }, - coords={ - 'subtask': all_subtasks, - } - ) - ds_splits['perf'] = ds_splits.k / ds_splits.n - - ds_splits = ds_splits.transpose('worker_split', 'split_iter', 'trial', 'subtask') - - ds_boot = xr.Dataset( - data_vars={ - 'k': (['boot_iter', 'subtask', 'trial'], i_boot_to_k), - 'n': (['boot_iter', 'subtask', 'trial'], i_boot_to_n) - }, - coords={ - 'subtask': all_subtasks, - } - ) - ds_boot['perf'] = ds_boot.k / ds_boot.n - ds_boot = ds_boot.transpose('boot_iter', 'trial', 'subtask') - - - rvalues = { - 'ds_boot':ds_boot, - 'ds_splits':ds_splits, - } - return rvalues - - -def get_policy_grain_splithalf_and_bootstrap_resampled_data(ds_raw, nsplits): - """ - Performs split-half and bootstrap resampling of the data, by worker. - - ds_raw: xr.Dataset with mandatory variables: - stimulus_url: (trial, session) - worker_id: (trial, session) - perf: (trial, session) - subtask: (session) - - ds_splits: - perf: (worker_split, split_iter, trial, subtask, stimulus_category) - """ - - all_subtasks = sorted(set(ds_raw.subtask.values)) - subtask_to_i_subtask = {sub: i_subtask for (i_subtask, sub) in enumerate(all_subtasks)} - ntrials = len(ds_raw.trial.values) - nsubtasks = len(all_subtasks) - - # %% Get workerwise data - nobjects_per_subtask = 2 - make_subtask_kn_table = lambda: np.zeros((nsubtasks, nobjects_per_subtask, ntrials, 2)).astype(int) - - # k: the number of times that action 1 was chosen - # n: the number of observations - - worker_to_kn = {} - nworkers = len(set(ds_raw.worker_id.values)) - import hobj.data.images.depr_imagesets as imagesets - ds_meta = imagesets.MutatorHighVarDeprImageset().ds_meta - - for worker_id, ws in tqdm(ds_raw.groupby('worker_id'), total=nworkers): - - worker_to_kn[worker_id] = make_subtask_kn_table() - - perfs = ws.perf.transpose('session', 'trial').values - subtask_seq = ws.subtask.values - - for i_sess in range(perfs.shape[0]): - subtask_cur = subtask_seq[i_sess] - i_subtask = subtask_to_i_subtask[subtask_cur] - perfs_cur = perfs[i_sess] - stimseq_cur = ws.stimulus_url.values[i_sess] - objseq_cur = ds_meta.sel(image_url = stimseq_cur).obj.values - subtask_objects = sorted(set(objseq_cur)) - assert len(subtask_objects) == nobjects_per_subtask - i_objseq_cur = [subtask_objects.index(obj) for obj in objseq_cur] - for t, (i_obj, p) in enumerate(zip(i_objseq_cur, perfs_cur)): - if p > 0: - performed_action1 = i_obj - else: - performed_action1 = 1 - i_obj - worker_to_kn[worker_id][i_subtask, i_obj, t, 0] += int(performed_action1) - worker_to_kn[worker_id][i_subtask, i_obj, t, 1] += 1 - all_workers = sorted(worker_to_kn.keys()) - - - - # %% Perform splitting and bootstrapping - i_split_to_k = [] - i_split_to_n = [] - - i_boot_to_k = [] - i_boot_to_n = [] - - for i_split in trange(nsplits): - RS = np.random.RandomState(i_split) - wcur = RS.permutation(all_workers) - workers0 = wcur[:len(wcur) // 2] - workers1 = wcur[len(wcur) // 2:] - workers_boot = RS.choice(all_workers, replace=True, size=nworkers) - - dat0 = make_subtask_kn_table() - dat1 = make_subtask_kn_table() - datboot = make_subtask_kn_table() - - for workers, dat in zip([workers0, workers1, workers_boot], [dat0, dat1, datboot]): - for w in workers: - dat += worker_to_kn[w] - - i_split_to_k.append((dat0[..., 0], dat1[..., 0])) - i_split_to_n.append((dat0[..., 1], dat1[..., 1])) - i_boot_to_k.append(datboot[..., 0]) - i_boot_to_n.append(datboot[..., 1]) - - ds_splits = xr.Dataset( - data_vars={ - 'k': (['split_iter', 'worker_split', 'subtask', 'stimulus_category', 'trial'], i_split_to_k), - 'n': (['split_iter', 'worker_split', 'subtask', 'stimulus_category','trial'], i_split_to_n) - }, - coords={ - 'subtask': all_subtasks, - } - ) - - ds_splits['action'] = ds_splits.k / ds_splits.n - ds_splits = ds_splits.transpose('worker_split', 'split_iter', 'trial', 'subtask', 'stimulus_category',) - - ds_boot = xr.Dataset( - data_vars={ - 'k': (['boot_iter', 'subtask', 'stimulus_category', 'trial'], i_boot_to_k), - 'n': (['boot_iter', 'subtask', 'stimulus_category', 'trial'], i_boot_to_n) - }, - coords={ - 'subtask': all_subtasks, - } - ) - ds_boot['action'] = ds_boot.k / ds_boot.n - ds_boot = ds_boot.transpose('boot_iter', 'trial', 'subtask', 'stimulus_category') - - - rvalues = { - 'ds_boot':ds_boot, - 'ds_splits':ds_splits, - } - return rvalues - - -if __name__ == '__main__': - import hobj.data.depr_behavior.human_data as human_data - ds_raw = human_data.MutatorHighVarDataset().load_ds_data() - result = get_policy_grain_splithalf_and_bootstrap_resampled_data(ds_raw = ds_raw, nsplits = 1000) diff --git a/hobj/benchmarks/leedicarlo_benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/mut_highvar_benchmark.py index 6bc5f3d..7ea43a4 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/mut_highvar_benchmark.py @@ -1,51 +1,27 @@ -import json -import os import warnings from dataclasses import dataclass -from pathlib import Path from typing import List import numpy as np import scipy.stats as ss -import xarray as xr -import hobj.statistics.hypothesis_testing.approximated_null_distribution as approximated_null_distribution -import hobj.statistics.hypothesis_testing.bootstrapped_confidence_intervals as bootstrapped_confidence_intervals import hobj.statistics.lapse_rate as lapse_rate_funcs -import hobj.statistics.score_statistics.MSEn as R2n_funcs import hobj.statistics.variance_estimates.binomial as binomial_funcs -from hobj.data.depr_behavior import human_data as human_data +from hobj.data.behavior.template import LearningDataset from hobj.learning_models import learning_model as lm -from hobj.statistics.resamplers.resamplers import LearningCurveResampler, WorkerResampler -from hobj.utils import stats as stats -from hobj.data.images import MutatorHighVarImageset -from hobj.benchmarks.experiment_simulation.task import SimpleBinaryTask class MutatorHighVarBenchmark: nboot = 1000 tearly = slice(1, 6) tlate = slice(94, 100) - def __init__(self): + dataset_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-highvar-human-learning-data.json' - self.dataset = human_data.MutatorHighVarDataset() - self.resampler = LearningCurveResampler( - ds=self.ds_worker_table, - condition_dim='subtask', - rep_dim='worker_id' - ) + def __init__(self): - self.worker_resampler = WorkerResampler( - ds_worker_table=self.ds_worker_table, - condition_dim='subtask', - worker_dim='worker_id' - ) + self.dataset = LearningDataset.from_url(dataset_url = self.dataset_url) - # Todo: move/load this manifest elsewhere – e.g. to imageset - subtask_manifest = Path(os.path.dirname(__file__)) / 'MutatorHighVarSubtasks.json' - subtask_manifest = json.loads(subtask_manifest.read_text()) - self.subtask_manifest = subtask_manifest @dataclass class SimulateExperimentResult: @@ -71,18 +47,24 @@ def _simulate_experiment(self, learner: lm.BinaryLearningModel, seed: int) -> Si @dataclass class EvaluateModelResult: - mse_n: float - subtask_vector_spearmanr: float - perf_early: float - perf_late: float - subtasks: List[str] + # Benchmark results: + msen: float + msen_sigma: float + msen_null_samples: List[float] + + # Point estimates that may be used for further downstream analysis: model_phat: np.ndarray # [trial, subtask] model_varhat_phat: np.ndarray # [trial, subtask] model_lapse_rate: float human_phat: np.ndarray # [trial, subtask] human_varhat_phat: np.ndarray # [trial, subtask] + human_phat_bootstrap_resamples: np.ndarray # [nboot, trial, subtask] - bootstrap resamples of these statistics over worker resamples. + + # Legacy version of msen, where variance correction from the human was not subtracted. + _legacy_msen: float + _legacy_msen_sigma: float def evaluate_model(self, learner: lm.BinaryLearningModel, seed: int = 0) -> EvaluateModelResult: """ @@ -118,205 +100,26 @@ def evaluate_model(self, learner: lm.BinaryLearningModel, seed: int = 0) -> Eval phat_model_lapsed = phat_model * (1 - lapse_rate) + 0.5 * lapse_rate varhat_phat_model_lapsed = varhat_phat_model * (1 - lapse_rate) ** 2 - msen_elementwise = np.square(phat_target - phat_model_lapsed) - varhat_phat_model_lapsed - MSEn_point = msen_elementwise.mean() - - # Get SpearmanR between the subtask difficulty patterns - predicted_subtask_vector = phat_model.mean(1) # [subtask] - actual_subtask_vector = phat_target.mean(1) # [subtask] - - SpearmanR_point = ss.spearmanr(predicted_subtask_vector, actual_subtask_vector).correlation + # Get elementwise errors: + msen_elementwise = np.square(phat_target - phat_model_lapsed) - varhat_phat_model_lapsed - varhat_phat_target + legacy_msen_elementwise = np.square(phat_target - phat_model) - varhat_phat_model_lapsed - # %% Get the (g)rand (l)earning (c)urve: the average over all subtasks, and the confidence intervals - glc = phat_model.mean(0) # [trial] - glc_lapsed = glc * (1 - lapse_rate) + 0.5 * lapse_rate - overall_perf = glc_lapsed.mean() # Prediction of lapse-rate adjusted model + # Get point estimates: + msen_point = msen_elementwise.mean() + msen_sigma = ss.sem(msen_elementwise, ddof = 1) - # %% Get early and late performances - perf_early = glc[self.tearly].mean() - perf_late = glc[self.tlate].mean() + # Package return return self.EvaluateModelResult( - mse_n=MSEn_point, - subtask_vector_spearmanr=SpearmanR_point, - subtasks=subtasks, + msen=msen_point, + msen_sigma = msen_sigma, model_phat=phat_model, model_varhat_phat=varhat_phat_model, model_lapse_rate=lapse_rate, human_phat=phat_target, - human_varhat_phat=varhat_phat_target + human_varhat_phat=varhat_phat_target, + msen_null_samples = msen_null_samples, + human_phat_bootstrap_resamples=phat_target_bootstrap_resamples, + _legacy_msen=legacy_msen_elementwise.mean(), + _legacy_msen_sigma=ss.sem(legacy_msen_elementwise, ddof=1), ) - - @property - def ds_ceilings(self): - """ - - The point estimate for the R2n noise ceiling, and associated CI. - - The approximate null (where the model is equal to humans) distribution of R2n scores, given the number of model simulations. - - The point estimates for the grand-human learning curve, and associated CI. - - - The expected range of SpearmanRs between the subtask difficulty vector between two repetitions of this experiment - - :return: - """ - if not hasattr(self, '_ds_ceilings'): - alpha = 0.05 - ds_ceilings = xr.Dataset() - - # %% Get point estimate of R2 ceiling, and confidence intervals - point = self.ds_worker_table.sum('worker_id') - ds_ceilings['MSEn'] = binomial_funcs.estimate_variance_of_binomial_proportion( - kvec=point.k, - nvec=point.n - ).mean(['subtask', 'trial']) - - # Approximate the null distribution of R2n, - # The null hypothesis is that null samples are drawn from the same distribution as the human data. - - def get_null_MSEn_functional(ds_data: xr.Dataset): - pvec = ds_data.k / ds_data.n - pvec_null = ds_data.knull / ds_data.nnull - var_pvec_null = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=ds_data.knull, nvec=ds_data.nnull) - - MSEn = R2n_funcs.estimate_MSEn( - hat_theta_model=pvec_null, - hat_var_hat_theta_model=var_pvec_null, - hat_theta_target=pvec, - condition_dims=('subtask', 'trial') - ) - return MSEn - - nsessions = self.resampler.nsessions.copy(deep=True) - n_null_sessions = nsessions - nsessions + 500 # Because 500 simulations per subtask were performed, for the models. - - ds_boot = self.resampler.get_ds_boot(self.nboot, seed=0) - ds_null = self.resampler.get_ds_resamples(nresamples=self.nboot, nsessions=n_null_sessions, seed=1) - ds_null = ds_null.rename({'k': 'knull', 'n': 'nnull'}) - - ds_data = xr.merge([ds_boot, ds_null]) - - ds_ceilings['MSEn_null'] = approximated_null_distribution.estimate_null_distribution( - statistical_functional=get_null_MSEn_functional, - ds_null_samples=ds_data, - hat_mu=ds_ceilings['MSEn'], - resample_dim='boot_iter' - ) - - # %% Approximate the distribution of SpearmanRs between the subtask difficulty vectors from two independent repetitions of this experiment - ds_boot1 = self.worker_resampler.get_ds_boot(nboot=self.nboot, seed=1) - ds_boot2 = self.worker_resampler.get_ds_boot(nboot=self.nboot, seed=2) - vec1 = ds_boot1.k.sum('trial') / ds_boot1.n.sum('trial') - vec2 = ds_boot2.k.sum('trial') / ds_boot2.n.sum('trial') - ds_ceilings['SpearmanR_repeat'] = stats.calc_correlation(obs_dim='subtask', y_pred=vec1, y_actual=vec2, spearman=True) - - # %% Approximate the distribution of overall performances over repetitions of this experiment - ds_ceilings['overall_perf'] = (self.ds_human.k / self.ds_human.n).mean(['subtask', 'trial']) - ds_ceilings['overall_perf_repeat'] = (ds_boot1.k / ds_boot1.n).mean(['subtask', 'trial']) - - # %% Get point estimate of the grand-human learning curve, and confidence intervals - ds_ceilings['glc'] = (self.ds_human.k / self.ds_human.n).mean('subtask') - ds_ceilings['glc_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval( - alpha=alpha, - ds_data=self.ds_human, - ds_boot=self.ds_boot, - statistic_functional=lambda ds_data: (ds_data.k / ds_data.n).mean('subtask'), - parameter_functional=None, - ) - - # %% Get point estimates of early and late performances - ds_ceilings['early_perf'] = ds_ceilings.glc.isel(trial=self.tearly).mean('trial') - ds_ceilings['early_perf_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval( - alpha=alpha, - ds_data=self.ds_human, - ds_boot=self.ds_boot, - statistic_functional=lambda ds_data: (ds_data.k / ds_data.n).mean('subtask').isel(trial=self.tearly).mean('trial'), - ) - ds_ceilings['late_perf'] = ds_ceilings.glc.isel(trial=self.tlate).mean('trial') - ds_ceilings['late_perf_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval( - alpha=alpha, - ds_data=self.ds_human, - ds_boot=self.ds_boot, - statistic_functional=lambda ds_data: (ds_data.k / ds_data.n).mean('subtask').isel(trial=self.tlate).mean('trial'), - ) - - # %% Save - self._ds_ceilings = ds_ceilings - - return self._ds_ceilings - - @property - def ds_worker_table(self): - if not hasattr(self, '_ds_worker_table'): - self._ds_worker_table = self.dataset.load_ds_worker_table() - return self._ds_worker_table - - @property - def ds_human(self): - if not hasattr(self, '_ds_human'): - self._ds_human = self.ds_worker_table.sum('worker_id') - return self._ds_human - - @property - def ds_behavioral_statistics(self): - """ - - perf: (subtask, trial) - perf_CI: (subtask, trial) - perf_std: (subtask, trial) - - glc: (trial) - glc_CI: (trial) - glc_std: (subtask, trial) - - subtask_vector: (subtask) - subtask_vector_CI: (subtask) - subtask_vector_std: (subtask) - - :return: - """ - if not hasattr(self, '_ds_behavioral_statistics'): - alpha = 0.05 - ds_stats = xr.Dataset() - - # Performance point statistics - ds_stats['perf'] = self.ds_human['k'] / self.ds_human['n'] - ds_stats['perf_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval( - alpha=alpha, - ds_data=self.ds_human, - ds_boot=self.ds_boot, - statistic_functional=lambda ds: ds['k'] / ds['n'], - parameter_functional=None, - ) - ds_stats['perf_std'] = (self.ds_boot['k'] / self.ds_boot['n']).std('boot_iter') - - # Overall learning curve - ds_stats['glc'] = ds_stats['perf'].mean('subtask') - ds_stats['glc_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval( - alpha=alpha, - ds_data=self.ds_human, - ds_boot=self.ds_boot, - statistic_functional=lambda ds: (ds['k'] / ds['n']).mean('subtask'), - parameter_functional=None, - ) - ds_stats['glc_std'] = (self.ds_boot['k'] / self.ds_boot['n']).mean('subtask').std('boot_iter') - - # Subtask difficulty vector - ds_stats['subtask_vector'] = ds_stats['perf'].mean('trial') - ds_stats['subtask_vector_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval( - alpha=alpha, - ds_data=self.ds_human, - ds_boot=self.ds_boot, - statistic_functional=lambda ds: (ds['k'] / ds['n']).mean('trial'), - parameter_functional=None, - ) - ds_stats['subtask_vector_std'] = (self.ds_boot['k'] / self.ds_boot['n']).mean('trial').std('boot_iter') - self._ds_behavioral_statistics = ds_stats - - return self._ds_behavioral_statistics - - @property - def ds_boot(self): - - if not hasattr(self, '_ds_boot'): - self._ds_boot = self.resampler.get_ds_boot(nboot=self.nboot, seed=0) - - return self._ds_boot diff --git a/hobj/data/behavior/highvar_behavior.py b/hobj/data/behavior/highvar_behavior.py new file mode 100644 index 0000000..b034a51 --- /dev/null +++ b/hobj/data/behavior/highvar_behavior.py @@ -0,0 +1,115 @@ +from typing import Dict, List, Tuple + +import numpy as np + +from hobj.data import images as imagesets, schema as schema +from hobj.data.behavior.template import LearningDataset + +from dataclasses import dataclass + + +# %% +class MutatorHighVarHumanLearningCurves: + """ + Class containing data from Experiment 1 from the paper, consisting of learning curves (100 trials) for 64 distinct binary subtasks. + """ + + dataset_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-highvar-human-learning-data.json' + + def __init__(self): + super().__init__() + + self.imageset = imagesets.MutatorHighVarImageset() + + learning_dataset = LearningDataset.from_url(dataset_url = self.dataset_url) + + # Package data + self._subtask_to_worker_to_perf_seq: Dict[str, Dict[str, np.ndarray]] = {} # subtask: worker_id: perf_seq + for session in learning_dataset.sessions: + worker_id = session.worker_id + reward_seq = session.reward_seq + stimulus_seq = session.stimulus_sha256_seq + assert len(reward_seq) == 104 + + # Infer the subtask + perf_seq = [] + all_categories = set() + for i_trial in range(len(reward_seq)): + + # Filter out probe trials + if i_trial in {25, 51, 77, 103}: + continue + + annotation = self.imageset.get_annotation(sha256=stimulus_seq[i_trial]).category + all_categories.add(annotation) + perf_seq.append(reward_seq[i_trial] > 0) + + assert len(all_categories) == 2 + assert len(perf_seq) == 100, len(perf_seq) + subtask = ','.join(sorted(all_categories)) + + if subtask not in self._subtask_to_worker_to_perf_seq: + self._subtask_to_worker_to_perf_seq[subtask] = {} + + if worker_id not in self._subtask_to_worker_to_perf_seq[subtask]: + self._subtask_to_worker_to_perf_seq[subtask][worker_id] = np.array(perf_seq) + else: + raise ValueError(f'Worker {worker_id} has already been added to subtask {subtask}') + + self._subtasks = sorted(list(self._subtask_to_worker_to_perf_seq.keys())) + assert len(self._subtasks) == 64, len(self._subtasks) + + # Get the images associated with each category + category_to_image_refs = {} + for ref in self.imageset.image_refs: + category = self.imageset.get_annotation(sha256=ref.sha256).category + if category not in category_to_image_refs: + category_to_image_refs[category] = [] + category_to_image_refs[category].append(ref) + + # Get the images associated with each subtask + self._subtask_to_images: Dict[str, Tuple[List[schema.ImageRef], List[schema.ImageRef]]] = {} + + for subtask in self.subtasks: + cat0, cat1 = subtask.split(',') + images0 = category_to_image_refs[cat0] + images1 = category_to_image_refs[cat1] + + assert len(images0) == len(images1) == 100 + self._subtask_to_images[subtask] = (category_to_image_refs[cat0], category_to_image_refs[cat1]) + + @property + def subtasks(self) -> List[str]: + """ + Lists the 64 subtasks. + :return: + """ + return self._subtasks + + def get_subtask_images(self, subtask: str) -> Tuple[List[schema.ImageRef], List[schema.ImageRef]]: + """ + Gives the images which comprise each of the two categories associated with this subtask. + :param subtask: + :return: + """ + return self._subtask_to_images[subtask] + + @property + def data(self) -> Dict[str, Dict[str, np.ndarray]]: + """ + Returns a nested dictionary of subtask : worker_id : [boolean performance across trials] + :param subtask: + :return: + """ + return self._subtask_to_worker_to_perf_seq + + +# %% + +if __name__ == '__main__': + + dataset = MutatorHighVarHumanLearningCurves() + print(dataset) + + x = dataset.data + diff --git a/hobj/data/behavior/oneshot_behavior.py b/hobj/data/behavior/oneshot_behavior.py new file mode 100644 index 0000000..648dd10 --- /dev/null +++ b/hobj/data/behavior/oneshot_behavior.py @@ -0,0 +1,2 @@ +#class MutatorOneShotDataset(LearningDataset): +# dataset_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-oneshot-human-learning-data.json' diff --git a/hobj/data/behavior/template.py b/hobj/data/behavior/template.py index 293e459..066bb67 100644 --- a/hobj/data/behavior/template.py +++ b/hobj/data/behavior/template.py @@ -10,6 +10,9 @@ # %% class HumanLearningSession(pydantic.BaseModel): + class Config: + frozen = True + worker_id: str = pydantic.Field( description='The anonymized worker ID of the participant.' ) @@ -45,7 +48,7 @@ def validate_lengths(self) -> 'HumanLearningSession': return self - +# %% class LearningDataset(pydantic.BaseModel): """ Model which wraps a set of "raw" human learning sessions. diff --git a/hobj/data/depr_behavior/depr_human_data.py b/hobj/data/depr_behavior/depr_human_data.py index c8d2bca..b636048 100644 --- a/hobj/data/depr_behavior/depr_human_data.py +++ b/hobj/data/depr_behavior/depr_human_data.py @@ -144,7 +144,3 @@ def apply_experiment_specific_filtering(self, ds_flat): # Remove data variables ds_flat = ds_flat[['perf', 'reaction_time_msec']] return ds_flat - -if __name__ == '__main__': - dataset = MutatorHighVarDataset() - ds_data_example = dataset.load_ds_data() \ No newline at end of file diff --git a/hobj/statistics/hypothesis_testing/bootstrapped_confidence_intervals.py b/hobj/statistics/hypothesis_testing/bootstrapped_confidence_intervals.py index 68d222a..bee010e 100644 --- a/hobj/statistics/hypothesis_testing/bootstrapped_confidence_intervals.py +++ b/hobj/statistics/hypothesis_testing/bootstrapped_confidence_intervals.py @@ -1,11 +1,12 @@ import xarray as xr + def estimate_basic_confidence_interval( alpha: float, ds_data: xr.Dataset, ds_boot: xr.Dataset, statistic_functional, - parameter_functional = None, + parameter_functional=None, ): assert 'boot_iter' in ds_boot.dims @@ -23,8 +24,8 @@ def estimate_basic_confidence_interval( else: empirical_parameter = ds_boot_stats.mean('boot_iter') - low = ds_boot_stats.quantile(alpha/2,'boot_iter', ) - high = ds_boot_stats.quantile( 1 - alpha / 2,'boot_iter',) + low = ds_boot_stats.quantile(alpha / 2, 'boot_iter') + high = ds_boot_stats.quantile(1 - alpha / 2, 'boot_iter') delta1 = empirical_parameter - low delta2 = high - empirical_parameter diff --git a/hobj/stats_new/__init__.py b/hobj/stats_new/__init__.py new file mode 100644 index 0000000..8be0822 --- /dev/null +++ b/hobj/stats_new/__init__.py @@ -0,0 +1 @@ +from hobj.stats_new.ci import estimate_basic_bootstrap_CI \ No newline at end of file diff --git a/hobj/stats_new/ci.py b/hobj/stats_new/ci.py new file mode 100644 index 0000000..419816e --- /dev/null +++ b/hobj/stats_new/ci.py @@ -0,0 +1,40 @@ +from typing import Tuple, Union +import numpy as np + + +def estimate_basic_bootstrap_CI( + alpha: float, + point_estimate: Union[np.ndarray, np.generic, float, int], # [*] + bootstrapped_point_estimates: np.ndarray, # [n_bootstraps, *] +) -> Tuple[float, float]: + """ + Estimates the basic confidence interval for a given point estimate(s) using the bootstrap method. + :param alpha: Sets the width of the confidence interval to be 1 - alpha. Must be in the range (0, 1). + :param point_estimate: The point estimate(s) for which the confidence interval is to be estimated. + :param bootstrapped_point_estimate: Bootstrap resamples of the point estimate in question. + :return: A tuple containing the lower and upper bounds of the confidence interval. + """ + + if not 0 < alpha < 1: + raise ValueError(f'alpha must be in the range (0, 1); got {alpha:.2f}') + + # Coerce point_estimate to a numpy array + point_estimate = np.array(point_estimate) # [*] + + # Ensure the dimensions are as expected + if not len(point_estimate.shape) == len(bootstrapped_point_estimates.shape) - 1: + raise ValueError(f'The dimensions of the point estimate and bootstrapped point estimates do not match: {point_estimate.shape} vs {bootstrapped_point_estimates.shape}') + + # Calculate confidence interval using the basic bootstrap method + low = np.quantile(bootstrapped_point_estimates, alpha / 2, axis=0) # [*] + high = np.quantile(bootstrapped_point_estimates, 1 - alpha / 2, axis=0) # [*] + + empirical_parameter = bootstrapped_point_estimates.mean(0) # [*] + + delta1 = empirical_parameter - low + delta2 = high - empirical_parameter + + CI_low = point_estimate - delta2 + CI_high = point_estimate + delta1 + + return CI_low, CI_high diff --git a/hobj/benchmarks/benchmark_utils/__init__.py b/hobj/stats_new/null_distribution.py similarity index 100% rename from hobj/benchmarks/benchmark_utils/__init__.py rename to hobj/stats_new/null_distribution.py diff --git a/requirements.txt b/requirements.txt index 4bb5f13..cb918f4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,30 +1,65 @@ annotated-types==0.7.0 -certifi==2024.12.14 +bokeh @ file:///home/conda/feedstock_root/build_artifacts/bokeh_1733754687975/work +Bottleneck @ file:///Users/runner/miniforge3/conda-bld/bottleneck_1729268876309/work +Brotli @ file:///Users/runner/miniforge3/conda-bld/brotli-split_1725267563793/work +certifi @ file:///home/conda/feedstock_root/build_artifacts/certifi_1734380492396/work/certifi +cffi @ file:///Users/runner/miniforge3/conda-bld/cffi_1725560551097/work +cftime @ file:///Users/runner/miniforge3/conda-bld/cftime_1725400488284/work charset-normalizer==3.4.1 -contourpy==1.3.1 +click @ file:///home/conda/feedstock_root/build_artifacts/click_1734858813237/work +cloudpickle @ file:///home/conda/feedstock_root/build_artifacts/cloudpickle_1736947526808/work +contourpy @ file:///Users/runner/miniforge3/conda-bld/contourpy_1731428370657/work cycler==0.12.1 +cytoolz @ file:///Users/runner/miniforge3/conda-bld/cytoolz_1734107258423/work +dask @ file:///home/conda/feedstock_root/build_artifacts/dask-core_1737242306144/work +distributed @ file:///home/conda/feedstock_root/build_artifacts/distributed_1737295222062/work fonttools==4.55.4 +fsspec @ file:///home/conda/feedstock_root/build_artifacts/fsspec_1734650190311/work +h2 @ file:///home/conda/feedstock_root/build_artifacts/h2_1733298745555/work +hpack @ file:///home/conda/feedstock_root/build_artifacts/hpack_1733299205993/work +hyperframe @ file:///home/conda/feedstock_root/build_artifacts/hyperframe_1733298771451/work idna==3.10 +importlib_metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1737420181517/work iniconfig==2.0.0 +Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1734823942230/work kiwisolver==1.4.8 +locket @ file:///home/conda/feedstock_root/build_artifacts/locket_1650660393415/work +lz4 @ file:///Users/runner/miniforge3/conda-bld/lz4_1733474285600/work +MarkupSafe @ file:///Users/runner/miniforge3/conda-bld/markupsafe_1733219682943/work matplotlib==3.9.2 -numpy==2.2.2 -packaging==24.2 -pandas==2.2.3 -pillow==11.1.0 +msgpack @ file:///Users/runner/miniforge3/conda-bld/msgpack-python_1725975032735/work +netCDF4 @ file:///Users/runner/miniforge3/conda-bld/netcdf4_1733254940771/work +numpy @ file:///Users/runner/miniforge3/conda-bld/numpy_1737331096427/work/dist/numpy-2.2.2-cp311-cp311-macosx_11_0_arm64.whl#sha256=69698c3c66a5271f366a81628a8204b86878f0137d06c09b34a9cfb83f9f8f2d +packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1733203243479/work +pandas @ file:///Users/runner/miniforge3/conda-bld/pandas_1726878432242/work +partd @ file:///home/conda/feedstock_root/build_artifacts/partd_1715026491486/work +pillow @ file:///Users/runner/miniforge3/conda-bld/pillow_1735929745664/work pluggy==1.5.0 +psutil @ file:///Users/runner/miniforge3/conda-bld/psutil_1735327389428/work +pyarrow==19.0.0 +pycparser @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_pycparser_1733195786/work pydantic==2.10.5 pydantic_core==2.27.2 pyparsing==3.2.1 +PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1733217236728/work pytest==8.3.4 -python-dateutil==2.9.0.post0 -pytz==2024.2 +python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1733215673016/work +pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1706886791323/work +PyYAML @ file:///Users/runner/miniforge3/conda-bld/pyyaml_1737454702303/work requests==2.32.3 scipy==1.15.1 -six==1.17.0 +six @ file:///home/conda/feedstock_root/build_artifacts/six_1733380938961/work +sortedcontainers @ file:///home/conda/feedstock_root/build_artifacts/sortedcontainers_1621217038088/work +tblib @ file:///home/conda/feedstock_root/build_artifacts/tblib_1733842374544/work +toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1733736030883/work +tornado @ file:///Users/runner/miniforge3/conda-bld/tornado_1732615936501/work tqdm==4.67.1 typing_extensions==4.12.2 tzdata==2025.1 url-normalize==1.4.3 -urllib3==2.3.0 -xarray==2025.1.1 +urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1734859416348/work +xarray @ file:///home/conda/feedstock_root/build_artifacts/xarray_1736442926911/work +xyzservices @ file:///home/conda/feedstock_root/build_artifacts/xyzservices_1737234886776/work +zict @ file:///home/conda/feedstock_root/build_artifacts/zict_1733261551178/work +zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1732827521216/work +zstandard==0.23.0 From d301459769360053dcb76ecf8521708202ce4d60 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 23 Jan 2025 10:02:02 -0500 Subject: [PATCH 051/156] delete depr behavior --- examples/score_example_model.py | 3 +- .../experiment_simulation2_tmp.py | 5 +- hobj/benchmarks/experiment_simulation/task.py | 2 +- hobj/benchmarks/learning_curve_benchmark.py | 63 ++++++++ .../MutatorOneshot/mut_oneshot_benchmark.py | 4 +- .../mut_highvar_benchmark.py | 8 +- hobj/data/depr_behavior/__init__.py | 0 hobj/data/depr_behavior/depr_human_data.py | 146 ------------------ .../depr_behavior/depr_template/__init__.py | 0 .../depr_behavior/depr_template/sr_data.py | 87 ----------- hobj/learning_models/__init__.py | 63 ++++++++ hobj/learning_models/learning_model.py | 62 -------- .../linear_learners/linear_learner.py | 5 +- .../tests/test_dummy_learner.py | 2 +- 14 files changed, 142 insertions(+), 308 deletions(-) create mode 100644 hobj/benchmarks/learning_curve_benchmark.py delete mode 100644 hobj/data/depr_behavior/__init__.py delete mode 100644 hobj/data/depr_behavior/depr_human_data.py delete mode 100644 hobj/data/depr_behavior/depr_template/__init__.py delete mode 100644 hobj/data/depr_behavior/depr_template/sr_data.py delete mode 100644 hobj/learning_models/learning_model.py diff --git a/examples/score_example_model.py b/examples/score_example_model.py index c63825f..a26a663 100644 --- a/examples/score_example_model.py +++ b/examples/score_example_model.py @@ -1,8 +1,9 @@ +import hobj.learning_models import hobj.learning_models.learning_model as lm import hobj.benchmarks.leedicarlo_benchmarks.mut_highvar_benchmark as highvar_benchmark import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_benchmark as oneshot_benchmark -dummy_model = lm.DummyBinaryLearner() +dummy_model = hobj.learning_models.DummyBinaryLearner() hv = highvar_benchmark.MutatorHighVarBenchmark() os = oneshot_benchmark.MutatorOneshotBenchmark() diff --git a/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py b/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py index dad2e1f..5514da7 100644 --- a/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py +++ b/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py @@ -1,4 +1,5 @@ import hobj.config as config +import hobj.learning_models import hobj.learning_models.learning_model as lm import hobj.benchmarks.experiment_simulation.environment_depr as env import hobj.utils.file_io as io @@ -30,7 +31,7 @@ def environments(self): raise NotImplementedError return [env.Environment()] - def run(self, learner: lm.BinaryLearningModel, seed: Union[type(None), int], show_pbar=True): + def run(self, learner: hobj.learning_models.BinaryLearningModel, seed: Union[type(None), int], show_pbar=True): dlist = [] @@ -60,7 +61,7 @@ def postprocess_behavioral_data(self, ds: xr.Dataset, environment: env.Environme @staticmethod def simulate_environment( - learner: lm.BinaryLearningModel, + learner: hobj.learning_models.BinaryLearningModel, environment: env.Environment, RS: np.random.RandomState, nreps: int, diff --git a/hobj/benchmarks/experiment_simulation/task.py b/hobj/benchmarks/experiment_simulation/task.py index 19e902d..cc90728 100644 --- a/hobj/benchmarks/experiment_simulation/task.py +++ b/hobj/benchmarks/experiment_simulation/task.py @@ -6,7 +6,7 @@ import pydantic import hobj.data.schema as schema -from hobj.learning_models.learning_model import BinaryLearningModel +from hobj.learning_models import BinaryLearningModel class BinaryTask(ABC): diff --git a/hobj/benchmarks/learning_curve_benchmark.py b/hobj/benchmarks/learning_curve_benchmark.py new file mode 100644 index 0000000..fba578a --- /dev/null +++ b/hobj/benchmarks/learning_curve_benchmark.py @@ -0,0 +1,63 @@ +from dataclasses import dataclass +import numpy as np +from typing import List + +from hobj.learning_models import BinaryLearningModel + + +class LearningCurveBenchmark: + + """ + This benchmark measures the divergence between a human and model learning curve across a variety of binary learning subtasks. + """ + + @dataclass + class LearningCurveBenchmarkResult: + + # Benchmark results: + msen: float + msen_sigma: float + msen_null_samples: List[float] + + # Point estimates that may be used for further downstream analysis: + model_phat: np.ndarray # [trial, subtask] + model_varhat_phat: np.ndarray # [trial, subtask] + model_lapse_rate: float + + target_phat: np.ndarray # [trial, subtask] + target_varhat_phat: np.ndarray # [trial, subtask] + target_phat_bootstrap_resamples: np.ndarray # [nboot, trial, subtask] - bootstrap resamples of these statistics over worker resamples. + + # Legacy version of msen, where variance correction from the human was not subtracted. + _legacy_msen: float + _legacy_msen_sigma: float + + def __init__( + self, + + ): + pass + + def __call__( + self, + learner: BinaryLearningModel + ) -> LearningCurveBenchmarkResult: + + pass + + + @dataclass + class SimulateExperimentResult: + """ + The result of simulating an experiment on this battery of experiments. + """ + subtasks: List[str] + k: np.ndarray # [trial, subtask] + n: np.ndarray # [trial, subtask] + + + def _simulate_experiment( + self, + learner: BinaryLearningModel, + seed: int, + ): \ No newline at end of file diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py index 459b0d2..bcb2b92 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py @@ -4,6 +4,7 @@ import numpy as np from tqdm import trange +import hobj.learning_models import hobj.learning_models.learning_model as lm import hobj.data.images.depr_imagesets as imagesets import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_experiment as mutator @@ -19,6 +20,7 @@ class MutatorOneshotBenchmark: environment_name_dim = 'session' + probe_trials = {9, 14, 19} def __init__(self): super().__init__() @@ -103,7 +105,7 @@ def __init__(self): 'MutatorB2000_2909,MutatorB2000_4256' ] - def evaluate_model(self, learner: lm.BinaryLearningModel, force_recompute: bool = False): + def evaluate_model(self, learner: hobj.learning_models.BinaryLearningModel, force_recompute: bool = False): """ :param learner: LearningModel :param force_recompute: bool. If True, recompute the results even if they are already cached. diff --git a/hobj/benchmarks/leedicarlo_benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/mut_highvar_benchmark.py index 7ea43a4..56a4b5d 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/leedicarlo_benchmarks/mut_highvar_benchmark.py @@ -5,6 +5,7 @@ import numpy as np import scipy.stats as ss +import hobj.learning_models import hobj.statistics.lapse_rate as lapse_rate_funcs import hobj.statistics.variance_estimates.binomial as binomial_funcs from hobj.data.behavior.template import LearningDataset @@ -13,8 +14,6 @@ class MutatorHighVarBenchmark: nboot = 1000 - tearly = slice(1, 6) - tlate = slice(94, 100) dataset_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-highvar-human-learning-data.json' @@ -32,7 +31,7 @@ class SimulateExperimentResult: k: np.ndarray # [trial, subtask] n: np.ndarray # [trial, subtask] - def _simulate_experiment(self, learner: lm.BinaryLearningModel, seed: int) -> SimulateExperimentResult: + def _simulate_experiment(self, learner: hobj.learning_models.BinaryLearningModel, seed: int) -> SimulateExperimentResult: # Todo nreps = 500 ntrials = 100 @@ -66,7 +65,7 @@ class EvaluateModelResult: _legacy_msen: float _legacy_msen_sigma: float - def evaluate_model(self, learner: lm.BinaryLearningModel, seed: int = 0) -> EvaluateModelResult: + def evaluate_model(self, learner: hobj.learning_models.BinaryLearningModel, seed: int = 0) -> EvaluateModelResult: """ :param learner: LearningModel :param force_recompute: bool. If True, recompute the model behavior, even if it is already cached. @@ -108,7 +107,6 @@ def evaluate_model(self, learner: lm.BinaryLearningModel, seed: int = 0) -> Eval msen_point = msen_elementwise.mean() msen_sigma = ss.sem(msen_elementwise, ddof = 1) - # Package return return self.EvaluateModelResult( msen=msen_point, diff --git a/hobj/data/depr_behavior/__init__.py b/hobj/data/depr_behavior/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/hobj/data/depr_behavior/depr_human_data.py b/hobj/data/depr_behavior/depr_human_data.py deleted file mode 100644 index b636048..0000000 --- a/hobj/data/depr_behavior/depr_human_data.py +++ /dev/null @@ -1,146 +0,0 @@ -import xarray as xr -import hobj.data.depr_behavior.depr_template.sr_data as template -import os -import numpy as np -import collections -from tqdm import trange - -_loc = os.path.dirname(__file__) - -class MutatorHighVarDataset(template.SR_Dataset): - """ - "Experiment 1" from the paper. - """ - data_url = 'https://hlbdatasets.s3.amazonaws.com/behavioral_data/ds_human_raw_MutatorHighVar.nc' - probe_trials = [25, 51, 77, 103] - - def apply_experiment_specific_filtering(self, ds_flat): - # Remove data variables - ds_flat = ds_flat[['perf', 'reaction_time_msec']] - - # Assign subtask names - import hobj.data.images.depr_imagesets as imagesets - ds_meta = imagesets.MutatorHighVarDeprImageset().ds_meta - urls = ds_flat.stimulus_url.transpose('session', 'trial').values - url_to_obj = {url: obj for url, obj in zip(ds_meta.image_url.values, ds_meta.obj.values)} - sseq = [] - for i in range(urls.shape[0]): - urlseq = urls[i] - urlseq = [u for u in urlseq if 'orange' not in u and 'blue' not in u] - objects = [url_to_obj[url] for url in urlseq] - objects = sorted(set(objects)) - assert len(objects) == 2 - subtask = ','.join(objects) - - sseq.append(subtask) - ds_flat = ds_flat.assign_coords(subtask = (['session'], sseq)) - return ds_flat - - def load_ds_worker_table(self): - """ - A worker-wise table of the human data. - - :return: - ds_data: - k: (worker_id, subtask, trial) - n: (worker_id, subtask, trial) - """ - - ds_data = self.load_ds_data() - - all_subtasks = sorted(np.unique(ds_data.subtask.values)) - assert len(all_subtasks) == 64 - - ntrials = len(ds_data.trial.values) - subtask_to_workers = collections.defaultdict(list) - for sub, worker in zip(ds_data.subtask.values, ds_data.worker_id.values): - subtask_to_workers[sub].append(worker) - max_reps_per_subtask = max(len(v) for v in subtask_to_workers.values()) - assert max_reps_per_subtask == 50 - - all_workers = sorted(list(set(ds_data.worker_id.values))) - nworkers = len(all_workers) - - k = np.zeros((nworkers, len(all_subtasks), ntrials), dtype=int) - n = np.zeros((nworkers, len(all_subtasks), ntrials), dtype=int) - - ds_data = ds_data.transpose('session', 'trial') - for i_session in trange(len(ds_data.session.values), desc='unpacking data', disable = True): - subtask = ds_data.subtask.values[i_session] - worker_id = ds_data.worker_id.values[i_session] - - i_subtask = all_subtasks.index(subtask) - i_worker = all_workers.index(worker_id) - perf_seq = ds_data.perf.values[i_session] - k[i_worker, i_subtask, :] = perf_seq - n[i_worker, i_subtask, :] += 1 - - ds_table = xr.Dataset( - data_vars=dict( - k=(['worker_id', 'subtask', 'trial'], k), - n=(['worker_id', 'subtask', 'trial'], n), - ), - coords=dict( - worker_id=all_workers, - subtask=all_subtasks, - trial=np.arange(ntrials), - ) - ) - - assert np.max(ds_table.n == 1) - assert np.max(ds_table.k == 1) - - return ds_table - - -class MutatorOneShotGeneralizationDataset(template.SR_Dataset): - - """ - "Experiment 2" from the paper. - """ - - data_url = 'https://hlbdatasets.s3.amazonaws.com/behavioral_data/ds_human_raw_MutatorOneShotGeneralization.nc' - - def apply_experiment_specific_filtering(self, ds_flat): - - import numpy as np - import hobj.data.images.depr_imagesets as imagesets - ds_meta = imagesets.MutatorOneshotDeprImageset().ds_meta - image_url_to_transformation = {url:trans for url, trans in zip(ds_meta.image_url.values, ds_meta.transformation.values)} - image_url_to_transformation_level = {url:trans_level for url, trans_level in zip(ds_meta.image_url.values, ds_meta.transformation_level.values)} - - valid_sess = (((ds_flat.stimulus_url == '').sum('trial') == 0)) - ds_flat = ds_flat.sel(session=valid_sess) - - - - get_trans_level = np.vectorize(lambda url: float(image_url_to_transformation_level[url])) - get_trans = np.vectorize(lambda url: str(image_url_to_transformation[url])) - - trans = get_trans(ds_flat.stimulus_url.values) - trans_level = get_trans_level(ds_flat.stimulus_url.values) - - trans_ids = [] - for i in range((trans.shape[0])): - cur = [] - for j in range((trans.shape[1])): - t = trans[i, j] - l = trans_level[i, j] - cur.append(' | '.join([str(t), str(l)])) - trans_ids.append(cur) - - - ds_flat = ds_flat.assign_coords( - transformation_type=(ds_flat.stimulus_url.dims, trans), - transformation_level=(ds_flat.stimulus_url.dims, trans_level), - transformation=(ds_flat.stimulus_url.dims, trans_ids) - ) - - ds_flat = ds_flat.transpose('trial', 'session') - ds_flat.transformation.values[9] = 'probe | 10' - ds_flat.transformation.values[14] = 'probe | 15' - ds_flat.transformation.values[19] = 'probe | 20' - - # Remove data variables - ds_flat = ds_flat[['perf', 'reaction_time_msec']] - return ds_flat diff --git a/hobj/data/depr_behavior/depr_template/__init__.py b/hobj/data/depr_behavior/depr_template/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/hobj/data/depr_behavior/depr_template/sr_data.py b/hobj/data/depr_behavior/depr_template/sr_data.py deleted file mode 100644 index 3d524e7..0000000 --- a/hobj/data/depr_behavior/depr_template/sr_data.py +++ /dev/null @@ -1,87 +0,0 @@ -import xarray as xr -import hobj.utils.hash -import hobj.utils.file_io as io -import hobj.config as config -import os - -class SR_Dataset(object): - """ - - Wraps data on disk - - Ensures data is valid - - """ - - """ - xr.DataArray - - mandatory coords: - stimulus_url: (trial, session) - worker_id: (trial, session) - timestamp_start: (trial, session) - - mandatory data variables: - perf: (trial, session) 0s or 1s - """ - - data_url = None - probe_trials = None - - def __init__(self, cachedir = config.behavior_cachedir): - self.cachedir = cachedir - self.savepath = io.get_local_save_location(url = self.data_url, cachedir = self.cachedir) - - @property - def name(self): - return str(type(self).__name__) - - - def load_raw_ds(self): - - if not os.path.exists(self.savepath): - io.download_file(url = self.data_url, cachedir=self.cachedir) - - ds_data = xr.load_dataset(self.savepath) - return ds_data - - def load_ds_data(self): - ds_data = self.load_raw_ds() - mandatory_coords = [ - 'stimulus_url', - 'worker_id', - 'timestamp_start', - ] - mandatory_vars = [ - 'perf', - ] - - mandatory_dims = ['trial', 'session'] - - for d in mandatory_dims: - assert d in ds_data.dims, d - - for v in mandatory_vars: - assert v in ds_data.data_vars, v - for c in mandatory_coords: - assert c in ds_data.coords, c - - # ds_flat = ds_data.reset_coords()[mandatory_coords + mandatory_vars] - ds_flat = ds_data.reset_coords().set_coords(mandatory_coords) - ds_flat = ds_flat.assign_coords(trial = range(len(ds_flat.trial.values))) - - hash = '' - for v in mandatory_vars + mandatory_coords: - hash += hobj.utils.hash.hash_ndarray(ds_flat[v].values) - ds_flat = ds_flat.assign_coords(behavioral_hash=hash) - - if self.probe_trials is not None: - ds_flat = ds_flat.assign_coords( - probe_perf=ds_flat.perf.sel(trial=self.probe_trials).mean('trial') - ) - ds_flat = ds_flat.sel(trial=[v for v in ds_flat.trial.values if v not in self.probe_trials]) - ds_flat = ds_flat.assign_coords(trial=range(len(ds_flat.trial.values))) - - ds_flat = self.apply_experiment_specific_filtering(ds_flat = ds_flat) - return ds_flat - - def apply_experiment_specific_filtering(self, ds_flat): - return ds_flat diff --git a/hobj/learning_models/__init__.py b/hobj/learning_models/__init__.py index e69de29..e914a14 100644 --- a/hobj/learning_models/__init__.py +++ b/hobj/learning_models/__init__.py @@ -0,0 +1,63 @@ +import typing +from abc import ABC, abstractmethod + +import PIL.Image +import numpy as np + +from hobj.data import schema as schema + + +class BinaryLearningModel(ABC): + + @abstractmethod + def reset_state(self, seed: int) -> None: + """ + This function resets the LearningModel to some initial state. + :param seed: if the LearningModel has any stochastic components, this seed should be used to seed them. + :return: + """ + raise NotImplementedError + + @abstractmethod + def get_response( + self, + image: typing.Union[schema.ImageRef, PIL.Image], + ) -> typing.Literal[0, 1]: + """ + This function takes the current stimulus image (given either as a PIL.Image or a ImageRef) and returns one of two possible actions (parameterized by an integer). + :param image: An image (or a reference to an image). + :return: + """ + raise NotImplementedError + + @abstractmethod + def give_feedback(self, reward: float) -> None: + """ + This function takes as input a scalar reward from the environment. The LearningModel may use this reward to update its parameters. + :return: + """ + raise NotImplementedError + + +class DummyBinaryLearner(BinaryLearningModel): + """ + A dummy model which simply returns random outputs, independent of the given image. + """ + + def __init__(self, seed: int = 0): + self.random_generator = np.random.default_rng(seed=seed) + + def reset_state(self, seed: int) -> None: + self.random_generator = np.random.default_rng(seed=seed) + + def get_response( + self, + image: typing.Union[schema.ImageRef, PIL.Image], + ) -> typing.Literal[0, 1]: + + action = self.random_generator.integers(2) + action = int(action) + return action + + def give_feedback(self, reward: float) -> None: + return diff --git a/hobj/learning_models/learning_model.py b/hobj/learning_models/learning_model.py deleted file mode 100644 index ee0b5ea..0000000 --- a/hobj/learning_models/learning_model.py +++ /dev/null @@ -1,62 +0,0 @@ -from abc import ABC, abstractmethod -import hobj.data.schema as schema -import typing -import PIL.Image -import numpy as np - - -class BinaryLearningModel(ABC): - - @abstractmethod - def reset_state(self, seed: int) -> None: - """ - This function resets the LearningModel to some initial state. - :param seed: if the LearningModel has any stochastic components, this seed should be used to seed them. - :return: - """ - raise NotImplementedError - - @abstractmethod - def get_response( - self, - image: typing.Union[schema.ImageRef, PIL.Image], - ) -> typing.Literal[0, 1]: - """ - This function takes the current stimulus image (given either as a PIL.Image or a ImageRef) and returns one of two possible actions (parameterized by an integer). - :param image: An image (or a reference to an image). - :return: - """ - raise NotImplementedError - - @abstractmethod - def give_feedback(self, reward: float) -> None: - """ - This function takes as input a scalar reward from the environment. The LearningModel may use this reward to update its parameters. - :return: - """ - raise NotImplementedError - - -# %% -class DummyBinaryLearner(BinaryLearningModel): - """ - A dummy model which simply returns random outputs, independent of the given image. - """ - - def __init__(self, seed: int = 0): - self.random_generator = np.random.default_rng(seed=seed) - - def reset_state(self, seed: int) -> None: - self.random_generator = np.random.default_rng(seed=seed) - - def get_response( - self, - image: typing.Union[schema.ImageRef, PIL.Image], - ) -> typing.Literal[0, 1]: - - action = self.random_generator.integers(2) - action = int(action) - return action - - def give_feedback(self, reward: float) -> None: - return diff --git a/hobj/learning_models/linear_learners/linear_learner.py b/hobj/learning_models/linear_learners/linear_learner.py index 7a69db7..6471837 100644 --- a/hobj/learning_models/linear_learners/linear_learner.py +++ b/hobj/learning_models/linear_learners/linear_learner.py @@ -1,11 +1,12 @@ -import hobj.learning_models.learning_model as lm +import hobj.learning_models +import hobj.learning_models as lm import os import hobj.learning_models.linear_learners.representational_models.representational_model as rms import hobj.learning_models.linear_learners.update_rules.update_rules as urs import numpy as np -class LinearLearner(lm.BinaryLearningModel): +class LinearLearner(hobj.learning_models.BinaryLearningModel): """ A learning model based on a standard cognitive theory of learning. """ diff --git a/hobj/learning_models/tests/test_dummy_learner.py b/hobj/learning_models/tests/test_dummy_learner.py index 5cc845d..4bd9427 100644 --- a/hobj/learning_models/tests/test_dummy_learner.py +++ b/hobj/learning_models/tests/test_dummy_learner.py @@ -1,5 +1,5 @@ import pytest -from hobj.learning_models.learning_model import DummyBinaryLearner +from hobj.learning_models import DummyBinaryLearner import hobj.data.schema as schema @pytest.fixture From b352b9355221392f23bf8c260a5d835a66f20ffe Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 23 Jan 2025 10:18:03 -0500 Subject: [PATCH 052/156] update linear learner --- hobj/learning_models/__init__.py | 2 +- .../linear_learners/linear_learner.py | 85 ++++++++++++------- .../representational_model.py | 75 +++++++++------- 3 files changed, 100 insertions(+), 62 deletions(-) diff --git a/hobj/learning_models/__init__.py b/hobj/learning_models/__init__.py index e914a14..9e5d23f 100644 --- a/hobj/learning_models/__init__.py +++ b/hobj/learning_models/__init__.py @@ -38,7 +38,7 @@ def give_feedback(self, reward: float) -> None: """ raise NotImplementedError - +# %% class DummyBinaryLearner(BinaryLearningModel): """ A dummy model which simply returns random outputs, independent of the given image. diff --git a/hobj/learning_models/linear_learners/linear_learner.py b/hobj/learning_models/linear_learners/linear_learner.py index 6471837..469688b 100644 --- a/hobj/learning_models/linear_learners/linear_learner.py +++ b/hobj/learning_models/linear_learners/linear_learner.py @@ -1,10 +1,13 @@ -import hobj.learning_models -import hobj.learning_models as lm -import os -import hobj.learning_models.linear_learners.representational_models.representational_model as rms -import hobj.learning_models.linear_learners.update_rules.update_rules as urs +import typing + +import PIL.Image import numpy as np +import hobj.data.schema as schema +import hobj.learning_models +from hobj.learning_models.linear_learners.representational_models.representational_model import RepresentationalModel +from hobj.learning_models.linear_learners.update_rules.update_rules import UpdateRule + class LinearLearner(hobj.learning_models.BinaryLearningModel): """ @@ -12,41 +15,65 @@ class LinearLearner(hobj.learning_models.BinaryLearningModel): """ def __init__( self, - representational_model: rms.RepresentationalModel, - update_rule: urs.UpdateRule, - nactions=2, + representational_model: RepresentationalModel, + update_rule: UpdateRule, ): - learner_id = representational_model.representational_model_id + '+' + update_rule.update_rule_id - super().__init__(learner_id) self.representational_model = representational_model self.update_rule = update_rule - self.nactions = nactions - self.w = np.zeros((self.representational_model.d, self.nactions)) - self.b = np.zeros((self.nactions,)) - self.reset_state() + + # State variables + self.w = None + self.b = None + self._f_last = None + self._logits_last = None + self._action_last = None + self._generator: np.random.Generator = np.random.default_rng() + + # Initialize state + self.reset_state(seed=0) return - def reset_state(self): - self.representational_model.reset() + def reset_state(self, seed: int) -> None: + """ + :param seed: + :return: + """ self.update_rule.reset() - self.w = np.zeros((self.representational_model.d, self.nactions)) - self.b = np.zeros((self.nactions,)) + self.w = np.zeros((self.representational_model.d, 2)) + self.b = np.zeros((2,)) + self._f_last = None + self._logits_last = None + self._action_last = None + self._generator = np.random.default_rng(seed=seed) + return - def get_response(self, image: str): - f = self.representational_model.get_features(image_url=image) - self.f = f - self.logits = self.f @ self.w + self.b # [action] - self.action = int(_random_tiebreaking_argmax(self.logits)) - return self.action + def get_response( + self, + image: typing.Union[schema.ImageRef, PIL.Image] + ) -> typing.Literal[0, 1]: + + f = self.representational_model.get_features(image=image) + logits = f @ self.w + self.b + action = self._random_tiebreaking_argmax(logits[0], logits[1]) - def give_feedback(self, reward: float): - delta_w, delta_b = self.update_rule.get_update(x=self.f, w=self.w, b=self.b, logits=self.logits, action=self.action, reward=reward) # [action] + # Update internal state with traces + self._f_last = f + self._logits_last = logits + self._action_last = action + return action + + def give_feedback(self, reward: float) -> None: + delta_w, delta_b = self.update_rule.get_update(x=self._f_last, w=self.w, b=self.b, logits=self._logits_last, action=self._action_last, reward=reward) # [action] self.w += delta_w self.b += delta_b return - -def _random_tiebreaking_argmax(x): - return np.random.choice(np.flatnonzero(x == x.max())) + def _random_tiebreaking_argmax(self, logit0, logit1) -> typing.Literal[0, 1]: + if logit0 > logit1: + return 0 + elif logit0 < logit1: + return 1 + else: + return 0 if self._generator.random() < 0.5 else 1 diff --git a/hobj/learning_models/linear_learners/representational_models/representational_model.py b/hobj/learning_models/linear_learners/representational_models/representational_model.py index 9dbba85..34f5a82 100644 --- a/hobj/learning_models/linear_learners/representational_models/representational_model.py +++ b/hobj/learning_models/linear_learners/representational_models/representational_model.py @@ -1,40 +1,32 @@ import numpy as np +import hobj.data.schema as schema +from typing import Union, Dict +import PIL.Image -class RepresentationalModel: +from abc import ABC, abstractmethod + + +class RepresentationalModel(ABC): """ Model which represents image_urls as feature vectors of shape (d,). Meant to be subclassed (see PrecachedRepresentationalModel below for an example). """ - def __init__(self, representational_model_id: str): - self.representational_model_id = representational_model_id - self._d = 1 - return + def __init__(self, d:int): + if not isinstance(d, int): + raise ValueError(f"Expected d to be an int, but got {d} of type {type(d)}") + if d <= 0: + raise ValueError(f"Expected d to be positive, but got {d}") - def reset(self): - """ - Resets the model to its initial state. Not used by all representational models (e.g., fixed intermediate layers of DCNNs), but - included here for completeness. - :return: None - """ + self.d = d return - @property - def d(self): - """ - Returns the dimension of the feature vectors. - :return: - """ - return self._d - - @d.setter - def d(self, value): - assert isinstance(value, int) - assert value > 0 - self._d = value - - def get_features(self, image_url: str) -> np.ndarray: + @abstractmethod + def get_features( + self, + image: Union[schema.ImageRef, PIL.Image] + ) -> np.ndarray: """ Returns a feature vector for the image_url. To work successfully with UpdateRule, the feature vector must be of shape (d,) and have norm 1. @@ -46,10 +38,29 @@ def get_features(self, image_url: str) -> np.ndarray: class PrecachedRepresentationalModel(RepresentationalModel): - def __init__(self, url_to_features: dict, representational_model_id: str): - self.url_to_features = url_to_features - super().__init__(representational_model_id=representational_model_id) - self.d = len(url_to_features[list(url_to_features.keys())[0]]) + def __init__( + self, + image_sha256_to_features: Dict[str, np.ndarray] + ): + self.image_sha256_to_features = image_sha256_to_features + + # Check that the features are all of the same dimension + d = None + for sha256, f in image_sha256_to_features.items(): + if not len(f.shape) == 1: + raise ValueError(f"Features for image {sha256} have shape {f.shape}, but expected a 1D array") + + if d is None: + d = len(f) + elif len(f) != d: + raise ValueError(f"Features for image {sha256} have dimension {len(f)}, but expected {d}") + + super().__init__(d = d) + + def get_features(self, image: Union[schema.ImageRef, PIL.Image]) -> np.ndarray: + + if isinstance(image, PIL.Image.Image): + image = schema.ImageRef.from_image(image) - def get_features(self, image_url: str) -> np.ndarray: - return self.url_to_features[image_url] + sha256 = image.sha256 + return self.image_sha256_to_features[sha256] From 7c4984d8e71bd51d639a1f60f0e07fbc091a4bd4 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 23 Jan 2025 10:20:51 -0500 Subject: [PATCH 053/156] clean up directory structure of learning models --- hobj/learning_models/__init__.py | 2 +- .../linear_learner.py => linear/__init__.py} | 7 ++++--- .../representational_model.py => linear/representation.py} | 0 .../update_rules => linear}/update_rules.py | 0 hobj/learning_models/linear_learners/__init__.py | 0 .../linear_learners/representational_models/__init__.py | 0 .../linear_learners/update_rules/__init__.py | 0 7 files changed, 5 insertions(+), 4 deletions(-) rename hobj/learning_models/{linear_learners/linear_learner.py => linear/__init__.py} (90%) rename hobj/learning_models/{linear_learners/representational_models/representational_model.py => linear/representation.py} (100%) rename hobj/learning_models/{linear_learners/update_rules => linear}/update_rules.py (100%) delete mode 100644 hobj/learning_models/linear_learners/__init__.py delete mode 100644 hobj/learning_models/linear_learners/representational_models/__init__.py delete mode 100644 hobj/learning_models/linear_learners/update_rules/__init__.py diff --git a/hobj/learning_models/__init__.py b/hobj/learning_models/__init__.py index 9e5d23f..836a18d 100644 --- a/hobj/learning_models/__init__.py +++ b/hobj/learning_models/__init__.py @@ -38,6 +38,7 @@ def give_feedback(self, reward: float) -> None: """ raise NotImplementedError + # %% class DummyBinaryLearner(BinaryLearningModel): """ @@ -54,7 +55,6 @@ def get_response( self, image: typing.Union[schema.ImageRef, PIL.Image], ) -> typing.Literal[0, 1]: - action = self.random_generator.integers(2) action = int(action) return action diff --git a/hobj/learning_models/linear_learners/linear_learner.py b/hobj/learning_models/linear/__init__.py similarity index 90% rename from hobj/learning_models/linear_learners/linear_learner.py rename to hobj/learning_models/linear/__init__.py index 469688b..a945848 100644 --- a/hobj/learning_models/linear_learners/linear_learner.py +++ b/hobj/learning_models/linear/__init__.py @@ -3,16 +3,17 @@ import PIL.Image import numpy as np -import hobj.data.schema as schema import hobj.learning_models -from hobj.learning_models.linear_learners.representational_models.representational_model import RepresentationalModel -from hobj.learning_models.linear_learners.update_rules.update_rules import UpdateRule +from hobj.data import schema as schema +from hobj.learning_models.linear.representation import RepresentationalModel +from hobj.learning_models.linear.update_rules import UpdateRule class LinearLearner(hobj.learning_models.BinaryLearningModel): """ A learning model based on a standard cognitive theory of learning. """ + def __init__( self, representational_model: RepresentationalModel, diff --git a/hobj/learning_models/linear_learners/representational_models/representational_model.py b/hobj/learning_models/linear/representation.py similarity index 100% rename from hobj/learning_models/linear_learners/representational_models/representational_model.py rename to hobj/learning_models/linear/representation.py diff --git a/hobj/learning_models/linear_learners/update_rules/update_rules.py b/hobj/learning_models/linear/update_rules.py similarity index 100% rename from hobj/learning_models/linear_learners/update_rules/update_rules.py rename to hobj/learning_models/linear/update_rules.py diff --git a/hobj/learning_models/linear_learners/__init__.py b/hobj/learning_models/linear_learners/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/hobj/learning_models/linear_learners/representational_models/__init__.py b/hobj/learning_models/linear_learners/representational_models/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/hobj/learning_models/linear_learners/update_rules/__init__.py b/hobj/learning_models/linear_learners/update_rules/__init__.py deleted file mode 100644 index e69de29..0000000 From 345aed258f92c68f37cb313750ade48ac5a72615 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 23 Jan 2025 10:22:09 -0500 Subject: [PATCH 054/156] clean up utils --- hobj/utils/file_io.py | 34 ---------------------------------- hobj/utils/hash.py | 8 -------- hobj/utils/stats.py | 37 ------------------------------------- 3 files changed, 79 deletions(-) delete mode 100644 hobj/utils/stats.py diff --git a/hobj/utils/file_io.py b/hobj/utils/file_io.py index a8081d0..b1cf07b 100644 --- a/hobj/utils/file_io.py +++ b/hobj/utils/file_io.py @@ -32,35 +32,6 @@ def unzip_file(zip_path: Path, output_dir: Path) -> None: zip_ref.extractall(output_dir) -def get_canonical_url(url: str): - return url_normalize.url_normalize(url=url) - - -def get_local_save_location(url: str, cachedir: str): - canonical_url = get_canonical_url(url) - parsed = urlparse(canonical_url) - - path = parsed.path - if path.startswith('/'): - path = path[1:] - - domain = parsed.netloc # domain is the hostname - save_location = os.path.join(cachedir, domain, path) - return save_location - - -def prepare_savepath(savepath): - savedir = os.path.dirname(savepath) - if not os.path.exists(savedir): - os.makedirs(savedir, exist_ok=True) - - -def get_image_from_url(image_url: str): - response = requests.get(image_url) - img = PIL.Image.open(BytesIO(response.content)) - return img - - def download_file(url: str, output_path: Path) -> None: # Send a GET request to fetch the file response = requests.get(url, stream=True) @@ -113,11 +84,6 @@ def get_bytes_size(num_bytes: int, output_units: str = None) -> (float, str): size = num_bytes / unit_conversion[output_units] return size, output_units -def load_json(json_path): - with open(json_path, 'r') as fb: - val = json.load(fb) - return val - def download_json(url: str) -> Any: """ diff --git a/hobj/utils/hash.py b/hobj/utils/hash.py index 1e17e5f..7431058 100644 --- a/hobj/utils/hash.py +++ b/hobj/utils/hash.py @@ -3,14 +3,6 @@ import PIL.Image -def hash_ndarray(x: np.ndarray): - assert isinstance(x, np.ndarray) - bytes = x.tobytes(order="C") - m = hashlib.sha256(string=bytes) - digest = m.hexdigest() - return digest - - def hash_image(image: PIL.Image) -> str: """ Hash an image based on its np.uint8 representation. diff --git a/hobj/utils/stats.py b/hobj/utils/stats.py deleted file mode 100644 index d3f8c3f..0000000 --- a/hobj/utils/stats.py +++ /dev/null @@ -1,37 +0,0 @@ -import xarray as xr -from typing import Union, List, Tuple -import numpy as np - - -def calc_correlation( - obs_dim:Union[str, List[str]], - y_pred: xr.DataArray, - y_actual: Union[xr.DataArray, type(None)] = None, - spearman=False, -): - """ - This function returns a DataArray populated by Pearson correlation coefficient values with the same dimensions as x, but reduced along obs_dim. - """ - - if spearman: - if isinstance(obs_dim, list): - y_pred = y_pred.stack(spearman_rank_dim = obs_dim) - y_actual = y_actual.stack(spearman_rank_dim=obs_dim) - y_actual = y_actual.rank('spearman_rank_dim') - y_pred = y_pred.rank('spearman_rank_dim') - obs_dim = 'spearman_rank_dim' - else: - y_actual = y_actual.rank(obs_dim) - y_pred = y_pred.rank(obs_dim) - assert y_actual is not None, 'If supplying pre-computed ysigma and yresids, do not pass y' - - ymean = y_actual.mean(obs_dim) - ysigma = np.sqrt(np.square(y_actual - ymean).sum(obs_dim)) - yresids = y_actual - ymean - - xmean = y_pred.mean(obs_dim) - xsigma = np.sqrt(np.square(y_pred - xmean).sum(obs_dim)) - xresids = y_pred - xmean - - r = (xresids * yresids).sum(obs_dim) / (xsigma * ysigma) - return r From 7b8d49327b6c19c3e34696e1771defd35e86b9d0 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 23 Jan 2025 10:37:08 -0500 Subject: [PATCH 055/156] simplify behavioral data loaders --- hobj/data/behavior.py | 56 ++++++++++++ hobj/data/behavior/__init__.py | 0 hobj/data/behavior/highvar_behavior.py | 115 ------------------------- hobj/data/behavior/oneshot_behavior.py | 2 - hobj/data/behavior/template.py | 100 --------------------- hobj/data/schema.py | 44 ++++++++++ 6 files changed, 100 insertions(+), 217 deletions(-) create mode 100644 hobj/data/behavior.py delete mode 100644 hobj/data/behavior/__init__.py delete mode 100644 hobj/data/behavior/highvar_behavior.py delete mode 100644 hobj/data/behavior/oneshot_behavior.py delete mode 100644 hobj/data/behavior/template.py diff --git a/hobj/data/behavior.py b/hobj/data/behavior.py new file mode 100644 index 0000000..6be6a2d --- /dev/null +++ b/hobj/data/behavior.py @@ -0,0 +1,56 @@ +from typing import List + +import pydantic + +from hobj.data.schema import HumanLearningSession +from hobj.data.store import DataStore, default_data_store + +__all__ = ['load_highvar_behavior', 'load_oneshot_behavior'] + + +def _load_learning_sessions( + dataset_url: str, + redownload: bool +) -> List[HumanLearningSession]: + data_store = default_data_store + # Download the data: + json_data = data_store.get_json_data_from_url( + url=dataset_url, + redownload=redownload + ) + + # + class LearningSessionDataset(pydantic.BaseModel): + sessions: List[HumanLearningSession] + + learning_dataset = LearningSessionDataset(**json_data) + return learning_dataset.sessions + + +# %% Data loaders +def load_highvar_behavior(redownload: bool = False) -> List[HumanLearningSession]: + """ + Load the "raw" human learning data from Experiment 1 of Lee and DiCarlo 2023. + :return: + """ + + sessions = _load_learning_sessions( + dataset_url='https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-highvar-human-learning-data.json', + redownload=redownload + ) + + return sessions + + +def load_oneshot_behavior(redownload: bool = False) -> List[HumanLearningSession]: + """ + Load the "raw" human learning data from Experiment 2 of Lee and DiCarlo 2023. + :return: + """ + + sessions = _load_learning_sessions( + dataset_url='https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-oneshot-human-learning-data.json', + redownload=redownload + ) + + return sessions diff --git a/hobj/data/behavior/__init__.py b/hobj/data/behavior/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/hobj/data/behavior/highvar_behavior.py b/hobj/data/behavior/highvar_behavior.py deleted file mode 100644 index b034a51..0000000 --- a/hobj/data/behavior/highvar_behavior.py +++ /dev/null @@ -1,115 +0,0 @@ -from typing import Dict, List, Tuple - -import numpy as np - -from hobj.data import images as imagesets, schema as schema -from hobj.data.behavior.template import LearningDataset - -from dataclasses import dataclass - - -# %% -class MutatorHighVarHumanLearningCurves: - """ - Class containing data from Experiment 1 from the paper, consisting of learning curves (100 trials) for 64 distinct binary subtasks. - """ - - dataset_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-highvar-human-learning-data.json' - - def __init__(self): - super().__init__() - - self.imageset = imagesets.MutatorHighVarImageset() - - learning_dataset = LearningDataset.from_url(dataset_url = self.dataset_url) - - # Package data - self._subtask_to_worker_to_perf_seq: Dict[str, Dict[str, np.ndarray]] = {} # subtask: worker_id: perf_seq - for session in learning_dataset.sessions: - worker_id = session.worker_id - reward_seq = session.reward_seq - stimulus_seq = session.stimulus_sha256_seq - assert len(reward_seq) == 104 - - # Infer the subtask - perf_seq = [] - all_categories = set() - for i_trial in range(len(reward_seq)): - - # Filter out probe trials - if i_trial in {25, 51, 77, 103}: - continue - - annotation = self.imageset.get_annotation(sha256=stimulus_seq[i_trial]).category - all_categories.add(annotation) - perf_seq.append(reward_seq[i_trial] > 0) - - assert len(all_categories) == 2 - assert len(perf_seq) == 100, len(perf_seq) - subtask = ','.join(sorted(all_categories)) - - if subtask not in self._subtask_to_worker_to_perf_seq: - self._subtask_to_worker_to_perf_seq[subtask] = {} - - if worker_id not in self._subtask_to_worker_to_perf_seq[subtask]: - self._subtask_to_worker_to_perf_seq[subtask][worker_id] = np.array(perf_seq) - else: - raise ValueError(f'Worker {worker_id} has already been added to subtask {subtask}') - - self._subtasks = sorted(list(self._subtask_to_worker_to_perf_seq.keys())) - assert len(self._subtasks) == 64, len(self._subtasks) - - # Get the images associated with each category - category_to_image_refs = {} - for ref in self.imageset.image_refs: - category = self.imageset.get_annotation(sha256=ref.sha256).category - if category not in category_to_image_refs: - category_to_image_refs[category] = [] - category_to_image_refs[category].append(ref) - - # Get the images associated with each subtask - self._subtask_to_images: Dict[str, Tuple[List[schema.ImageRef], List[schema.ImageRef]]] = {} - - for subtask in self.subtasks: - cat0, cat1 = subtask.split(',') - images0 = category_to_image_refs[cat0] - images1 = category_to_image_refs[cat1] - - assert len(images0) == len(images1) == 100 - self._subtask_to_images[subtask] = (category_to_image_refs[cat0], category_to_image_refs[cat1]) - - @property - def subtasks(self) -> List[str]: - """ - Lists the 64 subtasks. - :return: - """ - return self._subtasks - - def get_subtask_images(self, subtask: str) -> Tuple[List[schema.ImageRef], List[schema.ImageRef]]: - """ - Gives the images which comprise each of the two categories associated with this subtask. - :param subtask: - :return: - """ - return self._subtask_to_images[subtask] - - @property - def data(self) -> Dict[str, Dict[str, np.ndarray]]: - """ - Returns a nested dictionary of subtask : worker_id : [boolean performance across trials] - :param subtask: - :return: - """ - return self._subtask_to_worker_to_perf_seq - - -# %% - -if __name__ == '__main__': - - dataset = MutatorHighVarHumanLearningCurves() - print(dataset) - - x = dataset.data - diff --git a/hobj/data/behavior/oneshot_behavior.py b/hobj/data/behavior/oneshot_behavior.py deleted file mode 100644 index 648dd10..0000000 --- a/hobj/data/behavior/oneshot_behavior.py +++ /dev/null @@ -1,2 +0,0 @@ -#class MutatorOneShotDataset(LearningDataset): -# dataset_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-oneshot-human-learning-data.json' diff --git a/hobj/data/behavior/template.py b/hobj/data/behavior/template.py deleted file mode 100644 index 066bb67..0000000 --- a/hobj/data/behavior/template.py +++ /dev/null @@ -1,100 +0,0 @@ -import datetime -from abc import ABC -from typing import List, Literal - -import pydantic - -from hobj.data.store import DataStore, default_data_store - - -# %% -class HumanLearningSession(pydantic.BaseModel): - - class Config: - frozen = True - - worker_id: str = pydantic.Field( - description='The anonymized worker ID of the participant.' - ) - - stimulus_sha256_seq: List[str] = pydantic.Field( - description='The SHA256 hash of the image (in np.uint8 format and RGBA mode) shown to the participant.', - ) - - stimulus_duration_msec_seq: List[int] = pydantic.Field( - description='The duration of the stimulus presentation in milliseconds.', - ) - - action_seq: List[Literal[0, 1, None]] = pydantic.Field(description='The action taken by the participant. 0 maps to "F" (left) and 1 maps to "J" (right). None maps to no action taken.') - reward_seq: List[Literal[-1, 1]] = pydantic.Field(description='The reward received by the participant following their action.') - timestamp_start_seq: List[datetime.datetime] = pydantic.Field(description='The timestamp of the start of the trial.') - - @pydantic.field_serializer('timestamp_start_seq') - def serialize_timestamp_start(self, v: List[datetime.datetime], _info) -> List[float]: - # Serializes datetime objects into Unix timestamps (seconds) - return [entry.timestamp() for entry in v] - - @pydantic.model_validator(mode='after') - def validate_lengths(self) -> 'HumanLearningSession': - - stimulus_len = len(self.stimulus_sha256_seq) - duration_len = len(self.stimulus_duration_msec_seq) - action_len = len(self.action_seq) - reward_len = len(self.reward_seq) - timestamp_len = len(self.timestamp_start_seq) - - if not stimulus_len == duration_len == action_len == reward_len == timestamp_len: - raise ValueError(f'All lists must be of the same length. Got: {stimulus_len, duration_len, action_len, reward_len, timestamp_len}') - - return self - -# %% -class LearningDataset(pydantic.BaseModel): - """ - Model which wraps a set of "raw" human learning sessions. - - A .from_url method is provided to instantiate it from a URL. - """ - - class Config: - frozen = True - - sessions: List[HumanLearningSession] - - @classmethod - def from_url( - cls, - dataset_url: str, - redownload:bool = False, - data_store: DataStore = None, - ) -> 'LearningDataset': - """ - Instantiates a LearningDataset object from a given URL. - :param url: - :param redownload: - :return: - """ - if data_store is None: - data_store = default_data_store - - # Download the data: - json_data = data_store.get_json_data_from_url( - url=dataset_url, - redownload=redownload - ) - - return cls( - **json_data - ) - - def __str__(self): - unique_workers = set() - ntrials =0 - for session in self.sessions: - unique_workers.add(session.worker_id) - ntrials += len(session.action_seq) - nworkers = len(unique_workers) - return f'{self.__class__.__name__}(sessions={len(self.sessions)}, workers={nworkers}, trials={ntrials})' - - def __repr__(self): - return self.__str__() diff --git a/hobj/data/schema.py b/hobj/data/schema.py index dd191e2..1115cac 100644 --- a/hobj/data/schema.py +++ b/hobj/data/schema.py @@ -1,3 +1,6 @@ +import datetime +from typing import List, Literal + import PIL.Image import pydantic @@ -30,3 +33,44 @@ def __lt__(self, other) -> bool: def __hash__(self): return hash(self.sha256) + +# %% Behavior +class HumanLearningSession(pydantic.BaseModel): + + class Config: + frozen = True + + worker_id: str = pydantic.Field( + description='The anonymized worker ID of the participant.' + ) + + stimulus_sha256_seq: List[str] = pydantic.Field( + description='The SHA256 hash of the image (in np.uint8 format and RGBA mode) shown to the participant.', + ) + + stimulus_duration_msec_seq: List[int] = pydantic.Field( + description='The duration of the stimulus presentation in milliseconds.', + ) + + action_seq: List[Literal[0, 1, None]] = pydantic.Field(description='The action taken by the participant. 0 maps to "F" (left) and 1 maps to "J" (right). None maps to no action taken.') + reward_seq: List[Literal[-1, 1]] = pydantic.Field(description='The reward received by the participant following their action.') + timestamp_start_seq: List[datetime.datetime] = pydantic.Field(description='The timestamp of the start of the trial.') + + @pydantic.field_serializer('timestamp_start_seq') + def serialize_timestamp_start(self, v: List[datetime.datetime], _info) -> List[float]: + # Serializes datetime objects into Unix timestamps (seconds) + return [entry.timestamp() for entry in v] + + @pydantic.model_validator(mode='after') + def validate_lengths(self) -> 'HumanLearningSession': + + stimulus_len = len(self.stimulus_sha256_seq) + duration_len = len(self.stimulus_duration_msec_seq) + action_len = len(self.action_seq) + reward_len = len(self.reward_seq) + timestamp_len = len(self.timestamp_start_seq) + + if not stimulus_len == duration_len == action_len == reward_len == timestamp_len: + raise ValueError(f'All lists must be of the same length. Got: {stimulus_len, duration_len, action_len, reward_len, timestamp_len}') + + return self From 3ce04e04d2c510de403d6fab1dbe4b4155dca8ae Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 23 Jan 2025 11:11:45 -0500 Subject: [PATCH 056/156] convenience method --- hobj/data/images/imagesets/highvar.py | 23 +++++++++++++++++++++++ hobj/data/images/template.py | 8 ++++++-- 2 files changed, 29 insertions(+), 2 deletions(-) diff --git a/hobj/data/images/imagesets/highvar.py b/hobj/data/images/imagesets/highvar.py index 1f6fd77..a57844e 100644 --- a/hobj/data/images/imagesets/highvar.py +++ b/hobj/data/images/imagesets/highvar.py @@ -2,6 +2,10 @@ from hobj.data.images.template import Imageset, ImageManifestEntry, ImageManifest import pydantic +from typing import Dict, List + +import hobj.data.schema as schema + class MutatorHighVarAnnotation(pydantic.BaseModel): category: str = pydantic.Field( @@ -18,6 +22,25 @@ class MutatorHighVarImageset(Imageset[MutatorHighVarAnnotation]): zipped_images_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-highvar/MutatorB2000_Subset128_FullVar_Train.zip' annotation_schema = MutatorHighVarAnnotation + def __init__(self): + super().__init__() + + self._category_to_image_refs: Dict[str, List[schema.ImageRef]] = {} + + for ref in self.image_refs: + annotation = self.get_annotation(image_ref=ref) + category = annotation.category + if category not in self._category_to_image_refs: + self._category_to_image_refs[category] = [] + self._category_to_image_refs[category].append(ref) + + for category in self._category_to_image_refs: + self._category_to_image_refs[category] = sorted(self._category_to_image_refs[category]) + + @property + def category_to_image_refs(self) -> Dict[str, List[schema.ImageRef]]: + return self._category_to_image_refs + if __name__ == '__main__': diff --git a/hobj/data/images/template.py b/hobj/data/images/template.py index 96a93ca..08b2daa 100644 --- a/hobj/data/images/template.py +++ b/hobj/data/images/template.py @@ -78,7 +78,7 @@ def __init__( self._sha256_to_image_ids[image_ref.sha256] = [] self._sha256_to_image_ids[image_ref.sha256].append(image_id) - def _register_image_urls(self, manifest: ImageManifest, redownload:bool = False): + def _register_image_urls(self, manifest: ImageManifest, redownload: bool = False): """ Ensures the entries of the manifest are registered in the data store. """ @@ -124,10 +124,14 @@ def image_refs(self) -> List[ImageRef]: """ return self._image_refs - def get_annotation(self, *, sha256: str) -> IA: + def get_annotation(self, *, image_ref: ImageRef = None, sha256: str = None, ) -> IA: """ Get the annotation for a given image. If an image has multiple annotations, this will throw an error. """ + + if sha256 is None: + sha256 = image_ref.sha256 + image_ids = self._sha256_to_image_ids[sha256] if len(image_ids) > 1: warnings.warn(f"Image {sha256} has multiple annotations: {image_ids}. Returning the first one.") From a5ec9cd15e3ff5e97d2849d31fa38a26935b2072 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 23 Jan 2025 15:37:52 -0500 Subject: [PATCH 057/156] add test --- .../leedicarlo_benchmarks/__init__.py | 0 hobj/data/schema.py | 23 ++++++------- hobj/data/tests/test_load_behavior.py | 11 +++++++ hobj/statistics/lapse_rate.py | 32 +++---------------- 4 files changed, 27 insertions(+), 39 deletions(-) delete mode 100644 hobj/benchmarks/leedicarlo_benchmarks/__init__.py create mode 100644 hobj/data/tests/test_load_behavior.py diff --git a/hobj/benchmarks/leedicarlo_benchmarks/__init__.py b/hobj/benchmarks/leedicarlo_benchmarks/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/hobj/data/schema.py b/hobj/data/schema.py index 1115cac..3113d5f 100644 --- a/hobj/data/schema.py +++ b/hobj/data/schema.py @@ -4,16 +4,18 @@ import PIL.Image import pydantic -from hobj.utils.hash import hash_image from hobj.data.store import default_data_store +from hobj.utils.hash import hash_image -# %% Image -class ImageRef(pydantic.BaseModel): +class _Common(pydantic.BaseModel): + class Config: + frozen = True + + +# %% Image data +class ImageRef(_Common): sha256: str = pydantic.Field(pattern=r'^[a-f0-9]{64}$') - model_config = pydantic.ConfigDict( - frozen=True - ) @classmethod def from_image(cls, image: PIL.Image, register: bool = True): @@ -34,11 +36,11 @@ def __hash__(self): return hash(self.sha256) -# %% Behavior -class HumanLearningSession(pydantic.BaseModel): +# %% Learning tasks - class Config: - frozen = True + +# %% Behavior +class HumanLearningSession(_Common): worker_id: str = pydantic.Field( description='The anonymized worker ID of the participant.' @@ -63,7 +65,6 @@ def serialize_timestamp_start(self, v: List[datetime.datetime], _info) -> List[f @pydantic.model_validator(mode='after') def validate_lengths(self) -> 'HumanLearningSession': - stimulus_len = len(self.stimulus_sha256_seq) duration_len = len(self.stimulus_duration_msec_seq) action_len = len(self.action_seq) diff --git a/hobj/data/tests/test_load_behavior.py b/hobj/data/tests/test_load_behavior.py new file mode 100644 index 0000000..c76d948 --- /dev/null +++ b/hobj/data/tests/test_load_behavior.py @@ -0,0 +1,11 @@ +from hobj.data.behavior import load_highvar_behavior, load_oneshot_behavior + + +def test_load_highvar(): + sessions = load_highvar_behavior() + assert len(sessions) == 3199 + + +def test_load_oneshot(): + sessions = load_oneshot_behavior() + assert len(sessions) == 2547 diff --git a/hobj/statistics/lapse_rate.py b/hobj/statistics/lapse_rate.py index c557261..a860485 100644 --- a/hobj/statistics/lapse_rate.py +++ b/hobj/statistics/lapse_rate.py @@ -1,13 +1,14 @@ -import xarray as xr +from typing import Union + import numpy as np -from typing import Union, Tuple, List +import xarray as xr def fit_optimal_lapse_rate( phat: Union[np.ndarray, np.generic, float, int], p: Union[np.ndarray, np.generic, float, int], nway: int, -): +) -> float: """ Fits a lapse rate (gamma, which is the probability of a uniform random guess) that minimizes the empirical MSE loss between the phat and p: @@ -44,28 +45,3 @@ def fit_optimal_lapse_rate( gamma_star = float(gamma_star) return gamma_star - -def _test(): - import matplotlib.pyplot as plt - - np.random.seed(0) - gamma_true = np.random.rand() * 0.3 - phat = np.clip(np.random.rand(500) + gamma_true, 0, 1) - p = phat - gamma_true - - # Numerical solution - nway = 8 - gamma_seq = np.linspace(0.01, 1, 1000) - gamma_star = fit_optimal_lapse_rate(phat, p, nway=nway) - phat_gamma = phat[None, :] * (1 - gamma_seq[:, None]) + gamma_seq[:, None] * (1 / nway) # [gamma, cond] - mse = np.square(phat_gamma - p).mean(1) - plt.plot(gamma_seq, mse) - plt.axvline(gamma_star, color='black', ls='-', label='true') - plt.axvline(gamma_seq[np.argmin(mse)], color='red', ls='-', label='numerical') - plt.axvline(gamma_star, color='blue', ls=':', label='function') - plt.legend() - plt.show() - - -if __name__ == '__main__': - _test() From 44f0f1ffc7558f0503adaa978a12c5a24a80c748 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 23 Jan 2025 16:55:37 -0500 Subject: [PATCH 058/156] add BinaryClassificationSubtask --- hobj/benchmarks/binary_classification/task.py | 105 ++++++++++++++++++ 1 file changed, 105 insertions(+) create mode 100644 hobj/benchmarks/binary_classification/task.py diff --git a/hobj/benchmarks/binary_classification/task.py b/hobj/benchmarks/binary_classification/task.py new file mode 100644 index 0000000..7152e43 --- /dev/null +++ b/hobj/benchmarks/binary_classification/task.py @@ -0,0 +1,105 @@ +from typing import List, Optional + +import numpy as np +import pydantic + +from hobj.data.schema import ImageRef +from hobj.learning_models import BinaryLearningModel + + +# %% +class BinaryClassificationSubtask(pydantic.BaseModel): + """ + A representation of a simple binary classification task which samples uniformly (with or without replacement) from a pool of images. + Feedback consists of +1 or -1 rewards, depending on the class of the image. + """ + + class Config: + frozen = True + + classA: List[ImageRef] + classB: List[ImageRef] + ntrials: int = pydantic.Field(description='The number of trials in the subtask.', gt=0) + replace: bool = pydantic.Field(description='Whether to show stimulus images with replacement or not.') + name: Optional[str] = pydantic.Field(description='A human-readable name for the subtask.', default=None) + + @pydantic.field_validator('classA', 'classB', mode='after') + @classmethod + def sort_image_refs(cls, value: List[ImageRef]) -> List[ImageRef]: + return sorted(value) + + @pydantic.model_validator(mode='after') + def validate_image_refs(self) -> 'BinaryClassificationSubtask': + if self.replace and len(self.classA) + len(self.classB) < self.ntrials: + raise ValueError(f"Specified replace=True, but only {len(self.classA) + len(self.classB)} images available. (len(classA)={len(self.classA)}; len(classB)={len(self.classB)}") + return self + + def simulate_session( + self, + learner: BinaryLearningModel, + seed: int, + ) -> 'BinaryClassificationSubtaskResult': + """ + Convenience method to simulate a session of the binary classification task on a given BinaryLearningModel. + :param learner: + :param seed: + :return: + """ + + perf_seq: List[bool] = [] + stimulus_seq: List[ImageRef] = [] + + # Initialize random state of environment + gen = np.random.default_rng(seed=seed) + + # Sample the stimulus image sequence + num_classA = len(self.classA) + num_classB = len(self.classB) + n_all_images = num_classA + num_classB + if self.replace: + i_image_seq = gen.integers(n_all_images, size=self.ntrials) + else: + i_image_seq = gen.permutation(n_all_images)[:self.ntrials] + + # Iterate over trials + for i_image in i_image_seq: + # Retrieve trial information + image_ref_cur = self.classA[i_image] if i_image < num_classA else self.classB[i_image - num_classA] + correct_action_cur = 0 if i_image < num_classA else 1 + + # Get response from learner + a = learner.get_response(image=image_ref_cur) + + # Calculate feedback based on response + feedback = 1. if a == correct_action_cur else -1. + + # Deliver feedback to learner + learner.give_feedback(feedback) + + # Record results of trial + perf_seq.append(feedback > 0) + stimulus_seq.append(image_ref_cur) + + return BinaryClassificationSubtaskResult( + subtask=self, + perf_seq=perf_seq, + ) + + +# %% +class BinaryClassificationSubtaskResult(pydantic.BaseModel): + """ + A class which represents the results of performing a binary classification subtask. + """ + + subtask: BinaryClassificationSubtask + + perf_seq: List[bool] = pydantic.Field( + description='The performance (correct or incorrect) of the learner on each trial.' + ) + + @pydantic.model_validator(mode='after') + def validate_perf(self) -> 'BinaryClassificationSubtaskResult': + if not len(self.perf_seq) == self.subtask.ntrials: + raise ValueError(f'Performance sequence length does not match number of trials. Got: {len(self.perf_seq)}') + return self From 8c331827b1bfd36af998be422d0f8ed81aa315a7 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 23 Jan 2025 16:56:03 -0500 Subject: [PATCH 059/156] test --- .../tests/test_simulate_subtask.py | 39 +++++++++++++++++++ .../MutatorOneshot/__init__.py | 0 2 files changed, 39 insertions(+) create mode 100644 hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py delete mode 100644 hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/__init__.py diff --git a/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py b/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py new file mode 100644 index 0000000..b6ec569 --- /dev/null +++ b/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py @@ -0,0 +1,39 @@ +from hobj.benchmarks.binary_classification.task import BinaryClassificationSubtask +from hobj.learning_models import DummyBinaryLearner +from hobj.data.schema import ImageRef +from typing import List +import pytest +import PIL.Image +import numpy as np + + +def create_image_refs(nimages_per_class: int, seed: int) -> List[ImageRef]: + images = [] + np.random.seed(seed) + for _ in range(nimages_per_class): + image = PIL.Image.fromarray(np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)) + image_ref = ImageRef.from_image(image=image) + images.append(image_ref) + + return images + + +def test_simulate_subtask(): + nimages_per_class = 10 + + subtask = BinaryClassificationSubtask( + classA=create_image_refs(nimages_per_class=10, seed=0), + classB=create_image_refs(nimages_per_class=10, seed=1), + ntrials=nimages_per_class * 2, + replace=False, + ) + + learner = DummyBinaryLearner(seed=0) + + result = subtask.simulate_session( + learner=learner, + seed=0 + ) + + +# Todo: test deterministic \ No newline at end of file diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/__init__.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/__init__.py deleted file mode 100644 index e69de29..0000000 From 037f9b89dbe53c57cdf5d5f6fbf85de8298c76c7 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 23 Jan 2025 17:50:55 -0500 Subject: [PATCH 060/156] filter --- hobj/data/behavior.py | 32 ++++++++++++++++++++++++++++++-- 1 file changed, 30 insertions(+), 2 deletions(-) diff --git a/hobj/data/behavior.py b/hobj/data/behavior.py index 6be6a2d..983ef76 100644 --- a/hobj/data/behavior.py +++ b/hobj/data/behavior.py @@ -28,7 +28,10 @@ class LearningSessionDataset(pydantic.BaseModel): # %% Data loaders -def load_highvar_behavior(redownload: bool = False) -> List[HumanLearningSession]: +def load_highvar_behavior( + redownload: bool = False, + remove_probe_trials: bool = True +) -> List[HumanLearningSession]: """ Load the "raw" human learning data from Experiment 1 of Lee and DiCarlo 2023. :return: @@ -39,7 +42,29 @@ def load_highvar_behavior(redownload: bool = False) -> List[HumanLearningSession redownload=redownload ) - return sessions + if not remove_probe_trials: + return sessions + + + def filter(vals: list): + probe_trials = {25, 51, 77, 103} + return [vals[i] for i in range(len(vals)) if i not in probe_trials] + + filtered_sessions = [] + for session in sessions: + # Filter out indices in probe_trials + filtered_session = HumanLearningSession( + worker_id = session.worker_id, + stimulus_sha256_seq = filter(session.stimulus_sha256_seq), + stimulus_duration_msec_seq = filter(session.stimulus_duration_msec_seq), + action_seq = filter(session.action_seq), + reward_seq = filter(session.reward_seq), + timestamp_start_seq = filter(session.timestamp_start_seq) + ) + + filtered_sessions.append(filtered_session) + + return filtered_sessions def load_oneshot_behavior(redownload: bool = False) -> List[HumanLearningSession]: @@ -54,3 +79,6 @@ def load_oneshot_behavior(redownload: bool = False) -> List[HumanLearningSession ) return sessions + +if __name__ == '__main__': + sessions=load_highvar_behavior() \ No newline at end of file From 399f50fa410a7d7d7d8403b38d78231d6915f23f Mon Sep 17 00:00:00 2001 From: himjl Date: Fri, 24 Jan 2025 17:17:28 -0500 Subject: [PATCH 061/156] functional high var benchmark --- examples/score_example_model.py | 26 +- examples/view_behavioral_data.py | 22 +- hobj/benchmarks/__init__.py | 2 + .../__init__.py | 0 .../binary_classification/scoring.py | 343 + hobj/benchmarks/binary_classification/task.py | 47 +- .../tests/test_simulate_subtask.py | 2 +- .../experiment_simulation/environment_depr.py | 36 - .../experiment_simulation2_tmp.py | 102 - hobj/benchmarks/experiment_simulation/task.py | 109 - hobj/benchmarks/learning_curve_benchmark.py | 63 - .../MutatorHighVarSubtasks.json | 13186 ---------------- .../mut_highvar_benchmark.py | 123 - hobj/benchmarks/mut_highvar_benchmark.py | 95 + hobj/benchmarks/todo/__init__.py | 0 .../mut_oneshot_benchmark.py | 3 +- .../mut_oneshot_experiment.py | 0 hobj/data/behavior.py | 18 +- hobj/data/schema.py | 3 + hobj/learning_models/__init__.py | 6 +- hobj/statistics/lapse_rate.py | 47 - .../statistics/variance_estimates/binomial.py | 58 +- hobj/stats_new/ci.py | 2 +- hobj/stats_new/learning_curve.py | 60 + 24 files changed, 562 insertions(+), 13791 deletions(-) rename hobj/benchmarks/{experiment_simulation => binary_classification}/__init__.py (100%) create mode 100644 hobj/benchmarks/binary_classification/scoring.py delete mode 100644 hobj/benchmarks/experiment_simulation/environment_depr.py delete mode 100644 hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py delete mode 100644 hobj/benchmarks/experiment_simulation/task.py delete mode 100644 hobj/benchmarks/learning_curve_benchmark.py delete mode 100644 hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVarSubtasks.json delete mode 100644 hobj/benchmarks/leedicarlo_benchmarks/mut_highvar_benchmark.py create mode 100644 hobj/benchmarks/mut_highvar_benchmark.py create mode 100644 hobj/benchmarks/todo/__init__.py rename hobj/benchmarks/{leedicarlo_benchmarks/MutatorOneshot => todo}/mut_oneshot_benchmark.py (99%) rename hobj/benchmarks/{leedicarlo_benchmarks/MutatorOneshot => todo}/mut_oneshot_experiment.py (100%) delete mode 100644 hobj/statistics/lapse_rate.py create mode 100644 hobj/stats_new/learning_curve.py diff --git a/examples/score_example_model.py b/examples/score_example_model.py index a26a663..8620ee9 100644 --- a/examples/score_example_model.py +++ b/examples/score_example_model.py @@ -1,18 +1,16 @@ -import hobj.learning_models -import hobj.learning_models.learning_model as lm -import hobj.benchmarks.leedicarlo_benchmarks.mut_highvar_benchmark as highvar_benchmark -import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_benchmark as oneshot_benchmark +# %% Instantiate benchmarks +from hobj.benchmarks import MutatorHighVarBenchmark -dummy_model = hobj.learning_models.DummyBinaryLearner() +experiment1_benchmark = MutatorHighVarBenchmark() -hv = highvar_benchmark.MutatorHighVarBenchmark() -os = oneshot_benchmark.MutatorOneshotBenchmark() +# %% Instantiate learner +from hobj.learning_models import DummyBinaryLearner +learner = DummyBinaryLearner() -result = hv.evaluate_model( - learner=dummy_model, -) -print(result) +# %% Score +experiment1_result = experiment1_benchmark(learner=learner) +print(experiment1_result) + +# %% Visualize results +import matplotlib.pyplot as plt -result2 = os.evaluate_model( - learner=dummy_model, -) \ No newline at end of file diff --git a/examples/view_behavioral_data.py b/examples/view_behavioral_data.py index bfc386d..04ecefb 100644 --- a/examples/view_behavioral_data.py +++ b/examples/view_behavioral_data.py @@ -1,20 +1,30 @@ -import hobj.benchmarks.leedicarlo_benchmarks.mut_highvar_benchmark as highvar_benchmark -import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_benchmark as oneshot_benchmark +import hobj.benchmarks.mut_highvar_benchmark as highvar_benchmark +#import hobj.benchmarks.todo.mut_oneshot_benchmark as oneshot_benchmark import matplotlib.pyplot as plt import numpy as np -# %% View processed data for Experiment 1 +# %% View data for Experiment 1 experiment1_benchmark = highvar_benchmark.MutatorHighVarBenchmark() -ds_experiment1 = experiment1_benchmark.ds_behavioral_statistics +data = experiment1_benchmark.data + +stacked_data = [] +for subtask in data: + subtask_data = np.array([ + data[subtask][worker_id] for worker_id in data[subtask] + ]) + stacked_data.append(subtask_data) +stacked_data = np.concat(stacked_data, axis=0) plt.figure() -plt.errorbar(x=np.arange(100) + 1, y=ds_experiment1.glc, yerr=ds_experiment1.glc_std) +plt.imshow(stacked_data, aspect = 'auto') plt.xlabel('trial') -plt.ylabel('subtask-and-subject averaged accuracy') +plt.ylabel('session') +plt.title("Performance data") plt.show() # %% View processed data for Experiment 2 +raise NotImplementedError experiment2_benchmark = oneshot_benchmark.MutatorOneshotBenchmark() ds_experiment2 = experiment2_benchmark.ds_behavioral_statistics diff --git a/hobj/benchmarks/__init__.py b/hobj/benchmarks/__init__.py index e69de29..c173c7e 100644 --- a/hobj/benchmarks/__init__.py +++ b/hobj/benchmarks/__init__.py @@ -0,0 +1,2 @@ +from hobj.benchmarks.mut_highvar_benchmark import MutatorHighVarBenchmark +#from hobj.benchmarks.mut_oneshot_benchmark import MutatorOneshotBenchmark diff --git a/hobj/benchmarks/experiment_simulation/__init__.py b/hobj/benchmarks/binary_classification/__init__.py similarity index 100% rename from hobj/benchmarks/experiment_simulation/__init__.py rename to hobj/benchmarks/binary_classification/__init__.py diff --git a/hobj/benchmarks/binary_classification/scoring.py b/hobj/benchmarks/binary_classification/scoring.py new file mode 100644 index 0000000..fdd996b --- /dev/null +++ b/hobj/benchmarks/binary_classification/scoring.py @@ -0,0 +1,343 @@ +from dataclasses import dataclass +from typing import List, Dict, Union, Tuple, Optional, Iterator + +import numpy as np +from scipy import stats as ss + +from hobj.benchmarks.binary_classification.task import BinaryClassificationSubtask +from hobj.learning_models import BinaryLearningModel +from hobj.statistics.variance_estimates import binomial as binomial_funcs +import pydantic +from tqdm import tqdm +from hobj.stats_new.learning_curve import calculate_learning_curve +from hobj.stats_new.ci import estimate_basic_bootstrap_CI + +import hobj.data.schema as schema + +import xarray as xr +# %% Models for configuring a LearningCurveBenchmark: + +class TargetSubtaskData(pydantic.BaseModel): + subtask: BinaryClassificationSubtask # The subtask which generated the associated results + results: Dict[str, List[bool]] # worker_id -> perf_seq + + @pydantic.model_validator(mode='after') + def validate_results(self) -> 'TargetSubtaskData': + for worker_id, perf_seq in self.results.items(): + if len(perf_seq) != self.subtask.ntrials: + raise ValueError(f"Expected {self.subtask.ntrials} trials, but got {len(perf_seq)} trials for worker {worker_id}") + + return self + + +class LearningCurveBenchmarkConfig(pydantic.BaseModel): + subtask_name_to_data: Dict[str, 'TargetSubtaskData'] = pydantic.Field(default_factory=dict, description="A dictionary of subtask_name -> TargetSubtaskConfig") + num_simulations_per_subtask: int = pydantic.Field(ge=2) + num_bootstrap_samples: int = pydantic.Field(ge=2) + ntrials: Optional[int] = pydantic.Field(default=None) + + @pydantic.model_validator(mode='after') + def ensure_rectangular(self) -> 'LearningCurveBenchmarkConfig': + + ntrials_observed = set() + for name, data in self.subtask_name_to_data.items(): + ntrials_observed.add(data.subtask.ntrials) + + if not len(ntrials_observed) == 1: + raise ValueError(f"Expected all subtasks to have the same number of trials, but got {ntrials_observed}") + + if self.ntrials is not None: + if self.ntrials != ntrials_observed.pop(): + raise ValueError(f"Expected ntrials to be {ntrials_observed.pop()}, but got {self.ntrials}") + else: + self.ntrials = ntrials_observed.pop() + return self + + +class LearningCurveStatistics(xr.Dataset): + def __init__( + self, + subtask_names: List[str], + phat: np.ndarray, + varhat_phat: np.ndarray, + boot_phat: np.ndarray, + boot_varhat_phat: np.ndarray, + ): + data_vars = dict( + phat = (['subtask', 'trial'], phat), + varhat_phat = (['subtask', 'trial'], varhat_phat), + boot_phat = (['boot_iter', 'subtask', 'trial'], boot_phat), + boot_varhat_phat = (['boot_iter', 'subtask', 'trial'], boot_varhat_phat), + ) + + coords = dict( + subtask = subtask_names, + ) + + super().__init__( + data_vars = data_vars, + coords = coords + ) + + +# %% +class LearningCurveBenchmark: + + @property + def image_refs(self) -> Iterator[schema.ImageRef]: + """ + Returns all image references used in the benchmark. + :return: + """ + for subtask in self.subtask_name_to_subtask.values(): + for ref in subtask.classA + subtask.classB: + yield ref + + @staticmethod + def simulate_model_behavior( + subtask: BinaryClassificationSubtask, + learner: BinaryLearningModel, + nsimulations: int, + ) -> np.ndarray: + """ + Returns a [nsimulations, ntrials] matrix of model performance on the subtask. + :param subtask: + :param learner: + :param nsimulations: + :return: a [nsimulations, ntrials] matrix of model performance on the subtask. + """ + + # Instantiate array to store performance sequences for this subtask: + perf_mat = np.zeros((nsimulations, subtask.ntrials), dtype=np.bool) + + # Perform simulations for this subtask: + for i_simulation in range(nsimulations): + # Reset learner internal state + learner.reset_state(seed=None) + + # Simulate session: + perf_mat[i_simulation, :] = subtask.simulate_session( + learner=learner, + seed=None, + ) + + return perf_mat + + def __init__( + self, + config: LearningCurveBenchmarkConfig, + ): + self.config = config + + # Attach properties + self.subtask_names = sorted(config.subtask_name_to_data.keys()) + self.subtask_name_to_subtask: Dict[str, BinaryClassificationSubtask] = { + name: config.subtask_name_to_data[name].subtask for name in self.subtask_names + } + self.subtask_name_to_target_data: Dict[str, Dict[str, List[bool]]] = { + subtask_name: target_subtask.results for subtask_name, target_subtask in self.config.subtask_name_to_data.items() + } + + # Cache target learning curve statistics + phat = np.zeros((len(self.subtask_names), self.config.ntrials)) + varhat_phat = np.zeros((len(self.subtask_names), self.config.ntrials)) + boot_phat = np.zeros((self.config.num_bootstrap_samples, len(self.subtask_names), self.config.ntrials)) + boot_varhat_phat = np.zeros((self.config.num_bootstrap_samples, len(self.subtask_names), self.config.ntrials)) + + for i_subtask, subtask_name in enumerate(self.subtask_names): + subtask = self.subtask_name_to_subtask[subtask_name] + + nworkers = len(self.subtask_name_to_target_data[subtask_name]) + perf_mat = np.zeros((nworkers, subtask.ntrials), dtype=np.bool) + + for i_worker, (worker_id, perf_seq) in enumerate(self.subtask_name_to_target_data[subtask_name].items()): + perf_mat[i_worker] = perf_seq + + subtask_learning_curve_statistics = calculate_learning_curve( + perf_matrix=perf_mat, + bootstrap_seed=i_subtask, + nbootstrap_samples=self.config.num_bootstrap_samples, + repetition_axis=0, + ) + + phat[i_subtask] = subtask_learning_curve_statistics.phat + varhat_phat[i_subtask] = subtask_learning_curve_statistics.varhat_phat + boot_phat[:, i_subtask] = subtask_learning_curve_statistics.boot_phat + boot_varhat_phat[:, i_subtask] = subtask_learning_curve_statistics.boot_varhat_phat + + self._target_statistics = LearningCurveStatistics( + subtask_names=self.subtask_names, + phat=phat, + varhat_phat=varhat_phat, + boot_phat=boot_phat, + boot_varhat_phat=boot_varhat_phat, + ) + + + @property + def data(self) -> Dict[str, Dict[str, List[bool]]]: + """ + Returns the target data as a nested dictionary. Example usage + + target_data = self.get_target_as_json() + print(target_data[subtask_name][worker_id]) # List[bool] of trial performances on {subtask_name} by {worker_id}. + + Useful for performing custom analyses on the target data. + :return: + """ + + return self.subtask_name_to_target_data + + @property + def target_statistics(self) -> xr.Dataset: + """ + Returns point estimates of the mean (and an estimate of the variance of that estimator) of the binomial proportion for the target data, + and bootstrapped resamples of those statistics. + :return: + """ + return self._target_statistics + + @dataclass + class LearningCurveBenchmarkResult: + msen: float + msen_sigma: float + msen_CI95: Tuple[float, float] + lapse_rate: float + + model_statistics: LearningCurveStatistics + + def __call__( + self, + learner: BinaryLearningModel, + show_pbar: bool = False + ) -> LearningCurveBenchmarkResult: + """ + :param learner: LearningModel + :return: + """ + + # Get model learning curve statistics: + # Todo: deduplicate with constructor + phat = np.zeros((len(self.subtask_names), self.config.ntrials)) + varhat_phat = np.zeros((len(self.subtask_names), self.config.ntrials)) + boot_phat = np.zeros((self.config.num_bootstrap_samples, len(self.subtask_names), self.config.ntrials)) + boot_varhat_phat = np.zeros((self.config.num_bootstrap_samples, len(self.subtask_names), self.config.ntrials)) + + for i_subtask, subtask_name in enumerate(tqdm(self.subtask_names, desc='Subtask simulations:', disable=not show_pbar)): + # Get [simulation, trial] boolean performance matrix for the model + perf_matrix = self.simulate_model_behavior( + subtask=self.subtask_name_to_subtask[subtask_name], + learner=learner, + nsimulations=self.config.num_simulations_per_subtask, + ) + + # Calculate statistics + subtask_estimate = calculate_learning_curve( + perf_matrix=perf_matrix, + bootstrap_seed=None, + nbootstrap_samples=self.config.num_bootstrap_samples, + repetition_axis=0, + ) + phat[i_subtask] = subtask_estimate.phat + varhat_phat[i_subtask] = subtask_estimate.varhat_phat + boot_phat[:, i_subtask] = subtask_estimate.boot_phat + boot_varhat_phat[:, i_subtask] = subtask_estimate.boot_varhat_phat + + # Assemble into a single matrix + model_statistics = LearningCurveStatistics( + subtask_names=self.subtask_names, + phat=phat, + varhat_phat=varhat_phat, + boot_phat=boot_phat, + boot_varhat_phat=boot_varhat_phat, + ) + + # Calculate comparison statistics between target and model learning curves: + msen_point, lapse_rate = self._compare_learning_curves( + model_phat=model_statistics.phat, + model_varhat_phat=model_statistics.varhat_phat, + target_phat=self.target_statistics.phat, + target_varhat_phat=self.target_statistics.varhat_phat, + condition_dims=('subtask', 'trial'), + fit_lapse_rate=True, + ) + + # Calculate bootstrap replicates: + msen_boot, _ = self._compare_learning_curves( + model_phat=model_statistics.boot_phat, + model_varhat_phat=model_statistics.boot_varhat_phat, + target_phat=self.target_statistics.boot_phat, + target_varhat_phat=self.target_statistics.boot_varhat_phat, + condition_dims=('subtask', 'trial'), + fit_lapse_rate=True, + ) + + msen_sigma = np.std(msen_boot, ddof=1) + + msen_CI95 = estimate_basic_bootstrap_CI( + alpha = 0.05, + point_estimate = msen_point, + bootstrapped_point_estimates = np.array(msen_boot), + ) + + # Return result + return self.LearningCurveBenchmarkResult( + msen=float(msen_point), + msen_sigma=float(msen_sigma), + msen_CI95=msen_CI95, + model_statistics=model_statistics, + lapse_rate = lapse_rate, + ) + + @classmethod + def _compare_learning_curves( + cls, + model_phat: xr.DataArray, + model_varhat_phat: xr.DataArray, + target_phat: xr.DataArray, + target_varhat_phat: xr.DataArray, + condition_dims: Tuple[str, ...], + fit_lapse_rate: bool + ) -> Tuple[Union[np.ndarray, np.generic], Union[xr.DataArray, None]]: + + if fit_lapse_rate: + lapse_rate = cls._fit_lapse_rate( + pmodel=model_phat, + ptarget=target_phat, + condition_dims=condition_dims + ) + model_phat = model_phat * (1 - lapse_rate) + 0.5 * lapse_rate + model_varhat_phat = model_varhat_phat * (1 - lapse_rate) ** 2 + else : + lapse_rate = None + + msen = np.square(model_phat - target_phat).mean(condition_dims) - model_varhat_phat.mean(condition_dims) - target_varhat_phat.mean(condition_dims) + return msen, lapse_rate + + @staticmethod + def _fit_lapse_rate( + pmodel: xr.DataArray, + ptarget: xr.DataArray, + condition_dims: Tuple[str, ...] + ) -> Union[np.ndarray, np.generic]: + """ + Fits a "lapse rate" parameter (gamma), which takes on values between [0, 1]. It may be interpreted + as the probability that a uniform random guess is made. The value of gamma which minimizes the + empirical MSE between pmodel and ptarget is selected. + + + pmodel_adjusted := (1 - gamma) * pmodel + 1/2 * gamma + Loss = ((pmodel_adjusted - ptarget)**2).sum() + + :param pmodel: + :param ptarget: + :return: + """ + + nway = 2 + numerator = -(2 * pmodel / nway - 2 * np.square(pmodel) + 2 * pmodel * ptarget - 2 * ptarget / nway).sum(dim=condition_dims) + denominator = (2 / (nway ** 2) - 4 * pmodel / nway + 2 * (pmodel ** 2)).sum(dim=condition_dims) + gamma_star = numerator / denominator + gamma_star = np.clip(gamma_star, 0, 1) + print(gamma_star) + return gamma_star diff --git a/hobj/benchmarks/binary_classification/task.py b/hobj/benchmarks/binary_classification/task.py index 7152e43..93137a5 100644 --- a/hobj/benchmarks/binary_classification/task.py +++ b/hobj/benchmarks/binary_classification/task.py @@ -1,10 +1,11 @@ -from typing import List, Optional +from typing import List, Union import numpy as np import pydantic from hobj.data.schema import ImageRef from hobj.learning_models import BinaryLearningModel +from uuid import uuid4 # %% @@ -21,7 +22,6 @@ class Config: classB: List[ImageRef] ntrials: int = pydantic.Field(description='The number of trials in the subtask.', gt=0) replace: bool = pydantic.Field(description='Whether to show stimulus images with replacement or not.') - name: Optional[str] = pydantic.Field(description='A human-readable name for the subtask.', default=None) @pydantic.field_validator('classA', 'classB', mode='after') @classmethod @@ -37,17 +37,16 @@ def validate_image_refs(self) -> 'BinaryClassificationSubtask': def simulate_session( self, learner: BinaryLearningModel, - seed: int, - ) -> 'BinaryClassificationSubtaskResult': + seed: Union[int, None], + ) -> np.ndarray: """ Convenience method to simulate a session of the binary classification task on a given BinaryLearningModel. :param learner: :param seed: - :return: + :return: a [ntrials] boolean np.ndarray vector where True indicates a correct response. """ - perf_seq: List[bool] = [] - stimulus_seq: List[ImageRef] = [] + perf_seq = np.zeros(self.ntrials, dtype=bool) # Initialize random state of environment gen = np.random.default_rng(seed=seed) @@ -61,11 +60,16 @@ def simulate_session( else: i_image_seq = gen.permutation(n_all_images)[:self.ntrials] + # Randomly sample the reward contingency + classA_correct_action = 0 if gen.random() < 0.5 else 1 + classB_correct_action = 1 - classA_correct_action + # Iterate over trials + i_trial = 0 for i_image in i_image_seq: # Retrieve trial information image_ref_cur = self.classA[i_image] if i_image < num_classA else self.classB[i_image - num_classA] - correct_action_cur = 0 if i_image < num_classA else 1 + correct_action_cur = classA_correct_action if i_image < num_classA else classB_correct_action # Get response from learner a = learner.get_response(image=image_ref_cur) @@ -77,29 +81,8 @@ def simulate_session( learner.give_feedback(feedback) # Record results of trial - perf_seq.append(feedback > 0) - stimulus_seq.append(image_ref_cur) - - return BinaryClassificationSubtaskResult( - subtask=self, - perf_seq=perf_seq, - ) - - -# %% -class BinaryClassificationSubtaskResult(pydantic.BaseModel): - """ - A class which represents the results of performing a binary classification subtask. - """ - - subtask: BinaryClassificationSubtask + perf_seq[i_trial] = feedback > 0 - perf_seq: List[bool] = pydantic.Field( - description='The performance (correct or incorrect) of the learner on each trial.' - ) + i_trial += 1 - @pydantic.model_validator(mode='after') - def validate_perf(self) -> 'BinaryClassificationSubtaskResult': - if not len(self.perf_seq) == self.subtask.ntrials: - raise ValueError(f'Performance sequence length does not match number of trials. Got: {len(self.perf_seq)}') - return self + return perf_seq \ No newline at end of file diff --git a/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py b/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py index b6ec569..98621e4 100644 --- a/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py +++ b/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py @@ -30,7 +30,7 @@ def test_simulate_subtask(): learner = DummyBinaryLearner(seed=0) - result = subtask.simulate_session( + perf_seq = subtask.simulate_session( learner=learner, seed=0 ) diff --git a/hobj/benchmarks/experiment_simulation/environment_depr.py b/hobj/benchmarks/experiment_simulation/environment_depr.py deleted file mode 100644 index d4dea50..0000000 --- a/hobj/benchmarks/experiment_simulation/environment_depr.py +++ /dev/null @@ -1,36 +0,0 @@ -import numpy as np -from typing import Union, List -import xarray as xr - -class Environment(object): - - def __init__(self): - self._meta = {} - return - - def initialize(self, RS: np.random.RandomState): - raise NotImplementedError - - def sample_image(self): - image_url = '' - return image_url - - @property - def meta(self): - return self._meta - - @meta.setter - def meta(self, value): - assert isinstance(value, dict) or isinstance(value, xr.Dataset) - self._meta = value - - - @property - def current_state_meta(self): - # Returns meta information for the current environmental state, that will be logged in a simulation. Not allowed to be used by the learner. - return {} - - def provide_feedback(self, action: int): - reward = 0. - return reward - diff --git a/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py b/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py deleted file mode 100644 index 5514da7..0000000 --- a/hobj/benchmarks/experiment_simulation/experiment_simulation2_tmp.py +++ /dev/null @@ -1,102 +0,0 @@ -import hobj.config as config -import hobj.learning_models -import hobj.learning_models.learning_model as lm -import hobj.benchmarks.experiment_simulation.environment_depr as env -import hobj.utils.file_io as io -import os -import xarray as xr -import numpy as np -from tqdm import tqdm -from typing import Union - - -class ExperimentSimulation(object): - """ - Wraps a set of environments, and simulates them self.nreps times each - """ - nreps = None - ntrials = None - environment_name_dim = 'environment' - - def __init__(self): - return - - @property - def experiment_name(self): - raise NotImplementedError - return 'write_experiment_name_here' - - @property - def environments(self): - raise NotImplementedError - return [env.Environment()] - - def run(self, learner: hobj.learning_models.BinaryLearningModel, seed: Union[type(None), int], show_pbar=True): - - - dlist = [] - RS = np.random.RandomState(seed=seed) - if show_pbar: - pbar = tqdm(total=self.nreps * self.ntrials * len(self.environments), desc='simulation trials') - else: - pbar = None - - for environment in self.environments: - ds = self.simulate_environment(learner=learner, environment=environment, RS=RS, nreps=self.nreps, ntrials=self.ntrials, pbar=pbar) - ds = self.postprocess_behavioral_data(ds=ds, environment=environment) - dlist.append(ds) - - ds = xr.concat(dlist, dim=self.environment_name_dim, ) - - return ds - - def postprocess_behavioral_data(self, ds: xr.Dataset, environment: env.Environment): - """ - Called following the simulation of a single environment. Allows for postprocessing of the behavioral data. - :param ds: - :param environment: - :return: - """ - return ds - - @staticmethod - def simulate_environment( - learner: hobj.learning_models.BinaryLearningModel, - environment: env.Environment, - RS: np.random.RandomState, - nreps: int, - ntrials: int, - pbar: tqdm = None, - ): - - d = { - 'image_url': (['trial', 'rep', ], [['' for _ in range(nreps)] for _ in range(ntrials)]), - 'action': (['trial', 'rep', ], np.zeros((ntrials, nreps))), - 'reward': (['trial', 'rep', ], np.zeros((ntrials, nreps))), - } - - for i_rep in range(nreps): - learner.reset_state() - environment.initialize(RS=RS) - for i_trial in range(ntrials): - if pbar is not None: - pbar.update(1) - - image_url = environment.sample_image() - action = learner.get_response(image=image_url) - action = int(action) - reward = environment.provide_feedback(action=action) - reward = float(reward) - learner.give_feedback(reward=reward) - - d['image_url'][1][i_trial][i_rep] = image_url - d['action'][1][i_trial, i_rep] = action - d['reward'][1][i_trial, i_rep] = reward - - for k in environment.current_state_meta: - d[k][1][i_trial, i_rep] = (environment.current_state_meta[k]) - - ds = xr.Dataset(d) - ds = ds.assign_coords(environment.meta) - ds = ds.assign_coords(image_url=ds['image_url']) - return ds diff --git a/hobj/benchmarks/experiment_simulation/task.py b/hobj/benchmarks/experiment_simulation/task.py deleted file mode 100644 index cc90728..0000000 --- a/hobj/benchmarks/experiment_simulation/task.py +++ /dev/null @@ -1,109 +0,0 @@ -from abc import ABC, abstractmethod -from typing import List, Literal, Union - -import PIL.Image -import numpy as np -import pydantic - -import hobj.data.schema as schema -from hobj.learning_models import BinaryLearningModel - - -class BinaryTask(ABC): - class RunSessionResult(pydantic.BaseModel): - reward_sequence: List[float] - action_sequence: List[Literal[0, 1]] - image_sequence: List[schema.ImageRef] - - @abstractmethod - def run_session( - self, - learner: BinaryLearningModel, - seed: int - ) -> RunSessionResult: - """ - Runs a session of the task with the given learner. Note that the learner is not reset by this function; it runs the - session using the learner "as given". - :param learner: - :param seed: - :return: - """ - raise NotImplementedError - - -class SimpleBinaryTask(BinaryTask): - """ - A task which samples uniformly (with or without replacement) from a pool of images. - Feedback consists of +1 or -1 rewards, depending on the class of the image. - """ - - def __init__( - self, - classA: List[Union[schema.ImageRef, PIL.Image]], - classB: List[Union[schema.ImageRef, PIL.Image]], - ntrials: int, - replace: bool, - ): - - classA = self._process_images(images=classA) - classB = self._process_images(images=classB) - nclassA = len(classA) - nclassB = len(classB) - - if replace and nclassA + nclassB < ntrials: - raise ValueError(f"Specified replace=True, but only {nclassA + nclassB} images available. (len(classA)={nclassA}; len(classB)={nclassB})") - - self.image_refs = classA + classB - self.image_labels = [0] * nclassA + [1] * nclassB - self.ntrials = ntrials - self.replace = replace - - def run_session( - self, - learner: BinaryLearningModel, - seed: int - ) -> BinaryTask.RunSessionResult: - - reward_seq = [] - action_seq = [] - image_seq = [] - - gen = np.random.default_rng(seed=seed) - - if self.replace: - i_image_seq = gen.integers(len(self.image_refs), size=self.ntrials) - else: - i_image_seq = gen.permutation(len(self.image_refs))[:self.ntrials] - - # Iterate over trials - for i_image in i_image_seq: - # Get response from learner - a = learner.get_response(image=self.image_refs[i_image]) - - # Calculate feedback based on response - feedback = 1. if a == self.image_labels[i_image] else -1. - - # Deliver feedback to learner - learner.give_feedback(feedback) - - # Record results of trial - reward_seq.append(feedback) - action_seq.append(a) - image_seq.append(self.image_refs[i_image]) - - return BinaryTask.RunSessionResult( - reward_sequence=reward_seq, - action_sequence=action_seq, - image_sequence=image_seq - ) - - def _process_images(self, images: List[Union[schema.ImageRef, PIL.Image]]) -> List[schema.ImageRef]: - """ - Converts a list of images to a (sorted) list of unique ImageRefs. - :param images: - :return: - """ - - image_refs = [img if isinstance(img, schema.ImageRef) else schema.ImageRef.from_image(img) for img in images] - image_refs = sorted(set(image_refs)) - return image_refs diff --git a/hobj/benchmarks/learning_curve_benchmark.py b/hobj/benchmarks/learning_curve_benchmark.py deleted file mode 100644 index fba578a..0000000 --- a/hobj/benchmarks/learning_curve_benchmark.py +++ /dev/null @@ -1,63 +0,0 @@ -from dataclasses import dataclass -import numpy as np -from typing import List - -from hobj.learning_models import BinaryLearningModel - - -class LearningCurveBenchmark: - - """ - This benchmark measures the divergence between a human and model learning curve across a variety of binary learning subtasks. - """ - - @dataclass - class LearningCurveBenchmarkResult: - - # Benchmark results: - msen: float - msen_sigma: float - msen_null_samples: List[float] - - # Point estimates that may be used for further downstream analysis: - model_phat: np.ndarray # [trial, subtask] - model_varhat_phat: np.ndarray # [trial, subtask] - model_lapse_rate: float - - target_phat: np.ndarray # [trial, subtask] - target_varhat_phat: np.ndarray # [trial, subtask] - target_phat_bootstrap_resamples: np.ndarray # [nboot, trial, subtask] - bootstrap resamples of these statistics over worker resamples. - - # Legacy version of msen, where variance correction from the human was not subtracted. - _legacy_msen: float - _legacy_msen_sigma: float - - def __init__( - self, - - ): - pass - - def __call__( - self, - learner: BinaryLearningModel - ) -> LearningCurveBenchmarkResult: - - pass - - - @dataclass - class SimulateExperimentResult: - """ - The result of simulating an experiment on this battery of experiments. - """ - 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- -class MutatorHighVarBenchmark: - nboot = 1000 - - dataset_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-highvar-human-learning-data.json' - - def __init__(self): - - self.dataset = LearningDataset.from_url(dataset_url = self.dataset_url) - - - @dataclass - class SimulateExperimentResult: - """ - The result of simulating an experiment on this battery of experiments. - """ - subtasks: List[str] - k: np.ndarray # [trial, subtask] - n: np.ndarray # [trial, subtask] - - def _simulate_experiment(self, learner: hobj.learning_models.BinaryLearningModel, seed: int) -> SimulateExperimentResult: - # Todo - nreps = 500 - ntrials = 100 - warnings.warn("Returning dummy results.") - RS = np.random.RandomState(0) - - return self.SimulateExperimentResult( - subtasks=[], - k=RS.randint(low=0, high=n, size=(64, 100)), - n=np.ones((64, 100)) * n - ) - - @dataclass - class EvaluateModelResult: - - # Benchmark results: - msen: float - msen_sigma: float - msen_null_samples: List[float] - - # Point estimates that may be used for further downstream analysis: - model_phat: np.ndarray # [trial, subtask] - model_varhat_phat: np.ndarray # [trial, subtask] - model_lapse_rate: float - - human_phat: np.ndarray # [trial, subtask] - human_varhat_phat: np.ndarray # [trial, subtask] - human_phat_bootstrap_resamples: np.ndarray # [nboot, trial, subtask] - bootstrap resamples of these statistics over worker resamples. - - # Legacy version of msen, where variance correction from the human was not subtracted. - _legacy_msen: float - _legacy_msen_sigma: float - - def evaluate_model(self, learner: hobj.learning_models.BinaryLearningModel, seed: int = 0) -> EvaluateModelResult: - """ - :param learner: LearningModel - :param force_recompute: bool. If True, recompute the model behavior, even if it is already cached. - :return: - """ - - # Simulate the behavior of this model - model_behavior = self._simulate_experiment(learner=learner, seed=seed) - k = model_behavior.k - n = model_behavior.n - - # Fit a lapse rate which minimizes the MSE between the model and human data - lapse_rate = lapse_rate_funcs.fit_optimal_lapse_rate( - phat=model_behavior.k / model_behavior.n, - p=self.ds_behavioral_statistics.perf.values, - nway=2, - ) - - # Load human data - target_point = self.ds_worker_table.sum('worker_id') - ktarget = target_point.k.values - ntarget = target_point.n.values - phat_target = ktarget / ntarget - varhat_phat_target = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=ktarget, nvec=ntarget) - - # Get estimates of performance - phat_model = k / n - varhat_phat_model = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=k, nvec=n) - - # Get MSEn point estimate on the lapse-rate corrected predictions - phat_model_lapsed = phat_model * (1 - lapse_rate) + 0.5 * lapse_rate - varhat_phat_model_lapsed = varhat_phat_model * (1 - lapse_rate) ** 2 - - # Get elementwise errors: - msen_elementwise = np.square(phat_target - phat_model_lapsed) - varhat_phat_model_lapsed - varhat_phat_target - legacy_msen_elementwise = np.square(phat_target - phat_model) - varhat_phat_model_lapsed - - # Get point estimates: - msen_point = msen_elementwise.mean() - msen_sigma = ss.sem(msen_elementwise, ddof = 1) - - # Package return - return self.EvaluateModelResult( - msen=msen_point, - msen_sigma = msen_sigma, - model_phat=phat_model, - model_varhat_phat=varhat_phat_model, - model_lapse_rate=lapse_rate, - human_phat=phat_target, - human_varhat_phat=varhat_phat_target, - msen_null_samples = msen_null_samples, - human_phat_bootstrap_resamples=phat_target_bootstrap_resamples, - _legacy_msen=legacy_msen_elementwise.mean(), - _legacy_msen_sigma=ss.sem(legacy_msen_elementwise, ddof=1), - ) diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py new file mode 100644 index 0000000..ccb27a1 --- /dev/null +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -0,0 +1,95 @@ +from hobj.benchmarks.binary_classification.scoring import LearningCurveBenchmark, LearningCurveBenchmarkConfig, TargetSubtaskData +from hobj.benchmarks.binary_classification.task import BinaryClassificationSubtask +from hobj.data.behavior import load_highvar_behavior +from hobj.data.images import MutatorHighVarImageset +from hobj.data.schema import ImageRef + +from typing import List, Dict + + +# %% Load data into format expected by benchmark +class MutatorHighVarBenchmark(LearningCurveBenchmark): + + def __init__(self): + + # Load imageset: + imageset = MutatorHighVarImageset() + + # Load raw human session data for benchmark: + sessions = load_highvar_behavior(remove_probe_trials=True) + + # Normalize data for benchmark: + sha256_to_category = { + ref.sha256: imageset.get_annotation(image_ref=ref).category for ref in imageset.image_refs + } + + subtask_name_to_results = {} + subtask_name_to_subtask = {} + + # Iterate over worker sessions: + for session in sessions: + + categories = set() + perf_seq: List[bool] = [] + + # Iterate over trials: + for i_trial, (reward, sha256) in enumerate(zip(session.reward_seq, session.stimulus_sha256_seq)): + categories.add(sha256_to_category[sha256]) + perf_seq.append(reward > 0) + + # Infer subtask from the categories observed in the session: + assert len(categories) == 2, f"Expected two categories, but got {categories}" + subtask_name = ','.join(sorted(categories)) + cat0, cat1 = sorted(categories) + + # Instantiate the subtask if it does not exist: + if subtask_name not in subtask_name_to_subtask: + subtask = BinaryClassificationSubtask( + classA=imageset.category_to_image_refs[cat0], + classB=imageset.category_to_image_refs[cat1], + ntrials=100, + replace=False, + ) + subtask_name_to_subtask[subtask_name] = subtask + subtask_name_to_results[subtask_name] = {} + + subtask_name_to_results[subtask_name][session.worker_id] = perf_seq + + subtask_name_to_data: Dict[str, TargetSubtaskData] = {} + for name in subtask_name_to_results: + subtask_name_to_data[name] = TargetSubtaskData( + subtask=subtask_name_to_subtask[name], + results=subtask_name_to_results[name] + ) + + # Instantiate config object + config = LearningCurveBenchmarkConfig( + subtask_name_to_data=subtask_name_to_data, + num_simulations_per_subtask=500, + num_bootstrap_samples=1000, + ) + + super().__init__( + config=config + ) + + +# %% +if __name__ == '__main__': + benchmark = MutatorHighVarBenchmark() + + from hobj.learning_models import DummyBinaryLearner + + learner = DummyBinaryLearner() + + result = benchmark(learner=learner, show_pbar=True) + print(result.msen_CI95) + + # %% + import matplotlib.pyplot as plt + glc = benchmark.target_statistics.phat.mean('subtask') + glc_sigma = benchmark.target_statistics.boot_phat.mean('subtask').std('boot_iter') + x = glc.trial.values + 1 + plt.errorbar(x, glc, yerr = glc_sigma, marker = '.') + plt.xscale('log') + plt.show() \ No newline at end of file diff --git a/hobj/benchmarks/todo/__init__.py b/hobj/benchmarks/todo/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py b/hobj/benchmarks/todo/mut_oneshot_benchmark.py similarity index 99% rename from hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py rename to hobj/benchmarks/todo/mut_oneshot_benchmark.py index bcb2b92..babd7f0 100644 --- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/todo/mut_oneshot_benchmark.py @@ -5,9 +5,8 @@ from tqdm import trange import hobj.learning_models -import hobj.learning_models.learning_model as lm import hobj.data.images.depr_imagesets as imagesets -import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_experiment as mutator +import hobj.benchmarks.todo.mut_oneshot_experiment as mutator import hobj.data.depr_behavior.human_data as human_data diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_experiment.py b/hobj/benchmarks/todo/mut_oneshot_experiment.py similarity index 100% rename from hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_experiment.py rename to hobj/benchmarks/todo/mut_oneshot_experiment.py diff --git a/hobj/data/behavior.py b/hobj/data/behavior.py index 983ef76..3a0f469 100644 --- a/hobj/data/behavior.py +++ b/hobj/data/behavior.py @@ -3,7 +3,7 @@ import pydantic from hobj.data.schema import HumanLearningSession -from hobj.data.store import DataStore, default_data_store +from hobj.data.store import default_data_store __all__ = ['load_highvar_behavior', 'load_oneshot_behavior'] @@ -45,7 +45,6 @@ def load_highvar_behavior( if not remove_probe_trials: return sessions - def filter(vals: list): probe_trials = {25, 51, 77, 103} return [vals[i] for i in range(len(vals)) if i not in probe_trials] @@ -54,12 +53,12 @@ def filter(vals: list): for session in sessions: # Filter out indices in probe_trials filtered_session = HumanLearningSession( - worker_id = session.worker_id, - stimulus_sha256_seq = filter(session.stimulus_sha256_seq), - stimulus_duration_msec_seq = filter(session.stimulus_duration_msec_seq), - action_seq = filter(session.action_seq), - reward_seq = filter(session.reward_seq), - timestamp_start_seq = filter(session.timestamp_start_seq) + worker_id=session.worker_id, + stimulus_sha256_seq=filter(session.stimulus_sha256_seq), + stimulus_duration_msec_seq=filter(session.stimulus_duration_msec_seq), + action_seq=filter(session.action_seq), + reward_seq=filter(session.reward_seq), + timestamp_start_seq=filter(session.timestamp_start_seq) ) filtered_sessions.append(filtered_session) @@ -80,5 +79,6 @@ def load_oneshot_behavior(redownload: bool = False) -> List[HumanLearningSession return sessions + if __name__ == '__main__': - sessions=load_highvar_behavior() \ No newline at end of file + sessions = load_highvar_behavior() diff --git a/hobj/data/schema.py b/hobj/data/schema.py index 3113d5f..18813c5 100644 --- a/hobj/data/schema.py +++ b/hobj/data/schema.py @@ -41,6 +41,9 @@ def __hash__(self): # %% Behavior class HumanLearningSession(_Common): + """ + A representation of the "raw" data collected from a human learning session. + """ worker_id: str = pydantic.Field( description='The anonymized worker ID of the participant.' diff --git a/hobj/learning_models/__init__.py b/hobj/learning_models/__init__.py index 836a18d..ea063cd 100644 --- a/hobj/learning_models/__init__.py +++ b/hobj/learning_models/__init__.py @@ -6,11 +6,11 @@ from hobj.data import schema as schema - +# %% class BinaryLearningModel(ABC): @abstractmethod - def reset_state(self, seed: int) -> None: + def reset_state(self, seed: typing.Union[int, None]) -> None: """ This function resets the LearningModel to some initial state. :param seed: if the LearningModel has any stochastic components, this seed should be used to seed them. @@ -48,7 +48,7 @@ class DummyBinaryLearner(BinaryLearningModel): def __init__(self, seed: int = 0): self.random_generator = np.random.default_rng(seed=seed) - def reset_state(self, seed: int) -> None: + def reset_state(self, seed: typing.Union[int, None]) -> None: self.random_generator = np.random.default_rng(seed=seed) def get_response( diff --git a/hobj/statistics/lapse_rate.py b/hobj/statistics/lapse_rate.py deleted file mode 100644 index a860485..0000000 --- a/hobj/statistics/lapse_rate.py +++ /dev/null @@ -1,47 +0,0 @@ -from typing import Union - -import numpy as np -import xarray as xr - - -def fit_optimal_lapse_rate( - phat: Union[np.ndarray, np.generic, float, int], - p: Union[np.ndarray, np.generic, float, int], - nway: int, -) -> float: - """ - Fits a lapse rate (gamma, which is the probability of a uniform random guess) that minimizes the empirical MSE loss between the phat and p: - - z = phat * (1 - gamma) + 1/nway * (gamma) - Loss = ((z - p)**2).sum() - - :param phat: - :param p: - :param nway: - :return: - """ - - assert nway >= 2 - assert np.all(phat <= 1) - assert np.all(phat >= 0) - assert np.all(p <= 1) - assert np.all(p >= 0) - - if not isinstance(phat, (np.ndarray, xr.DataArray)): - phat = np.array(phat) - - if not isinstance(p, (np.ndarray, xr.DataArray)): - p = np.array(p) - - assert p.shape == phat.shape - - numerator = -(2 * phat / nway - 2 * np.square(phat) + 2 * phat * p - 2 * p / nway).sum() - denominator = (2 / (nway ** 2) - 4 * phat / nway + 2 * (phat ** 2)).sum() - gamma_star = numerator / denominator - gamma_star = np.clip(gamma_star, 0, 1) - - if isinstance(gamma_star, np.ndarray): - if gamma_star.shape == (): - gamma_star = float(gamma_star) - return gamma_star - diff --git a/hobj/statistics/variance_estimates/binomial.py b/hobj/statistics/variance_estimates/binomial.py index e83e058..e7c10ba 100644 --- a/hobj/statistics/variance_estimates/binomial.py +++ b/hobj/statistics/variance_estimates/binomial.py @@ -1,9 +1,3 @@ -from scipy.stats import beta -import numpy as np -import xarray as xr -from typing import Union - - def estimate_variance_of_binomial_proportion(kvec, nvec): """ @@ -15,56 +9,6 @@ def estimate_variance_of_binomial_proportion(kvec, nvec): :param nvec: :return: """ + # todo: add checking on argument phat = kvec / nvec return (phat * (1 - phat)) / (nvec - 1) - - -def get_variance_of_binomial_proportion(pvec, nvec): - - """ - Returns the ground-truth variance associated with the proportion estimator associated with this distribution, phat = k/n: - - kvec ~ Binomial(nvec, pvec) - phat = kvec / nvec - \hat var(phat) = (phat * (1 - phat)) / (nvec - 1) - :param kvec: - :param nvec: - :return: - """ - - return (pvec * (1 - pvec)) / (nvec) - - -def get_CI_of_binomial_proportion(kvec, nvec, alpha:float): - - """ - Returns the exact (Clopper-Pearson) confidence interval for the Binomial proportion estimator, phat: - - kvec ~ Binomial(nvec, pvec) - phat = kvec / nvec - - These confidence intervals may be conservative (wide), due to the discrete nature of the Binomial distribution: they may have higher coverage than alpha. - :param kvec: - :param nvec: - :return: - """ - - assert np.all(kvec <= nvec) - assert np.all(kvec >= 0) - assert np.all(nvec >= 2), np.min(nvec) - - template = kvec - kvec - alpha_lower = (template + 1) * alpha / 2 - alpha_upper = (template + 1) * (1 - alpha / 2) - - p_lower, p_upper = beta.ppf([alpha_lower, alpha_upper], [kvec, kvec + 1], [nvec - kvec + 1, nvec - kvec]) - - p_lower = (template) + p_lower - p_upper = (template) + p_upper - - if isinstance(template, xr.DataArray): - CI = xr.concat([p_lower, p_upper], dim='CI') - else: - CI = p_lower, p_upper - return CI - diff --git a/hobj/stats_new/ci.py b/hobj/stats_new/ci.py index 419816e..9a713d4 100644 --- a/hobj/stats_new/ci.py +++ b/hobj/stats_new/ci.py @@ -37,4 +37,4 @@ def estimate_basic_bootstrap_CI( CI_low = point_estimate - delta2 CI_high = point_estimate + delta1 - return CI_low, CI_high + return float(CI_low), float(CI_high) diff --git a/hobj/stats_new/learning_curve.py b/hobj/stats_new/learning_curve.py new file mode 100644 index 0000000..a8b68f0 --- /dev/null +++ b/hobj/stats_new/learning_curve.py @@ -0,0 +1,60 @@ +import numpy as np +from typing import Union +from hobj.statistics.variance_estimates import binomial as binomial_funcs +from dataclasses import dataclass + +@dataclass +class CalculateLearningCurveResult: + phat: np.ndarray # [trial] + varhat_phat: np.ndarray # [trial] + + boot_phat: np.ndarray # [boot, trial] + boot_varhat_phat: np.ndarray # [boot, trial] + + +def calculate_learning_curve( + perf_matrix: np.ndarray, + bootstrap_seed: Union[None, int], + nbootstrap_samples: int, + repetition_axis: int, +) -> CalculateLearningCurveResult: + + """ + Calculates the learning curve statistics for a given set of performance data for a single subtask. + Returns the point estimates and bootstrapped estimates of the mean and variance of the binomial proportion. + :param perf_matrix: a boolean np.ndarray of shape (nrepetitions, ntrials) where True indicates a correct response. + :param bootstrap_seed: + :param nbootstrap_samples: + :return: + """ + + nrepetitions = perf_matrix.shape[repetition_axis] + ntrials = perf_matrix.shape[1] + + # Calculate statistics: + k = perf_matrix.sum(axis=repetition_axis) + n = nrepetitions + phat = k / n + varhat_phat = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=k, nvec=n) + + # Perform bootstrapping of the model + boot_phat = np.zeros((nbootstrap_samples, ntrials)) + boot_varhat_phat = np.zeros((nbootstrap_samples, ntrials)) + + # Bootstrap resample model simulations + gen = np.random.default_rng(seed=bootstrap_seed) + + for i_boot_iter in range(nbootstrap_samples): + i_boot_sessions = gen.integers(low =0, high = nrepetitions, size = nrepetitions) + + kboot = perf_matrix[i_boot_sessions].sum(repetition_axis) + boot_phat[i_boot_iter] = kboot / n + boot_varhat_phat[i_boot_iter] = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=kboot, nvec=n) + + return CalculateLearningCurveResult( + phat=phat, + varhat_phat=varhat_phat, + boot_phat=boot_phat, + boot_varhat_phat=boot_varhat_phat, + ) + From 8fcb0312704465c0188759ccf2032046f7824d0b Mon Sep 17 00:00:00 2001 From: himjl Date: Fri, 24 Jan 2025 17:27:56 -0500 Subject: [PATCH 062/156] update requirements --- .../{todo => generalization}/__init__.py | 0 .../mut_oneshot_benchmark.py | 2 +- .../mut_oneshot_experiment.py | 0 hobj/benchmarks/mut_highvar_benchmark.py | 10 +-- requirements.txt | 68 ++++--------------- 5 files changed, 18 insertions(+), 62 deletions(-) rename hobj/benchmarks/{todo => generalization}/__init__.py (100%) rename hobj/benchmarks/{todo => generalization}/mut_oneshot_benchmark.py (99%) rename hobj/benchmarks/{todo => generalization}/mut_oneshot_experiment.py (100%) diff --git a/hobj/benchmarks/todo/__init__.py b/hobj/benchmarks/generalization/__init__.py similarity index 100% rename from hobj/benchmarks/todo/__init__.py rename to hobj/benchmarks/generalization/__init__.py diff --git a/hobj/benchmarks/todo/mut_oneshot_benchmark.py b/hobj/benchmarks/generalization/mut_oneshot_benchmark.py similarity index 99% rename from hobj/benchmarks/todo/mut_oneshot_benchmark.py rename to hobj/benchmarks/generalization/mut_oneshot_benchmark.py index babd7f0..4cc3291 100644 --- a/hobj/benchmarks/todo/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/generalization/mut_oneshot_benchmark.py @@ -6,7 +6,7 @@ import hobj.learning_models import hobj.data.images.depr_imagesets as imagesets -import hobj.benchmarks.todo.mut_oneshot_experiment as mutator +import hobj.benchmarks.generalization.mut_oneshot_experiment as mutator import hobj.data.depr_behavior.human_data as human_data diff --git a/hobj/benchmarks/todo/mut_oneshot_experiment.py b/hobj/benchmarks/generalization/mut_oneshot_experiment.py similarity index 100% rename from hobj/benchmarks/todo/mut_oneshot_experiment.py rename to hobj/benchmarks/generalization/mut_oneshot_experiment.py diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index ccb27a1..37c4fde 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -1,16 +1,16 @@ +from typing import List, Dict + from hobj.benchmarks.binary_classification.scoring import LearningCurveBenchmark, LearningCurveBenchmarkConfig, TargetSubtaskData from hobj.benchmarks.binary_classification.task import BinaryClassificationSubtask from hobj.data.behavior import load_highvar_behavior from hobj.data.images import MutatorHighVarImageset -from hobj.data.schema import ImageRef -from typing import List, Dict - -# %% Load data into format expected by benchmark +# %% class MutatorHighVarBenchmark(LearningCurveBenchmark): def __init__(self): + # Load data into format expected by benchmark # Load imageset: imageset = MutatorHighVarImageset() @@ -62,7 +62,7 @@ def __init__(self): results=subtask_name_to_results[name] ) - # Instantiate config object + # Instantiate benchmark config object config = LearningCurveBenchmarkConfig( subtask_name_to_data=subtask_name_to_data, num_simulations_per_subtask=500, diff --git a/requirements.txt b/requirements.txt index cb918f4..84bcf89 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,65 +1,21 @@ annotated-types==0.7.0 -bokeh @ file:///home/conda/feedstock_root/build_artifacts/bokeh_1733754687975/work -Bottleneck @ file:///Users/runner/miniforge3/conda-bld/bottleneck_1729268876309/work -Brotli @ file:///Users/runner/miniforge3/conda-bld/brotli-split_1725267563793/work -certifi @ file:///home/conda/feedstock_root/build_artifacts/certifi_1734380492396/work/certifi -cffi @ file:///Users/runner/miniforge3/conda-bld/cffi_1725560551097/work -cftime @ file:///Users/runner/miniforge3/conda-bld/cftime_1725400488284/work -charset-normalizer==3.4.1 -click @ file:///home/conda/feedstock_root/build_artifacts/click_1734858813237/work -cloudpickle @ file:///home/conda/feedstock_root/build_artifacts/cloudpickle_1736947526808/work -contourpy @ file:///Users/runner/miniforge3/conda-bld/contourpy_1731428370657/work +contourpy==1.3.1 cycler==0.12.1 -cytoolz @ file:///Users/runner/miniforge3/conda-bld/cytoolz_1734107258423/work -dask @ file:///home/conda/feedstock_root/build_artifacts/dask-core_1737242306144/work -distributed @ file:///home/conda/feedstock_root/build_artifacts/distributed_1737295222062/work -fonttools==4.55.4 -fsspec @ file:///home/conda/feedstock_root/build_artifacts/fsspec_1734650190311/work -h2 @ file:///home/conda/feedstock_root/build_artifacts/h2_1733298745555/work -hpack @ file:///home/conda/feedstock_root/build_artifacts/hpack_1733299205993/work -hyperframe @ file:///home/conda/feedstock_root/build_artifacts/hyperframe_1733298771451/work -idna==3.10 -importlib_metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1737420181517/work -iniconfig==2.0.0 -Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1734823942230/work +fonttools==4.55.6 kiwisolver==1.4.8 -locket @ file:///home/conda/feedstock_root/build_artifacts/locket_1650660393415/work -lz4 @ file:///Users/runner/miniforge3/conda-bld/lz4_1733474285600/work -MarkupSafe @ file:///Users/runner/miniforge3/conda-bld/markupsafe_1733219682943/work -matplotlib==3.9.2 -msgpack @ file:///Users/runner/miniforge3/conda-bld/msgpack-python_1725975032735/work -netCDF4 @ file:///Users/runner/miniforge3/conda-bld/netcdf4_1733254940771/work -numpy @ file:///Users/runner/miniforge3/conda-bld/numpy_1737331096427/work/dist/numpy-2.2.2-cp311-cp311-macosx_11_0_arm64.whl#sha256=69698c3c66a5271f366a81628a8204b86878f0137d06c09b34a9cfb83f9f8f2d -packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1733203243479/work -pandas @ file:///Users/runner/miniforge3/conda-bld/pandas_1726878432242/work -partd @ file:///home/conda/feedstock_root/build_artifacts/partd_1715026491486/work -pillow @ file:///Users/runner/miniforge3/conda-bld/pillow_1735929745664/work -pluggy==1.5.0 -psutil @ file:///Users/runner/miniforge3/conda-bld/psutil_1735327389428/work -pyarrow==19.0.0 -pycparser @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_pycparser_1733195786/work -pydantic==2.10.5 +matplotlib==3.10.0 +numpy==2.2.2 +packaging==24.2 +pandas==2.2.3 +pillow==11.1.0 +pydantic==2.10.6 pydantic_core==2.27.2 pyparsing==3.2.1 -PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1733217236728/work -pytest==8.3.4 -python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1733215673016/work -pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1706886791323/work -PyYAML @ file:///Users/runner/miniforge3/conda-bld/pyyaml_1737454702303/work -requests==2.32.3 +python-dateutil==2.9.0.post0 +pytz==2024.2 scipy==1.15.1 -six @ file:///home/conda/feedstock_root/build_artifacts/six_1733380938961/work -sortedcontainers @ file:///home/conda/feedstock_root/build_artifacts/sortedcontainers_1621217038088/work -tblib @ file:///home/conda/feedstock_root/build_artifacts/tblib_1733842374544/work -toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1733736030883/work -tornado @ file:///Users/runner/miniforge3/conda-bld/tornado_1732615936501/work +six==1.17.0 tqdm==4.67.1 typing_extensions==4.12.2 tzdata==2025.1 -url-normalize==1.4.3 -urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1734859416348/work -xarray @ file:///home/conda/feedstock_root/build_artifacts/xarray_1736442926911/work -xyzservices @ file:///home/conda/feedstock_root/build_artifacts/xyzservices_1737234886776/work -zict @ file:///home/conda/feedstock_root/build_artifacts/zict_1733261551178/work -zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1732827521216/work -zstandard==0.23.0 +xarray==2025.1.1 From 7a9cd789d25485f4313c69849e96341f4aeea138 Mon Sep 17 00:00:00 2001 From: himjl Date: Fri, 24 Jan 2025 17:28:20 -0500 Subject: [PATCH 063/156] install pytest --- requirements.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/requirements.txt b/requirements.txt index 84bcf89..eb2344a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,15 +2,18 @@ annotated-types==0.7.0 contourpy==1.3.1 cycler==0.12.1 fonttools==4.55.6 +iniconfig==2.0.0 kiwisolver==1.4.8 matplotlib==3.10.0 numpy==2.2.2 packaging==24.2 pandas==2.2.3 pillow==11.1.0 +pluggy==1.5.0 pydantic==2.10.6 pydantic_core==2.27.2 pyparsing==3.2.1 +pytest==8.3.4 python-dateutil==2.9.0.post0 pytz==2024.2 scipy==1.15.1 From 64a28a338643d922dd59e23f6a42a2c38d12291c Mon Sep 17 00:00:00 2001 From: himjl Date: Fri, 24 Jan 2025 17:30:10 -0500 Subject: [PATCH 064/156] install requests --- requirements.txt | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/requirements.txt b/requirements.txt index eb2344a..b2d5010 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,10 @@ annotated-types==0.7.0 +certifi==2024.12.14 +charset-normalizer==3.4.1 contourpy==1.3.1 cycler==0.12.1 fonttools==4.55.6 +idna==3.10 iniconfig==2.0.0 kiwisolver==1.4.8 matplotlib==3.10.0 @@ -16,9 +19,11 @@ pyparsing==3.2.1 pytest==8.3.4 python-dateutil==2.9.0.post0 pytz==2024.2 +requests==2.32.3 scipy==1.15.1 six==1.17.0 tqdm==4.67.1 typing_extensions==4.12.2 tzdata==2025.1 +urllib3==2.3.0 xarray==2025.1.1 From 9a61f2564d58fdd1379da4ce8fd9bf377072af28 Mon Sep 17 00:00:00 2001 From: himjl Date: Fri, 24 Jan 2025 17:31:15 -0500 Subject: [PATCH 065/156] remove unused imports --- hobj/utils/file_io.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/hobj/utils/file_io.py b/hobj/utils/file_io.py index b1cf07b..d44ac71 100644 --- a/hobj/utils/file_io.py +++ b/hobj/utils/file_io.py @@ -1,16 +1,12 @@ import json -import os import urllib.request import zipfile -from io import BytesIO from pathlib import Path -from urllib.parse import urlparse +from typing import Any -import PIL.Image import requests -import url_normalize from tqdm import tqdm -from typing import Any + def unzip_file(zip_path: Path, output_dir: Path) -> None: """ From 4d9665a377ee486115ecb756c5060e34d2c013b1 Mon Sep 17 00:00:00 2001 From: himjl Date: Sun, 26 Jan 2025 11:13:49 -0500 Subject: [PATCH 066/156] clean up data model wrapping --- .../binary_classification/estimate.py | 62 ++++++++ .../binary_classification/scoring.py | 147 +++++------------- hobj/benchmarks/mut_highvar_benchmark.py | 9 +- requirements.txt | 2 +- 4 files changed, 107 insertions(+), 113 deletions(-) create mode 100644 hobj/benchmarks/binary_classification/estimate.py diff --git a/hobj/benchmarks/binary_classification/estimate.py b/hobj/benchmarks/binary_classification/estimate.py new file mode 100644 index 0000000..c2c296a --- /dev/null +++ b/hobj/benchmarks/binary_classification/estimate.py @@ -0,0 +1,62 @@ +from typing import List, Dict + +import numpy as np +import xarray as xr +from hobj.stats_new.learning_curve import calculate_learning_curve + + +class LearningCurveStatistics(xr.Dataset): + + def __init__( + self, + subtask_name_to_perf: Dict[str, np.ndarray], # subtask: [session, trial] + nbootstrap_samples: int, + ): + # Ensure rectangular data + ntrials_observed = set() + subtask_name_to_nsessions: Dict[str, int] = {} + for subtask_name, perf_data in subtask_name_to_perf.items(): + ntrials_observed.add(perf_data.shape[1]) + subtask_name_to_nsessions[subtask_name] = perf_data.shape[0] + + if len(ntrials_observed) != 1: + raise ValueError(f"Expected all subtask data to have the same number of trials, but got ntrials={ntrials_observed}") + + # Preallocate arrays + subtask_names = sorted(subtask_name_to_perf.keys()) + nsubtasks = len(subtask_names) + ntrials = ntrials_observed.pop() + + phat = np.zeros(shape=(nsubtasks, ntrials)) + varhat_phat = np.zeros(shape=(nsubtasks, ntrials)) + boot_phat = np.zeros(shape=(nbootstrap_samples, nsubtasks, ntrials)) + boot_varhat_phat = np.zeros(shape=(nbootstrap_samples, nsubtasks, ntrials)) + + # Compute statistics + for i_subtask, (subtask_name, perf_mat) in enumerate(subtask_name_to_perf.items()): + + # todo: move this function here + subtask_learning_curve_statistics = calculate_learning_curve( + perf_matrix=perf_mat, + bootstrap_seed=i_subtask, + nbootstrap_samples=nbootstrap_samples, + repetition_axis=0, + ) + + phat[i_subtask] = subtask_learning_curve_statistics.phat + varhat_phat[i_subtask] = subtask_learning_curve_statistics.varhat_phat + boot_phat[:, i_subtask] = subtask_learning_curve_statistics.boot_phat + boot_varhat_phat[:, i_subtask] = subtask_learning_curve_statistics.boot_varhat_phat + + super().__init__( + data_vars=dict( + phat=(['subtask', 'trial'], phat), + varhat_phat=(['subtask', 'trial'], varhat_phat), + boot_phat=(['boot_iter', 'subtask', 'trial'], boot_phat), + boot_varhat_phat=(['boot_iter', 'subtask', 'trial'], boot_varhat_phat), + ), + coords=dict( + subtask=subtask_names, + nsessions=('subtask', [subtask_name_to_nsessions[name] for name in subtask_names]), + ) + ) diff --git a/hobj/benchmarks/binary_classification/scoring.py b/hobj/benchmarks/binary_classification/scoring.py index fdd996b..fb31704 100644 --- a/hobj/benchmarks/binary_classification/scoring.py +++ b/hobj/benchmarks/binary_classification/scoring.py @@ -2,30 +2,38 @@ from typing import List, Dict, Union, Tuple, Optional, Iterator import numpy as np -from scipy import stats as ss +import pydantic +import xarray as xr +from tqdm import tqdm +import hobj.data.schema as schema +from hobj.benchmarks.binary_classification.estimate import LearningCurveStatistics from hobj.benchmarks.binary_classification.task import BinaryClassificationSubtask from hobj.learning_models import BinaryLearningModel -from hobj.statistics.variance_estimates import binomial as binomial_funcs -import pydantic -from tqdm import tqdm -from hobj.stats_new.learning_curve import calculate_learning_curve from hobj.stats_new.ci import estimate_basic_bootstrap_CI -import hobj.data.schema as schema -import xarray as xr # %% Models for configuring a LearningCurveBenchmark: class TargetSubtaskData(pydantic.BaseModel): subtask: BinaryClassificationSubtask # The subtask which generated the associated results - results: Dict[str, List[bool]] # worker_id -> perf_seq + results: np.ndarray # [session, trial] boolean matrix of performance + + class Config: + arbitrary_types_allowed = True @pydantic.model_validator(mode='after') def validate_results(self) -> 'TargetSubtaskData': - for worker_id, perf_seq in self.results.items(): - if len(perf_seq) != self.subtask.ntrials: - raise ValueError(f"Expected {self.subtask.ntrials} trials, but got {len(perf_seq)} trials for worker {worker_id}") + # Check shape + if len(self.results.shape) != 2: + raise ValueError(f"Expected 2D results matrix [session, trial], but got {len(self.results.shape)} dimensions") + + if self.subtask.ntrials != self.results.shape[1]: + raise ValueError(f"Expected {self.subtask.ntrials} trials, but got {self.results.shape[1]} trials") + + # Check dtype + if self.results.dtype != np.bool: + raise ValueError(f"Expected boolean dtype, but got {self.results.dtype}") return self @@ -54,32 +62,6 @@ def ensure_rectangular(self) -> 'LearningCurveBenchmarkConfig': return self -class LearningCurveStatistics(xr.Dataset): - def __init__( - self, - subtask_names: List[str], - phat: np.ndarray, - varhat_phat: np.ndarray, - boot_phat: np.ndarray, - boot_varhat_phat: np.ndarray, - ): - data_vars = dict( - phat = (['subtask', 'trial'], phat), - varhat_phat = (['subtask', 'trial'], varhat_phat), - boot_phat = (['boot_iter', 'subtask', 'trial'], boot_phat), - boot_varhat_phat = (['boot_iter', 'subtask', 'trial'], boot_varhat_phat), - ) - - coords = dict( - subtask = subtask_names, - ) - - super().__init__( - data_vars = data_vars, - coords = coords - ) - - # %% class LearningCurveBenchmark: @@ -134,62 +116,26 @@ def __init__( self.subtask_name_to_subtask: Dict[str, BinaryClassificationSubtask] = { name: config.subtask_name_to_data[name].subtask for name in self.subtask_names } - self.subtask_name_to_target_data: Dict[str, Dict[str, List[bool]]] = { - subtask_name: target_subtask.results for subtask_name, target_subtask in self.config.subtask_name_to_data.items() - } - - # Cache target learning curve statistics - phat = np.zeros((len(self.subtask_names), self.config.ntrials)) - varhat_phat = np.zeros((len(self.subtask_names), self.config.ntrials)) - boot_phat = np.zeros((self.config.num_bootstrap_samples, len(self.subtask_names), self.config.ntrials)) - boot_varhat_phat = np.zeros((self.config.num_bootstrap_samples, len(self.subtask_names), self.config.ntrials)) - for i_subtask, subtask_name in enumerate(self.subtask_names): - subtask = self.subtask_name_to_subtask[subtask_name] - - nworkers = len(self.subtask_name_to_target_data[subtask_name]) - perf_mat = np.zeros((nworkers, subtask.ntrials), dtype=np.bool) - - for i_worker, (worker_id, perf_seq) in enumerate(self.subtask_name_to_target_data[subtask_name].items()): - perf_mat[i_worker] = perf_seq - - subtask_learning_curve_statistics = calculate_learning_curve( - perf_matrix=perf_mat, - bootstrap_seed=i_subtask, - nbootstrap_samples=self.config.num_bootstrap_samples, - repetition_axis=0, - ) - - phat[i_subtask] = subtask_learning_curve_statistics.phat - varhat_phat[i_subtask] = subtask_learning_curve_statistics.varhat_phat - boot_phat[:, i_subtask] = subtask_learning_curve_statistics.boot_phat - boot_varhat_phat[:, i_subtask] = subtask_learning_curve_statistics.boot_varhat_phat + self.subtask_name_to_perf: Dict[str, np.ndarray] = {} + for name in self.subtask_names: + self.subtask_name_to_perf[name] = config.subtask_name_to_data[name].results self._target_statistics = LearningCurveStatistics( - subtask_names=self.subtask_names, - phat=phat, - varhat_phat=varhat_phat, - boot_phat=boot_phat, - boot_varhat_phat=boot_varhat_phat, + subtask_name_to_perf=self.subtask_name_to_perf, + nbootstrap_samples=self.config.num_bootstrap_samples, ) - @property - def data(self) -> Dict[str, Dict[str, List[bool]]]: + def target_data(self) -> Dict[str, np.ndarray]: """ - Returns the target data as a nested dictionary. Example usage - - target_data = self.get_target_as_json() - print(target_data[subtask_name][worker_id]) # List[bool] of trial performances on {subtask_name} by {worker_id}. - - Useful for performing custom analyses on the target data. + Returns the target data for the benchmark, which is a dictionary of subtask_name -> [session, trial] boolean matrix of performance. :return: """ - - return self.subtask_name_to_target_data + return self.subtask_name_to_perf @property - def target_statistics(self) -> xr.Dataset: + def target_statistics(self) -> LearningCurveStatistics: """ Returns point estimates of the mean (and an estimate of the variance of that estimator) of the binomial proportion for the target data, and bootstrapped resamples of those statistics. @@ -217,12 +163,7 @@ def __call__( """ # Get model learning curve statistics: - # Todo: deduplicate with constructor - phat = np.zeros((len(self.subtask_names), self.config.ntrials)) - varhat_phat = np.zeros((len(self.subtask_names), self.config.ntrials)) - boot_phat = np.zeros((self.config.num_bootstrap_samples, len(self.subtask_names), self.config.ntrials)) - boot_varhat_phat = np.zeros((self.config.num_bootstrap_samples, len(self.subtask_names), self.config.ntrials)) - + subtask_name_to_model_perfs: Dict[str, np.ndarray] = {} for i_subtask, subtask_name in enumerate(tqdm(self.subtask_names, desc='Subtask simulations:', disable=not show_pbar)): # Get [simulation, trial] boolean performance matrix for the model perf_matrix = self.simulate_model_behavior( @@ -231,25 +172,12 @@ def __call__( nsimulations=self.config.num_simulations_per_subtask, ) - # Calculate statistics - subtask_estimate = calculate_learning_curve( - perf_matrix=perf_matrix, - bootstrap_seed=None, - nbootstrap_samples=self.config.num_bootstrap_samples, - repetition_axis=0, - ) - phat[i_subtask] = subtask_estimate.phat - varhat_phat[i_subtask] = subtask_estimate.varhat_phat - boot_phat[:, i_subtask] = subtask_estimate.boot_phat - boot_varhat_phat[:, i_subtask] = subtask_estimate.boot_varhat_phat + subtask_name_to_model_perfs[subtask_name] = perf_matrix # Assemble into a single matrix model_statistics = LearningCurveStatistics( - subtask_names=self.subtask_names, - phat=phat, - varhat_phat=varhat_phat, - boot_phat=boot_phat, - boot_varhat_phat=boot_varhat_phat, + subtask_name_to_perf=subtask_name_to_model_perfs, + nbootstrap_samples=self.config.num_bootstrap_samples, ) # Calculate comparison statistics between target and model learning curves: @@ -275,9 +203,9 @@ def __call__( msen_sigma = np.std(msen_boot, ddof=1) msen_CI95 = estimate_basic_bootstrap_CI( - alpha = 0.05, - point_estimate = msen_point, - bootstrapped_point_estimates = np.array(msen_boot), + alpha=0.05, + point_estimate=msen_point, + bootstrapped_point_estimates=np.array(msen_boot), ) # Return result @@ -286,7 +214,7 @@ def __call__( msen_sigma=float(msen_sigma), msen_CI95=msen_CI95, model_statistics=model_statistics, - lapse_rate = lapse_rate, + lapse_rate=lapse_rate, ) @classmethod @@ -308,7 +236,7 @@ def _compare_learning_curves( ) model_phat = model_phat * (1 - lapse_rate) + 0.5 * lapse_rate model_varhat_phat = model_varhat_phat * (1 - lapse_rate) ** 2 - else : + else: lapse_rate = None msen = np.square(model_phat - target_phat).mean(condition_dims) - model_varhat_phat.mean(condition_dims) - target_varhat_phat.mean(condition_dims) @@ -339,5 +267,4 @@ def _fit_lapse_rate( denominator = (2 / (nway ** 2) - 4 * pmodel / nway + 2 * (pmodel ** 2)).sum(dim=condition_dims) gamma_star = numerator / denominator gamma_star = np.clip(gamma_star, 0, 1) - print(gamma_star) return gamma_star diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index 37c4fde..f54642b 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -1,4 +1,5 @@ from typing import List, Dict +import numpy as np from hobj.benchmarks.binary_classification.scoring import LearningCurveBenchmark, LearningCurveBenchmarkConfig, TargetSubtaskData from hobj.benchmarks.binary_classification.task import BinaryClassificationSubtask @@ -51,9 +52,13 @@ def __init__(self): replace=False, ) subtask_name_to_subtask[subtask_name] = subtask - subtask_name_to_results[subtask_name] = {} + subtask_name_to_results[subtask_name] = [] - subtask_name_to_results[subtask_name][session.worker_id] = perf_seq + subtask_name_to_results[subtask_name].append(perf_seq) + + # Cast as numpy + for name in subtask_name_to_results: + subtask_name_to_results[name] = np.array(subtask_name_to_results[name]) # [session, trial] subtask_name_to_data: Dict[str, TargetSubtaskData] = {} for name in subtask_name_to_results: diff --git a/requirements.txt b/requirements.txt index b2d5010..0f44eb2 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,7 +7,7 @@ fonttools==4.55.6 idna==3.10 iniconfig==2.0.0 kiwisolver==1.4.8 -matplotlib==3.10.0 +matplotlib==3.9.2 numpy==2.2.2 packaging==24.2 pandas==2.2.3 From 5a4c7f9aeac42504c1f0d9bdf2c6ca68370af0b5 Mon Sep 17 00:00:00 2001 From: himjl Date: Sun, 26 Jan 2025 11:23:18 -0500 Subject: [PATCH 067/156] fix slots warning? --- hobj/benchmarks/binary_classification/estimate.py | 2 ++ hobj/benchmarks/binary_classification/scoring.py | 5 +++-- hobj/benchmarks/binary_classification/task.py | 7 ++++--- hobj/data/schema.py | 5 +++-- 4 files changed, 12 insertions(+), 7 deletions(-) diff --git a/hobj/benchmarks/binary_classification/estimate.py b/hobj/benchmarks/binary_classification/estimate.py index c2c296a..b8941ed 100644 --- a/hobj/benchmarks/binary_classification/estimate.py +++ b/hobj/benchmarks/binary_classification/estimate.py @@ -7,6 +7,8 @@ class LearningCurveStatistics(xr.Dataset): + __slots__ = () + def __init__( self, subtask_name_to_perf: Dict[str, np.ndarray], # subtask: [session, trial] diff --git a/hobj/benchmarks/binary_classification/scoring.py b/hobj/benchmarks/binary_classification/scoring.py index fb31704..901a715 100644 --- a/hobj/benchmarks/binary_classification/scoring.py +++ b/hobj/benchmarks/binary_classification/scoring.py @@ -19,8 +19,9 @@ class TargetSubtaskData(pydantic.BaseModel): subtask: BinaryClassificationSubtask # The subtask which generated the associated results results: np.ndarray # [session, trial] boolean matrix of performance - class Config: - arbitrary_types_allowed = True + model_config = dict( + arbitrary_types_allowed=True + ) @pydantic.model_validator(mode='after') def validate_results(self) -> 'TargetSubtaskData': diff --git a/hobj/benchmarks/binary_classification/task.py b/hobj/benchmarks/binary_classification/task.py index 93137a5..36fa28e 100644 --- a/hobj/benchmarks/binary_classification/task.py +++ b/hobj/benchmarks/binary_classification/task.py @@ -15,8 +15,9 @@ class BinaryClassificationSubtask(pydantic.BaseModel): Feedback consists of +1 or -1 rewards, depending on the class of the image. """ - class Config: - frozen = True + model_config = dict( + frozen=True + ) classA: List[ImageRef] classB: List[ImageRef] @@ -85,4 +86,4 @@ def simulate_session( i_trial += 1 - return perf_seq \ No newline at end of file + return perf_seq diff --git a/hobj/data/schema.py b/hobj/data/schema.py index 18813c5..3f4b0a5 100644 --- a/hobj/data/schema.py +++ b/hobj/data/schema.py @@ -9,8 +9,9 @@ class _Common(pydantic.BaseModel): - class Config: - frozen = True + model_config = dict( + frozen=True + ) # %% Image data From 2e6fbb3b2032fe8f92481f9e8ec69ddfff81cb9f Mon Sep 17 00:00:00 2001 From: himjl Date: Sun, 26 Jan 2025 16:06:10 -0500 Subject: [PATCH 068/156] core oneshot code --- .../binary_classification/estimate.py | 1 + hobj/benchmarks/binary_classification/task.py | 4 +- hobj/benchmarks/generalization/benchmark.py | 153 ++++++++++++++++++ hobj/benchmarks/generalization/estimate.py | 115 +++++++++++++ .../benchmarks/generalization/ref/__init__.py | 0 .../{ => ref}/mut_oneshot_benchmark.py | 31 ++-- .../{ => ref}/mut_oneshot_experiment.py | 0 hobj/benchmarks/generalization/task.py | 147 +++++++++++++++++ hobj/benchmarks/mut_highvar_benchmark.py | 21 ++- hobj/benchmarks/mut_oneshot_benchmark.py | 44 +++++ hobj/learning_models/linear/representation.py | 4 +- 11 files changed, 495 insertions(+), 25 deletions(-) create mode 100644 hobj/benchmarks/generalization/benchmark.py create mode 100644 hobj/benchmarks/generalization/estimate.py create mode 100644 hobj/benchmarks/generalization/ref/__init__.py rename hobj/benchmarks/generalization/{ => ref}/mut_oneshot_benchmark.py (97%) rename hobj/benchmarks/generalization/{ => ref}/mut_oneshot_experiment.py (100%) create mode 100644 hobj/benchmarks/generalization/task.py create mode 100644 hobj/benchmarks/mut_oneshot_benchmark.py diff --git a/hobj/benchmarks/binary_classification/estimate.py b/hobj/benchmarks/binary_classification/estimate.py index b8941ed..63b699a 100644 --- a/hobj/benchmarks/binary_classification/estimate.py +++ b/hobj/benchmarks/binary_classification/estimate.py @@ -14,6 +14,7 @@ def __init__( subtask_name_to_perf: Dict[str, np.ndarray], # subtask: [session, trial] nbootstrap_samples: int, ): + # Ensure rectangular data ntrials_observed = set() subtask_name_to_nsessions: Dict[str, int] = {} diff --git a/hobj/benchmarks/binary_classification/task.py b/hobj/benchmarks/binary_classification/task.py index 36fa28e..be66cdc 100644 --- a/hobj/benchmarks/binary_classification/task.py +++ b/hobj/benchmarks/binary_classification/task.py @@ -31,8 +31,8 @@ def sort_image_refs(cls, value: List[ImageRef]) -> List[ImageRef]: @pydantic.model_validator(mode='after') def validate_image_refs(self) -> 'BinaryClassificationSubtask': - if self.replace and len(self.classA) + len(self.classB) < self.ntrials: - raise ValueError(f"Specified replace=True, but only {len(self.classA) + len(self.classB)} images available. (len(classA)={len(self.classA)}; len(classB)={len(self.classB)}") + if not self.replace and len(self.classA) + len(self.classB) < self.ntrials: + raise ValueError(f"Specified replace=False, but only {len(self.classA) + len(self.classB)} images available. (len(classA)={len(self.classA)}; len(classB)={len(self.classB)}") return self def simulate_session( diff --git a/hobj/benchmarks/generalization/benchmark.py b/hobj/benchmarks/generalization/benchmark.py new file mode 100644 index 0000000..9dae7c3 --- /dev/null +++ b/hobj/benchmarks/generalization/benchmark.py @@ -0,0 +1,153 @@ +from dataclasses import dataclass +from typing import List, Dict, Union, Tuple, Optional, Iterator + +import numpy as np +import pydantic +import xarray as xr +from tqdm import tqdm + +import hobj.data.schema as schema +from hobj.benchmarks.generalization.estimate import GeneralizationStatistics +from hobj.benchmarks.generalization.task import GeneralizationSubtask, GeneralizationSessionResult +from hobj.learning_models import BinaryLearningModel +from hobj.stats_new.ci import estimate_basic_bootstrap_CI + + +# %% Models for configuring a LearningCurveBenchmark: +class GeneralizationBenchmarkConfig(pydantic.BaseModel): + subtasks: List[GeneralizationSubtask] + results: List[GeneralizationSessionResult] + num_simulations_per_subtask: int = pydantic.Field(ge=2) + num_bootstrap_samples: int = pydantic.Field(ge=2) + + +# %% +class GeneralizationBenchmark: + + def __init__( + self, + config: GeneralizationBenchmarkConfig, + ): + self.config = config + self._generalization_statistics = GeneralizationStatistics( + results = self.config.results, + perform_lapse_rate_correction=True, + n_bootstrap_iterations=self.config.num_bootstrap_samples + ) + + @property + def target_statistics(self) -> GeneralizationStatistics: + """ + Returns the target data for the benchmark, which is a dictionary of subtask_name -> [session, trial] boolean matrix of performance. + :return: + """ + return self._generalization_statistics + + @dataclass + class GeneralizationBenchmarkResult: + msen: float + msen_sigma: float + msen_CI95: Tuple[float, float] + model_statistics: GeneralizationStatistics + + def __call__( + self, + learner: BinaryLearningModel, + show_pbar: bool = False + ) -> GeneralizationBenchmarkResult: + """ + :param learner: LearningModel + :return: + """ + + # Get model generalization behaviors: + results: List[GeneralizationSessionResult] = [] + for subtask in tqdm(self.config.subtasks, desc='Subtask simulations:', disable=not show_pbar): + # Get [simulation, trial] boolean performance matrix for the model + subtask_results = self.simulate_model_behavior( + subtask=subtask, + learner=learner, + nsimulations=self.config.num_simulations_per_subtask, + ) + results.extend(subtask_results) + + # Assemble into a single matrix + model_statistics = GeneralizationStatistics( + results=results, + perform_lapse_rate_correction=False, + n_bootstrap_iterations=self.config.num_bootstrap_samples, + ) + + # Calculate comparison statistics between target and model learning curves: + msen_point = self._compare_generalization_patterns( + model_phat=model_statistics.phat, + model_varhat_phat=model_statistics.varhat_phat, + target_phat=self.target_statistics.phat, + target_varhat_phat=self.target_statistics.varhat_phat, + condition_dims=('transformation',), + ) + + # Calculate bootstrap replicates: + msen_boot = self._compare_generalization_patterns( + model_phat=model_statistics.boot_phat, + model_varhat_phat=model_statistics.boot_varhat_phat, + target_phat=self.target_statistics.boot_phat, + target_varhat_phat=self.target_statistics.boot_varhat_phat, + condition_dims=('transformation',), + ) + + msen_sigma = np.std(msen_boot, ddof=1) + msen_CI95 = estimate_basic_bootstrap_CI( + alpha=0.05, + point_estimate=msen_point, + bootstrapped_point_estimates=np.array(msen_boot), + ) + + # Return result + return self.GeneralizationBenchmarkResult( + msen=float(msen_point), + msen_sigma=float(msen_sigma), + msen_CI95=msen_CI95, + model_statistics=model_statistics, + ) + + @staticmethod + def simulate_model_behavior( + subtask: GeneralizationSubtask, + learner: BinaryLearningModel, + nsimulations: int, + ) -> List[GeneralizationSessionResult]: + """ + Returns a [nsimulations, ntrials] matrix of model performance on the subtask. + :param subtask: + :param learner: + :param nsimulations: + :return: a [nsimulations, ntrials] matrix of model performance on the subtask. + """ + + results = [] + + # Perform simulations for this subtask: + for i_simulation in range(nsimulations): + # Reset learner internal state + learner.reset_state(seed=None) + + # Simulate session: + results.append(subtask.simulate_session(learner=learner,seed=None,)) + + return results + + + + @classmethod + def _compare_generalization_patterns( + cls, + model_phat: xr.DataArray, + model_varhat_phat: xr.DataArray, + target_phat: xr.DataArray, + target_varhat_phat: xr.DataArray, + condition_dims: Tuple[str, ...], + ): + + msen = np.square(model_phat - target_phat).mean(condition_dims) - model_varhat_phat.mean(condition_dims) - target_varhat_phat.mean(condition_dims) + return msen \ No newline at end of file diff --git a/hobj/benchmarks/generalization/estimate.py b/hobj/benchmarks/generalization/estimate.py new file mode 100644 index 0000000..5e121ae --- /dev/null +++ b/hobj/benchmarks/generalization/estimate.py @@ -0,0 +1,115 @@ +from typing import Dict, Set, List + +import xarray as xr +import numpy as np +from hobj.benchmarks.generalization.task import GeneralizationSessionResult + +from hobj.statistics.variance_estimates import binomial as binomial_funcs + + +class GeneralizationStatistics(xr.Dataset): + __slots__ = () + + def __init__( + self, + results: List[GeneralizationSessionResult], + perform_lapse_rate_correction: bool, + n_bootstrap_iterations: int, + ): + + # Get all transformations + all_transformations = set() + for result in results: + all_transformations.update(result.transformation_to_kn.keys()) + + all_transformations = sorted(all_transformations) + transformation_to_i = {transformation: i for i, transformation in enumerate(all_transformations)} + + # Package data into [session, transformation] kn matrices + nsessions = len(results) + ntransformations = len(all_transformations) + k = np.zeros(shape=(nsessions, ntransformations)) + n = np.zeros(shape=(nsessions, ntransformations)) + kcatch = np.zeros(shape = nsessions) + ncatch = np.zeros(shape = nsessions) + + for i_session, result in enumerate(results): + kcatch[i_session] = result.kcatch + ncatch[i_session] = result.ncatch + + for transformation, (k, n) in result.transformation_to_kn.items(): + i_transformation = transformation_to_i[transformation] + k[i_session, i_transformation] = k + n[i_session, i_transformation] = n + + # Calculate statistics + phat, varhat_phat = self._get_point_estimates( + k = k.sum(0), + n = n.sum(0), + kcatch = kcatch.sum(), + ncatch = ncatch.sum(), + perform_lapse_rate_correction = perform_lapse_rate_correction, + ) + + # Compute bootstrap replicates by resampling sessions + gen = np.random.default_rng() + boot_phat = np.zeros(shape=(n_bootstrap_iterations, ntransformations)) + boot_varhat_phat = np.zeros(shape=(n_bootstrap_iterations, ntransformations)) + for i_boot_iter in range(n_bootstrap_iterations): + + # Resample sessions + i_boot_sessions = gen.integers(low = 0, high = nsessions, size = nsessions) + kboot = k[i_boot_sessions].sum(0) + nboot = n[i_boot_sessions].sum(0) + kcatch_boot = kcatch[i_boot_sessions].sum() + ncatch_boot = ncatch[i_boot_sessions].sum() + + # Compute bootstrapped point estimates + phat_cur, varhat_phat_cur = self._get_point_estimates( + k=kboot, + n=nboot, + kcatch=kcatch_boot, + ncatch=ncatch_boot, + perform_lapse_rate_correction=perform_lapse_rate_correction, + ) + + # Save + boot_phat[i_boot_iter] = phat_cur + boot_varhat_phat[i_boot_iter] = varhat_phat_cur + + super().__init__( + data_vars=dict( + phat=(['transformation'], phat), + varhat_phat=(['transformation'], varhat_phat), + boot_phat=(['boot_iter', 'transformation'], boot_phat), + boot_varhat_phat=(['boot_iter', 'transformation'], boot_varhat_phat), + ), + ) + + @staticmethod + def _get_point_estimates( + k: np.ndarray, # [transformation] + n: np.ndarray, # [transformation] + kcatch: np.ndarray, # () + ncatch: np.ndarray, # () + perform_lapse_rate_correction: bool, + ): + + phat = k / n + varhat_phat = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=k, nvec=n) + + if perform_lapse_rate_correction: + # Return an estimate of the performance that would be expected if the system had a lapse rate of 0. + # The lapse rate is estimated using the catch trials. + hat_lapse_rate = 2 - 2 * (kcatch / ncatch) + + # Estimate the performance that would be expected if the system had a lapse rate of 0, using the plug-in estimator: + phat = (phat - hat_lapse_rate / 2) / (1 - hat_lapse_rate) + + # Estimate the variance of the corrected performance by treating the lapse rate as a constant: + varhat_phat = varhat_phat / (1 - hat_lapse_rate)**2 + + phat = np.clip(phat, 0, 1) + + return phat, varhat_phat + diff --git a/hobj/benchmarks/generalization/ref/__init__.py b/hobj/benchmarks/generalization/ref/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/hobj/benchmarks/generalization/mut_oneshot_benchmark.py b/hobj/benchmarks/generalization/ref/mut_oneshot_benchmark.py similarity index 97% rename from hobj/benchmarks/generalization/mut_oneshot_benchmark.py rename to hobj/benchmarks/generalization/ref/mut_oneshot_benchmark.py index 4cc3291..ae3f00a 100644 --- a/hobj/benchmarks/generalization/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/generalization/ref/mut_oneshot_benchmark.py @@ -6,8 +6,7 @@ import hobj.learning_models import hobj.data.images.depr_imagesets as imagesets -import hobj.benchmarks.generalization.mut_oneshot_experiment as mutator - +import hobj.benchmarks.generalization.ref.mut_oneshot_experiment as mutator import hobj.data.depr_behavior.human_data as human_data from hobj.statistics.resamplers.resamplers import Resampler @@ -202,7 +201,7 @@ def score_predictions( # %% Assemble scores ds_scores = xr.Dataset( data_vars=dict( - MSEn = MSEn_point, + MSEn=MSEn_point, perf=hat_prob, perf_by_type=hat_prob.groupby('transformation').mean(), ), @@ -326,7 +325,6 @@ def _process_raw_experiment(self, da_perf: xr.DataArray): return ds - @property def ds_table(self): @@ -340,10 +338,9 @@ def ds_table(self): :return: """ if not hasattr(self, '_ds_table'): - self._ds_table = self._process_raw_experiment(da_perf = self.ds_raw.perf) + self._ds_table = self._process_raw_experiment(da_perf=self.ds_raw.perf) return self._ds_table - @property def ds_ceilings(self): """ @@ -397,22 +394,24 @@ def get_null_MSE_functional(ds_data: xr.Dataset): MSEn = R2n_funcs.estimate_MSEn( hat_theta_model=pvec_null, hat_var_hat_theta_model=var_pvec_null, - hat_theta_target = pvec, + hat_theta_target=pvec, condition_dims=('transformation_id',), ) - return MSEn + return MSEn ds_boot = self.ds_boot ds_null = self.ds_null - ds_null = ds_null.rename({ - 'k': 'knull', - 'n': 'nnull', - 'k_catch': 'k_catch_null', - 'n_catch': 'n_catch_null', - 'k_train': 'k_train_null', - 'n_train': 'n_train_null', - }) + ds_null = ds_null.rename( + { + 'k': 'knull', + 'n': 'nnull', + 'k_catch': 'k_catch_null', + 'n_catch': 'n_catch_null', + 'k_train': 'k_train_null', + 'n_train': 'n_train_null', + } + ) ds_data = xr.merge([ds_boot, ds_null]) ds_ceilings['MSEn_null'] = approximated_null_distribution.estimate_null_distribution( diff --git a/hobj/benchmarks/generalization/mut_oneshot_experiment.py b/hobj/benchmarks/generalization/ref/mut_oneshot_experiment.py similarity index 100% rename from hobj/benchmarks/generalization/mut_oneshot_experiment.py rename to hobj/benchmarks/generalization/ref/mut_oneshot_experiment.py diff --git a/hobj/benchmarks/generalization/task.py b/hobj/benchmarks/generalization/task.py new file mode 100644 index 0000000..c97b8fa --- /dev/null +++ b/hobj/benchmarks/generalization/task.py @@ -0,0 +1,147 @@ +from dataclasses import dataclass +from typing import List, Set, Union, Dict, Optional + +import numpy as np +import pydantic + +from hobj.data.schema import ImageRef +from hobj.learning_models import BinaryLearningModel +import collections + + +# %% +class GeneralizationSessionResult(pydantic.BaseModel): + transformation_to_kn: Dict[str, List[int]] + kcatch: int + ncatch: int + + @pydantic.model_validator(mode='after') + def validate_results(self) -> 'GeneralizationSessionResult': + for transformation in self.transformation_to_kn: + k, n = self.transformation_to_kn[transformation] + if k < 0 or n < 0 or k > n: + raise ValueError(f"Got invalid Binomial parameters k={k}, n={n} for transformation {transformation}") + + if self.kcatch < 0 or self.ncatch < 0 or self.kcatch > self.ncatch: + raise ValueError(f"Got invalid Binomial parameters k={self.kcatch}, n={self.ncatch} for catch trials") + + return self + + +class GeneralizationSubtask(pydantic.BaseModel): + model_config = dict( + frozen=True + ) + + support_imageA: ImageRef + support_imageB: ImageRef + test_imagesA: List[ImageRef] + test_imagesB: List[ImageRef] + image_ref_to_transformation: Dict[ImageRef, str] + + support_trials: Set[int] # Trials in which a support image will be given + catch_trials: Set[int] # Trials in which a support image will be given, and recorded for analysis + ntrials: int = pydantic.Field(description='The number of trials in the subtask.', gt=0) + replace_test_trials: bool + + @pydantic.field_validator('test_imagesA', 'test_imagesB', mode='after') + @classmethod + def sort_image_refs(cls, value: List[ImageRef]) -> List[ImageRef]: + return sorted(value) + + @pydantic.model_validator(mode='after') + def validate_model(self) -> 'GeneralizationSubtask': + + if len(self.support_trials.intersection(self.catch_trials)) > 0: + raise ValueError(f"Expected support_trials and catch_trials to be disjoint, but got {self.support_trials.intersection(self.catch_trials)}") + + if max(self.support_trials) >= self.ntrials: + raise ValueError(f"Expected support_trials to be less than ntrials, but got {max(self.support_trials)} >= {self.ntrials}") + if min(self.support_trials) < 0: + raise ValueError(f"Expected support_trials to be non-negative, but got {min(self.support_trials)}") + + if not self.replace_test_trials and len(self.test_imagesA) + len(self.test_imagesB) < self.ntrials: + raise ValueError(f"Specified replace=True, but only {len(self.test_imagesA) + len(self.test_imagesB)} images available. (len(classA)={len(self.classA)}; len(classB)={len(self.classB)}") + + for ref in self.test_imagesA + self.test_imagesB: + if not ref in self.image_ref_to_transformation: + raise ValueError(f"Expected all test images to have an associated transformation, but {ref} was missing.") + + return self + + def simulate_session( + self, + learner: BinaryLearningModel, + seed: Union[int, None], + ) -> GeneralizationSessionResult: + """ + Convenience method to simulate a session of the one-shot task on a given BinaryLearningModel. + :param learner: + :param seed: + :return: a [ntrials] boolean np.ndarray vector where True indicates a correct response. + """ + # Allocate results + transformation_to_kn = collections.defaultdict(lambda: [0, 0]) + kcatch = 0 + ncatch = 0 + + # Initialize random state of environment + gen = np.random.default_rng(seed=seed) + + # Sample the stimulus image sequence + num_test_classA = len(self.test_imagesA) + num_test_classB = len(self.test_imagesB) + + n_all_test_images = num_test_classA + num_test_classB + ntest_trials = self.ntrials - len(self.support_trials) + if self.replace_test_trials: + i_image_seq = gen.integers(n_all_test_images, size=ntest_trials) + else: + i_image_seq = gen.permutation(n_all_test_images)[:ntest_trials] + i_image_seq = list(i_image_seq) + + # Randomly sample the reward contingency + classA_correct_action = 0 if gen.random() < 0.5 else 1 + classB_correct_action = 1 - classA_correct_action + + # Iterate over trials + perf_seq = np.zeros(self.ntrials, dtype=bool) + + for i_trial in range(self.ntrials): + # Retrieve trial information + stimulus_category_is_A = gen.random() < 0.5 + correct_action_cur = classA_correct_action if stimulus_category_is_A else classB_correct_action + + if i_trial in self.support_trials or i_trial in self.catch_trials: + # Sample a support image + image_ref_cur = self.support_imageA if stimulus_category_is_A else self.support_imageB + else: + # Sample a test image + i_test_image = i_image_seq.pop() + image_ref_cur = self.test_imagesA[i_test_image] if stimulus_category_is_A else self.test_imagesA[i_test_image - num_test_classA] + + # Get response from learner + a = learner.get_response(image=image_ref_cur) + + # Calculate feedback based on response + correct = a == correct_action_cur + feedback = 1. if correct else -1. + + # Deliver feedback to learner + learner.give_feedback(feedback) + + # Record results of trial: + + if i_trial in self.support_trials or i_trial in self.catch_trials: + kcatch += correct + ncatch += 1 + else: + transformation_cur = self.image_ref_to_transformation[image_ref_cur] + transformation_to_kn[transformation_cur][0] += correct + transformation_to_kn[transformation_cur][1] += 1 + + return GeneralizationSessionResult( + transformation_to_kn=transformation_to_kn, + kcatch=kcatch, + ncatch=ncatch + ) diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index f54642b..176451c 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -79,7 +79,7 @@ def __init__(self): ) -# %% +# %% todo: move to example if __name__ == '__main__': benchmark = MutatorHighVarBenchmark() @@ -92,9 +92,20 @@ def __init__(self): # %% import matplotlib.pyplot as plt - glc = benchmark.target_statistics.phat.mean('subtask') - glc_sigma = benchmark.target_statistics.boot_phat.mean('subtask').std('boot_iter') - x = glc.trial.values + 1 - plt.errorbar(x, glc, yerr = glc_sigma, marker = '.') + target_statistics = benchmark.target_statistics + + def get_glc(statistics): + glc = statistics.phat.mean('subtask') + glc_sigma = statistics.boot_phat.mean('subtask').std('boot_iter') + x = glc.trial.values + 1 + return x, glc, glc_sigma + + + x, glc, glc_sigma = get_glc(target_statistics) + plt.errorbar(x, glc, yerr = glc_sigma, marker = '.', label = 'human') + + xmod, glcmod, glc_sigmamod = get_glc(result.model_statistics) + plt.errorbar(xmod, glcmod, yerr = glc_sigmamod, marker = '.', label = 'model') + plt.legend() plt.xscale('log') plt.show() \ No newline at end of file diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py new file mode 100644 index 0000000..46f1450 --- /dev/null +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -0,0 +1,44 @@ + +# Coercing human data +from hobj.data.behavior import load_oneshot_behavior +from hobj.data.images import MutatorOneShotImageset + +imageset = MutatorOneShotImageset() +oneshot_sessions = load_oneshot_behavior() + +from hobj.benchmarks.generalization.task import GeneralizationSessionResult + +image_ref_to_transformation_id = {} +for ref in imageset.image_refs: + annotation = imageset.get_annotation(image_ref=ref) + transformation_id = f"{annotation.transformation} | {annotation.transformation_level}" + image_ref_to_transformation_id[ref] = transformation_id + +ids = { + 'inplanerotation | 180.0', + 'outplanerotation | 45.0', + 'outplanerotation | 180.0', 'blur | 0.03125', + 'scale | 0.25', + 'noise | 0.5', + 'inplanetranslation | 0.75', + 'inplanetranslation | 0.5', + 'noise | 0.375', + 'backgrounds | 1.0', + 'noise | 0.125', + 'inplanetranslation | 0.25', + 'backgrounds | 0.215443', + 'delpixels | 0.25', + 'delpixels | 0.95', + 'scale | 0.5', + 'combinednat | 1.0', # excluded + 'backgrounds | 0.464159', + 'delpixels | 0.75', + 'original | 0.0', # excluded + 'outplanerotation | 90.0', + 'scale | 1.5', + 'inplanerotation | 135.0', 'blur | 0.015625', 'scale | 0.125', 'contrast | -0.4', 'backgrounds | 0.1', 'inplanerotation | 45.0', 'outplanerotation | 135.0', 'blur | 0.007812', 'inplanerotation | 90.0', 'noise | 0.25', + 'inplanetranslation | 0.125', 'blur | 0.0625', 'contrast | 0.8', 'contrast | 0.4', 'delpixels | 0.5', 'contrast | -0.8'} + + +for session in oneshot_sessions: + # Parse raw data by worker \ No newline at end of file diff --git a/hobj/learning_models/linear/representation.py b/hobj/learning_models/linear/representation.py index 34f5a82..41d268f 100644 --- a/hobj/learning_models/linear/representation.py +++ b/hobj/learning_models/linear/representation.py @@ -13,7 +13,7 @@ class RepresentationalModel(ABC): Meant to be subclassed (see PrecachedRepresentationalModel below for an example). """ - def __init__(self, d:int): + def __init__(self, d: int): if not isinstance(d, int): raise ValueError(f"Expected d to be an int, but got {d} of type {type(d)}") if d <= 0: @@ -55,7 +55,7 @@ def __init__( elif len(f) != d: raise ValueError(f"Features for image {sha256} have dimension {len(f)}, but expected {d}") - super().__init__(d = d) + super().__init__(d=d) def get_features(self, image: Union[schema.ImageRef, PIL.Image]) -> np.ndarray: From 1b33259083bea3b171608b204c844174bbfe22b8 Mon Sep 17 00:00:00 2001 From: himjl Date: Mon, 27 Jan 2025 09:48:19 -0500 Subject: [PATCH 069/156] clean up oneshot --- examples/score_example_model.py | 13 +- examples/view_oneshot_behavioral_data.py | 26 ++ hobj/benchmarks/__init__.py | 2 +- .../{scoring.py => benchmark.py} | 4 +- .../{estimate.py => estimator.py} | 0 .../{task.py => simulation.py} | 0 .../tests/test_simulate_subtask.py | 4 +- hobj/benchmarks/generalization/benchmark.py | 21 +- .../{estimate.py => estimator.py} | 85 ++++-- .../generalization/{task.py => simulator.py} | 81 +++--- hobj/benchmarks/mut_highvar_benchmark.py | 8 +- hobj/benchmarks/mut_oneshot_benchmark.py | 251 +++++++++++++++--- hobj/learning_models/__init__.py | 2 +- .../tests/test_dummy_learner.py | 6 +- 14 files changed, 370 insertions(+), 133 deletions(-) create mode 100644 examples/view_oneshot_behavioral_data.py rename hobj/benchmarks/binary_classification/{scoring.py => benchmark.py} (98%) rename hobj/benchmarks/binary_classification/{estimate.py => estimator.py} (100%) rename hobj/benchmarks/binary_classification/{task.py => simulation.py} (100%) rename hobj/benchmarks/generalization/{estimate.py => estimator.py} (54%) rename hobj/benchmarks/generalization/{task.py => simulator.py} (61%) diff --git a/examples/score_example_model.py b/examples/score_example_model.py index 8620ee9..346a66b 100644 --- a/examples/score_example_model.py +++ b/examples/score_example_model.py @@ -1,16 +1,19 @@ # %% Instantiate benchmarks -from hobj.benchmarks import MutatorHighVarBenchmark +from hobj.benchmarks import MutatorHighVarBenchmark, MutatorOneshotBenchmark experiment1_benchmark = MutatorHighVarBenchmark() + # %% Instantiate learner -from hobj.learning_models import DummyBinaryLearner -learner = DummyBinaryLearner() +from hobj.learning_models import RandomGuesser +learner = RandomGuesser() # %% Score -experiment1_result = experiment1_benchmark(learner=learner) -print(experiment1_result) +#experiment1_result = experiment1_benchmark(learner=learner) +#print(experiment1_result) # %% Visualize results import matplotlib.pyplot as plt +experiment2_benchmark = MutatorOneshotBenchmark() +experiment2_result = experiment2_benchmark(learner=learner) \ No newline at end of file diff --git a/examples/view_oneshot_behavioral_data.py b/examples/view_oneshot_behavioral_data.py new file mode 100644 index 0000000..c73f579 --- /dev/null +++ b/examples/view_oneshot_behavioral_data.py @@ -0,0 +1,26 @@ + +from hobj.benchmarks import MutatorOneshotBenchmark +import matplotlib.pyplot as plt +import numpy as np + +# %% View processed data for Experiment 2 +experiment2_benchmark = MutatorOneshotBenchmark() +target_statistics = experiment2_benchmark.target_statistics + +# %% + +plt.figure(figsize=(12, 4)) +xx = np.arange(len(target_statistics.transformation.values)) +xlabels = target_statistics.transformation.values +plt.errorbar(x=xx, y=target_statistics.phat, yerr=np.sqrt(target_statistics.varhat_phat)) +plt.xlabel('trial') +plt.ylabel('generalization accuracy') +plt.xticks(xx, xlabels, rotation=45, ha='right') +plt.ylim([0.3, 1.05]) +plt.tight_layout() +plt.show() + +# %% +plt.plot(target_statistics.varhat_phat, target_statistics.boot_phat.var('boot_iter'), '.') +plt.axis([0, 0.002, 0, 0.002]) +plt.show() \ No newline at end of file diff --git a/hobj/benchmarks/__init__.py b/hobj/benchmarks/__init__.py index c173c7e..17797ed 100644 --- a/hobj/benchmarks/__init__.py +++ b/hobj/benchmarks/__init__.py @@ -1,2 +1,2 @@ from hobj.benchmarks.mut_highvar_benchmark import MutatorHighVarBenchmark -#from hobj.benchmarks.mut_oneshot_benchmark import MutatorOneshotBenchmark +from hobj.benchmarks.mut_oneshot_benchmark import MutatorOneshotBenchmark diff --git a/hobj/benchmarks/binary_classification/scoring.py b/hobj/benchmarks/binary_classification/benchmark.py similarity index 98% rename from hobj/benchmarks/binary_classification/scoring.py rename to hobj/benchmarks/binary_classification/benchmark.py index 901a715..98bba80 100644 --- a/hobj/benchmarks/binary_classification/scoring.py +++ b/hobj/benchmarks/binary_classification/benchmark.py @@ -7,8 +7,8 @@ from tqdm import tqdm import hobj.data.schema as schema -from hobj.benchmarks.binary_classification.estimate import LearningCurveStatistics -from hobj.benchmarks.binary_classification.task import BinaryClassificationSubtask +from hobj.benchmarks.binary_classification.estimator import LearningCurveStatistics +from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask from hobj.learning_models import BinaryLearningModel from hobj.stats_new.ci import estimate_basic_bootstrap_CI diff --git a/hobj/benchmarks/binary_classification/estimate.py b/hobj/benchmarks/binary_classification/estimator.py similarity index 100% rename from hobj/benchmarks/binary_classification/estimate.py rename to hobj/benchmarks/binary_classification/estimator.py diff --git a/hobj/benchmarks/binary_classification/task.py b/hobj/benchmarks/binary_classification/simulation.py similarity index 100% rename from hobj/benchmarks/binary_classification/task.py rename to hobj/benchmarks/binary_classification/simulation.py diff --git a/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py b/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py index 98621e4..13966a5 100644 --- a/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py +++ b/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py @@ -1,5 +1,5 @@ from hobj.benchmarks.binary_classification.task import BinaryClassificationSubtask -from hobj.learning_models import DummyBinaryLearner +from hobj.learning_models import RandomGuesser from hobj.data.schema import ImageRef from typing import List import pytest @@ -28,7 +28,7 @@ def test_simulate_subtask(): replace=False, ) - learner = DummyBinaryLearner(seed=0) + learner = RandomGuesser(seed=0) perf_seq = subtask.simulate_session( learner=learner, diff --git a/hobj/benchmarks/generalization/benchmark.py b/hobj/benchmarks/generalization/benchmark.py index 9dae7c3..945b567 100644 --- a/hobj/benchmarks/generalization/benchmark.py +++ b/hobj/benchmarks/generalization/benchmark.py @@ -6,9 +6,8 @@ import xarray as xr from tqdm import tqdm -import hobj.data.schema as schema -from hobj.benchmarks.generalization.estimate import GeneralizationStatistics -from hobj.benchmarks.generalization.task import GeneralizationSubtask, GeneralizationSessionResult +from hobj.benchmarks.generalization.estimator import GeneralizationStatistics +from hobj.benchmarks.generalization.simulator import GeneralizationSubtask, GeneralizationSessionResult from hobj.learning_models import BinaryLearningModel from hobj.stats_new.ci import estimate_basic_bootstrap_CI @@ -19,6 +18,7 @@ class GeneralizationBenchmarkConfig(pydantic.BaseModel): results: List[GeneralizationSessionResult] num_simulations_per_subtask: int = pydantic.Field(ge=2) num_bootstrap_samples: int = pydantic.Field(ge=2) + bootstrap_target_by_worker: bool # %% @@ -30,9 +30,10 @@ def __init__( ): self.config = config self._generalization_statistics = GeneralizationStatistics( - results = self.config.results, + results=self.config.results, perform_lapse_rate_correction=True, - n_bootstrap_iterations=self.config.num_bootstrap_samples + n_bootstrap_iterations=self.config.num_bootstrap_samples, + bootstrap_by_worker=self.config.bootstrap_target_by_worker, ) @property @@ -76,8 +77,12 @@ def __call__( results=results, perform_lapse_rate_correction=False, n_bootstrap_iterations=self.config.num_bootstrap_samples, + bootstrap_by_worker=False, # bootstrap simulations ) + # Get target transformation + model_statistics = model_statistics.sel(transformation = self.target_statistics.transformation) + # Calculate comparison statistics between target and model learning curves: msen_point = self._compare_generalization_patterns( model_phat=model_statistics.phat, @@ -133,12 +138,10 @@ def simulate_model_behavior( learner.reset_state(seed=None) # Simulate session: - results.append(subtask.simulate_session(learner=learner,seed=None,)) + results.append(subtask.simulate_session(learner=learner, seed=None, )) return results - - @classmethod def _compare_generalization_patterns( cls, @@ -150,4 +153,4 @@ def _compare_generalization_patterns( ): msen = np.square(model_phat - target_phat).mean(condition_dims) - model_varhat_phat.mean(condition_dims) - target_varhat_phat.mean(condition_dims) - return msen \ No newline at end of file + return msen diff --git a/hobj/benchmarks/generalization/estimate.py b/hobj/benchmarks/generalization/estimator.py similarity index 54% rename from hobj/benchmarks/generalization/estimate.py rename to hobj/benchmarks/generalization/estimator.py index 5e121ae..e470835 100644 --- a/hobj/benchmarks/generalization/estimate.py +++ b/hobj/benchmarks/generalization/estimator.py @@ -2,9 +2,10 @@ import xarray as xr import numpy as np -from hobj.benchmarks.generalization.task import GeneralizationSessionResult +from hobj.benchmarks.generalization.simulator import GeneralizationSessionResult from hobj.statistics.variance_estimates import binomial as binomial_funcs +import warnings class GeneralizationStatistics(xr.Dataset): @@ -15,6 +16,7 @@ def __init__( results: List[GeneralizationSessionResult], perform_lapse_rate_correction: bool, n_bootstrap_iterations: int, + bootstrap_by_worker: bool, # versus by session ): # Get all transformations @@ -25,44 +27,71 @@ def __init__( all_transformations = sorted(all_transformations) transformation_to_i = {transformation: i for i, transformation in enumerate(all_transformations)} + # Get all workers + all_workers = sorted({result.worker_id for result in results}) + print(len(all_workers)) + nworkers = len(all_workers) + worker_to_i = {worker: i for i, worker in enumerate(all_workers)} + # Package data into [session, transformation] kn matrices nsessions = len(results) ntransformations = len(all_transformations) - k = np.zeros(shape=(nsessions, ntransformations)) - n = np.zeros(shape=(nsessions, ntransformations)) - kcatch = np.zeros(shape = nsessions) - ncatch = np.zeros(shape = nsessions) + + if bootstrap_by_worker: + if nworkers == 1: + raise ValueError(f"Only one worker {all_workers}, cannot perform valid bootstrap by worker.") + elif nworkers < 20: + warnings.warn(f"Only {nworkers} unique workers, which is less than 20. Bootstrapping by worker may not be reliable.") + + kmat = np.zeros(shape=(nworkers, ntransformations)) + nmat = np.zeros(shape=(nworkers, ntransformations)) + kcatch = np.zeros(shape=nworkers) + ncatch = np.zeros(shape=nworkers) + else: + # Will be bootstrapping by session + kmat = np.zeros(shape=(nsessions, ntransformations)) + nmat = np.zeros(shape=(nsessions, ntransformations)) + kcatch = np.zeros(shape=nsessions) + ncatch = np.zeros(shape=nsessions) for i_session, result in enumerate(results): - kcatch[i_session] = result.kcatch - ncatch[i_session] = result.ncatch + i = worker_to_i[result.worker_id] if bootstrap_by_worker else i_session + kcatch[i] = result.kcatch + ncatch[i] = result.ncatch for transformation, (k, n) in result.transformation_to_kn.items(): i_transformation = transformation_to_i[transformation] - k[i_session, i_transformation] = k - n[i_session, i_transformation] = n + kmat[i, i_transformation] += k + nmat[i, i_transformation] += n # Calculate statistics + print(kmat.sum(0)) + print(nmat.sum(0)) phat, varhat_phat = self._get_point_estimates( - k = k.sum(0), - n = n.sum(0), - kcatch = kcatch.sum(), - ncatch = ncatch.sum(), - perform_lapse_rate_correction = perform_lapse_rate_correction, + k=kmat.sum(0), + n=nmat.sum(0), + kcatch=kcatch.sum(), + ncatch=ncatch.sum(), + perform_lapse_rate_correction=perform_lapse_rate_correction, ) - # Compute bootstrap replicates by resampling sessions + # Compute bootstrap replicates by resampling sessions or workers gen = np.random.default_rng() boot_phat = np.zeros(shape=(n_bootstrap_iterations, ntransformations)) boot_varhat_phat = np.zeros(shape=(n_bootstrap_iterations, ntransformations)) + for i_boot_iter in range(n_bootstrap_iterations): - # Resample sessions - i_boot_sessions = gen.integers(low = 0, high = nsessions, size = nsessions) - kboot = k[i_boot_sessions].sum(0) - nboot = n[i_boot_sessions].sum(0) - kcatch_boot = kcatch[i_boot_sessions].sum() - ncatch_boot = ncatch[i_boot_sessions].sum() + if bootstrap_by_worker: + # Resample by worker + i_boot = gen.integers(low=0, high=nworkers, size=nworkers) + else: + # Resample by session + i_boot = gen.integers(low=0, high=nsessions, size=nsessions) + kboot = kmat[i_boot].sum(0) + nboot = nmat[i_boot].sum(0) + kcatch_boot = kcatch[i_boot].sum() + ncatch_boot = ncatch[i_boot].sum() # Compute bootstrapped point estimates phat_cur, varhat_phat_cur = self._get_point_estimates( @@ -84,14 +113,17 @@ def __init__( boot_phat=(['boot_iter', 'transformation'], boot_phat), boot_varhat_phat=(['boot_iter', 'transformation'], boot_varhat_phat), ), + coords=dict( + transformation=all_transformations + ) ) @staticmethod def _get_point_estimates( - k: np.ndarray, # [transformation] - n: np.ndarray, # [transformation] - kcatch: np.ndarray, # () - ncatch: np.ndarray, # () + k: np.ndarray, # [transformation] + n: np.ndarray, # [transformation] + kcatch: np.ndarray, # () + ncatch: np.ndarray, # () perform_lapse_rate_correction: bool, ): @@ -107,9 +139,8 @@ def _get_point_estimates( phat = (phat - hat_lapse_rate / 2) / (1 - hat_lapse_rate) # Estimate the variance of the corrected performance by treating the lapse rate as a constant: - varhat_phat = varhat_phat / (1 - hat_lapse_rate)**2 + varhat_phat = varhat_phat / (1 - hat_lapse_rate) ** 2 phat = np.clip(phat, 0, 1) return phat, varhat_phat - diff --git a/hobj/benchmarks/generalization/task.py b/hobj/benchmarks/generalization/simulator.py similarity index 61% rename from hobj/benchmarks/generalization/task.py rename to hobj/benchmarks/generalization/simulator.py index c97b8fa..34e3802 100644 --- a/hobj/benchmarks/generalization/task.py +++ b/hobj/benchmarks/generalization/simulator.py @@ -14,6 +14,7 @@ class GeneralizationSessionResult(pydantic.BaseModel): transformation_to_kn: Dict[str, List[int]] kcatch: int ncatch: int + worker_id: Optional[str] = pydantic.Field(default="NO_WORKER") @pydantic.model_validator(mode='after') def validate_results(self) -> 'GeneralizationSessionResult': @@ -29,6 +30,10 @@ def validate_results(self) -> 'GeneralizationSessionResult': class GeneralizationSubtask(pydantic.BaseModel): + """ + A simulator for a single session of the specific "one-shot" task used in Lee and DiCarlo 2023. + """ + model_config = dict( frozen=True ) @@ -39,11 +44,6 @@ class GeneralizationSubtask(pydantic.BaseModel): test_imagesB: List[ImageRef] image_ref_to_transformation: Dict[ImageRef, str] - support_trials: Set[int] # Trials in which a support image will be given - catch_trials: Set[int] # Trials in which a support image will be given, and recorded for analysis - ntrials: int = pydantic.Field(description='The number of trials in the subtask.', gt=0) - replace_test_trials: bool - @pydantic.field_validator('test_imagesA', 'test_imagesB', mode='after') @classmethod def sort_image_refs(cls, value: List[ImageRef]) -> List[ImageRef]: @@ -52,17 +52,6 @@ def sort_image_refs(cls, value: List[ImageRef]) -> List[ImageRef]: @pydantic.model_validator(mode='after') def validate_model(self) -> 'GeneralizationSubtask': - if len(self.support_trials.intersection(self.catch_trials)) > 0: - raise ValueError(f"Expected support_trials and catch_trials to be disjoint, but got {self.support_trials.intersection(self.catch_trials)}") - - if max(self.support_trials) >= self.ntrials: - raise ValueError(f"Expected support_trials to be less than ntrials, but got {max(self.support_trials)} >= {self.ntrials}") - if min(self.support_trials) < 0: - raise ValueError(f"Expected support_trials to be non-negative, but got {min(self.support_trials)}") - - if not self.replace_test_trials and len(self.test_imagesA) + len(self.test_imagesB) < self.ntrials: - raise ValueError(f"Specified replace=True, but only {len(self.test_imagesA) + len(self.test_imagesB)} images available. (len(classA)={len(self.classA)}; len(classB)={len(self.classB)}") - for ref in self.test_imagesA + self.test_imagesB: if not ref in self.image_ref_to_transformation: raise ValueError(f"Expected all test images to have an associated transformation, but {ref} was missing.") @@ -80,45 +69,56 @@ def simulate_session( :param seed: :return: a [ntrials] boolean np.ndarray vector where True indicates a correct response. """ + + # Initialize random state of environment + gen = np.random.default_rng(seed=seed) + # Allocate results transformation_to_kn = collections.defaultdict(lambda: [0, 0]) kcatch = 0 ncatch = 0 - # Initialize random state of environment - gen = np.random.default_rng(seed=seed) + # Sample the sequence of trials using the procedure described in Lee and DiCarlo 2023 + support_trials = set(range(10)) + catch_trials = {9, 14, 19} + ntrials = 20 + stimulus_category_is_A_seq = [] - # Sample the stimulus image sequence - num_test_classA = len(self.test_imagesA) - num_test_classB = len(self.test_imagesB) + # An initial phase of 10 support trials, with classes sampled in equal number (5 each) + support_sequence = [True] * 5 + [False] * 5 + support_sequence = [support_sequence[i] for i in gen.permutation(len(support_sequence))] + stimulus_category_is_A_seq.extend(support_sequence) - n_all_test_images = num_test_classA + num_test_classB - ntest_trials = self.ntrials - len(self.support_trials) - if self.replace_test_trials: - i_image_seq = gen.integers(n_all_test_images, size=ntest_trials) - else: - i_image_seq = gen.permutation(n_all_test_images)[:ntest_trials] - i_image_seq = list(i_image_seq) + # A testing phase of 8 test trials with 2 catch trials, where classes are sampled i.i.d. + # with 50-50 probability, and test images for each class are sampled without replacement + test_sequence = [bool(v) for v in gen.random(size=10) < 0.5] + stimulus_category_is_A_seq.extend(test_sequence) + assert len(stimulus_category_is_A_seq) == ntrials # Randomly sample the reward contingency classA_correct_action = 0 if gen.random() < 0.5 else 1 classB_correct_action = 1 - classA_correct_action - # Iterate over trials - perf_seq = np.zeros(self.ntrials, dtype=bool) + i_test_seq_A = list(gen.permutation(len(self.test_imagesA))) + i_test_seq_B = list(gen.permutation(len(self.test_imagesB))) - for i_trial in range(self.ntrials): + # Iterate over trials + for i_trial in range(ntrials): # Retrieve trial information - stimulus_category_is_A = gen.random() < 0.5 + stimulus_category_is_A = stimulus_category_is_A_seq[i_trial] correct_action_cur = classA_correct_action if stimulus_category_is_A else classB_correct_action - if i_trial in self.support_trials or i_trial in self.catch_trials: - # Sample a support image + if i_trial in support_trials or i_trial in catch_trials: + # Supply a support image image_ref_cur = self.support_imageA if stimulus_category_is_A else self.support_imageB + else: - # Sample a test image - i_test_image = i_image_seq.pop() - image_ref_cur = self.test_imagesA[i_test_image] if stimulus_category_is_A else self.test_imagesA[i_test_image - num_test_classA] + # Sample a test image without replacement + if stimulus_category_is_A: + image_ref_cur = self.test_imagesA[i_test_seq_A.pop()] + + else: + image_ref_cur = self.test_imagesB[i_test_seq_B.pop()] # Get response from learner a = learner.get_response(image=image_ref_cur) @@ -130,11 +130,12 @@ def simulate_session( # Deliver feedback to learner learner.give_feedback(feedback) - # Record results of trial: - - if i_trial in self.support_trials or i_trial in self.catch_trials: + # Record results of trial if catch or test: + if i_trial in catch_trials: kcatch += correct ncatch += 1 + elif i_trial in support_trials: + pass else: transformation_cur = self.image_ref_to_transformation[image_ref_cur] transformation_to_kn[transformation_cur][0] += correct diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index 176451c..dd3c0ef 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -1,8 +1,8 @@ from typing import List, Dict import numpy as np -from hobj.benchmarks.binary_classification.scoring import LearningCurveBenchmark, LearningCurveBenchmarkConfig, TargetSubtaskData -from hobj.benchmarks.binary_classification.task import BinaryClassificationSubtask +from hobj.benchmarks.binary_classification.benchmark import LearningCurveBenchmark, LearningCurveBenchmarkConfig, TargetSubtaskData +from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask from hobj.data.behavior import load_highvar_behavior from hobj.data.images import MutatorHighVarImageset @@ -83,9 +83,9 @@ def __init__(self): if __name__ == '__main__': benchmark = MutatorHighVarBenchmark() - from hobj.learning_models import DummyBinaryLearner + from hobj.learning_models import RandomGuesser - learner = DummyBinaryLearner() + learner = RandomGuesser() result = benchmark(learner=learner, show_pbar=True) print(result.msen_CI95) diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index 46f1450..caa964a 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -1,44 +1,217 @@ +import collections # Coercing human data +from typing import Dict, Set, Optional, List from hobj.data.behavior import load_oneshot_behavior from hobj.data.images import MutatorOneShotImageset +from hobj.data.schema import ImageRef +import matplotlib.pyplot as plt +import pandas as pd +from hobj.benchmarks.generalization.benchmark import GeneralizationBenchmarkConfig, GeneralizationBenchmark +from hobj.benchmarks.generalization.benchmark import GeneralizationSessionResult +from hobj.benchmarks.generalization.estimator import GeneralizationStatistics +from hobj.benchmarks.generalization.simulator import GeneralizationSubtask + + +# %% + +class MutatorOneshotBenchmark(GeneralizationBenchmark): + + subtask_names = [ + 'MutatorB2000_2292,MutatorB2000_2444', + 'MutatorB2000_138,MutatorB2000_2344', + 'MutatorB2000_1251,MutatorB2000_953', + 'MutatorB2000_3043,MutatorB2000_694', + 'MutatorB2000_3496,MutatorB2000_496', + 'MutatorB2000_1219,MutatorB2000_296', + 'MutatorB2000_1825,MutatorB2000_2757', + 'MutatorB2000_3077,MutatorB2000_4703', + 'MutatorB2000_270,MutatorB2000_3615', + 'MutatorB2000_3066,MutatorB2000_3585', + 'MutatorB2000_2139,MutatorB2000_746', + 'MutatorB2000_116,MutatorB2000_2365', + 'MutatorB2000_2130,MutatorB2000_4628', + 'MutatorB2000_462,MutatorB2000_926', + 'MutatorB2000_2304,MutatorB2000_3733', + 'MutatorB2000_1363,MutatorB2000_3278', + 'MutatorB2000_4049,MutatorB2000_663', + 'MutatorB2000_2722,MutatorB2000_3527', + 'MutatorB2000_2832,MutatorB2000_801', + 'MutatorB2000_1258,MutatorB2000_3123', + 'MutatorB2000_1865,MutatorB2000_613', + 'MutatorB2000_1164,MutatorB2000_2106', + 'MutatorB2000_1229,MutatorB2000_1280', + 'MutatorB2000_1767,MutatorB2000_2122', + 'MutatorB2000_2198,MutatorB2000_701', + 'MutatorB2000_3636,MutatorB2000_4305', + 'MutatorB2000_3035,MutatorB2000_46', + 'MutatorB2000_3601,MutatorB2000_4792', + 'MutatorB2000_2092,MutatorB2000_288', + 'MutatorB2000_1424,MutatorB2000_2314', + 'MutatorB2000_3308,MutatorB2000_3525', + 'MutatorB2000_2909,MutatorB2000_4256' + ] + transformation_ids = [ + 'backgrounds | 0.1', + 'backgrounds | 0.215443', + 'backgrounds | 0.464159', + 'backgrounds | 1.0', + 'blur | 0.007812', + 'blur | 0.015625', + 'blur | 0.03125', + 'blur | 0.0625', + 'contrast | -0.4', + 'contrast | -0.8', + 'contrast | 0.4', + 'contrast | 0.8', + 'delpixels | 0.25', + 'delpixels | 0.5', + 'delpixels | 0.75', + 'delpixels | 0.95', + 'inplanerotation | 135.0', + 'inplanerotation | 180.0', + 'inplanerotation | 45.0', + 'inplanerotation | 90.0', + 'inplanetranslation | 0.125', + 'inplanetranslation | 0.25', + 'inplanetranslation | 0.5', + 'inplanetranslation | 0.75', + 'noise | 0.125', + 'noise | 0.25', + 'noise | 0.375', + 'noise | 0.5', + 'outplanerotation | 135.0', + 'outplanerotation | 180.0', + 'outplanerotation | 45.0', + 'outplanerotation | 90.0', + 'scale | 0.125', + 'scale | 0.25', + 'scale | 0.5', + 'scale | 1.5' + ] + + def __init__(self): + support_trials = {0, 1, 2, 3, 4, 5, 6, 7, 8} + catch_trials = {9, 14, 19} + + # Load images + imageset = MutatorOneShotImageset() + + # Map image refs to transformation ids + image_ref_to_transformation_id = {} + cat_to_support_image: Dict[str, ImageRef] = {} + cat_to_test_images: Dict[str, List[ImageRef]] = {} + + for ref in imageset.image_refs: + annotation = imageset.get_annotation(image_ref=ref) + transformation_id = f"{annotation.transformation} | {annotation.transformation_level}" + image_ref_to_transformation_id[ref] = transformation_id + + if annotation.transformation == 'original': + if annotation.category not in cat_to_support_image: + cat_to_support_image[annotation.category] = ref + else: + raise ValueError(f"Multiple support images for category {annotation.category}") + else: + if annotation.category not in cat_to_test_images: + cat_to_test_images[annotation.category] = [] + + cat_to_test_images[annotation.category].append(ref) + + # Assemble subtask simulators + subtasks = [] + for subtask_name in self.subtask_names: + catA, catB = sorted(subtask_name.split(',')) + + support_imageA = cat_to_support_image[catA] + support_imageB = cat_to_support_image[catB] + test_imagesA = cat_to_test_images[catA] + test_imagesB = cat_to_test_images[catB] + assert len(test_imagesA) == len(test_imagesB) == 60 + + subtask = GeneralizationSubtask( + support_imageA=support_imageA, + support_imageB=support_imageB, + test_imagesA=test_imagesA, + test_imagesB=test_imagesB, + image_ref_to_transformation=image_ref_to_transformation_id + ) + subtasks.append(subtask) + + # Package human data into format expected by benchmark + oneshot_sessions = load_oneshot_behavior() + + results = [] + + for session in oneshot_sessions: + # Parse raw data by worker + transformation_to_kn = collections.defaultdict(lambda: [0, 0]) + kcatch = 0 + ncatch = 0 + observed_categories = set() + + for i_trial, sha in enumerate(session.stimulus_sha256_seq): + ref = ImageRef(sha256=sha) + annotation = imageset.get_annotation(image_ref=ref) + + # Add stimulus category to observed categories + observed_categories.add(annotation.category) + + # Calculate performance + perf = session.reward_seq[i_trial] > 0 + + # Record performance in relevant slot + if i_trial in support_trials: + assert annotation.transformation == 'original' + elif i_trial in catch_trials: + assert annotation.transformation == 'original' + kcatch += perf + ncatch += 1 + else: + assert annotation.transformation != 'original' + transformation_id = image_ref_to_transformation_id[ref] + + # Keep only benchmarked transformations + if transformation_id in self.transformation_ids: + transformation_to_kn[transformation_id][0] += perf + transformation_to_kn[transformation_id][1] += 1 + + # Infer subtask name from observed categories + assert len(observed_categories) == 2 + subtask_name = ','.join(sorted(observed_categories)) + if subtask_name not in self.subtask_names: + raise ValueError(f"Unexpected subtask name: {subtask_name}") + + # Package result + result = GeneralizationSessionResult( + transformation_to_kn=transformation_to_kn, + kcatch=kcatch, + ncatch=ncatch, + worker_id=session.worker_id + ) + results.append(result) + + # %% Assemble benchmark config + config = GeneralizationBenchmarkConfig( + results=results, + subtasks=subtasks, + num_simulations_per_subtask=500, + num_bootstrap_samples=1000, + bootstrap_target_by_worker=True, + ) + + super().__init__(config=config) + + @property + def target_statistics(self) -> GeneralizationStatistics: + + gen_statistics: GeneralizationStatistics = self._generalization_statistics + return gen_statistics.assign_coords( + transformation_type=(['transformation'], [transformation.split(' | ')[0] for transformation in gen_statistics.transformation.values]), + transformation_level=(['transformation'], [float(transformation.split(' | ')[1]) for transformation in gen_statistics.transformation.values]) + ) + + +if __name__ == '__main__': + benchmark = MutatorOneshotBenchmark() -imageset = MutatorOneShotImageset() -oneshot_sessions = load_oneshot_behavior() - -from hobj.benchmarks.generalization.task import GeneralizationSessionResult - -image_ref_to_transformation_id = {} -for ref in imageset.image_refs: - annotation = imageset.get_annotation(image_ref=ref) - transformation_id = f"{annotation.transformation} | {annotation.transformation_level}" - image_ref_to_transformation_id[ref] = transformation_id - -ids = { - 'inplanerotation | 180.0', - 'outplanerotation | 45.0', - 'outplanerotation | 180.0', 'blur | 0.03125', - 'scale | 0.25', - 'noise | 0.5', - 'inplanetranslation | 0.75', - 'inplanetranslation | 0.5', - 'noise | 0.375', - 'backgrounds | 1.0', - 'noise | 0.125', - 'inplanetranslation | 0.25', - 'backgrounds | 0.215443', - 'delpixels | 0.25', - 'delpixels | 0.95', - 'scale | 0.5', - 'combinednat | 1.0', # excluded - 'backgrounds | 0.464159', - 'delpixels | 0.75', - 'original | 0.0', # excluded - 'outplanerotation | 90.0', - 'scale | 1.5', - 'inplanerotation | 135.0', 'blur | 0.015625', 'scale | 0.125', 'contrast | -0.4', 'backgrounds | 0.1', 'inplanerotation | 45.0', 'outplanerotation | 135.0', 'blur | 0.007812', 'inplanerotation | 90.0', 'noise | 0.25', - 'inplanetranslation | 0.125', 'blur | 0.0625', 'contrast | 0.8', 'contrast | 0.4', 'delpixels | 0.5', 'contrast | -0.8'} - - -for session in oneshot_sessions: - # Parse raw data by worker \ No newline at end of file diff --git a/hobj/learning_models/__init__.py b/hobj/learning_models/__init__.py index ea063cd..bce9f03 100644 --- a/hobj/learning_models/__init__.py +++ b/hobj/learning_models/__init__.py @@ -40,7 +40,7 @@ def give_feedback(self, reward: float) -> None: # %% -class DummyBinaryLearner(BinaryLearningModel): +class RandomGuesser(BinaryLearningModel): """ A dummy model which simply returns random outputs, independent of the given image. """ diff --git a/hobj/learning_models/tests/test_dummy_learner.py b/hobj/learning_models/tests/test_dummy_learner.py index 4bd9427..6addaa4 100644 --- a/hobj/learning_models/tests/test_dummy_learner.py +++ b/hobj/learning_models/tests/test_dummy_learner.py @@ -1,10 +1,10 @@ import pytest -from hobj.learning_models import DummyBinaryLearner +from hobj.learning_models import RandomGuesser import hobj.data.schema as schema @pytest.fixture -def dummy_learner() -> DummyBinaryLearner: - return DummyBinaryLearner(seed=0) +def dummy_learner() -> RandomGuesser: + return RandomGuesser(seed=0) @pytest.fixture def test_image() -> schema.ImageRef: From f3a127b2b059d763aff512ebd45f91baf32b523c Mon Sep 17 00:00:00 2001 From: himjl Date: Mon, 27 Jan 2025 11:53:00 -0500 Subject: [PATCH 070/156] bootstrapping by worker mut highvar benchmark --- examples/view_behavioral_data.py | 37 ++-- .../binary_classification/benchmark.py | 113 +++++----- .../binary_classification/estimator.py | 194 +++++++++++++++--- .../binary_classification/simulation.py | 17 +- .../tests/test_simulate_subtask.py | 9 +- hobj/benchmarks/mut_highvar_benchmark.py | 43 +--- hobj/stats_new/learning_curve.py | 1 + 7 files changed, 251 insertions(+), 163 deletions(-) diff --git a/examples/view_behavioral_data.py b/examples/view_behavioral_data.py index 04ecefb..3749fff 100644 --- a/examples/view_behavioral_data.py +++ b/examples/view_behavioral_data.py @@ -1,39 +1,26 @@ import hobj.benchmarks.mut_highvar_benchmark as highvar_benchmark -#import hobj.benchmarks.todo.mut_oneshot_benchmark as oneshot_benchmark import matplotlib.pyplot as plt import numpy as np # %% View data for Experiment 1 experiment1_benchmark = highvar_benchmark.MutatorHighVarBenchmark() -data = experiment1_benchmark.data - -stacked_data = [] -for subtask in data: - subtask_data = np.array([ - data[subtask][worker_id] for worker_id in data[subtask] - ]) - stacked_data.append(subtask_data) -stacked_data = np.concat(stacked_data, axis=0) +data = experiment1_benchmark.target_statistics +# %% plt.figure() -plt.imshow(stacked_data, aspect = 'auto') +plt.imshow(data.phat, aspect='auto') plt.xlabel('trial') plt.ylabel('session') plt.title("Performance data") plt.show() +# %% +plt.figure() +plt.errorbar(x=np.arange(100) + 1, y=data.phat.values[0], yerr=np.sqrt(data.varhat_phat.values[0])) +plt.show() -# %% View processed data for Experiment 2 -raise NotImplementedError -experiment2_benchmark = oneshot_benchmark.MutatorOneshotBenchmark() -ds_experiment2 = experiment2_benchmark.ds_behavioral_statistics - -plt.figure(figsize=(12, 4)) -xx = np.arange(len(ds_experiment2.transformation_id)) -xlabels = ds_experiment2.transformation_id.values -plt.errorbar(x=xx, y=ds_experiment2.perf, yerr=np.sqrt(ds_experiment2.hat_var_perf)) -plt.xlabel('trial') -plt.ylabel('generalization accuracy') -plt.xticks(xx, xlabels, rotation=45, ha='right') -plt.tight_layout() -plt.show() \ No newline at end of file +# %% +yerr = data.boot_phat.mean('subtask').std('boot_iter') +glc = data.phat.mean('subtask') +plt.errorbar(x=np.arange(100) + 1, y=glc, yerr=yerr) +plt.show() diff --git a/hobj/benchmarks/binary_classification/benchmark.py b/hobj/benchmarks/binary_classification/benchmark.py index 98bba80..0f90808 100644 --- a/hobj/benchmarks/binary_classification/benchmark.py +++ b/hobj/benchmarks/binary_classification/benchmark.py @@ -8,16 +8,15 @@ import hobj.data.schema as schema from hobj.benchmarks.binary_classification.estimator import LearningCurveStatistics -from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask +from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask, BinaryClassificationSubtaskResult from hobj.learning_models import BinaryLearningModel from hobj.stats_new.ci import estimate_basic_bootstrap_CI # %% Models for configuring a LearningCurveBenchmark: - class TargetSubtaskData(pydantic.BaseModel): subtask: BinaryClassificationSubtask # The subtask which generated the associated results - results: np.ndarray # [session, trial] boolean matrix of performance + results: List[BinaryClassificationSubtaskResult] # [session, trial] boolean matrix of performance model_config = dict( arbitrary_types_allowed=True @@ -26,15 +25,9 @@ class TargetSubtaskData(pydantic.BaseModel): @pydantic.model_validator(mode='after') def validate_results(self) -> 'TargetSubtaskData': # Check shape - if len(self.results.shape) != 2: - raise ValueError(f"Expected 2D results matrix [session, trial], but got {len(self.results.shape)} dimensions") - - if self.subtask.ntrials != self.results.shape[1]: - raise ValueError(f"Expected {self.subtask.ntrials} trials, but got {self.results.shape[1]} trials") - - # Check dtype - if self.results.dtype != np.bool: - raise ValueError(f"Expected boolean dtype, but got {self.results.dtype}") + for result in self.results: + if self.subtask.ntrials != len(result.perf_seq): + raise ValueError(f"Expected {self.subtask.ntrials} trials, but got {result.perf_seq} trials") return self @@ -43,6 +36,7 @@ class LearningCurveBenchmarkConfig(pydantic.BaseModel): subtask_name_to_data: Dict[str, 'TargetSubtaskData'] = pydantic.Field(default_factory=dict, description="A dictionary of subtask_name -> TargetSubtaskConfig") num_simulations_per_subtask: int = pydantic.Field(ge=2) num_bootstrap_samples: int = pydantic.Field(ge=2) + bootstrap_by_worker: bool ntrials: Optional[int] = pydantic.Field(default=None) @pydantic.model_validator(mode='after') @@ -66,46 +60,6 @@ def ensure_rectangular(self) -> 'LearningCurveBenchmarkConfig': # %% class LearningCurveBenchmark: - @property - def image_refs(self) -> Iterator[schema.ImageRef]: - """ - Returns all image references used in the benchmark. - :return: - """ - for subtask in self.subtask_name_to_subtask.values(): - for ref in subtask.classA + subtask.classB: - yield ref - - @staticmethod - def simulate_model_behavior( - subtask: BinaryClassificationSubtask, - learner: BinaryLearningModel, - nsimulations: int, - ) -> np.ndarray: - """ - Returns a [nsimulations, ntrials] matrix of model performance on the subtask. - :param subtask: - :param learner: - :param nsimulations: - :return: a [nsimulations, ntrials] matrix of model performance on the subtask. - """ - - # Instantiate array to store performance sequences for this subtask: - perf_mat = np.zeros((nsimulations, subtask.ntrials), dtype=np.bool) - - # Perform simulations for this subtask: - for i_simulation in range(nsimulations): - # Reset learner internal state - learner.reset_state(seed=None) - - # Simulate session: - perf_mat[i_simulation, :] = subtask.simulate_session( - learner=learner, - seed=None, - ) - - return perf_mat - def __init__( self, config: LearningCurveBenchmarkConfig, @@ -118,23 +72,16 @@ def __init__( name: config.subtask_name_to_data[name].subtask for name in self.subtask_names } - self.subtask_name_to_perf: Dict[str, np.ndarray] = {} + self.subtask_name_to_results: Dict[str, List[BinaryClassificationSubtaskResult]] = {} for name in self.subtask_names: - self.subtask_name_to_perf[name] = config.subtask_name_to_data[name].results + self.subtask_name_to_results[name] = config.subtask_name_to_data[name].results self._target_statistics = LearningCurveStatistics( - subtask_name_to_perf=self.subtask_name_to_perf, + subtask_name_to_results=self.subtask_name_to_results, nbootstrap_samples=self.config.num_bootstrap_samples, + bootstrap_by_worker=self.config.bootstrap_by_worker, ) - @property - def target_data(self) -> Dict[str, np.ndarray]: - """ - Returns the target data for the benchmark, which is a dictionary of subtask_name -> [session, trial] boolean matrix of performance. - :return: - """ - return self.subtask_name_to_perf - @property def target_statistics(self) -> LearningCurveStatistics: """ @@ -164,21 +111,22 @@ def __call__( """ # Get model learning curve statistics: - subtask_name_to_model_perfs: Dict[str, np.ndarray] = {} + subtask_name_to_model_results: Dict[str, List[BinaryClassificationSubtaskResult]] = {} for i_subtask, subtask_name in enumerate(tqdm(self.subtask_names, desc='Subtask simulations:', disable=not show_pbar)): # Get [simulation, trial] boolean performance matrix for the model - perf_matrix = self.simulate_model_behavior( + subtask_results = self.simulate_model_behavior( subtask=self.subtask_name_to_subtask[subtask_name], learner=learner, nsimulations=self.config.num_simulations_per_subtask, ) - subtask_name_to_model_perfs[subtask_name] = perf_matrix + subtask_name_to_model_results[subtask_name] = subtask_results # Assemble into a single matrix model_statistics = LearningCurveStatistics( - subtask_name_to_perf=subtask_name_to_model_perfs, + subtask_name_to_results=subtask_name_to_model_results, nbootstrap_samples=self.config.num_bootstrap_samples, + bootstrap_by_worker=False, ) # Calculate comparison statistics between target and model learning curves: @@ -218,6 +166,37 @@ def __call__( lapse_rate=lapse_rate, ) + @staticmethod + def simulate_model_behavior( + subtask: BinaryClassificationSubtask, + learner: BinaryLearningModel, + nsimulations: int, + ) -> List[BinaryClassificationSubtaskResult]: + """ + Returns a [nsimulations, ntrials] matrix of model performance on the subtask. + :param subtask: + :param learner: + :param nsimulations: + :return: a [nsimulations, ntrials] matrix of model performance on the subtask. + """ + + results = [] + + # Perform simulations for this subtask: + for i_simulation in range(nsimulations): + # Reset learner internal state + learner.reset_state(seed=None) + + # Simulate session: + results.append( + subtask.simulate_session( + learner=learner, + seed=None, + ) + ) + + return results + @classmethod def _compare_learning_curves( cls, diff --git a/hobj/benchmarks/binary_classification/estimator.py b/hobj/benchmarks/binary_classification/estimator.py index 63b699a..724648b 100644 --- a/hobj/benchmarks/binary_classification/estimator.py +++ b/hobj/benchmarks/binary_classification/estimator.py @@ -1,55 +1,89 @@ -from typing import List, Dict +from typing import List, Dict, Tuple import numpy as np import xarray as xr -from hobj.stats_new.learning_curve import calculate_learning_curve +from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtaskResult +from dataclasses import dataclass +from hobj.statistics.variance_estimates import binomial as binomial_funcs -class LearningCurveStatistics(xr.Dataset): +# %% +class LearningCurveStatistics(xr.Dataset): __slots__ = () def __init__( self, - subtask_name_to_perf: Dict[str, np.ndarray], # subtask: [session, trial] + subtask_name_to_results: Dict[str, List[BinaryClassificationSubtaskResult]], nbootstrap_samples: int, + bootstrap_by_worker: bool, # versus by session ): + # Get canonical order of subtask names + subtask_names = sorted(subtask_name_to_results.keys()) + subtask_name_to_i = {name: i for i, name in enumerate(subtask_names)} + nsubtasks = len(subtask_names) - # Ensure rectangular data + # Ensure all subtasks have the same number of trials ntrials_observed = set() - subtask_name_to_nsessions: Dict[str, int] = {} - for subtask_name, perf_data in subtask_name_to_perf.items(): - ntrials_observed.add(perf_data.shape[1]) - subtask_name_to_nsessions[subtask_name] = perf_data.shape[0] - - if len(ntrials_observed) != 1: - raise ValueError(f"Expected all subtask data to have the same number of trials, but got ntrials={ntrials_observed}") + all_workers = set() + nsessions = 0 + for subtask, results in subtask_name_to_results.items(): + nsessions += len(results) + for r in results: + ntrials_observed.add(len(r.perf_seq)) + all_workers.add(r.worker_id) - # Preallocate arrays - subtask_names = sorted(subtask_name_to_perf.keys()) - nsubtasks = len(subtask_names) + if not len(ntrials_observed) == 1: + raise ValueError(f"Expected all subtasks to have the same number of trials, but got {ntrials_observed}") ntrials = ntrials_observed.pop() + all_workers = sorted(all_workers) + worker_to_i = {worker: i for i, worker in enumerate(all_workers)} + + # Reshape data into a [session, trial] matrix and associated [session] meta vectors + i_subtasks = [] + i_workers = [] + i_session = 0 + perf_mat = np.zeros((nsessions, ntrials), dtype=np.bool) + for subtask, results in subtask_name_to_results.items(): + i_subtask = subtask_name_to_i[subtask] + for result in results: + i_worker = worker_to_i[result.worker_id] + perf_mat[i_session] = result.perf_seq + i_session += 1 + i_subtasks.append(i_subtask) + i_workers.append(i_worker) - phat = np.zeros(shape=(nsubtasks, ntrials)) - varhat_phat = np.zeros(shape=(nsubtasks, ntrials)) - boot_phat = np.zeros(shape=(nbootstrap_samples, nsubtasks, ntrials)) - boot_varhat_phat = np.zeros(shape=(nbootstrap_samples, nsubtasks, ntrials)) + # Collate data into [subtask, trial] matrices + k_mat = np.zeros(shape=(nsubtasks, ntrials), dtype=int) + n_mat = np.zeros(shape=nsubtasks, dtype=int) - # Compute statistics - for i_subtask, (subtask_name, perf_mat) in enumerate(subtask_name_to_perf.items()): + for i_subtask, perf in zip(i_subtasks, perf_mat): + k_mat[i_subtask] += perf + n_mat[i_subtask] += 1 - # todo: move this function here - subtask_learning_curve_statistics = calculate_learning_curve( - perf_matrix=perf_mat, - bootstrap_seed=i_subtask, + # Get point estimate of the [subtask, trial] of learning curve first and second moments + phat, varhat_phat = self.get_point_estimates(k=k_mat, n=n_mat[:, None]) + + # Perform bootstrap resampling of data + if bootstrap_by_worker: + bootstrap_resamples = self._get_bootstrap_resamples_by_worker( + perf_mat=perf_mat, + i_subtasks = i_subtasks, + i_workers = i_workers, + nbootstrap_samples=nbootstrap_samples, + ) + else: + bootstrap_resamples = self._get_bootstrap_resamples_by_session( + perf_mat=perf_mat, + i_subtasks = i_subtasks, nbootstrap_samples=nbootstrap_samples, - repetition_axis=0, ) - phat[i_subtask] = subtask_learning_curve_statistics.phat - varhat_phat[i_subtask] = subtask_learning_curve_statistics.varhat_phat - boot_phat[:, i_subtask] = subtask_learning_curve_statistics.boot_phat - boot_varhat_phat[:, i_subtask] = subtask_learning_curve_statistics.boot_varhat_phat + # Calculate bootstrapped point estimates + boot_phat, boot_varhat_phat = self.get_point_estimates( + k=bootstrap_resamples.boot_k, # [boot_iter, subtask, trial] + n=bootstrap_resamples.boot_n[..., None], # [boot_iter, subtask] + ) super().__init__( data_vars=dict( @@ -60,6 +94,104 @@ def __init__( ), coords=dict( subtask=subtask_names, - nsessions=('subtask', [subtask_name_to_nsessions[name] for name in subtask_names]), ) ) + + @staticmethod + def get_point_estimates( + k: np.ndarray, + n: np.ndarray, + ) -> Tuple[np.ndarray, np.ndarray]: + phat = k / n + varhat_phat = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=k, nvec=n) + return phat, varhat_phat + + @dataclass + class BootstrapSamples: + boot_k: np.ndarray # [boot_iter, subtask, trial] + boot_n: np.ndarray # [boot_iter, subtask] + + @staticmethod + def _get_bootstrap_resamples_by_worker( + perf_mat: np.ndarray, # [session, trial] + i_subtasks: List[int], # [session] + i_workers: List[int], # [session] + nbootstrap_samples: int, + ) -> BootstrapSamples: + + if not perf_mat.dtype == np.bool: + raise ValueError(f"Expected perf_mat to have dtype bool, but got {perf_mat.dtype}") + + ntrials = perf_mat.shape[1] + nworkers = max(i_workers) + 1 + nsubtasks = max(i_subtasks) + 1 + + if not len(set(i_subtasks)) == nsubtasks: + raise ValueError(f"Expected i_subtasks to be a contiguous range, but got {set(i_subtasks)}") + + if not len(set(i_workers)) == nworkers: + raise ValueError(f"Expected i_workers to be a contiguous range, but got {set(i_workers)}") + + # Reshape data + perf_table = np.zeros(shape=(nworkers, nsubtasks, ntrials), dtype=np.bool) + was_recorded_table = np.zeros(shape=(nworkers, nsubtasks), dtype=np.bool) + + for i_subtask, i_worker, perf in zip(i_subtasks, i_workers, perf_mat): + perf_table[i_worker, i_subtask] = perf + was_recorded_table[i_worker, i_subtask] = True + + # Perform bootstrapping + gen = np.random.default_rng() + boot_k = np.zeros(shape=(nbootstrap_samples, nsubtasks, ntrials), dtype=int) + boot_n = np.zeros(shape=(nbootstrap_samples, nsubtasks), dtype=int) + for i_bootstrap in range(nbootstrap_samples): + # Bootstrap resample workers + i_workers_boot = gen.integers(low=0, high=nworkers, size=nworkers) + boot_k[i_bootstrap] = perf_table[i_workers_boot].sum(axis=0) + boot_n[i_bootstrap] = was_recorded_table[i_workers_boot].sum(axis=0) + + return LearningCurveStatistics.BootstrapSamples( + boot_k=boot_k, + boot_n=boot_n, + ) + + @staticmethod + def _get_bootstrap_resamples_by_session( + perf_mat: np.ndarray, # [session, trial] + i_subtasks: List[int], # [session] + nbootstrap_samples: int, + ) -> BootstrapSamples: + # Bootstrap by session, within each subtask + + ntrials = perf_mat.shape[1] + nsubtasks = max(i_subtasks) + 1 + + if not len(set(i_subtasks)) == nsubtasks: + raise ValueError(f"Expected i_subtasks to be a contiguous range, but got {set(i_subtasks)}") + + # Reshape data to {i_subtask: [session, trial]} + i_subtask_to_perf_data = {} + for i_subtask, perf in zip(i_subtasks, perf_mat): + if i_subtask not in i_subtask_to_perf_data: + i_subtask_to_perf_data[i_subtask] = [] + i_subtask_to_perf_data[i_subtask].append(perf) + + # Cast to np arrays + for i_subtask in i_subtask_to_perf_data: + i_subtask_to_perf_data[i_subtask] = np.array(i_subtask_to_perf_data[i_subtask], dtype = np.bool) + + # Perform bootstrapping + gen = np.random.default_rng() + boot_k = np.zeros(shape=(nbootstrap_samples, nsubtasks, ntrials), dtype=int) + boot_n = np.zeros(shape=(nbootstrap_samples, nsubtasks), dtype=int) + for i_bootstrap in range(nbootstrap_samples): + for i_subtask in range(nsubtasks): + nsessions_for_subtask = len(i_subtask_to_perf_data[i_subtask]) + i_sessions = gen.integers(low=0, high=nsessions_for_subtask, size=nsessions_for_subtask) + boot_k[i_bootstrap, i_subtask] = i_subtask_to_perf_data[i_subtask][i_sessions].sum(axis=0) + boot_n[i_bootstrap, i_subtask] = len(i_sessions) + + return LearningCurveStatistics.BootstrapSamples( + boot_k=boot_k, + boot_n=boot_n, + ) \ No newline at end of file diff --git a/hobj/benchmarks/binary_classification/simulation.py b/hobj/benchmarks/binary_classification/simulation.py index be66cdc..f3fc254 100644 --- a/hobj/benchmarks/binary_classification/simulation.py +++ b/hobj/benchmarks/binary_classification/simulation.py @@ -1,4 +1,4 @@ -from typing import List, Union +from typing import List, Union, Optional import numpy as np import pydantic @@ -9,6 +9,15 @@ # %% +class BinaryClassificationSubtaskResult(pydantic.BaseModel): + model_config = dict( + arbitrary_types_allowed=True + ) + + perf_seq: np.ndarray + worker_id: Optional[str] = pydantic.Field(default="NO_WORKER") + + class BinaryClassificationSubtask(pydantic.BaseModel): """ A representation of a simple binary classification task which samples uniformly (with or without replacement) from a pool of images. @@ -39,7 +48,7 @@ def simulate_session( self, learner: BinaryLearningModel, seed: Union[int, None], - ) -> np.ndarray: + ) -> BinaryClassificationSubtaskResult: """ Convenience method to simulate a session of the binary classification task on a given BinaryLearningModel. :param learner: @@ -86,4 +95,6 @@ def simulate_session( i_trial += 1 - return perf_seq + return BinaryClassificationSubtaskResult( + perf_seq=perf_seq, + ) diff --git a/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py b/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py index 13966a5..afdd97e 100644 --- a/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py +++ b/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py @@ -1,4 +1,4 @@ -from hobj.benchmarks.binary_classification.task import BinaryClassificationSubtask +from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask from hobj.learning_models import RandomGuesser from hobj.data.schema import ImageRef from typing import List @@ -20,20 +20,23 @@ def create_image_refs(nimages_per_class: int, seed: int) -> List[ImageRef]: def test_simulate_subtask(): nimages_per_class = 10 + ntrials = nimages_per_class * 2 subtask = BinaryClassificationSubtask( classA=create_image_refs(nimages_per_class=10, seed=0), classB=create_image_refs(nimages_per_class=10, seed=1), - ntrials=nimages_per_class * 2, + ntrials=ntrials, replace=False, ) learner = RandomGuesser(seed=0) - perf_seq = subtask.simulate_session( + result = subtask.simulate_session( learner=learner, seed=0 ) + assert len(result.perf_seq) == ntrials + # Todo: test deterministic \ No newline at end of file diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index dd3c0ef..dc0f7c2 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -2,7 +2,7 @@ import numpy as np from hobj.benchmarks.binary_classification.benchmark import LearningCurveBenchmark, LearningCurveBenchmarkConfig, TargetSubtaskData -from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask +from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask, BinaryClassificationSubtaskResult from hobj.data.behavior import load_highvar_behavior from hobj.data.images import MutatorHighVarImageset @@ -54,7 +54,12 @@ def __init__(self): subtask_name_to_subtask[subtask_name] = subtask subtask_name_to_results[subtask_name] = [] - subtask_name_to_results[subtask_name].append(perf_seq) + subtask_name_to_results[subtask_name].append( + BinaryClassificationSubtaskResult( + perf_seq = np.array(perf_seq), + worker_id = session.worker_id + ) + ) # Cast as numpy for name in subtask_name_to_results: @@ -72,40 +77,10 @@ def __init__(self): subtask_name_to_data=subtask_name_to_data, num_simulations_per_subtask=500, num_bootstrap_samples=1000, + bootstrap_by_worker=False, + ntrials = 100 ) super().__init__( config=config ) - - -# %% todo: move to example -if __name__ == '__main__': - benchmark = MutatorHighVarBenchmark() - - from hobj.learning_models import RandomGuesser - - learner = RandomGuesser() - - result = benchmark(learner=learner, show_pbar=True) - print(result.msen_CI95) - - # %% - import matplotlib.pyplot as plt - target_statistics = benchmark.target_statistics - - def get_glc(statistics): - glc = statistics.phat.mean('subtask') - glc_sigma = statistics.boot_phat.mean('subtask').std('boot_iter') - x = glc.trial.values + 1 - return x, glc, glc_sigma - - - x, glc, glc_sigma = get_glc(target_statistics) - plt.errorbar(x, glc, yerr = glc_sigma, marker = '.', label = 'human') - - xmod, glcmod, glc_sigmamod = get_glc(result.model_statistics) - plt.errorbar(xmod, glcmod, yerr = glc_sigmamod, marker = '.', label = 'model') - plt.legend() - plt.xscale('log') - plt.show() \ No newline at end of file diff --git a/hobj/stats_new/learning_curve.py b/hobj/stats_new/learning_curve.py index a8b68f0..e921168 100644 --- a/hobj/stats_new/learning_curve.py +++ b/hobj/stats_new/learning_curve.py @@ -3,6 +3,7 @@ from hobj.statistics.variance_estimates import binomial as binomial_funcs from dataclasses import dataclass + @dataclass class CalculateLearningCurveResult: phat: np.ndarray # [trial] From 337fd3047b71fb371b4d60e4960add9d83351df5 Mon Sep 17 00:00:00 2001 From: himjl Date: Mon, 27 Jan 2025 12:11:50 -0500 Subject: [PATCH 071/156] remove unused --- .../binary_classification/estimator.py | 2 +- hobj/benchmarks/generalization/estimator.py | 4 +- .../benchmarks/generalization/ref/__init__.py | 0 .../ref/mut_oneshot_benchmark.py | 617 ------------------ .../ref/mut_oneshot_experiment.py | 280 -------- hobj/statistics/__init__.py | 30 - .../statistics/hypothesis_testing/__init__.py | 0 .../approximated_null_distribution.py | 18 - .../bootstrapped_confidence_intervals.py | 33 - hobj/statistics/resamplers/__init__.py | 0 hobj/statistics/resamplers/resamplers.py | 194 ------ hobj/statistics/score_statistics/MSEn.py | 30 - hobj/statistics/score_statistics/__init__.py | 0 .../statistics/variance_estimates/__init__.py | 0 hobj/{stats_new => stats}/__init__.py | 0 .../variance_estimates => stats}/binomial.py | 5 +- hobj/{stats_new => stats}/ci.py | 0 hobj/stats_new/learning_curve.py | 61 -- hobj/stats_new/null_distribution.py | 0 19 files changed, 4 insertions(+), 1270 deletions(-) delete mode 100644 hobj/benchmarks/generalization/ref/__init__.py delete mode 100644 hobj/benchmarks/generalization/ref/mut_oneshot_benchmark.py delete mode 100644 hobj/benchmarks/generalization/ref/mut_oneshot_experiment.py delete mode 100644 hobj/statistics/__init__.py delete mode 100644 hobj/statistics/hypothesis_testing/__init__.py delete mode 100644 hobj/statistics/hypothesis_testing/approximated_null_distribution.py delete mode 100644 hobj/statistics/hypothesis_testing/bootstrapped_confidence_intervals.py delete mode 100644 hobj/statistics/resamplers/__init__.py delete mode 100644 hobj/statistics/resamplers/resamplers.py delete mode 100644 hobj/statistics/score_statistics/MSEn.py delete mode 100644 hobj/statistics/score_statistics/__init__.py delete mode 100644 hobj/statistics/variance_estimates/__init__.py rename hobj/{stats_new => stats}/__init__.py (100%) rename hobj/{statistics/variance_estimates => stats}/binomial.py (55%) rename hobj/{stats_new => stats}/ci.py (100%) delete mode 100644 hobj/stats_new/learning_curve.py delete mode 100644 hobj/stats_new/null_distribution.py diff --git a/hobj/benchmarks/binary_classification/estimator.py b/hobj/benchmarks/binary_classification/estimator.py index 724648b..ebc9468 100644 --- a/hobj/benchmarks/binary_classification/estimator.py +++ b/hobj/benchmarks/binary_classification/estimator.py @@ -5,7 +5,7 @@ from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtaskResult from dataclasses import dataclass -from hobj.statistics.variance_estimates import binomial as binomial_funcs +from hobj.stats_new import binomial as binomial_funcs # %% diff --git a/hobj/benchmarks/generalization/estimator.py b/hobj/benchmarks/generalization/estimator.py index e470835..cb33030 100644 --- a/hobj/benchmarks/generalization/estimator.py +++ b/hobj/benchmarks/generalization/estimator.py @@ -1,10 +1,10 @@ -from typing import Dict, Set, List +from typing import List import xarray as xr import numpy as np from hobj.benchmarks.generalization.simulator import GeneralizationSessionResult -from hobj.statistics.variance_estimates import binomial as binomial_funcs +from hobj.stats_new import binomial as binomial_funcs import warnings diff --git a/hobj/benchmarks/generalization/ref/__init__.py b/hobj/benchmarks/generalization/ref/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/hobj/benchmarks/generalization/ref/mut_oneshot_benchmark.py b/hobj/benchmarks/generalization/ref/mut_oneshot_benchmark.py deleted file mode 100644 index ae3f00a..0000000 --- a/hobj/benchmarks/generalization/ref/mut_oneshot_benchmark.py +++ /dev/null @@ -1,617 +0,0 @@ -import collections - -import xarray as xr -import numpy as np -from tqdm import trange - -import hobj.learning_models -import hobj.data.images.depr_imagesets as imagesets -import hobj.benchmarks.generalization.ref.mut_oneshot_experiment as mutator - -import hobj.data.depr_behavior.human_data as human_data -from hobj.statistics.resamplers.resamplers import Resampler -import hobj.statistics.score_statistics.MSEn as R2n_funcs -import hobj.statistics.variance_estimates.binomial as binomial_funcs -import hobj.statistics.hypothesis_testing.bootstrapped_confidence_intervals as bootstrapped_confidence_intervals -import hobj.statistics.hypothesis_testing.approximated_null_distribution as approximated_null_distribution - - -class MutatorOneshotBenchmark: - environment_name_dim = 'session' - probe_trials = {9, 14, 19} - - def __init__(self): - super().__init__() - self.nboot = 1000 - self.dataset = human_data.MutatorOneShotGeneralizationDataset() - self.ds_raw = self.dataset.load_ds_data() - - # Remove sessions with less than 100% catch performance - self.ds_meta = imagesets.MutatorOneshotDeprImageset().ds_meta - self.url_to_trans_id = {url: trans for (url, trans) in zip(self.ds_meta.image_url.values, self.ds_meta.transformation_id.values)} - self.url_to_obj = {url: obj for (url, obj) in zip(self.ds_meta.image_url.values, self.ds_meta.obj.values)} - self.experiment = mutator.MutatorOneShotExperiment() - - self.transformation_ids = ['backgrounds | 0.1', - 'backgrounds | 0.215443', - 'backgrounds | 0.464159', - 'backgrounds | 1.0', - 'blur | 0.007812', - 'blur | 0.015625', - 'blur | 0.03125', - 'blur | 0.0625', - 'contrast | -0.4', - 'contrast | -0.8', - 'contrast | 0.4', - 'contrast | 0.8', - 'delpixels | 0.25', - 'delpixels | 0.5', - 'delpixels | 0.75', - 'delpixels | 0.95', - 'inplanerotation | 135.0', - 'inplanerotation | 180.0', - 'inplanerotation | 45.0', - 'inplanerotation | 90.0', - 'inplanetranslation | 0.125', - 'inplanetranslation | 0.25', - 'inplanetranslation | 0.5', - 'inplanetranslation | 0.75', - 'noise | 0.125', - 'noise | 0.25', - 'noise | 0.375', - 'noise | 0.5', - 'outplanerotation | 135.0', - 'outplanerotation | 180.0', - 'outplanerotation | 45.0', - 'outplanerotation | 90.0', - 'scale | 0.125', - 'scale | 0.25', - 'scale | 0.5', - 'scale | 1.5'] - self.subtasks = [ - 'MutatorB2000_2292,MutatorB2000_2444', - 'MutatorB2000_138,MutatorB2000_2344', - 'MutatorB2000_1251,MutatorB2000_953', - 'MutatorB2000_3043,MutatorB2000_694', - 'MutatorB2000_3496,MutatorB2000_496', - 'MutatorB2000_1219,MutatorB2000_296', - 'MutatorB2000_1825,MutatorB2000_2757', - 'MutatorB2000_3077,MutatorB2000_4703', - 'MutatorB2000_270,MutatorB2000_3615', - 'MutatorB2000_3066,MutatorB2000_3585', - 'MutatorB2000_2139,MutatorB2000_746', - 'MutatorB2000_116,MutatorB2000_2365', - 'MutatorB2000_2130,MutatorB2000_4628', - 'MutatorB2000_462,MutatorB2000_926', - 'MutatorB2000_2304,MutatorB2000_3733', - 'MutatorB2000_1363,MutatorB2000_3278', - 'MutatorB2000_4049,MutatorB2000_663', - 'MutatorB2000_2722,MutatorB2000_3527', - 'MutatorB2000_2832,MutatorB2000_801', - 'MutatorB2000_1258,MutatorB2000_3123', - 'MutatorB2000_1865,MutatorB2000_613', - 'MutatorB2000_1164,MutatorB2000_2106', - 'MutatorB2000_1229,MutatorB2000_1280', - 'MutatorB2000_1767,MutatorB2000_2122', - 'MutatorB2000_2198,MutatorB2000_701', - 'MutatorB2000_3636,MutatorB2000_4305', - 'MutatorB2000_3035,MutatorB2000_46', - 'MutatorB2000_3601,MutatorB2000_4792', - 'MutatorB2000_2092,MutatorB2000_288', - 'MutatorB2000_1424,MutatorB2000_2314', - 'MutatorB2000_3308,MutatorB2000_3525', - 'MutatorB2000_2909,MutatorB2000_4256' - ] - - def evaluate_model(self, learner: hobj.learning_models.BinaryLearningModel, force_recompute: bool = False): - """ - :param learner: LearningModel - :param force_recompute: bool. If True, recompute the results even if they are already cached. - :return: - """ - ds_model_behavior = self.experiment.run(learner=learner, seed=0, force_recompute=force_recompute) - ds_model_behavior = ds_model_behavior.sel(transformation_id=self.ds_table.transformation_id) - ds_model_behavior = ds_model_behavior.sum('subtask') - - # Compute scores - ds_score = self.score_predictions( - k=ds_model_behavior.k, - n=ds_model_behavior.n, - ) - - return ds_score - - def get_lapse_rate_corrected_performance( - self, - k, - n, - kcatch, - ncatch, - ): - """ - - Returns an estimate of the performance that would be expected if the system had a lapse rate of 0. - The values kcatch and ncatch are assumed to be drawn from a Bernoulli distribution with parameter p = (1 - lapse rate). - This function also returns an estimate of the variance associated with that estimate, using bootstrapping. - - We assume - kcatch ~ Binomial(ncatch, (1 - lapse_rate) * 1 + lapse_rate * 0.5) - k ~ Binomial(n, (1 - lapse_rate) * p + (lapse_rate) * 0.5) - - Denote - pcatch = (1 - lapse_rate) * 1 + lapse_rate * 0.5) - - Then, - lapse_rate = 2 - 2 * pcatch - praw = (1 - lapse_rate) * p + (lapse_rate) * 0.5 (the empirically observed probability of a correct response) - - We have the relation: - p = (praw - lapse_rate / 2) / (1 - lapse_rate) - - Here, we estimate p using the plug-in estimator for p. - - :param k: - :param n: - :param kcatch: - :param ncatch: - :return: - """ - - assert np.all(kcatch <= ncatch) - assert np.all(kcatch >= 0) - - hat_praw = k / n - hat_pcatch = kcatch / ncatch - hat_lapse_rate = 2 - 2 * hat_pcatch - hat_p_adjusted = (hat_praw - hat_lapse_rate / 2) / (1 - hat_lapse_rate) - - return hat_p_adjusted - - def score_predictions( - self, - k: xr.DataArray, - n: xr.DataArray, - ): - - """ - k: (transformation_id) - n: (transformation_id) - kcatch: int - ncatch: int - returns ds_scores - """ - - # Compute R^2 score - assert isinstance(k, xr.DataArray) - assert isinstance(n, xr.DataArray) - assert set(k.dims) == {'transformation_id'}, (k.dims) - assert set(k.dims) == set(n.dims), (k.dims, n.dims) - - hat_prob = k / n - hat_var_hat_prob = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=k, nvec=n) - - # %% Get MSEn point estimate - MSEn_point = R2n_funcs.estimate_MSEn( - hat_theta_model=hat_prob, - hat_var_hat_theta_model=hat_var_hat_prob, - hat_theta_target=self.ds_behavioral_statistics['perf'], - condition_dims=('transformation_id',), - ) - - # %% Assemble scores - ds_scores = xr.Dataset( - data_vars=dict( - MSEn=MSEn_point, - perf=hat_prob, - perf_by_type=hat_prob.groupby('transformation').mean(), - ), - ) - - return ds_scores - - @property - def ds_behavioral_statistics(self): - if not hasattr(self, '_ds_behavioral_statistics'): - p = self.ds_table.k.sum('session') / self.ds_table.n.sum('session') - pvec_boot = self.get_lapse_rate_corrected_performance( - k=self.ds_boot.k, - n=self.ds_boot.n, - kcatch=self.ds_boot.k_catch.sum(['catch_trial']), - ncatch=self.ds_boot.n_catch.sum(['catch_trial']), - ) - - var_pvec = pvec_boot.var('boot_iter', ddof=1) - - ds_behavioral_statistics = xr.Dataset( - { - 'perf_raw': p, - 'perf': self.get_lapse_rate_corrected_performance( - k=self.ds_table.k.sum('session'), - n=self.ds_table.n.sum('session'), - kcatch=int(self.ds_table.k_catch.sum(['session', 'catch_trial'])), - ncatch=int(self.ds_table.n_catch.sum(['session', 'catch_trial'])), - ), - 'hat_var_perf': var_pvec, - } - ) - - self._ds_behavioral_statistics = ds_behavioral_statistics - return self._ds_behavioral_statistics - - def _process_raw_experiment(self, da_perf: xr.DataArray): - """ - - :param da_perf: (session, trial) - :return: ds_oneshot - perf: (transformation_id, stimulus_category) - catch_perf: () - """ - - """ - ktraining: (session, trial) - ntraining: (session, trial) - k: (session, condition) - n: (session, condition) - kprobe: (session, condition) - nprobe: (session, condition) - :return: - """ - - train_trials = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] - catch_trials = [14, 19] # trials used to estimate the lapse rate (each of these trials presented one of the two support images) - test_trials = [10, 11, 12, 13, 15, 16, 17, 18, ] # trials containing a test trial (defined as a randomly sampled, transformed version of one of the two support images) - assert np.all(np.mod(da_perf, 1) == 0) - da_perf = da_perf.astype(int) - k_catch = da_perf.isel(trial=catch_trials) - n_catch = k_catch - k_catch + 1 - - k_training = da_perf.isel(trial=train_trials) - n_training = k_training - k_training + 1 - - da_train = da_perf.isel(trial=train_trials).transpose('session', 'trial') - da_test = da_perf.isel(trial=test_trials).transpose('session', 'trial') - - nsessions = len(da_perf.session) - ntransformations = len(self.transformation_ids) - k = np.zeros((nsessions, ntransformations)) - n = np.zeros((nsessions, ntransformations)) - - trans_id_to_i = collections.defaultdict(lambda: np.nan) - for i, t in enumerate(self.transformation_ids): - trans_id_to_i[t] = i - - worker_ids = [] - for i_session in range(da_test.session.values.size): - worker_ids.append(str(da_train.isel(session=i_session).worker_id.values)) - support_ids = sorted(set(da_train.stimulus_url.values[i_session])) - support_objects = sorted([self.url_to_obj[u] for u in support_ids]) - assert len(support_objects) == 2, support_objects - - url_seq = da_test.stimulus_url.values[i_session] - perf_seq = da_test.values[i_session] - trans_id_seq = [self.url_to_trans_id[u] for u in url_seq] - - for i_trial in range(len(trans_id_seq)): - trans = trans_id_seq[i_trial] - i_trans = trans_id_to_i[trans] - if np.isnan(i_trans): - continue - - perf = int(perf_seq[i_trial]) - - k[i_session, i_trans] += perf - n[i_session, i_trans] += 1 - - ds = xr.Dataset( - { - 'k': (['session', 'transformation_id'], k), - 'n': (['session', 'transformation_id'], n), - 'k_catch': (['session', 'catch_trial'], k_catch.transpose('session', 'trial').values), - 'n_catch': (['session', 'catch_trial'], n_catch.transpose('session', 'trial').values), - 'k_train': (['session', 'train_trial'], k_training.transpose('session', 'trial').values), - 'n_train': (['session', 'train_trial'], n_training.transpose('session', 'trial').values), - - }, - coords={ - 'transformation_id': self.transformation_ids, - 'transformation': (['transformation_id'], [t.split(" | ")[0] for t in self.transformation_ids]), - 'transformation_level': (['transformation_id'], [float(t.split(" | ")[1]) for t in self.transformation_ids]), - 'session': da_perf.session.values, - 'catch_trial': catch_trials, - 'train_trial': train_trials, - 'worker_id': (['session'], worker_ids) - } - ) - - return ds - - @property - def ds_table(self): - - """ - ktraining: (session, trial) - ntraining: (session, trial) - k: (session, condition) - n: (session, condition) - kprobe: (session, condition) - nprobe: (session, condition) - :return: - """ - if not hasattr(self, '_ds_table'): - self._ds_table = self._process_raw_experiment(da_perf=self.ds_raw.perf) - return self._ds_table - - @property - def ds_ceilings(self): - """ - - The point estimate for the R2n noise ceiling, and associated CI. - - The approximate null (where the model is equal to humans) distribution of R2n scores, given the number of model simulations. - - The point estimates for each transformation, associated CIs, and standard errors. - - The point estimates for each transformation type, and its associate CI, and standard errors. - - :return: - """ - if not hasattr(self, '_ds_ceilings'): - alpha = 0.05 - ds_ceilings = xr.Dataset() - - # %% Get point estimate of MSE floor - pvec_boot = self.get_lapse_rate_corrected_performance( - k=self.ds_boot.k, - n=self.ds_boot.n, - kcatch=self.ds_boot.k_catch.sum(['catch_trial']), - ncatch=self.ds_boot.n_catch.sum(['catch_trial']), - ) - - var_pvec = pvec_boot.var('boot_iter', ddof=1) - - ds_ceilings['MSEn'] = var_pvec.mean('transformation_id') - - # %% Approximate the null distribution of MSEn, using the null hypothesis that the model is equal to the (lapse-corrected) human performance - - var_pvec_null = self.get_lapse_rate_corrected_performance( - k=self.ds_null.k, - n=self.ds_null.n, - kcatch=self.ds_null.k_catch.sum(['catch_trial']), - ncatch=self.ds_null.n_catch.sum(['catch_trial']), - ).var('boot_iter', ddof=1) - - def get_null_MSE_functional(ds_data: xr.Dataset): - pvec = self.get_lapse_rate_corrected_performance( - k=ds_data.k, - n=ds_data.n, - kcatch=(ds_data.k_catch.sum('catch_trial')), - ncatch=(ds_data.n_catch.sum('catch_trial')), - ) - - pvec_null = self.get_lapse_rate_corrected_performance( - k=ds_data.knull, - n=ds_data.nnull, - kcatch=(ds_data.k_catch_null.sum('catch_trial')), - ncatch=(ds_data.n_catch_null.sum('catch_trial')), - ) - - MSEn = R2n_funcs.estimate_MSEn( - hat_theta_model=pvec_null, - hat_var_hat_theta_model=var_pvec_null, - hat_theta_target=pvec, - condition_dims=('transformation_id',), - ) - - return MSEn - - ds_boot = self.ds_boot - ds_null = self.ds_null - ds_null = ds_null.rename( - { - 'k': 'knull', - 'n': 'nnull', - 'k_catch': 'k_catch_null', - 'n_catch': 'n_catch_null', - 'k_train': 'k_train_null', - 'n_train': 'n_train_null', - } - ) - ds_data = xr.merge([ds_boot, ds_null]) - - ds_ceilings['MSEn_null'] = approximated_null_distribution.estimate_null_distribution( - statistical_functional=get_null_MSE_functional, - ds_null_samples=ds_data, - hat_mu=ds_ceilings['MSEn'], - resample_dim='boot_iter' - ) - - # %% Get point estimate of per-transformation performances, confidence intervals, and standard errors (lapse-rate adjusted) - perf_lapsed_functional = lambda ds_data: self.get_lapse_rate_corrected_performance( - k=ds_data.k, - n=ds_data.n, - kcatch=ds_data.k_catch.sum('catch_trial'), - ncatch=ds_data.n_catch.sum('catch_trial'), - ) - - ds_data = self.ds_table.sum('session') - perf_lapse = perf_lapsed_functional(ds_data) - - perf_lapsed_boot = perf_lapsed_functional(self.ds_boot) - ds_ceilings['perf'] = perf_lapse - ds_ceilings['perf_std'] = perf_lapsed_boot.std('boot_iter', ddof=1) # Get via bootstrapping - ds_ceilings['perf_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval( - alpha=alpha, - ds_data=ds_data, - ds_boot=self.ds_boot, - statistic_functional=perf_lapsed_functional, - parameter_functional=None, - ) - - # %% Get point estimate of per-transformation performances, confidence intervals, and standard errors - point = self.ds_table.sum('session') - ds_ceilings['perf_raw'] = point.k / point.n - ds_ceilings['perf_raw_std'] = (self.ds_boot.k / self.ds_boot.n).std('boot_iter', ddof=1) - ds_ceilings['perf_raw_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval( - alpha=alpha, - ds_data=self.ds_table.sum('session')[['k', 'n']], - ds_boot=self.ds_boot, - statistic_functional=lambda ds_data: ds_data.k / ds_data.n, - parameter_functional=None, - ) - - # %% Get point estimate of per-transformation-type performances, confidence intervals, and standard errors - - def perf_by_type_functional(ds_data): - pvec = self.get_lapse_rate_corrected_performance( - k=ds_data.k, - n=ds_data.n, - kcatch=ds_data.k_catch.sum('catch_trial'), - ncatch=ds_data.n_catch.sum('catch_trial'), - ) - perf_by_type = pvec.groupby("transformation").mean() - perf_by_type = perf_by_type.rename({'transformation': 'transformation_type'}) - return perf_by_type - - perf_by_type = perf_by_type_functional(self.ds_table.sum('session')) - perf_by_type_boot = perf_by_type_functional(self.ds_boot) - - ds_ceilings['perf_by_type'] = perf_by_type - ds_ceilings['perf_by_type_std'] = perf_by_type_boot.std('boot_iter', ddof=1) - ds_ceilings['perf_by_type_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval( - alpha=alpha, - ds_data=self.ds_table.sum('session'), - ds_boot=self.ds_boot, - statistic_functional=perf_by_type_functional, - parameter_functional=None, - ) - - self._ds_ceilings = ds_ceilings - - return self._ds_ceilings - - @property - def worker_resampler(self): - if not hasattr(self, '_worker_resampler'): - self._worker_resampler = ExperimentResampler( - ds_session=self.ds_table, - ) - return self._worker_resampler - - @property - def ds_boot(self): - if not hasattr(self, '_ds_boot'): - dlist = [] - RS = np.random.RandomState(seed=0) - for i_boot in trange(self.nboot, desc='resampling experiment'): - ds_resampled = self.worker_resampler.get_ds_resampled(RS=RS).sum('session') - dlist.append(ds_resampled) - - self._ds_boot = xr.concat(dlist, dim='boot_iter').assign_coords( - transformation=(['transformation_id'], self.ds_table.transformation.values), - transformation_level=(['transformation_id'], self.ds_table.transformation_level.values), - ) - - return self._ds_boot - - @property - def ds_null(self): - if not hasattr(self, '_ds_null'): - RS = np.random.RandomState(seed=1) - dlist = [] - for i_boot in trange(self.nboot, desc='resampling experiment'): - ds_resampled = self.worker_resampler.get_ds_resampled(RS=RS, nsessions=16000).sum('session') - dlist.append(ds_resampled) - - self._ds_null = xr.concat(dlist, dim='boot_iter').assign_coords( - transformation=(['transformation_id'], self.ds_table.transformation.values), - transformation_level=(['transformation_id'], self.ds_table.transformation_level), - ) - - return self._ds_null - - -class ExperimentResampler(Resampler): - """ - Resamples the experiment, simulating the data-generating process: - 1) First, re-samples a subject randomly - 2) Then for that re-sampled subject, resamples a session associated with that subject. - - The probability of re-sampling a particular session is then given by: - - Prob(session) = Prob(worker) * Prob(session | worker) - Prob(worker) = 1 /nworkers - Prob(session | worker) = 1 / nsessions[i_worker] - Prob(session) = 1 / (nworkers * nsessions[i_worker]) - - """ - - def __init__( - self, - ds_session: xr.Dataset, - ): - - super().__init__() - mandatory_vars = ['k_catch', 'n_catch', 'k_train', 'n_train', 'k', 'n'] - for var in mandatory_vars: - assert var in ds_session.data_vars, ds_session.data_vars - - all_dims = ds_session.dims - assert 'session' in all_dims - assert 'worker_id' in ds_session.coords - - prob_session = [] - nworkers = len(set(ds_session['worker_id'].values)) - worker_to_nsessions = collections.defaultdict(lambda: 0) - for worker_id in ds_session['worker_id'].values: - worker_to_nsessions[worker_id] += 1 - - for worker_id in ds_session['worker_id'].values: - prob_session.append(1 / (nworkers * worker_to_nsessions[worker_id])) - - assert np.isclose(np.sum(prob_session), 1) - - self.ds_session = ds_session.transpose('session', 'transformation_id', 'catch_trial', 'train_trial') - self.transformation_ids = self.ds_session['transformation_id'].values - self.catch_trial = self.ds_session['catch_trial'].values - self.train_trial = self.ds_session['train_trial'].values - - self.prob_session = np.array(prob_session) - self.nsessions_empirical = len(self.prob_session) - self.worker_id_seq = self.ds_session['worker_id'].values - - def get_ds_resampled(self, RS: np.random.RandomState, nsessions=None, ): - """ - Returns a single bootstrap re-sample - :param seed: - :return: - """ - - if nsessions is None: - # Take a bootstrap resample - nsessions = len(self.ds_session['session']) - - i_sessions = RS.choice(self.nsessions_empirical, size=nsessions, p=self.prob_session, replace=True) - - kbootstrapped_data = self.ds_session['k'].values[i_sessions] - nbootstrapped_data = self.ds_session['n'].values[i_sessions] - kcatch_resampled_data = self.ds_session['k_catch'].values[i_sessions] - ncatch_resampled_data = self.ds_session['n_catch'].values[i_sessions] - ktrain_resampled_data = self.ds_session['k_train'].values[i_sessions] - ntrain_resampled_data = self.ds_session['n_train'].values[i_sessions] - worker_ids = self.worker_id_seq[i_sessions] - - ds_resampled = xr.Dataset( - data_vars={ - 'k': (['session', 'transformation_id'], kbootstrapped_data), - 'n': (['session', 'transformation_id'], nbootstrapped_data), - 'k_catch': (['session', 'catch_trial'], kcatch_resampled_data), - 'n_catch': (['session', 'catch_trial'], ncatch_resampled_data), - 'k_train': (['session', 'train_trial'], ktrain_resampled_data), - 'n_train': (['session', 'train_trial'], ntrain_resampled_data), - }, - coords={ - 'transformation_id': self.transformation_ids, - 'catch_trial': self.catch_trial, - 'train_trial': self.train_trial, - 'session': np.arange(nsessions), - 'worker_id': (['session'], worker_ids), - } - ) - - return ds_resampled diff --git a/hobj/benchmarks/generalization/ref/mut_oneshot_experiment.py b/hobj/benchmarks/generalization/ref/mut_oneshot_experiment.py deleted file mode 100644 index 2f027ec..0000000 --- a/hobj/benchmarks/generalization/ref/mut_oneshot_experiment.py +++ /dev/null @@ -1,280 +0,0 @@ -import os -from typing import List - -import xarray as xr -import numpy as np - -import hobj.benchmarks.experiment_simulation.environment_depr as env_template -from hobj.benchmarks.experiment_simulation.experiment_simulation2_tmp import ExperimentSimulation -import hobj.data.images.depr_imagesets as imagesets - -_loc = os.path.dirname(__file__) - - -class OneshotTestEnvironment(env_template.Environment): - """ - Trials 0-9 are training trials involving two support images. They are shown an equal amount of times. - Trials 10-13 are test trials involving transformed versions of the support images. - Trial 14 is a catch trial. - Trials 15-18 are test trials involving transformed versions of the support images. - Trial 19 is a catch trial. - - In all of the test trials, the test images are shown *without replacement*. - - """ - - def __init__( - self, - support_imageA_url: str, - support_imageB_url: str, - test_imageA_urls: List[str], - test_imageB_urls: List[str], - ): - super().__init__() - - assert isinstance(support_imageA_url, str) - assert isinstance(support_imageB_url, str) - assert isinstance(test_imageA_urls, list) - assert isinstance(test_imageB_urls, list) - - assert len(test_imageA_urls) == len(test_imageB_urls), 'Imbalanced test images' - assert np.all([isinstance(url, str) for url in test_imageA_urls]) - assert np.all([isinstance(url, str) for url in test_imageB_urls]) - assert support_imageA_url not in test_imageA_urls - assert support_imageB_url not in test_imageB_urls - assert support_imageA_url != support_imageB_url - - self.support_image_urls = [support_imageA_url, support_imageB_url] - self.test_imageA_urls = test_imageA_urls - self.test_imageB_urls = test_imageB_urls - self.step = None - return - - def initialize(self, RS: np.random.RandomState): - self.step = 0 - - url_sequence = [] - correct_action_sequence = [] - - # Sample the order of the support images during the training phase, balancing the times they appear - i_training_sequence = RS.permutation([0] * 5 + [1] * 5) - for i in i_training_sequence: - url_sequence.append(self.support_image_urls[i]) - correct_action_sequence.append(i) - - # Assemble the test sequence - test_class_sequence = RS.choice([0, 1], size=10, replace=True) - testA_seq = RS.permutation(self.test_imageA_urls) - testB_seq = RS.permutation(self.test_imageB_urls) - - for i_test_trial in np.arange(10): - class_cur = test_class_sequence[i_test_trial] - - if i_test_trial in [4, 9]: - # Catch trial - url_sequence.append(self.support_image_urls[class_cur]) - correct_action_sequence.append(class_cur) - continue - - if class_cur == 0: - url_sequence.append(testA_seq[i_test_trial]) - else: - url_sequence.append(testB_seq[i_test_trial]) - - correct_action_sequence.append(class_cur) - - self.url_seq = url_sequence - self.correct_action_seq = correct_action_sequence - - - def sample_image(self): - - image_url = self.url_seq[self.step] - return image_url - - def provide_feedback(self, action: int): - - correct_action = self.correct_action_seq[self.step] - assert action in [0, 1] - assert correct_action in [0, 1] - - if action == correct_action: - reward = 1. - else: - reward = -1. - - self.step += 1 - - return reward - - -class MutatorOneShotExperiment(ExperimentSimulation): - nreps = 500 # per subtask (n=32) - ntrials = 20 - imageset = imagesets.MutatorOneshotDeprImageset() - environment_name_dim = 'subtask' - - @property - def experiment_name(self): - return 'MutatorOneShotExperiment' - - @property - def ds_meta(self): - if not hasattr(self, '_ds_meta'): - self._ds_meta = self.imageset.ds_meta - return self._ds_meta - - @property - def url_to_transformation_id(self): - if not hasattr(self, '_url_to_transformation_id'): - self._url_to_transformation_id = {url: trans for (url, trans) in zip(self.ds_meta.image_url.values, self.ds_meta.transformation_id.values)} - return self._url_to_transformation_id - - @property - def transformation_ids(self): - if not hasattr(self, '_transformation_ids'): - self._transformation_ids = sorted(np.unique(self.ds_meta.transformation_id.values)) - return self._transformation_ids - - def get_i_transformation_id_from_url(self, url: str): - transformation_id = self.url_to_transformation_id[url] - if not hasattr(self, '_transformation_id_to_i'): - self._transformation_id_to_i = {transformation_id: i for (i, transformation_id) in enumerate(self.transformation_ids)} - i_transformation_id = self._transformation_id_to_i[transformation_id] - return i_transformation_id - - def postprocess_behavioral_data(self, ds: xr.Dataset, environment: env_template.Environment): - - """ - - :param ds: [rep, trial] - :param environment: - :return: - - k: (transformation_id) - n: (transformation_id) - - k_train: (train_trial) - n_train: (train_trial) - - k_test: (test_trial) - n_test: (test_trial) - - k_catch: (catch_trial) - n_catch: (catch_trial) - - """ - assert set(ds.dims) == {'rep', 'trial'} - assert 'reward' in ds.data_vars - ds['perf'] = ds.reward > 0 - ds_table = xr.Dataset( - dict( - k=(['transformation_id'], np.zeros(len(self.transformation_ids), dtype=int)), - n=(['transformation_id'], np.zeros(len(self.transformation_ids), dtype=int)), - k_train=(['train_trial'], np.zeros(10, dtype=int)), - n_train=(['train_trial'], np.zeros(10, dtype=int)), - k_catch=(['catch_trial'], np.zeros(3, dtype=int)), - n_catch=(['catch_trial'], np.zeros(3, dtype=int)), - ), - coords={ - 'transformation_id': self.transformation_ids, - 'train_trial': np.arange(10), - 'catch_trial': [9, 14, 19], - } - ) - - perf_dat = ds.transpose('rep', 'trial').perf.values - url_dat = ds.transpose('rep', 'trial').image_url.values - for i_rep in ds.rep.values: - for i_trial in range(self.ntrials): - url = url_dat[i_rep, i_trial] - perf = int(perf_dat[i_rep, i_trial]) - - if i_trial < 10: - ds_table.k_train.values[i_trial] += perf - ds_table.n_train.values[i_trial] += 1 - if i_trial == 9: - ds_table.k_catch.values[0] += perf - ds_table.n_catch.values[0] += 1 - if i_trial == 14: - ds_table.k_catch.values[1] += perf - ds_table.n_catch.values[1] += 1 - if i_trial == 19: - ds_table.k_catch.values[2] += perf - ds_table.n_catch.values[2] += 1 - - i_transformation_id = self.get_i_transformation_id_from_url(url) - - ds_table.k.values[i_transformation_id] += perf - ds_table.n.values[i_transformation_id] += 1 - - return ds_table - - @property - def environments(self): - subtasks = ['MutatorB2000_2292,MutatorB2000_2444', - 'MutatorB2000_138,MutatorB2000_2344', - 'MutatorB2000_1251,MutatorB2000_953', - 'MutatorB2000_3043,MutatorB2000_694', - 'MutatorB2000_3496,MutatorB2000_496', - 'MutatorB2000_1219,MutatorB2000_296', - 'MutatorB2000_1825,MutatorB2000_2757', - 'MutatorB2000_3077,MutatorB2000_4703', - 'MutatorB2000_270,MutatorB2000_3615', - 'MutatorB2000_3066,MutatorB2000_3585', - 'MutatorB2000_2139,MutatorB2000_746', - 'MutatorB2000_116,MutatorB2000_2365', - 'MutatorB2000_2130,MutatorB2000_4628', - 'MutatorB2000_462,MutatorB2000_926', - 'MutatorB2000_2304,MutatorB2000_3733', - 'MutatorB2000_1363,MutatorB2000_3278', - 'MutatorB2000_4049,MutatorB2000_663', - 'MutatorB2000_2722,MutatorB2000_3527', - 'MutatorB2000_2832,MutatorB2000_801', - 'MutatorB2000_1258,MutatorB2000_3123', - 'MutatorB2000_1865,MutatorB2000_613', - 'MutatorB2000_1164,MutatorB2000_2106', - 'MutatorB2000_1229,MutatorB2000_1280', - 'MutatorB2000_1767,MutatorB2000_2122', - 'MutatorB2000_2198,MutatorB2000_701', - 'MutatorB2000_3636,MutatorB2000_4305', - 'MutatorB2000_3035,MutatorB2000_46', - 'MutatorB2000_3601,MutatorB2000_4792', - 'MutatorB2000_2092,MutatorB2000_288', - 'MutatorB2000_1424,MutatorB2000_2314', - 'MutatorB2000_3308,MutatorB2000_3525', - 'MutatorB2000_2909,MutatorB2000_4256'] - - # Replicate the same kinds of trials done in humans - if not hasattr(self, '_environments'): - ds_meta = imagesets.MutatorOneshotDeprImageset().ds_meta - - obj_to_support_url = {} - obj_to_test_urls = {} - for obj, ds_obj in ds_meta.groupby('obj'): - ds_support = ds_obj.sel(image_url=ds_obj.transformation == 'original') - ds_test = ds_obj.sel(image_url=ds_obj.transformation != 'original') - assert len(ds_support.image_url) + len(ds_test.image_url) == len(ds_obj.image_url) - assert len(ds_support.image_url) == 1 - support_url = ds_support.image_url.values[0] - test_urls = ds_test.image_url.values - obj_to_support_url[obj] = str(np.array(support_url)) - obj_to_test_urls[obj] = list(np.array(test_urls)) - - environments = [] - for subtask in subtasks: - objA, objB = subtask.split(',') - support_imageA_url = obj_to_support_url[objA] - support_imageB_url = obj_to_support_url[objB] - test_imageA_urls = obj_to_test_urls[objA] - test_imageB_urls = obj_to_test_urls[objB] - - env_cur = OneshotTestEnvironment( - support_imageA_url=support_imageA_url, - support_imageB_url=support_imageB_url, - test_imageA_urls=test_imageA_urls, - test_imageB_urls=test_imageB_urls, - ) - environments.append(env_cur) - self._environments = environments - return self._environments diff --git a/hobj/statistics/__init__.py b/hobj/statistics/__init__.py deleted file mode 100644 index 3cbba05..0000000 --- a/hobj/statistics/__init__.py +++ /dev/null @@ -1,30 +0,0 @@ -""" - -Benchmark 1: -- CI for the raw R^2 ceiling -- Approximate null distribution for R^2_n, given # of model simulations -- CI for overall/early/late performance -- Approximate null distribution for SpearmanR upon experimental repeat - - Scoring model: - - R^2_n point estimate - - Overall/early/late point estimates - - Raw SpearmanR point estimate - -Scoring heldout human split-half -- R^2_n point estimate, and CI -- Overall/early/late point estimates, and CIs -- Raw SpearmanR point estimate - -Benchmark 2: -- CI for the raw R^2 ceiling -- Approximate null distribution, given the null hypothesis -- Bootstrapped standard errors for the performances - -Scoring model on Benchmark 2: -- R^2_n point estimate - -Scoring heldout human split-half -- R^2_n point estimate, and CI - -""" diff --git a/hobj/statistics/hypothesis_testing/__init__.py b/hobj/statistics/hypothesis_testing/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/hobj/statistics/hypothesis_testing/approximated_null_distribution.py b/hobj/statistics/hypothesis_testing/approximated_null_distribution.py deleted file mode 100644 index 1ad6e3e..0000000 --- a/hobj/statistics/hypothesis_testing/approximated_null_distribution.py +++ /dev/null @@ -1,18 +0,0 @@ -import xarray as xr -from typing import Union, List - - -def estimate_null_distribution( - statistical_functional, - ds_null_samples: xr.Dataset, - hat_mu: Union[xr.Dataset, xr.DataArray] = None, - hat_sigma: Union[xr.Dataset, xr.DataArray] = None, - resample_dim='boot_iter' -): - null_stats = statistical_functional(ds_null_samples) - - if hat_mu is not None: - null_stats = null_stats - null_stats.mean(resample_dim) + hat_mu - if hat_sigma is not None: - null_stats = (null_stats - null_stats.mean(resample_dim)) * hat_sigma / null_stats.std(resample_dim, ddof=1) + null_stats.mean(resample_dim) - return null_stats diff --git a/hobj/statistics/hypothesis_testing/bootstrapped_confidence_intervals.py b/hobj/statistics/hypothesis_testing/bootstrapped_confidence_intervals.py deleted file mode 100644 index bee010e..0000000 --- a/hobj/statistics/hypothesis_testing/bootstrapped_confidence_intervals.py +++ /dev/null @@ -1,33 +0,0 @@ -import xarray as xr - - -def estimate_basic_confidence_interval( - alpha: float, - ds_data: xr.Dataset, - ds_boot: xr.Dataset, - statistic_functional, - parameter_functional=None, - -): - assert 'boot_iter' in ds_boot.dims - for dim in ds_data.dims: - assert dim in ds_boot.dims, dim - - assert alpha <= 0.1 - assert alpha > 0 - - point_estimate = statistic_functional(ds_data) - ds_boot_stats = statistic_functional(ds_boot) - - if parameter_functional is not None: - empirical_parameter = parameter_functional(ds_data) - else: - empirical_parameter = ds_boot_stats.mean('boot_iter') - - low = ds_boot_stats.quantile(alpha / 2, 'boot_iter') - high = ds_boot_stats.quantile(1 - alpha / 2, 'boot_iter') - - delta1 = empirical_parameter - low - delta2 = high - empirical_parameter - CI = xr.concat([point_estimate - delta2, point_estimate + delta1], 'CI') - return CI diff --git a/hobj/statistics/resamplers/__init__.py b/hobj/statistics/resamplers/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/hobj/statistics/resamplers/resamplers.py b/hobj/statistics/resamplers/resamplers.py deleted file mode 100644 index fdb0593..0000000 --- a/hobj/statistics/resamplers/resamplers.py +++ /dev/null @@ -1,194 +0,0 @@ -import numpy as np -import xarray as xr -from tqdm import trange - - -class Resampler(object): - def __init__(self): - return - - -class WorkerResampler(Resampler): - def __init__(self, ds_worker_table: xr.Dataset, condition_dim='subtask', worker_dim='worker_id'): - """ - Bootstrap resamples workers. - ds: - :param k, n: (worker_id, subtask, trial) - """ - super().__init__() - assert 'k' in ds_worker_table.data_vars - assert 'n' in ds_worker_table.data_vars - assert 'trial' in ds_worker_table.dims - assert worker_dim in ds_worker_table.dims, ds_worker_table.dims - assert condition_dim in ds_worker_table.dims, ds_worker_table.dims - assert np.all(ds_worker_table.n <= 1) - assert np.all(ds_worker_table.k <= ds_worker_table.n), ds_worker_table - assert np.all(ds_worker_table.k >= 0) - - self.ds_worker_table = ds_worker_table.transpose('worker_id', 'trial', 'subtask') - self.worker_dim = worker_dim - self.condition_dim = condition_dim - - return - - def get_ds_boot(self, nboot: int, seed: int): - RS = np.random.RandomState(seed=seed) - kbootstrapped = np.zeros((nboot, len(self.ds_worker_table[self.condition_dim]), len(self.ds_worker_table.trial),)) - nbootstrapped = np.zeros((nboot, len(self.ds_worker_table[self.condition_dim]), len(self.ds_worker_table.trial),)) - - kdat = self.ds_worker_table.k.transpose('worker_id', 'subtask', 'trial').values - ndat = self.ds_worker_table.n.transpose('worker_id', 'subtask', 'trial').values - for i_resample in trange(nboot, desc='bootstrap resampling workers'): - i_workers = RS.choice(len(self.ds_worker_table[self.worker_dim]), size=len(self.ds_worker_table[self.worker_dim]), replace=True).astype(int) - kbootstrapped[i_resample] = kdat[i_workers].sum(0) - nbootstrapped[i_resample] = ndat[i_workers].sum(0) - - ds_boot = xr.Dataset( - data_vars={ - 'k': (('boot_iter', self.condition_dim, 'trial',), kbootstrapped), - 'n': (('boot_iter', self.condition_dim, 'trial',), nbootstrapped), - }, - coords={ - 'boot_iter': np.arange(nboot), - 'trial': self.ds_worker_table.trial, - self.condition_dim: self.ds_worker_table[self.condition_dim], - } - ) - return ds_boot - - -class LearningCurveResampler(Resampler): - - def __init__(self, ds: xr.Dataset, condition_dim='subtask', rep_dim='worker_id'): - """ - For each condition, bootstraps sequences of Bernoulli trials. - ds: - :param ds: An xr.Dataset with data variables 'k' and 'n' with dims ('trial', condition_dim, rep_dim) - """ - super().__init__() - assert 'k' in ds.data_vars - assert 'n' in ds.data_vars - assert 'trial' in ds.dims - assert rep_dim in ds.dims, ds.dims - assert condition_dim in ds.dims, ds.dims - assert np.all(ds.n <= 1) - assert np.all(ds.k <= ds.n), ds - assert np.all(ds.k >= 0) - - self.ds = ds - self.rep_dim = rep_dim - - condition_to_kn_dat = {} - ntrials = len(ds.trial) - for subtask, ds_subtask in ds.groupby(condition_dim): - ds_subtask = ds_subtask.sel({condition_dim: subtask}) - - n = ds_subtask.n - nsessions_with_data = ((n > 0).sum('trial') > 0).sum(rep_dim) - valid_sessions = (n > 0).sum('trial') == ntrials - assert valid_sessions.sum() == nsessions_with_data - ds_valid = ds_subtask.sel({rep_dim: valid_sessions}) - ds_valid = ds_valid.transpose(rep_dim, 'trial') - condition_to_kn_dat[subtask] = ds_valid['k'].values, ds_valid['n'].values # [session, trial] - - self.condition_dim = condition_dim - self.condition_to_kn_dat = condition_to_kn_dat - self.conditions = sorted(condition_to_kn_dat.keys()) - - # Number of sessions for each condition - self.nsessions = xr.DataArray([self.condition_to_kn_dat[condition][0].shape[0] for condition in self.conditions], - dims=self.condition_dim, - coords={self.condition_dim: self.conditions} - ) - - return - - def get_ds_resamples(self, nresamples: int, nsessions: xr.DataArray, seed: int): - """ - Simulate taking nsessions samples for each condition. - :param nsessions: - :return: - """ - - assert nresamples >= 1 - assert nsessions.dims == (self.condition_dim,) - nsessions = nsessions.sel({self.condition_dim: self.conditions}) - nsessions = np.array(nsessions.values) - RS = np.random.RandomState(seed=seed) - kresamp = np.zeros((nresamples, len(self.ds.trial), len(self.ds[self.condition_dim]))) - nresamp = np.zeros((nresamples, len(self.ds.trial), len(self.ds[self.condition_dim]))) - - for i_resample in trange(nresamples, desc='resampling learning curves'): - - for i_condition, condition in enumerate(self.conditions): - kdat, ndat = self.condition_to_kn_dat[condition] # [session, trial] - nsessions_cur = nsessions[i_condition] - navailable_lcs = kdat.shape[0] - - i_sessions = RS.choice(navailable_lcs, size=nsessions_cur, replace=True) - kboot_samp = kdat[i_sessions].sum(0) - nboot_samp = ndat[i_sessions].sum(0) - - kresamp[i_resample, :, i_condition] = kboot_samp - nresamp[i_resample, :, i_condition] = nboot_samp - - ds_resamp = xr.Dataset( - data_vars={ - 'k': (('boot_iter', 'trial', self.condition_dim), kresamp), - 'n': (('boot_iter', 'trial', self.condition_dim), nresamp), - }, - coords={ - 'boot_iter': np.arange(nresamples), - 'trial': self.ds.trial, - self.condition_dim: self.conditions, - } - ) - - return ds_resamp - - def get_ds_resamples_path2(self, nresamples: int, nsessions: xr.DataArray, seed: int): - """ - Simulate taking nsessions samples for each condition. - :param nsessions: - :return: - """ - - assert nresamples >= 1 - assert nsessions.dims == (self.condition_dim,) - nsessions = nsessions.sel({self.condition_dim: self.conditions}) - nsessions = np.array(nsessions.values) - RS = np.random.RandomState(seed=seed) - kresamp = np.zeros((nresamples, len(self.ds.trial), len(self.ds[self.condition_dim]))) - nresamp = np.zeros((nresamples, len(self.ds.trial), len(self.ds[self.condition_dim]))) - - for i_resample in trange(nresamples, desc='resampling learning curves'): - - for i_condition, condition in enumerate(self.conditions): - kdat, ndat = self.condition_to_kn_dat[condition] # [session, trial] - nsessions_cur = nsessions[i_condition] - navailable_lcs = kdat.shape[0] - - ntimes_resampled = RS.multinomial(n=nsessions_cur, pvals=1 / navailable_lcs * np.ones(navailable_lcs), ) # [navailable_lcs] - kboot_samp = (kdat * ntimes_resampled[:, None]).sum(0) - nboot_samp = (ndat * ntimes_resampled[:, None]).sum(0) - - kresamp[i_resample, :, i_condition] = kboot_samp - nresamp[i_resample, :, i_condition] = nboot_samp - - ds_resamp = xr.Dataset( - data_vars={ - 'k': (('boot_iter', 'trial', self.condition_dim), kresamp), - 'n': (('boot_iter', 'trial', self.condition_dim), nresamp), - }, - coords={ - 'boot_iter': np.arange(nresamples), - 'trial': self.ds.trial, - self.condition_dim: self.conditions, - } - ) - - return ds_resamp - - def get_ds_boot(self, nboot: int, seed: int): - ds_boot = self.get_ds_resamples_path2(nresamples=nboot, nsessions=self.nsessions, seed=seed) - return ds_boot diff --git a/hobj/statistics/score_statistics/MSEn.py b/hobj/statistics/score_statistics/MSEn.py deleted file mode 100644 index 7dc8f78..0000000 --- a/hobj/statistics/score_statistics/MSEn.py +++ /dev/null @@ -1,30 +0,0 @@ -import xarray as xr -import numpy as np -from typing import Union, List - - - -def estimate_MSEn( - hat_theta_model: Union[xr.DataArray, np.ndarray], - hat_var_hat_theta_model: Union[xr.DataArray, np.ndarray], - hat_theta_target: Union[xr.DataArray, np.ndarray], - condition_dims: tuple, -): - """ - The expected value of the raw MSE is: - - (E[x] - E[y])^2 + var(x) + var(y) - - The MSEn performs a bias correction using the estimated variance of the model. - - :param hat_theta_model: - :param hat_var_hat_theta_model: - :param hat_theta_target: - :param hat_var_hat_theta_target: - :param condition_dims: - :return: - """ - hat_MSE_raw = np.square(hat_theta_model - hat_theta_target).mean(condition_dims) - hat_MSE_n = hat_MSE_raw - hat_var_hat_theta_model.mean(condition_dims) - return hat_MSE_n - diff --git a/hobj/statistics/score_statistics/__init__.py b/hobj/statistics/score_statistics/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/hobj/statistics/variance_estimates/__init__.py b/hobj/statistics/variance_estimates/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/hobj/stats_new/__init__.py b/hobj/stats/__init__.py similarity index 100% rename from hobj/stats_new/__init__.py rename to hobj/stats/__init__.py diff --git a/hobj/statistics/variance_estimates/binomial.py b/hobj/stats/binomial.py similarity index 55% rename from hobj/statistics/variance_estimates/binomial.py rename to hobj/stats/binomial.py index e7c10ba..aad1b90 100644 --- a/hobj/statistics/variance_estimates/binomial.py +++ b/hobj/stats/binomial.py @@ -1,10 +1,7 @@ def estimate_variance_of_binomial_proportion(kvec, nvec): """ - Returns the *unbiased* estimate of the variance associated with the proportion estimator, phat = k/n: - - phat = kvec / nvec - \hat var(phat) = (phat * (1 - phat)) / (nvec - 1) + Returns the unbiased estimate of the variance associated with the proportion estimator, phat = k/n: :param kvec: :param nvec: :return: diff --git a/hobj/stats_new/ci.py b/hobj/stats/ci.py similarity index 100% rename from hobj/stats_new/ci.py rename to hobj/stats/ci.py diff --git a/hobj/stats_new/learning_curve.py b/hobj/stats_new/learning_curve.py deleted file mode 100644 index e921168..0000000 --- a/hobj/stats_new/learning_curve.py +++ /dev/null @@ -1,61 +0,0 @@ -import numpy as np -from typing import Union -from hobj.statistics.variance_estimates import binomial as binomial_funcs -from dataclasses import dataclass - - -@dataclass -class CalculateLearningCurveResult: - phat: np.ndarray # [trial] - varhat_phat: np.ndarray # [trial] - - boot_phat: np.ndarray # [boot, trial] - boot_varhat_phat: np.ndarray # [boot, trial] - - -def calculate_learning_curve( - perf_matrix: np.ndarray, - bootstrap_seed: Union[None, int], - nbootstrap_samples: int, - repetition_axis: int, -) -> CalculateLearningCurveResult: - - """ - Calculates the learning curve statistics for a given set of performance data for a single subtask. - Returns the point estimates and bootstrapped estimates of the mean and variance of the binomial proportion. - :param perf_matrix: a boolean np.ndarray of shape (nrepetitions, ntrials) where True indicates a correct response. - :param bootstrap_seed: - :param nbootstrap_samples: - :return: - """ - - nrepetitions = perf_matrix.shape[repetition_axis] - ntrials = perf_matrix.shape[1] - - # Calculate statistics: - k = perf_matrix.sum(axis=repetition_axis) - n = nrepetitions - phat = k / n - varhat_phat = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=k, nvec=n) - - # Perform bootstrapping of the model - boot_phat = np.zeros((nbootstrap_samples, ntrials)) - boot_varhat_phat = np.zeros((nbootstrap_samples, ntrials)) - - # Bootstrap resample model simulations - gen = np.random.default_rng(seed=bootstrap_seed) - - for i_boot_iter in range(nbootstrap_samples): - i_boot_sessions = gen.integers(low =0, high = nrepetitions, size = nrepetitions) - - kboot = perf_matrix[i_boot_sessions].sum(repetition_axis) - boot_phat[i_boot_iter] = kboot / n - boot_varhat_phat[i_boot_iter] = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=kboot, nvec=n) - - return CalculateLearningCurveResult( - phat=phat, - varhat_phat=varhat_phat, - boot_phat=boot_phat, - boot_varhat_phat=boot_varhat_phat, - ) - diff --git a/hobj/stats_new/null_distribution.py b/hobj/stats_new/null_distribution.py deleted file mode 100644 index e69de29..0000000 From 57b90fa2ef67b5c41ad5a1a630bd49362912a9aa Mon Sep 17 00:00:00 2001 From: himjl Date: Mon, 27 Jan 2025 12:12:40 -0500 Subject: [PATCH 072/156] correct imports --- hobj/benchmarks/binary_classification/benchmark.py | 2 +- hobj/benchmarks/binary_classification/estimator.py | 2 +- hobj/benchmarks/generalization/benchmark.py | 2 +- hobj/benchmarks/generalization/estimator.py | 2 +- hobj/stats/__init__.py | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/hobj/benchmarks/binary_classification/benchmark.py b/hobj/benchmarks/binary_classification/benchmark.py index 0f90808..0a46ad6 100644 --- a/hobj/benchmarks/binary_classification/benchmark.py +++ b/hobj/benchmarks/binary_classification/benchmark.py @@ -10,7 +10,7 @@ from hobj.benchmarks.binary_classification.estimator import LearningCurveStatistics from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask, BinaryClassificationSubtaskResult from hobj.learning_models import BinaryLearningModel -from hobj.stats_new.ci import estimate_basic_bootstrap_CI +from hobj.stats.ci import estimate_basic_bootstrap_CI # %% Models for configuring a LearningCurveBenchmark: diff --git a/hobj/benchmarks/binary_classification/estimator.py b/hobj/benchmarks/binary_classification/estimator.py index ebc9468..8e18668 100644 --- a/hobj/benchmarks/binary_classification/estimator.py +++ b/hobj/benchmarks/binary_classification/estimator.py @@ -5,7 +5,7 @@ from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtaskResult from dataclasses import dataclass -from hobj.stats_new import binomial as binomial_funcs +from hobj.stats import binomial as binomial_funcs # %% diff --git a/hobj/benchmarks/generalization/benchmark.py b/hobj/benchmarks/generalization/benchmark.py index 945b567..d9eb08b 100644 --- a/hobj/benchmarks/generalization/benchmark.py +++ b/hobj/benchmarks/generalization/benchmark.py @@ -9,7 +9,7 @@ from hobj.benchmarks.generalization.estimator import GeneralizationStatistics from hobj.benchmarks.generalization.simulator import GeneralizationSubtask, GeneralizationSessionResult from hobj.learning_models import BinaryLearningModel -from hobj.stats_new.ci import estimate_basic_bootstrap_CI +from hobj.stats.ci import estimate_basic_bootstrap_CI # %% Models for configuring a LearningCurveBenchmark: diff --git a/hobj/benchmarks/generalization/estimator.py b/hobj/benchmarks/generalization/estimator.py index cb33030..d2de06d 100644 --- a/hobj/benchmarks/generalization/estimator.py +++ b/hobj/benchmarks/generalization/estimator.py @@ -4,7 +4,7 @@ import numpy as np from hobj.benchmarks.generalization.simulator import GeneralizationSessionResult -from hobj.stats_new import binomial as binomial_funcs +from hobj.stats import binomial as binomial_funcs import warnings diff --git a/hobj/stats/__init__.py b/hobj/stats/__init__.py index 8be0822..e6723e2 100644 --- a/hobj/stats/__init__.py +++ b/hobj/stats/__init__.py @@ -1 +1 @@ -from hobj.stats_new.ci import estimate_basic_bootstrap_CI \ No newline at end of file +from hobj.stats.ci import estimate_basic_bootstrap_CI \ No newline at end of file From ab6ed11390eb41b46b5979c6e9c54ef53a11d333 Mon Sep 17 00:00:00 2001 From: himjl Date: Mon, 27 Jan 2025 12:36:27 -0500 Subject: [PATCH 073/156] add notebook --- examples/view_behavioral_data.ipynb | 254 ++++++++++++++++++ examples/view_behavioral_data.py | 26 -- .../binary_classification/benchmark.py | 17 +- 3 files changed, 270 insertions(+), 27 deletions(-) create mode 100644 examples/view_behavioral_data.ipynb delete mode 100644 examples/view_behavioral_data.py diff --git a/examples/view_behavioral_data.ipynb b/examples/view_behavioral_data.ipynb new file mode 100644 index 0000000..fb8de69 --- /dev/null +++ b/examples/view_behavioral_data.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": "## View human learning curve statistics for Experiment 1", + "id": "bb5fb3d6fa3feb0" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T17:36:10.876431Z", + "start_time": "2025-01-27T17:36:10.709906Z" + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ], + "id": "initial_id", + "outputs": [], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T17:36:12.159340Z", + "start_time": "2025-01-27T17:36:10.877431Z" + } + }, + "cell_type": "code", + "source": [ + "from hobj.benchmarks import MutatorHighVarBenchmark\n", + "benchmark1 = MutatorHighVarBenchmark()\n", + "target_statistics = benchmark1.target_statistics\n", + "print(target_statistics)" + ], + "id": "40343652efc6724e", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 103MB\n", + "Dimensions: (subtask: 64, trial: 100, boot_iter: 1000)\n", + "Coordinates:\n", + " * subtask (subtask) " + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 3 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T17:36:12.333276Z", + "start_time": "2025-01-27T17:36:12.218249Z" + } + }, + "cell_type": "code", + "source": [ + "# Calculate the subtask-averaged learning curve\n", + "glc = target_statistics.phat.mean('subtask')\n", + "\n", + "# Compute standard error using the bootstrap samples:\n", + "glc_se = target_statistics.boot_phat.mean('subtask').std('boot_iter', ddof = 1) \n", + "\n", + "plt.figure()\n", + "plt.errorbar(np.arange(len(glc))+1, glc, yerr = glc_se, lw = 1, elinewidth = 1, marker = '.')\n", + "plt.axhline(0.5, ls = ':', color = 'gray')\n", + "plt.xscale('log')\n", + "plt.xlabel('trial')\n", + "plt.ylabel('p(correct)')\n", + "plt.title(\"Human learning curve\\n(averaged over all subtasks)\")\n", + "plt.show()" + ], + "id": "e0d81a639257d99e", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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ubqqwsDCNfWfMmKGys7NTRUdHi/tbt24Vj/3888/V+9y5c0fVp08fsX316tWq0kydOlXsx+dBwue8UaNGqsDAQFVhYaHY9sMPP4j9zp8/r/H41q1ba5x3Q9t97do1cTwPDw9VYmKiyhCGnGPWt29fcZNIz1XWuejQoYPqoYceKnUf7WNL+L3jz4f2c7q4uKhiY2PV248dOya2T5s2Tb1t8uTJYps+hnzWFy1aJB6flJRUYrv37t0r9tm8ebN4f/k1eHl5qU6fPq2xn6enp2iPIZo3b64aNmyYQfsCyKELDKAU/Jcn/5XP6Xxt8jWbuJsqIyOD+vTpo5Gmb968OQUFBYkMhXzhS/7rlrMv0jE2b94sugD4L/zk5GT1jbsa+Ln37t2r8dyNGjUSGZvS2sTt4WNwJoS7l/g+27Nnj6hz4ayF9vpU2rhd/Jpq1aql0S7uEuTXceDAAXUBLZ8nXs5BwhkZfcfUhx/P2Q7uPpHw6+aMDHdTSV1Sw4cPF88jP5/clcM/52xbedstefLJJ6lu3boGtdWQc1wR3H3E2ZTw8HCqLI8//rjocpXwue7evbs474Yw5LPO7WZ//PFHmdNF8ON58dfLly+LofH8OyLHxzp27JjISpVFeo8BygsBEICROP3PdSpcp8FdJnwB5e4R7YsgX5g5Rc/dMYz/w09MTNS4YPPFjh/HtQ58HPmNCzx5f+0ASB9+Hr7Iu7m5iYsIP16qwZDadf36dfGVa2Lk+DXwxUSO27Vjxw6dNkk1UVK7+Jh+fn46gaLUNVcWfry+fbkrTd5mLy8vGjhwoOgGk3AwxEERB0flbXdZ59PYc1wR3H3E9UscPLdr147efvttUYdWEVxPpI2Pz8FlZX3W+fPcq1cv0XXH3Wrcvcvvk75giOt7Tpw4Ibq9uDtRG3fPcmDL9VscrHG3IAeY+nD3q7xeCcBQqAECKAXXonC2hOsn3N3d1dsPHjwoaiLuv/9+Wrp0qbj4c40O15LwQpdyfGHg2hPOSvB//HxR4GzP0KFD1fvwRYKDn/Xr1+tth3Z2Qt+K4VevXhXBAU/YuHDhQnHxcHR0FH/lcwGsMZM48mM4K/XOO+/o/TlfRKsbX1i5TurMmTMic8Dnk183B0fGttvQFdir4hxr488UPw9nUv7991/68ccfxbGXLVsmggvGF3x9yzhydquyGfpZ53PImTXOVnLxOgegHJxyrRG/Ds4ISrjuh+uI5s+fL+qTtOfX4tFdnGHiOjJ+7BdffCEK2HnE17BhwzT25YyUvgAPoCwIgABKwRc6xgXJXNwp4QnY+K9hHonFxZ4Sviho4+wC/xXLFwMu6uT/xLlLQv44Hn3Efw3zX9CGXoy1cTEuj6jZtm2bKPqVaHef8UggxgWq8swHd/dpjzjjdnEGSt8oOO1jctca7yvPAnGxtiH48fr25S4SeZsZnzsu2pW6wcLCwkSAaUy7q+ocVxRnWTjI4xu/Dg4+OAsiBUCcqdOXEZEyZdr0dafxeZOPGCspi1KezzoHMhwg8o0DxM8++4zef/99cX7k7wW/h9wFxqPP+A8LfaPoONDiblq+ccaOi58//fRTjQCI/ziJiYkRARpAeaELDKAUPXr0EF95lIoc/zXLFwz5X9zcnbB161a9x+Es0NGjR8WkilyvIO/+kv7i5WPNnTtX57H8n7whw8mlv7DlmQHuotC+UPHFibuMtC868iHk8nYFBweLi582bhO3jT344IPie/kx+fV8++23ZbZbevzx48fFc0l4ePPy5cvFRZpH+0i424nrnzjzw1kEzsDwBdWYdpeXoee4IjgQleOAkrsrOfCSB3gcHCYlJam38Ui+kkZc8edS6oJlfK65xkYeTHCXHtP+rBn6WU9NTdV5Xqm2R952yejRo+mbb74RmS2e1VnCz6PdlcjZUZ6yQfs4XPvFo9p4BCFAuWmURAOADh7tMnLkSI1te/bsEaNZeJTT999/r/r4449V3t7eqvbt2+sdSRMTE6OysbFRubu7q2rXri1G0Wh7+eWXxWN5RAuPqPnuu+9UU6ZMUfn7+4tRMxIe5aNvlNDly5dVjo6Oqnbt2onHzp8/X9WkSRMxqoiPyyOCJG+++abY9sgjj6iWLFmimjhxoiogIECMyBk7dqx6v+zsbFWnTp3EqLHx48eL1/rll1+K0UY8ykoa8cOjtHr16qWytbUVI9z4+XmEkHQ+yhr5xCPTfHx8xOifDz/8ULz+oKAgcc62bNmis//PP/8sjsvnk1+DNkPbLY2S+uKLL0ptnzHn2NhRYPw5euaZZ1QLFixQrVixQnwu+Dy8/vrr6n0uXbokznXHjh1FO2bNmiUex+3SNwqMt/NoOj7mnDlzxGewTp06qps3b6r33bRpk9h31KhR4vz++uuv5fqs82eV28MjJ7ndn376qapevXqq+vXrq9LT03VGgUl4P97GX1laWpp4j/i9WrhwoWr58uXifPA+X331lca54vfU1dVVlZmZadD7ByCHAAigDPyfsHzIumTlypWqZs2aqZycnFQtW7YUF7bZs2eXOJSYAwT+GV+QS8L/2Xfu3FkMW+aLO1+43nnnHY0LVUkBENu2bZu4MDk7O6sveKtWrdK5OPMQdQ40fH19xXNxsBIaGiouiq+88orGMXm48syZM1VNmzYVF38Oknr27CkuPvJALiUlRVw8eUg5BzL8PQ9vNuSiz65evap66qmnVDVr1hTt79atm+qvv/7Suy9f8LjdfGy+WOtjSLvLGwCV5xwbGwB98skn4rXzeeDXyJ8tDg60g2Z+3Tz0nl8bB4s7d+4scRg8vz4OHjjI5c8rBzNnz57VOB5/JjjIqlu3rgi45J9jQz7rHCg99thjImDnNvFX/sNBPhWBvgCI8Wect3Mwl5eXp3r77bdFUMm/AxwM8fdLly7VOVfdu3dXvfDCC6WeT4CS2PA/5c8bAVgPTsc3btxYjEx56aWXyFJx1wfXlnzyySeibgPAlHERPNcF8VB87WH0AIZADRBAGXjEFo8m4pEolTHKxxToWztMmh1Z3xILAKaGR5DxUjIIfsBYyAABWCFecoNvXHzMRba8zMCvv/4qRuboKxwGALA0GAYPYIV4SD+PBONuPV5riieumzJliuj+AgCwBsgAAQAAgNVBDRAAAABYHQRAAAAAYHVQA6QHj/ThVYh5inYssgcAAGAeuKqH127kmcO115jThgBIDw5+eJFDAAAAMD+8Rlz9+vVL3QcBkB7Sqt98Aj08PJRuDgAAABiAR7VyAkO6jpcGAZAeUrcXBz8IgAAAAMyLIeUrKIIGAAAAq4MACAAAAKwOAiAAAACwOgiAAAAAwOogAAIAAACrgwAIAAAArA4CIAAAALA6CIAAAADA6iAAAgAAAKuDAAgAAACsDgIgAAAAsDoIgAAAAMDqIAACAAAAq4MACAAAAKpNYmYuLdoVJr4qCQEQAAAAVJvEW3n09Z5w8VVJCIAAAACg2iRn5Wl8VQoCIAAAAKgWG09E07g1J8T3/JXvKwUBEAAAAFS5uIzbNHPLeVKpiu/z1/e2XBDblYAACAAAAKrcteRsKrob/EgKVSqKSs4hJSAAAgAAgCof1dXIy41sbTS32dnYUKCXKykBARAAAABU+aguP08Xmjmslfo+B0OfDW8rtisBARAAAAAYneUpz6iuHk3qqL9fNbYrjejagJSCAAgAAACMyvIYOqpLCqAiEm+pt3nVcCIlIQACAACwYokG1PDoy/KUZ1SXFEBN23hWve3fi/GkJARAAAAAZhqkVMayEoll1PCUlOUpz6guKXCS7/7d3gjFhsAzBEAAAAAmrqQgRd/28gZFyaXU8JSW5SnPqK7QuEydbRw8KTUEniEAAgAAMHH6ghQOcFYcuKq7vRxrbW0so4bn9PW0ErM8PHprzmNtyxzVxcdcsOOKznMrOQSeIQACAAAwYSUFKT8fvU5/nI3T2W6oOANqeFyd7EsNXHo19VJvXzG6i3pUl5SFOh+bLp5Dn8c7+is2BJ4hAAIAAFCAIV1VJQUpZ2PS6Nu9Eer95MGLocPSr5VQw/PlzivqNmmP1JKyPBwEcduDryarf+Zkb6uThTp/I0PnOSRbT9+07hqgJUuWUGBgIDk7O1P37t3p+PHjpe6/ePFiatGiBbm4uFBAQABNmzaNcnPvfXg++ugjsrGx0bi1bNmyGl4JAACA4QzpqiopSFlx4Jo6KJJvX30oqtQuLXnQ1UhPDQ/f/z3khrpNUhAl7bbshc4iy6MOcGIz1I+1kR1Lepyzg53Oc8jba7U1QBs3bqTp06fT7NmzKSQkhDp06EBDhgyhxMREvfv/8ssvNGPGDLF/aGgorVy5Uhzjvffe09ivTZs2FBcXp74dOnSoml4RAABA6bTnxCktU6M3SCGiv88Xd33J8W4/HoostUtLHnT5ebrQvOHt1MENP8+YHoHqNsm73qRYy/Fulkdqc1zGvQREYVHxV/njpm86Sz1lkx/KWXUN0MKFC2nChAk0btw4at26NS1btoxcXV1p1apVevc/cuQI9erVi5577jmRNXrggQdo5MiROlkje3t78vX1Vd+8vO71UeqTl5dHmZmZGjcAAICqoD0nDgcLL609odEVJgVJHCRIQYkUpIzv00hjOLkksI5rmcPStbvHRnRtQN0b1RbfP9GxHq0JjhLfj119gmb8fq/rTZJxu0AjwNkXlqT+2cqDkTTz93MaXXbs8NUUnSyR0stgiDYo9cT5+fl06tQpGjRo0L3G2NqK+8HBwXof07NnT/EYKeCJjIyk7du304MPPqixX3h4OPn7+1Pjxo3p+eefp+jo0gvD5s2bR56enuobd60BAABUBe05cThY2BOaSEeupqi7p6Qg6XL8LXGTLx8xrncjje4mybUU3e4kDjSkLEtJxdS5d4pTN1tCbmgELvqCrPCELJ0AR7I/PJl+PRGjE4TJ9539cGuTWAZD0QAoOTmZCgsLycfHR2M734+P1z87JGd+5syZQ7179yYHBwdq0qQJ9evXT6MLjOuI1qxZQzt27KDvv/+erl27Rn369KFbt+59gLTNnDmTMjIy1LeYmJhKfKUAAGCt9BU665sTh52OTtOpCUrNzqfgyHsZFC5K5qzJ0Da+Go91c7TTe8zX+jcVWaQ5f14sccRXek5+8TYDXk/SrdwSi5ol+oIzSUxajkksg2ESRdDlsW/fPvrss89o6dKlomZoy5Yt9Pfff9PcuXPV+wwbNoyefvppat++vagn4gxReno6bdq0qcTjOjk5kYeHh8YNAACgsgudS5oTh60Nvq7OEElZIik4qenqIL7m5N8RwVRkcrbo8mKdGtSk7PxC9XFa+967hnVqWEs896rDUSV2j2Xl3RH3S4lbBDsbInu7ssMGfdkhyerDUfRir0DydrfiAIjrcuzs7CghIUFjO9/nuh19PvzwQxo1ahSNHz+e2rVrR0888YQIiLgLq6jobvWVlpo1a1Lz5s0pIuLecEEAAIDKWEairOPIa26kIe1l4fobvrGP/rwkvrbxKw5qiopUIqC5En+L/GsW18+08df8o/1S/L0ME3d1cfZHX2bGzsaGGtZxofScAnUNUGk8XBzoZFQqVQQHYYNb+5K3hzNZbQDk6OhInTt3pj179qi3cRDD93v06KH3MTk5OaJOSI6DKKYqIeTMysqiq1evkp+fX6W2HwAAzFt5Zkw25jjaNTerD10rs/uoJFwfxAqKVFRwd7hVvbsB0E9HS65z5Uvj8ag08f1z3e7V3NjeLUJ2c3SgO3cb5edZelBSWKiiKwlZVBFKj/wymS4wHgK/YsUKWrt2rRjWPmnSJMrOzhajwtjo0aNFfY7kkUceEXU9GzZsELU9u3btElkh3i4FQm+99Rbt37+foqKixKgxzhLxz3i0GAAAWLbyZHUMnTDQmOPom8Dwx4PXSpwTpyxS3HQ5LpPOxaaL7/09nWl4GVkbuV+O3QuU7m/mRf1beFNK9r02OznoryOSZN7tKjMWv3SlR37J6c5xXY1GjBhBSUlJNGvWLFH4HBQUJIqXpcJoHr0lz/h88MEHYmJD/nrjxg2qW7euCH4+/fRT9T6xsbEi2ElJSRE/54Lpo0ePiu8BAMCySdmYwa19Su1m4ezMjLvdUZydmT+8XYmjkjiYWn8smp7v3kDjmLx95v/O03+XE3WOo28CQ87bTOzdmFYcjBQBDXdJlVYvo8/Oiwl06npxRuebvRE0Y2hLEVgYchj5PvvCksW5ul1wr3ZIyixVpfubm861WNEAiL322mviVlLRs/b8PjwJIt9KwtkhAABrV9JF29IZktUpaXkJvjhzF432eSspqLoUlymGr0vkx+EJDLUDHD72uN6BdCkugw5FpNCiZ4JEoNTIy5Wm3p0TqCwn7wY/0vN9vuMKzXiwJc3fflkjwDE0KIpMKu7SsrO1oaS7XXiGPlb+XCO61qcNJ2JL3Y+PKS2iagrMahQYAABUb32LOSlrZfOylpfgi3N5ztvNdN11rOQrpT/Q6t40L/KJ/24XFGdamnrXoGmDm1NTb3dxf0yPhiU+Fz/eR8/IKX6+9vVq0upxXdXb1ozrSl8906HEY3k4F3d1cZAYk1r8Gmq7OerMT1SWZ7rUF1/5uV+4795kjaW9BlOp/2EIgAAAwOwZsrJ5actLlFacW1JWide50sbH3XUpXmTg5EPG5RP/ZeXeobb1PHSGgnO7SsKPb+lXHCjpa7d8Xh3+PlfWtaUtM7f4Zxwk/naqeN47BztZBsjAOqWggJrq5+PX0i2wVon78nnhZTdMJfvDEAABAFigyirwNRelZXW08UVYe3kJKTujfd60s0rSkhW8/c3Nut1WE/o0FnPucAaJJzyUJij0dCmex4el386ngS3vdadx8DBlYDNqXNdNIwDhLy/1DlQHGbVdHTWeS95uPsb43o3Ejb9vV8+zzHl9VCqi+Mzi13kzPZeiUrLF9+8ObakOEPnrM52LMz3az83Pwe3m5+NzLe+e084SmcLMz9oQAAEAWGlXkCUpb1ZHmkOHff98J3Fx1j5vP+y/Kgql5Vklrvk5FJFc4nIQ0pQsN9Jy6FpKNvW4uxDojbvdZTyPT3JWPtWVZX84EOKusJa+HiKgWHS3+4q7lp7oWBxA/HsxnraeuanxXPKggo/xwcOtxY2/b1e/Js1/sl25Rp1l3C4e5fVs1wBxbOk5RvcM1MkM8QzT/Bzcbn4+fQGodpbI1CAAAgCw0q4gcxu6Xtp+nAUZ3ulepqKsxTYjErPI2aH4EpiZe0fveZv3z2W9Qc7xa6l6L/a1XB1o+cFr4vtXfg4Rj+17d9RT5u3iyQbTcvKpsEilEQBpB0JSTZDUtcQzJ3+3N6JchcmMgyMpkCkPNyd7dcAitUEemD0W5Ecv3New7FXrtbJEpgYBEACAlXYFmQpDC4/L2o8DDmf74svazGEt1dkRfYFTRFIWdWlYvAr61aSsEjMY+mw4EaO3e0maUVm05e5XHw8nquPmSFHJ2aINPIMz0xcA6cNB0aDWPnrbpq8IW1t5My8OdjbkoLXchXZgNqFPE52RhRxoco2PbSlZIlODAAgAwIKI4dcKzL5bkWUlDK1XKmu/o5EpNPDuyCt5kKQdOHE3FWeAuBC5bg0nUf+i77xJ9G2XxyN80Z/Yp5HeDM3VpGxqUreGWNGd28DBFuPnLYmUcZGyJvqyK3yXsytlkTJIhnaF1XCy19uG8mac9GWJTA0CIAAAC8J/ictHE1XX7LvGDrs3tF6prP1i03JEnY1UcyJlWvRJysqjjNsFFBRQi/q3rEuRyVk6503CC46+M7SF3uM42RdHFXzRH9e7kd4g4/OdV8SbEJNWnK1Ju5slKi0DJGVcpKyJdnaFv3J9D2dXysLHmPVIG53sTGndX/raYGhQJGWc9GWJTA0CIAAAC8Kz+d7MuE0j7o6+4TqXh9r7kznXK5W1H2ed5vx5SQR7/VrUFUEQd3GVlDni7I80D08bf08RLM3bHkrRqTliAkS56yk5lHh3pJS2Rl411Bd9KUjRjjG4rSeiUinm7ggrDtTcnez1DqE3NLtizIgq+eM/eqS1TjtttDJA+ugLiswZAiAAAAty8WYm5RYUUddGxfUt+YUq2nA82iSH3ZdUr/TlzisaXWll1TVx1unfSwnUsI4rNfNxpxd7NxLdWilZeXozR1cTs8jelldCdxUrqRcUquiHA5FiUdC2Wiur89OuDY6i3k3r6GRQOHAa2MpbnRHhIENftoiDoDt327/pZCw53S2+Li95YXJFHt8lsLbOCLFudz8vrneH7RvLmK4zpSAAAgCwICejUsnJ3pZ6NK4jLkRD2vjQykPXqnSdJ2OH3euru+GL8u8hN9RdaRwI7b6UYFBdk1QT0/FuN9jW0zdoxu+6maOzMekU6OUmin1b+Xmoj80XbQ/ZfD0SDr4m929GXz6tObsyH3bf5SQRjEn4eGXhYfDn7y5oWp3BhfzxI7Tqde5rXEejC8xY5pQlQgAEAGBBTkaliS6gerVcxYXojYHNKC4jl/48e1NvoXJFipdL657iC3xZx+Vuo/b1PXUmEpTjQIgnFuTRSRKej0Ze1xSWUFzv07B2cUBUv5aLGHl1MCJZpzCZg5X9YUkUUMtFfcH3uXuxbuXnTp0a1ipxPiF9sytrj7CTsizyyQz1OX8jg6o7uNB+vJdGvU7x926Oii8RWm0QAAEAWAge3XTyeip1DSzuzmA8uR7XxSw/EEkJmbk6hcrGFi9LgdPp62l6u6f4Al/WcW/nF1J4YhY92bGeuqiYu7v0daXxc0jz0NzXqLa6BkbMyLypeEbmJfuvivs2NjYiCEzSU7vDwU1SVj55uxcHAbx//N0g7UBYMh0IS9IpOJaCLfnkiSVlorTnzOE1ufQVH+s7lpJc7tYkVTQDZE4QAAEAWIiolBzRvdJFa02mifc3FsOw915JqrTlMaTASZrhWI4v+IZc4P+7nEg5+YU0tJ2vuN+5QS1RyyPvSpPmuunZpI6o72FnYjIoJjWb5vx5sTj7RLrF0RwA8egubZ0aFJ+b+rVd1NkrCR9HWs1dX8Fxaz8PUfOjLzgqac6c5j7uBo/Aqm7esi4xqfYnIvGW0dlAc4MACADAQvBoI+564W4cOa4HqlfThRbuCtOp0zGkeLm0brK1wddFd5L8At/C152K7vaJ6TuudLzfT8WIuh0pgAi+lqrehx/O9Tsv/3RK3D8YniyWg2C3CwrpnwvxomuspOLo9NsF6lXXJVzIfDq6eL0qD2f7Uour9RUcc3CzckzXco/G0jcjsyksUeIt6xJzudv1dTY2o9zZQHOFAAgAwIIKoFv4uJOHs2YhL3fxyGcN5uCCMx/f/RduUPGyvm4yKbCJS78tLu7SBX7qwGYUGneLXlpzssTjSsc7FJFCD7f3ExmI4R3r6Sw7wXfV2R0isRwET+rX3KeGKIzWh7ukXB1tafXh4iUp5DiYKbx7wI/+vETnYzPKtX6Y/Djyr8YULJvaEiWuFRz9ZY4QAAEAWFABtLz+R8KZDu1iYM58fPlvmFFrholRX6tPqI8TEp2mDgaCGtQUhb/6uqW05RcW0YPt/EQG4u2hLcrsJuLnGtzaV6yafjzq3srj0sOkLqns/EK9S0f8IVtMlNv1+Y4r9O4wzZXP5aurV2jElVbBcU5e8UKjprpEiYtsXqLK6CI1BwiAAAAsAM95E5mcrVP/U9pSCoZekOXdZOpRX1p1M9I+nGlSlXFcad/GXm4UcHfklvZsx/zFpoTszr8X9Wd/pC4pvQtzai1fIbWrfb2aeru0ShtxZUxw1LGU0WWmYN+VRJPqnqsOCIAAACzAyevFGRF9GSB9SynMkGU+JHyfL8jymh+NOX5Wn6Cnlh3RWzfDI7o4KOCantIu9PLjcWZKfqGV18qsHtdVY7K+0rI70l0pC6Xv9cozPdrtKu8Eg8YMR9fXpupYosQQcRm31fVhptg9V1UQAAEAWEj9j7+nc4mjr7SXUni5bxOd0Um8LISvh7O6RudSXKbmHD9EdCMtV29mhru+OCjg9ankS0LIL/Q6cwaR7oVWHozoW/5Bb3bHpvyvtzK7u6prOYuqcq2MmbYtFQIgAAALcCIqTSxxUBrtTIf8gjypb2Mxd4+8TobXrdJXS8OTFZaWyeDjSiuBrxjdRX2hN+ZCq91mfZmUmcNa6Q1gSnu9hnZ3VbaKLmdRFRrpCSpNqXuuqiAAAgAwc9z9dOFGBnXVU/9TFulCzAumPhbkTx/+cUG9kvr6o7p1IHxhHNc7sMxMRkvf4nlw7kjDrgy80BqSjdEOZCbc31hvAKPvWKYYgCjNz4S756oSAiAAADN3NjZdLORZVgZIX0Ag3zbn0bai6+rNzcUzK4fG36JeskVA5RfGsgIJXo5CyiJpX2glpU0kKAUzJQVEBg1FN8F1qUx1sdARJto9V5UQAAEAmDEuVP56dzjVcLIXsw6XRl9AIN+WU3CHbmkN1z56NVW9CGh5LoycAXK0s6W0nAKN7UPb+qm/N+R4lR3EKB2AmGJQZq3ZMetZ9AMAwAJxwXJwZIpYRsKugustiPmC9NToSIuAasyKXEYg4ePpIhYX5fXH5MLvLlyqfbzqCmSkAAQAGSAAADNQ0nIUhXerilv7e1T4OUqq0dE3ssyQTEaTujXoapLmelxhCVmVsi6WKWdSzJW3iXbPVRUEQAAARihtfayqOG5Jq7afiSme/4fX+qqqYlheBNSYC2MTbw6AOKt0L60UlnCLGtR2taoLrbnwtrKgEgEQAIARSgpIqvO4PIngR9suie8X7LxcKbP36iuGNfbC2KSuG2XcLqCU7HyNAKiVn4dVXWjBNCEAAgAwISWtzq69PSo5W6yWbsiaW0oVw3IXGLuamKURAJVVrA1QHRAAAQBUYqBSERrLTsjWY9JejmLED8H04NcHylxzS+lakAZ1XEVhdsTdOiBeryw5Kx8BEJgEBEAAAJUUqFSkRkhnmQgViQzPtI1nijM9suUjjl1LFauoV9XsvZVVC+Jkbyfqfa4mZqsLoFlzn+LMEICSEAABAJQjeNEXqOjreipvjZC+ZSL47t7LiTqZHvZk5wCzmL1XPhIsPPEWOdhxkOamdLMAEAABAGgrLXj53+kbetezikwqznIYi7M3+ratfbFriZkec5i9t4m3mzoA4vqfxl41yMEOlx5QnuKfwiVLllBgYCA5OztT9+7d6fjx46Xuv3jxYmrRogW5uLhQQEAATZs2jXJzcyt0TAAAQzI0L605QZ/vuKL35+9tOUdbT99Qz8tTnhqh7Lw79MHWC+RVw1Eno9MhoFapmR5Tn72XM0A30m+L9crC4rOo+d01wgCsOgDauHEjTZ8+nWbPnk0hISHUoUMHGjJkCCUmJurd/5dffqEZM2aI/UNDQ2nlypXiGO+9957RxwQA0CYPXrLy7tC8f0LpgUX76XL8Lfr++U40Xysgmdy/KTXxdqepG8/Q4IX76Z3fzpZaIyTvYuM5ct757RzdTL9NGybepzejU1qmx9Qnr+MAiLsJI5OzKCzxFjX3Rv0PmAZFl8JYuHAhTZgwgcaNGyfuL1u2jP7++29atWqVCHS0HTlyhHr16kXPPfecuM9ZnpEjR9KxY8eMPiYAgBwHKzO2nFePuHJzsqc7RUX0Wv9m9HLfxuTsYCd+5uvpTGNXnxABSb8W3mLb+dgMWrDjMm06Gas+nlQjdH/zuuqsjdTFNri1D209c4P+Ph9Hy17oRE293Sm3oEhvRqekTI+pL+3AcwGxo5GplJ5TgAwQmAzFMkD5+fl06tQpGjRo0L3G2NqK+8HBwXof07NnT/EYqUsrMjKStm/fTg8++KDRx2R5eXmUmZmpcQMA66NT4Hy3e+rXCffRlEHN1MFPSQFJu/qe9Gr/JjrHLR6erlsjdCYmneb/c5km9WuisUioJanp6ii69raduSHu13FzVLpJAMoGQMnJyVRYWEg+Pj4a2/l+fHy83sdw5mfOnDnUu3dvcnBwoCZNmlC/fv3UXWDGHJPNmzePPD091TeuLQIA61PSSCwpK2PselqMA52TUakaXWzz/wmlXk296K0HWpTZpWXqXV2laVy3Bp2LzRDfowAaTIVZfRL37dtHn332GS1dulTU92zZskV0b82dO7dCx505cyZlZGSobzExMZXWZgAwH9Kq54bMrVNSQKJvPa1xvQIpv1BFTy0LpmFfHxBdaywrr5D6t/DWWMW9pDl4zHmdJlEHdPf7iq5YD2D2NUBeXl5kZ2dHCQkJGtv5vq+vr97HfPjhhzRq1CgaP368uN+uXTvKzs6miRMn0vvvv2/UMZmTk5O4AYD54oLi9cei6fnuxWtXldexyBR649cz1KC2C8Wm3RaZoNLm1imt9oaLlH08NGuEiopU9NPRKJp9d+0uyad/h9Kwdr4mN39PVdQBVfbM2QBmmQFydHSkzp070549e9TbioqKxP0ePXrofUxOTo6o6ZHjgIfxSApjjgkAlsHQiQf1TXK470oijVl9nNrX96R/ptxfKXPraNcI2draUDM9S0BU1vIVpoyHwUtKmjkbwKq6wHi4+ooVK2jt2rViWPukSZNERkcawTV69GjRPSV55JFH6Pvvv6cNGzbQtWvXaNeuXSIrxNulQKisYwKAddMOlP45H0cT1p2k3k29RMDDo76qam4dffVBlbV8hSkXlq89EqW+X5mLtgKY7TD4ESNGUFJSEs2aNUsUKQcFBdGOHTvURczR0dEaGZ8PPviAbGxsxNcbN25Q3bp1RfDz6aefGnxMALBMxixO+vupWHr7t7NiXa1FI4IqtUBXX42QVB/EI83K6mKz5MJyKetlya8bTJ+NivuOQAMPg+fRYFwQ7eHhoXRzAMDAuXv4fzNeUYInKiyp64q7u7g2Z9R9Demno9dpRJcA+mx4O43i3IrWE5VFasOacffmELJUnOnpNf8/jSCIs16HZvRHAASKXr8VzQABAFTGBVYKfuSrqEen5IhJ93w9nMWF1tvDif44c0M9ySEHP9ztNf/JdiKzXJ2TC5r68hWVyRqzXmAeEAABgEkyJAsTkZhFb20+ow5+JHx31eFrdLuM+XuCryZTfGYuLsZVTN+oOAClIQACAJMkXy5COwBKz8kXP/sp+Dp5uTsR52/kMRB3sfz3Vj9yd3ag+IxccTsUkUzL9l/VOE6hihSpRTHnSQ2NZU1ZLzAPCIAAwGzcKSyiX45H08JdYVRwp4imP9CcXuzVSHRtldTF0tS7hrg18Xaj5Qeu6tSiKDECy9TX7wKwBmY1EzQAmCd9c++Ud1TXgbAkGvb1QZq97SI90NqH9r7Vj17t11Ssz1XaaumlzdCMWpTqY41ZLzBtyAABgKLdWYasyN7C150ux9+iboG16c/XelPbep5GdbGgFkU5yHqBqUEABAAmvyI7Bz+fPtGWnuvWQGfEVnmhFgUAGAIgADBIRebGKWuSwpSsPDobm05notNpX1iSzsR5rLFXjVKDH3SxAEB5IAACgCrpxtLbnbXmBM15tC219HOnszHpdObujRcfZbXdHKmlr7veUV1lFSsb2sWCQAkAGAIgAKgykUlZOpMUfvjHBfG9k70ttavnSUPb+FKHgJoUFFCT6tdyEVkeDpqqauI81KIAAEMABAAV7sbiFXV4xe/QuFsUGpdJl+Mzxfe8DpQ+84a3pac6B5S49haKlQGgqiEAAoByd2Px8HPO1lyOKw50QuMz6VbuHfFzTxcHauXnTv1a1KWR3QJo3j+XNWZq5u4sDmjKWngUxcoAUJUQAAFA+UZlqYiW7I0QdTpNvGuImp2+LZpQaz8PUdvDa2/Ji5U5IMI6UABgahAAAUCpLsRm6B2VtebFrtS3edldU+jOAgBThJmgAaBEXMsz+8+LOtu5G6u5j7vBxzGmOwujtQCgKiEAAgC9tp+Po+FLj5CHswO9O7RFtS8hIY3WKu+cQwAAhkAXGABoKCxS0cJdV2jJ3qv0cHs/+vyp9uTqaE+t/DyM7sZCNgcATA0CIABQy7hdQFM2nBYLj84Y1pJevr+xuqC5IqOyMPcOAJgaBEAAIIQn3KKJP50Sy1KsHteN+javq3STAACqDAIgAKAdF+LpzU1nqH4tV/rz9d7UsI6bzj7oxgIAS4IACMCKFRWpaPHuMPrmvwh6sJ0vffFUB3Jz0v/fArqxAMCSIAACsNJV3DNzC2jahjP035VEentIC3q1X5NSV1sHALAkCIAArHAV94jELJr400lKupUnRnX1x+SEAGBlEAABWJldlxJo2sYz5OvpTNte602NvHTrfQAALB0CIAALJ63enngrlxbvTqDFu8PpgdY+tHBEENUood4HAMDS4X8/ACtZxf3FNSfF1+mDm9Nr/ZuSrTS1MwCAFcJSGABWsoo745jn6S71EfwAgNVDAARgoa4lZ+us4s73o5JzlGoSAIDJQAAEYKEC67jqXcU90Et3OwCAtUEABGChDoQli69SZ1d1reIOAGAOEAABWKDQuEyave0ijezWgFaP6yq28Xw/I7o2ULppAAAmAQEQgIW5lVtAr64PoSZ1a9DsR1pXaBV3AABLhWHwABZEpVKJkV88w/Nfr/cmZwc7pZsEAGCSTCIDtGTJEgoMDCRnZ2fq3r07HT9+vMR9+/XrJ9Yr0r499NBD6n3Gjh2r8/OhQ4dW06sBUM7Px6Lpr3NxtODJ9hSIGZ4BAEw3A7Rx40aaPn06LVu2TAQ/ixcvpiFDhtCVK1fI21t3faItW7ZQfn6++n5KSgp16NCBnn76aY39OOBZvXq1+r6TE9L/YNnOx2bQ3D8v0ZgeDemh9n7q7d7uTjRlYDPxFQAATCQDtHDhQpowYQKNGzeOWrduLQIhV1dXWrVqld79a9euTb6+vurbrl27xP7aARAHPPL9atWqVU2vCKD68cruk38JoZZ+7vTeQ600fsYLoE4b3FxjJXgAAGunaADEmZxTp07RoEGD7jXI1lbcDw4ONugYK1eupGeffZbc3DTT/fv27RMZpBYtWtCkSZNEpqgkeXl5lJmZqXEDMKe6n3c2n6O0nHxa8lwncrJH3Q8AgEkHQMnJyVRYWEg+Pj4a2/l+fHx8mY/nWqELFy7Q+PHjdbq/1q1bR3v27KEFCxbQ/v37adiwYeK59Jk3bx55enqqbwEBARV8ZQDVZ/XhKNpxMZ6+fLoDBdTGJIcAAGZRA1QRnP1p164ddevWTWM7Z4Qk/PP27dtTkyZNRFZo4MCBOseZOXOmqEOScAYIQRCYg9PRaTTvn1B6qXcjGtLGV+nmAACYDUUzQF5eXmRnZ0cJCQka2/k+1+2UJjs7mzZs2EAvvfRSmc/TuHFj8VwRERF6f871Qh4eHho3AFOXnpNPr/1ymtrW86R3h7ZUujkAAGZF0QDI0dGROnfuLLqqJEVFReJ+jx49Sn3s5s2bRe3OCy+8UObzxMbGihogP797I2MAzL3u563NZyk7/w5991wncrRXfDwDAIBZUfx/Te56WrFiBa1du5ZCQ0NFwTJnd3hUGBs9erTootLX/fX4449TnTp1NLZnZWXR22+/TUePHqWoqCgRTD322GPUtGlTMbwewBKsOBhJu0MTaeEzHaheTaztBQBgdjVAI0aMoKSkJJo1a5YofA4KCqIdO3aoC6Ojo6PFyDA5niPo0KFD9O+//+ocj7vUzp07JwKq9PR08vf3pwceeIDmzp2LuYDAIpyMSqUFO67QK32b0ICWmgMIAADAMDYqzqWDBi6C5tFgGRkZqAcCk5KSlUcPfXOIAmq70K8T7iN7O8WTuAAAZnn9xv+eAGaiqEhF0zadpfzCIvp2ZCcEPwAA5twFBgCG+X7/VToYnkRrx3UjX0/M6gwAUBH4ExLADByNTKGv/r1Cr/VvSvc3r6t0cwAAzB4CIAATl3Qrj17/9TR1b1SHpg5qrnRzAAAsAgIgABNWWKSiqRtPEw9V+HpkENnZ2ijdJAAAi4AACMBEJGbm0qJdYeKr5Nv/win4agp982wQebuj7gcAoLIgAAIwEYm38ujrPeHiKzsUnizuc7dXz6ZeSjcPAMCiIAACMEEJmbmi66t3Uy+a3L+p0s0BALA4CIAATLDuh4ueud5n0QjU/QAAVAXMAwRgIpKziru+Vhy8Sqeup4mZnr1qYPkWAICqgAwQgAnYeCKaxq05Ib7/61w8DW7tTd0a1Va6WQAAFgsBEIDC4jJu08wt58VQd8muiwliOwAAVA0EQAAKOxyeTEVaSxIXqoiiknOUahIAgMVDDRCAQiKTsuibPeG09cxNnZ/Z2dhQoJerIu0CALAGCIAAqll0Sg598184bQmJFZMbzn28LfE4r1l/XBCZIB709dnwtuTn6aJ0UwEALBYCIIBqEpOaQ0v2RtDmU7FU282RZj3cmp7t1oCcHezEz+vXcqGxq0/QqrFdqV8Lb6WbCwBg0RAAAVSxm+m3ReCz6WQMebo40MxhLemF+xqqAx+JNOQdQ98BAKoeAiCAKpzNeeneCPr1eAy5OdnRWw+0oFE9GpKrI37tAACUhv+JASpZ4q1cWrYvktYfuy6yPFMGNaMxPQOphhN+3QAATAX+RwYoAa/Kvv5YND3fvQF5e5S9EntKVh79cCCS1gVHkYOdLb3arymN6x1IHs4OBj2ft7sTTRnYTHwFAICqhQAIoIzV2Qe39ik1AErLzqcVByNpzZEosrWxoYl9GtNLvRuTp6thgY+En2Pa4OaV0HIAACgLAiAAI2XkFNDKQ5G06nAUFalUopuLg59abo5KNw0AAMqAAAignDJzC2j1oSj68VAkFRQW0egegTTx/sYYvQUAYA0BUHR0NF2/fp1ycnKobt261KZNG3JywgUALFdW3h1aeySKlh+IpNsFhfRC94b0Sr/GYjJDAACw4AAoKiqKvv/+e9qwYQPFxsaSSrZ6o6OjI/Xp04cmTpxITz75JNnaYpkxMG/JWXni6420HDockSwKnLNy79DIbgH0av+m5GNAYTQAAJgmG5U8iinFG2+8QWvXrqUhQ4bQI488Qt26dSN/f39ycXGh1NRUunDhAh08eFAER3Z2drR69Wrq2rUrmaPMzEzy9PSkjIwM8vDwULo5oICNJ6JphmyFdjsbErM2T+7flPxrYokKAABzv34bnAFyc3OjyMhIqlOnjs7PvL29acCAAeI2e/Zs2rFjB8XExJhtAATWLS7jNs2UBT+Mv31tQFOszwUAYCEMDoDmzZtn8EGHDh1qbHsAFHctOVssSirH96OScxAAAQBYCKMKdTjTk56erjf1xD8DMPdiZ212NjYU6OWqSHsAAMBEAqB9+/ZRfn6+zvbc3FxRBwRgznP7fLztEjWo7UK2NsXb+Otnw9si+wMAYK2jwM6dO6f+/tKlSxQfH6++X1hYKGp/6tWrV7ktBKgmPB7gnd/PigzQ32/0pojELBq7+gStGtuV+rXwVrp5AACgVAAUFBRENjY24qavq4tHhH377beV2T6AasNz/Oy8mEDLR3Wm+rVcKT2nQGzHBIcAAFYeAF27dk38ldy4cWM6fvy4mABRPg8QjwbjIfAA5uZ8bAZ9tv0yjesVSA+08VW6OQAAYEo1QA0bNqTAwEAqKiqiLl26iPvSzc/Pz+jgZ8mSJeK4zs7O1L17dxFclaRfv37qLJT89tBDD6n34SBt1qxZok2clRo0aBCFh4cb1TawfLdyC+i1X0Ooha87zRjWUunmAACAqRZB85D4VatW6WznbQsWLCjXsTZu3EjTp08X8weFhIRQhw4dxGSLiYmJevffsmULxcXFqW88ASMHXk8//bR6n88//5y++eYbWrZsGR07dkzMYcTH5CJtADkOlnnOn5SsfPruuY7kZH8viPd2d6IpA5uJrwAAYGFURmjYsKHq8OHDOtuPHj2qCgwMLNexunXrppo8ebL6fmFhocrf3181b948gx6/aNEilbu7uyorK0vcLyoqUvn6+qq++OIL9T7p6ekqJycn1a+//mrQMTMyMngWGPEVLNsvx66rGr77l+rPszeUbgoAAFRQea7fRmWAePQXdy9p45ogzsoYiofSnzp1SnRRSXgNMb4fHBxs0DFWrlxJzz77rMjySHVK3D75MXlabO5aK+mYeXl5Yg4j+Q0sX2hcJn207SI9370BPdzeX+nmAABANTIqAAoICKDDhw/rbOdtvD6YoZKTk8XweR8fH43tfF8+xL4kXCvEXWDjx49Xb5MeV55jcpceB0nSjV8fWLbsvDv02i8h1MjLjT58uLXSzQEAAFMeBSaZMGECTZ06lQoKCtTD4ffs2UPvvPMOvfnmm1RdOPvTrl07sTBrRcycOVPUIUk4A4QgyLLN+uMixWXk0rbXepOzA0YuAgBYG6MCoLfffptSUlLo1VdfVc8IzSO43n33XRFMGMrLy0sUMCckJGhs5/u+vqUPRc7OzhYrz8+ZM0dju/Q4Poa8m47v8zxG+jg5OYkbWIffTsXS7yGxtPCZDtTUu4bSzQEAAHPpAuNh5zzaKykpiY4ePUpnz56l1NRUMfS8PHjuoM6dO4vskYSH2PP9Hj16lPrYzZs3i9qdF154QWN7o0aNRBAkPyZndHg0WFnHBMvHszt/uPUCPdW5Pg3vVF/p5gAAgDkFQBKuqeHAp0mTJiKDwkOKy4u7nlasWEFr166l0NBQmjRpksjujBs3Tvx89OjRerNK3P31+OOPU506dXSCM+6e++STT2jbtm10/vx5cQyuTeL9wXrlFhSKup96tVxozmNtlG4OAACYWxcYd38988wztHfvXhFw8CSDPDv0Sy+9RLVq1aKvvvrK4GONGDFCZJI4e8QBFXdT8ZpiUhFzdHS0GBkmd+XKFTp06BD9+++/eo/JtUgcRE2cOFGsWt+7d29xTO6mA+v18Z+X6Fpytqj7cXU06qMPAAAWwobHwpf3QZxR4YkKf/zxR2rVqpXoAuMAaOfOnSKjc/HiRTJn3GXGo8EyMjLIw8ND6eZAJdh29ia98etpmj+8HT3brYHSzQEAAIWv30b9GcyZFw526tfXrKFo1qwZXb9+3ZhDAlSZqORsem/LeXq0gz+N6IrRfQAAYGQNEHcvubq66mzneiCMpgJTknenUKzz5VXDkT59oq3osgUAADAqAOrTpw+tW7dOfZ8vKjx6i9fg6t+/f2W2D6BC5m2/TGHxWfTdc53I3dlB6eYAAICJMKoLjAOdgQMH0smTJ8U8QFx0zHU/nAHSN0M0gBJ2XIinNUeixIivtvU8lW4OAACYewaobdu2FBYWJkZXPfbYY6JLbPjw4XT69GkxJB5AaTGpOfTOb2dpaBtfGnVfQ6WbAwAA5p4B4uUvhg4dSsuWLaP333+/aloFUAEFhUX0+q+nycPFgRY81R51PwAAUPEAyMHBgc6dO1fehwFUmy92XqELNzJo8ys9yNMFdT8AAFBJXWC8/ATPxAxgav67nEDLD0TSu0NbUscGtZRuDgAAWFIR9J07d2jVqlW0e/dusZaXm5ubxs8XLlxYWe0DMFhcxm16c9NZGtjSm8b3aaR0cwAAwNICoAsXLlCnTp3E91wMLYd6C1DCncIimvLrGXJ2sKMvn+6AzyEAAFRuAFRYWEgff/wxtWvXTqz7BWAKvt4TTqei02jDxPuolpuj0s0BAABLqwGys7OjBx54QCwyCmAKDoUn03d7I2j64ObUNbC20s0BAABLngcoMjKy8lsDUE6Jt3Jp6sbT1LupF03qizmoAACgCgOgTz75hN566y3666+/KC4uTqy+Kr8BVIfCIhVN3XBG1PssfCaIbG1R9wMAAFVYBP3ggw+Kr48++qhGsalKpRL3uU4IoKot3RtBwZEptP6l7lTXHYvwAgBAFQdAe/fuNeZhAJXmWGQKLdodRq8PaEY9m3op3RwAALCGAKhv376V3xIAA6Vk5dEbG06LgucpA5sp3RwAALCWAIjxKDCeDTo0NFTcb9OmDb344ovk6YlVt6HqFBWp6M3NZ6mgUEVfP9uR7FD3AwAA1VUEffLkSbHq+6JFiyg1NVXcePZn3hYSEmLMIQEMsuJgJO27kkQLn+lAvp7OSjcHAADMlI2KK5fLqU+fPtS0aVNasWIF2dvbq5fHGD9+vBgef+DAATJnPJKNM1kZGRnk4eGhdHPgrlPX02jED8E0vk9jmjGspdLNAQAAM75+GxUAubi40OnTp6llS82L0KVLl6hLly6Uk5ND5gwBkOnJyCmgB785SD4eTrTx5R7kYGdU8hIAACxYZjmu30ZdRfig0dHROttjYmLI3d3dmEMClIhj9Ld+O0tZeXfom5EdEfwAAECFGXUlGTFiBL300ku0ceNGEfTwbcOGDaILbOTIkRVvFYDMmiNRtOtSAn3xVHuqX8tV6eYAAIC1jgL78ssvxYSHo0ePFrU/zMHBgSZNmkTz58+v7DaCFTsXm06fbQ+lcb0C6YE2vko3BwAALIRRNUASrvW5evWq+J5HgLm6WsZf56gBMg2ZuQX08DeHqKarA/32Sk9ytEfXFwAAVM7126gMEB+Yl7uoXbs2tWvXTr2dh8PzqDAEDVBRHJfP3HKe0rLz6aeXuiH4AQCASmXUVeXZZ58VNT/aNm3aJH4GUFG/HI+mv8/F0bwn21HDOm5KNwcAACyMUQHQsWPHqH///jrb+/XrJ34GUBGhcZn08Z+X6PnuDejh9v5KNwcAACyQUQFQXl6euvhZrqCggG7fvl0Z7QIrlZ13hyb/EkKNvdzow4dbK90cAACwUEYFQN26daPly5frbF+2bBl17ty5MtoFViAxM5cW7QoTXyUf/nGB4jNyacnzncjZwU7R9gEAgOUyqgj6k08+oUGDBtHZs2dp4MCBYtuePXvoxIkT9O+//1Z2G8FCJd7Ko6/3hNPg1j7k7eFMv52KpS0hN2jRiA7UpG4NpZsHAAAWzKgMUK9evSg4OJgCAgJE4fOff/4p1gY7d+6cWCcMoLzCE27Rh1sv0NOd69MTHesr3RwAALBwRmWAWFBQEK1fv75yWwNWKbegkN7cdJbq1XKhjx9ro3RzAADAChicAcrOzi7XgQ3df8mSJRQYGEjOzs7UvXt3On78eKn7p6en0+TJk8nPz4+cnJyoefPmtH37dvXPP/roIzFLtfymvWgrmIbkrDzx9dv/wikqJZuWPNeJXB2NjskBAAAqPwDiLi5e5iIuLq7Uyet27dpFw4YNo2+++abMY/JaYtOnT6fZs2dTSEgIdejQgYYMGUKJiYl698/Pz6fBgwdTVFQU/fbbb3TlyhVasWIF1atXT2O/Nm3aiHZKt0OHDhn6MqGabDwRTePWnBDf7w9Lpofb+1ELXyykCwAA1cPgP7f37dtH7733nsiwcKDSpUsX8vf3F5mbtLQ0unTpkqgL4pmgZ86cSS+//HKZx1y4cCFNmDCBxo0bpx5F9vfff9OqVatoxowZOvvzdp5t+siRI2LtMcbZI50XZW9Pvr5YN8pUxWXcFrM8yxdh2Xr6Br01pAX5eboo2TQAALASBmeAWrRoQb///juFhYXRM888Qzdu3BBZGM7AcHDEWRj+nrMzr776KtnZlT6EmbM5p06dEqPJ1I2xtRX3OZDSZ9u2bdSjRw/RBebj40Nt27alzz77TCzLIRceHi6Cs8aNG9Pzzz9P0dHRZc5rxOuHyG9Qda4lZ1OR1gp0hSqiqOQcpZoEAABWptwFFw0aNKA333xT3Ji0lirX2pRHcnKyCFw4kJHj+5cvX9b7mMjISPrvv/9EUMN1PxERESLY4gkYuRuNcR3RmjVrRMDG3V8ff/yxGJl24cIFcnfX38Uyb948sR9Uj0ZebmRrQxpBkJ2NDQV6WcZiugAAYPqMXmFy5cqVIgPDXWB84+9//PFHqkpFRUXk7e0tJmHkCRdHjBhB77//vug6k3D90dNPP03t27cX9UQcKHHhNA/XLwl32fECr9ItJiamSl+HteNurqCAmur7HAx9Nrwtur8AAKDaGDXkZtasWaJ+5/XXXxddUoy7raZNmya6m+bMmVPmMby8vEQ3WUJCgsZ2vl9S/Q6P/OLaH3n3WqtWrSg+Pl50qTk6Ouo8pmbNmmKkGGeLSsKjyfgG1eNqUhadiUkXa32tPxZNq8Z2pX4tvJVuFgAAWBGjMkDff/+9qPfhrqNHH31U3Ph7zswsXbrUoGNwsMJZHJ5BWp7h4ftSUKVvAkYOZHg/CdckcWCkL/hhWVlZdPXqVbEPmIZv9oSTj4czPdmpeMJDrxoIPgEAwAwCIK654VFg2jig0bdIakl4CDwHUmvXrqXQ0FCaNGmSmD9IGhU2evRo0T0l4Z/zKLApU6aIwIdHjHERNBdFS9566y3av3+/KMbm0WJPPPGEyBiNHDnSmJcKVTDj87azN2ly/6bkaG90DywAAECFGNUFNmrUKJEF4m4wOc4AcYGyobiGJykpSXSpcTcWzy69Y8cOdWE0d6fxyDAJL72xc+dO0dXGNT488oyDoXfffVe9T2xsrAh2UlJSqG7dutS7d286evSo+B6Ut3hPOPl7utAzXQIoLOGW0s0BAAArZaOShnGVA9f+rFu3TgQk9913n9h27NgxEbBw1kaao4dpB0nmgIfBe3p6ioJoDw8PpZtjMS7HZ9LQxQdp/vB29Gy3BmIVeK4B4logXgwVAACguq7fRgVA/fv3N2g/HhrPw9bNDQKgqvHKT6foUlwm7XmzLznYofsLAACUu34b1QW2d+9eY9sGVurCjQzacTGevny6A4IfAABQHK5EUC0W7w4XEyA+HuSvdFMAAAAQAEHVOxebTrtDE2jKwGZkj+wPAACYAFyNoMot2hVGTeq60SMdkP0BAADTgAAIqlRIdBrtvZJEUwc1Jzte8wIAAMAEIACCKs/+NPepQQ+1w0zcAABgOhAAQZU5EZVKB8OTadqg5mSL7A8AAJgQBEBQpdmfVn4eNKSN/sVtAQAAlIIACKpE8NUUOnI1haYNaobsDwAAmBwEQFDpeHLxRbvDqG09DxrcunhdNwAAAFOCAAgqHWd+jl9LpemDm4vlUAAAAEwNAiCo9OzPwl1h1CGgJvVv4a10cwAAAPRCAASV6kB4Mp26nobsDwAAmDQEQFDp2Z/ODWvR/c28lG4OAABAiRAAQaXZeyWRzsakI/sDAAAmDwEQVGr2p1uj2tSzSR2lmwMAAFAqBEBQKXZdSqALNzKR/QEAALOAAAgqrKiI5/0JF5mf+xoj+wMAAKYPARBU2M6L8RQal0nTBjdXuikAAAAGQQAElZD9CaM+zbyoa2BtpZsDAABgEARAUCF/n4+jsIQsZH8AAMCsIAACoxUWqWjx7jDq36IudWpQS+nmAAAAGAwBEBjtz7M36WpSNrI/AABgdhAAgVHuFBbR13vCaVArH2pfv6bSzQEAACgXBEBglK1nbtK15GyaOqiZ0k0BAAAoNwRAUG4FhUX0zZ5wGtrGl9rW81S6OQAAAOWGAAjK7fdTsRSdmkNTByP7AwAA5gkBEJRL/p0i+va/CHqovR+19PVQujkAAABGQQAE5bLpZAzdzLhNUwci+wMAAOYLARAYLLegkJbsjaBHO/hTMx93pZsDAABgNARAYLCNJ2IoITOX3kD2BwAAzBwCIChX9ufxjvWoSd0aSjcHAADAvAOgJUuWUGBgIDk7O1P37t3p+PHjpe6fnp5OkydPJj8/P3JycqLmzZvT9u3bK3RMKNv6Y9GUkp1PbwxA9gcAAMyfogHQxo0bafr06TR79mwKCQmhDh060JAhQygxMVHv/vn5+TR48GCKioqi3377ja5cuUIrVqygevXqGX1MKFtO/h36fl8EPdmpHgV6uSndHAAAgAqzUalUKlIIZ2e6du1K3333nbhfVFREAQEB9Prrr9OMGTN09l+2bBl98cUXdPnyZXJwcKiUY+qTmZlJnp6elJGRQR4eGOq9/MBV+nzHFdr7Vj8KqO2qdHMAAAAqfP1WLAPE2ZxTp07RoEGD7jXG1lbcDw4O1vuYbdu2UY8ePUQXmI+PD7Vt25Y+++wzKiwsNPqYLC8vT5w0+Q2KZefdoWX7I+npLgEIfgAAwGIoFgAlJyeLwIUDGTm+Hx8fr/cxkZGRouuLH8d1Px9++CF99dVX9Mknnxh9TDZv3jwRMUo3zhhBsbXBUXQrt4BeG9BU6aYAAABYThF0eXB3lre3Ny1fvpw6d+5MI0aMoPfff190jVXEzJkzRbpMusXExFRam80ZBz7LD0TSs10bUL2aLko3BwAAoNLYk0K8vLzIzs6OEhISNLbzfV9fX72P4ZFfXPvDj5O0atVKZHe4+8uYYzIeTcY30LTmcBTl5BfSq/2bKN0UAAAAy8gAOTo6iizOnj17NDI8fJ/rfPTp1asXRUREiP0kYWFhIjDi4xlzTNAv43YBrTgYSc91a0B+nsj+AACAZVG0C4yHq/Mw9rVr11JoaChNmjSJsrOzady4ceLno0ePFt1TEv55amoqTZkyRQQ+f//9tyiC5qJoQ48Jhll16Brl3SmiV/sh+wMAAJZHsS4wxjU8SUlJNGvWLNGNFRQURDt27FAXMUdHR4tRXBIuTt65cydNmzaN2rdvL+b/4WDo3XffNfiYULb0nHwRAI26ryF5ezgr3RwAAADLmgfIVFn7PEBf7rxCKw9dowPv9Ke67qiNAgAA82AW8wCBaUrNzqfVh6/R6J4NEfwAAIDFQgAEGnjYO3v5ftT+AACA5UIABGrJWXm09kgUje0VSLXdHJVuDgAAQJVBAARqP+y/Sna2NjShT2OlmwIAAFClEACBkJiZS+uCr9OLvRtRTVdkfwAAwLIhAALh+/1XydHell7q3UjppgAAAFQ5BEBA8Rm5tP5YtOj68nRxULo5AAAAVQ4BENDSfRHk6mhH43oFKt0UAACAaoEAyMrdSL9NG47H0MT7G5O7M7I/AABgHRAAWbkleyOohrM9jemB7A8AAFgPBEBWLCY1hzadiKFX+jYmNydFl4UDAACoVgiArNh3/0WIIe+j7kP2BwAArAsCICt1PSWbfguJpUn9mpCLo53SzQEAAKhWCICs1Dd7IqiOmyM9372B0k0BAACodgiArFBkUhb973QsTe7flJwdkP0BAADrgwDICn2zJ5x8PJxpRNcApZsCAACgCARAViYi8Rb9cfYmsj8AAGDVEABZmcW7w8nf04We6YLsDwAAWC8EQFbkcnwm/X0+jl4f0FQsfAoAAGCtcBW0Il/vDqf6tVzoyc71lW4KAACAohAAWYmLNzPonwvx9MaAZuRgh7cdAACsG66EVlT7E1jHlZ7oWE/ppgAAACgOAZAVOB+bQbsuJdCUQc3IHtkfAAAABEDWYNHuMGpc140e7YDsDwAAAEMAZOFOR6fRf5cTaeqg5mRna6N0cwAAAEwCAiALt2h3ODX3qUEPtfNTuikAAAAmAwGQBTsZlUoHwpKQ/QEAANCCAMiCLdwVRi193WloG1+lmwIAAGBSEABZqOCrKXTkagpNG9ycbJH9AQAA0IAAyAKpVCox8quNvwc90NpH6eYAAACYHARAFogzP8evpdL0wc3JxgbZHwAAAG0IgCww+8O1Px3qe9KAlt5KNwcAAMAkIQCyMAfCk+nU9TRR+4PsDwAAgAkHQEuWLKHAwEBydnam7t270/Hjx0vcd82aNeLCLr/x4+TGjh2rs8/QoUPJWrI/nRrUpL7N6yrdHAAAAJNlr3QDNm7cSNOnT6dly5aJ4Gfx4sU0ZMgQunLlCnl76+/C8fDwED+X6Mt0cMCzevVq9X0nJyeydHuvJNLZmHT6+aXuyP4AAACYcgZo4cKFNGHCBBo3bhy1bt1aBEKurq60atWqEh/DF3dfX1/1zcdHd6QTBzzyfWrVqkXWkP3pFlibejWto3RzAAAATJqiAVB+fj6dOnWKBg0adK9BtrbifnBwcImPy8rKooYNG1JAQAA99thjdPHiRZ199u3bJzJILVq0oEmTJlFKSkqJx8vLy6PMzEyNm7nh1d4v3MhE7Q8AAICpB0DJyclUWFiok8Hh+/Hx8XofwwENZ4f++OMP+vnnn6moqIh69uxJsbGxGt1f69atoz179tCCBQto//79NGzYMPFc+sybN488PT3VNw6szElREc/7E049GtehHk2Q/QEAADD5GqDy6tGjh7hJOPhp1aoV/fDDDzR37lyx7dlnn1X/vF27dtS+fXtq0qSJyAoNHDhQ55gzZ84UdUgSzgCZUxC082I8hcZl0qaX750XAAAAMNEMkJeXF9nZ2VFCQoLGdr7PdTuGcHBwoI4dO1JERESJ+zRu3Fg8V0n7cL0QF1bLb+aV/QmjPs28qFuj2ko3BwAAwCwoGgA5OjpS586dRVeVhLu0+L48y1Ma7tY6f/48+fn5lbgPd49xDVBp+5irv8/HUVhClljxHQAAAMxkFBh3Pa1YsYLWrl1LoaGhomA5OztbjApjo0ePFl1Ukjlz5tC///5LkZGRFBISQi+88AJdv36dxo8fry6Qfvvtt+no0aMUFRUlgikulG7atKkYXm9JCotUtHh3GPVrUZc6N7TsUW4AAAAWVQM0YsQISkpKolmzZonC56CgINqxY4e6MDo6OlqMDJOkpaWJYfO8Lw9t5wzSkSNHxBB6xl1q586dEwFVeno6+fv70wMPPCDqgyxtLqA/z96kq0nZtPCZIKWbAgAAYFZsVDyBDGjgImgeDZaRkWGy9UB3Coto8KID1KSuG/04pqvSzQEAADCr67fiXWBgnK1nbtK15GzU/gAAABgBAZAZKigsom/2hNOQNj7Utp6n0s0BAAAwOwiAzNCWkFiKTs1B9gcAAMBICIDMTP4dzv5E0EPt/KiVn2nWJwEAAJg6BEBmZvOpGLqZcZumDGqmdFMAAADMFgIgM5J3p5C++y+CHmnvT8193JVuDgAAgNlCAGRGNp6IoYTMXHpjILI/AAAAFYEAyEzkFhTSkr0R9HhQPWrqXUPp5gAAAJg1BEBm4pdj0ZSclU+vI/sDAABQYQiAzMDt/EJauu8qDe9Yjxp5uSndHAAAALOHAMgM/Hz0OqXn5NPrA5D9AQAAqAwIgExcdt4dWrb/Kj3dpT41qOOqdHMAAAAsAgIgE7cu+Dpl5hbQ5P5NlW4KAACAxUAAZMJu5RbQDweu0oiuAVS/FrI/AAAAlQUBkAlbeySKcvILkf0BAACoZAiATBR3ey0/EEnPdWtAfp4uSjcHAADAoiAAMlGrDl2jvDtF9Gq/Jko3BQAAwOIgADJBGTkFtPLgNRp1X0Py9nBWujkAAAAWBwGQCfrxUCTdKVLRy32R/QEAAKgKCIBMTFp2vuj+Gt2zIdV1d1K6OQAAABYJAZCJWX4wUnx9+X5kfwAAAKoKAiATkpyVJ4a+j+0VSLXdHJVuDgAAgMVCAGRCeNi7rY0NTejTWOmmAAAAWDQEQCYi8VYurQuOohd7N6Karsj+AAAAVCUEQCZi2b5IcrCzpZd6N1K6KQAAABYPAZAJSMjMpZ+PXRddX54uDko3BwAAwOIhADIBS/dGkIuDHY3rFah0UwAAAKwCAiCF3Uy/Tb8ej6GJ9zcmd2dkfwAAAKoDAiCFfbc3gmo429OYnsj+AAAAVBcEQAqKSc2hTSdi6OX7G1MNJ3ulmwMAAGA1EABVs8TMXFq0K0x8/e6/CKrp6kCjejRUulkAAABWBQFQNUu8lUdf7wmns7Hp9FtILL3Stwm5OiL7AwAAUJ0QAClkw/EYsdzFC/ch+wMAAGCVAdCSJUsoMDCQnJ2dqXv37nT8+PES912zZg3Z2Nho3PhxciqVimbNmkV+fn7k4uJCgwYNovDwcDIle68k0uR+TcjZwU7ppgAAAFgdxQOgjRs30vTp02n27NkUEhJCHTp0oCFDhlBiYmKJj/Hw8KC4uDj17fr16xo///zzz+mbb76hZcuW0bFjx8jNzU0cMzc3l0xFLVdHerZbA6WbAQAAYJUUD4AWLlxIEyZMoHHjxlHr1q1F0OLq6kqrVq0q8TGc9fH19VXffHx8NLI/ixcvpg8++IAee+wxat++Pa1bt45u3rxJW7duLVfb8vPzxfEkhYWFYtudO3d09jN035CoZG4lPdjOV539KWnfgoICsb2oqEi9jb/nbfwzU9uX28/b+fUYsy+fP+lcVtW++t6j8uxbkfe+PPvqO++W/Dmpjve+PPsq+d5X9HNibu+9qX1O8H9EgeL7VvT9NIsAiBt96tQp0UWlbpCtrbgfHBxc4uOysrKoYcOGFBAQIIKcixcvqn927do1io+P1zimp6en6For6Zh5eXmUmZmpcWNfffUV5eTkqPc7fPgwzZs3j7Zv367x+C+//FJsz8jIUG87ceKE2LZt2zb1to0nomnWn6EcwtHPR6PFfXbmzBmx72+//abTNcjbOcsluXDhgti2YcMGjX1XrFghtkdHFx+ThYWFiW0//fSTTjcib4+IiNA4b7xt5cqVGvuuX79ebA8N5XYXi42NFds4WJXbtGmT2H7+/Hn1Ns7k8bZvv/1WY9///e9/Yju//5LU1FSxjYNiub/++ktsP3r0qHrbrVu3xLYFCxZo7Ltz506x/eDBgxrvL2/jm/yXb8+ePWIbf5Xwz6V9+XESPh5v4+PL8fPzdm6PhNvJ27jdcvy6eDu/Tgm/ft7G50OOzxdvl2dC+bzyNj7Pcvw+8HZ+XyT8fvE2fv/k+P3l7fx+S/hzwNv4cyHHnxvezp8jCX++eBt/3uT488jb+fMp4c8tb+PPsRx/znk7f+4lSUlJYtvXX3+tsS///vB2/n2S8O8Zb+PfOzn+veTt/Hsq4d9f6f2U2717t9i2b98+9Tb+T1jaV/4fMu/D2/gxctK+lfl/BONzwNv5nEjwf0Qx/B9xD/6PKPn/CLMIgJKTk0XUJs/gML7PQYw+LVq0ENmhP/74g37++WfxYezZs6f6jZUeV55j8snjIEm6cWBV2eIybtPMLfxLbyPuc3z/3pYLYjsAAABULxuVPNdWzbhbql69enTkyBHq0aOHevs777xD+/fvF/U7ZeG/1Fq1akUjR46kuXPnimP16tVLHJuLoCXPPPOM6DrjmiNtHMXLI3nOAHEQxBFnnTp1xOMYB2t84yyVvf29oetSSs7BwaHEfY9cTabnVui+nl8n3EfdAmvqPS6/Nn57eBv/jHHAxyk/fh5+PlPal7fxz+zs7MStvPvy80h/eTs6OlbJvvreo/Lsa8x7b8y++s57efY1t89Jdbz3Ff2cVNd7X9HPibm996b2OcH/EQWK71uR95Ov35zI4Gwr1wuXRtEJaLy8vESDExISNLbzfa7tMQSftI4dO6pTtdLj+BjyAIjvBwUF6T2Gk5OTuGnjkyx9CJn8zdDeT5v2vo283MjWhqhIFm7a2dhQoJdriceVfyAk/MHR93ymsK/8F86YfflcV+e++s57efZlVbWvvvNuyZ+T6n7vK/o5Yaawrym8n/g/ouR9Gf6PqP7/I8yiC4xfTOfOnXX6V/m+PCNUGo52ud9TCnYaNWokgiD5MTki5GySocesCn6eLjRveDsRBDH++tnwtmI7AAAAVC/FpyDmIfBjxoyhLl26ULdu3cQIruzsbDEqjI0ePVp0k0lFjHPmzKH77ruPmjZtSunp6fTFF1+IYfDjx49XR4lTp06lTz75hJo1ayYCog8//JD8/f3p8ccfV/S1jujagHw8nGns6hO0amxX6tfCW9H2AAAAWCvFA6ARI0aIWhueuJCLlLmbaseOHeoiZq4ol/oLWVpamhg2z/vWqlVLZJC47oeH0MtriDiImjhxogiSevfuLY6pPWGiErxqOGl8BQAAACsrgjZV5SmiKq8LNzLo4W8P0V+v96a29Twr9dgAAADWLLMc12/FJ0IEAAAAqG4IgKqZt7sTTRnYTHwFAAAAK60BsjbeHs40bXBzpZsBAABg1ZABAgAAAKuDAAgAAACsDgIgAAAAsDoIgAAAAMDqIAACAAAAq4MACAAAAKwOAiAAAACwOgiAAAAAwOogAAIAAACrgwAIAAAArA4CIAAAALA6CIAAAADA6iAAAgAAAKuDAAgAAACsjr3SDTBFKpVKfM3MzFS6KQAAAGAg6botXcdLgwBIj1u3bomvAQEBSjcFAAAAjLiOe3p6lrqPjcqQMMnKFBUV0c2bN2nAgAF08uRJgx7TtWtXOnHihEHRKQdWMTEx5OHhQdbO0PNmLe2rquerrONW5DjGPLa8jzFkf/wO6sLvYfU8X2Uct6LHqOrfw64K/w5ySMPBj7+/P9nall7lgwyQHnzS6tevT/b29ga/OXZ2duV6I3lf/Odb/vNm6e2rquerrONW5DjGPLa8jynP/vgdvAe/h9XzfJVx3Ioeo6p/D+1M4HewrMyPBEXQpZg8eXKV7Avmc96qu31V9XyVddyKHMeYx5b3Mab+eTJVpn7e8HtYeceo6t/DySb+WZJDF1g149QfR6cZGRkm/RcXgKXC7yCAskzldxAZoGrm5OREs2fPFl8BoPrhdxBAWabyO4gMEAAAAFgdZIAAAADA6iAAAgAAAKuDAAgAAACsDgIgAAAAsDoIgAAAAMDqIAAyIX/99Re1aNGCmjVrRj/++KPSzQGwSk888QTVqlWLnnrqKaWbAmB1YmJiqF+/ftS6dWtq3749bd68ucqeC8PgTcSdO3fEG753714xQVTnzp3pyJEjVKdOHaWbBmBV9u3bJ9YSWrt2Lf32229KNwfAqsTFxVFCQgIFBQVRfHy8uBaGhYWRm5tbpT8XMkAm4vjx49SmTRuqV68e1ahRg4YNG0b//vuv0s0CsDr816e7u7vSzQCwSn5+fiL4Yb6+vuTl5UWpqalV8lwIgCrJgQMH6JFHHhEr0NrY2NDWrVt19lmyZAkFBgaSs7Mzde/eXQQ9El59noMfCX9/48aNams/gCWo6O8hAJjO7+CpU6eosLBQrBxfFRAAVZLs7Gzq0KGDeGP12bhxI02fPl1M/x0SEiL2HTJkCCUmJlZ7WwEsFX4PASzjdzA1NZVGjx5Ny5cvr7rGcg0QVC4+rf/73/80tnXr1k01efJk9f3CwkKVv7+/at68eeL+4cOHVY8//rj651OmTFGtX7++GlsNYFmM+T2U7N27V/Xkk09WW1sBLBEZ+TuYm5ur6tOnj2rdunVV2j5kgKpBfn6+SOUNGjRIvc3W1lbcDw4OFve7detGFy5cEN1eWVlZ9M8//4ioGACq7/cQAJT9HeS4aezYsTRgwAAaNWpUFbYGXWDVIjk5WfRj+vj4aGzn+1zlzuzt7emrr76i/v37iwKwN998EyPAAKr595Dxf8ZPP/00bd++nerXr4/gCKAafwcPHz4susm4doivhXw7f/48VQX7KjkqGOXRRx8VNwBQzu7du5VuAoDV6t27NxUVFVXLcyEDVA14GJ+dnZ2Y20CO7/MwPwCoevg9BFCWl4n9DiIAqgaOjo5iMqc9e/aot3GEy/d79OihaNsArAV+DwGU5Whiv4PoAqskXLgcERGhvn/t2jU6c+YM1a5dmxo0aCCG/Y0ZM4a6dOkiCp4XL14shguOGzdO0XYDWBL8HgIoK8ucfgerdIyZFeFhs3w6tW9jxoxR7/Ptt9+qGjRooHJ0dBRDAY8ePapomwEsDX4PAZS114x+B7EWGAAAAFgd1AABAACA1UEABAAAAFYHARAAAABYHQRAAAAAYHUQAAEAAIDVQQAEAAAAVgcBEAAAAFgdBEAAAABgdRAAAQAAgNVBAAQAVuOjjz6ioKCgcj3GxsaGtm7dWmVtAgBlIAACALPXr18/mjp1apn7vfXWWxorUQOA9cJq8ABg8XjJw8LCQqpRo4a4AQAgAwQAZm3s2LG0f/9++vrrr0V3Fd/WrFkjvv7zzz/UuXNncnJyokOHDul0gZ04cYIGDx5MXl5e5OnpSX379qWQkBBFXw8AVA8EQABg1jjw6dGjB02YMIHi4uLELSAgQPxsxowZNH/+fAoNDaX27dvrPPbWrVs0ZswYERwdPXqUmjVrRg8++KDYDgCWDV1gAGDWOHPj6OhIrq6u5OvrK7ZdvnxZfJ0zZ47I8JRkwIABGveXL19ONWvWFBmlhx9+uIpbDgBKQgYIACxWly5dSv15QkKCyBxx5ocDKQ8PD8rKyqLo6OhqayMAKAMZIACwWG5ubqX+nLu/UlJSRDdaw4YNRa0Qd6fl5+dXWxsBQBkIgADA7HEXGI/yKq/Dhw/T0qVLRd0Pi4mJoeTk5CpoIQCYGgRAAGD2AgMD6dixYxQVFSWGuRcVFRn0OO76+umnn0RXWWZmJr399tvk4uJS5e0FAOWhBggAzB5PcGhnZ0etW7emunXrGlzDs3LlSkpLS6NOnTrRqFGj6I033iBvb+8qby8AKM9GxTOEAQAAAFgRZIAAAADA6iAAAgAAAKuDAAgAAACsDgIgAAAAsDoIgAAAAMDqIAACAAAAq4MACAAAAKwOAiAAAACwOgiAAAAAwOogAAIAAACrgwAIAAAAyNr8H4KIwykAzL6LAAAAAElFTkSuQmCC" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 4 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## View behavioral and image data for a specific subtask", + "id": "b205d0bd45d304c3" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T17:36:12.336689Z", + "start_time": "2025-01-27T17:36:12.334729Z" + } + }, + "cell_type": "code", + "source": [ + "# Response data for each subject\n", + "target_data = benchmark1.target_data # subtask_name: worker_id: [bool]\n", + "\n", + "# View example data for the first subtask\n", + "example_subtask = list(target_data.keys())[0]\n", + "example_worker_id = list(target_data[example_subtask].keys())[0]\n", + "print(f'Showing behavioral data for subtask {example_subtask}, Worker {example_worker_id}:\\n')\n", + "print(target_data[example_subtask][example_worker_id])" + ], + "id": "d3b013ded98e937", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Showing behavioral data for subtask MutatorB2000_0,MutatorB2000_4718, Worker 95396ff08c73aef1e33db1893f8b53d13bef93b0047fd222cd5a79e3df65cb1c:\n", + "\n", + "[True, False, True, False, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, False, True, True, False, True, False, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, False, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True]\n" + ] + } + ], + "execution_count": 5 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T17:36:12.376387Z", + "start_time": "2025-01-27T17:36:12.337297Z" + } + }, + "cell_type": "code", + "source": [ + "# Get the image distribution for this subtask \n", + "simulator = benchmark1.subtask_name_to_subtask[example_subtask]\n", + "classA_images = simulator.classA\n", + "classB_images = simulator.classB\n", + "\n", + "# Show some example images for this subtask:\n", + "example_imageA = classA_images[0].get_image_data()\n", + "example_imageB = classB_images[0].get_image_data()\n", + "\n", + "fig, ax = plt.subplots(1, 2)\n", + "ax[0].imshow(example_imageA)\n", + "ax[0].axis('off')\n", + "ax[0].set_title('Class A')\n", + "ax[1].imshow(example_imageB)\n", + "ax[1].axis('off')\n", + "ax[1].set_title('Class B')\n", + "plt.show()\n", + "\n", + "\n" + ], + "id": "c1e4ac70358e2312", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 6 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T17:36:12.378118Z", + "start_time": "2025-01-27T17:36:12.377015Z" + } + }, + "cell_type": "code", + "source": "", + "id": "e7acf7cf09b2af78", + "outputs": [], + "execution_count": 6 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/view_behavioral_data.py b/examples/view_behavioral_data.py deleted file mode 100644 index 3749fff..0000000 --- a/examples/view_behavioral_data.py +++ /dev/null @@ -1,26 +0,0 @@ -import hobj.benchmarks.mut_highvar_benchmark as highvar_benchmark -import matplotlib.pyplot as plt -import numpy as np - -# %% View data for Experiment 1 -experiment1_benchmark = highvar_benchmark.MutatorHighVarBenchmark() -data = experiment1_benchmark.target_statistics - -# %% -plt.figure() -plt.imshow(data.phat, aspect='auto') -plt.xlabel('trial') -plt.ylabel('session') -plt.title("Performance data") -plt.show() - -# %% -plt.figure() -plt.errorbar(x=np.arange(100) + 1, y=data.phat.values[0], yerr=np.sqrt(data.varhat_phat.values[0])) -plt.show() - -# %% -yerr = data.boot_phat.mean('subtask').std('boot_iter') -glc = data.phat.mean('subtask') -plt.errorbar(x=np.arange(100) + 1, y=glc, yerr=yerr) -plt.show() diff --git a/hobj/benchmarks/binary_classification/benchmark.py b/hobj/benchmarks/binary_classification/benchmark.py index 0a46ad6..549b1b1 100644 --- a/hobj/benchmarks/binary_classification/benchmark.py +++ b/hobj/benchmarks/binary_classification/benchmark.py @@ -73,8 +73,18 @@ def __init__( } self.subtask_name_to_results: Dict[str, List[BinaryClassificationSubtaskResult]] = {} + self._target_data = {} + for name in self.subtask_names: - self.subtask_name_to_results[name] = config.subtask_name_to_data[name].results + results = config.subtask_name_to_data[name].results + self.subtask_name_to_results[name] = results + + self._target_data[name] = {} + for result in results: + worker_id = result.worker_id + if worker_id in self._target_data[name]: + raise ValueError(f"Worker {worker_id} has already been seen for subtask {name}") + self._target_data[name][result.worker_id] = list([bool(v) for v in result.perf_seq]) self._target_statistics = LearningCurveStatistics( subtask_name_to_results=self.subtask_name_to_results, @@ -82,6 +92,11 @@ def __init__( bootstrap_by_worker=self.config.bootstrap_by_worker, ) + @property + def target_data(self) -> Dict[str, Dict[str, List[bool]]]: + return self._target_data + + @property def target_statistics(self) -> LearningCurveStatistics: """ From d7a245de25aaaf9cd3cc7908f19879d992681a9f Mon Sep 17 00:00:00 2001 From: himjl Date: Mon, 27 Jan 2025 12:38:26 -0500 Subject: [PATCH 074/156] remove print statements --- ...=> view_experiment1_behavioral_data.ipynb} | 0 .../view_experiment2_behavioral_data.ipynb | 91 +++++++++++++++++++ hobj/benchmarks/generalization/estimator.py | 3 - 3 files changed, 91 insertions(+), 3 deletions(-) rename examples/{view_behavioral_data.ipynb => view_experiment1_behavioral_data.ipynb} (100%) create mode 100644 examples/view_experiment2_behavioral_data.ipynb diff --git a/examples/view_behavioral_data.ipynb b/examples/view_experiment1_behavioral_data.ipynb similarity index 100% rename from examples/view_behavioral_data.ipynb rename to examples/view_experiment1_behavioral_data.ipynb diff --git a/examples/view_experiment2_behavioral_data.ipynb b/examples/view_experiment2_behavioral_data.ipynb new file mode 100644 index 0000000..e8c0457 --- /dev/null +++ b/examples/view_experiment2_behavioral_data.ipynb @@ -0,0 +1,91 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": "## View human one-shot generalization statistics from Experiment 2", + "id": "bb5fb3d6fa3feb0" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T17:37:30.996083Z", + "start_time": "2025-01-27T17:37:30.843416Z" + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ], + "id": "initial_id", + "outputs": [], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T17:37:31.495017Z", + "start_time": "2025-01-27T17:37:30.997049Z" + } + }, + "cell_type": "code", + "source": [ + "from hobj.benchmarks import MutatorOneshotBenchmark\n", + "benchmark2 = MutatorOneshotBenchmark()\n", + "target_statistics = benchmark2.target_statistics\n", + "print(target_statistics)" + ], + "id": "40343652efc6724e", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "170\n", + "[462. 410. 385. 542. 476. 375. 265. 419. 417. 376. 406. 555. 557. 449.\n", + " 406. 290. 253. 352. 462. 589. 587. 448. 446. 367. 414. 489. 337. 297.\n", + " 380. 110. 564. 545. 165. 555. 421. 391.]\n", + "[525. 446. 460. 632. 530. 440. 340. 701. 455. 471. 462. 626. 610. 492.\n", + " 494. 446. 301. 408. 534. 720. 653. 486. 505. 405. 488. 612. 485. 502.\n", + " 506. 123. 699. 776. 302. 760. 501. 438.]\n", + " Size: 583kB\n", + "Dimensions: (transformation: 36, boot_iter: 1000)\n", + "Coordinates:\n", + " * transformation (transformation) Date: Mon, 27 Jan 2025 12:47:16 -0500 Subject: [PATCH 075/156] add experiment2 notebook --- examples/view_experiment1_behavior.ipynb | 154 +++++++++++ .../view_experiment1_behavioral_data.ipynb | 254 ------------------ examples/view_experiment2_behavior.ipynb | 133 +++++++++ .../view_experiment2_behavioral_data.ipynb | 91 ------- examples/view_oneshot_behavioral_data.py | 26 -- 5 files changed, 287 insertions(+), 371 deletions(-) create mode 100644 examples/view_experiment1_behavior.ipynb delete mode 100644 examples/view_experiment1_behavioral_data.ipynb create mode 100644 examples/view_experiment2_behavior.ipynb delete mode 100644 examples/view_experiment2_behavioral_data.ipynb delete mode 100644 examples/view_oneshot_behavioral_data.py diff --git a/examples/view_experiment1_behavior.ipynb b/examples/view_experiment1_behavior.ipynb new file mode 100644 index 0000000..d8dfac8 --- /dev/null +++ b/examples/view_experiment1_behavior.ipynb @@ -0,0 +1,154 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": "## View human learning curve statistics for Experiment 1", + "id": "bb5fb3d6fa3feb0" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T17:46:41.977347Z", + "start_time": "2025-01-27T17:46:41.730178Z" + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ], + "id": "initial_id", + "outputs": [], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T17:46:43.483534Z", + "start_time": "2025-01-27T17:46:41.981541Z" + } + }, + "cell_type": "code", + "source": [ + "from hobj.benchmarks import MutatorHighVarBenchmark\n", + "benchmark1 = MutatorHighVarBenchmark()\n", + "target_statistics = benchmark1.target_statistics\n", + "print(target_statistics)" + ], + "id": "40343652efc6724e", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 103MB\n", + "Dimensions: (subtask: 64, trial: 100, boot_iter: 1000)\n", + "Coordinates:\n", + " * subtask (subtask) " + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 3 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T17:46:43.656327Z", + "start_time": "2025-01-27T17:46:43.541136Z" + } + }, + "cell_type": "code", + "source": [ + "# Calculate the subtask-averaged learning curve\n", + "glc = target_statistics.phat.mean('subtask')\n", + "\n", + "# Compute standard error using the bootstrap samples:\n", + "glc_se = target_statistics.boot_phat.mean('subtask').std('boot_iter', ddof = 1) \n", + "\n", + "plt.figure()\n", + "plt.errorbar(np.arange(len(glc))+1, glc, yerr = glc_se, lw = 1, elinewidth = 1, marker = '.')\n", + "plt.axhline(0.5, ls = ':', color = 'gray')\n", + "plt.xscale('log')\n", + "plt.xlabel('trial')\n", + "plt.ylabel('p(correct)')\n", + "plt.title(\"Human learning curve\\n(averaged over all subtasks)\")\n", + "plt.show()" + ], + "id": "e0d81a639257d99e", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 4 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/view_experiment1_behavioral_data.ipynb b/examples/view_experiment1_behavioral_data.ipynb deleted file mode 100644 index fb8de69..0000000 --- a/examples/view_experiment1_behavioral_data.ipynb +++ /dev/null @@ -1,254 +0,0 @@ -{ - "cells": [ - { - "metadata": {}, - "cell_type": "markdown", - "source": "## View human learning curve statistics for Experiment 1", - "id": "bb5fb3d6fa3feb0" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-01-27T17:36:10.876431Z", - "start_time": "2025-01-27T17:36:10.709906Z" - } - }, - "cell_type": "code", - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ], - "id": "initial_id", - "outputs": [], - "execution_count": 1 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-01-27T17:36:12.159340Z", - "start_time": "2025-01-27T17:36:10.877431Z" - } - }, - "cell_type": "code", - "source": [ - "from hobj.benchmarks import MutatorHighVarBenchmark\n", - "benchmark1 = MutatorHighVarBenchmark()\n", - "target_statistics = benchmark1.target_statistics\n", - "print(target_statistics)" - ], - "id": "40343652efc6724e", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Size: 103MB\n", - "Dimensions: (subtask: 64, trial: 100, boot_iter: 1000)\n", - "Coordinates:\n", - " * subtask (subtask) " - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 3 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-01-27T17:36:12.333276Z", - "start_time": "2025-01-27T17:36:12.218249Z" - } - }, - "cell_type": "code", - "source": [ - "# Calculate the subtask-averaged learning curve\n", - "glc = target_statistics.phat.mean('subtask')\n", - "\n", - "# Compute standard error using the bootstrap samples:\n", - "glc_se = target_statistics.boot_phat.mean('subtask').std('boot_iter', ddof = 1) \n", - "\n", - "plt.figure()\n", - "plt.errorbar(np.arange(len(glc))+1, glc, yerr = glc_se, lw = 1, elinewidth = 1, marker = '.')\n", - "plt.axhline(0.5, ls = ':', color = 'gray')\n", - "plt.xscale('log')\n", - "plt.xlabel('trial')\n", - "plt.ylabel('p(correct)')\n", - "plt.title(\"Human learning curve\\n(averaged over all subtasks)\")\n", - "plt.show()" - ], - "id": "e0d81a639257d99e", - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 4 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## View behavioral and image data for a specific subtask", - "id": "b205d0bd45d304c3" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-01-27T17:36:12.336689Z", - "start_time": "2025-01-27T17:36:12.334729Z" - } - }, - "cell_type": "code", - "source": [ - "# Response data for each subject\n", - "target_data = benchmark1.target_data # subtask_name: worker_id: [bool]\n", - "\n", - "# View example data for the first subtask\n", - "example_subtask = list(target_data.keys())[0]\n", - "example_worker_id = list(target_data[example_subtask].keys())[0]\n", - "print(f'Showing behavioral data for subtask {example_subtask}, Worker {example_worker_id}:\\n')\n", - "print(target_data[example_subtask][example_worker_id])" - ], - "id": "d3b013ded98e937", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Showing behavioral data for subtask MutatorB2000_0,MutatorB2000_4718, Worker 95396ff08c73aef1e33db1893f8b53d13bef93b0047fd222cd5a79e3df65cb1c:\n", - "\n", - "[True, False, True, False, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, False, True, True, False, True, False, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, False, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True]\n" - ] - } - ], - "execution_count": 5 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-01-27T17:36:12.376387Z", - "start_time": "2025-01-27T17:36:12.337297Z" - } - }, - "cell_type": "code", - "source": [ - "# Get the image distribution for this subtask \n", - "simulator = benchmark1.subtask_name_to_subtask[example_subtask]\n", - "classA_images = simulator.classA\n", - "classB_images = simulator.classB\n", - "\n", - "# Show some example images for this subtask:\n", - "example_imageA = classA_images[0].get_image_data()\n", - "example_imageB = classB_images[0].get_image_data()\n", - "\n", - "fig, ax = plt.subplots(1, 2)\n", - "ax[0].imshow(example_imageA)\n", - "ax[0].axis('off')\n", - "ax[0].set_title('Class A')\n", - "ax[1].imshow(example_imageB)\n", - "ax[1].axis('off')\n", - "ax[1].set_title('Class B')\n", - "plt.show()\n", - "\n", - "\n" - ], - "id": "c1e4ac70358e2312", - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 6 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-01-27T17:36:12.378118Z", - "start_time": "2025-01-27T17:36:12.377015Z" - } - }, - "cell_type": "code", - "source": "", - "id": "e7acf7cf09b2af78", - "outputs": [], - "execution_count": 6 - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/view_experiment2_behavior.ipynb b/examples/view_experiment2_behavior.ipynb new file mode 100644 index 0000000..82594e5 --- /dev/null +++ b/examples/view_experiment2_behavior.ipynb @@ -0,0 +1,133 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": "## View human one-shot generalization statistics from Experiment 2", + "id": "bb5fb3d6fa3feb0" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T17:46:35.297586Z", + "start_time": "2025-01-27T17:46:35.063832Z" + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ], + "id": "initial_id", + "outputs": [], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T17:46:35.851984Z", + "start_time": "2025-01-27T17:46:35.299577Z" + } + }, + "cell_type": "code", + "source": [ + "from hobj.benchmarks import MutatorOneshotBenchmark\n", + "benchmark2 = MutatorOneshotBenchmark()\n", + "target_statistics = benchmark2.target_statistics\n", + "print(target_statistics)" + ], + "id": "40343652efc6724e", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 583kB\n", + "Dimensions: (transformation: 36, boot_iter: 1000)\n", + "Coordinates:\n", + " * transformation (transformation) " + ], + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 3 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/view_experiment2_behavioral_data.ipynb b/examples/view_experiment2_behavioral_data.ipynb deleted file mode 100644 index e8c0457..0000000 --- a/examples/view_experiment2_behavioral_data.ipynb +++ /dev/null @@ -1,91 +0,0 @@ -{ - "cells": [ - { - "metadata": {}, - "cell_type": "markdown", - "source": "## View human one-shot generalization statistics from Experiment 2", - "id": "bb5fb3d6fa3feb0" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-01-27T17:37:30.996083Z", - "start_time": "2025-01-27T17:37:30.843416Z" - } - }, - "cell_type": "code", - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ], - "id": "initial_id", - "outputs": [], - "execution_count": 1 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-01-27T17:37:31.495017Z", - "start_time": "2025-01-27T17:37:30.997049Z" - } - }, - "cell_type": "code", - "source": [ - "from hobj.benchmarks import MutatorOneshotBenchmark\n", - "benchmark2 = MutatorOneshotBenchmark()\n", - "target_statistics = benchmark2.target_statistics\n", - "print(target_statistics)" - ], - "id": "40343652efc6724e", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "170\n", - "[462. 410. 385. 542. 476. 375. 265. 419. 417. 376. 406. 555. 557. 449.\n", - " 406. 290. 253. 352. 462. 589. 587. 448. 446. 367. 414. 489. 337. 297.\n", - " 380. 110. 564. 545. 165. 555. 421. 391.]\n", - "[525. 446. 460. 632. 530. 440. 340. 701. 455. 471. 462. 626. 610. 492.\n", - " 494. 446. 301. 408. 534. 720. 653. 486. 505. 405. 488. 612. 485. 502.\n", - " 506. 123. 699. 776. 302. 760. 501. 438.]\n", - " Size: 583kB\n", - "Dimensions: (transformation: 36, boot_iter: 1000)\n", - "Coordinates:\n", - " * transformation (transformation) Date: Mon, 27 Jan 2025 13:06:35 -0500 Subject: [PATCH 076/156] remove precached model --- examples/score_example_model.py | 19 -- examples/score_example_models.ipynb | 260 ++++++++++++++++++ examples/view_experiment1_behavior.ipynb | 4 +- examples/view_experiment2_behavior.ipynb | 2 +- hobj/learning_models/linear/representation.py | 29 -- 5 files changed, 263 insertions(+), 51 deletions(-) delete mode 100644 examples/score_example_model.py create mode 100644 examples/score_example_models.ipynb diff --git a/examples/score_example_model.py b/examples/score_example_model.py deleted file mode 100644 index 346a66b..0000000 --- a/examples/score_example_model.py +++ /dev/null @@ -1,19 +0,0 @@ -# %% Instantiate benchmarks -from hobj.benchmarks import MutatorHighVarBenchmark, MutatorOneshotBenchmark - -experiment1_benchmark = MutatorHighVarBenchmark() - - -# %% Instantiate learner -from hobj.learning_models import RandomGuesser -learner = RandomGuesser() - -# %% Score -#experiment1_result = experiment1_benchmark(learner=learner) -#print(experiment1_result) - -# %% Visualize results -import matplotlib.pyplot as plt - -experiment2_benchmark = MutatorOneshotBenchmark() -experiment2_result = experiment2_benchmark(learner=learner) \ No newline at end of file diff --git a/examples/score_example_models.ipynb b/examples/score_example_models.ipynb new file mode 100644 index 0000000..fe14145 --- /dev/null +++ b/examples/score_example_models.ipynb @@ -0,0 +1,260 @@ +{ + "cells": [ + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T18:01:57.628744Z", + "start_time": "2025-01-27T18:01:57.627246Z" + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ], + "id": "e72341e539b8abd3", + "outputs": [], + "execution_count": 18 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T17:55:16.496111Z", + "start_time": "2025-01-27T17:55:14.601513Z" + } + }, + "cell_type": "code", + "source": [ + "# Load benchmarks\n", + "from hobj.benchmarks import MutatorHighVarBenchmark, MutatorOneshotBenchmark\n", + "\n", + "benchmark1 = MutatorHighVarBenchmark()\n", + "benchmark2 = MutatorOneshotBenchmark()" + ], + "id": "fac0e73cb1bd321", + "outputs": [], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# Benchmark a random guesser", + "id": "220ecf6a69d69874" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T17:55:59.084006Z", + "start_time": "2025-01-27T17:55:51.249885Z" + } + }, + "cell_type": "code", + "source": [ + "# Load example model – a random guesser\n", + "from hobj.learning_models import RandomGuesser\n", + "\n", + "learner = RandomGuesser()" + ], + "id": "41c8eaea44ac8454", + "outputs": [], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Experiment 1 benchmark", + "id": "df34925233babff6" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T18:00:21.859699Z", + "start_time": "2025-01-27T18:00:16.229050Z" + } + }, + "cell_type": "code", + "source": [ + "# Benchmark on Experiment 1\n", + "result1 = benchmark1(learner=learner)\n", + "print(result1)" + ], + "id": "e0d04f193baadd3c", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LearningCurveBenchmark.LearningCurveBenchmarkResult(msen=0.11339913360940175, msen_sigma=0.0010368901029760523, msen_CI95=(0.11132244337522731, 0.11535028532288642), lapse_rate= Size: 8B\n", + "array(0.77936087), model_statistics= Size: 103MB\n", + "Dimensions: (subtask: 64, trial: 100, boot_iter: 1000)\n", + "Coordinates:\n", + " * subtask (subtask) " + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 14 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Experiment 2 benchmark", + "id": "cb2d9eed7df96023" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-27T18:01:08.415374Z", + "start_time": "2025-01-27T18:01:05.690437Z" + } + }, + "cell_type": "code", + "source": [ + "result2 = benchmark2(learner = learner)\n", + "print(result2)" + ], + "id": "e2495422b0877523", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GeneralizationBenchmark.GeneralizationBenchmarkResult(msen=0.1845594088021789, msen_sigma=0.007595730994685912, msen_CI95=(0.1701146907720955, 0.19975036371625196), model_statistics= Size: 583kB\n", + "Dimensions: (transformation: 36, boot_iter: 1000)\n", + "Coordinates:\n", + " * transformation (transformation) " + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 28 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# Benchmark a linear learning model", + "id": "b1931dc9c7cbac92" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "9e93a5585d87fbf3" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/view_experiment1_behavior.ipynb b/examples/view_experiment1_behavior.ipynb index d8dfac8..03fd520 100644 --- a/examples/view_experiment1_behavior.ipynb +++ b/examples/view_experiment1_behavior.ipynb @@ -3,8 +3,8 @@ { "metadata": {}, "cell_type": "markdown", - "source": "## View human learning curve statistics for Experiment 1", - "id": "bb5fb3d6fa3feb0" + "source": "## View human learning curves from Experiment 1", + "id": "be362495b3302922" }, { "metadata": { diff --git a/examples/view_experiment2_behavior.ipynb b/examples/view_experiment2_behavior.ipynb index 82594e5..59e6661 100644 --- a/examples/view_experiment2_behavior.ipynb +++ b/examples/view_experiment2_behavior.ipynb @@ -3,7 +3,7 @@ { "metadata": {}, "cell_type": "markdown", - "source": "## View human one-shot generalization statistics from Experiment 2", + "source": "## View human one-shot behavior from Experiment 2", "id": "bb5fb3d6fa3feb0" }, { diff --git a/hobj/learning_models/linear/representation.py b/hobj/learning_models/linear/representation.py index 41d268f..4949c9c 100644 --- a/hobj/learning_models/linear/representation.py +++ b/hobj/learning_models/linear/representation.py @@ -35,32 +35,3 @@ def get_features( """ return np.zeros(self.d) - -class PrecachedRepresentationalModel(RepresentationalModel): - - def __init__( - self, - image_sha256_to_features: Dict[str, np.ndarray] - ): - self.image_sha256_to_features = image_sha256_to_features - - # Check that the features are all of the same dimension - d = None - for sha256, f in image_sha256_to_features.items(): - if not len(f.shape) == 1: - raise ValueError(f"Features for image {sha256} have shape {f.shape}, but expected a 1D array") - - if d is None: - d = len(f) - elif len(f) != d: - raise ValueError(f"Features for image {sha256} have dimension {len(f)}, but expected {d}") - - super().__init__(d=d) - - def get_features(self, image: Union[schema.ImageRef, PIL.Image]) -> np.ndarray: - - if isinstance(image, PIL.Image.Image): - image = schema.ImageRef.from_image(image) - - sha256 = image.sha256 - return self.image_sha256_to_features[sha256] From e3b0000cee01208ea48d0cc4832e56d98f623fbf Mon Sep 17 00:00:00 2001 From: himjl Date: Mon, 27 Jan 2025 13:11:08 -0500 Subject: [PATCH 077/156] lint --- hobj/learning_models/linear/representation.py | 1 - 1 file changed, 1 deletion(-) diff --git a/hobj/learning_models/linear/representation.py b/hobj/learning_models/linear/representation.py index 4949c9c..6294155 100644 --- a/hobj/learning_models/linear/representation.py +++ b/hobj/learning_models/linear/representation.py @@ -34,4 +34,3 @@ def get_features( :return: np.ndarray, shape=(d,) """ return np.zeros(self.d) - From d4bae33cfbf6e002600c6fe7105c43adfe6faa76 Mon Sep 17 00:00:00 2001 From: himjl Date: Mon, 27 Jan 2025 13:11:45 -0500 Subject: [PATCH 078/156] improve import --- examples/score_example_models.ipynb | 14 -------------- hobj/learning_models/linear/__init__.py | 4 ++-- 2 files changed, 2 insertions(+), 16 deletions(-) diff --git a/examples/score_example_models.ipynb b/examples/score_example_models.ipynb index fe14145..5253729 100644 --- a/examples/score_example_models.ipynb +++ b/examples/score_example_models.ipynb @@ -220,20 +220,6 @@ } ], "execution_count": 28 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "# Benchmark a linear learning model", - "id": "b1931dc9c7cbac92" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "", - "id": "9e93a5585d87fbf3" } ], "metadata": { diff --git a/hobj/learning_models/linear/__init__.py b/hobj/learning_models/linear/__init__.py index a945848..3fcf02c 100644 --- a/hobj/learning_models/linear/__init__.py +++ b/hobj/learning_models/linear/__init__.py @@ -3,13 +3,13 @@ import PIL.Image import numpy as np -import hobj.learning_models from hobj.data import schema as schema +from hobj.learning_models import BinaryLearningModel from hobj.learning_models.linear.representation import RepresentationalModel from hobj.learning_models.linear.update_rules import UpdateRule -class LinearLearner(hobj.learning_models.BinaryLearningModel): +class LinearLearner(BinaryLearningModel): """ A learning model based on a standard cognitive theory of learning. """ From c26260cfae5cdaf522a50881aefcc9a60a43e580 Mon Sep 17 00:00:00 2001 From: himjl Date: Mon, 27 Jan 2025 13:12:24 -0500 Subject: [PATCH 079/156] refresh --- examples/score_example_models.ipynb | 68 ++++++++++++------------ examples/view_experiment1_behavior.ipynb | 22 ++++---- examples/view_experiment2_behavior.ipynb | 16 +++--- 3 files changed, 53 insertions(+), 53 deletions(-) diff --git a/examples/score_example_models.ipynb b/examples/score_example_models.ipynb index 5253729..23b7c61 100644 --- a/examples/score_example_models.ipynb +++ b/examples/score_example_models.ipynb @@ -3,8 +3,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T18:01:57.628744Z", - "start_time": "2025-01-27T18:01:57.627246Z" + "end_time": "2025-01-27T18:12:05.068082Z", + "start_time": "2025-01-27T18:12:04.841722Z" } }, "cell_type": "code", @@ -14,13 +14,13 @@ ], "id": "e72341e539b8abd3", "outputs": [], - "execution_count": 18 + "execution_count": 1 }, { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T17:55:16.496111Z", - "start_time": "2025-01-27T17:55:14.601513Z" + "end_time": "2025-01-27T18:12:06.972624Z", + "start_time": "2025-01-27T18:12:05.071466Z" } }, "cell_type": "code", @@ -33,7 +33,7 @@ ], "id": "fac0e73cb1bd321", "outputs": [], - "execution_count": 1 + "execution_count": 2 }, { "metadata": {}, @@ -44,8 +44,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T17:55:59.084006Z", - "start_time": "2025-01-27T17:55:51.249885Z" + "end_time": "2025-01-27T18:12:06.974944Z", + "start_time": "2025-01-27T18:12:06.973320Z" } }, "cell_type": "code", @@ -57,7 +57,7 @@ ], "id": "41c8eaea44ac8454", "outputs": [], - "execution_count": 2 + "execution_count": 3 }, { "metadata": {}, @@ -68,8 +68,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T18:00:21.859699Z", - "start_time": "2025-01-27T18:00:16.229050Z" + "end_time": "2025-01-27T18:12:12.334469Z", + "start_time": "2025-01-27T18:12:06.975697Z" } }, "cell_type": "code", @@ -84,27 +84,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "LearningCurveBenchmark.LearningCurveBenchmarkResult(msen=0.11339913360940175, msen_sigma=0.0010368901029760523, msen_CI95=(0.11132244337522731, 0.11535028532288642), lapse_rate= Size: 8B\n", - "array(0.77936087), model_statistics= Size: 103MB\n", + "LearningCurveBenchmark.LearningCurveBenchmarkResult(msen=0.11344782631802722, msen_sigma=0.0010224279429435987, msen_CI95=(0.1113092143859505, 0.11539759036915627), lapse_rate= Size: 8B\n", + "array(1.), model_statistics= Size: 103MB\n", "Dimensions: (subtask: 64, trial: 100, boot_iter: 1000)\n", "Coordinates:\n", " * subtask (subtask) " ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 14 + "execution_count": 5 }, { "metadata": {}, @@ -151,8 +151,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T18:01:08.415374Z", - "start_time": "2025-01-27T18:01:05.690437Z" + "end_time": "2025-01-27T18:12:14.724472Z", + "start_time": "2025-01-27T18:12:12.378736Z" } }, "cell_type": "code", @@ -166,7 +166,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "GeneralizationBenchmark.GeneralizationBenchmarkResult(msen=0.1845594088021789, msen_sigma=0.007595730994685912, msen_CI95=(0.1701146907720955, 0.19975036371625196), model_statistics= Size: 583kB\n", + "GeneralizationBenchmark.GeneralizationBenchmarkResult(msen=0.18410921895147417, msen_sigma=0.0075863415108709436, msen_CI95=(0.1707254164624649, 0.20003988718928112), model_statistics= Size: 583kB\n", "Dimensions: (transformation: 36, boot_iter: 1000)\n", "Coordinates:\n", " * transformation (transformation) " ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 28 + "execution_count": 7 } ], "metadata": { diff --git a/examples/view_experiment1_behavior.ipynb b/examples/view_experiment1_behavior.ipynb index 03fd520..a7423a8 100644 --- a/examples/view_experiment1_behavior.ipynb +++ b/examples/view_experiment1_behavior.ipynb @@ -9,8 +9,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T17:46:41.977347Z", - "start_time": "2025-01-27T17:46:41.730178Z" + "end_time": "2025-01-27T18:11:55.739221Z", + "start_time": "2025-01-27T18:11:55.495694Z" } }, "cell_type": "code", @@ -25,8 +25,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T17:46:43.483534Z", - "start_time": "2025-01-27T17:46:41.981541Z" + "end_time": "2025-01-27T18:11:57.282743Z", + "start_time": "2025-01-27T18:11:55.741133Z" } }, "cell_type": "code", @@ -50,8 +50,8 @@ "Data variables:\n", " phat (subtask, trial) float64 51kB 0.44 0.68 0.62 ... 0.68 0.86\n", " varhat_phat (subtask, trial) float64 51kB 0.005029 ... 0.002457\n", - " boot_phat (boot_iter, subtask, trial) float64 51MB 0.42 ... 0.94\n", - " boot_varhat_phat (boot_iter, subtask, trial) float64 51MB 0.004971 ... 0...\n" + " boot_phat (boot_iter, subtask, trial) float64 51MB 0.5 0.68 ... 0.78\n", + " boot_varhat_phat (boot_iter, subtask, trial) float64 51MB 0.005102 ... 0...\n" ] } ], @@ -60,8 +60,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T17:46:43.540614Z", - "start_time": "2025-01-27T17:46:43.484360Z" + "end_time": "2025-01-27T18:11:57.342066Z", + "start_time": "2025-01-27T18:11:57.283421Z" } }, "cell_type": "code", @@ -93,8 +93,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T17:46:43.656327Z", - "start_time": "2025-01-27T17:46:43.541136Z" + "end_time": "2025-01-27T18:11:57.460173Z", + "start_time": "2025-01-27T18:11:57.343196Z" } }, "cell_type": "code", @@ -121,7 +121,7 @@ "text/plain": [ "
" ], - "image/png": 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r8vLyUl24cEFr31mzZqmcnJxUCQkJ4v7PP/8sHvvBBx+o97l9+7aqd+/eYvu6detUpXnxxRfFfnweJHzOGzZsqAoJCVEVFRWJbV988YXY79SpU1qPb9WqldZ5N7bdFy9eFMfz9vZWpaSkqIxhzDlmffv2FTeJ9FxlnYt27dqp7rvvvlL30T22hN87/nzoPqeHh4fq8uXL6u1Hjx4V22fMmKHeNm3aNLFNjjGf9U8++UQ8PjU11WC79+zZI/b5/vvvxfvLr8HX11d1/Phxrf18fHxEe4zRrFkz1dChQ43aF0ATusAASsF/efJf+ZzO16W5ZhN3U2VmZlLv3r210vTNmjWj9u3biwyF5sKX/NctZ1+kY3z//feiC4D/wk9LS1PfuKuBn3vPnj1az92wYUORsSmtTdwePgZnQrh7ie+z3bt3izoXzlrork+li9vFr6lmzZpa7eIuQX4d//zzj7qAls8TL+cg4YyM3DHl8OM528HdJxJ+3ZyR4W4qqUvqoYceEs+jeT65K4d/ztk2U9stGTVqFNWpU8eothpzjsuDu484mxIdHU0VZeTIkaLLVcLnumvXruK8G8OYzzq3m23btq3M6SL48bz467lz58TQeP4d0cTHOnr0qMhKlUV6jwFMhQAIwEyc/uc6Fa7T4C4TvoBy94juRZAvzJyi5+4Yxv/hp6SkaF2w+WLHj+NaBz6O5o0LPHl/3QBIDj8PX+S9vLzERYQfL9VgSO26dOmS+Mo1MZr4NfDFRBO3a+fOnXptkmqipHbxMQMDA/UCRalrriz8eLl9uStNs82+vr50zz33iG4wCQdDHBRxcGRqu8s6n+ae4/Lg7iOuX+LgOTQ0lF555RVRh1YeXE+ki4/PwWVFfdb589yzZ0/Rdcfdaty9y++TXDDE9T0RERGi24u7E3Vx9ywHtly/xcEadwtygCmHu18165UAjIUaIIBScC0KZ0u4fqJ69erq7fv37xc1EX369KHly5eLiz/X6HAtCS90qYkvDFx7wlkJ/o+fLwqc7RkyZIh6H75IcPCzadMm2XboZifkVgyPjY0VwQFP2Lh48WJx8XB1dRV/5XMBrDmTOPJjOCv16quvyv6cL6JVjS+sXCf133//icwBn09+3RwcmdtuY1dgr4xzrIs/U/w8nEn5888/6csvvxTHXrlypQguGF/w5ZZx5OxWRTP2s87nkDNrnK3k4nUOQDk45Vojfh2cEZRw3Q/XEb3//vuiPkl3fi0e3cUZJq4j48d++OGHooCdR3wNHTpUa1/OSMkFeABlQQAEUAq+0DEuSObiTglPwMZ/DfNILC72lPBFQRdnF/ivWL4YcFEn/yfOXRKaj+PRR/zXMP8FbezFWBcX4/KImu3bt4uiX4lu9xmPBGJcoKqZ+eDuPt0RZ9wuzkDJjYLTPSZ3rfG+mlkgLtY2Bj9ebl/uItFsM+Nzx0W7UjfYhQsXRIBpTrsr6xyXF2dZOMjjG78ODj44CyIFQJypk8uISJkyXXLdaXzeNEeMGcqimPJZ50CGA0S+cYC4YMECeuONN8T50Xwv+D3kLjAefcZ/WMiNouNAi7tp+cYZOy5+fu+997QCIP7jJDExUQRoAKZCFxhAKbp37y6+8igVTfzXLF8wNP/i5u6En3/+WfY4nAU6cuSImFSR6xU0u7+kv3j5WO+++67eY/k/eWOGk0t/YWtmBriLQvdCxRcn7jLSvehoDiHXbNfhw4fFxU8Xt4nbxoYNGya+1zwmv56lS5eW2W7p8eHh4eK5JDy8edWqVeIizaN9JNztxPVPnPnhLAJnYPiCak67TWXsOS4PDkQ1cUDJ3ZUceGkGeBwcpqamqrfxSD5DI674cyl1wTI+11xjoxlMcJce0/2sGftZT09P13teqbZHs+2S8ePH02effSYyWzyrs4SfR7crkbOjPGWD7nG49otHtfEIQgCTaZVEA4AeHu0yduxYrW27d+8Wo1l4lNOKFStU77zzjsrPz0/Vtm1b2ZE0iYmJKgcHB1X16tVVtWrVEqNodD3zzDPisTyihUfUfP7556rp06ergoKCxKgZCY/ykRsldO7cOZWrq6sqNDRUPPb9999XNW7cWIwq4uPyiCDJSy+9JLYNHz5ctWzZMtXTTz+tCg4OFiNyJk6cqN4vNzdX1bFjRzFqbPLkyeK1fvTRR2K0EY+ykkb88Citnj17qhwdHcUIN35+HiEknY+yRj7xyDR/f38x+uett94Sr799+/binG3dulVv/6+//locl88nvwZdxrZbGiX14Ycflto+c86xuaPA+HM0evRo1aJFi1SrV68Wnws+D88//7x6n7Nnz4pz3aFDB9GOOXPmiMdxu+RGgfF2Hk3Hx5w3b574DNauXVt19epV9b7fffed2HfcuHHi/H777bcmfdb5s8rt4ZGT3O733ntPVbduXVW9evVUGRkZeqPAJLwfb+Ov7MaNG+I94vdq8eLFqlWrVonzwft8/PHHWueK31NPT09VVlaWUe8fgCYEQABl4P+ENYesS9asWaNq2rSpys3NTdWiRQtxYZs7d67BocQcIPDP+IJsCP9nHxYWJoYt88WdL1yvvvqq1oXKUADEtm/fLi5M7u7u6gve2rVr9S7OPESdA42AgADxXBysREVFiYvis88+q3VMHq48e/ZsVZMmTcTFn4OkHj16iIuPZiB3/fp1cfHkIeUcyPD3PLzZmIs+i42NVT388MOqGjVqiPZ36dJF9euvv8ruyxc8bjcfmy/Wcoxpt6kBkCnn2NwAaP78+eK183ng18ifLQ4OdINmft089J5fGweLf/zxh8Fh8Pz6OHjgIJc/rxzMnDhxQut4/JngIKtOnToi4NL8HBvzWedAacSIESJg5zbxV/7DQXMqArkAiPFnnLdzMFdQUKB65ZVXRFDJvwMcDPH3y5cv1ztXXbt2VT3xxBOlnk8AQxz4H9PzRgD2g9PxjRo1EiNTnnrqKbJV3PXBtSXz588XdRsAloyL4LkuiIfi6w6jBzAGaoAAysAjtng0EY9EqYhRPpZAbu0waXZkuSUWACwNjyDjpWQQ/IC5kAECsEO85AbfuPiYi2x5mYFvv/1WjMyRKxwGALA1GAYPYId4SD+PBONuPV5riieumz59uuj+AgCwB8gAAQAAgN1BDRAAAADYHQRAAAAAYHdQAySDR/rwKsQ8RTsW2QMAALAOXNXDazfyzOG6a8zpQgAkg4MfXuQQAAAArA+vEVevXr1S90EAJENa9ZtPoLe3t9LNAQAAACPwqFZOYEjX8dIgAJIhdXtx8IMACAAAwLoYU76CImgAAACwOwiAAAAAwO4gAAIAAAC7gwAIAAAA7A4CIAAAALA7CIAAAADA7iAAAgAAALuDAAgAAADsDgIgAAAAsDsIgAAAAMDuIAACAAAAu4MACAAAAOwOAiAAAACwOwiAAAAAoMqkZOXTJ7suiK9KQgAEAAAAVSYlu4A+3R0tvioJARAAAADYHQRAAAAAUGXScgq0vioFARAAAABUiS0RCTRpfYT4nr/yfaUgAAIAAIBKdy3zJs3eeopUqpL7/PX1rafFdiUgAAIAAIBKH9V1MS2Xiu8EP5IilYri0/JICQiAAAAAoNJHdTX09SJHB+1tTg4OFOLrSUpAAAQAAABmZ3mMLWoO9PGg//Vvor7PwdCCh9qI7UpAAAQAAABmZXlMLWru2KCm+vu1EzvTmM71SSkIgAAAAOxYihE1PHJZHlOKmvnYT22IUAdLLPLSDVISAiAAAAArDVIqYlmJlDJqeAxleUwpaj57LYt2R6WogyX2+Z4YxUaAMQRAAAAAFs5QkFIRy0qklVLDU1qWx5Si5qhrWXrbOHhSagQYQwAEAABghTjrs/qfWL3gxZSs0JYyaniOX7phMMvDxcvzRrQps6iZj7lo53m951ZyBBhDAAQAAGDh5LI0Xx+5RNtOXNMLXozNCl0zoobH08251MClT9M66u1fTuikLmqWgrBTlzPEc8gZ2SFIsRFgDAEQAACAAozN1MhlaThIWbonRr2PZvBi7LD0iwZqeD7647y6Tb7V3LR+LmV5OAjith+KTVX/zN3Z6e5ruxOEnbqSqfcckp+PX7XvGqBly5ZRSEgIubu7U9euXSk8PLzU/ZcsWULNmzcnDw8PCg4OphkzZlB+/t0Pz9tvv00ODg5atxYtWlTBKwEAADCeMZkaQ1mapX9HaxUUS8HLugPxpXZpaQZdDWVqePj+j5FX1G2SgigpWPhiXJjI8khtP3n5bm2Pg8axpMe5uzjpPYdme+22BmjLli00c+ZMmjt3LkVGRlK7du1o8ODBlJKSIrv/N998Q7NmzRL7R0VF0Zo1a8QxXn/9da39WrduTdeuXVPfDhw4UEWvCAAAoHRSEBKTkl1mpsZQluabo4l6+3Kc8eWBuFK7tDSDrkAfD1r4UKh4HONAZUL3EHWbNDNPxXf2cbuT5ZHarHnsojs7aT5u5ncnqEfj2rKvTekaIP3OvSq0ePFimjJlCk2aNEncX7lyJf3222+0du1aEejoOnToEPXs2ZMee+wxcZ8zR2PHjqWjR49q7efs7EwBAQFV9CoAAACMJwUhUuDBwcKAFn608MFQ8vN2L9knK582HU2ggS39RGZFM9vjqBGQaAqp7UkXr+cZLFhmut1jYzrXp5+OX6Ejcen0YIe6tP5wvNg+cV2EaJ9u71XmzUIR4My6U9ez5/zdLrDV++Pot5NXacu/iVrtPRh7Xf29dEylZ4FWNANUWFhIx44do4EDB95tjKOjuH/48GHZx/To0UM8Ruomi4uLox07dtCwYcO09ouOjqagoCBq1KgRPf7445SQUPrMlAUFBZSVlaV1AwAAqAxS8CHFCBws8Bw5h2Kvq7un1EGSgwMNbn33D3oOHF4b2kKru0miG/xI+0tZFkMjvgpul4RTWyOvaAUucqU70Uk5Wl1ymvZdSKVvIxL1Mlaa+84d3soiZoFWNABKS0ujoqIi8vf319rO95OSkmQfw5mfefPmUa9evcjFxYUaN25M/fr10+oC4zqi9evX086dO2nFihV08eJF6t27N2Vnl6Qa5SxcuJB8fHzUN64tAgAAqIxCZ7k5cdjxhBt6NUHpuYUUm5Kjvs+BwzN9G9OwNoFaj60uM1qL8dpb3NU075czBkd8ZebdKtlmxOtJzi4wWNQskQvOJIk38mSLq+2yCNoUe/fupQULFtDy5ctFzdDWrVtFl9m7776r3mfo0KH0yCOPUNu2bUU9EWeIMjIy6LvvvjN43NmzZ1NmZqb6lpio37cKAABQ3kJnQ3PisI2HL+llieJScyg6JYequ5cEOF6uziKYikvLofo1S7qP2gfXoOyC2+rHNvWrprX2Fj/32oPxBufzybnz2FLilpKfOxC5OpcdNshlhyTrDsbTkz1DyK+6HQdAvr6+5OTkRMnJyVrb+b6h+p233nqLxo0bR5MnT6bQ0FB68MEHRUDEGZziYrkeUaIaNWpQs2bNKCbm7nBBXW5ubuTt7a11AwAA21YRy0iUdRzNmhtpRJchUtyw6p9YUYPD3v7lrPjaKqC6ei8OaKKuZVPQnQCoVZD2NYsDJgl3dXH2Ry4z4+TgQA1qe1DGnQzQQx3qlvo6vd1dKOLi3Xoec3AQNqhVgLrWyS4DIFdXVwoLC6Pdu3ert3EQw/e7d+8u+5i8vDxRJ6SJgyimMhBy5uTkUGxsLAUGaqcLAQDAvlXEMhKlHUe35mbdgYtldh+x7XcmN9QUHl+ycOitIhUV3TlIE79qIpvy7VHDda58aZQe+3iXuzU3jneKkKu5u1DhneFbgTVKD0qKi1V0LvlucGUOpUd+WUwXGA+BX716NW3YsEEMa586dSrl5uaqR4WNHz9edE9Jhg8fLup6Nm/eLGp7du3aJbJCvF0KhF5++WXat28fxcfHi1FjnCXin/FoMQAAsG0VldUxhbErpX+5/6LBOXHKIsVN55Ky6NSVDPF9vRqeNLCVv1G1O4xHlUn6NPOl/s39KD2nUL2N5+wpjWY3mzn4pSs98stihsGPGTOGUlNTac6cOaLwuX379qJ4WSqM5tFbmhmfN998U1TE89crV65QnTp1RPDz3nvvqfe5fPmyCHauX78ufs4F00eOHBHfAwCAbZOyMYNa+ZfZzWLsjMnSkPTHu9bXOiZvn/3TKfr7XIo6y/P+Q6FidJPc/D2cZ3m6VyMxXJx/pDu83Ri/n06if+9kdBb9cY5mDWkhO1xdjuY+e8+niXMljQBjhUUmNsYMfZpZzrVY0QCI/e9//xM3Q0XPuvP78CSIfDOEs0MAAPbO0EUbSmjOZaMZuMidN0NB1dlrWWL4ukQaWcUXeZ5lWTfA4e6fSb1CRBbnn+g0+mR0exEo1fR0prd/iTKq3RF3gh/p+T7YeZ5mDWtB7+84pxXgGBsUxaWWdGlxZkoKBI19rOZzjelcjzZHXC51Pz6m5pxESrOqUWAAAFC19S3WxpisTmmLgJpy3q5m6K9jpTnx4BCd+Xuk7p+bt4rUNTwzBjWjTiHyMyVr4scHygSy/Hxt69agdZM6q7etn9SZPh7dzuCxqrvdnc05Ib1kWHotL1dKvfOajQ1+RneqJ77ycz/RLcSo12Ap9T8MARAAANgEQxP9Gbu8hKF1qQwFVXI1M3yR33U2SWSSNIeMa078x8PO2wR56w0FnzGwqcHXxo9v6n93eLtuUbHmvDr8ff6dIEtOdkHJz/gcff9vSdaG2yoFQMaWKfHwe+n5+LV0CalpcF8+L7zshqVkfxgCIAAAG2RsfYutKC2ro0vqnpILJHTPm25Q9dSGCBHc8PaXvj+hd+xn+jQSc+6UDFXPIrc7QVANDxf1PjzsnJe+kLrTOHiYfk9T6lC/JKCQ2sZfnuoVog4yanrePYZuVomPMblXQ3Hj70Pr+pQZyKhUREl3isWvZuTTpeu54vvn+jXWWh9sdFhJpkf3ufk5uN38fBxA/nvpbvecbpbIEmZ+1oUACADATjMhtsSUrA4HDPeHBuoFEv9cSNU6b1/si9ULqrjm50BMmsHlIHigjtQ9FpuaS90alXRvXcvMVw8l5+Cqjkb2hwMh7gprEeAtAopP7nRfcdfSM30ai22HY6/rDY/XDCr4GG/e30rc+PvQejXo/VGhJo06u3FnPqDJvRupu9T4Ocb3KAnCNINGnmGan4Pbzc8nd/51s0SWBgEQAICdZkKsbeh6afuJrI4Jc85U93AhqYfqzftaisJl3fO28Pdzshf18Ivpstt9PJxp+d5Y8f0zXx0T8/X0beYr7mfllwQXmTdvibl85AICKRBq4lf9bteStzs92iWYFv4eZVJhMuPgiAMYU3m5Oavbp9nOufeXrOM1on0gPdGtgd751w22dLNElgYBEACADTG1vsUSGFt4XNp+nNXp3PBuDYqDRveQXOAUk5JDnRrUEt+n594ymMGQszkiUbZ7KfPm3XlypEPVreFBPh4u4vxzG3gEGNPMAJXFUNvkirB1mZp5cXbUX+5C6qJrVMdL3J/Su7HeyEI+z1zj41hKlsjSIAACALAhpmZCLGECQmPrlcraLzW7kHo3Lcm48DB2qXtILnDiAKhtPR+q7eUqAgm581YazXiEL/pP924ou19cWq4Y7cWBD7eBu8XKCoCkgEPKmshlV/guZ1fKwsfg2aKN7Qqr5u6i1wYpM1XLy83ojJNclsjSIAACALAh/Jc4X3CrevZdc4fdG1uvVNZ+HHhxpqRH45Kamys3DGdHeIV1vrULrkl9m9URQYo4bzKjrIJquNOrg5vJHsfVqSSq4Iv+pF4NZYOMD/44L96DxDvtycgrLDMzIwUcUtZEN7vCX7m+h7MrZeFjzBneWi87Y4iXq7NsG+QCMznS65LLElkaBEAAADaE60644HZUx5KFLau5O9OI9qUvcmnp9UrG7HfkYrr42q9ZHZHZkTItcpkjzv4wDhR5IVEerbXo93OUcD2PxtwZtaR+7ox8kVmSo1mrIwUpujEGt/XYpRuUcL3kOS/fuElerk6izsYUmtkVc0ZUaT7+7eGt9Nop3fe6M0eQHLmgyJohAAIAsCHc1cLzzHQOKalvycm/TT9Glj5Dr1LD7o2tVyprP87+rP4njkJqe1LLIB+a2CNETPCXmXdLNnPEAZA0KV+buj5iOYgV+2LFV76viZ92w+F46tWktl4GhQMtzYwIBxmvDmmu9zr5GIVFd+uHylpzyxC5wmRzHt8ppJbeCLEuDUs+L6YGZrqMyRJZCgRAAAA2hNeJcnVypGb+JdmJ7o1ri0U4pRXELWnYvdx8PJoTCTL++tfZ5FLrmrjb7dSVTGoR6K019Prn/y7TrB/1M0cnEjOoQW0vcnN2EhkgCc+zwzMi6+JTN61/U70RVVv+TRRdZJoZkZZ32lCa67mFdOpyyYKmVRlcaD5+jE69TrdGJQFQtfIGQFaUJUIABABgQyLi06lNXW+qV9NDXOx4YjzOoEhBhW6hcnlXTzfUPcUX+LKOy91GHevXNDiRoGhfdoG4r5k10RzhxWLvrGfFGSApsOKRV/9cSNMbOs6Zo73nUyi4Zsljvd1d1AEFBy8dG9TUy/QYKiKX64qTsiyakxnK4YCtqoML3cf7atTr1Kless3T1bzslDVCAAQAYCNUKpUIgLj7S7rYDWjpL7o3vvgnjpKz8vUKlcu7Zpih7im+wJd13ILbRXQhOZseaFcyKSHXFEtDu3W70njfRaNCxfd9mvqqa2A42/Ti5v/E9/wa+T5PRtguuIZ6aQdNHNwkZxeogwDeX2ojTzbIkyHqFhxLwVZpa3/pZlmkyQx5TS654uOgGpazJATzuBNglrcLzJogAAIAsBFcYJucVSBqPHSzKscTMsTq5RVFyhzJ1fzwBd+YC/z+C2mUnX+bBt9ZNLRD/Zq07c5sx1JXGgdtrHODmtQ6qKQ+JzIhg67cyKN5v5wpyT7JZGS4GywurSQzpEmamZkzQFL2SsLHkVZzlys4llteQjc7pDuZIXdFGjsCq6r5aXSJSZkfro8yNxtobRAAAQDYiH8vlYyECmug3a3Uv7mfGPH0bXiiuK8ZtBhTvCzbdXYnc/TpX9Hk7+2mdYHn4IOzUYaOKx3vh2OXqZl/NVGPw44l3F1Lih/O9TuTN/wr7ofH36A/zySJ7zlo+vNssugaM1QcnX3zFuXcWfRTwot1Hrtzjqq7O5daXC1XcKy7vIRmdsjUGZktYYkSP40uMY87AdDJy5lmZwOtDQIgAAAbKoDmQEe3kNfR0UEEJf8lZqgvvt+GJxhdvCzXTSYFNnGpubRqXCf1BZ4nBOQMzZPr/zU8Z8+d4+27kEr3tw0SGYiHOtTVW1uL76qzO0T0+Z4YmtQjRNT67DqbLNtWzsh4ujqKkVu6ePLBgtslR3z3tyg6dTnT6HofU4ejl1WwbGlLlHjemf/HniAAAgCwoQCoc4h29ofxRXarxlB4vvhy189rMiOkjLkgi8BpXUngpLoz9F7KlPRoUjITs1y3lK6bt4ro/raBIgPxypDmZXYTcbbm3tYB1NivGh2Kva7errlyOWdkcguLZJeO2HGqJIMkteuDnefptaEtZDM6pQUwxgxH1y04ziu4u0yGJS5R4qlR/GzKVAbWDAEQAIAN4DlvzidnU9id9a00GbvOlaELsmY3mXrUl8bPOcCR9jGmUFjat35ND2pUp5rsbMf8xcFAdmfPuRTZ9ksZGdmFOXWWr5Da1bZuDdmMTmkjrswZjt7BhNFlSthzPsWiuueqAgIgAAAbcCyhpLZFLgNkKCCQW72bL8iaNT9a3WTrImj8mqOydTM3C4tEUMCFwqVd6DWPx8tDaF5oNbuX1k3qLFtvI5fdke5KGRm5pSM0Mz267TJ1gkFzhqPLtakqligxxrXMm+L9ttTuucqCAAgAwAZExN8QNS71a3kadfFdOCpUb3RSp5CaJaun36nR4VFjWnP8EFF0Sq5sZqZ9/ZJVv7lQmI8r0bzQ680ZRIbn0eGvcvU2ssGcTNeZ7mOf6dvYYABSVbMXl3c5i8py0cgZuW0NAiAAABvwr5j/p6aYA8fYi6/mtondQyj84g0x67KEh5rLdZ1N6d2o1EwGH/exriUX99XjO6kv9OZcaHWzM3LB3OyhLWUDGN3HGgpAqnL24vIuZ1EZGsoElZbUPVdZEAABAFg5niTwxOVM6iRT/1PWxVf6flRYXRrQwo9mbT2pnj/n5/+u6B2DL4yTeoWUmcloEVAyD47myC5jLrTGZGN0A5kpfRoZHcBYYgCitEAL7p6rTAiAAACs3OkrmVR4u1i9AKohpQUXnDnimpu8wiJ64duSmZUj4jOok0bxruaFsaxAgpfiYIk38vQutBK5C61uNsZQm40aiWWBC3NaYpssuXuuMiEAAgCwYlyovHjXBbGUQcvAkqyLIXJdPZoXZF4wlYuZNfEM0h890s7kC2OrQG9ycXKgjLxbWtvvaxuk/t6Y45Wne6qs16sES14s1NfOsmP2N/MRAIAN4YLlgzHXqV09H3J2cjT7gswOxcovHpp/q0jvwlhWIBHg40HNA6qrl7KQRCdnq78vz4XW3EBG8/WCfUMGCADAghhand3Q9uI7RTatgrzL/dyGanTk1vUyJpPRuE419UrtEl781MHGMynWys9Cu+cqCwIgAAAzGApIyn1cA6uzG9rOq66zejU8K60YlruzzLkwlgRAuVrbLiTniPoge7rQWgs/OwsqEQABAJjBUEBSlXgSwTd/Oi2+/2jX+QqZvVeuGNbcCyMHQOm5heKmmQFqGehtVxdasEwIgAAALIih1dl1t/McPbM0lqSoyNl7K6oYtrFfySrvcRrdYBwANfMvvVgboCogAAIAqMBApTwMrc6uuxzFk+sjaMTnB/VWT7e02XtDansRz8so1QHxemXJWQXU1L9k/S8AJSEAAgCooEClPDVCestEqEhkeN7ZdoZmaa7aTkR/n0uhjg1qiOCiMmbvrahiWHcXJwqu6amuA7qQUjICjEeHASgNARAAgAnBi1ygItf1ZGqNkNwyEXzsr8Mv6Q1NZ5N6NqL3K2n23ooshm1cx4tiU3LU3V9Ojg5itBmA0hAAAQDoKC144bWy5Nez0h7tZCovVye9bU4ORD88293g8hHWMHsvF0LH3OkCu5CULYIfN2f91wpgdwHQsmXLKCQkhNzd3alr164UHh5e6v5Lliyh5s2bk4eHBwUHB9OMGTMoPz+/XMcEAChLSnY+vfz9CXpr2xnZny/YcY7+uZBKqjupIVNqhHgtrznbz1INDxedjE4otQuuWeo6TZY+e29jv2qUmJ4nJlPkIfDNUP8DFkLRAGjLli00c+ZMmjt3LkVGRlK7du1o8ODBlJKSIrv/N998Q7NmzRL7R0VF0Zo1a8QxXn/9dbOPCQCgSzN44TW2vtgXSwM+2ke7o5Jp/sg2egHJpB4h5OjoQOPXhtODyw/RO7+cKbVGSLeL7e3tZyjqWhZtfKqLbEbHGjI9hjTxqyYyZpeu52EEGFgURZfCWLx4MU2ZMoUmTZok7q9cuZJ+++03Wrt2rQh0dB06dIh69uxJjz32mLjPWZ6xY8fS0aNHzT4mAIAmDla4+FgacVW7mivdyLtF47o1oBcHNqUanq7iZ4E+7jRxXYQISPo19xOZn3+i0+jDnedo3cF49fGkGqE+zeqoszZSF9ugVv60+1wKfRueSB883Jba1qshFjaVy+gYyvRY+uy93AXGOHi8nltI/hbaTrA/imWACgsL6dixYzRw4MC7jXF0FPcPHz4s+5gePXqIx0hdWnFxcbRjxw4aNmyY2cdkBQUFlJWVpXUDAPujV+AsskCFtGFSF3r7gdbq4EcuIOHV1Ps2q0OvD2upd1xDw9PPJ2XT3G1n6PGu9Wl0p2CbnL23lpcr1fR0oZ1nksR9zXMIYJcBUFpaGhUVFZG/v7/Wdr6flFTyi6KLMz/z5s2jXr16kYuLCzVu3Jj69eun7gIz55hs4cKF5OPjo75xbREA2B+5kViMRy4Zq2Ed/fW02Iq9MeqFQKUutnd/PUut63rT3OGty8zoWHqmp6wskJTZkltXDMAui6BNsXfvXlqwYAEtX75c1Pds3bpVdG+9++675Tru7NmzKTMzU31LTEyssDYDgPVwkFmm09DcOoYCErn1tMZ0rkdxabk0eMk/9PCKQ+r6oIybt2hom0BydXYsM6Nj6ZmesgIgKbA0JZgEsMkaIF9fX3JycqLk5GSt7Xw/ICBA9jFvvfUWjRs3jiZPnizuh4aGUm5uLj399NP0xhtvmHVM5ubmJm4AYL24oHjT0QTRnWROkMAZimnfRJK/txulZheIC3Zpc+tIAYkcLlL299auEeJi6pX7Ymnxrgta+y76/RwNbxdYIfP3WCppSYyKnjkbwCozQK6urhQWFka7d+9WbysuLhb3u3fvLvuYvLw8UdOjiQMexgWI5hwTAGyDsRMPyk1yeOzSDRq7+ggF1/SgP17sUyEjrnRrhDjL0ymkpt5+lrZ8RWW4mnF3kkhDM2cD2FUXGA9XX716NW3YsEEMa586darI6EgjuMaPHy+6pyTDhw+nFStW0ObNm+nixYu0a9cukRXi7VIgVNYxAcC+6QZKB2PSaNyao2KF8q8ndxVFupU1tw5PAmhoUkNbLizfePiS+n5FLtoKYLXD4MeMGUOpqak0Z84cUaTcvn172rlzp7qIOSEhQSvj8+abb4qRFvz1ypUrVKdOHRH8vPfee0YfEwBskzmLk/Kszs99E0ndG9WmlU+EkYfMbMzmkqsRkuqDeKRZWV1stlxYLmW9bPl1g+VzUEnTloIaD4Pn0WBcEO3t7a10cwDAyLl7+H8zXiCU18gy1HW193yKqM15tm8jWr3/It3byp8+fbSDViFyeeuJyiK1Yf2kkvogW8aZnp7v/60VBHHW68Cs/giAQNHrt1WNAgMAkLvASsGPehX1H0/Rqn2xYmkKnn04K/+WqBPUXMV95b44alevBi0dqx38VMWIK0tfvqIiyY2Ks/WsF1gHRbvAAAAMMSYLk5SZT69+f1Id/Ej47oLfz2ltc3dxpPxbxVrbTiTeoNScAlyMK5ncqDgApSEAAgCLpLlchG4AdLOwiFbvj6MVe2PJzdlRzN6jGQNxF8uel/uK9bk4SErKyqcjsdfp66Pao4+KVKRILYo1T2poLnvKeoF1QAAEAFaDu7G2n7gq5s7hzM2TPRvStAFN6PdT1/QKi+vXLpl7pl7NkhFWYQ1q0jfhCXq1KEqMwCptDiEAqBqoAQKASic3946po7r+S8ygUSsO0fTN/1FoPR/aNaMvzR7WkrzdXYxaLR21KMqyx6wXWDZkgABA0e6sMldkXx9BHYJrUGRChpir55spXalHY1+zulhQi6IcZL3A0iAAAgDLXpFdRSL4mTWkBU3p06jca0mhFgUAGAIgADBKeebGKWuSwvxbRXTmahadSMygv6KSZVdkbxdco9TgB10sAGAKBEAAUCndWIa6sxY8GEqdGtQUNT0nLmfQicRMirqWRbeLVWI+nmZ+1WRHdZVVrGxsFwsCJQBgCIAAoNJcuZGn153F9xnP2Ny4TjVqH1yDRncOpvb1alDzgOoiCOKgqbKWi0AtCgAwBEAAUCHdWDzb8vmkbJHNKbll09mrWbLdWW8Oa0mjuwSLEVxyUKwMAJUNARAAmNyN9dK9zamxr5cIdM5ey6ZzSVl0+UbJ6t4uTg7UxK86tQyoTr2b+NLne2O0Zmrm7qz72gUaDH4kKFYGgMqEAAgATB6V9dEf59XBScvA6jQsNFB85WHqjXyraa2tVa+Wh12tfg4A1gEBEACU6tw1+W6slU90pCFtAst8PLqzAMASYSZoADAo4Xoezf8tSm87d2PxsHRjmdOdhdFaAFCZEAABgKz90ak0/PMDVFSsohmDmlb5EhLSaC1T5xwCADAGusAAQG/BUV5p/f3fz1GfZnXo0zEdyMfThdrVq4FuLACwGQiAAEDtZmERvfbjSbHi+nP9GovRXtLsy+UZlYXuLACwNAiAAEBITM+jZ746RhfTcmnZYx3pvrZlFzgbC5MPAoClQQAEAHQwJo3+900kVXd3oZ+m9aAWAd56+yCLAwC2BAEQgJ3X+6w5cJEW7Iiink18aenYDlTD01V2X2RxAMCWYBQYgB2s4v7Jrgviq269z4wt/4lh7lP6NKL1k7oYDH4AAGwNMkAAdriK++UbJfU+sak59NnYDvRAuyClmwkAUKUQAAHYmUOxXO9znDxdnWjr1J7UKki/3gcAwNYhAAKwcdLq7anZ+bT2QDq9tyOKujWqRZ+P7Ug1vdDlBQD2CQEQgN2s4v6v+Dqld0N6bUgLcnZCCSAA2C/8DwhgJ6u4MwcHoid7NUTwAwB2D/8LAtgontBQdxV3Dobi0/KUahIAgMVAAARgoxr6esmu4h7i66lIewAALAkCIAAbFX4xXXy9s4h7la3iDgBgDRAAAdigmJQcUf8zsn0QrZ3YSWzjVdzHdK6vdNMAACwCAiAAG8MzPE/bFEmBPu703oOhVKe6u9mruAMA2CoMgwewMXO3n6ZL6bm0bVov8nLDrzgAgMVmgJYtW0YhISHk7u5OXbt2pfDwcIP79uvXjxwcHPRu9913n3qfiRMn6v18yJAhVfRqAJTzw7HL9N2/l2n+yFBqHlBd6eYAAFgsxf883LJlC82cOZNWrlwpgp8lS5bQ4MGD6fz58+Tn56e3/9atW6mwsFB9//r169SuXTt65JFHtPbjgGfdunXq+25uSP+DbTuflE1v/nyKHgmrRw+H1VNv96vuRtPvaSq+AgCAhWSAFi9eTFOmTKFJkyZRq1atRCDk6elJa9euld2/Vq1aFBAQoL7t2rVL7K8bAHHAo7lfzZo1q+gVAVS93ILb9NymY9SglhfNG9FG62e8AOqMQc3UC6ECAIDCARBnco4dO0YDBw682yBHR3H/8OHDRh1jzZo19Oijj5KXl/acJ3v37hUZpObNm9PUqVNFpsiQgoICysrK0roBWAuVSkVv/HSKrmXm07LHO5KHq5PSTQIAsHiKBkBpaWlUVFRE/v7+Wtv5flJSUpmP51qh06dP0+TJk/W6vzZu3Ei7d++mRYsW0b59+2jo0KHiueQsXLiQfHx81Lfg4OByvjKAqrM5IpF+/u8qLXwolJr4VVO6OQAAVkHxGqDy4OxPaGgodenSRWs7Z4Qk/PO2bdtS48aNRVbonnvu0TvO7NmzRR2ShDNACILAGpy5mklzt5+hx7rWpxHt6yrdHAAAq6FoBsjX15ecnJwoOTlZazvf57qd0uTm5tLmzZvpqaeeKvN5GjVqJJ4rJiZG9udcL+Tt7a11A7B02fm3xHw/TepUozn3t1K6OQAAVkXRAMjV1ZXCwsJEV5WkuLhY3O/evXupj/3+++9F7c4TTzxR5vNcvnxZ1AAFBgZWSLsBLKHuZ9aPpygtp5CWP96R3F1Q9wMAYFWjwLjrafXq1bRhwwaKiooSBcuc3eFRYWz8+PGii0qu+2vkyJFUu3Ztre05OTn0yiuv0JEjRyg+Pl4EUyNGjKAmTZqI4fUAtuCrI5fot1PX6IOH21KIzKKnAABg4TVAY8aModTUVJozZ44ofG7fvj3t3LlTXRidkJAgRoZp4jmCDhw4QH/++afe8bhL7eTJkyKgysjIoKCgILr33nvp3XffxVxAYBNOXs6g+b9G0cQeITQsFFlNAABzOKg4lw5auAiaR4NlZmaiHggsSubNW3T/0v1Uy9OVvnu2O7k5o+sLAMCc67fiGSAAMA7/rfLK9ycoM+8WfTO5G4IfAIByQAAEYCXWHLhIf55NplXjwii4lqfSzQEAsGqKF0EDQNkiE27Q+7+foym9G9K9rUufIgIAAMqGAAjAwt3ILaTnvzlObev50KtDWijdHAAAm4AACMCCFRer6KXvT1Be4W36/LGO5OKEX1kAgIqAGiAAC7Zqfxz9fS6F1k3qTEE1PJRuDgCAzcCfkwAWIiUrnz7ZdUF8ZRHx6fThH+fpuX6NqX9zP6WbBwBgUxAAAViIlOwC+nR3tPh6PaeA/vdNJIU1qEkzBzVTumkAADYHARCAhSlWqejFLf/R7SIVLR3bgZxR9wMAUOFQAwRgIdJyCsTXdQcv0oGYNPrqya7k7+2udLMAAGwS/rQEsABbIhJo0voI8f1Px6/SgBZ+1Kupr9LNAgCwWQiAABR2LfMmzd56ijRX5dtzLkVsBwCAyoEACEBh4XHpVKyzJDHfj0/LU6pJAAA2DzVAAAq5knGTPv87hr6LSND7mZODA4X4Yr0vAIDKggAIoIpx19byPbG0OSKBqru70GtDW5CHixPN3X5GZH4cHYgWPNSGAn0w8SEAQGVBAARQRXiCw+V7Y+mb8ATydHWiGYOa0YTuIeTlVvJryCu8T1wXQWsndqZ+mPgQAKBSIQACqGSp2QX0xb5Y+urIJXJzdqTn+zehiT1DRPZHk281N62vAABQeRAAAVSS9NxC+uKfWNp46BI5OzrQs30b05O9GpKPh3bgAwAAVQ8BEEAFy8grpNX742j9wXhx/6leDWly74ZUw9NV6aYBAMAdCIAAKkjmzVu05sBFWnvgIhUVq2hCjxB6uk8jquVlXODjV92Npt/TVHwFAIDKhQAIoJSi5U1HE+jxrvXJr5QlKbLzb9G6g/Ei61N4u5jGd29Az/RtbHItDz8HF0YDAEDlQwAEUMbq7INa+csGQLkFt2n9oZLAJ6+wSARKU/s2LjVYAgAAy4AACMBEeYW36avDl+iLf+JE9mdsl/r0XL8mFOCDwAcAwOYDoISEBLp06RLl5eVRnTp1qHXr1uTmhtoFsF35t4ro6yOXaOW+WMrIu0WjOwfTtP5NqG4NTFgIAGDTAVB8fDytWLGCNm/eTJcvXyaVxuqNrq6u1Lt3b3r66adp1KhR5OiIZcbAuqXlFIivVzNu0r/x6bRsb6wY2v5wx3r0vwFNxMSFAABgnRxUmlFMKV544QXasGEDDR48mIYPH05dunShoKAg8vDwoPT0dDp9+jTt379fBEdOTk60bt066ty5M1mjrKws8vHxoczMTPL29la6OaCALREJNEtjhXYHInqwY116YUBTCvH1Urp5AABQzuu30RkgLy8viouLo9q1a+v9zM/PjwYMGCBuc+fOpZ07d1JiYqLVBkBg33itrtkawQ9zcCB6ZXBzrM8FAGAjjA6AFi5caPRBhwwZYm57ABR3MS1XLEqqie/Hp+UhAAIAsBFmFepwpicjI0M29cQ/A7BmBbeK9bY5OThQiC9qfgAA7DoA2rt3LxUWFuptz8/PF3VAANYqK/8Wzd1+hurV9CBHhzu/JA5ECx5qg+wPAIC9jgI7efKk+vuzZ89SUlKS+n5RUZGo/albt27FthCgivB4AK79uZFbSL++0Et0hU1cF0FrJ3amfs39lG4eAAAoFQC1b9+eHBwcxE2uq4tHhC1durQi2wdQZb4JT6DfTl6jZY91pAa1vSg7/7bYbuqSFgAAYGMB0MWLF8VfyY0aNaLw8HAxAaLmPEA8GoyHwANYm6hrWfTOL2fpiW716b62gUo3BwAALKkGqEGDBhQSEkLFxcXUqVMncV+6BQYGmh38LFu2TBzX3d2dunbtKoIrQ/r166fOQmne7rvvPvU+HKTNmTNHtImzUgMHDqTo6Giz2ga2j9f0mvZNJDXy9aI372ul3o7V2QEAbJdZRdA8JH7t2rV623nbokWLTDrWli1baObMmWL+oMjISGrXrp2YbDElJUV2/61bt9K1a9fUN56AkQOvRx55RL3PBx98QJ999hmtXLmSjh49KuYw4mNykTaAJg6W3/r5NCVl5tOyxzuSu4uT3ursWNwUAMAGqczQoEED1cGDB/W2HzlyRBUSEmLSsbp06aKaNm2a+n5RUZEqKChItXDhQqMe/8knn6iqV6+uysnJEfeLi4tVAQEBqg8//FC9T0ZGhsrNzU317bffGnXMzMxMngVGfAXb9l1EgqrBa7+qtkYmKt0UAAAoJ1Ou32ZlgHj0F3cv6eKaIM7KGIuH0h87dkx0UUl4DTG+f/jwYaOOsWbNGnr00UdFlkeqU+L2aR6Tp8XmrjVDxywoKBBzGGnewPZFJ2fTnG1naHSnevRgh3pKNwcAAKqQWQFQcHAwHTx4UG87b+P1wYyVlpYmhs/7+/trbef7mkPsDeFaIe4Cmzx5snqb9DhTjsldehwkSTd+fWDbbhYWibofnu/n7QdaK90cAACw5FFgkilTptCLL75It27dUg+H3717N7366qv00ksvUVXh7E9oaKhYmLU8Zs+eLeqQJJwBQhBk29755QwlpOfR9v/1Ik9Xs34NAADAipn1P/8rr7xC169fp+eee049IzSP4HrttddEMGEsX19fUcCcnJystZ3vBwQElPrY3NxcsfL8vHnztLZLj+NjaHbT8X2ex0iOm5ubuIF92PbfFdockUgfjGpLzfyrK90cAACwli4wHnbOo71SU1PpyJEjdOLECUpPTxdDz03BcweFhYWJ7JGEh9jz/e7du5f62O+//17U7jzxxBNa2xs2bCiCIM1jckaHR4OVdUywfTy78+tbT9HI9kH0SCfU/QAA2CuzAiAJ19Rw4NO4cWORQeEhxabirqfVq1fThg0bKCoqiqZOnSqyO5MmTRI/Hz9+vGxWibu/Ro4cSbVr19YLzrh7bv78+bR9+3Y6deqUOAbXJvH+YL/ybxXRtE2RYlj7/AdDxWcFAADsk1ldYNz9NXr0aNqzZ4+4iPAkgzw79FNPPUU1a9akjz/+2OhjjRkzRmSSOHvEARV3U/GaYlIRc0JCghgZpun8+fN04MAB+vPPP2WPybVIHEQ9/fTTYtX6Xr16iWNyNx3YrwU7oigmNYd+eq4HVXND3Q8AgD1z4LHwpj6IMyo8UeGXX35JLVu2FF1gHAD98ccfIqNz5swZsmbcZcajwTIzM8nb21vp5kAF+P3UNZq6KZLeHdmGxnVroHRzAABA4eu3WX8Gc+aFg5169bRrKJo2bUqXLl0y55AAlSbheh69+uNJGhYaQE90ra90cwAAwFprgLh7ydPTU2871wNhNBVYksLbxfT8t5FUw9OF3h/VFnU/AABgfgDUu3dv2rhxo/o+X1R49BavwdW/f39zDglQKT7YeY7OXsuiz8d2JG93F6WbAwAAFsKsLjAOdO655x76999/xTxAXHTMdT+cAZKbIRpACX+dTaYvD1ykt+5vRe2CayjdHAAAsPYMUJs2bejChQtidNWIESNEl9hDDz1Ex48fF0PiAZR2JeMmvfT9CRrY0p+e7BmidHMAAMDaM0C8/MWQIUNo5cqV9MYbb1ROqwDK4VZRMb3w7XHycnWijx5B3Q8AAFRAAOTi4kInT5409WEAVWbxrgv0X2IGffdMN6rh6ap0cwAAwFa6wHj5CZ6JGcDS7LuQSiv2xtLL9zansAa1lG4OAADYUhH07du3ae3atfTXX3+Jtby8vLy0fr548eKKah+A0ZKz8mnmlv+ob7M69EyfRko3BwAAbC0AOn36NHXs2FF8z8XQmlBvAUooKlbR9M3HydnJgRaPbkeOjvgcAgBABQZARUVF9M4771BoaKhY9wvAEny2O5rCL6bTN1O6Ue1qmIwTAAAquAbIycmJ7r33XrHIKIAlOBSTRp/9HU3T72lG3RrVVro5AABgy/MAxcXFVXxrAEyUllNA07f8R90b1ab/DWiidHMAAMCWA6D58+fTyy+/TL/++itdu3ZNrL6qeQOoCsXFKpqx5T9SqVS0ZEx7ckLdDwAAVGYR9LBhw8TXBx54QKvomS9EfJ/rhAAq24p9sXQgJo02PtmF/LzdlW4OAADYegC0Z8+eim8JgAki4tPFhIfP9WtMvZvWUbo5AABgDwFQ3759K74lAEa6kVsolrroWL8GzRjYTOnmAACAvQRAjEeB8WzQUVFR4n7r1q3pySefJB8fn4psH4AW7mblRU7zbxXRZ2M7kLOTWWVsAABg58y6evz7779i1fdPPvmE0tPTxY1nf+ZtkZGRFd9KgDvWHLhIf59LoY9Ht6NAHw+lmwMAAFbKQcV/Upuod+/e1KRJE1q9ejU5Ozurl8eYPHmyGB7/zz//kDXjkWycycrMzCRvb2+lmwN3HE+4QY+sPEyTeobQG/e1Uro5AABgxddvswIgDw8POn78OLVo0UJr+9mzZ6lTp06Ul5dH1gwBkOXJzLtFwz7bT3Wqu9F3z3QnV2d0fQEAgPnXb7OuInzQhIQEve2JiYlUvXp1cw4JYBDH6K/9eJKy8m/R0rEdEPwAAEC5mXUlGTNmDD311FO0ZcsWEfTwbfPmzaILbOzYseVvFYCGjYcv0c4zSfThw+0ouJan0s0BAAB7HQX20UcfiQkPx48fL2p/mIuLC02dOpXef//9im4j2LHTVzLpvd+iaGKPEBrSJkDp5gAAgI0wqwZIwrU+sbGx4nseAebpaRt/naMGyDJk59+i4UsPUDV3Z/pxag9yc3ZSukkAAGAj12+zMkB8YF7uolatWhQaGqrezsPheVQYggYoL47LX//pNKXlFNL6SV0Q/AAAgPI1QI8++qio+dH13XffiZ8BlNfmiET65cRVWvBQKIX4eindHAAAsDFmBUBHjx6l/v37623v16+f+BlAeZxLyqK3t5+hsV3q0wPtgpRuDgAA2CCzAqCCggJ18bOmW7du0c2bNyuiXWAHUrLy6ZNdF8RXSW7BbZq2KZJCanvR3OGY7BAAACwoAOrSpQutWrVKb/vKlSspLCysItoFdiAlu4A+3R0tvkrmbDtDVzPyadnjHcjdBXU/AABQOcwqgp4/fz4NHDiQTpw4Qffcc4/Ytnv3boqIiKA///yzotsIduKHY5fpx8jL9PEj7aiJHybUBAAAC8sA9ezZkw4fPkzBwcGi8PmXX34Ra4OdPHlSrBMGYKqYlBx66+fTNKpjPRoVVk/p5gAAgI0zKwPE2rdvT5s2barY1oBdKrhdRC9/f4KCarjTvBGtlW4OAADYAaMzQLm5uSYd2Nj9ly1bRiEhIeTu7k5du3al8PDwUvfPyMigadOmUWBgILm5uVGzZs1ox44d6p+//fbbYpZqzZvuoq1gGdJySmp/lu6OoYtpubTs8Y7k5WZ2TA4AAFDxARB3cfEyF9euXSt18rpdu3bR0KFD6bPPPivzmLyW2MyZM2nu3LkUGRlJ7dq1o8GDB1NKSors/oWFhTRo0CCKj4+nH374gc6fP0+rV6+munXrau3XunVr0U7pduDAAWNfJlSRLREJNGl9hPh+74VUuq9tILUIwASaAABQNYz+c3vv3r30+uuviwwLByqdOnWioKAgkbm5ceMGnT17VtQF8UzQs2fPpmeeeabMYy5evJimTJlCkyZNUo8i++2332jt2rU0a9Ysvf15O882fejQIbH2GOPskd6LcnamgACsG2WprmXepNlbT5HmIizbjl+hVwY3p0AfDyWbBgAAdsLoDFDz5s3pxx9/pAsXLtDo0aPpypUrIgvDGRgOjjgLw99zdua5554jJ6fShzBzNufYsWNiNJm6MY6O4j4HUnK2b99O3bt3F11g/v7+1KZNG1qwYIFYlkNTdHS0CM4aNWpEjz/+OCUkJJQ5rxGvH6J5g8rD3V3FOivQFamI4tPylGoSAADYGZMLLurXr08vvfSSuDFpLVWutTFFWlqaCFw4kNHE98+dOyf7mLi4OPr7779FUMN1PzExMSLY4gkYuRuNcR3R+vXrRcDG3V/vvPOOGJl2+vRpql5dfmj1woULxX5QNRr6epGjA2kFQU4ODhTiaxuL6QIAgI0Og2dr1qwRGRjuAuMbf//ll19SZSouLiY/Pz8xCSNPuDhmzBh64403RNeZhOuPHnnkEWrbtq2oJ+JAiQunebi+Idxlxwu8SrfExMRKfR32jru52gfXUN/nYGjBQ23Q/QUAAFXGrCE3c+bMEfU7zz//vOiSYtxtNWPGDNHdNG/evDKP4evrK7rJkpOTtbbzfUP1Ozzyi2t/NLvXWrZsSUlJSaJLzdXVVe8xNWrUECPFOFtkCI8m4xtUjdjUHPovMYMe71qfNh1NoLUTO1O/5n5KNwsAAOyIWRmgFStWiHof7jp64IEHxI2/58zM8uXLjToGByucxeEZpDUzPHxfCqrkJmDkQIb3k3BNEgdGcsEPy8nJodjYWLEPWIbPdkeTv7e7mPSQ+VZD8AkAAFYQAHHNDY8C08UBjdwiqYbwEHgOpDZs2EBRUVE0depUMX+QNCps/PjxontKwj/nUWDTp08XgQ+PGOMiaC6Klrz88su0b98+UYzNo8UefPBBkTEaO3asOS8VKlh0cjZtP3GVpvVvQq7OZvfAAgAAlItZXWDjxo0TWSDuBtPEGSAuUDYW1/CkpqaKLjXuxuLZpXfu3KkujObuNB4ZJuGlN/744w/R1cY1PjzyjIOh1157Tb3P5cuXRbBz/fp1qlOnDvXq1YuOHDkivgflLdkdTUE+HjS6UzBdSM5WujkAAGCnHFTSMC4TcO3Pxo0bRUDSrVs3se3o0aMiYOGsjTRHD9MNkqwBD4P38fERBdHe3picr6KcS8qiIUv20/sPhdKjXepTSla+qAHiWiA/b3elmwcAAFbOlOu3WQFQ//79jdqPh8bzsHVrgwCocjz71TE6cy2T/n6pH7k4ofsLAACUu36b1QW2Z88ec9sGdur0lUzaeSaJPny4LYIfAABQHK5EUCWW/BVNIbU96cEO2uu2AQAAKAEBEFS6k5cz6K+oZJo+sCk5I/sDAAAWAFcjqHSf7LpAjet40QPtkP0BAADLgAAIKtWxSzdoz/lUenFgM3LiNS8AAAAsAAIgqFRL/rpAzfyr0X2hmIkbAAAsBwIgqDThF9Npf3QazRjYjByR/QEAAAuCAAgqtfanZaA3DW4tv7gtAACAUhAAQaU4FJtGh+Ou04yBTZH9AQAAi4MACCocTy6+ZFc0tanrTYNalazrBgAAYEkQAEGFOxhzncLj02nmoGZiORQAAABLgwAIKjz7s3jXeWoXXIP6N/dTujkAAACyEABBhdp3IZUiEzKQ/QEAAIuGAAgqNPvDI7/CGtSkPk19lW4OAACAQQiAoMLsjkqhE5czkf0BAACLhwAIKrD25wJ1bViLejSurXRzAAAASoUACCrEH2eS6ey1LJqB7A8AAFgBBEBQbsXFKrHmV88mtalbI2R/AADA8iEAgnL7/XQSnUvKFmt+AQAAWAMEQFAuRXeyP32a1aFOIbWUbg4AAIBREABBufx68ipFp+SINb8AAACsBQIgMNvtomL69K9oGtDCjzrUr6l0cwAAAIyGAAjMtv3EVYpLy0XtDwAAWB0EQGB+9md3tFjtPbSej9LNAQAAMAkCIDDL1uNX6NL1PGR/AADAKiEAApMV3i6mz3ZH07DQAGoV5K10cwAAAEyGAAhM9sOxy3Ql4yZNvwfZHwAAsE4IgMAkBbeL6PO/o+n+tkHUPKC60s0BAAAwCwIgMMl3EYmUlJVP0+/BvD8AAGC9EACB0fJvFdHne2JoRPu61MSvmtLNAQAAMBsCIDDat+EJlJZTSC8g+wMAAFYOARAY5WZhES3fG0sPdqhLDX29lG4OAACAdQdAy5Yto5CQEHJ3d6euXbtSeHh4qftnZGTQtGnTKDAwkNzc3KhZs2a0Y8eOch0Tyrbp6CVKzy2kFwYg+wMAANZP0QBoy5YtNHPmTJo7dy5FRkZSu3btaPDgwZSSkiK7f2FhIQ0aNIji4+Pphx9+oPPnz9Pq1aupbt26Zh8TypZXeJtW7I2lR8LqUf3anko3BwAAoNwcVCqVihTC2ZnOnTvT559/Lu4XFxdTcHAwPf/88zRr1iy9/VeuXEkffvghnTt3jlxcXCrkmHKysrLIx8eHMjMzydsbE/2t3BdLH/95nv5+qR8F10IABAAAlsmU67diGSDO5hw7dowGDhx4tzGOjuL+4cOHZR+zfft26t69u+gC8/f3pzZt2tCCBQuoqKjI7GOygoICcdI0b1Aip+A2fbEvlkZ3CkbwAwAANkOxACgtLU0ELhzIaOL7SUlJso+Ji4sTXV/8OK77eeutt+jjjz+m+fPnm31MtnDhQhExSjfOGEGJ9QcvUm5BEf1vQBOlmwIAAGA7RdCm4O4sPz8/WrVqFYWFhdGYMWPojTfeEF1j5TF79myRLpNuiYmJFdZma5aVf4tW/RNHj3WtT4E+Hko3BwAAoMI4k0J8fX3JycmJkpOTtbbz/YCAANnH8Mgvrv3hx0latmwpsjvc/WXOMRmPJuMbaFt74CIV3C6mqf0aK90UAAAA28gAubq6iizO7t27tTI8fJ/rfOT07NmTYmJixH6SCxcuiMCIj2fOMUFeZt4tWrP/Ij3RrQH5e7sr3RwAAADb6QLj4eo8jH3Dhg0UFRVFU6dOpdzcXJo0aZL4+fjx40X3lIR/np6eTtOnTxeBz2+//SaKoLko2thjgnG+PBBHt4qL6dm+yP4AAIDtUawLjHENT2pqKs2ZM0d0Y7Vv35527typLmJOSEgQo7gkXJz8xx9/0IwZM6ht27Zi/h8Ohl577TWjjwllu5FbKLq/JnQPoTrV0TUIAAC2R9F5gCyVvc8DtGjnOdpwKJ72v9qfaldDAAQAANbBKuYBAsuUllMggp+JPUIQ/AAAgM1CAARaeNi7o4MDTendSOmmAAAAVBoEQKCWkp1PGw/H05M9Q6iml6vSzQEAAKg0CIBAjRc8dXFypKeQ/QEAABuHAAiEpMx82nQ0QXR9+XjILzQLAABgKxAAgbB8bwx5uDjRpJ4hSjcFAACg0iEAArqScZM2hyfS030aUXV3ZH8AAMD2IQACWrYnhqq5O9OEHsj+AACAfUAAZOcS0/Pou4hEeqZPI6rmpujE4AAAAFUGAZCd+/zvGKrh6ULjujdQuikAAABVBgGQHbt0PZd+iLwsFjz1dEX2BwAA7AcCIDv22e4YquXlSk90Q/YHAADsCwIgOxWXmkM/Hb9M0/o1JncXJ6WbAwAAUKUQANmpz3ZHk7+3Oz3apb7STQEAAKhyCIDsUHRyNm07cZWm9W+C7A8AANglBEB2aMnuaAry8aDRnYKVbgoAAIAiEADZmXNJWfTbyWv0/IAm5OqMtx8AAOwTroB2ZsmuaAqu5UGjwuop3RQAAADFIACyI6evZNLOM0n0woCm5OKEtx4AAOwXroJ2ZMlf0RRS25Me7FBX6aYAAAAoCgGQnTh5OYP+ikqm6QObkjOyPwAAYOdwJbQTn+y6QI3qeNED7ZD9AQAAQABkByITbtCe86n04sBm5OTooHRzAAAAFIcAyE6yP838q9H9oYFKNwUAAMAiIACycRHx6bQ/Oo1mDGxGjsj+AAAACAiAbNziPy9Qy0BvGtw6QOmmAAAAWAwEQDbsUGwaHY67TjMGNkX2BwAAQAMCIBulUqnErM9t6nrToFb+SjcHAADAoiAAslEHY65TeHw6zRzUjBwckP0BAADQhADIRrM/i3edp3bBNah/cz+lmwMAAGBxEADZoH0XUikyIQPZHwAAAAMQANlg9ofn/QlrUJP6NPVVujkAAAAWCQGQjfn7XAqduJyJ7A8AAIClB0DLli2jkJAQcnd3p65du1J4eLjBfdevXy8u7Jo3fpymiRMn6u0zZMgQso/anwvUpWEt6tG4ttLNAQAAsFjOSjdgy5YtNHPmTFq5cqUIfpYsWUKDBw+m8+fPk5+ffAGvt7e3+LlELtPBAc+6devU993c3MjW/Xk2mc5czaLNT3dD9gcAAMCSM0CLFy+mKVOm0KRJk6hVq1YiEPL09KS1a9cafAxf3AMCAtQ3f3/9eW444NHcp2bNmmTLiotLan96NqlN3Roh+wMAAGCxAVBhYSEdO3aMBg4ceLdBjo7i/uHDhw0+Licnhxo0aEDBwcE0YsQIOnPmjN4+e/fuFRmk5s2b09SpU+n69esGj1dQUEBZWVlaN2vz++kkOpeULdb8AgAAAAsOgNLS0qioqEgvg8P3k5KSZB/DAQ1nh7Zt20Zff/01FRcXU48ePejy5cta3V8bN26k3bt306JFi2jfvn00dOhQ8VxyFi5cSD4+PuobB1bWpKhYRUv+ukB9mtWhTiG1lG4OAACAxVO8BshU3bt3FzcJBz8tW7akL774gt59912x7dFHH1X/PDQ0lNq2bUuNGzcWWaF77rlH75izZ88WdUgSzgBZUxD068mrFJ2SQx883FbppgAAAFgFRTNAvr6+5OTkRMnJyVrb+T7X7RjDxcWFOnToQDExMQb3adSokXguQ/twvRAXVmverMXtomL69K9oGtDCjzrUt+06JwAAAJsIgFxdXSksLEx0VUm4S4vva2Z5SsPdWqdOnaLAwECD+3D3GNcAlbaPtdp+4irFpeWi9gcAAMCaRoFx19Pq1atpw4YNFBUVJQqWc3NzxagwNn78eNFFJZk3bx79+eefFBcXR5GRkfTEE0/QpUuXaPLkyeoC6VdeeYWOHDlC8fHxIpjiQukmTZqI4fW2RGR/dkeL1d5D6/ko3RwAAACroXgN0JgxYyg1NZXmzJkjCp/bt29PO3fuVBdGJyQkiJFhkhs3bohh87wvD23nDNKhQ4fEEHrGXWonT54UAVVGRgYFBQXRvffeK+qDbG0uoK3Hr9Cl63m0/PGOSjcFAADAqjioePpg0MJF0DwaLDMz02LrgW4VFVP/j/ZSaF0fWvFEmNLNAQAAsKrrt+JdYGCeH45dpisZN+lF1P4AAACYDAGQFSq4XUSf/x1D97cNouYB1ZVuDgAAgNVBAGSFvotIpGuZN2n6PU2VbgoAAIBVQgBkZfJvFdHne2JoRPu61MSvmtLNAQAAsEoIgKzMt+EJlJZTSC8g+wMAAGA2BEBW5GZhES3fG0sPdqhLDX29lG4OAACA1UIAZEU2Hb1E6bmF9MIAZH8AAADKAwGQlcgrvE0r9sbSI2H1qH5tT6WbAwAAYNUQAFmJjYcvUVb+LZrWv4nSTQEAALB6CICsQE7BbfpiXyyN7hRMwbWQ/QEAACgvBEBWYMOheMotKEL2BwAAoIIgALJw3O216p84GtslmIJqeCjdHAAAAJuAAMjCrT1wUUx++ByyPwAAABUGAZAFy8y7RWv2X6QnujUgf293pZsDAABgMxAAWbAvD8TRreJierZvY6WbAgAAYFMQAFmoG7mFovtrQvcQqlPdTenmAAAA2BQEQBZq1f44UhHR030aKd0UAAAAm4MAyAKl5RSIoe8Te4RQ7WrI/gAAAFQ0BEAWiIe9Ozo40JTeyP4AAABUBgRAFiYlO582Ho6nJ3uGUE0vV6WbAwAAYJMQAFmYlXvjyMXJkZ7qhewPAABAZUEAZEGSs/Lp66OXaHKvRuTj6aJ0cwAAAGwWAiALsnxPDHm4ONGTvUKUbgoAAIBNQwBkIa5k3KRvwxPFsPfq7sj+AAAAVCYEQBZi2Z4YqubuTBN6IPsDAABQ2RAAWYDE9Dz6LiKRnunTiKq5OSvdHAAAAJuHAMgCfP53DNXwdKFx3Rso3RQAAAC7gABIYZeu59IPkZfFgqeersj+AAAAVAUEQAr7bHcM1fJypSe6IfsDAABQVRAAKSguNYd+On6ZpvVrTO4uTko3BwAAwG4gAKpiKVn59MmuC+LrZ7ujya+6Oz3apb7SzQIAALArCICqWEp2AX26O5qOXbpB205cpWkDmiD7AwAAUMUQACnkm/AECvLxoNGd6indFAAAALtjEQHQsmXLKCQkhNzd3alr164UHh5ucN/169eTg4OD1o0fp0mlUtGcOXMoMDCQPDw8aODAgRQdHU2W5EB0Gj0/oAm5OSP7AwAAYHcB0JYtW2jmzJk0d+5cioyMpHbt2tHgwYMpJSXF4GO8vb3p2rVr6tulS5e0fv7BBx/QZ599RitXrqSjR4+Sl5eXOGZ+fj5ZCj9vNxoVhuwPAACAXQZAixcvpilTptCkSZOoVatWImjx9PSktWvXGnwMZ30CAgLUN39/f63sz5IlS+jNN9+kESNGUNu2bWnjxo109epV+vnnn01qW2FhoTiepKioSGy7ffu23n7G7hsemyq+3hcaSC5OjqXue+vWLbG9uLhYvY2/5238M0vbl9vP2/n1mLMvnz/pXFbWvnLvkSn7lue9N2VfufNuy5+TqnjvTdlXyfe+vJ8Ta3vvLe1zgv8jbim+b3nfT6sIgLjRx44dE11U6gY5Oor7hw8fNvi4nJwcatCgAQUHB4sg58yZM+qfXbx4kZKSkrSO6ePjI7rWDB2zoKCAsrKytG7s448/pry8PPV+Bw8epIULF9KOHTu0Hv/RRx+J7ZmZmeptERERYtv27dvV27ZEJNC8HefE9+sOxov77L///hP7/vDDD3pdg7yds1yS06dPi22bN2/W2nf16tVie0JCyTHZhQsXxLavvvpKrxuRt8fExGidN962Zs0arX03bdoktkdFRam3Xb58WWzjYFXTd999J7afOnVKvY0zebxt6dKlWvv+9NNPYju//5L09HSxjYNiTb/++qvYfuTIEfW27OxssW3RokVa+/7xxx9i+/79+7XeX97GN81fvt27d4tt/FXCP5f25cdJ+Hi8jY+viZ+ft3N7JNxO3sbt1sSvi7fz65Tw6+dtfD408fni7ZqZUD6vvI3PsyZ+H3g7vy8Sfr94G79/mvj95e38fkv4c8Db+HOhiT83vJ0/RxL+fPE2/rxp4s8jb+fPp4Q/t7yNP8ea+HPO2/lzL0lNTRXbPv30U619+feHt/Pvk4R/z3gb/95p4t9L3s6/pxL+/ZXeT01//fWX2LZ37171Nv5PWNpX8z9k3oe38WM0SftW5P8RjM8Bb+dzIsH/ESXwf8Rd+D/C8P8RVhEApaWliahNM4PD+D4HMXKaN28uskPbtm2jr7/+WnwYe/TooX5jpceZckw+eRwkSTcOrCratcybNHsr/9I7iPsc37++9bTYDgAAAFXLQaWZa6ti3C1Vt25dOnToEHXv3l29/dVXX6V9+/aJ+p2y8F9qLVu2pLFjx9K7774rjtWzZ09xbC6ClowePVp0nXHNkS6O4jUjec4AcRDEEWft2rXF4xgHa3zjLJWz891lK6SUnIuLi8F9D8Wm0WOr9V/Pt1O6UZeQGrLH5dfGbw9v458xDvg45cfPw89nSfvyNv6Zk5OTuJm6Lz+P9Je3q6trpewr9x6Zsq857705+8qdd1P2tbbPSVW89+X9nFTVe1/ez4m1vfeW9jnB/xG3FN+3PO8nX785kcHZVq4XLo2ii0/5+vqKBicnJ2tt5/tc22MMPmkdOnRQp2qlx/ExNAMgvt++fXvZY7i5uYmbLj7J0oeQab4Zuvvp0t23oa8XOToQFWuEm04ODhTi62nwuJofCAl/cOSezxL21fyFM2dfPtdVua/ceTdlX1ZZ+8qdd1v+nFT1e1/ezwmzhH0t4f3E/xGG92X4P6Lq/4+wii4wfjFhYWF6/at8XzMjVBqOdrnfUwp2GjZsKIIgzWNyRMjZJGOPWRkCfTxo4UOhIghi/HXBQ23EdgAAAKhaii8/zkPgJ0yYQJ06daIuXbqIEVy5ubliVBgbP3686CaTihjnzZtH3bp1oyZNmlBGRgZ9+OGHYhj85MmT1VHiiy++SPPnz6emTZuKgOitt96ioKAgGjlypKKvdUzn+uTv7U4T10XQ2omdqV9zP0XbAwAAYK8UD4DGjBkjam144kIuUuZuqp07d6qLmLmiXOovZDdu3BDD5nnfmjVrigwS1/3wEHrNGiIOop5++mkRJPXq1UscU3fCRCX4VnPT+goAAAB2VgRtqUwpojLV6SuZdP/SA/Tr872oTV2fCj02AACAPcsy4fqt+ESIAAAAAFUNAVAV86vuRtPvaSq+AgAAgJ3WANkbP293mjGomdLNAAAAsGvIAAEAAIDdQQAEAAAAdgcBEAAAANgdBEAAAABgdxAAAQAAgN1BAAQAAAB2BwEQAAAA2B0EQAAAAGB3EAABAACA3UEABAAAAHYHARAAAADYHQRAAAAAYHcQAAEAAIDdQQAEAAAAdsdZ6QZYIpVKJb5mZWUp3RQAAAAwknTdlq7jpUEAJCM7O1t8DQ4OVropAAAAYMZ13MfHp9R9HFTGhEl2pri4mK5evUoDBgygf//916jHdO7cmSIiIoyKTjmwSkxMJG9vb7J3xp43e2lfZT1fRR23PMcx57GmPsaY/fE7qA+/h1XzfBVx3PIeo7J/Dzsr/DvIIQ0HP0FBQeToWHqVDzJAMvik1atXj5ydnY1+c5ycnEx6I3lf/Odr+nmz9fZV1vNV1HHLcxxzHmvqY0zZH7+Dd+H3sGqeryKOW95jVPbvoZMF/A6WlfmRoAi6FNOmTauUfcF6zltVt6+ynq+ijlue45jzWFMfY+mfJ0tl6ecNv4cVd4zK/j2cZuGfJU3oAqtinPrj6DQzM9Oi/+ICsFX4HQRQlqX8DiIDVMXc3Nxo7ty54isAVD38DgIoy1J+B5EBAgAAALuDDBAAAADYHQRAAAAAYHcQAAEAAIDdQQAEAAAAdgcBEAAAANgdBEAW5Ndff6XmzZtT06ZN6csvv1S6OQB26cEHH6SaNWvSww8/rHRTAOxOYmIi9evXj1q1akVt27al77//vtKeC8PgLcTt27fFG75nzx4xQVRYWBgdOnSIateurXTTAOzK3r17xVpCGzZsoB9++EHp5gDYlWvXrlFycjK1b9+ekpKSxLXwwoUL5OXlVeHPhQyQhQgPD6fWrVtT3bp1qVq1ajR06FD6888/lW4WgN3hvz6rV6+udDMA7FJgYKAIflhAQAD5+vpSenp6pTwXAqAK8s8//9Dw4cPFCrQODg70888/6+2zbNkyCgkJIXd3d+ratasIeiS8+jwHPxL+/sqVK1XWfgBbUN7fQwCwnN/BY8eOUVFRkVg5vjIgAKogubm51K5dO/HGytmyZQvNnDlTTP8dGRkp9h08eDClpKRUeVsBbBV+DwFs43cwPT2dxo8fT6tWraq8xnINEFQsPq0//fST1rYuXbqopk2bpr5fVFSkCgoKUi1cuFDcP3jwoGrkyJHqn0+fPl21adOmKmw1gG0x5/dQsmfPHtWoUaOqrK0AtojM/B3Mz89X9e7dW7Vx48ZKbR8yQFWgsLBQpPIGDhyo3ubo6CjuHz58WNzv0qULnT59WnR75eTk0O+//y6iYgCout9DAFD2d5DjpokTJ9KAAQNo3LhxldgadIFVibS0NNGP6e/vr7Wd73OVO3N2dqaPP/6Y+vfvLwrAXnrpJYwAA6ji30PG/xk/8sgjtGPHDqpXrx6CI4Aq/B08ePCg6Cbj2iG+FvLt1KlTVBmcK+WoYJYHHnhA3ABAOX/99ZfSTQCwW7169aLi4uIqeS5kgKoAD+NzcnIScxto4vs8zA8AKh9+DwGU5Wthv4MIgKqAq6urmMxp9+7d6m0c4fL97t27K9o2AHuB30MAZbla2O8gusAqCBcux8TEqO9fvHiR/vvvP6pVqxbVr19fDPubMGECderUSRQ8L1myRAwXnDRpkqLtBrAl+D0EUFaONf0OVuoYMzvCw2b5dOreJkyYoN5n6dKlqvr166tcXV3FUMAjR44o2mYAW4PfQwBl7bGi30GsBQYAAAB2BzVAAAAAYHcQAAEAAIDdQQAEAAAAdgcBEAAAANgdBEAAAABgdxAAAQAAgN1BAAQAAAB2BwEQAAAA2B0EQAAAAGB3EAABgN14++23qX379iY9xsHBgX7++edKaxMAKAMBEABYvX79+tGLL75Y5n4vv/yy1krUAGC/sBo8ANg8XvKwqKiIqlWrJm4AAMgAAYBVmzhxIu3bt48+/fRT0V3Ft/Xr14uvv//+O4WFhZGbmxsdOHBArwssIiKCBg0aRL6+vuTj40N9+/alyMhIRV8PAFQNBEAAYNU48OnevTtNmTKFrl27Jm7BwcHiZ7NmzaL333+foqKiqG3btnqPzc7OpgkTJojg6MiRI9S0aVMaNmyY2A4Atg1dYABg1Thz4+rqSp6enhQQECC2nTt3TnydN2+eyPAYMmDAAK37q1atoho1aoiM0v3331/JLQcAJSEDBAA2q1OnTqX+PDk5WWSOOPPDgZS3tzfl5ORQQkJClbURAJSBDBAA2CwvL69Sf87dX9evXxfdaA0aNBC1QtydVlhYWGVtBABlIAACAKvHXWA8ystUBw8epOXLl4u6H5aYmEhpaWmV0EIAsDQIgADA6oWEhNDRo0cpPj5eDHMvLi426nHc9fXVV1+JrrKsrCx65ZVXyMPDo9LbCwDKQw0QAFg9nuDQycmJWrVqRXXq1DG6hmfNmjV048YN6tixI40bN45eeOEF8vPzq/T2AoDyHFQ8QxgAAACAHUEGCAAAAOwOAiAAAACwOwiAAAAAwO4gAAIAAAC7gwAIAAAA7A4CIAAAALA7CIAAAADA7iAAAgAAALuDAAgAAADsDgIgAAAAsDsIgAAAAIDszf8BjyTIGGUuZHEAAAAASUVORK5CYII=" 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" }, "metadata": {}, "output_type": "display_data" diff --git a/examples/view_experiment2_behavior.ipynb b/examples/view_experiment2_behavior.ipynb index 59e6661..9a9666c 100644 --- a/examples/view_experiment2_behavior.ipynb +++ b/examples/view_experiment2_behavior.ipynb @@ -9,8 +9,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T17:46:35.297586Z", - "start_time": "2025-01-27T17:46:35.063832Z" + "end_time": "2025-01-27T18:11:58.286738Z", + "start_time": "2025-01-27T18:11:58.063475Z" } }, "cell_type": "code", @@ -25,8 +25,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T17:46:35.851984Z", - "start_time": "2025-01-27T17:46:35.299577Z" + "end_time": "2025-01-27T18:11:58.914027Z", + "start_time": "2025-01-27T18:11:58.295149Z" } }, "cell_type": "code", @@ -52,8 +52,8 @@ "Data variables:\n", " phat (transformation) float64 288B 1.0 1.0 ... 0.952 1.0\n", " varhat_phat (transformation) float64 288B 0.0003555 ... 0.0003867\n", - " boot_phat (boot_iter, transformation) float64 288kB 0.9509 .....\n", - " boot_varhat_phat (boot_iter, transformation) float64 288kB 0.0003529...\n" + " boot_phat (boot_iter, transformation) float64 288kB 0.991 ......\n", + " boot_varhat_phat (boot_iter, transformation) float64 288kB 0.0004516...\n" ] } ], @@ -62,8 +62,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T17:46:36.231158Z", - "start_time": "2025-01-27T17:46:35.852920Z" + "end_time": "2025-01-27T18:11:59.312918Z", + "start_time": "2025-01-27T18:11:58.914641Z" } }, "cell_type": "code", From 970afc5efae4a9e6488f4ec85f52df838a82ac2d Mon Sep 17 00:00:00 2001 From: himjl Date: Mon, 27 Jan 2025 13:48:20 -0500 Subject: [PATCH 080/156] add banner image --- README.md | 18 + readme.md | 41 - site/changelist.md | 8 + site/readme_images/human_learning_curves.svg | 8132 ++++++++++++++++++ site/readme_images/run_make_images.py | 52 + 5 files changed, 8210 insertions(+), 41 deletions(-) create mode 100644 README.md delete mode 100644 readme.md create mode 100644 site/changelist.md create mode 100644 site/readme_images/human_learning_curves.svg create mode 100644 site/readme_images/run_make_images.py diff --git a/README.md b/README.md new file mode 100644 index 0000000..d2e1243 --- /dev/null +++ b/README.md @@ -0,0 +1,18 @@ +# `hobj`: human object learning benchmarks + +[![CI](https://github.com/himjl/hobj/actions/workflows/ci.yml/badge.svg)](https://github.com/himjl/hobj/actions/workflows/ci.yml) + +This repository contains benchmarks for comparing models of object learning against measurements of human behavior, from Lee and DiCarlo 2023 (["How well do rudimentary plasticity rules predict adult visual object learning?"](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011713)). + +![Example of human object learning data](site/readme_images/human_learning_curves.svg) + +### Installation +This package is supported for Python >=3.11. After cloning `hobj` on your machine, simply navigate to this directory in your shell, then run + +``` pip install hobj``` + +To see how to view behavioral data and/or score an example learning model, check out `examples/`. + + +### Changes to codebase since publication +This codebase was refactored in 2025, improving the performance and quality of the code, as well as introducing changes to the statistics (see [changelist](site/changelist.md)). To see the codebase that reflects its contents at the time of publication, check out the repo with the `v1` tag. diff --git a/readme.md b/readme.md deleted file mode 100644 index 6e7b0ae..0000000 --- a/readme.md +++ /dev/null @@ -1,41 +0,0 @@ -# Human object learning benchmarks - -[![CI](https://github.com/himjl/hobj/actions/workflows/ci.yml/badge.svg)](https://github.com/himjl/hobj/actions/workflows/ci.yml) - -This repository contains benchmarks for comparing models of object learning against psychometric measurements of human behavior. - -# Installation - -We developed this repository using Python 3.9. Follow the steps below to use it: - -1. Begin by cloning the `hobj` repository to your local machine. -2. Make and activate a new conda environment. -3. Install the `hobj` package itself. To do so, `cd` to the `hobj` directory (the top-level one), then run: - -```bash -pip install -e . -``` - -4. Then, install [xarray](https://docs.xarray.dev/en/latest/getting-started-guide/installing.html) and its dependencies by running the following command: -```bash -conda install -c conda-forge xarray dask netCDF4 bottleneck -``` - -### Download all images (recommended) - -The code in this repository works without this step. But to save time, it is recommended to download the images in a batch. -They are located [here](https://hlbdatasets.s3.amazonaws.com/images/LeeDiCarlo_hobj_Images.zip). - -Once it is downloaded, unzip it (it should turn into an `images` folder). Move that `images` folder to `~/hlb_cache/`. - -# Usage - -To see how to view the raw behavioral data and/or score an example learning model, check out the examples in -`examples/`. - - -# Paper -This codebase originally accompanied the 2023 paper ["How well do rudimentary plasticity rules predict adult visual object learning?"](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011713). - - -Since then, this codebase has been refactored and streamlined, introducing breaking changes to the API. To see the codebase that reflects its contents at the time of publication, check out the `v1` tag. diff --git a/site/changelist.md b/site/changelist.md new file mode 100644 index 0000000..69875a8 --- /dev/null +++ b/site/changelist.md @@ -0,0 +1,8 @@ + +We note the following changes since this codebase was released alongisde the paper: + + +## 1/27/2025: +* Benchmarks now report MSEn scores which subtract the sampling variance of the human target, meaning that a "correct model" will have an expected score of 0. Before, these benchmarks did not perform this subtraction, meaning a "correct model" would have an expected score equal to the human sampling variance. +* Experiment 2: The lapse-rate corrected performance is now clipped between [0, 1]. +* Experiment 2: The estimation of the variance of a lapse-rate corrected performance estimate from using bootstrapping to using the formula $\frac{1}{1-\hat p_{l}} \hat \sigma^2$, which treats the estimated lapse rate as a constant. Because a high number (~1000s) of Binomial observations are used to estimate $\hat p_{l}$, we consider this a decent approximation that is less computationally expensive. diff --git a/site/readme_images/human_learning_curves.svg b/site/readme_images/human_learning_curves.svg new file mode 100644 index 0000000..24ac401 --- /dev/null +++ b/site/readme_images/human_learning_curves.svg @@ -0,0 +1,8132 @@ + + + + + + + + 2025-01-27T13:47:46.663343 + image/svg+xml + + + Matplotlib v3.9.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/site/readme_images/run_make_images.py b/site/readme_images/run_make_images.py new file mode 100644 index 0000000..b55fb68 --- /dev/null +++ b/site/readme_images/run_make_images.py @@ -0,0 +1,52 @@ +if __name__ == '__main__': + + # %% + import matplotlib.pyplot as plt + from hobj.data.images import MutatorHighVarImageset + from hobj.benchmarks import MutatorHighVarBenchmark + import numpy as np + + imageset = MutatorHighVarImageset() + benchmark = MutatorHighVarBenchmark() + + target_stats = benchmark.target_statistics + x = np.arange(100) + 1 + + # %% + plt.figure(figsize=(6, 4)) + + for subtask in target_stats.subtask.values: + y = target_stats.phat.sel(subtask=subtask) + yerr = np.sqrt(target_stats.varhat_phat.sel(subtask=subtask)) + plt.plot( + x, y, + alpha=0.05, + color='gray', + lw=1, + ) + + glc = target_stats.phat.mean(dim='subtask') + glc_se = target_stats.boot_phat.mean('subtask').std('boot_iter', ddof=1) + + plt.errorbar( + x, + glc, + yerr=glc_se, + color='blue', + label='human average' + ) + plt.axhline(0.5, ls=':', color='black', label='chance', zorder=0) + plt.ylabel('test accuracy') + plt.xscale('log') + plt.xlabel('# examples') + plt.title('Human learning curves') + plt.xlim([None, 100]) + plt.legend(loc=(1.05, 0.5)) + + # Remove top and right spines + plt.gca().spines['top'].set_visible(False) + plt.gca().spines['right'].set_visible(False) + + plt.tight_layout() + plt.savefig('./human_learning_curves.svg', transparent=True) + plt.show() From 6f0d02bdaf4aa0b875e514a5e0399d35942bf908 Mon Sep 17 00:00:00 2001 From: himjl Date: Mon, 27 Jan 2025 13:50:03 -0500 Subject: [PATCH 081/156] center image --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index d2e1243..120d38a 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,9 @@ This repository contains benchmarks for comparing models of object learning against measurements of human behavior, from Lee and DiCarlo 2023 (["How well do rudimentary plasticity rules predict adult visual object learning?"](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011713)). -![Example of human object learning data](site/readme_images/human_learning_curves.svg) +
+ Alt text +
### Installation This package is supported for Python >=3.11. After cloning `hobj` on your machine, simply navigate to this directory in your shell, then run From 5b6e3369a7c96b7a73d16776e09a09841ec3fa1c Mon Sep 17 00:00:00 2001 From: himjl Date: Mon, 27 Jan 2025 18:05:52 -0500 Subject: [PATCH 082/156] Add example usage section --- README.md | 27 ++++++++++++++++++++++++--- hobj/__init__.py | 2 ++ hobj/learning_models/__init__.py | 1 + 3 files changed, 27 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 120d38a..09f1682 100644 --- a/README.md +++ b/README.md @@ -2,16 +2,37 @@ [![CI](https://github.com/himjl/hobj/actions/workflows/ci.yml/badge.svg)](https://github.com/himjl/hobj/actions/workflows/ci.yml) -This repository contains benchmarks for comparing models of object learning against measurements of human behavior, from Lee and DiCarlo 2023 (["How well do rudimentary plasticity rules predict adult visual object learning?"](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011713)). +This repository contains benchmarks for comparing models of object learning against measurements of human behavior, from Lee and DiCarlo 2023 (["How well do rudimentary plasticity rules predict adult visual object learning?"](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011713)).
Alt text
+ +### Example usage + +Benchmarking a learning model simply requires the user to define a `hobj.learning_models.BinaryLearningModel` subclass. +Then, an instance of that subclass may be passed in as an argument to a benchmark object: + +```python +import hobj + +# Load model: +model = hobj.learning_models.RandomGuesser() # A subclass of BinaryLearningModel + +# Load benchmark: +benchmark = hobj.benchmarks.MutatorHighVarBenchmark() # Try benchmark 2: hobj.benchmarks.MutatorOneshotBenchmark() +result = benchmark(model) + +# Print the score and its CI: +print(result.msen, result.msen_CI95) +``` + + ### Installation -This package is supported for Python >=3.11. After cloning `hobj` on your machine, simply navigate to this directory in your shell, then run +The `hobj` package works for Python >=3.11. After cloning this repository on your machine, navigate to this directory in your shell and run: -``` pip install hobj``` +``` pip install -e .``` To see how to view behavioral data and/or score an example learning model, check out `examples/`. diff --git a/hobj/__init__.py b/hobj/__init__.py index e69de29..d70fc75 100644 --- a/hobj/__init__.py +++ b/hobj/__init__.py @@ -0,0 +1,2 @@ +import hobj.benchmarks as benchmarks +import hobj.learning_models as learning_models diff --git a/hobj/learning_models/__init__.py b/hobj/learning_models/__init__.py index bce9f03..165a81f 100644 --- a/hobj/learning_models/__init__.py +++ b/hobj/learning_models/__init__.py @@ -6,6 +6,7 @@ from hobj.data import schema as schema + # %% class BinaryLearningModel(ABC): From c78fc38ed475adbdf95fc082d9f3c66cd4032571 Mon Sep 17 00:00:00 2001 From: himjl Date: Mon, 27 Jan 2025 18:08:41 -0500 Subject: [PATCH 083/156] improve readme --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 09f1682..b6aa5f4 100644 --- a/README.md +++ b/README.md @@ -28,14 +28,14 @@ result = benchmark(model) print(result.msen, result.msen_CI95) ``` +For more details, check out `examples/`. ### Installation + The `hobj` package works for Python >=3.11. After cloning this repository on your machine, navigate to this directory in your shell and run: ``` pip install -e .``` -To see how to view behavioral data and/or score an example learning model, check out `examples/`. - ### Changes to codebase since publication -This codebase was refactored in 2025, improving the performance and quality of the code, as well as introducing changes to the statistics (see [changelist](site/changelist.md)). To see the codebase that reflects its contents at the time of publication, check out the repo with the `v1` tag. +This codebase was refactored in January 2025 to improve the performance and quality of the code, and is now designated as `v2`. Along the way, minor refinements to the statistical analysis of the original codebase were introduced (see [changelist](site/changelist.md)). To see the codebase at the time of publication, check out the repo with the `v1` tag [here](https://github.com/himjl/hobj/releases/tag/v1). From 76a272e124e929af1860a6e782017e9dc4a24feb Mon Sep 17 00:00:00 2001 From: himjl Date: Mon, 27 Jan 2025 18:12:40 -0500 Subject: [PATCH 084/156] added changelist --- site/changelist.md | 1 + 1 file changed, 1 insertion(+) diff --git a/site/changelist.md b/site/changelist.md index 69875a8..38dba67 100644 --- a/site/changelist.md +++ b/site/changelist.md @@ -4,5 +4,6 @@ We note the following changes since this codebase was released alongisde the pap ## 1/27/2025: * Benchmarks now report MSEn scores which subtract the sampling variance of the human target, meaning that a "correct model" will have an expected score of 0. Before, these benchmarks did not perform this subtraction, meaning a "correct model" would have an expected score equal to the human sampling variance. +* Statistical analyses other than the calculation of MSEn scores, their 95% confidence intervals, and generation of bootstrap resamples have been removed from this codebase. * Experiment 2: The lapse-rate corrected performance is now clipped between [0, 1]. * Experiment 2: The estimation of the variance of a lapse-rate corrected performance estimate from using bootstrapping to using the formula $\frac{1}{1-\hat p_{l}} \hat \sigma^2$, which treats the estimated lapse rate as a constant. Because a high number (~1000s) of Binomial observations are used to estimate $\hat p_{l}$, we consider this a decent approximation that is less computationally expensive. From e6bc53d7b1dcda4131352be7c46d236d74b95c9a Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 28 Jan 2025 06:59:35 -0500 Subject: [PATCH 085/156] write toml --- pyproject.toml | 37 +++++++++++++++++++++++++++++++++++++ requirements.txt | 29 ----------------------------- setup.py | 17 ----------------- 3 files changed, 37 insertions(+), 46 deletions(-) create mode 100644 pyproject.toml delete mode 100644 requirements.txt delete mode 100644 setup.py diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..9957d09 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,37 @@ +[build-system] +requires = ["setuptools>=42", "wheel"] # Add build dependencies +build-backend = "setuptools.build_meta" + +[project] +name = "hobj" +version = "2.0.0" +description = "Human object learning benchmarks" +authors = [ + { name = "Michael J. Lee", email = "mil@mit.edu" } +] +license = { file = "license.md" } +readme = "README.md" +requires-python = ">=3.11" + +# Dependencies from requirements.txt +dependencies = [ + "numpy>=2.2", + "pandas>=2.2", + "matplotlib>=3.9", + "requests>=2.32", + "tqdm>=4.67", + "pydantic>=2.10", + "xarray>=2025.1" +] + + +[project.optional-dependencies] +dev = [ + "pytest>=8.3", +] + +[project.urls] +repository = "https://github.com/himjl/hobj" + +[tool.setuptools] +packages = ["hobj"] \ No newline at end of file diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 0f44eb2..0000000 --- a/requirements.txt +++ /dev/null @@ -1,29 +0,0 @@ -annotated-types==0.7.0 -certifi==2024.12.14 -charset-normalizer==3.4.1 -contourpy==1.3.1 -cycler==0.12.1 -fonttools==4.55.6 -idna==3.10 -iniconfig==2.0.0 -kiwisolver==1.4.8 -matplotlib==3.9.2 -numpy==2.2.2 -packaging==24.2 -pandas==2.2.3 -pillow==11.1.0 -pluggy==1.5.0 -pydantic==2.10.6 -pydantic_core==2.27.2 -pyparsing==3.2.1 -pytest==8.3.4 -python-dateutil==2.9.0.post0 -pytz==2024.2 -requests==2.32.3 -scipy==1.15.1 -six==1.17.0 -tqdm==4.67.1 -typing_extensions==4.12.2 -tzdata==2025.1 -urllib3==2.3.0 -xarray==2025.1.1 diff --git a/setup.py b/setup.py deleted file mode 100644 index 89ecb51..0000000 --- a/setup.py +++ /dev/null @@ -1,17 +0,0 @@ -from setuptools import setup - -# Read dependencies from requirements.txt -with open('requirements.txt', 'r') as file: - requirements = [line.strip() for line in file if line.strip() and not line.startswith('#')] - -setup( - name="hobj", - version="2.0.0", - packages=['hobj'], - install_requires=requirements, - author="Michael J. Lee", - author_email="mil@mit.edu", - description="Human object learning benchmarks", - keywords="", - python_requires='>=3.11', -) From 28130080ee21ecbbe478b3017ca4b8ffe49e18a5 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 28 Jan 2025 07:01:16 -0500 Subject: [PATCH 086/156] move pytest to dep --- pyproject.toml | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 9957d09..33ed5f1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,13 +21,8 @@ dependencies = [ "requests>=2.32", "tqdm>=4.67", "pydantic>=2.10", - "xarray>=2025.1" -] - - -[project.optional-dependencies] -dev = [ - "pytest>=8.3", + "xarray>=2025.1", + "pytest>=8.3" ] [project.urls] From 9ad27e6f44677f6cde5a15ffeb0fd044271bdcba Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 28 Jan 2025 07:04:24 -0500 Subject: [PATCH 087/156] trigger --- .github/workflows/ci.yml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index ccf34cb..b45a188 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -7,9 +7,8 @@ on: - hobj/** # Changes to workflows - .github/workflows/ci.yml - # Changes to setup.py and requirements.txt - - 'setup.py' - - 'requirements.txt' + # Changes to pyproject.toml + - 'pyproject.toml' jobs: unit_tests: runs-on: ubuntu-latest From fabf3273b4f43f49c77b159c6a1582638cb108ad Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 28 Jan 2025 08:39:05 -0500 Subject: [PATCH 088/156] edit pyproject.toml --- pyproject.toml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 33ed5f1..b67260b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,5 +28,6 @@ dependencies = [ [project.urls] repository = "https://github.com/himjl/hobj" -[tool.setuptools] -packages = ["hobj"] \ No newline at end of file +[tool.setuptools.packages.find] +where = ["hobj"] +namespaces = false From d6da92cad03d3b658e3e4ae48cc8ae2f01ab284c Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 28 Jan 2025 08:40:46 -0500 Subject: [PATCH 089/156] remove line? --- pyproject.toml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index b67260b..359db72 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,5 +29,4 @@ dependencies = [ repository = "https://github.com/himjl/hobj" [tool.setuptools.packages.find] -where = ["hobj"] -namespaces = false +where = ["hobj"] \ No newline at end of file From 7fa136905c2fb3ba02421d92c0aaa7dfdf9dd17f Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 28 Jan 2025 08:41:55 -0500 Subject: [PATCH 090/156] dot? --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 359db72..1a8e824 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,4 +29,4 @@ dependencies = [ repository = "https://github.com/himjl/hobj" [tool.setuptools.packages.find] -where = ["hobj"] \ No newline at end of file +where = ["."] \ No newline at end of file From adcc930080009f735b3bc4fa1fd4a28cd25c51f5 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 28 Jan 2025 08:43:08 -0500 Subject: [PATCH 091/156] turn off namespaces again --- pyproject.toml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 1a8e824..980dea6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,4 +29,5 @@ dependencies = [ repository = "https://github.com/himjl/hobj" [tool.setuptools.packages.find] -where = ["."] \ No newline at end of file +where = ["."] +namespaces = false \ No newline at end of file From fb2be4e42cf21a1416899a945dc5c3f3a9d3f46e Mon Sep 17 00:00:00 2001 From: himjl Date: Fri, 31 Jan 2025 16:12:07 -0500 Subject: [PATCH 092/156] commit --- .../binary_classification/benchmark.py | 6 +- .../binary_classification/estimator.py | 5 +- .../binary_classification/simulation.py | 6 +- .../tests/test_simulate_subtask.py | 2 +- hobj/benchmarks/generalization/benchmark.py | 5 +- hobj/benchmarks/generalization/estimator.py | 7 +- hobj/benchmarks/generalization/simulator.py | 2 +- hobj/benchmarks/mut_highvar_benchmark.py | 2 +- hobj/benchmarks/mut_oneshot_benchmark.py | 15 +- hobj/config.py | 2 +- hobj/data/behavior.py | 51 +++- hobj/data/images/imagesets/highvar.py | 7 +- hobj/data/images/imagesets/oneshot.py | 3 + hobj/data/images/imagesets/warmup.py | 1 + hobj/data/images/template.py | 28 ++- hobj/data/schema.py | 81 ------- hobj/data/store.py | 125 +--------- hobj/learning_models/__init__.py | 6 +- hobj/learning_models/linear/__init__.py | 7 +- hobj/learning_models/linear/representation.py | 74 +++++- hobj/learning_models/linear/update_rules.py | 218 ++++++++---------- .../tests/test_dummy_learner.py | 7 +- hobj/utils/file_io.py | 4 +- pyproject.toml | 3 +- 24 files changed, 263 insertions(+), 404 deletions(-) delete mode 100644 hobj/data/schema.py diff --git a/hobj/benchmarks/binary_classification/benchmark.py b/hobj/benchmarks/binary_classification/benchmark.py index 549b1b1..8f5b3f6 100644 --- a/hobj/benchmarks/binary_classification/benchmark.py +++ b/hobj/benchmarks/binary_classification/benchmark.py @@ -1,12 +1,10 @@ -from dataclasses import dataclass -from typing import List, Dict, Union, Tuple, Optional, Iterator - import numpy as np import pydantic import xarray as xr +from dataclasses import dataclass from tqdm import tqdm +from typing import List, Dict, Union, Tuple, Optional -import hobj.data.schema as schema from hobj.benchmarks.binary_classification.estimator import LearningCurveStatistics from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask, BinaryClassificationSubtaskResult from hobj.learning_models import BinaryLearningModel diff --git a/hobj/benchmarks/binary_classification/estimator.py b/hobj/benchmarks/binary_classification/estimator.py index 8e18668..370f74c 100644 --- a/hobj/benchmarks/binary_classification/estimator.py +++ b/hobj/benchmarks/binary_classification/estimator.py @@ -1,10 +1,9 @@ -from typing import List, Dict, Tuple - import numpy as np import xarray as xr +from dataclasses import dataclass +from typing import List, Dict, Tuple from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtaskResult -from dataclasses import dataclass from hobj.stats import binomial as binomial_funcs diff --git a/hobj/benchmarks/binary_classification/simulation.py b/hobj/benchmarks/binary_classification/simulation.py index f3fc254..6e364f4 100644 --- a/hobj/benchmarks/binary_classification/simulation.py +++ b/hobj/benchmarks/binary_classification/simulation.py @@ -1,11 +1,9 @@ -from typing import List, Union, Optional - import numpy as np import pydantic +from typing import List, Union, Optional -from hobj.data.schema import ImageRef from hobj.learning_models import BinaryLearningModel -from uuid import uuid4 +from mref import ImageRef # %% diff --git a/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py b/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py index afdd97e..2bc2232 100644 --- a/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py +++ b/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py @@ -1,6 +1,6 @@ from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask from hobj.learning_models import RandomGuesser -from hobj.data.schema import ImageRef +from mref import ImageRef from typing import List import pytest import PIL.Image diff --git a/hobj/benchmarks/generalization/benchmark.py b/hobj/benchmarks/generalization/benchmark.py index d9eb08b..3c9e856 100644 --- a/hobj/benchmarks/generalization/benchmark.py +++ b/hobj/benchmarks/generalization/benchmark.py @@ -1,10 +1,9 @@ -from dataclasses import dataclass -from typing import List, Dict, Union, Tuple, Optional, Iterator - import numpy as np import pydantic import xarray as xr +from dataclasses import dataclass from tqdm import tqdm +from typing import List, Tuple from hobj.benchmarks.generalization.estimator import GeneralizationStatistics from hobj.benchmarks.generalization.simulator import GeneralizationSubtask, GeneralizationSessionResult diff --git a/hobj/benchmarks/generalization/estimator.py b/hobj/benchmarks/generalization/estimator.py index 2ce815b..cc77697 100644 --- a/hobj/benchmarks/generalization/estimator.py +++ b/hobj/benchmarks/generalization/estimator.py @@ -1,11 +1,10 @@ +import numpy as np +import warnings +import xarray as xr from typing import List -import xarray as xr -import numpy as np from hobj.benchmarks.generalization.simulator import GeneralizationSessionResult - from hobj.stats import binomial as binomial_funcs -import warnings class GeneralizationStatistics(xr.Dataset): diff --git a/hobj/benchmarks/generalization/simulator.py b/hobj/benchmarks/generalization/simulator.py index 34e3802..e794729 100644 --- a/hobj/benchmarks/generalization/simulator.py +++ b/hobj/benchmarks/generalization/simulator.py @@ -4,7 +4,7 @@ import numpy as np import pydantic -from hobj.data.schema import ImageRef +from mref import ImageRef from hobj.learning_models import BinaryLearningModel import collections diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index dc0f7c2..27ad1ad 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -1,5 +1,5 @@ -from typing import List, Dict import numpy as np +from typing import List, Dict from hobj.benchmarks.binary_classification.benchmark import LearningCurveBenchmark, LearningCurveBenchmarkConfig, TargetSubtaskData from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask, BinaryClassificationSubtaskResult diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index caa964a..226b1de 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -1,16 +1,13 @@ import collections - # Coercing human data -from typing import Dict, Set, Optional, List -from hobj.data.behavior import load_oneshot_behavior -from hobj.data.images import MutatorOneShotImageset -from hobj.data.schema import ImageRef -import matplotlib.pyplot as plt -import pandas as pd -from hobj.benchmarks.generalization.benchmark import GeneralizationBenchmarkConfig, GeneralizationBenchmark -from hobj.benchmarks.generalization.benchmark import GeneralizationSessionResult +from typing import Dict, List + +from hobj.benchmarks.generalization.benchmark import GeneralizationBenchmarkConfig, GeneralizationBenchmark, GeneralizationSessionResult from hobj.benchmarks.generalization.estimator import GeneralizationStatistics from hobj.benchmarks.generalization.simulator import GeneralizationSubtask +from hobj.data.behavior import load_oneshot_behavior +from hobj.data.images import MutatorOneShotImageset +from mref import ImageRef # %% diff --git a/hobj/config.py b/hobj/config.py index 07bc6e0..fe623da 100644 --- a/hobj/config.py +++ b/hobj/config.py @@ -1,3 +1,3 @@ from pathlib import Path -cachedir: Path = Path.home() / 'hobj_cache' +cachedir: Path = Path.home() / 'hobj_cache2' diff --git a/hobj/data/behavior.py b/hobj/data/behavior.py index 3a0f469..d44ade8 100644 --- a/hobj/data/behavior.py +++ b/hobj/data/behavior.py @@ -1,22 +1,65 @@ -from typing import List +from typing import List, Literal +import datetime import pydantic -from hobj.data.schema import HumanLearningSession from hobj.data.store import default_data_store __all__ = ['load_highvar_behavior', 'load_oneshot_behavior'] +class HumanLearningSession(pydantic.BaseModel): + """ + A representation of the "raw" data collected from a human learning session. + """ + model_config = dict( + frozen=True + ) + + worker_id: str = pydantic.Field( + description='The anonymized worker ID of the participant.' + ) + + stimulus_sha256_seq: List[str] = pydantic.Field( + description='The SHA256 hash of the image (in np.uint8 format and RGBA mode) shown to the participant.', + ) + + stimulus_duration_msec_seq: List[int] = pydantic.Field( + description='The duration of the stimulus presentation in milliseconds.', + ) + + action_seq: List[Literal[0, 1, None]] = pydantic.Field(description='The action taken by the participant. 0 maps to "F" (left) and 1 maps to "J" (right). None maps to no action taken.') + reward_seq: List[Literal[-1, 1]] = pydantic.Field(description='The reward received by the participant following their action.') + timestamp_start_seq: List[datetime.datetime] = pydantic.Field(description='The timestamp of the start of the trial.') + + @pydantic.field_serializer('timestamp_start_seq') + def serialize_timestamp_start(self, v: List[datetime.datetime], _info) -> List[float]: + # Serializes datetime objects into Unix timestamps (seconds) + return [entry.timestamp() for entry in v] + + @pydantic.model_validator(mode='after') + def validate_lengths(self) -> 'HumanLearningSession': + stimulus_len = len(self.stimulus_sha256_seq) + duration_len = len(self.stimulus_duration_msec_seq) + action_len = len(self.action_seq) + reward_len = len(self.reward_seq) + timestamp_len = len(self.timestamp_start_seq) + + if not stimulus_len == duration_len == action_len == reward_len == timestamp_len: + raise ValueError(f'All lists must be of the same length. Got: {stimulus_len, duration_len, action_len, reward_len, timestamp_len}') + + return self + +# %% def _load_learning_sessions( dataset_url: str, redownload: bool ) -> List[HumanLearningSession]: data_store = default_data_store # Download the data: - json_data = data_store.get_json_data_from_url( + json_data = data_store.download_json_from_url( url=dataset_url, - redownload=redownload + register=True, ) # diff --git a/hobj/data/images/imagesets/highvar.py b/hobj/data/images/imagesets/highvar.py index a57844e..2999d62 100644 --- a/hobj/data/images/imagesets/highvar.py +++ b/hobj/data/images/imagesets/highvar.py @@ -1,10 +1,9 @@ - +import mref.media_references from hobj.data.images.template import Imageset, ImageManifestEntry, ImageManifest import pydantic from typing import Dict, List -import hobj.data.schema as schema class MutatorHighVarAnnotation(pydantic.BaseModel): @@ -25,7 +24,7 @@ class MutatorHighVarImageset(Imageset[MutatorHighVarAnnotation]): def __init__(self): super().__init__() - self._category_to_image_refs: Dict[str, List[schema.ImageRef]] = {} + self._category_to_image_refs: Dict[str, List[mref.media_references.ImageRef]] = {} for ref in self.image_refs: annotation = self.get_annotation(image_ref=ref) @@ -38,7 +37,7 @@ def __init__(self): self._category_to_image_refs[category] = sorted(self._category_to_image_refs[category]) @property - def category_to_image_refs(self) -> Dict[str, List[schema.ImageRef]]: + def category_to_image_refs(self) -> Dict[str, List[mref.media_references.ImageRef]]: return self._category_to_image_refs diff --git a/hobj/data/images/imagesets/oneshot.py b/hobj/data/images/imagesets/oneshot.py index 7ab54e6..80d1646 100644 --- a/hobj/data/images/imagesets/oneshot.py +++ b/hobj/data/images/imagesets/oneshot.py @@ -48,3 +48,6 @@ class MutatorOneShotImageset(Imageset[MutatorOneShotAnnotation]): zipped_images_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-oneshot/MutatorB2000_Oneshot64.zip' annotation_schema = MutatorOneShotAnnotation + +if __name__ == '__main__': + imageset = MutatorOneShotImageset() \ No newline at end of file diff --git a/hobj/data/images/imagesets/warmup.py b/hobj/data/images/imagesets/warmup.py index 9ce4501..1a53dc8 100644 --- a/hobj/data/images/imagesets/warmup.py +++ b/hobj/data/images/imagesets/warmup.py @@ -3,6 +3,7 @@ from hobj.data.images.template import Imageset + class MutatorWarmupAnnotation(pydantic.BaseModel): category: str = pydantic.Field( examples=[ diff --git a/hobj/data/images/template.py b/hobj/data/images/template.py index 08b2daa..3be066a 100644 --- a/hobj/data/images/template.py +++ b/hobj/data/images/template.py @@ -9,8 +9,9 @@ from hobj.utils.file_io import unzip_file from hobj.utils.hash import hash_image -from hobj.data.schema import ImageRef -from hobj.data.store import DataStore, default_data_store +from mref import ImageRef +from hobj.data.store import default_data_store +from mref import FileSystemStorage import warnings @@ -47,7 +48,7 @@ class Imageset(Generic[IA], ABC): def __init__( self, - data_store: DataStore = None, + data_store: FileSystemStorage = None, redownload=False, ): """ @@ -55,10 +56,10 @@ def __init__( """ if not data_store: - self.data_store: DataStore = default_data_store + self.data_store: FileSystemStorage = default_data_store # Load the manifest if it is already cached - manifest_data = self.data_store.get_json_data_from_url(url=self.manifest_url, redownload=redownload) + manifest_data = self.data_store.download_json_from_url(url=self.manifest_url, register=True) # Todo image_manifest = ImageManifest(**manifest_data) self._register_image_urls(manifest=image_manifest, redownload=redownload) @@ -85,8 +86,8 @@ def _register_image_urls(self, manifest: ImageManifest, redownload: bool = False num_undownloaded_images = 0 for image_id, manifest_entry in manifest.entries.items(): - - if not self.data_store.check_image_exists(sha256=manifest_entry.sha256): + ref = ImageRef(sha256=manifest_entry.sha256) + if not self.data_store.check_data_exists(ref=ref): num_undownloaded_images += 1 if num_undownloaded_images == 0: @@ -95,7 +96,7 @@ def _register_image_urls(self, manifest: ImageManifest, redownload: bool = False print(f'Missing {num_undownloaded_images}/{len(manifest.entries)} images for this imageset.') # Download the images - zipped_images_path = self.data_store.get_zipfile(zipfile_url=self.zipped_images_url, redownload=redownload) + zipped_images_path = self.data_store.download_zip_path_from_url(url=self.zipped_images_url, register=True) # todo type correctly # Make a tempdir to unzip the images with tempfile.TemporaryDirectory() as tempdir: @@ -109,11 +110,14 @@ def _register_image_urls(self, manifest: ImageManifest, redownload: bool = False relpath = manifest_entry.relpath image_path = tempdir / relpath image_data = PIL.Image.open(image_path) - actual_sha256 = hash_image(image=image_data) - if not actual_sha256 == reported_sha256: - raise ValueError(f"SHA256 mismatch for image {manifest_entry}: {actual_sha256} != {reported_sha256}") - self.data_store.register_image(image_data=image_data) + image_ref = ImageRef.from_image(image=image_data) + + if not image_ref.sha256 == reported_sha256: + raise ValueError(f"SHA256 mismatch for image {manifest_entry}: {image_ref.sha256} != {reported_sha256}") + + # Store image + self.data_store.register_image(image=image_data) pbar.update(1) @property diff --git a/hobj/data/schema.py b/hobj/data/schema.py deleted file mode 100644 index 3f4b0a5..0000000 --- a/hobj/data/schema.py +++ /dev/null @@ -1,81 +0,0 @@ -import datetime -from typing import List, Literal - -import PIL.Image -import pydantic - -from hobj.data.store import default_data_store -from hobj.utils.hash import hash_image - - -class _Common(pydantic.BaseModel): - model_config = dict( - frozen=True - ) - - -# %% Image data -class ImageRef(_Common): - sha256: str = pydantic.Field(pattern=r'^[a-f0-9]{64}$') - - @classmethod - def from_image(cls, image: PIL.Image, register: bool = True): - if register: - default_data_store.register_image(image_data=image) - - return cls( - sha256=hash_image(image=image) - ) - - def get_image_data(self) -> PIL.Image: - return default_data_store.load_image(sha256=self.sha256) - - def __lt__(self, other) -> bool: - return self.sha256 < other.sha256 - - def __hash__(self): - return hash(self.sha256) - - -# %% Learning tasks - - -# %% Behavior -class HumanLearningSession(_Common): - """ - A representation of the "raw" data collected from a human learning session. - """ - - worker_id: str = pydantic.Field( - description='The anonymized worker ID of the participant.' - ) - - stimulus_sha256_seq: List[str] = pydantic.Field( - description='The SHA256 hash of the image (in np.uint8 format and RGBA mode) shown to the participant.', - ) - - stimulus_duration_msec_seq: List[int] = pydantic.Field( - description='The duration of the stimulus presentation in milliseconds.', - ) - - action_seq: List[Literal[0, 1, None]] = pydantic.Field(description='The action taken by the participant. 0 maps to "F" (left) and 1 maps to "J" (right). None maps to no action taken.') - reward_seq: List[Literal[-1, 1]] = pydantic.Field(description='The reward received by the participant following their action.') - timestamp_start_seq: List[datetime.datetime] = pydantic.Field(description='The timestamp of the start of the trial.') - - @pydantic.field_serializer('timestamp_start_seq') - def serialize_timestamp_start(self, v: List[datetime.datetime], _info) -> List[float]: - # Serializes datetime objects into Unix timestamps (seconds) - return [entry.timestamp() for entry in v] - - @pydantic.model_validator(mode='after') - def validate_lengths(self) -> 'HumanLearningSession': - stimulus_len = len(self.stimulus_sha256_seq) - duration_len = len(self.stimulus_duration_msec_seq) - action_len = len(self.action_seq) - reward_len = len(self.reward_seq) - timestamp_len = len(self.timestamp_start_seq) - - if not stimulus_len == duration_len == action_len == reward_len == timestamp_len: - raise ValueError(f'All lists must be of the same length. Got: {stimulus_len, duration_len, action_len, reward_len, timestamp_len}') - - return self diff --git a/hobj/data/store.py b/hobj/data/store.py index 8b94282..d8fef15 100644 --- a/hobj/data/store.py +++ b/hobj/data/store.py @@ -8,127 +8,12 @@ import json import tempfile -__all__ = ['DataStore', 'default_data_store'] +import mref +__all__ = [ + 'default_data_store' +] # %% -class _PathManager: - - def __init__(self, cachedir: Path): - self.cachedir = cachedir - - def get_image_cache_path(self, sha256: str) -> Path: - path = self.cachedir / 'image_cache' / (sha256 + '.png') - return path - - # JSON caching from web - def get_json_cache_path(self, url: str) -> Path: - """ - Construct a cache path for a URL. - :param url: - :return: - """ - if not url.endswith('.json'): - raise ValueError(f"URL {url} does not point to a JSON file.") - - path = self.cachedir / 'json_cache' / (hash_string(url) + '.json') - return path - - def get_zipped_file_cache_path(self, zipfile_url: str) -> Path: - if not zipfile_url.endswith('.zip'): - raise ValueError(f"URL {zipfile_url} does not point to a ZIP file") - - path = self.cachedir / 'zipfile_cache' / (hash_string(zipfile_url) + '.zip') - return path - - -class DataStore: - - def __init__(self, cachedir: Path): - self.path_manager = _PathManager(cachedir=cachedir) - self._image_cache_manifest = set() - - def register_image(self, image_data: PIL.Image) -> str: - """ - Register an image in the data store. - :param image_data: - :return: The SHA256 hash of the image. - """ - sha256 = hash_image(image=image_data) - path = self.path_manager.get_image_cache_path(sha256=sha256) - if not path.parent.exists(): - path.parent.mkdir(parents=True) - image_data.save(path) - return sha256 - - def check_image_exists(self, sha256: str) -> bool: - """ - Checks whether an image is already cached in the data store. - :param sha256: - :return: - """ - if sha256 not in self._image_cache_manifest: - path = self.path_manager.get_image_cache_path(sha256=sha256) - if path.exists(): - self._image_cache_manifest.add(sha256) - return sha256 in self._image_cache_manifest - - def load_image(self, sha256: str, as_rgb: bool = True) -> PIL.Image: - """ - Load an image from the data store. - If the image is not there, registers it. - :param image_ref: - :return: - """ - path = self.path_manager.get_image_cache_path(sha256=sha256) - with PIL.Image.open(path) as img: - if as_rgb: - img = img.convert('RGB') - loaded_image = img.copy() - - return loaded_image - - def get_json_data_from_url(self, url: str, redownload: bool = False): - """ - Loads JSON data file from a url, caching it if it is not already present. - :param url: - :param redownload: - :return: - """ - path = self.path_manager.get_json_cache_path(url=url) - - if not path.exists() or redownload: - json_data = download_json(url=url) - if not path.parent.exists(): - path.parent.mkdir(parents=True) - path.write_text(json.dumps(json_data)) - print(f"Downloaded JSON data from {url} to {path}") - - return json.loads(path.read_text()) - - def get_zipfile(self, zipfile_url: str, redownload: bool = False) -> Path: - """ - Get the path to the compressed imageset file. - :param zipfile_url: - :return: - """ - path = self.path_manager.get_zipped_file_cache_path(zipfile_url) - if not path.exists() or redownload: - - # Save it in a tempdir - with tempfile.TemporaryDirectory() as tempdir: - tempdir = Path(tempdir) - savepath = tempdir / 'tmp.zip' - download_file(url=zipfile_url, output_path=savepath) - - if not path.parent.exists(): - path.parent.mkdir(parents=True) - - # Move the file to the correct location - savepath.rename(path) - - return path - - # Default data store -default_data_store = DataStore(cachedir=hobj.config.cachedir) +default_data_store = mref.FileSystemStorage(cachedir=hobj.config.cachedir) diff --git a/hobj/learning_models/__init__.py b/hobj/learning_models/__init__.py index 165a81f..97e597c 100644 --- a/hobj/learning_models/__init__.py +++ b/hobj/learning_models/__init__.py @@ -4,7 +4,7 @@ import PIL.Image import numpy as np -from hobj.data import schema as schema +import mref.media_references # %% @@ -22,7 +22,7 @@ def reset_state(self, seed: typing.Union[int, None]) -> None: @abstractmethod def get_response( self, - image: typing.Union[schema.ImageRef, PIL.Image], + image: typing.Union[mref.media_references.ImageRef, PIL.Image], ) -> typing.Literal[0, 1]: """ This function takes the current stimulus image (given either as a PIL.Image or a ImageRef) and returns one of two possible actions (parameterized by an integer). @@ -54,7 +54,7 @@ def reset_state(self, seed: typing.Union[int, None]) -> None: def get_response( self, - image: typing.Union[schema.ImageRef, PIL.Image], + image: typing.Union[mref.media_references.ImageRef, PIL.Image], ) -> typing.Literal[0, 1]: action = self.random_generator.integers(2) action = int(action) diff --git a/hobj/learning_models/linear/__init__.py b/hobj/learning_models/linear/__init__.py index 3fcf02c..23db07d 100644 --- a/hobj/learning_models/linear/__init__.py +++ b/hobj/learning_models/linear/__init__.py @@ -3,6 +3,7 @@ import PIL.Image import numpy as np +import mref.media_references from hobj.data import schema as schema from hobj.learning_models import BinaryLearningModel from hobj.learning_models.linear.representation import RepresentationalModel @@ -10,10 +11,6 @@ class LinearLearner(BinaryLearningModel): - """ - A learning model based on a standard cognitive theory of learning. - """ - def __init__( self, representational_model: RepresentationalModel, @@ -52,7 +49,7 @@ def reset_state(self, seed: int) -> None: def get_response( self, - image: typing.Union[schema.ImageRef, PIL.Image] + image: typing.Union[mref.media_references.ImageRef, PIL.Image] ) -> typing.Literal[0, 1]: f = self.representational_model.get_features(image=image) diff --git a/hobj/learning_models/linear/representation.py b/hobj/learning_models/linear/representation.py index 6294155..890b941 100644 --- a/hobj/learning_models/linear/representation.py +++ b/hobj/learning_models/linear/representation.py @@ -1,36 +1,86 @@ import numpy as np -import hobj.data.schema as schema -from typing import Union, Dict +from typing import Union, Dict, Callable, List import PIL.Image -from abc import ABC, abstractmethod +from mref import ImageRef -class RepresentationalModel(ABC): +class RepresentationalModel: """ - Model which represents image_urls as feature vectors of shape (d,). - Meant to be subclassed (see PrecachedRepresentationalModel below for an example). + Class which maps images to feature vectors of shape (d,). """ - def __init__(self, d: int): + def __init__( + self, + d: int, + image_to_features_func: Callable[[Union[ImageRef, PIL.Image]], np.ndarray], + ): + if not isinstance(d, int): raise ValueError(f"Expected d to be an int, but got {d} of type {type(d)}") if d <= 0: raise ValueError(f"Expected d to be positive, but got {d}") - self.d = d + self._d = d + self._image_to_features_func = image_to_features_func return - @abstractmethod + @property + def d(self) -> int: + return self._d + def get_features( self, - image: Union[schema.ImageRef, PIL.Image] + image: Union[ImageRef, PIL.Image] ) -> np.ndarray: """ Returns a feature vector for the image_url. To work successfully with UpdateRule, the feature vector must be of shape (d,) and have norm 1. - :param image_url: str, url of the image + + :param image: schema.ImageRef or PIL.Image :return: np.ndarray, shape=(d,) """ - return np.zeros(self.d) + + f = self._image_to_features_func(image) + + if f.shape != (self.d,): + raise ValueError(f"Expected feature vector of shape {(self.d,)}, but got {f.shape}") + + return f + + @classmethod + def from_precomputed_features( + cls, + image_ref_to_features: Dict[ImageRef, np.ndarray] + ) -> 'RepresentationalModel': + + """ + Convenience method for creating a RepresentationalModel, when one has a precomputed dict which maps ImageRefs to np.ndarray feature vectors. + + If get_features is called with an ImageRef (or PIL.Image with an ImageRef) not in image_ref_to_features, a KeyError will be raised. + """ + + def image_to_features_func(image: Union[ImageRef, PIL.Image]) -> np.ndarray: + if isinstance(image, PIL.Image.Image): + ref = ImageRef.from_image(image) + else: + ref = image + return image_ref_to_features[ref] + + # Ensure all feature vectors are the same shape + d = None + for ref in image_ref_to_features: + f = image_ref_to_features[ref] + if not isinstance(f, np.ndarray): + raise ValueError(f"Expected feature vector to be a np.ndarray, but got {f} of type {type(f)}") + if not len(f.shape) == 1: + raise ValueError(f"Expected feature vector to be 1D, but got {f.shape}") + + if d is None: + d = f.shape[0] + + if not f.shape[0] == d: + raise ValueError(f"Expected feature vector to be of shape ({d},), but got {f.shape}") + + return cls(d=d, image_to_features_func=image_to_features_func) \ No newline at end of file diff --git a/hobj/learning_models/linear/update_rules.py b/hobj/learning_models/linear/update_rules.py index 9de1b5e..6911f11 100644 --- a/hobj/learning_models/linear/update_rules.py +++ b/hobj/learning_models/linear/update_rules.py @@ -1,31 +1,39 @@ import numpy as np import scipy.special +from abc import ABC, abstractmethod +from typing import Tuple, Union -class UpdateRule(object): +class UpdateRule(ABC): def __init__(self, alpha: float): """ Implements an update rule for a linear learner. Assumes that all features have at most norm 1. :param alpha: the normalized learning rate. Between 0 and 1. """ - assert 0 <= alpha <= 1 + if not 0 <= alpha <= 1: + raise ValueError("alpha must be in the interval [0, 1]") + self.alpha = alpha - self._update_rule_id = None self.reset() return - def reset(self): + def reset(self) -> None: """ - Resets the update rule to its initial state. Not used by all update rules. + Called to reset the update rule to some initial state. Not used by all update rules. :return: """ return - @property - def update_rule_id(self): - return str(self.__class__.__name__) + '_%0.3e' % (self.alpha) - - def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float): + @abstractmethod + def get_update( + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float + ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: """ Returns delta_w and delta_b, where w{t+1} = w{t} + delta_w @@ -37,7 +45,7 @@ def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.nda :param logits: np.ndarray, shape=(action,) :param action: int, action taken by the learner :param reward: float, reward received by the learner - :return: delta, np.ndarray, shape=(actions) + :return: delta_w, delta_b, which are np.ndarrays of the same shape as w and b, respectively. """ delta_w = np.zeros(w.shape) delta_b = np.zeros(b.shape) @@ -52,20 +60,15 @@ class Prototype(UpdateRule): def reset(self): self.ncounts = None - def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float): - """ - Returns delta_w and delta_b, where - w{t+1} = w{t} + delta_w - b{t+1} = b{t} + delta_b - - :param x: np.ndarray, shape=(d) - :param w: np.ndarray, shape=(d, actions) - :param b: np.ndarray, shape=(actions,) - :param logits: np.ndarray, shape=(action,) - :param action: int, action taken by the learner - :param reward: float, reward received by the learner - :return: delta, np.ndarray, shape=(actions) - """ + def get_update( + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float + ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: if self.ncounts is None: self.ncounts = np.zeros(w.shape[1]) @@ -77,7 +80,7 @@ def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.nda if w.shape[1] == 2: i_updated_class = 1 - action else: - return 0, 0 + return np.zeros_like(w), 0 mu_cur = w[:, i_updated_class] n = self.ncounts[i_updated_class] @@ -95,20 +98,15 @@ def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.nda class Square(UpdateRule): - def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float): - """ - Returns delta_w and delta_b, where - w{t+1} = w{t} + delta_w - b{t+1} = b{t} + delta_b - - :param x: np.ndarray, shape=(d) - :param w: np.ndarray, shape=(d, actions) - :param b: np.ndarray, shape=(actions,) - :param logits: np.ndarray, shape=(action,) - :param action: int, action taken by the learner - :param reward: float, reward received by the learner - :return: delta, np.ndarray, shape=(actions) - """ + def get_update( + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float + ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: delta = np.zeros(w.shape) reward_prediction = logits[action] @@ -120,21 +118,15 @@ def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.nda class Perceptron(UpdateRule): - def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float): - """ - Returns delta_w and delta_b, where - w{t+1} = w{t} + delta_w - b{t+1} = b{t} + delta_b - - :param x: np.ndarray, shape=(d) - :param w: np.ndarray, shape=(d, actions) - :param b: np.ndarray, shape=(actions,) - :param logits: np.ndarray, shape=(action,) - :param action: int, action taken by the learner - :param reward: float, reward received by the learner - :return: delta, np.ndarray, shape=(actions) - """ - + def get_update( + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float + ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: nactions = len(logits) gts = np.zeros(nactions) @@ -148,21 +140,15 @@ def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.nda class Hinge(UpdateRule): - def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float): - """ - Returns delta_w and delta_b, where - w{t+1} = w{t} + delta_w - b{t+1} = b{t} + delta_b - - :param x: np.ndarray, shape=(d) - :param w: np.ndarray, shape=(d, actions) - :param b: np.ndarray, shape=(actions,) - :param logits: np.ndarray, shape=(action,) - :param action: int, action taken by the learner - :param reward: float, reward received by the learner - :return: delta, np.ndarray, shape=(actions) - """ - + def get_update( + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float + ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: nactions = len(logits) gts = np.zeros(nactions) @@ -176,20 +162,15 @@ def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.nda class MAE(UpdateRule): - def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float): - """ - Returns delta_w and delta_b, where - w{t+1} = w{t} + delta_w - b{t+1} = b{t} + delta_b - - :param x: np.ndarray, shape=(d) - :param w: np.ndarray, shape=(d, actions) - :param b: np.ndarray, shape=(actions,) - :param logits: np.ndarray, shape=(action,) - :param action: int, action taken by the learner - :param reward: float, reward received by the learner - :return: delta, np.ndarray, shape=(actions) - """ + def get_update( + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float + ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: nactions = len(logits) reward_prediction = logits[action] @@ -207,20 +188,15 @@ def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.nda class Exponential(UpdateRule): max_weight_norm = 10.0 - def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float): - """ - Returns delta_w and delta_b, where - w{t+1} = w{t} + delta_w - b{t+1} = b{t} + delta_b - - :param x: np.ndarray, shape=(d) - :param w: np.ndarray, shape=(d, actions) - :param b: np.ndarray, shape=(actions,) - :param logits: np.ndarray, shape=(action,) - :param action: int, action taken by the learner - :param reward: float, reward received by the learner - :return: delta, np.ndarray, shape=(actions) - """ + def get_update( + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float + ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: nactions = len(logits) gts = np.zeros(nactions) @@ -237,20 +213,15 @@ def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.nda class CE(UpdateRule): - def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float): - """ - Returns delta_w and delta_b, where - w{t+1} = w{t} + delta_w - b{t+1} = b{t} + delta_b - - :param x: np.ndarray, shape=(d) - :param w: np.ndarray, shape=(d, actions) - :param b: np.ndarray, shape=(actions,) - :param logits: np.ndarray, shape=(action,) - :param action: int, action taken by the learner - :param reward: float, reward received by the learner - :return: delta, np.ndarray, shape=(actions) - """ + def get_update( + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float + ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: nactions = len(logits) gts = np.zeros(nactions) reward_prediction = logits[action] @@ -290,20 +261,15 @@ def _compute_trace( expression[action_taken] = 1 + expression[action_taken] return expression - def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float): - """ - Returns delta_w and delta_b, where - w{t+1} = w{t} + delta_w - b{t+1} = b{t} + delta_b - - :param x: np.ndarray, shape=(d) - :param w: np.ndarray, shape=(d, actions) - :param b: np.ndarray, shape=(actions,) - :param logits: np.ndarray, shape=(action,) - :param action: int, action taken by the learner - :param reward: float, reward received by the learner - :return: delta, np.ndarray, shape=(actions) - """ + def get_update( + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float + ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: gt = self.alpha * reward * self._compute_trace(action, logits) delta = x[:, None] * gt[None, :] diff --git a/hobj/learning_models/tests/test_dummy_learner.py b/hobj/learning_models/tests/test_dummy_learner.py index 6addaa4..9c8656c 100644 --- a/hobj/learning_models/tests/test_dummy_learner.py +++ b/hobj/learning_models/tests/test_dummy_learner.py @@ -1,14 +1,15 @@ import pytest + +import mref.media_references from hobj.learning_models import RandomGuesser -import hobj.data.schema as schema @pytest.fixture def dummy_learner() -> RandomGuesser: return RandomGuesser(seed=0) @pytest.fixture -def test_image() -> schema.ImageRef: - return schema.ImageRef(sha256='0'*64) +def test_image() -> mref.media_references.ImageRef: + return mref.media_references.ImageRef(sha256='0' * 64) def test_dummy_learner_deterministic(dummy_learner, test_image): diff --git a/hobj/utils/file_io.py b/hobj/utils/file_io.py index d44ac71..86be9cb 100644 --- a/hobj/utils/file_io.py +++ b/hobj/utils/file_io.py @@ -38,7 +38,7 @@ def download_file(url: str, output_path: Path) -> None: output_path.parent.mkdir(parents=True) size, unit = get_bytes_size(num_bytes=total_size_in_bytes) - # Create a progress bar + with tqdm(total=size, unit=unit, unit_scale=True, disable=False, desc='Download progress') as progress_bar: with open(output_path.as_posix(), 'wb') as file: # Iterate over the response data in chunks and write to file @@ -47,7 +47,7 @@ def download_file(url: str, output_path: Path) -> None: file.write(chunk) chunk_size, _ = get_bytes_size(num_bytes=len(chunk), output_units=unit) progress_bar.update(chunk_size) - + file.flush() def get_bytes_size(num_bytes: int, output_units: str = None) -> (float, str): diff --git a/pyproject.toml b/pyproject.toml index 980dea6..919d6fd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,7 +22,8 @@ dependencies = [ "tqdm>=4.67", "pydantic>=2.10", "xarray>=2025.1", - "pytest>=8.3" + "pytest>=8.3", + #"mref @ git+https://github.com/himjl/mref.git", ] [project.urls] From 0f2ef26a859e9c6f8a5d85e2868efc925d1086b5 Mon Sep 17 00:00:00 2001 From: himjl Date: Sat, 1 Feb 2025 09:38:57 -0500 Subject: [PATCH 093/156] initial --- hobj/learning_models/linear/__init__.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/hobj/learning_models/linear/__init__.py b/hobj/learning_models/linear/__init__.py index 23db07d..77b934d 100644 --- a/hobj/learning_models/linear/__init__.py +++ b/hobj/learning_models/linear/__init__.py @@ -1,10 +1,8 @@ -import typing - import PIL.Image import numpy as np +import typing import mref.media_references -from hobj.data import schema as schema from hobj.learning_models import BinaryLearningModel from hobj.learning_models.linear.representation import RepresentationalModel from hobj.learning_models.linear.update_rules import UpdateRule From 1e6426b4bc95dfdc83dff44ad4ff5760d1d32b51 Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 5 Feb 2025 08:06:34 -0500 Subject: [PATCH 094/156] alternative interface for instantiating a linear learning model --- hobj/benchmarks/make_model.py | 58 +++++++++++++++++++++++++++++++++++ hobj/config.py | 2 +- 2 files changed, 59 insertions(+), 1 deletion(-) create mode 100644 hobj/benchmarks/make_model.py diff --git a/hobj/benchmarks/make_model.py b/hobj/benchmarks/make_model.py new file mode 100644 index 0000000..2df5f58 --- /dev/null +++ b/hobj/benchmarks/make_model.py @@ -0,0 +1,58 @@ +""" +This module provides an alternative interface for instantiating a linear learning model. +""" +from hobj.learning_models.linear import LinearLearner, RepresentationalModel +import hobj.learning_models.linear.update_rules as update_rules +from typing import Literal, Dict +import mref +import numpy as np +from typing import List + + +# %% +def make_linear_learner_from_features( + ref_to_features: Dict[mref.ImageRef, np.ndarray], + calibration_images: List[mref.ImageRef], + update_rule_name: Literal[ + 'Prototype', + 'Square', + 'Perceptron', + 'Hinge', + 'MAE', + 'Exponential', + 'CE', + 'REINFORCE' + ] = 'Square', + alpha: float = 1, +) -> LinearLearner: + """ + Instantiates a linear learning model from precomputed features. + :param ref_to_features: Dict[mref.ImageRef, np.ndarray], the features to use. + :param calibration_images: List[mref.ImageRef], the images that will be used to calibrate the features (i.e. for mean centering and ensuring they fit within a unit ball). + :param update_rule_name: str, the name of the update rule to use. + :param alpha: float, the learning rate. + :return: LinearLearner + """ + + f_calibration = np.array([ref_to_features[ref] for ref in calibration_images]) + mu_calibration = np.mean(f_calibration, axis=0) + norms_calibration = np.linalg.norm(f_calibration - mu_calibration, axis=1) + norm_cutoff = np.quantile(norms_calibration, 0.999) # Will clip the rest + + ref_to_calibrated_features = {} + for ref in ref_to_features: + f = ref_to_features[ref] + fc = f - mu_calibration + fcn = fc / norm_cutoff + norm_cur = np.linalg.norm(fcn) + if norm_cur > 1: + fcn = fcn / norm_cur + ref_to_calibrated_features[ref] = np.array(fcn) + + update_rule_name = getattr(update_rules, update_rule_name) + return LinearLearner( + representational_model=RepresentationalModel.from_precomputed_features( + image_ref_to_features=ref_to_calibrated_features + ), + update_rule=update_rule_name(alpha=alpha) + ) \ No newline at end of file diff --git a/hobj/config.py b/hobj/config.py index fe623da..07bc6e0 100644 --- a/hobj/config.py +++ b/hobj/config.py @@ -1,3 +1,3 @@ from pathlib import Path -cachedir: Path = Path.home() / 'hobj_cache2' +cachedir: Path = Path.home() / 'hobj_cache' From 7c3e5b7e437d4683f2c3ca9612ba6c44d7b54ed6 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 25 Feb 2025 13:35:03 -0500 Subject: [PATCH 095/156] before rework triplet collection --- hobj/benchmarks/mut_highvar_benchmark.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index 27ad1ad..2d76f7e 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -84,3 +84,7 @@ def __init__(self): super().__init__( config=config ) + +if __name__ == '__main__': + experiment = MutatorHighVarBenchmark() + print(sorted(experiment.config.subtask_name_to_data.keys())) \ No newline at end of file From 7a7cd9c25bf3a96d31e60a83d60b92e199fb4f29 Mon Sep 17 00:00:00 2001 From: himjl Date: Fri, 14 Mar 2025 17:22:01 -0400 Subject: [PATCH 096/156] add mref dep --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 919d6fd..e6b4da5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,7 +23,7 @@ dependencies = [ "pydantic>=2.10", "xarray>=2025.1", "pytest>=8.3", - #"mref @ git+https://github.com/himjl/mref.git", + "mref @ git+https://github.com/himjl/mref.git", ] [project.urls] From 6f87a22fc613d74479aa792c3824c5fb83e760e3 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 17:14:20 -0400 Subject: [PATCH 097/156] uv install --- .gitignore | 6 +- .python-version | 1 + pyproject.toml | 22 +- uv.lock | 619 ++++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 638 insertions(+), 10 deletions(-) create mode 100644 .python-version create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore index 1f1025f..f511459 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,6 @@ .idea -.DS_Store \ No newline at end of file +.DS_Store +.venv +__pycache__/ +*.egg-info/ +dist/ diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/pyproject.toml b/pyproject.toml index e6b4da5..f65aef6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [build-system] -requires = ["setuptools>=42", "wheel"] # Add build dependencies -build-backend = "setuptools.build_meta" +requires = ["uv_build>=0.10.9,<0.11.0"] +build-backend = "uv_build" [project] name = "hobj" @@ -9,11 +9,11 @@ description = "Human object learning benchmarks" authors = [ { name = "Michael J. Lee", email = "mil@mit.edu" } ] -license = { file = "license.md" } +license = "MIT" +license-files = ["license.md"] readme = "README.md" -requires-python = ">=3.11" +requires-python = ">=3.11,<3.13" -# Dependencies from requirements.txt dependencies = [ "numpy>=2.2", "pandas>=2.2", @@ -22,13 +22,17 @@ dependencies = [ "tqdm>=4.67", "pydantic>=2.10", "xarray>=2025.1", - "pytest>=8.3", "mref @ git+https://github.com/himjl/mref.git", ] +[dependency-groups] +dev = [ + "pytest>=8.3", +] + [project.urls] repository = "https://github.com/himjl/hobj" -[tool.setuptools.packages.find] -where = ["."] -namespaces = false \ No newline at end of file +[tool.uv.build-backend] +module-root = "" + diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..1a2781a --- /dev/null +++ b/uv.lock @@ -0,0 +1,619 @@ +version = 1 +revision = 3 +requires-python = ">=3.11, <3.13" +resolution-markers = [ + "sys_platform == 'win32'", + "sys_platform == 'emscripten'", + "sys_platform != 'emscripten' and 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+[[package]] +name = "xarray" +version = "2026.2.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "numpy" }, + { name = "packaging" }, + { name = "pandas" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/0f/03/e3353b72e518574b32993989d8f696277bf878e9d508c7dd22e86c0dab5b/xarray-2026.2.0.tar.gz", hash = "sha256:978b6acb018770554f8fd964af4eb02f9bcc165d4085dbb7326190d92aa74bcf", size = 3111388, upload-time = "2026-02-13T22:20:50.18Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/99/92/545eb2ca17fc0e05456728d7e4378bfee48d66433ae3b7e71948e46826fb/xarray-2026.2.0-py3-none-any.whl", hash = "sha256:e927d7d716ea71dea78a13417970850a640447d8dd2ceeb65c5687f6373837c9", size = 1405358, upload-time = "2026-02-13T22:20:47.847Z" }, +] From a7d2b279e60da9419b8e3d8e5892e93f065dd0bb Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 17:16:27 -0400 Subject: [PATCH 098/156] add citation --- README.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/README.md b/README.md index b6aa5f4..9b4cab4 100644 --- a/README.md +++ b/README.md @@ -39,3 +39,17 @@ The `hobj` package works for Python >=3.11. After cloning this repository on you ### Changes to codebase since publication This codebase was refactored in January 2025 to improve the performance and quality of the code, and is now designated as `v2`. Along the way, minor refinements to the statistical analysis of the original codebase were introduced (see [changelist](site/changelist.md)). To see the codebase at the time of publication, check out the repo with the `v1` tag [here](https://github.com/himjl/hobj/releases/tag/v1). + +### Citation + +``` +@article{lee2023well, + title={How well do rudimentary plasticity rules predict adult visual object learning?}, + author={Lee, Michael J and DiCarlo, James J}, + journal={PLOS Computational Biology}, + volume={19}, + number={12}, + pages={e1011713}, + year={2023}, + publisher={Public Library of Science San Francisco, CA USA} +}``` From 212c20e452127ff3c0901703174688526d90c3fb Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 17:35:28 -0400 Subject: [PATCH 099/156] check --- Makefile | 8 ++++++++ examples/dev.py | 1 + .../tests/test_simulate_subtask.py | 1 - hobj/benchmarks/generalization/simulator.py | 5 ++--- hobj/data/images/imagesets/highvar.py | 2 +- hobj/data/images/imagesets/probe.py | 2 +- hobj/data/images/template.py | 1 - hobj/data/store.py | 6 ------ hobj/learning_models/linear/representation.py | 2 +- pyproject.toml | 1 - uv.lock | 16 ---------------- 11 files changed, 14 insertions(+), 31 deletions(-) create mode 100644 Makefile create mode 100644 examples/dev.py diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..c596605 --- /dev/null +++ b/Makefile @@ -0,0 +1,8 @@ +lint: + uv run ruff check --fix && \ + uv run ruff format + +check: + uv run ty check && \ + uv run ruff check && \ + uv run ruff format --check diff --git a/examples/dev.py b/examples/dev.py new file mode 100644 index 0000000..29661e1 --- /dev/null +++ b/examples/dev.py @@ -0,0 +1 @@ +import hobj diff --git a/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py b/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py index 2bc2232..3503df8 100644 --- a/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py +++ b/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py @@ -2,7 +2,6 @@ from hobj.learning_models import RandomGuesser from mref import ImageRef from typing import List -import pytest import PIL.Image import numpy as np diff --git a/hobj/benchmarks/generalization/simulator.py b/hobj/benchmarks/generalization/simulator.py index e794729..dc690ad 100644 --- a/hobj/benchmarks/generalization/simulator.py +++ b/hobj/benchmarks/generalization/simulator.py @@ -1,5 +1,4 @@ -from dataclasses import dataclass -from typing import List, Set, Union, Dict, Optional +from typing import List, Union, Dict, Optional import numpy as np import pydantic @@ -53,7 +52,7 @@ def sort_image_refs(cls, value: List[ImageRef]) -> List[ImageRef]: def validate_model(self) -> 'GeneralizationSubtask': for ref in self.test_imagesA + self.test_imagesB: - if not ref in self.image_ref_to_transformation: + if ref not in self.image_ref_to_transformation: raise ValueError(f"Expected all test images to have an associated transformation, but {ref} was missing.") return self diff --git a/hobj/data/images/imagesets/highvar.py b/hobj/data/images/imagesets/highvar.py index 2999d62..f39778c 100644 --- a/hobj/data/images/imagesets/highvar.py +++ b/hobj/data/images/imagesets/highvar.py @@ -1,5 +1,5 @@ import mref.media_references -from hobj.data.images.template import Imageset, ImageManifestEntry, ImageManifest +from hobj.data.images.template import Imageset import pydantic from typing import Dict, List diff --git a/hobj/data/images/imagesets/probe.py b/hobj/data/images/imagesets/probe.py index 350dede..d9a7a0f 100644 --- a/hobj/data/images/imagesets/probe.py +++ b/hobj/data/images/imagesets/probe.py @@ -1,5 +1,5 @@ -from hobj.data.images.template import Imageset, ImageManifestEntry, ImageManifest +from hobj.data.images.template import Imageset import pydantic from typing import Literal diff --git a/hobj/data/images/template.py b/hobj/data/images/template.py index 3be066a..f8f93d7 100644 --- a/hobj/data/images/template.py +++ b/hobj/data/images/template.py @@ -8,7 +8,6 @@ from tqdm import tqdm from hobj.utils.file_io import unzip_file -from hobj.utils.hash import hash_image from mref import ImageRef from hobj.data.store import default_data_store from mref import FileSystemStorage diff --git a/hobj/data/store.py b/hobj/data/store.py index d8fef15..c10786a 100644 --- a/hobj/data/store.py +++ b/hobj/data/store.py @@ -1,12 +1,6 @@ -from pathlib import Path -import PIL.Image import hobj.config -from hobj.utils.hash import hash_image, hash_string -from hobj.utils.file_io import download_json, download_file -import json -import tempfile import mref __all__ = [ diff --git a/hobj/learning_models/linear/representation.py b/hobj/learning_models/linear/representation.py index 890b941..846870d 100644 --- a/hobj/learning_models/linear/representation.py +++ b/hobj/learning_models/linear/representation.py @@ -1,6 +1,6 @@ import numpy as np -from typing import Union, Dict, Callable, List +from typing import Union, Dict, Callable import PIL.Image from mref import ImageRef diff --git a/pyproject.toml b/pyproject.toml index f65aef6..4104969 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,7 +22,6 @@ dependencies = [ "tqdm>=4.67", "pydantic>=2.10", "xarray>=2025.1", - "mref @ git+https://github.com/himjl/mref.git", ] [dependency-groups] diff --git a/uv.lock b/uv.lock index 1a2781a..f65af68 100644 --- a/uv.lock +++ b/uv.lock @@ -153,7 +153,6 @@ version = "2.0.0" source = { editable = "." } dependencies = [ { name = "matplotlib" }, - { name = "mref" }, { name = "numpy" }, { name = "pandas" }, { name = "pydantic" }, @@ -170,7 +169,6 @@ dev = [ [package.metadata] requires-dist = [ { name = "matplotlib", specifier = ">=3.9" }, - { name = "mref", git = "https://github.com/himjl/mref.git" }, { name = "numpy", specifier = ">=2.2" }, { name = "pandas", specifier = ">=2.2" }, { name = "pydantic", specifier = ">=2.10" }, @@ -278,20 +276,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/17/91/febbb6c1063ae05a62fdbe038c2917b348b1b35f0482cee4738e6870a44a/matplotlib-3.9.0-cp312-cp312-win_amd64.whl", hash = "sha256:d91a4ffc587bacf5c4ce4ecfe4bcd23a4b675e76315f2866e588686cc97fccdf", size = 7968581, upload-time = "2024-05-15T23:37:47.839Z" }, ] -[[package]] -name = "mref" -version = "0.0.2" -source = { git = "https://github.com/himjl/mref.git#5b6f03d54184dec33aa81006dab3144b66f41c9d" } -dependencies = [ - { name = "matplotlib" }, - { name = "numpy" }, - { name = "pillow" }, - { name = "pydantic" }, - { name = "pytest" }, - { name = "requests" }, - { name = "tqdm" }, -] - [[package]] name = "numpy" version = "2.4.3" From 1f8f9c1c4725d983400c9c91318c9feb81981c3b Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 17:44:08 -0400 Subject: [PATCH 100/156] fix docstring --- hobj/stats/ci.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hobj/stats/ci.py b/hobj/stats/ci.py index 9a713d4..7ce343c 100644 --- a/hobj/stats/ci.py +++ b/hobj/stats/ci.py @@ -11,7 +11,7 @@ def estimate_basic_bootstrap_CI( Estimates the basic confidence interval for a given point estimate(s) using the bootstrap method. :param alpha: Sets the width of the confidence interval to be 1 - alpha. Must be in the range (0, 1). :param point_estimate: The point estimate(s) for which the confidence interval is to be estimated. - :param bootstrapped_point_estimate: Bootstrap resamples of the point estimate in question. + :param bootstrapped_point_estimates: Bootstrap resamples of the point estimate in question. :return: A tuple containing the lower and upper bounds of the confidence interval. """ From 45c89f14f9b943c4bc921d43f4afb978a9ceb150 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 17:48:04 -0400 Subject: [PATCH 101/156] test --- Makefile | 3 +++ hobj/benchmarks/mut_highvar_benchmark.py | 4 ++-- hobj/benchmarks/mut_oneshot_benchmark.py | 2 +- hobj/data/images/__init__.py | 4 ---- hobj/images/__init__.py | 4 ++++ hobj/{data => }/images/imagesets/__init__.py | 0 hobj/{data => }/images/imagesets/highvar.py | 2 +- hobj/{data => }/images/imagesets/oneshot.py | 4 ++-- hobj/{data => }/images/imagesets/probe.py | 2 +- hobj/{data => }/images/imagesets/warmup.py | 2 +- hobj/{data => }/images/template.py | 0 hobj/utils/hash.py | 2 +- site/readme_images/run_make_images.py | 2 +- {hobj/learning_models/tests => tests}/test_dummy_learner.py | 0 14 files changed, 17 insertions(+), 14 deletions(-) delete mode 100644 hobj/data/images/__init__.py create mode 100644 hobj/images/__init__.py rename hobj/{data => }/images/imagesets/__init__.py (100%) rename hobj/{data => }/images/imagesets/highvar.py (97%) rename hobj/{data => }/images/imagesets/oneshot.py (95%) rename hobj/{data => }/images/imagesets/probe.py (92%) rename hobj/{data => }/images/imagesets/warmup.py (93%) rename hobj/{data => }/images/template.py (100%) rename {hobj/learning_models/tests => tests}/test_dummy_learner.py (100%) diff --git a/Makefile b/Makefile index c596605..bf3fd22 100644 --- a/Makefile +++ b/Makefile @@ -6,3 +6,6 @@ check: uv run ty check && \ uv run ruff check && \ uv run ruff format --check + +test: + uv run pytest tests diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index 2d76f7e..92a544a 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -4,7 +4,7 @@ from hobj.benchmarks.binary_classification.benchmark import LearningCurveBenchmark, LearningCurveBenchmarkConfig, TargetSubtaskData from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask, BinaryClassificationSubtaskResult from hobj.data.behavior import load_highvar_behavior -from hobj.data.images import MutatorHighVarImageset +from hobj.images import MutatorHighVarImageset # %% @@ -87,4 +87,4 @@ def __init__(self): if __name__ == '__main__': experiment = MutatorHighVarBenchmark() - print(sorted(experiment.config.subtask_name_to_data.keys())) \ No newline at end of file + print(sorted(experiment.config.subtask_name_to_data.keys())) diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index 226b1de..a5ad68f 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -6,7 +6,7 @@ from hobj.benchmarks.generalization.estimator import GeneralizationStatistics from hobj.benchmarks.generalization.simulator import GeneralizationSubtask from hobj.data.behavior import load_oneshot_behavior -from hobj.data.images import MutatorOneShotImageset +from hobj.images import MutatorOneShotImageset from mref import ImageRef diff --git a/hobj/data/images/__init__.py b/hobj/data/images/__init__.py deleted file mode 100644 index 0fc51d1..0000000 --- a/hobj/data/images/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from hobj.data.images.imagesets.highvar import MutatorHighVarImageset -from hobj.data.images.imagesets.oneshot import MutatorOneShotImageset -from hobj.data.images.imagesets.warmup import MutatorWarmupImageset -from hobj.data.images.imagesets.probe import ProbeImageset \ No newline at end of file diff --git a/hobj/images/__init__.py b/hobj/images/__init__.py new file mode 100644 index 0000000..471101d --- /dev/null +++ b/hobj/images/__init__.py @@ -0,0 +1,4 @@ +from hobj.images.imagesets.highvar import MutatorHighVarImageset +from hobj.images.imagesets.oneshot import MutatorOneShotImageset +from hobj.images.imagesets.warmup import MutatorWarmupImageset +from hobj.images.imagesets.probe import ProbeImageset diff --git a/hobj/data/images/imagesets/__init__.py b/hobj/images/imagesets/__init__.py similarity index 100% rename from hobj/data/images/imagesets/__init__.py rename to hobj/images/imagesets/__init__.py diff --git a/hobj/data/images/imagesets/highvar.py b/hobj/images/imagesets/highvar.py similarity index 97% rename from hobj/data/images/imagesets/highvar.py rename to hobj/images/imagesets/highvar.py index f39778c..c87a4df 100644 --- a/hobj/data/images/imagesets/highvar.py +++ b/hobj/images/imagesets/highvar.py @@ -1,5 +1,5 @@ import mref.media_references -from hobj.data.images.template import Imageset +from hobj.images.template import Imageset import pydantic from typing import Dict, List diff --git a/hobj/data/images/imagesets/oneshot.py b/hobj/images/imagesets/oneshot.py similarity index 95% rename from hobj/data/images/imagesets/oneshot.py rename to hobj/images/imagesets/oneshot.py index 80d1646..e334b1d 100644 --- a/hobj/data/images/imagesets/oneshot.py +++ b/hobj/images/imagesets/oneshot.py @@ -3,7 +3,7 @@ import pydantic -from hobj.data.images.template import Imageset +from hobj.images.template import Imageset class MutatorOneShotAnnotation(pydantic.BaseModel): @@ -50,4 +50,4 @@ class MutatorOneShotImageset(Imageset[MutatorOneShotAnnotation]): if __name__ == '__main__': - imageset = MutatorOneShotImageset() \ No newline at end of file + imageset = MutatorOneShotImageset() diff --git a/hobj/data/images/imagesets/probe.py b/hobj/images/imagesets/probe.py similarity index 92% rename from hobj/data/images/imagesets/probe.py rename to hobj/images/imagesets/probe.py index d9a7a0f..594dc9f 100644 --- a/hobj/data/images/imagesets/probe.py +++ b/hobj/images/imagesets/probe.py @@ -1,5 +1,5 @@ -from hobj.data.images.template import Imageset +from hobj.images.template import Imageset import pydantic from typing import Literal diff --git a/hobj/data/images/imagesets/warmup.py b/hobj/images/imagesets/warmup.py similarity index 93% rename from hobj/data/images/imagesets/warmup.py rename to hobj/images/imagesets/warmup.py index 1a53dc8..73808bb 100644 --- a/hobj/data/images/imagesets/warmup.py +++ b/hobj/images/imagesets/warmup.py @@ -1,6 +1,6 @@ import pydantic -from hobj.data.images.template import Imageset +from hobj.images.template import Imageset diff --git a/hobj/data/images/template.py b/hobj/images/template.py similarity index 100% rename from hobj/data/images/template.py rename to hobj/images/template.py diff --git a/hobj/utils/hash.py b/hobj/utils/hash.py index 7431058..6c6b0fc 100644 --- a/hobj/utils/hash.py +++ b/hobj/utils/hash.py @@ -3,7 +3,7 @@ import PIL.Image -def hash_image(image: PIL.Image) -> str: +def hash_image(image: PIL.Image.Image) -> str: """ Hash an image based on its np.uint8 representation. :param image: diff --git a/site/readme_images/run_make_images.py b/site/readme_images/run_make_images.py index b55fb68..f084ed8 100644 --- a/site/readme_images/run_make_images.py +++ b/site/readme_images/run_make_images.py @@ -2,7 +2,7 @@ # %% import matplotlib.pyplot as plt - from hobj.data.images import MutatorHighVarImageset + from hobj.images import MutatorHighVarImageset from hobj.benchmarks import MutatorHighVarBenchmark import numpy as np diff --git a/hobj/learning_models/tests/test_dummy_learner.py b/tests/test_dummy_learner.py similarity index 100% rename from hobj/learning_models/tests/test_dummy_learner.py rename to tests/test_dummy_learner.py From 1dd8c0e059c264660e2d2ac5f6d7294d516a550d Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 17:50:43 -0400 Subject: [PATCH 102/156] move --- hobj/images/__init__.py | 8 ++++---- hobj/images/{imagesets => }/highvar.py | 0 hobj/images/imagesets/__init__.py | 0 hobj/images/{imagesets => }/oneshot.py | 0 hobj/images/{imagesets => }/probe.py | 0 hobj/images/{imagesets => }/warmup.py | 0 site/readme_images/run_make_images.py | 2 -- 7 files changed, 4 insertions(+), 6 deletions(-) rename hobj/images/{imagesets => }/highvar.py (100%) delete mode 100644 hobj/images/imagesets/__init__.py rename hobj/images/{imagesets => }/oneshot.py (100%) rename hobj/images/{imagesets => }/probe.py (100%) rename hobj/images/{imagesets => }/warmup.py (100%) diff --git a/hobj/images/__init__.py b/hobj/images/__init__.py index 471101d..bd9a855 100644 --- a/hobj/images/__init__.py +++ b/hobj/images/__init__.py @@ -1,4 +1,4 @@ -from hobj.images.imagesets.highvar import MutatorHighVarImageset -from hobj.images.imagesets.oneshot import MutatorOneShotImageset -from hobj.images.imagesets.warmup import MutatorWarmupImageset -from hobj.images.imagesets.probe import ProbeImageset +from hobj.images.highvar import MutatorHighVarImageset +from hobj.images.oneshot import MutatorOneShotImageset +from hobj.images.warmup import MutatorWarmupImageset +from hobj.images.probe import ProbeImageset diff --git a/hobj/images/imagesets/highvar.py b/hobj/images/highvar.py similarity index 100% rename from hobj/images/imagesets/highvar.py rename to hobj/images/highvar.py diff --git a/hobj/images/imagesets/__init__.py b/hobj/images/imagesets/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/hobj/images/imagesets/oneshot.py b/hobj/images/oneshot.py similarity index 100% rename from hobj/images/imagesets/oneshot.py rename to hobj/images/oneshot.py diff --git a/hobj/images/imagesets/probe.py b/hobj/images/probe.py similarity index 100% rename from hobj/images/imagesets/probe.py rename to hobj/images/probe.py diff --git a/hobj/images/imagesets/warmup.py b/hobj/images/warmup.py similarity index 100% rename from hobj/images/imagesets/warmup.py rename to hobj/images/warmup.py diff --git a/site/readme_images/run_make_images.py b/site/readme_images/run_make_images.py index f084ed8..c7ec90c 100644 --- a/site/readme_images/run_make_images.py +++ b/site/readme_images/run_make_images.py @@ -2,11 +2,9 @@ # %% import matplotlib.pyplot as plt - from hobj.images import MutatorHighVarImageset from hobj.benchmarks import MutatorHighVarBenchmark import numpy as np - imageset = MutatorHighVarImageset() benchmark = MutatorHighVarBenchmark() target_stats = benchmark.target_statistics From 893e7b614eed4daf40c2afc00604b86f61289058 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 17:59:55 -0400 Subject: [PATCH 103/156] rename to loaders --- hobj/benchmarks/mut_highvar_benchmark.py | 2 +- hobj/benchmarks/mut_oneshot_benchmark.py | 2 +- hobj/images/template.py | 2 +- hobj/{data => loaders}/__init__.py | 0 hobj/{data => loaders}/behavior.py | 2 +- hobj/{data => loaders}/store.py | 0 {hobj/data/tests => tests}/test_load_behavior.py | 2 +- 7 files changed, 5 insertions(+), 5 deletions(-) rename hobj/{data => loaders}/__init__.py (100%) rename hobj/{data => loaders}/behavior.py (98%) rename hobj/{data => loaders}/store.py (100%) rename {hobj/data/tests => tests}/test_load_behavior.py (71%) diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index 92a544a..385b5b4 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -3,7 +3,7 @@ from hobj.benchmarks.binary_classification.benchmark import LearningCurveBenchmark, LearningCurveBenchmarkConfig, TargetSubtaskData from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask, BinaryClassificationSubtaskResult -from hobj.data.behavior import load_highvar_behavior +from hobj.loaders.behavior import load_highvar_behavior from hobj.images import MutatorHighVarImageset diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index a5ad68f..211b609 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -5,7 +5,7 @@ from hobj.benchmarks.generalization.benchmark import GeneralizationBenchmarkConfig, GeneralizationBenchmark, GeneralizationSessionResult from hobj.benchmarks.generalization.estimator import GeneralizationStatistics from hobj.benchmarks.generalization.simulator import GeneralizationSubtask -from hobj.data.behavior import load_oneshot_behavior +from hobj.loaders.behavior import load_oneshot_behavior from hobj.images import MutatorOneShotImageset from mref import ImageRef diff --git a/hobj/images/template.py b/hobj/images/template.py index f8f93d7..4fd26e5 100644 --- a/hobj/images/template.py +++ b/hobj/images/template.py @@ -9,7 +9,7 @@ from hobj.utils.file_io import unzip_file from mref import ImageRef -from hobj.data.store import default_data_store +from hobj.loaders.store import default_data_store from mref import FileSystemStorage import warnings diff --git a/hobj/data/__init__.py b/hobj/loaders/__init__.py similarity index 100% rename from hobj/data/__init__.py rename to hobj/loaders/__init__.py diff --git a/hobj/data/behavior.py b/hobj/loaders/behavior.py similarity index 98% rename from hobj/data/behavior.py rename to hobj/loaders/behavior.py index d44ade8..615f642 100644 --- a/hobj/data/behavior.py +++ b/hobj/loaders/behavior.py @@ -3,7 +3,7 @@ import pydantic -from hobj.data.store import default_data_store +from hobj.loaders.store import default_data_store __all__ = ['load_highvar_behavior', 'load_oneshot_behavior'] diff --git a/hobj/data/store.py b/hobj/loaders/store.py similarity index 100% rename from hobj/data/store.py rename to hobj/loaders/store.py diff --git a/hobj/data/tests/test_load_behavior.py b/tests/test_load_behavior.py similarity index 71% rename from hobj/data/tests/test_load_behavior.py rename to tests/test_load_behavior.py index c76d948..e7a4741 100644 --- a/hobj/data/tests/test_load_behavior.py +++ b/tests/test_load_behavior.py @@ -1,4 +1,4 @@ -from hobj.data.behavior import load_highvar_behavior, load_oneshot_behavior +from hobj.loaders.behavior import load_highvar_behavior, load_oneshot_behavior def test_load_highvar(): From 65a8a5148b2ff07490953596a2421e4a6986e5fa Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 18:00:41 -0400 Subject: [PATCH 104/156] dir refactor --- hobj/benchmarks/mut_highvar_benchmark.py | 4 ++-- hobj/benchmarks/mut_oneshot_benchmark.py | 4 ++-- hobj/{loaders => data_loaders}/__init__.py | 0 hobj/{loaders => data_loaders}/behavior.py | 2 +- hobj/data_loaders/images/__init__.py | 4 ++++ hobj/{ => data_loaders}/images/highvar.py | 2 +- hobj/{ => data_loaders}/images/oneshot.py | 2 +- hobj/{ => data_loaders}/images/probe.py | 2 +- hobj/{ => data_loaders}/images/template.py | 2 +- hobj/{ => data_loaders}/images/warmup.py | 2 +- hobj/{loaders => data_loaders}/store.py | 0 hobj/images/__init__.py | 4 ---- tests/test_load_behavior.py | 2 +- 13 files changed, 15 insertions(+), 15 deletions(-) rename hobj/{loaders => data_loaders}/__init__.py (100%) rename hobj/{loaders => data_loaders}/behavior.py (98%) create mode 100644 hobj/data_loaders/images/__init__.py rename hobj/{ => data_loaders}/images/highvar.py (96%) rename hobj/{ => data_loaders}/images/oneshot.py (96%) rename hobj/{ => data_loaders}/images/probe.py (91%) rename hobj/{ => data_loaders}/images/template.py (98%) rename hobj/{ => data_loaders}/images/warmup.py (92%) rename hobj/{loaders => data_loaders}/store.py (100%) delete mode 100644 hobj/images/__init__.py diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index 385b5b4..91f5409 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -3,8 +3,8 @@ from hobj.benchmarks.binary_classification.benchmark import LearningCurveBenchmark, LearningCurveBenchmarkConfig, TargetSubtaskData from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask, BinaryClassificationSubtaskResult -from hobj.loaders.behavior import load_highvar_behavior -from hobj.images import MutatorHighVarImageset +from hobj.data_loaders.behavior import load_highvar_behavior +from hobj.data_loaders.images import MutatorHighVarImageset # %% diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index 211b609..3a0c0aa 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -5,8 +5,8 @@ from hobj.benchmarks.generalization.benchmark import GeneralizationBenchmarkConfig, GeneralizationBenchmark, GeneralizationSessionResult from hobj.benchmarks.generalization.estimator import GeneralizationStatistics from hobj.benchmarks.generalization.simulator import GeneralizationSubtask -from hobj.loaders.behavior import load_oneshot_behavior -from hobj.images import MutatorOneShotImageset +from hobj.data_loaders.behavior import load_oneshot_behavior +from hobj.data_loaders.images import MutatorOneShotImageset from mref import ImageRef diff --git a/hobj/loaders/__init__.py b/hobj/data_loaders/__init__.py similarity index 100% rename from hobj/loaders/__init__.py rename to hobj/data_loaders/__init__.py diff --git a/hobj/loaders/behavior.py b/hobj/data_loaders/behavior.py similarity index 98% rename from hobj/loaders/behavior.py rename to hobj/data_loaders/behavior.py index 615f642..fc36b81 100644 --- a/hobj/loaders/behavior.py +++ b/hobj/data_loaders/behavior.py @@ -3,7 +3,7 @@ import pydantic -from hobj.loaders.store import default_data_store +from hobj.data_loaders.store import default_data_store __all__ = ['load_highvar_behavior', 'load_oneshot_behavior'] diff --git a/hobj/data_loaders/images/__init__.py b/hobj/data_loaders/images/__init__.py new file mode 100644 index 0000000..3c15cf9 --- /dev/null +++ b/hobj/data_loaders/images/__init__.py @@ -0,0 +1,4 @@ +from hobj.data_loaders.images.highvar import MutatorHighVarImageset +from hobj.data_loaders.images.oneshot import MutatorOneShotImageset +from hobj.data_loaders.images.warmup import MutatorWarmupImageset +from hobj.data_loaders.images.probe import ProbeImageset diff --git a/hobj/images/highvar.py b/hobj/data_loaders/images/highvar.py similarity index 96% rename from hobj/images/highvar.py rename to hobj/data_loaders/images/highvar.py index c87a4df..42c5530 100644 --- a/hobj/images/highvar.py +++ b/hobj/data_loaders/images/highvar.py @@ -1,5 +1,5 @@ import mref.media_references -from hobj.images.template import Imageset +from hobj.data_loaders.images.template import Imageset import pydantic from typing import Dict, List diff --git a/hobj/images/oneshot.py b/hobj/data_loaders/images/oneshot.py similarity index 96% rename from hobj/images/oneshot.py rename to hobj/data_loaders/images/oneshot.py index e334b1d..ae253cf 100644 --- a/hobj/images/oneshot.py +++ b/hobj/data_loaders/images/oneshot.py @@ -3,7 +3,7 @@ import pydantic -from hobj.images.template import Imageset +from hobj.data_loaders.images.template import Imageset class MutatorOneShotAnnotation(pydantic.BaseModel): diff --git a/hobj/images/probe.py b/hobj/data_loaders/images/probe.py similarity index 91% rename from hobj/images/probe.py rename to hobj/data_loaders/images/probe.py index 594dc9f..7945099 100644 --- a/hobj/images/probe.py +++ b/hobj/data_loaders/images/probe.py @@ -1,5 +1,5 @@ -from hobj.images.template import Imageset +from hobj.data_loaders.images.template import Imageset import pydantic from typing import Literal diff --git a/hobj/images/template.py b/hobj/data_loaders/images/template.py similarity index 98% rename from hobj/images/template.py rename to hobj/data_loaders/images/template.py index 4fd26e5..91695d2 100644 --- a/hobj/images/template.py +++ b/hobj/data_loaders/images/template.py @@ -9,7 +9,7 @@ from hobj.utils.file_io import unzip_file from mref import ImageRef -from hobj.loaders.store import default_data_store +from hobj.data_loaders.store import default_data_store from mref import FileSystemStorage import warnings diff --git a/hobj/images/warmup.py b/hobj/data_loaders/images/warmup.py similarity index 92% rename from hobj/images/warmup.py rename to hobj/data_loaders/images/warmup.py index 73808bb..ec5a60c 100644 --- a/hobj/images/warmup.py +++ b/hobj/data_loaders/images/warmup.py @@ -1,6 +1,6 @@ import pydantic -from hobj.images.template import Imageset +from hobj.data_loaders.images.template import Imageset diff --git a/hobj/loaders/store.py b/hobj/data_loaders/store.py similarity index 100% rename from hobj/loaders/store.py rename to hobj/data_loaders/store.py diff --git a/hobj/images/__init__.py b/hobj/images/__init__.py deleted file mode 100644 index bd9a855..0000000 --- a/hobj/images/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from hobj.images.highvar import MutatorHighVarImageset -from hobj.images.oneshot import MutatorOneShotImageset -from hobj.images.warmup import MutatorWarmupImageset -from hobj.images.probe import ProbeImageset diff --git a/tests/test_load_behavior.py b/tests/test_load_behavior.py index e7a4741..83b6c8d 100644 --- a/tests/test_load_behavior.py +++ b/tests/test_load_behavior.py @@ -1,4 +1,4 @@ -from hobj.loaders.behavior import load_highvar_behavior, load_oneshot_behavior +from hobj.data_loaders.behavior import load_highvar_behavior, load_oneshot_behavior def test_load_highvar(): From bffd0d26a988bed3e6880f82b6b43cecbde87773 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 18:19:48 -0400 Subject: [PATCH 105/156] remove mref from learning models --- hobj/learning_models/__init__.py | 7 ++-- hobj/learning_models/linear/__init__.py | 5 +-- hobj/learning_models/linear/representation.py | 23 ++++++------- hobj/types.py | 1 + pyproject.toml | 1 + uv.lock | 33 +++++++++++++++++++ 6 files changed, 51 insertions(+), 19 deletions(-) create mode 100644 hobj/types.py diff --git a/hobj/learning_models/__init__.py b/hobj/learning_models/__init__.py index 97e597c..cc2a510 100644 --- a/hobj/learning_models/__init__.py +++ b/hobj/learning_models/__init__.py @@ -1,10 +1,9 @@ import typing from abc import ABC, abstractmethod -import PIL.Image import numpy as np -import mref.media_references +from hobj.types import ImageId # %% @@ -22,7 +21,7 @@ def reset_state(self, seed: typing.Union[int, None]) -> None: @abstractmethod def get_response( self, - image: typing.Union[mref.media_references.ImageRef, PIL.Image], + image: ImageId, ) -> typing.Literal[0, 1]: """ This function takes the current stimulus image (given either as a PIL.Image or a ImageRef) and returns one of two possible actions (parameterized by an integer). @@ -54,7 +53,7 @@ def reset_state(self, seed: typing.Union[int, None]) -> None: def get_response( self, - image: typing.Union[mref.media_references.ImageRef, PIL.Image], + image: ImageId, ) -> typing.Literal[0, 1]: action = self.random_generator.integers(2) action = int(action) diff --git a/hobj/learning_models/linear/__init__.py b/hobj/learning_models/linear/__init__.py index 77b934d..cc133b7 100644 --- a/hobj/learning_models/linear/__init__.py +++ b/hobj/learning_models/linear/__init__.py @@ -6,8 +6,9 @@ from hobj.learning_models import BinaryLearningModel from hobj.learning_models.linear.representation import RepresentationalModel from hobj.learning_models.linear.update_rules import UpdateRule +from hobj.types import ImageId - +# %% class LinearLearner(BinaryLearningModel): def __init__( self, @@ -47,7 +48,7 @@ def reset_state(self, seed: int) -> None: def get_response( self, - image: typing.Union[mref.media_references.ImageRef, PIL.Image] + image: ImageId, ) -> typing.Literal[0, 1]: f = self.representational_model.get_features(image=image) diff --git a/hobj/learning_models/linear/representation.py b/hobj/learning_models/linear/representation.py index 846870d..da28183 100644 --- a/hobj/learning_models/linear/representation.py +++ b/hobj/learning_models/linear/representation.py @@ -1,10 +1,11 @@ +from typing import Callable, Dict + import numpy as np -from typing import Union, Dict, Callable -import PIL.Image +from hobj.types import ImageId -from mref import ImageRef +# %% class RepresentationalModel: """ @@ -14,7 +15,7 @@ class RepresentationalModel: def __init__( self, d: int, - image_to_features_func: Callable[[Union[ImageRef, PIL.Image]], np.ndarray], + image_to_features_func: Callable[[ImageId], np.ndarray], ): if not isinstance(d, int): @@ -32,7 +33,7 @@ def d(self) -> int: def get_features( self, - image: Union[ImageRef, PIL.Image] + image: ImageId ) -> np.ndarray: """ Returns a feature vector for the image_url. @@ -52,7 +53,7 @@ def get_features( @classmethod def from_precomputed_features( cls, - image_ref_to_features: Dict[ImageRef, np.ndarray] + image_ref_to_features: Dict[ImageId, np.ndarray] ) -> 'RepresentationalModel': """ @@ -61,12 +62,8 @@ def from_precomputed_features( If get_features is called with an ImageRef (or PIL.Image with an ImageRef) not in image_ref_to_features, a KeyError will be raised. """ - def image_to_features_func(image: Union[ImageRef, PIL.Image]) -> np.ndarray: - if isinstance(image, PIL.Image.Image): - ref = ImageRef.from_image(image) - else: - ref = image - return image_ref_to_features[ref] + def image_to_features_func(image: ImageId) -> np.ndarray: + return image_ref_to_features[image] # Ensure all feature vectors are the same shape d = None @@ -83,4 +80,4 @@ def image_to_features_func(image: Union[ImageRef, PIL.Image]) -> np.ndarray: if not f.shape[0] == d: raise ValueError(f"Expected feature vector to be of shape ({d},), but got {f.shape}") - return cls(d=d, image_to_features_func=image_to_features_func) \ No newline at end of file + return cls(d=d, image_to_features_func=image_to_features_func) diff --git a/hobj/types.py b/hobj/types.py new file mode 100644 index 0000000..0f0952f --- /dev/null +++ b/hobj/types.py @@ -0,0 +1 @@ +type ImageId = str diff --git a/pyproject.toml b/pyproject.toml index 4104969..526a8b8 100644 --- a/pyproject.toml +++ 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Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 18:20:30 -0400 Subject: [PATCH 106/156] optimize imports --- examples/dev.py | 1 - hobj/benchmarks/binary_classification/benchmark.py | 5 +++-- hobj/benchmarks/binary_classification/estimator.py | 7 ++++--- hobj/benchmarks/binary_classification/simulation.py | 5 +++-- .../tests/test_simulate_subtask.py | 10 ++++++---- hobj/benchmarks/generalization/benchmark.py | 7 ++++--- hobj/benchmarks/generalization/estimator.py | 5 +++-- hobj/benchmarks/generalization/simulator.py | 6 +++--- hobj/benchmarks/make_model.py | 11 ++++++----- hobj/benchmarks/mut_highvar_benchmark.py | 3 ++- hobj/benchmarks/mut_oneshot_benchmark.py | 5 +++-- hobj/data_loaders/behavior.py | 2 +- hobj/data_loaders/images/__init__.py | 2 +- hobj/data_loaders/images/highvar.py | 6 +++--- hobj/data_loaders/images/probe.py | 6 ++++-- hobj/data_loaders/images/template.py | 9 ++++----- hobj/data_loaders/images/warmup.py | 1 - hobj/data_loaders/store.py | 4 ++-- hobj/learning_models/linear/__init__.py | 6 +++--- hobj/learning_models/linear/update_rules.py | 5 +++-- hobj/stats/ci.py | 1 + hobj/utils/hash.py | 3 ++- 22 files changed, 61 insertions(+), 49 deletions(-) delete mode 100644 examples/dev.py diff --git a/examples/dev.py b/examples/dev.py deleted file mode 100644 index 29661e1..0000000 --- a/examples/dev.py +++ /dev/null @@ -1 +0,0 @@ -import hobj diff --git a/hobj/benchmarks/binary_classification/benchmark.py b/hobj/benchmarks/binary_classification/benchmark.py index 8f5b3f6..90820a6 100644 --- a/hobj/benchmarks/binary_classification/benchmark.py +++ b/hobj/benchmarks/binary_classification/benchmark.py @@ -1,9 +1,10 @@ +from dataclasses import dataclass +from typing import Dict, List, Optional, Tuple, Union + import numpy as np import pydantic import xarray as xr -from dataclasses import dataclass from tqdm import tqdm -from typing import List, Dict, Union, Tuple, Optional from hobj.benchmarks.binary_classification.estimator import LearningCurveStatistics from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask, BinaryClassificationSubtaskResult diff --git a/hobj/benchmarks/binary_classification/estimator.py b/hobj/benchmarks/binary_classification/estimator.py index 370f74c..0b80cb7 100644 --- a/hobj/benchmarks/binary_classification/estimator.py +++ b/hobj/benchmarks/binary_classification/estimator.py @@ -1,7 +1,8 @@ +from dataclasses import dataclass +from typing import Dict, List, Tuple + import numpy as np import xarray as xr -from dataclasses import dataclass -from typing import List, Dict, Tuple from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtaskResult from hobj.stats import binomial as binomial_funcs @@ -193,4 +194,4 @@ def _get_bootstrap_resamples_by_session( return LearningCurveStatistics.BootstrapSamples( boot_k=boot_k, boot_n=boot_n, - ) \ No newline at end of file + ) diff --git a/hobj/benchmarks/binary_classification/simulation.py b/hobj/benchmarks/binary_classification/simulation.py index 6e364f4..fe5833b 100644 --- a/hobj/benchmarks/binary_classification/simulation.py +++ b/hobj/benchmarks/binary_classification/simulation.py @@ -1,9 +1,10 @@ +from typing import List, Optional, Union + import numpy as np import pydantic -from typing import List, Union, Optional +from mref import ImageRef from hobj.learning_models import BinaryLearningModel -from mref import ImageRef # %% diff --git a/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py b/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py index 3503df8..a6c0415 100644 --- a/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py +++ b/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py @@ -1,9 +1,11 @@ -from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask -from hobj.learning_models import RandomGuesser -from mref import ImageRef from typing import List + import PIL.Image import numpy as np +from mref import ImageRef + +from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask +from hobj.learning_models import RandomGuesser def create_image_refs(nimages_per_class: int, seed: int) -> List[ImageRef]: @@ -38,4 +40,4 @@ def test_simulate_subtask(): assert len(result.perf_seq) == ntrials -# Todo: test deterministic \ No newline at end of file +# Todo: test deterministic diff --git a/hobj/benchmarks/generalization/benchmark.py b/hobj/benchmarks/generalization/benchmark.py index 3c9e856..69266ac 100644 --- a/hobj/benchmarks/generalization/benchmark.py +++ b/hobj/benchmarks/generalization/benchmark.py @@ -1,12 +1,13 @@ +from dataclasses import dataclass +from typing import List, Tuple + import numpy as np import pydantic import xarray as xr -from dataclasses import dataclass from tqdm import tqdm -from typing import List, Tuple from hobj.benchmarks.generalization.estimator import GeneralizationStatistics -from hobj.benchmarks.generalization.simulator import GeneralizationSubtask, GeneralizationSessionResult +from hobj.benchmarks.generalization.simulator import GeneralizationSessionResult, GeneralizationSubtask from hobj.learning_models import BinaryLearningModel from hobj.stats.ci import estimate_basic_bootstrap_CI diff --git a/hobj/benchmarks/generalization/estimator.py b/hobj/benchmarks/generalization/estimator.py index cc77697..6dbd337 100644 --- a/hobj/benchmarks/generalization/estimator.py +++ b/hobj/benchmarks/generalization/estimator.py @@ -1,8 +1,9 @@ -import numpy as np import warnings -import xarray as xr from typing import List +import numpy as np +import xarray as xr + from hobj.benchmarks.generalization.simulator import GeneralizationSessionResult from hobj.stats import binomial as binomial_funcs diff --git a/hobj/benchmarks/generalization/simulator.py b/hobj/benchmarks/generalization/simulator.py index dc690ad..9234410 100644 --- a/hobj/benchmarks/generalization/simulator.py +++ b/hobj/benchmarks/generalization/simulator.py @@ -1,11 +1,11 @@ -from typing import List, Union, Dict, Optional +import collections +from typing import Dict, List, Optional, Union import numpy as np import pydantic - from mref import ImageRef + from hobj.learning_models import BinaryLearningModel -import collections # %% diff --git a/hobj/benchmarks/make_model.py b/hobj/benchmarks/make_model.py index 2df5f58..4baa090 100644 --- a/hobj/benchmarks/make_model.py +++ b/hobj/benchmarks/make_model.py @@ -1,12 +1,13 @@ """ This module provides an alternative interface for instantiating a linear learning model. """ -from hobj.learning_models.linear import LinearLearner, RepresentationalModel -import hobj.learning_models.linear.update_rules as update_rules -from typing import Literal, Dict +from typing import Dict, List, Literal + import mref import numpy as np -from typing import List + +import hobj.learning_models.linear.update_rules as update_rules +from hobj.learning_models.linear import LinearLearner, RepresentationalModel # %% @@ -55,4 +56,4 @@ def make_linear_learner_from_features( image_ref_to_features=ref_to_calibrated_features ), update_rule=update_rule_name(alpha=alpha) - ) \ No newline at end of file + ) diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index 91f5409..a57ad5a 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -1,5 +1,6 @@ +from typing import Dict, List + import numpy as np -from typing import List, Dict from hobj.benchmarks.binary_classification.benchmark import LearningCurveBenchmark, LearningCurveBenchmarkConfig, TargetSubtaskData from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask, BinaryClassificationSubtaskResult diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index 3a0c0aa..0329c99 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -2,12 +2,13 @@ # Coercing human data from typing import Dict, List -from hobj.benchmarks.generalization.benchmark import GeneralizationBenchmarkConfig, GeneralizationBenchmark, GeneralizationSessionResult +from mref import ImageRef + +from hobj.benchmarks.generalization.benchmark import GeneralizationBenchmark, GeneralizationBenchmarkConfig, GeneralizationSessionResult from hobj.benchmarks.generalization.estimator import GeneralizationStatistics from hobj.benchmarks.generalization.simulator import GeneralizationSubtask from hobj.data_loaders.behavior import load_oneshot_behavior from hobj.data_loaders.images import MutatorOneShotImageset -from mref import ImageRef # %% diff --git a/hobj/data_loaders/behavior.py b/hobj/data_loaders/behavior.py index fc36b81..a26fbdb 100644 --- a/hobj/data_loaders/behavior.py +++ b/hobj/data_loaders/behavior.py @@ -1,5 +1,5 @@ -from typing import List, Literal import datetime +from typing import List, Literal import pydantic diff --git a/hobj/data_loaders/images/__init__.py b/hobj/data_loaders/images/__init__.py index 3c15cf9..1b8c648 100644 --- a/hobj/data_loaders/images/__init__.py +++ b/hobj/data_loaders/images/__init__.py @@ -1,4 +1,4 @@ from hobj.data_loaders.images.highvar import MutatorHighVarImageset from hobj.data_loaders.images.oneshot import MutatorOneShotImageset -from hobj.data_loaders.images.warmup import MutatorWarmupImageset from hobj.data_loaders.images.probe import ProbeImageset +from hobj.data_loaders.images.warmup import MutatorWarmupImageset diff --git a/hobj/data_loaders/images/highvar.py b/hobj/data_loaders/images/highvar.py index 42c5530..aa34156 100644 --- a/hobj/data_loaders/images/highvar.py +++ b/hobj/data_loaders/images/highvar.py @@ -1,9 +1,9 @@ +from typing import Dict, List + import mref.media_references -from hobj.data_loaders.images.template import Imageset import pydantic -from typing import Dict, List - +from hobj.data_loaders.images.template import Imageset class MutatorHighVarAnnotation(pydantic.BaseModel): diff --git a/hobj/data_loaders/images/probe.py b/hobj/data_loaders/images/probe.py index 7945099..473ce35 100644 --- a/hobj/data_loaders/images/probe.py +++ b/hobj/data_loaders/images/probe.py @@ -1,8 +1,10 @@ -from hobj.data_loaders.images.template import Imageset -import pydantic from typing import Literal +import pydantic + +from hobj.data_loaders.images.template import Imageset + class ProbeAnnotation(pydantic.BaseModel): color: Literal['blue', 'orange'] diff --git a/hobj/data_loaders/images/template.py b/hobj/data_loaders/images/template.py index 91695d2..150608a 100644 --- a/hobj/data_loaders/images/template.py +++ b/hobj/data_loaders/images/template.py @@ -1,17 +1,16 @@ import tempfile +import warnings from abc import ABC from pathlib import Path -from typing import Any, Dict, TypeVar, Generic, List +from typing import Any, Dict, Generic, List, TypeVar import PIL.Image import pydantic +from mref import FileSystemStorage, ImageRef from tqdm import tqdm -from hobj.utils.file_io import unzip_file -from mref import ImageRef from hobj.data_loaders.store import default_data_store -from mref import FileSystemStorage -import warnings +from hobj.utils.file_io import unzip_file class ImageManifestEntry(pydantic.BaseModel, ABC): diff --git a/hobj/data_loaders/images/warmup.py b/hobj/data_loaders/images/warmup.py index ec5a60c..3fdd9c9 100644 --- a/hobj/data_loaders/images/warmup.py +++ b/hobj/data_loaders/images/warmup.py @@ -3,7 +3,6 @@ from hobj.data_loaders.images.template import Imageset - class MutatorWarmupAnnotation(pydantic.BaseModel): category: str = pydantic.Field( examples=[ diff --git a/hobj/data_loaders/store.py b/hobj/data_loaders/store.py index c10786a..fcb2419 100644 --- a/hobj/data_loaders/store.py +++ b/hobj/data_loaders/store.py @@ -1,8 +1,8 @@ -import hobj.config +import mref +import hobj.config -import mref __all__ = [ 'default_data_store' ] diff --git a/hobj/learning_models/linear/__init__.py b/hobj/learning_models/linear/__init__.py index cc133b7..ac70b74 100644 --- a/hobj/learning_models/linear/__init__.py +++ b/hobj/learning_models/linear/__init__.py @@ -1,13 +1,13 @@ -import PIL.Image -import numpy as np import typing -import mref.media_references +import numpy as np + from hobj.learning_models import BinaryLearningModel from hobj.learning_models.linear.representation import RepresentationalModel from hobj.learning_models.linear.update_rules import UpdateRule from hobj.types import ImageId + # %% class LinearLearner(BinaryLearningModel): def __init__( diff --git a/hobj/learning_models/linear/update_rules.py b/hobj/learning_models/linear/update_rules.py index 6911f11..ea7b917 100644 --- a/hobj/learning_models/linear/update_rules.py +++ b/hobj/learning_models/linear/update_rules.py @@ -1,8 +1,9 @@ -import numpy as np -import scipy.special from abc import ABC, abstractmethod from typing import Tuple, Union +import numpy as np +import scipy.special + class UpdateRule(ABC): def __init__(self, alpha: float): diff --git a/hobj/stats/ci.py b/hobj/stats/ci.py index 7ce343c..13979a5 100644 --- a/hobj/stats/ci.py +++ b/hobj/stats/ci.py @@ -1,4 +1,5 @@ from typing import Tuple, Union + import numpy as np diff --git a/hobj/utils/hash.py b/hobj/utils/hash.py index 6c6b0fc..f321fbe 100644 --- a/hobj/utils/hash.py +++ b/hobj/utils/hash.py @@ -1,6 +1,7 @@ -import numpy as np import hashlib + import PIL.Image +import numpy as np def hash_image(image: PIL.Image.Image) -> str: From e1328243489c55e374cf5c0a89ddbc55d963e299 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 18:21:54 -0400 Subject: [PATCH 107/156] flatten --- hobj/benchmarks/make_model.py | 5 +- hobj/learning_models/__init__.py | 71 +++++++++++++++++ hobj/learning_models/linear/__init__.py | 76 ------------------- .../{linear => }/representation.py | 1 - .../{linear => }/update_rules.py | 0 5 files changed, 74 insertions(+), 79 deletions(-) delete mode 100644 hobj/learning_models/linear/__init__.py rename hobj/learning_models/{linear => }/representation.py (99%) rename hobj/learning_models/{linear => }/update_rules.py (100%) diff --git a/hobj/benchmarks/make_model.py b/hobj/benchmarks/make_model.py index 4baa090..3086be8 100644 --- a/hobj/benchmarks/make_model.py +++ b/hobj/benchmarks/make_model.py @@ -6,8 +6,9 @@ import mref import numpy as np -import hobj.learning_models.linear.update_rules as update_rules -from hobj.learning_models.linear import LinearLearner, RepresentationalModel +import hobj.learning_models.update_rules as update_rules +from hobj.learning_models.linear import RepresentationalModel +from hobj.learning_models import LinearLearner # %% diff --git a/hobj/learning_models/__init__.py b/hobj/learning_models/__init__.py index cc2a510..b409900 100644 --- a/hobj/learning_models/__init__.py +++ b/hobj/learning_models/__init__.py @@ -3,6 +3,9 @@ import numpy as np +from hobj.learning_models.representation import RepresentationalModel +from hobj.learning_models.update_rules import UpdateRule + from hobj.types import ImageId @@ -61,3 +64,71 @@ def get_response( def give_feedback(self, reward: float) -> None: return + + +# %% +class LinearLearner(BinaryLearningModel): + def __init__( + self, + representational_model: RepresentationalModel, + update_rule: UpdateRule, + ): + + self.representational_model = representational_model + self.update_rule = update_rule + + # State variables + self.w = None + self.b = None + self._f_last = None + self._logits_last = None + self._action_last = None + self._generator: np.random.Generator = np.random.default_rng() + + # Initialize state + self.reset_state(seed=0) + return + + def reset_state(self, seed: int) -> None: + """ + :param seed: + :return: + """ + self.update_rule.reset() + self.w = np.zeros((self.representational_model.d, 2)) + self.b = np.zeros((2,)) + self._f_last = None + self._logits_last = None + self._action_last = None + self._generator = np.random.default_rng(seed=seed) + + return + + def get_response( + self, + image: ImageId, + ) -> typing.Literal[0, 1]: + + f = self.representational_model.get_features(image=image) + logits = f @ self.w + self.b + action = self._random_tiebreaking_argmax(logits[0], logits[1]) + + # Update internal state with traces + self._f_last = f + self._logits_last = logits + self._action_last = action + return action + + def give_feedback(self, reward: float) -> None: + delta_w, delta_b = self.update_rule.get_update(x=self._f_last, w=self.w, b=self.b, logits=self._logits_last, action=self._action_last, reward=reward) # [action] + self.w += delta_w + self.b += delta_b + return + + def _random_tiebreaking_argmax(self, logit0, logit1) -> typing.Literal[0, 1]: + if logit0 > logit1: + return 0 + elif logit0 < logit1: + return 1 + else: + return 0 if self._generator.random() < 0.5 else 1 diff --git a/hobj/learning_models/linear/__init__.py b/hobj/learning_models/linear/__init__.py deleted file mode 100644 index ac70b74..0000000 --- a/hobj/learning_models/linear/__init__.py +++ /dev/null @@ -1,76 +0,0 @@ -import typing - -import numpy as np - -from hobj.learning_models import BinaryLearningModel -from hobj.learning_models.linear.representation import RepresentationalModel -from hobj.learning_models.linear.update_rules import UpdateRule -from hobj.types import ImageId - - -# %% -class LinearLearner(BinaryLearningModel): - def __init__( - self, - representational_model: RepresentationalModel, - update_rule: UpdateRule, - ): - - self.representational_model = representational_model - self.update_rule = update_rule - - # State variables - self.w = None - self.b = None - self._f_last = None - self._logits_last = None - self._action_last = None - self._generator: np.random.Generator = np.random.default_rng() - - # Initialize state - self.reset_state(seed=0) - return - - def reset_state(self, seed: int) -> None: - """ - :param seed: - :return: - """ - self.update_rule.reset() - self.w = np.zeros((self.representational_model.d, 2)) - self.b = np.zeros((2,)) - self._f_last = None - self._logits_last = None - self._action_last = None - self._generator = np.random.default_rng(seed=seed) - - return - - def get_response( - self, - image: ImageId, - ) -> typing.Literal[0, 1]: - - f = self.representational_model.get_features(image=image) - logits = f @ self.w + self.b - action = self._random_tiebreaking_argmax(logits[0], logits[1]) - - # Update internal state with traces - self._f_last = f - self._logits_last = logits - self._action_last = action - return action - - def give_feedback(self, reward: float) -> None: - delta_w, delta_b = self.update_rule.get_update(x=self._f_last, w=self.w, b=self.b, logits=self._logits_last, action=self._action_last, reward=reward) # [action] - self.w += delta_w - self.b += delta_b - return - - def _random_tiebreaking_argmax(self, logit0, logit1) -> typing.Literal[0, 1]: - if logit0 > logit1: - return 0 - elif logit0 < logit1: - return 1 - else: - return 0 if self._generator.random() < 0.5 else 1 diff --git a/hobj/learning_models/linear/representation.py b/hobj/learning_models/representation.py similarity index 99% rename from hobj/learning_models/linear/representation.py rename to hobj/learning_models/representation.py index da28183..bd3d1f4 100644 --- a/hobj/learning_models/linear/representation.py +++ b/hobj/learning_models/representation.py @@ -6,7 +6,6 @@ # %% - class RepresentationalModel: """ Class which maps images to feature vectors of shape (d,). diff --git a/hobj/learning_models/linear/update_rules.py b/hobj/learning_models/update_rules.py similarity index 100% rename from hobj/learning_models/linear/update_rules.py rename to hobj/learning_models/update_rules.py From 2699b531aecd8e3e9b3f51560986e659a5296db0 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 18:23:45 -0400 Subject: [PATCH 108/156] 3.12 only --- .github/workflows/ci.yml | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index b45a188..00db357 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -7,21 +7,27 @@ on: - hobj/** # Changes to workflows - .github/workflows/ci.yml - # Changes to pyproject.toml + # Changes to project/dependency metadata - 'pyproject.toml' + - 'uv.lock' jobs: unit_tests: runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.12"] steps: - name: Checkout code uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 with: - python-version: '3.12' - cache: 'pip' - cache-dependency-path: setup.py # See https://github.com/actions/setup-python/blob/main/docs/advanced-usage.md#caching-packages - - name: Install hobj - run: pip3 install -e . + python-version: ${{ matrix.python-version }} + - name: Set up uv + uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + - name: Install dependencies + run: uv sync --locked --dev - name: Run pytests - run: pytest -s + run: uv run pytest -s From 2520cd510779a3b4b243cb546b2c1e447239a603 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 18:26:39 -0400 Subject: [PATCH 109/156] remove mref from tests --- tests/test_dummy_learner.py | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) diff --git a/tests/test_dummy_learner.py b/tests/test_dummy_learner.py index 9c8656c..f164712 100644 --- a/tests/test_dummy_learner.py +++ b/tests/test_dummy_learner.py @@ -1,24 +1,19 @@ import pytest -import mref.media_references from hobj.learning_models import RandomGuesser @pytest.fixture def dummy_learner() -> RandomGuesser: return RandomGuesser(seed=0) -@pytest.fixture -def test_image() -> mref.media_references.ImageRef: - return mref.media_references.ImageRef(sha256='0' * 64) - -def test_dummy_learner_deterministic(dummy_learner, test_image): +def test_dummy_learner_deterministic(dummy_learner): ntests = 10 actions = [] expected_actions = [1, 1, 1, 0, 0, 0, 0, 0, 0, 1] for i in range(ntests): - a = dummy_learner.get_response(image=test_image) + a = dummy_learner.get_response(image='hi') actions.append(a) - assert actions == expected_actions \ No newline at end of file + assert actions == expected_actions From ee3dd34dfd3755baea88487595c054de56bd0b50 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 19:23:45 -0400 Subject: [PATCH 110/156] updated getters for images --- .gitignore | 1 + hobj/benchmarks/make_model.py | 26 ++-- hobj/benchmarks/mut_highvar_benchmark.py | 6 +- hobj/benchmarks/mut_oneshot_benchmark.py | 6 +- hobj/data_loaders/behavior.py | 3 - hobj/data_loaders/images/__init__.py | 1 + hobj/data_loaders/images/highvar.py | 31 ++--- hobj/data_loaders/images/oneshot.py | 5 +- hobj/data_loaders/images/template.py | 156 ++++++++++++----------- hobj/learning_models/__init__.py | 2 - hobj/learning_models/update_rules.py | 1 + 11 files changed, 132 insertions(+), 106 deletions(-) diff --git a/.gitignore b/.gitignore index f511459..ac631e5 100644 --- a/.gitignore +++ b/.gitignore @@ -4,3 +4,4 @@ __pycache__/ *.egg-info/ dist/ +/data/ diff --git a/hobj/benchmarks/make_model.py b/hobj/benchmarks/make_model.py index 3086be8..0593021 100644 --- a/hobj/benchmarks/make_model.py +++ b/hobj/benchmarks/make_model.py @@ -1,20 +1,30 @@ """ This module provides an alternative interface for instantiating a linear learning model. """ -from typing import Dict, List, Literal +from functools import lru_cache +from typing import Literal -import mref import numpy as np import hobj.learning_models.update_rules as update_rules -from hobj.learning_models.linear import RepresentationalModel -from hobj.learning_models import LinearLearner +from hobj.learning_models import LinearLearner, RepresentationalModel +from hobj.types import ImageId + + +# %% +@lru_cache(maxsize=1) +def _get_calibration_image_ids() -> list[ImageId]: + """ + Returns the ImageIds of the warmup images that are used for calibrating the features of the linear learner. + Caches the result to avoid redundant computation. + """ + raise NotImplementedError # %% def make_linear_learner_from_features( - ref_to_features: Dict[mref.ImageRef, np.ndarray], - calibration_images: List[mref.ImageRef], + features: np.ndarray, + image_ids: list[ImageId], update_rule_name: Literal[ 'Prototype', 'Square', @@ -30,13 +40,13 @@ def make_linear_learner_from_features( """ Instantiates a linear learning model from precomputed features. :param ref_to_features: Dict[mref.ImageRef, np.ndarray], the features to use. - :param calibration_images: List[mref.ImageRef], the images that will be used to calibrate the features (i.e. for mean centering and ensuring they fit within a unit ball). :param update_rule_name: str, the name of the update rule to use. :param alpha: float, the learning rate. :return: LinearLearner """ - f_calibration = np.array([ref_to_features[ref] for ref in calibration_images]) + ref_to_features = {ref: features[i] for i, ref in enumerate(image_ids)} + f_calibration = np.array([ref_to_features[ref] for ref in _get_calibration_image_ids()]) mu_calibration = np.mean(f_calibration, axis=0) norms_calibration = np.linalg.norm(f_calibration - mu_calibration, axis=1) norm_cutoff = np.quantile(norms_calibration, 0.999) # Will clip the rest diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index a57ad5a..5068538 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -22,7 +22,7 @@ def __init__(self): # Normalize data for benchmark: sha256_to_category = { - ref.sha256: imageset.get_annotation(image_ref=ref).category for ref in imageset.image_refs + ref.sha256: imageset.get_annotation(image_id=ref).category for ref in imageset.image_ids } subtask_name_to_results = {} @@ -47,8 +47,8 @@ def __init__(self): # Instantiate the subtask if it does not exist: if subtask_name not in subtask_name_to_subtask: subtask = BinaryClassificationSubtask( - classA=imageset.category_to_image_refs[cat0], - classB=imageset.category_to_image_refs[cat1], + classA=imageset.category_to_image_ids[cat0], + classB=imageset.category_to_image_ids[cat1], ntrials=100, replace=False, ) diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index 0329c99..9004a38 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -100,8 +100,8 @@ def __init__(self): cat_to_support_image: Dict[str, ImageRef] = {} cat_to_test_images: Dict[str, List[ImageRef]] = {} - for ref in imageset.image_refs: - annotation = imageset.get_annotation(image_ref=ref) + for ref in imageset.image_ids: + annotation = imageset.get_annotation(image_id=ref) transformation_id = f"{annotation.transformation} | {annotation.transformation_level}" image_ref_to_transformation_id[ref] = transformation_id @@ -150,7 +150,7 @@ def __init__(self): for i_trial, sha in enumerate(session.stimulus_sha256_seq): ref = ImageRef(sha256=sha) - annotation = imageset.get_annotation(image_ref=ref) + annotation = imageset.get_annotation(image_id=ref) # Add stimulus category to observed categories observed_categories.add(annotation.category) diff --git a/hobj/data_loaders/behavior.py b/hobj/data_loaders/behavior.py index a26fbdb..8eb9327 100644 --- a/hobj/data_loaders/behavior.py +++ b/hobj/data_loaders/behavior.py @@ -53,7 +53,6 @@ def validate_lengths(self) -> 'HumanLearningSession': # %% def _load_learning_sessions( dataset_url: str, - redownload: bool ) -> List[HumanLearningSession]: data_store = default_data_store # Download the data: @@ -72,7 +71,6 @@ class LearningSessionDataset(pydantic.BaseModel): # %% Data loaders def load_highvar_behavior( - redownload: bool = False, remove_probe_trials: bool = True ) -> List[HumanLearningSession]: """ @@ -82,7 +80,6 @@ def load_highvar_behavior( sessions = _load_learning_sessions( dataset_url='https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-highvar-human-learning-data.json', - redownload=redownload ) if not remove_probe_trials: diff --git a/hobj/data_loaders/images/__init__.py b/hobj/data_loaders/images/__init__.py index 1b8c648..1e11a53 100644 --- a/hobj/data_loaders/images/__init__.py +++ b/hobj/data_loaders/images/__init__.py @@ -2,3 +2,4 @@ from hobj.data_loaders.images.oneshot import MutatorOneShotImageset from hobj.data_loaders.images.probe import ProbeImageset from hobj.data_loaders.images.warmup import MutatorWarmupImageset + diff --git a/hobj/data_loaders/images/highvar.py b/hobj/data_loaders/images/highvar.py index aa34156..ea98517 100644 --- a/hobj/data_loaders/images/highvar.py +++ b/hobj/data_loaders/images/highvar.py @@ -1,10 +1,11 @@ -from typing import Dict, List - -import mref.media_references +from pathlib import Path import pydantic from hobj.data_loaders.images.template import Imageset +from hobj.types import ImageId + +# %% class MutatorHighVarAnnotation(pydantic.BaseModel): category: str = pydantic.Field( @@ -21,24 +22,24 @@ class MutatorHighVarImageset(Imageset[MutatorHighVarAnnotation]): zipped_images_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-highvar/MutatorB2000_Subset128_FullVar_Train.zip' annotation_schema = MutatorHighVarAnnotation - def __init__(self): - super().__init__() + def __init__(self, cachedir: Path | None = None, redownload: bool = False): + super().__init__(cachedir=cachedir, redownload=redownload) - self._category_to_image_refs: Dict[str, List[mref.media_references.ImageRef]] = {} + self._category_to_image_ids: dict[str, list[ImageId]] = {} - for ref in self.image_refs: - annotation = self.get_annotation(image_ref=ref) + for ref in self.image_ids: + annotation = self.get_annotation(image_id=ref) category = annotation.category - if category not in self._category_to_image_refs: - self._category_to_image_refs[category] = [] - self._category_to_image_refs[category].append(ref) + if category not in self._category_to_image_ids: + self._category_to_image_ids[category] = [] + self._category_to_image_ids[category].append(ref) - for category in self._category_to_image_refs: - self._category_to_image_refs[category] = sorted(self._category_to_image_refs[category]) + for category in self._category_to_image_ids: + self._category_to_image_ids[category] = sorted(self._category_to_image_ids[category]) @property - def category_to_image_refs(self) -> Dict[str, List[mref.media_references.ImageRef]]: - return self._category_to_image_refs + def category_to_image_ids(self) -> dict[str, list[ImageId]]: + return self._category_to_image_ids if __name__ == '__main__': diff --git a/hobj/data_loaders/images/oneshot.py b/hobj/data_loaders/images/oneshot.py index ae253cf..15c58fd 100644 --- a/hobj/data_loaders/images/oneshot.py +++ b/hobj/data_loaders/images/oneshot.py @@ -1,4 +1,4 @@ - +from pathlib import Path from typing import Literal import pydantic @@ -48,6 +48,9 @@ class MutatorOneShotImageset(Imageset[MutatorOneShotAnnotation]): zipped_images_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-oneshot/MutatorB2000_Oneshot64.zip' annotation_schema = MutatorOneShotAnnotation + def __init__(self, cachedir: Path | None = None, redownload: bool = False): + super().__init__(cachedir=cachedir, redownload=redownload) + if __name__ == '__main__': imageset = MutatorOneShotImageset() diff --git a/hobj/data_loaders/images/template.py b/hobj/data_loaders/images/template.py index 150608a..51e527e 100644 --- a/hobj/data_loaders/images/template.py +++ b/hobj/data_loaders/images/template.py @@ -1,18 +1,17 @@ -import tempfile -import warnings +import json from abc import ABC from pathlib import Path from typing import Any, Dict, Generic, List, TypeVar import PIL.Image import pydantic -from mref import FileSystemStorage, ImageRef -from tqdm import tqdm -from hobj.data_loaders.store import default_data_store -from hobj.utils.file_io import unzip_file +from hobj.types import ImageId +from hobj.utils.file_io import download_file, download_json, unzip_file +from hobj.utils.hash import hash_image +# %% class ImageManifestEntry(pydantic.BaseModel, ABC): sha256: str = pydantic.Field(pattern=r'^[a-f0-9]{64}$') relpath: Path = pydantic.Field( @@ -24,7 +23,7 @@ class ImageManifestEntry(pydantic.BaseModel, ABC): class ImageManifest(pydantic.BaseModel): - entries: Dict[str, ImageManifestEntry] = pydantic.Field( + entries: Dict[ImageId, ImageManifestEntry] = pydantic.Field( description='A mapping from a unique image ID to image manifest entries.' ) @@ -46,103 +45,118 @@ class Imageset(Generic[IA], ABC): def __init__( self, - data_store: FileSystemStorage = None, + cachedir: Path | None = None, redownload=False, ): """ - Unwrap the image manifest and save the images to the cache. + Download and materialize the imageset into a local cache directory. """ - if not data_store: - self.data_store: FileSystemStorage = default_data_store + repo_root = Path(__file__).resolve().parents[3] + self.cachedir = (cachedir if cachedir is not None else repo_root / 'data').resolve() + self.cachedir.mkdir(parents=True, exist_ok=True) - # Load the manifest if it is already cached - manifest_data = self.data_store.download_json_from_url(url=self.manifest_url, register=True) # Todo - image_manifest = ImageManifest(**manifest_data) + self._dataset_dir = self.cachedir / self.__class__.__name__ + self._dataset_dir.mkdir(parents=True, exist_ok=True) + self._images_dir = self._dataset_dir / 'images' - self._register_image_urls(manifest=image_manifest, redownload=redownload) + manifest_data = self._load_manifest_json(redownload=redownload) + image_manifest = ImageManifest(**manifest_data) self._manifest = image_manifest + self._ensure_images_present(manifest=image_manifest, redownload=redownload) - self._image_id_to_annotation: Dict[str, IA] = {} - self._image_id_to_sha256: Dict[str, str] = {} - self._sha256_to_image_ids: Dict[str, List[str]] = {} - self._image_refs: List[ImageRef] = [] + self._image_id_to_annotation: Dict[ImageId, IA] = {} + self._image_id_to_sha256: Dict[ImageId, str] = {} + self._image_id_to_relpath: Dict[ImageId, Path] = {} + self._image_ids: List[ImageId] = [] for image_id, entry in image_manifest.entries.items(): - image_ref = ImageRef(sha256=entry.sha256) - self._image_refs.append(image_ref) - self._image_id_to_sha256[image_id] = image_ref.sha256 + self._image_ids.append(image_id) + self._image_id_to_sha256[image_id] = entry.sha256 + self._image_id_to_relpath[image_id] = entry.relpath self._image_id_to_annotation[image_id] = self.annotation_schema(**entry.annotation) - if image_ref.sha256 not in self._sha256_to_image_ids: - self._sha256_to_image_ids[image_ref.sha256] = [] - self._sha256_to_image_ids[image_ref.sha256].append(image_id) - def _register_image_urls(self, manifest: ImageManifest, redownload: bool = False): + def _load_manifest_json(self, redownload: bool) -> dict[str, Any]: + if redownload or not self.manifest_path.exists(): + manifest_data = download_json(self.manifest_url) + self.manifest_path.write_text(json.dumps(manifest_data, indent=2)) + return json.loads(self.manifest_path.read_text()) + + def _ensure_images_present(self, manifest: ImageManifest, redownload: bool = False) -> None: """ - Ensures the entries of the manifest are registered in the data store. + Ensure the images for this imageset exist locally. """ + if redownload or not self._all_images_present(manifest): + self._download_and_extract_images(manifest) + + def _all_images_present(self, manifest: ImageManifest) -> bool: + for entry in manifest.entries.values(): + if not (self._images_dir / entry.relpath).exists(): + return False + return True + + def _download_and_extract_images(self, manifest: ImageManifest) -> None: + self._dataset_dir.mkdir(parents=True, exist_ok=True) + if self._images_dir.exists(): + for path in sorted(self._images_dir.rglob('*'), reverse=True): + if path.is_file(): + path.unlink() + elif path.is_dir(): + path.rmdir() + self._images_dir.rmdir() + + download_file(self.zipped_images_url, self.archive_path) + unzip_file(zip_path=self.archive_path, output_dir=self._images_dir) + self._verify_images(manifest) + + def _verify_images(self, manifest: ImageManifest) -> None: + for image_id, entry in manifest.entries.items(): + image_path = self._images_dir / entry.relpath + if not image_path.exists(): + raise FileNotFoundError(f"Missing image file for {image_id}: {image_path}") + + with PIL.Image.open(image_path) as image_data: + observed_sha256 = hash_image(image_data) + + if observed_sha256 != entry.sha256: + raise ValueError( + f"SHA256 mismatch for image {image_id}: {observed_sha256} != {entry.sha256}" + ) - num_undownloaded_images = 0 - for image_id, manifest_entry in manifest.entries.items(): - ref = ImageRef(sha256=manifest_entry.sha256) - if not self.data_store.check_data_exists(ref=ref): - num_undownloaded_images += 1 - - if num_undownloaded_images == 0: - return - - print(f'Missing {num_undownloaded_images}/{len(manifest.entries)} images for this imageset.') - - # Download the images - zipped_images_path = self.data_store.download_zip_path_from_url(url=self.zipped_images_url, register=True) # todo type correctly - - # Make a tempdir to unzip the images - with tempfile.TemporaryDirectory() as tempdir: - tempdir = Path(tempdir) - unzip_file(zip_path=zipped_images_path, output_dir=tempdir) - - # Register the images - pbar = tqdm(total=len(manifest.entries)) - for image_id, manifest_entry in manifest.entries.items(): - reported_sha256 = manifest_entry.sha256 - relpath = manifest_entry.relpath - image_path = tempdir / relpath - image_data = PIL.Image.open(image_path) - - image_ref = ImageRef.from_image(image=image_data) + @property + def manifest_path(self) -> Path: + return self._dataset_dir / 'manifest.json' - if not image_ref.sha256 == reported_sha256: - raise ValueError(f"SHA256 mismatch for image {manifest_entry}: {image_ref.sha256} != {reported_sha256}") + @property + def archive_path(self) -> Path: + archive_name = Path(self.zipped_images_url).name + return self._dataset_dir / archive_name - # Store image - self.data_store.register_image(image=image_data) - pbar.update(1) + @property + def images_dir(self) -> Path: + return self._images_dir @property - def image_refs(self) -> List[ImageRef]: + def image_ids(self) -> list[ImageId]: """ List of image refs in this imageset. :return: """ - return self._image_refs + return self._image_ids - def get_annotation(self, *, image_ref: ImageRef = None, sha256: str = None, ) -> IA: + def get_annotation(self, *, image_id: ImageId) -> IA: """ Get the annotation for a given image. If an image has multiple annotations, this will throw an error. """ - if sha256 is None: - sha256 = image_ref.sha256 - - image_ids = self._sha256_to_image_ids[sha256] - if len(image_ids) > 1: - warnings.warn(f"Image {sha256} has multiple annotations: {image_ids}. Returning the first one.") - image_id = image_ids[0] entry = self._image_id_to_annotation[image_id] return entry + def get_image_path(self, *, image_id: ImageId) -> Path: + return self.images_dir / self._image_id_to_relpath[image_id] + def __len__(self) -> int: - return len(self.image_refs) + return len(self.image_ids) def __repr__(self): return f"{self.__class__.__name__}({len(self)})" diff --git a/hobj/learning_models/__init__.py b/hobj/learning_models/__init__.py index b409900..ad3411b 100644 --- a/hobj/learning_models/__init__.py +++ b/hobj/learning_models/__init__.py @@ -5,13 +5,11 @@ from hobj.learning_models.representation import RepresentationalModel from hobj.learning_models.update_rules import UpdateRule - from hobj.types import ImageId # %% class BinaryLearningModel(ABC): - @abstractmethod def reset_state(self, seed: typing.Union[int, None]) -> None: """ diff --git a/hobj/learning_models/update_rules.py b/hobj/learning_models/update_rules.py index ea7b917..3297e26 100644 --- a/hobj/learning_models/update_rules.py +++ b/hobj/learning_models/update_rules.py @@ -53,6 +53,7 @@ def get_update( return delta_w, delta_b +# %% class Prototype(UpdateRule): """ Simulates the decision boundary implemented by a prototype learner. From a1e059baa510c574fbbbf68118bb5b091cff78c6 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 19:31:53 -0400 Subject: [PATCH 111/156] download behavior without mref --- .../binary_classification/simulation.py | 9 ++--- hobj/benchmarks/generalization/simulator.py | 15 ++++--- hobj/benchmarks/mut_oneshot_benchmark.py | 23 ++++++----- hobj/data_loaders/behavior.py | 39 ++++++++++++++----- hobj/data_loaders/store.py | 12 ------ hobj/utils/file_io.py | 9 ++--- .../tests => tests}/test_simulate_subtask.py | 13 ++----- 7 files changed, 58 insertions(+), 62 deletions(-) rename {hobj/benchmarks/binary_classification/tests => tests}/test_simulate_subtask.py (73%) diff --git a/hobj/benchmarks/binary_classification/simulation.py b/hobj/benchmarks/binary_classification/simulation.py index fe5833b..ad35f89 100644 --- a/hobj/benchmarks/binary_classification/simulation.py +++ b/hobj/benchmarks/binary_classification/simulation.py @@ -2,8 +2,7 @@ import numpy as np import pydantic -from mref import ImageRef - +from hobj.types import ImageId from hobj.learning_models import BinaryLearningModel @@ -27,14 +26,14 @@ class BinaryClassificationSubtask(pydantic.BaseModel): frozen=True ) - classA: List[ImageRef] - classB: List[ImageRef] + classA: List[ImageId] + classB: List[ImageId] ntrials: int = pydantic.Field(description='The number of trials in the subtask.', gt=0) replace: bool = pydantic.Field(description='Whether to show stimulus images with replacement or not.') @pydantic.field_validator('classA', 'classB', mode='after') @classmethod - def sort_image_refs(cls, value: List[ImageRef]) -> List[ImageRef]: + def sort_image_refs(cls, value: List[ImageId]) -> List[ImageId]: return sorted(value) @pydantic.model_validator(mode='after') diff --git a/hobj/benchmarks/generalization/simulator.py b/hobj/benchmarks/generalization/simulator.py index 9234410..9bddd95 100644 --- a/hobj/benchmarks/generalization/simulator.py +++ b/hobj/benchmarks/generalization/simulator.py @@ -3,10 +3,9 @@ import numpy as np import pydantic -from mref import ImageRef from hobj.learning_models import BinaryLearningModel - +from hobj.types import ImageId # %% class GeneralizationSessionResult(pydantic.BaseModel): @@ -37,15 +36,15 @@ class GeneralizationSubtask(pydantic.BaseModel): frozen=True ) - support_imageA: ImageRef - support_imageB: ImageRef - test_imagesA: List[ImageRef] - test_imagesB: List[ImageRef] - image_ref_to_transformation: Dict[ImageRef, str] + support_imageA: ImageId + support_imageB: ImageId + test_imagesA: List[ImageId] + test_imagesB: List[ImageId] + image_ref_to_transformation: Dict[ImageId, str] @pydantic.field_validator('test_imagesA', 'test_imagesB', mode='after') @classmethod - def sort_image_refs(cls, value: List[ImageRef]) -> List[ImageRef]: + def sort_image_refs(cls, value: List[ImageId]) -> List[ImageId]: return sorted(value) @pydantic.model_validator(mode='after') diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index 9004a38..71851d3 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -2,14 +2,13 @@ # Coercing human data from typing import Dict, List -from mref import ImageRef - from hobj.benchmarks.generalization.benchmark import GeneralizationBenchmark, GeneralizationBenchmarkConfig, GeneralizationSessionResult from hobj.benchmarks.generalization.estimator import GeneralizationStatistics from hobj.benchmarks.generalization.simulator import GeneralizationSubtask from hobj.data_loaders.behavior import load_oneshot_behavior from hobj.data_loaders.images import MutatorOneShotImageset +from hobj.types import ImageId # %% @@ -97,24 +96,24 @@ def __init__(self): # Map image refs to transformation ids image_ref_to_transformation_id = {} - cat_to_support_image: Dict[str, ImageRef] = {} - cat_to_test_images: Dict[str, List[ImageRef]] = {} + cat_to_support_image: Dict[str, ImageId] = {} + cat_to_test_images: Dict[str, List[ImageId]] = {} - for ref in imageset.image_ids: - annotation = imageset.get_annotation(image_id=ref) + for image_id in imageset.image_ids: + annotation = imageset.get_annotation(image_id=image_id) transformation_id = f"{annotation.transformation} | {annotation.transformation_level}" - image_ref_to_transformation_id[ref] = transformation_id + image_ref_to_transformation_id[image_id] = transformation_id if annotation.transformation == 'original': if annotation.category not in cat_to_support_image: - cat_to_support_image[annotation.category] = ref + cat_to_support_image[annotation.category] = image_id else: raise ValueError(f"Multiple support images for category {annotation.category}") else: if annotation.category not in cat_to_test_images: cat_to_test_images[annotation.category] = [] - cat_to_test_images[annotation.category].append(ref) + cat_to_test_images[annotation.category].append(image_id) # Assemble subtask simulators subtasks = [] @@ -149,8 +148,8 @@ def __init__(self): observed_categories = set() for i_trial, sha in enumerate(session.stimulus_sha256_seq): - ref = ImageRef(sha256=sha) - annotation = imageset.get_annotation(image_id=ref) + image_id = sha + annotation = imageset.get_annotation(image_id=image_id) # Add stimulus category to observed categories observed_categories.add(annotation.category) @@ -167,7 +166,7 @@ def __init__(self): ncatch += 1 else: assert annotation.transformation != 'original' - transformation_id = image_ref_to_transformation_id[ref] + transformation_id = image_ref_to_transformation_id[image_id] # Keep only benchmarked transformations if transformation_id in self.transformation_ids: diff --git a/hobj/data_loaders/behavior.py b/hobj/data_loaders/behavior.py index 8eb9327..3452ce7 100644 --- a/hobj/data_loaders/behavior.py +++ b/hobj/data_loaders/behavior.py @@ -1,9 +1,11 @@ import datetime +import json +from pathlib import Path from typing import List, Literal import pydantic -from hobj.data_loaders.store import default_data_store +from hobj.utils.file_io import download_json __all__ = ['load_highvar_behavior', 'load_oneshot_behavior'] @@ -53,15 +55,22 @@ def validate_lengths(self) -> 'HumanLearningSession': # %% def _load_learning_sessions( dataset_url: str, + cache_filename: str, + cachedir: Path | None = None, + redownload: bool = False, ) -> List[HumanLearningSession]: - data_store = default_data_store - # Download the data: - json_data = data_store.download_json_from_url( - url=dataset_url, - register=True, - ) + repo_root = Path(__file__).resolve().parents[2] + cache_root = (cachedir if cachedir is not None else repo_root / 'data').resolve() + behavior_dir = cache_root / 'behavior' + behavior_dir.mkdir(parents=True, exist_ok=True) + dataset_path = behavior_dir / cache_filename + + if redownload or not dataset_path.exists(): + json_data = download_json(dataset_url) + dataset_path.write_text(json.dumps(json_data, indent=2)) + else: + json_data = json.loads(dataset_path.read_text()) - # class LearningSessionDataset(pydantic.BaseModel): sessions: List[HumanLearningSession] @@ -71,7 +80,9 @@ class LearningSessionDataset(pydantic.BaseModel): # %% Data loaders def load_highvar_behavior( - remove_probe_trials: bool = True + remove_probe_trials: bool = True, + cachedir: Path | None = None, + redownload: bool = False, ) -> List[HumanLearningSession]: """ Load the "raw" human learning data from Experiment 1 of Lee and DiCarlo 2023. @@ -80,6 +91,9 @@ def load_highvar_behavior( sessions = _load_learning_sessions( dataset_url='https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-highvar-human-learning-data.json', + cache_filename='mutator-highvar-human-learning-data.json', + cachedir=cachedir, + redownload=redownload, ) if not remove_probe_trials: @@ -106,7 +120,10 @@ def filter(vals: list): return filtered_sessions -def load_oneshot_behavior(redownload: bool = False) -> List[HumanLearningSession]: +def load_oneshot_behavior( + cachedir: Path | None = None, + redownload: bool = False, +) -> List[HumanLearningSession]: """ Load the "raw" human learning data from Experiment 2 of Lee and DiCarlo 2023. :return: @@ -114,6 +131,8 @@ def load_oneshot_behavior(redownload: bool = False) -> List[HumanLearningSession sessions = _load_learning_sessions( dataset_url='https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-oneshot-human-learning-data.json', + cache_filename='mutator-oneshot-human-learning-data.json', + cachedir=cachedir, redownload=redownload ) diff --git a/hobj/data_loaders/store.py b/hobj/data_loaders/store.py index fcb2419..8b13789 100644 --- a/hobj/data_loaders/store.py +++ b/hobj/data_loaders/store.py @@ -1,13 +1 @@ -import mref - -import hobj.config - -__all__ = [ - 'default_data_store' -] - - -# %% -# Default data store -default_data_store = mref.FileSystemStorage(cachedir=hobj.config.cachedir) diff --git a/hobj/utils/file_io.py b/hobj/utils/file_io.py index 86be9cb..6778353 100644 --- a/hobj/utils/file_io.py +++ b/hobj/utils/file_io.py @@ -37,16 +37,13 @@ def download_file(url: str, output_path: Path) -> None: if not output_path.parent.exists(): output_path.parent.mkdir(parents=True) - size, unit = get_bytes_size(num_bytes=total_size_in_bytes) - - with tqdm(total=size, unit=unit, unit_scale=True, disable=False, desc='Download progress') as progress_bar: + with tqdm(total=total_size_in_bytes, unit='B', unit_scale=True, disable=False, desc='Download progress') as progress_bar: with open(output_path.as_posix(), 'wb') as file: # Iterate over the response data in chunks and write to file for chunk in response.iter_content(chunk_size=1024): if chunk: file.write(chunk) - chunk_size, _ = get_bytes_size(num_bytes=len(chunk), output_units=unit) - progress_bar.update(chunk_size) + progress_bar.update(len(chunk)) file.flush() @@ -93,4 +90,4 @@ def download_json(url: str) -> Any: data = response.read().decode('utf-8') json_data = json.loads(data) - return json_data \ No newline at end of file + return json_data diff --git a/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py b/tests/test_simulate_subtask.py similarity index 73% rename from hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py rename to tests/test_simulate_subtask.py index a6c0415..3772a21 100644 --- a/hobj/benchmarks/binary_classification/tests/test_simulate_subtask.py +++ b/tests/test_simulate_subtask.py @@ -2,19 +2,17 @@ import PIL.Image import numpy as np -from mref import ImageRef from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask from hobj.learning_models import RandomGuesser +from hobj.types import ImageId -def create_image_refs(nimages_per_class: int, seed: int) -> List[ImageRef]: +def create_image_refs(nimages_per_class: int, seed: int) -> List[ImageId]: images = [] np.random.seed(seed) - for _ in range(nimages_per_class): - image = PIL.Image.fromarray(np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)) - image_ref = ImageRef.from_image(image=image) - images.append(image_ref) + for i in range(nimages_per_class): + images.append(f'seed{seed}_image{i}') return images @@ -38,6 +36,3 @@ def test_simulate_subtask(): ) assert len(result.perf_seq) == ntrials - - -# Todo: test deterministic From 6eafa67ab246a41c42fd78266f1e45614c28919f Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 20:59:06 -0400 Subject: [PATCH 112/156] load highvar from CSV --- hobj/benchmarks/mut_highvar_benchmark.py | 15 +++--- hobj/data_loaders/behavior.py | 64 +++++++++++++----------- hobj/data_loaders/store.py | 1 - pyproject.toml | 1 + tests/test_load_behavior.py | 4 +- uv.lock | 55 +++++++++++++++++++- 6 files changed, 99 insertions(+), 41 deletions(-) delete mode 100644 hobj/data_loaders/store.py diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index 5068538..d3e77fe 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -21,23 +21,24 @@ def __init__(self): sessions = load_highvar_behavior(remove_probe_trials=True) # Normalize data for benchmark: - sha256_to_category = { - ref.sha256: imageset.get_annotation(image_id=ref).category for ref in imageset.image_ids + stimulus_id_to_category = { + image_id: imageset.get_annotation(image_id=image_id).category for image_id in imageset.image_ids } subtask_name_to_results = {} subtask_name_to_subtask = {} # Iterate over worker sessions: - for session in sessions: + for assignment_id, session in sessions.groupby('assignment_id', sort=False): categories = set() perf_seq: List[bool] = [] # Iterate over trials: - for i_trial, (reward, sha256) in enumerate(zip(session.reward_seq, session.stimulus_sha256_seq)): - categories.add(sha256_to_category[sha256]) - perf_seq.append(reward > 0) + session = session.sort_values('trial') + for reward, stimulus_id in zip(session.perf, session.stimulus_id): + categories.add(stimulus_id_to_category[stimulus_id]) + perf_seq.append(bool(reward)) # Infer subtask from the categories observed in the session: assert len(categories) == 2, f"Expected two categories, but got {categories}" @@ -58,7 +59,7 @@ def __init__(self): subtask_name_to_results[subtask_name].append( BinaryClassificationSubtaskResult( perf_seq = np.array(perf_seq), - worker_id = session.worker_id + worker_id = session.worker_id.iloc[0] ) ) diff --git a/hobj/data_loaders/behavior.py b/hobj/data_loaders/behavior.py index 3452ce7..7aeaf83 100644 --- a/hobj/data_loaders/behavior.py +++ b/hobj/data_loaders/behavior.py @@ -3,6 +3,7 @@ from pathlib import Path from typing import List, Literal +import pandas as pd import pydantic from hobj.utils.file_io import download_json @@ -83,41 +84,46 @@ def load_highvar_behavior( remove_probe_trials: bool = True, cachedir: Path | None = None, redownload: bool = False, -) -> List[HumanLearningSession]: +) -> pd.DataFrame: """ - Load the "raw" human learning data from Experiment 1 of Lee and DiCarlo 2023. + Load the trial-level human learning data from Experiment 1 of Lee and DiCarlo 2023. :return: """ + if redownload: + raise ValueError("load_highvar_behavior no longer supports redownload; expected cached CSV artifact instead.") - sessions = _load_learning_sessions( - dataset_url='https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-highvar-human-learning-data.json', - cache_filename='mutator-highvar-human-learning-data.json', - cachedir=cachedir, - redownload=redownload, - ) - - if not remove_probe_trials: - return sessions - - def filter(vals: list): - probe_trials = {25, 51, 77, 103} - return [vals[i] for i in range(len(vals)) if i not in probe_trials] - - filtered_sessions = [] - for session in sessions: - # Filter out indices in probe_trials - filtered_session = HumanLearningSession( - worker_id=session.worker_id, - stimulus_sha256_seq=filter(session.stimulus_sha256_seq), - stimulus_duration_msec_seq=filter(session.stimulus_duration_msec_seq), - action_seq=filter(session.action_seq), - reward_seq=filter(session.reward_seq), - timestamp_start_seq=filter(session.timestamp_start_seq) + repo_root = Path(__file__).resolve().parents[2] + cache_root = (cachedir if cachedir is not None else repo_root / 'data').resolve() + dataset_path = cache_root / 'behavior' / 'human-behavior-highvar-tasks.csv' + if not dataset_path.exists(): + raise FileNotFoundError( + "Expected cached highvar behavior CSV to already exist at:\n" + f" - {dataset_path}" ) - filtered_sessions.append(filtered_session) - - return filtered_sessions + df = pd.read_csv(dataset_path) + + required_columns = { + 'trial', + 'assignment_id', + 'worker_id', + 'subtask', + 'stimulus_id', + 'is_probe_trial', + 'stimulus_duration_msec', + 'reaction_time_msec', + 'timed_out', + 'perf', + 'timestamp_start', + } + missing_columns = required_columns - set(df.columns) + if missing_columns: + raise ValueError(f"Highvar behavior CSV missing required columns: {sorted(missing_columns)}") + + if remove_probe_trials: + df = df.loc[~df['is_probe_trial']].copy() + + return df def load_oneshot_behavior( diff --git a/hobj/data_loaders/store.py b/hobj/data_loaders/store.py deleted file mode 100644 index 8b13789..0000000 --- a/hobj/data_loaders/store.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/pyproject.toml b/pyproject.toml index 526a8b8..17209f6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,6 +27,7 @@ dependencies = [ [dependency-groups] dev = [ + "netcdf4>=1.7.4", "pytest>=8.3", ] diff --git a/tests/test_load_behavior.py b/tests/test_load_behavior.py index 83b6c8d..a719e05 100644 --- a/tests/test_load_behavior.py +++ b/tests/test_load_behavior.py @@ -2,8 +2,8 @@ def test_load_highvar(): - sessions = load_highvar_behavior() - assert len(sessions) == 3199 + df = load_highvar_behavior() + assert len(df) == 3199 * 100 def test_load_oneshot(): diff --git a/uv.lock b/uv.lock index 8d370d2..af32679 100644 --- a/uv.lock +++ b/uv.lock @@ -2,7 +2,8 @@ version = 1 revision 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insertions(+), 18 deletions(-) diff --git a/examples/view_experiment1_behavior.ipynb b/examples/view_experiment1_behavior.ipynb index a7423a8..ef5c5b7 100644 --- a/examples/view_experiment1_behavior.ipynb +++ b/examples/view_experiment1_behavior.ipynb @@ -9,8 +9,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T18:11:55.739221Z", - "start_time": "2025-01-27T18:11:55.495694Z" + "end_time": "2026-03-25T02:04:21.064613Z", + "start_time": "2026-03-25T02:04:21.050111Z" } }, "cell_type": "code", @@ -25,8 +25,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T18:11:57.282743Z", - "start_time": "2025-01-27T18:11:55.741133Z" + "end_time": "2026-03-25T02:04:23.133150Z", + "start_time": "2026-03-25T02:04:21.073087Z" } }, "cell_type": "code", @@ -50,8 +50,8 @@ "Data variables:\n", " phat (subtask, trial) float64 51kB 0.44 0.68 0.62 ... 0.68 0.86\n", " varhat_phat (subtask, trial) float64 51kB 0.005029 ... 0.002457\n", - " boot_phat (boot_iter, subtask, trial) float64 51MB 0.5 0.68 ... 0.78\n", - " boot_varhat_phat (boot_iter, subtask, trial) float64 51MB 0.005102 ... 0...\n" + " boot_phat (boot_iter, subtask, trial) float64 51MB 0.44 ... 0.88\n", + " boot_varhat_phat (boot_iter, subtask, trial) float64 51MB 0.005029 ... 0...\n" ] } ], @@ -60,8 +60,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T18:11:57.342066Z", - "start_time": "2025-01-27T18:11:57.283421Z" + "end_time": "2026-03-25T02:04:23.243048Z", + "start_time": "2026-03-25T02:04:23.168747Z" } }, "cell_type": "code", @@ -82,10 +82,13 @@ "text/plain": [ "
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" 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" }, "metadata": {}, - "output_type": "display_data" + "output_type": "display_data", + "jetTransient": { + "display_id": null + } } ], "execution_count": 3 @@ -93,8 +96,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T18:11:57.460173Z", - "start_time": "2025-01-27T18:11:57.343196Z" + "end_time": "2026-03-25T02:04:23.386221Z", + "start_time": "2026-03-25T02:04:23.249727Z" } }, "cell_type": "code", @@ -121,10 +124,13 @@ "text/plain": [ "
" ], - "image/png": 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4KKKjo9X2nTZtmsLBwUGRmJgo7m/atEk89pNPPlHuc/v2bUWvXr3E9lWrVinK8+qrr4r9+DxI+Jw3btxYERISoigpKRHbvvnmG7HfmTNn1B7funVrtfNuaLsvX74sjufl5aVITU1VGMKQc8x69+4tbhLpuSo6Fx06dFA8+OCD5e6jeWwJv3f8+dB8Tjc3N8WVK1eU2w8fPiy2T5kyRblt0qRJYpsuhnzWFyxYIB6flpamt907d+4U+/z888/i/eXX4OPjozhx4oTaft7e3qI9hmjevLli8ODBBu0LoApdYADl4L88+a98TudrUl2zibupsrOzqVevXmpp+ubNm1NYWJjIUKgufMl/3XL2RTrGzz//LLoA+C/89PR05Y27Gvi5d+7cqfbcjRs3Fhmb8trE7eFjcCaEu5f4PtuxY4eoc+Gsheb6VJq4Xfya6tSpo9Yu7hLk17Fnzx5lAS2fJ17OQcIZGV3H1IUfz9kO7j6R8OvmjAx3U0ldUo899ph4HtXzyV05/HPOthnbbsnjjz9O9evXN6ithpzjyuDuI86mxMTEUFUZOnSo6HKV8LmOiIgQ590QhnzWud3s999/r3C6CH48L/564cIFMTSef0dU8bEOHz4sslIVkd5jAGMhAAIwEaf/uU6F6zS4y4QvoNw9onkR5Aszp+i5O4bxf/ipqalqF2y+2PHjuNaBj6N64wJP3l8zANKFn4cv8h4eHuIiwo+XajCkdiUkJIivXBOjil8DX0xUcbu2bt2q1SapJkpqFx8zICBAK1CUuuYqwo/XtS93pam22cfHh/r37y+6wSQcDHFQxMGRse2u6Hyaeo4rg7uPuH6Jg+d27drRG2+8IerQKoPriTTx8Tm4rKrPOn+ee/ToIbruuFuNu3f5fdIVDHF9z9GjR0W3F3cnauLuWQ5suX6LgzXuFuQAUxfuflWtVwIwFGqAAMrBtSicLeH6CU9PT+X2vXv3ipqIe++9lxYvXiwu/lyjw7UkvNClKr4wcO0JZyX4P36+KHC25/7771fuwxcJDn7WrVunsx2a2QldK4ZfunRJBAc8YeP8+fPFxcPZ2Vn8lc8FsKZM4siP4azUm2++qfPnfBGtaXxh5TqpkydPiswBn09+3RwcmdpuQ1dgr45zrIk/U/w8nEn5999/6dtvvxXHXrp0qQguGF/wdS3jyNmtqmboZ53PIWfWOFvJxescgHJwyrVG/Do4Iyjhuh+uI5o3b56oT9KcX4tHd3GGievI+LGffvqpKGDnEV+DBw9W25czUroCPICKIAACKAdf6BgXJHNxp4QnYOO/hnkkFhd7SviioImzC/xXLF8MuKiT/xPnLgnVx/HoI/5rmP+CNvRirImLcXlEzebNm0XRr0Sz+4xHAjEuUFXNfHB3n+aIM24XZ6B0jYLTPCZ3rfG+qlkgLtY2BD9e177cRaLaZsbnjot2pW6w6OhoEWCa0u7qOseVxVkWDvL4xq+Dgw/OgkgBEGfqdGVEpEyZJl3daXzeVEeM6cuiGPNZ50CGA0S+cYD40Ucf0TvvvCPOj+p7we8hd4Hx6DP+w0LXKDoOtLiblm+csePi5w8//FAtAOI/TpKSkkSABmAsdIEBlKNbt27iK49SUcV/zfIFQ/Uvbu5O2LRpk87jcBbo0KFDYlJFrldQ7f6S/uLlY82ePVvrsfyfvCHDyaW/sFUzA9xFoXmh4osTdxlpXnRUh5CrtuvgwYPi4qeJ28RtYw888ID4XvWY/Hq++uqrCtstPf7IkSPiuSQ8vHnZsmXiIs2jfSTc7cT1T5z54SwCZ2D4gmpKu41l6DmuDA5EVXFAyd2VHHipBngcHKalpSm38Ug+fSOu+HMpdcEyPtdcY6MaTHCXHtP8rBn6Wc/IyNB6Xqm2R7XtkpEjR9KXX34pMls8q7OEn0ezK5Gzozxlg+ZxuPaLR7XxCEIAo6mVRAOAFh7tMnz4cLVtO3bsEKNZeJTTkiVLFB988IHC19dX0b59e50jaZKSkhR2dnYKT09PRd26dcUoGk3PP/+8eCyPaOERNV9//bVi8uTJisDAQDFqRsKjfHSNErpw4YLC2dlZ0a5dO/HYefPmKZo2bSpGFfFxeUSQ5LXXXhPbHn74YcWiRYsUEyZMUAQFBYkROaNHj1bul5+fr+jUqZMYNTZu3DjxWj/77DMx2ohHWUkjfniUVo8ePRT29vZihBs/P48Qks5HRSOfeGSan5+fGP3z3nvvidcfFhYmztnGjRu19v/+++/Fcfl88mvQZGi7pVFSn376abntM+UcmzoKjD9HTz31lOLjjz9WLF++XHwu+Dy8/PLLyn3Onz8vznXHjh1FO2bMmCEex+3SNQqMt/NoOj7mrFmzxGewXr16imvXrin3/emnn8S+I0aMEOf3xx9/NOqzzp9Vbg+PnOR2f/jhh4oGDRooGjZsqMjKytIaBSbh/Xgbf2WZmZniPeL3av78+Yply5aJ88H7fP7552rnit9Td3d3RU5OjkHvH4AqBEAAFeD/hFWHrEtWrFihCA0NVbi4uChatmwpLmwzZ87UO5SYAwT+GV+Q9eH/7MPDw8WwZb6484XrzTffVLtQ6QuA2ObNm8WFydXVVXnBW7lypdbFmYeoc6Dh7+8vnouDlaioKHFRfOGFF9SOycOVp0+frmjWrJm4+HOQ1L17d3HxUQ3kbty4IS6ePKScAxn+noc3G3LRZ5cuXVI88cQTitq1a4v2d+3aVfHnn3/q3JcveNxuPjZfrHUxpN3GBkDGnGNTA6A5c+aI187ngV8jf7Y4ONAMmvl189B7fm0cLP7zzz96h8Hz6+PggYNc/rxyMHPq1Cm14/FngoOs+vXri4BL9XNsyGedA6UhQ4aIgJ3bxF/5DwfVqQh0BUCMP+O8nYO5oqIixRtvvCGCSv4d4GCIv1+8eLHWuYqIiFA8++yz5Z5PAH3s+B/j80YAtoPT8U2aNBEjU8aOHUvWirs+uLZkzpw5om4DwJxxETzXBfFQfM1h9ACGQA0QQAV4xBaPJuKRKFUxyscc6Fo7TJodWdcSCwDmhkeQ8VIyCH7AVMgAAdggXnKDb1x8zEW2vMzAjz/+KEbm6CocBgCwNhgGD2CDeEg/jwTjbj1ea4onrps8ebLo/gIAsAXIAAEAAIDNQQ0QAAAA2BwEQAAAAGBzUAOkA4/04VWIeYp2LLIHAABgGbiqh9du5JnDNdeY04QASAcOfniRQwAAALA8vEZcw4YNy90HAZAO0qrffAK9vLzkbg4AAAAYgEe1cgJDuo6XBwGQDlK3Fwc/CIAAAAAsiyHlKyiCBgAAAJuDAAgAAABsDgIgAAAAsDkIgAAAAMDmIAACAAAAm4MACAAAAGwOAiAAAACwOQiAAAAAwOYgAAIAAACbgwAIAAAAbA4CIAAAALA5CIAAAADA5iAAAgAAAJuDAAgAAABqTGpOIS3YFi2+ygkBEAAAANSY1Nwi+mJHjPgqJwRAAAAAYHMQAAEAAECNSc8rUvsqFwRAAAAAUCM2HE2kMauPiu/5K9+XCwIgAAAAqHbXs2/S9I1nSKEou89f3954VmyXAwIgAAAAqPZRXZfT86n0TvAjKVEoKD69gOSAAAgAAACqfVRXYx8PsrdT3+ZgZ0chPu4kBwRAAAAAYHKWx9Ci5gBvN3qpbzPlfQ6GPnqsrdguBwRAAAAAYFKWx9ii5k7BdZTfrxzdhYZ1aURyQQAEAABgw1INqOHRleUxpqiZjz12zVFlsMQiEzJJTgiAAAAALDRIqYplJVIrqOHRl+Uxpqj5/PUc2hGVqgyW2Nc7Y2UbAcYQAAEAAJg5fUFKdS8rcb2cLI8xRc1R13O0tnHwJNcIMIYACAAAwAJx1mf5nktaXVPGZoXSyyliPpGQqTfLw8XLc4a2rbComTNGH2+9qHVsOUeAMQRAAAAAZk5XkPL9oQT6/dR1ra4pY7JCGyooYnZ3cSw3cOkVWl+5fcWozsqiZikIO3MlS2SQdBnaMVC2EWAMARAAAIAMDM3U6ApSuAvqq52xyn1Uu6YMHZZ+XUf3Ft+f9cc5ZZt8armoPUbK8nAQxG0/cCld+TM3p7vBkhSEnbmarZVBkmw6cc22a4AWLVpEISEh5OrqShEREXTkyJFy91+4cCG1aNGC3NzcKCgoiKZMmUKFhXc/PO+//z7Z2dmp3Vq2bFkDrwQAAMBwhmRq9NXgfPVfjFpBsdQ1tWpffLkZHdWg67KOIma+v3J/vLJNUhDlcKfWZ9mIcJHlUQY4V7KVj7VTqQeSHufq5KBVJ6TaXputAdqwYQNNnTqVZs6cSZGRkdShQwcaNGgQpaam6tz/hx9+oGnTpon9o6KiaMWKFeIYb7/9ttp+bdq0oevXrytv+/btq6FXBAAAUD4pCMnIr7iLSt9Iqx8OJ2nty3HGt/viyh2Wrhp0NdZRxGynEsCoZp5K7hzTxdFB+XN2LftuAmLD0STx2lQfN/WnU9S3pa/O12bTNUDz58+n8ePH05gxY6h169a0dOlScnd3p5UrV+rc/8CBA9SjRw/63//+J7JG9913Hw0fPlwra+To6Ej+/v7Km4+PTw29IgAAgPJJQUhcWr64H5uap9UVJgVJni6OapmV8i7cIfXcKxyWrto9FuDtRnMfa6f8merTjF51lKb9ejfzJMm+WawW4Px34W7CYuOJq/ThlvNqGSvNfezMZBZo0Qa5nri4uJiOHz9OAwYMuNsYe3tx/+DBgzof0717d/EYKeCJi4ujLVu20AMPPKC2X0xMDAUGBlKTJk3omWeeocTE8memLCoqopycHLUbAABAdfrgj/Pi65QNJ0VAdCE5VxkISUESl3EMbuuvfAwHDm8NbqkVFLHLN7S7k3h/Kcuiq5ZoWJdG1DbQS7m/aryjq3QnJiVPK8BR9fvJ61pBmOq+Mx9ubRazQMsaAKWnp1NJSQn5+fmpbef7ycnJOh/DmZ9Zs2ZRz549ycnJiZo2bUp9+vRR6wLjOqLVq1fT1q1bacmSJXT58mXq1asX5ebm6m3L3LlzydvbW3nj2iIAAIDqKHSOTim7HklxgfT1RGKmVk1QRn6xyBBJOHB4vndTeqhdgNrzeLlqj9ZivPYWZ1nKm8/n5q0StXaUJyW3SG9Rs0RXcCZJyizQWVxtk0XQxti1axd99NFHtHjxYlEztHHjRvrrr79o9uzZyn0GDx5MTz75JLVv317UE3GGKCsri3766Se9x50+fTplZ2crb0lJ2n2rAAAAxo7m0ix05qwL18XosnB7jFZXVVxaHkWn5JGHc1ntTa07w9LdnB0ouG5Z91FYkDflFN5WPraJSl0Nr73F7fp060W93WO5dx5bTtxS9nM7ImeHisMGfdkhtmp/PD3XI4R8PeUPgHSHjDWA63IcHBwoJSVFbTvf57odXd577z0aMWIEjRs3Ttxv164d5efn04QJE+idd94RXWiaateuTc2bN6fY2LvDBTW5uLiIGwAA2A4ODNYdTqRnIhqRr5er6ce5E+QMbO2ndRzVmhspC6OPFDcs23OJNt+Z3+f9O91krQI96Vh8lrLduy6mUQt/T0rIuEmt/L3oZNLd0VhxKjU/3NXVJbgOHYnPFAGManDiYGdHwfXcKOvmLXH/sU4N6NfIq3rb5+3mRLmFZfuaioOwga39K3W+LT4D5OzsTOHh4bRjxw7lttLSUnG/W7duOh9TUFCgFeRwEMUUekLOvLw8unTpEgUEqKcLAQDAtlX3MhKaNTer9l2usPuIScGPquN3gp/iklK6nl1WI1TPw1ls+/Go/l4LvjRy8MOe7Xq35sb+ThGyl5szFd8uFdsCvMsPSkpLFfTbyWtUGXKP/DKbLjAeAr98+XJas2aNGNY+ceJEkdHhUWFs5MiRontK8vDDD4u6nvXr14vanm3btomsEG+XAqHXX3+ddu/eTfHx8WLU2KOPPip+xqPFAADAulXF4qDGMnSl9G/3XtY7J05FpLjpYnIOnbtWlu0JrO1Gj3VsYPAxvj98d0BQ7xb1qW8LX7qh0mZX5/I7hVS72UzBL13ukV9m0QXGhg0bRmlpaTRjxgxR+BwWFiaKl6XCaB69pZrxeffdd0VFPH+9evUq1a9fXwQ/H374oXKfK1euiGDnxo0b4udcMH3o0CHxPQAAWLfyuqM0GTpjsr6uMt4+/bczymHenOWZ91g7MbpJ5ySDRDShZxNavjdOBDSaXVKG+PtMMh29k9FZsvsSTbu/pQgsDDmM6j47L6SJc8UZJcmtO5mg6nRvc/O5FssaALGXXnpJ3PQVPWvO78OTIPJNH84OAQDYuqqqb7FW3D017U49jmrgouu86Quqzl/PoR1Rd+e4kUZW8UWeJxnUVXMzpmcIXUzJod3R6bTgqTARKNVxd6T3/4gyqN1Sd5b0fJ9svUjTHmhJ87ZcUAtwDA2K4u6MMOPMVFoFgaA+/FzDujSk9UevlLsft0daRNUcWNQoMAAAMI/6FnNlSFanvCHhxpy3a1na61iprpSuOX+P1P1z81ZZpqWZby2aMrA5dQ6pV+Fz8eN9PZ11Pl/7BrVp1Zguym2rx3Shz5/qoPdY0pD59LwiSswoK5iu4+5M6UZ+Vp7q3FB85ed+9p4Qg16DudT/MARAAABgFSpa2byi5SX0rUulL6jida50XeS3nU8WmSRp2QjNif/yCm9Tm0AvraHgUwaE6n1t/Pjmfp56i4pV59Xh7wvvzO1TXi3PmNVH6adjZVkbZ0d75esztEwpLKi28vn4tXQNqaN3Xz4vPOu0uWR/GAIgAAArZGh9i7UoL6ujSeqe0hVIaJ43zaBq7JqjyvWuXvtZez6f53s3US4mGnU9RzlvDmdYJFkFxdSvpa+yO42Dh8n9Q6ljo7KAQmobfxnbM0QZZNS9M+pLV1aJjzGuZ2Nx4++5QLoiCgVR8p1icR5ZdvlG2dIcL/ZpqrZkxVPhZZkezedWfQ4OII8l3O2e08wSmcPMz5oQAAEA2GgmxJoYk9XhgOHh9gFagcSe6DS18/bN7ktaQRXX/OyLTde7HISjXdlllQMvnsG5a+O64n7ynUVDecoWrrWpr5L94UCIu8Ja+nuJQGjBne4r7lp6/t6mYtvBSzfoD43h8apBBR/j3Ydaixt/3zrAi/q38jVq1FlmftkcP2N7NVF2qfFzjOxeFoSpBo08wzQ/B7eNAy5d518zS2RuEAABANhoJsTShq6Xt5/I6hgx54yXmxM53HnAjIdai8JlzfP28d8XdF7Uj1zO0Lmda2u+2lk26e6EtcfpdqmCejcvW4w7584Egtk3b9GtEoXOgEAKhJr5et7tWvJypae7BtHcv6MMKmpWPdaKUV1EAGMsDxcHZftU2znzobJ1vIaEBdCz9wQr28tfda0sz/fbNfBWBknmBgEQAIAVMba+xRwYWnhc3n6c1ZGyLVK2Quoe0hU4cXaGl4lgN/KL9Q5b12X90SSddTKq8+RIh2pYx408XR0pPj1ftOHC9bLFtlUzQBXRl125Way/zkdibObF0Z7UapdUu+ia1PcQ98f3aqo1slBaWd5eI0vUrmFtZZBkbhAAAQBYEWMzIeYwAaGh9UoV7cfbuzctG1E1IiJY2T2kK3DiAKhDw9qiruZ61k2d5608qvEIX/Sl+h1N8TcKxGivqORc0YZLaWV1NvXLCUykgEPKmujKrvB7GqbnOTWPxWtvGdoV5uHipNUGzS46fdkcPt9SxknKEpkzBEAAAFaE/xJv7lerxmffNXXYvaH1ShXtl5ZbJIKLHs3Kupyu6hiirlqEnJ5XTB2CatO9oT4Ul56vdd4k/l6u9Oag5nqzJYwv+ouf6aQzyPjkn4siWEm6M9xcWnervAyQateSruyKavFzRfgYMx5uo5Wd0UdabFWzDfq26cs46coSmRsEQAAAVqSkVEHXsguVSyRwXcpQI5ZLMMd6JUP2O3z5hvjat0V9UXdyKa1sgj9dmSPO/jDOzLQJ9BYTGn6y9QIlZBTQkxojnlJyCiktt1hn+1VrdaQgRTPG4LYeT8ikxDsjrK5kFJC7swN53Ak0DKWaXTFlRJXq499/uLVWO+1U6n9sBQIgAAArEp2SS7mFt6nznTlZuC7l9xOVW8Cyuobd66tX+uyfi2pdaRXVNfG+y3bHUaO67tQ60JtGdQ8RXU9ccKwrcxSTmieyIdy11KaBFxXeKqXFuy6Jr20beKk9Dz/tmoPx1LNZPa0MSnuNAl8OMt68v4XW6+RjFJUolAuX6po/yBC6CpNNeXznkLo073H1jJBUP+VewXpgFdHsvjNnCIAAAKzIsfgMcrS3U06aF9G4Ln2z55JYydvcht3rmo+HL8q/Rl5V60qrqK6J9z19NZtaBXiqDb3+/eQVmvarduboVFKWCJY4EGkT4K08Zm03J53BBZ+6SX1DtUZU/Rx5hQJru6p19bQKUA+gdMnIL6YzV8pWd69JqsHJMI16Hf6cqHaBmfwcBnSTmQsEQAAAVoQXymzbwFtc4PliN65XY1Ebwwt26ipUruzq6fq6p/gCX9FxuduocyP12YNf6N1U7T4//vuDCaLbSNcILxaXXtalFVKvLCBq4uMhRl7tiU7XGjrOmaOdF1MpqG7Zvt7uHPSUTTDIARSPDNNVcKyriFxXV5wUQKlOZqjLmatlK7rXZHZFMzjxUanXqX9nG7rAAADAYjNAXULqKC92A1r7U3hwHZEF0lWobGrxshQ4nUjI1Nk9xRf4io5bfLuUolPz6KH2ZWtmceAhTRgodaXx4xftukQFxSU079F2Ylvv5vWVNTCcbZr840nx/bK9l8V9e3s7kQXiwmhN/BwpOUXKIIL354JodiguQ0yGqK/guLy1vzSDFGkyQ16TS1fxsSEzNVd3dsVXJaByv9Mt51HJLjBLggAIAMBK8MgnLoDmGg9VE+5tIjJDF5LL5qCpClLglHlnVFN5yyTosz82XdTpDGpTNiszBy0bT1xV60rjImQWHlKb2jYs666KTMika5kFNOuPc2XZJx0ZGT4Wj+7SJA2T5wyQlL2S8HGk1dx1FRxzcXVFUwxoTmbIXZGGjsCqab4qAZWUYeMCcVOzgZYGARAAgBVlf1jnOxP8SQa28hPdQj8eSdQqVDakeLm8brK1BxJEF5LqBZ4zTprH13W8n48nUdP6HsquqxNJd+tiOJjh+p1xa46J+8fjs+jfc8nKwu5tUalizS19xdG5hbdEMbiq8ODadCy+bL0qTxfHcourdRUc86R+qsXDhg5HV623kZjbEiVudwIgrqUyNhtoqRAAAQBYiaPxGWK23noahbyiS6hRbTqekKV28TW0eFlXN5kU2FxMyaVvRoQrL/BjeoSIbNOYVfqPKx1v18U0eqh9IPl5uYph+5pra/FdZXaHiL7eGUtjuodQUF032huTprOtnJFxd7antQcTtH7m5+lKhbfL5neesyWKzlzJ1lvvo6/extDh6BXV65jbEiXuNtT1JUEABABgJTi70SVYvfuL8UV2052uJeni+9avZ8TNlDXDROB0J8CRuk2kTAnX54jjUcXH5bqehzsEiC6YN+5vUWE3EWdr7mvjT4+GNRDdZxI7jYxMfnGJzqUj/j5blkGS2vXJ1ov01uCWOjM65dXbGDIcXfPxBUXq2ShzW6LEXaXI3JipDCwZAiAAACvAtTScjZHm/zFkLSlDL8iq3WTKUV8qP+cAR9rHkEJhad+GtV2VtTKasx3zF331Ng4OdnTz1t2VukZ3C1HLyOhcmFNj+QqpXe0b1DZ6gkFTRmN1NGJ0mRx2Xkw12+656oIACADACkQmZoqsRheNAmimLyDQtXo3X5BVa37UuslWHaXRK4/orJvhhTk5KOBC4fIu9KrHu5JVdnxd3UurxnTRWW/Dvtgeo3Z8nqhQNSOja+kI1UyPZruMnWDQlNFYlVnOorpdz74p3m9z7Z6rLgiAAACspACaL+DBdwqKK7r4zn28ndboJJ4NWKyefqdGh5eIUJvjR9T85OnMzHCNEQcFXCjMx5WoXug15wxi+ubR4a+66m10rtquI7ul+djnezfVG4DU1OzFlV3OorpcrmCmbWuFAAgAwApw4TGP/rLTnFq5nIuv6rYR9wSLeXBUu0K4O0tXcDG+V5NyMxl83OFdg8T3y0d2Vl7oTbnQamZndGaz7EiseK4ZwGg+Vl8AUpOzF1d2OYvq0FjPavPm0j1XXRAAAQBYuKLbJWJ5B131PxVdfKXvn+rcUMx/8+Yvp+nynZmVN5/UXkOML4xjeoZUmMlo6V+2JIRqtseQC21F2Rid2azH2okVzzUDGF3HMscARG4BZtw9V50QAAEAWLizV3Oo6HapzvofVeUFF5w5+vSJ9pRXeJtevjOz8qHLGRTeqI7OC2NFgURQnbKL55XMAq0LrUTXhVYzG6OrzQYPRdeR2ZF7sU65n9/SuueqEwIgAAALJhUsuzrZU+vA8hfirCggKFUoqPBWidpjTiZl0WdPdjD6wsht4UVZMwvUZ4rmeX8khhxPX/eUqZkcuRfrlPv5y+NjY9kxBEAAABaMC5b3xaaLJRecHOwrdUHmGh1dQ8WloEj1wlhRJsPf241a+HtSaq767NExqWXda5rHs5ZMClgOBEAAABZA33IUnLVhbQLKz/4YQl+Njq51vQzJZDStX4supaqvxxWdnKt3hXRryaRYKl8bCyoRAAEAmKC89bGq47j6Vm0/ezVbfG1wp+amOophWwd4mXRhFAFQ2t2MD4tOyRVttaULraXwtbGgEgEQAIAJ9AUkNXlcnkTwnd/Oiu8/3xZdJbP36iqGNfXC2NTXg27kF1NmfrFyG89W3SrAy6YutGCeEAABAJgRfauza26/mllA01SWpKjK2XurqhiWM0BMNQsUk5JHzf3KtgPICQEQAEAVBiqVoW91ds3tY9ccpaGL92utnm5us/dyTRHPyygFQLxeWXJOoSjYBpAbAiAAgCoKVCpTI6S5TAR/5QzPB5vP0TSNVdt3RKVS+wbeylXQq3r23qoqhnV1cqCgOu50Ka2sEDomJVd8RQAE5gABEACAEcGLrkBFV9eTsTVCupaJ4GN/dyhBa2g6G9erKc2rptl7q7IYtml9D7p0Z+g71/842NtRk/oelT4uQGUhAAIA0FBe8LIjKkXPelbqw72N5eHsoLXNwY7o14nd9C4fYQmz96qOBOP6n5B67uTiqP1aAWwuAFq0aBGFhISQq6srRURE0JEjR8rdf+HChdSiRQtyc3OjoKAgmjJlChUWFlbqmAAAFUnLLaI3fzlF7246p/Pn8/6+QHtj0khxJzVkTI0Qr+U1c/N58nZz1MjotKMOQXXKXafJ3GfvbepbixIzCsRrvJicKyZHBCBbD4A2bNhAU6dOpZkzZ1JkZCR16NCBBg0aRKmpd1cjVvXDDz/QtGnTxP5RUVG0YsUKcYy3337b5GMCAGhSDV6Kb5fSsj2XqO9nu+jf8yk0e0gb+ujRtmoByejuweL7ESuO0ONLDtAHf5wrt0ZIs4vt/c3n6fz1HFr7XITOjI4lZHrKywBxxoyLs2NScynUFwEQmAdHOZ98/vz5NH78eBozZoy4v3TpUvrrr79o5cqVItDRdODAAerRowf973//E/c5yzN8+HA6fPiwyccEAFDFwQoXH7Mxq45SvVrOlJFfTM/eE0xTBzan2u7O4mc8O/LoVUdFQNKnha/I/OyOTqNP/7lIq/bHK48n1QjxSutS1kbqYhvY2o/+u5BKPx5JpE8eb08dgmorJzbUzOjoy/SY++y9XAPEjsRnUHpeMTJAYDZkywAVFxfT8ePHacCAAXcbY28v7h88eFDnY7p37y4eI3VpxcXF0ZYtW+iBBx4w+ZisqKiIcnJy1G4AYHu0CpxFFqiY1jzXlWYNaasMfnQFJLyaOgdC7zzQSuu4+oanc5fQjN/P0TMRjeipLkFWOXtvvVouVMfdiTafvFp23+PuOQSwyQxQeno6lZSUkJ+fn9p2vn/hwgWdj+HMDz+uZ8+e4q+t27dv0wsvvKDsAjPlmGzu3Ln0wQcfVMnrAgDLpWskFnO0N/xvxcb1y9bT0jzOkl2x5FPLmUL9PJVdbLP/PE9tGnjRzIfbVJjRMfdMT0XdYMcTMsX3Lo6yl54CCBb1Sdy1axd99NFHtHjxYlHfs3HjRtG9NXv27Eodd/r06ZSdna28JSUlVVmbAcBy6FqkU9/cOvoCEl3raQ3r0pDi0vNp0MI9okZIqg/KunmLHmgbQM4qQYG+jI65Z3oMqQNijiasWA9gVRkgHx8fcnBwoJSUFLXtfN/f31/nY9577z0aMWIEjRs3Ttxv164d5efn04QJE+idd94x6ZjMxcVF3ADAcnFB8brDiaI7yZQggWtvJv1wgvy8XMSIL75glze3jhSQ6MJFyn5ermo1QjwK6pvdcTR/W7TW6LGHOgRUyfw95orXBJNU5czZAJUhWyju7OxM4eHhtGPHDuW20tJScb9bt246H1NQUCBqelRxwMO4S8yUYwKAdTB04kFdkxweT8ig4csPUVAdN9o6+d4qGXGlWSPEc990DqmjtZ+5LV9RHa5l3Z0kUt/M2QA1TdZcJA9XX758Oa1Zs0YMa584caLI6EgjuEaOHCm6pyQPP/wwLVmyhNavX0+XL1+mbdu2iawQb5cCoYqOCQC2TTNQ2heTTs9+e0SsUP79uAiq4+FcbXPr8NpY+iY1tObC8rUHE5T3q3LRVgCLHQY/bNgwSktLoxkzZlBycjKFhYXR1q1blUXMiYmJahmfd999V4y04K9Xr16l+vXri+Dnww8/NPiYAGCdTFmc9N9zyfTSDyeoe7N6tOSZcHLTMRuzqXTVCEn1QTzSrKIuNmsuLJeyXtb8usH82SmkaUtBiYfBe3t7i4JoLy8vuZsDAAbO3cP/m/ECobxGlr6uq10XU0VtzvP3NqZv98XToDZ+tHBYR7VC5MrWE1VEasPqMWX1QdaMMz095v2nFgRx1mvftL4IgEDW67esGSAAgKq4wErBj3IV9V/PUGb+LWoR4En+Xq4U4O1K3m5O9NOxJOUkh9/suUzhwXXoy6c7ao1MKq/AuSqY+/IVVckWs15gGRAAAYBZMiQLw8W1b/x8Shn8SPjuvK3qc3+5ONpR0W31HU8mZlJaXhEuxtVM16g4ALkhAAIAs6S6XIRmAFRQfFsMKf9mzyVyc3IU8/eohjbcxbLzjd5iAsPr2YWUklNIBy/doO8O3S3GZSV31qiq6QDIkic1NJUtZb3AMiAAAgCLUVqqoN9PXaWP/74o1uca26sxvdinKW05c12ri6VRXQ/lml2sY6PatO5wglYtihwjsKq7iw0AKoYpOQGg2umae8fYUV2RiZn02JIDNGXDKeoUXJu2T+1Nb93fkjxdnQxaLV3XDM2oRak5tpj1AvOGDBAAyNqdVeGK7KuPUoeG3nQyKZvaBHrRhgn3UESTeiZ1saAWRT7IeoG5QQAEAOa9IruCRPDz9uCWNLZXE3LQnEnQSKhFAQCGAAgADFKZuXEqmqSQi5rPXs2hk0mZtCMqVeeK7O0a1i43+EEXCwAYAwEQAFRLN5a+7qwPh7aljo3q0KmkLDp55xadkiuCHjcnB2rh56lzVFdFxcqGdrEgUAIAhgAIAKpNUka+VnfW27+dFd9zMqe5nyeFBdWmUd1DxNdQ31piUkIOmqpr4jzUogAAQwAEAFXSjZWZX0xR13MoKjlXfL2QnEMXrpdldjTNeKgVPd21Ebk76/4vCMXKAFDdEAABgNHdWFMGhFJwPQ+6cCfY4VtKTllg5OJoTy38PalNgDcNbOVHC3fEqM3UzN1Zg9sF6A1+JChWBoDqhAAIAIwelTV/W4z4PtDblVoFeNET4Q3F15b+XtTYx0OtWNnf2xXrQAGA2UEABADl4uyOrm6sb0d2pgGt/Sp8PLqzAMAcYSZoANArLi2PZv1xXms7d2O1aeBl8HFM6c7CaC0AqE4IgABAp/8upNCQRftFd9Zr9zWv8SUkpNFaxs45BABgCHSBAYDWgqOLdsbS/O3R1L+lHy0Y1kGst9WugbfJ3VjI5gCAuUEABABKeUW36fWfTtHWc8n06oBQeqVfKNnfSf1UZlQW5t4BAHODAAgAhMvp+TRh7TG6nl1Iy0d2FjM+AwBYKwRAAEA7L6bSKz+eoPqeLrRpUg9q5ltLax90YwGANUEABGDDFAoFLd51iT779yL1b+lL84eFkZerk8590Y0FANYEARCAja7ins/1Pj+for/PJovMDt+keh8AAGuHAAjABldxj0/Pp+e/O05Xs27SNyPCaVAbf7mbCQBQoxAAAdiYXXfqfXg016ZJ3amZr6fcTQIAqHEIgACsnLR6e1puIS3elUaf/nOR+rXwpQVP66/3AQCwdgiAAGxmFfdj4usr/ZrRqwOao94HAGwalsIAsJFV3BnHPMMjGiH4AQCbhwAIwIonNtRcxZ3vx6cXyNUkAACzgQAIwEo19vHQuYp7iI+7LO0BADAnCIAArNTemHTxVersqqlV3AEALAECIAArFHU9h97bdJae7hJEq8Z0Edt4FfdhXRrJ3TQAALOAAAjACld0n7QuUnSBvf9Im0qt4g4AYK0wDB7Aytb24pFfPPvz5pd6kKuTg9xNAgAwS2aRAVq0aBGFhISQq6srRURE0JEjR/Tu26dPH7Kzs9O6Pfjgg8p9Ro8erfXz+++/v4ZeDYB8vj+cSH+cukbzHm9HTeprr+gOAABmkgHasGEDTZ06lZYuXSqCn4ULF9KgQYPo4sWL5Ovrq7X/xo0bqbi4WHn/xo0b1KFDB3ryySfV9uOAZ9WqVcr7Li5I/4N1O3s1m2b/cZ5Gdgumh9oHKrf7erqIhU75KwAAmEkGaP78+TR+/HgaM2YMtW7dWgRC7u7utHLlSp37161bl/z9/ZW3bdu2if01AyAOeFT3q1OnTg29IoCal33zFr24LpJa+HvSOw+2UvsZL4A6ZWBztZXgAQBsnawBEGdyjh8/TgMGDLjbIHt7cf/gwYMGHWPFihX09NNPk4eH+pwnu3btEhmkFi1a0MSJE0WmSJ+ioiLKyclRuwFYUt3Pm7+cosyCYlr8TCdycUTdDwCAWQdA6enpVFJSQn5+fmrb+X5ycnKFj+daobNnz9K4ceO0ur/Wrl1LO3bsoI8//ph2795NgwcPFs+ly9y5c8nb21t5CwoKquQrA6g5K/fH0z/nUuizJztQUF1McggAYBE1QJXB2Z927dpR165d1bZzRkjCP2/fvj01bdpUZIX69++vdZzp06eLOiQJZ4AQBIEliEzMpLlbomhcz8Y0qI2/3M0BALAYsmaAfHx8yMHBgVJSUtS2832u2ylPfn4+rV+/nsaOHVvh8zRp0kQ8V2xsrM6fc72Ql5eX2g3A3GXmF9PLP5yg9g296a3BLeVuDgCARZE1AHJ2dqbw8HDRVSUpLS0V97t161buY3/++WdRu/Pss89W+DxXrlwRNUABAQFV0m4AuZWWKmjqTycpv/g2ff2/TuTkIPt4BgAAiyL7/5rc9bR8+XJas2YNRUVFiYJlzu7wqDA2cuRI0UWlq/tr6NChVK9ePbXteXl59MYbb9ChQ4coPj5eBFNDhgyhZs2aieH1ANbgmz1xtPNiGi14KowCa2NtLwAAi6sBGjZsGKWlpdGMGTNE4XNYWBht3bpVWRidmJgoRoap4jmC9u3bR//++6/W8bhL7fTp0yKgysrKosDAQLrvvvto9uzZmAsIrMKRyxn02b8X6cU+TalvS+25sgAAoGJ2Ch5DC2q4CJpHg2VnZ6MeCMxKel4RPfjlXgqu50E/jIsgR3R9AQCYdP3G/54AFqKkVEGvrj9Jt0sU9NXwjgh+AAAsuQsMAAzz9X+xtP9SOn33XAT5YVZnAIBKwZ+QABZgf2w6LdwRLdb06hnqI3dzAAAsHgIgADOXmlNIk9efoB5NfejlfqFyNwcAwCogAAIwY7dLSunlH0+QvZ0dLRgWRg72dnI3CQDAKqAGCMCMLdgeTUfjM+jH8fdQfU9M4wAAUFWQAQIwo66uBduixVe282IqLdp5iV4f1IIimqhP+AkAAJWDAAjATKTmFtEXO2LE12tZN2nqhpPUt0V9euHepnI3DQDA6iAAAjDDup+XfogkNycHmv9UGNmj7gcAoMqhBgjAjGZ5Zkt3x9HpK9m04fluVMfDWe5mAQBYJWSAAMzAhqOJNGb1UfH91nPJNKiNP4UH15G7WQAAVgsBEIDMrmffpOkbz5Dqqnxbz14X2wEAoHogAAKQ2c4LqVSqsSRxiYIoPr1AriYBAFg91AAByCTqeg4t3B5N/5xL0fqZg50dhfi4y9IuAABbgAAIoIZdTM6lL3ZE05YzydSorjt99mQHKr5dQu9uOisyQTzo66PH2lKAt5vcTQUAsFoIgABqSGxqnpjn58/T1yjQ240+frwdPdapITk5lPVEB9Z2o9GrjtLK0V2oTwtfuZsLAGDVEAABVLPL6fn05Y4Y+v3kVfL3cqU5Q9vSk+FB5OyoXoLnU8tF7SsAAFQfBEAA1SThBgc+sfTbiStiHa8PHmlDT3UJIhdHB7mbBgBg8xAAAVSxpIwC+vq/WPol8grV9XCm9x5qTcO7NiJXJwQ+AADmAgEQQBW5mnWTFu2MpZ+OJlFtdyeaPrglPXtPsMGBj6+nC03uHyq+AgBA9UIABKAHr8q+7nAiPRPRiHy9XPXul5xdKAKfDUeTqJarI70xqAWN6BZM7s7G/Xrxc0wZ2LwKWg4AABVBAARQwersA1v76QyAOEBavOsS/XAkkdydHejVgaE0qlsIebjg1woAwNzhf2oAI6XlFtE3uy/Rd4cSyMXRnl7u24xG9wghT1cnuZsGAADVHQAlJiZSQkICFRQUUP369alNmzbk4oLaBbC+1dmlrxn5xfTNnku09kACOdrb0Qu9m9JzPRuTtxsCHwAAqw6A4uPjacmSJbR+/Xq6cuUKKVRWb3R2dqZevXrRhAkT6PHHHyd7eywzBpa9Ovu0jWfE97xKe+/m9enI5QyyI6JxvRrT2J6Nqba7s9zNBAAAExkcpbzyyivUoUMHunz5Ms2ZM4fOnz9P2dnZVFxcTMnJybRlyxbq2bMnzZgxg9q3b09Hjx41tU0AZrU6O3/ddTGNHu/UkPa+1Y9eu68Fgh8AAFvJAHl4eFBcXBzVq1dP62e+vr7Ur18/cZs5cyZt3bqVkpKSqEuXLlXdXoAamblZc3V29kC7ADGvDwAA2FAANHfuXIMPev/995vaHgDZNfbxEF1dqjEQVmcHALAuJhXqcKYnKytLa3tOTo74GYAlu5yWL4IfDoIYVmcHALA+JgVAu3btErU/mgoLC2nv3r1V0S4A2Ya4T95wkno286FvR3UW23h19mFdGsndNAAAkGsU2OnTp5XfcxE0Fz9LSkpKRO1PgwYNqrJ9ADWmtFRBUzacFEXPC4aFUUpOodiO1dkBAGw8AAoLCyM7Oztx09XV5ebmRl999VVVtg+gxizeFUv7L6XT92MjxOrtUgAEAAA2HgDxEHie+6dJkyZ05MgRMQGi6jxAPBrMwQErXoPl4Tl+5m+LFrM692jmI3dzAADAnGqAgoODKSQkhEpLS6lz587ivnQLCAgwOfhZtGiROK6rqytFRESI4EqfPn36KLNQqrcHH3xQuQ8HaTwfEbeJs1IDBgygmJgYk9oG1o9neH7lxxPUJaQuTR5wdzFSrM4OAGC9TCqC5iHxK1eu1NrO2z7++GOjjrVhwwaaOnWqmD8oMjJSTLY4aNAgSk1N1bn/xo0b6fr168rb2bNnReD15JNPKvf55JNP6Msvv6SlS5fS4cOHxRxGfEwu0gbQrPuZ+tNJKi4ppS+e7kgOPORLY3X28laCBwAAC6UwQXBwsGL//v1a2w8dOqQICQkx6lhdu3ZVTJo0SXm/pKREERgYqJg7d65Bj1+wYIHC09NTkZeXJ+6XlpYq/P39FZ9++qlyn6ysLIWLi4vixx9/NOiY2dnZPApafAXrtnRXrCL4rT8VOy+kyN0UAACoJGOu3yZlgHj0F3cvaeKaIM7KGIqH0h8/flx0UUl4DTG+f/DgQYOOsWLFCnr66adFlkeqU+L2qR7T29tbdK3pO2ZRUZGYw0j1BtbveEImffLPRbGoaZ8WvnI3BwAAapBJAVBQUBDt379faztvCwwMNPg46enpYvi8n5+f2na+rzrEXh+uFeIusHHjxim3SY8z5pjcpcdBknTj1wfWLaugrO4nLKg2vXbf3bofAACwDUaNApOMHz+eXn31Vbp165ZyOPyOHTvozTffpNdee41qCmd/2rVrR127dq3UcaZPny7qkCScAUIQZL24SP71n09TXtFt+nJ4R3JyMOnvAAAAsLUA6I033qAbN27Qiy++qJwRmkdwvfXWWyKYMJSPj48oYE5JSVHbzvf9/f3LfWx+fj6tX7+eZs2apbZdehwfQ7Wbju/zPEa6uLi4iBvYhlX742l7VAp9O7IzNaiN5S0AAGyRSX/68rBzHu2VlpZGhw4dolOnTlFGRoYYem4MnjsoPDxcZI8kPMSe73fr1q3cx/7888+idufZZ59V2964cWMRBKkekzM6PBqsomOC9TuVlEVz/46isT0b04DW6t2kAABgOyqV++eaGg58mjZtKjIo3LVgLO56Wr58Oa1Zs4aioqJo4sSJIrszZswY8fORI0fqzCpx99fQoUOpXr16WsEZd8/NmTOHNm/eTGfOnBHH4Nok3h9sV07hLXrpx0hqHeBFb93fUu7mAACApXWBcffXU089RTt37hQBB08yyLNDjx07lurUqUOff/65wccaNmyYyCRx9ogDKu6m4jXFpCLmxMREMTJM1cWLF2nfvn3077//6jwm1yJxEDVhwgSxan3Pnj3FMbmbDmwTB+fTfj1NWQW36Idx95CzI+p+AABsmR2PhTf2QZxR4YkKv/32W2rVqpXoAuMA6J9//hEZnXPnzpEl4y4zHg2WnZ1NXl5ecjcHqsB3hxLovU1nackznWhwO+0pHAAAwPIZc/02KQPEmRcOdho2bKi2PTQ0lBISEkw5JEC1OXctm2b/eZ5GdgtG8AMAAIJJ/QDcveTu7q61neuBMJoKzAkPdX/phxMU6luL3n6gldzNAQAASw6AevXqRWvXrlXe5zogHr3Fa3D17du3KtsHYDLu3X174xlKzSmkr//XiVydTFusFwAArI9JXWAc6PTv35+OHTsm5gHiomOu++EMkK4ZogHksOFoEm0+dU1MdtjYp2ypFAAAAJMzQG3btqXo6GgxumrIkCGiS+yxxx6jEydOiCHxAHK7mJxLMzefo+Fdg+iRDoYvzwIAALbB6AwQL39x//3309KlS+mdd96pnlYBVEJB8W2a9EOkyPrMfLiN3M0BAABrCICcnJzo9OnT1dMagCow4/dzdDXzJv3xck/U/QAAQNV1gfHyEzwTM4C5+fX4Ffrl+BWaM7QtNfOtJXdzAADAmoqgb9++TStXrqTt27eLtbw8PNQLTOfPn19V7QMwWGxqLr276Sw9Ed6QHg9Xn6MKAACg0gHQ2bNnqVOnTuJ7LoZWxUPiAWpa4a0SmrTuBDWo40azhqDuBwAAqjgAKikpoQ8++IDatWsn1v0CMAcf/HGO4m/k0+aXepK7s0lxPQAA2BCja4AcHBzovvvuE4uMApiD309epR+PJNEHj7ShFv6ecjcHAACseR6guLi4qm8NgJEup+eL2Z6HhAXSsC5BcjcHAACsOQCaM2cOvf766/Tnn3/S9evXxeqrqjeAmqv7iSRfL1f68NF2qD8DAACDmVQs8cADD4ivjzzyiNpFh9de4vtcJwRQ3T7aEkWxaXn024vdqZYL6n4AAMBwJl01du7cacrDAKrMljPXae3BBJo9pA21CfSWuzkAAGALAVDv3r2rviUABkq8UUBv/XKaHmjnT8/eEyx3cwAAwAKZ3G/Ao8B4NuioqChxv02bNvTcc8+Rtzf+GofqU3y7lF7+MZJqezjRvMfbo+4HAABqrgj62LFjYtX3BQsWUEZGhrjx7M+8LTIy0rSWABjg460X6Pz1HPp6eCfycnWSuzkAAGCh7BRcuWykXr16UbNmzWj58uXk6OioXB5j3LhxYnj8nj17yJLxSDbOZGVnZ5OXl5fczYE7tp1PofFrj9F7D7WmsT0by90cAACw4Ou3SQGQm5sbnThxglq2bKm2/fz589S5c2cqKCggS4YAyPxczbpJD3yxl7qE1KXlI8PR9QUAAJW6fpvUBcYHTUxM1NqelJREnp6YiReq1q2SUnr5h0gx1P2zJ1H3AwAAlWdSADRs2DAaO3YsbdiwQQQ9fFu/fr3oAhs+fHgVNAvgrs/+vUinrmTTl8M7Um13Z7mbAwAAtjoK7LPPPhN/hY8cOVLU/jAnJyeaOHEizZs3r6rbCDZs58VU+mZ3HE0b3JLCg7H4LgAAVA2TaoAkXOtz6dIl8T2PAHN3dydrgBog85CcXUgPfLmX2jf0ppWjupC9Pbq+AACgaq7fJmWA+MC83EXdunWpXbt2yu08HJ5HhSFogMq6XVJKr/x4gpwc7OjzJzsg+AEAAPlrgJ5++mlR86Ppp59+Ej8DqKwvdsTQsYQM+vLpjlSvlovczQEAACtjUgB0+PBh6tu3r9b2Pn36iJ8BVMa+mHT6emcsTR3YnCKa1JO7OQAAYIVMCoCKioqUxc+qbt26RTdv3qyKdoGNSs0tpFc3nKQeTX1oYp9mcjcHAACslEkBUNeuXWnZsmVa25cuXUrh4eFV0S6wAak5hbRgW7T4ykpKFfTq+pPi+wXDwsgBdT8AAFBNTCqCnjNnDg0YMIBOnTpF/fv3F9t27NhBR48epX///beq2whWKjW3SNT6DGztR75errRoZywdjLtB34+NoPqeqPsBAAAzywD16NGDDh48SEFBQaLw+Y8//hBrg50+fVqsEwZgrENxN2jh9mh6uV8o9WjmI3dzAADAypmUAWJhYWG0bt26qm0N2KTsm7doyoaTYp2vyf1D5W4OAADYAIMzQPn5+UYd2ND9Fy1aRCEhIeTq6koRERF05MiRcvfPysqiSZMmUUBAALm4uFDz5s1py5Ytyp+///77YpZq1Zvmoq1gHtLzisTXuVui6HapQix1gbofAAAwqwCIu7h4mYvr16/r3Ycnld62bRsNHjyYvvzyywqPyWuJTZ06lWbOnEmRkZHUoUMHGjRoEKWmpurcv7i4mAYOHEjx8fH0yy+/0MWLF2n58uXUoEEDtf3atGkj2ind9u3bZ+jLhBqy4WgijVl9VHx/9loODQkLJD8vV7mbBQAANsLgLrBdu3bR22+/LTIsHKh07tyZAgMDReYmMzOTzp8/L+qCeCbo6dOn0/PPP1/hMefPn0/jx4+nMWPGKEeR/fXXX7Ry5UqaNm2a1v68nWebPnDggFh7jHH2SOtFOTqSv7+/oS8Natj17Js0feMZUl2EZe2BBJpwbxMK8HaTs2kAAGAjDM4AtWjRgn799VeKjo6mp556iq5evSqyMJyB4eCIszD8PWdnXnzxRXJwcCj3eJzNOX78uBhNpmyMvb24z4GULps3b6Zu3bqJLjA/Pz9q27YtffTRR2JZDlUxMTEiOGvSpAk988wzlJiYWOG8Rrx+iOoNqs/l9Hwq1ViBrkShoPj0ArmaBAAANsboIuhGjRrRa6+9Jm5MWkuVa22MkZ6eLgIXDmRU8f0LFy7ofExcXBz9999/Iqjhup/Y2FgRbPEEjNyNxriOaPXq1SJg4+6vDz74QIxMO3v2LHl6euo87ty5c8V+UDMa+3gQf1xUM0AOdnYU4mMdi+kCAICVDoNnK1asEBkY7gLjG3//7bffUnUqLS0lX19fMQkjT7g4bNgweuedd0TXmYTrj5588klq3769qCfiQIkLp3m4vj7cZccLvEq3pKSkan0dto67uUJ9aynvc93zR4+1RfcXAACY9zD4GTNmiPqdl19+WXRJMe62mjJliuhumjVrVoXH8PHxEd1kKSkpatv5vr76HR75xbU/qt1rrVq1ouTkZNGl5uzsrPWY2rVri5FinC3Sh0eT8Q1qxvGEDIpOyaOJfZrQkl1xtHJ0F+rTwlfuZgEAgA0xKQO0ZMkSUe/DXUePPPKIuPH3nJlZvHixQcfgYIWzODyDtGqGh+9LQZWuCRg5kOH9JFyTxIGRruCH5eXl0aVLl8Q+YB7mb4umFn6eNLht2Xvig9XeAQDAEgIgrrnhUWCaOKDRtUiqPjwEngOpNWvWUFRUFE2cOFHMHySNChs5cqTonpLwz3kU2OTJk0XgwyPGuAiai6Ilr7/+Ou3evVsUY/NosUcffVRkjIYPH27KS4VqmPF5f+wNmjIwlOyNrBsDAACoKiZ1gY0YMUJkgbgbTBVngLhA2VBcw5OWlia61Lgbi2eX3rp1q7IwmrvTeGSYhJfe+Oeff0RXG9f48MgzDobeeust5T5XrlwRwc6NGzeofv361LNnTzp06JD4HuTFBfOc/WkT6EWD2vjTuWsYbQcAAPKwU0jDuIzAtT9r164VAck999wjth0+fFgELJy1keboYZpBkiXgYfDe3t6iINrLy0vu5liNA7Hp9L9vD9O3IzvTgNZ+YhX4dYcT6ZmIRmIxVAAAgJq6fpsUAPXt29eg/XhoPA9btzQIgKoef8yeWHqQbpWU0u+Tehg9bQIAAEBVXr9N6gLbuXOnKQ8DG7YnJp2OJ2TSqjFdEPwAAIDlzgMEYGztT6dGtalPc9RiAQCA/BAAQbXbeTGVTiVl0dSBLZD9AQAAs4AACGok+9M1pC71aFZP7uYAAAAICICgWv17PoXOXs2hqfc1R/YHAADMBgIgqDalpQpasC2aujetR/c0QfYHAADMBwIgqDZbzyXTheRcmjKwudxNAQAAUIMACKpFyZ3sT69QH+oSUlfu5gAAAKhBAATV4s/T1ygmNY+mIvsDAABmCAEQVLnbJaX0xfYY6tfSlzo2qiN3cwAAALQgAIIqt/nUNYpLz6cpA5D9AQAA84QACKoUr/X1xY4YGtjaj9o19Ja7OQAAADohAIIq9VvkVUq4UYDsDwAAmDUEQFBlim+X0pf/xdAD7fypdWD5q/ACAADICQEQVJmfjyfR1aybNLk/sj8AAGDeEABBlSi6XUJf/xdLD7UPpBb+nnI3BwAAoFwIgKBKbDiaRCk5hTS5f6jcTQEAAKgQAiCotMJbJbRoZywNDWtAzXxryd0cAACACiEAgkpbdziR0vOK6WVkfwAAwEIgAIJKKSi+TUt2xdJjHRtQYx8PuZsDAABgEARAUCnfH0qgrIJb9AqyPwAAYEEQAIHJ8otu09LdcfRk5yAKqusud3MAAAAMhgAITLb6QDzlFd6ml/o1k7spAAAARkEABCbJLbxFy/bE0dNdg6hBbTe5mwMAAGAUBEBgklX74+nmrRJ6sQ+yPwAAYHkQAIHRsm/eouV74+iZiEbk7+0qd3MAAACMhgAIjLZibxzdKimliX2ayt0UAAAAkyAAAqNk5hfTyv3xNLJbCPl6IvsDAACWCQEQGIW7vkoVCnr+3iZyNwUAAMBkCIDAYDfyisTQ91HdQ6heLRe5mwMAAGAyBEBgsG/2xJG9nR1N6IXsDwAAWDYEQGCQ1NxCWnswnp7rEUJ1PJzlbg4AAIBlB0CLFi2ikJAQcnV1pYiICDpy5Ei5+2dlZdGkSZMoICCAXFxcqHnz5rRly5ZKHRMqtnRXHDk52NPYnsj+AACA5ZM1ANqwYQNNnTqVZs6cSZGRkdShQwcaNGgQpaam6ty/uLiYBg4cSPHx8fTLL7/QxYsXafny5dSgQQOTjwkVS84upO8PJ9C4nk3I291J7uYAAABUmp1CoVCQTDg706VLF/r666/F/dLSUgoKCqKXX36Zpk2bprX/0qVL6dNPP6ULFy6Qk5NTlRxTl5ycHPL29qbs7Gzy8vIiWzfj97P0+8lrtPetvuTligAIAADMkzHXb9kyQJzNOX78OA0YMOBuY+ztxf2DBw/qfMzmzZupW7duogvMz8+P2rZtSx999BGVlJSYfExWVFQkTprqDcpczbpJ648k0YR7myD4AQAAqyFbAJSeni4CFw5kVPH95ORknY+Ji4sTXV/8OK77ee+99+jzzz+nOXPmmHxMNnfuXBExSjfOGEGZRTtjqZaroxj6DgAAYC1kL4I2Bndn+fr60rJlyyg8PJyGDRtG77zzjugaq4zp06eLdJl0S0pKqrI2W7KkjAL66WiSmPSwlouj3M0BAACoMrJd1Xx8fMjBwYFSUlLUtvN9f39/nY/hkV9c+8OPk7Rq1Upkd7j7y5RjMh5NxjdQ99V/MVTb3YlGdAuWuykAAADWkQFydnYWWZwdO3aoZXj4Ptf56NKjRw+KjY0V+0mio6NFYMTHM+WYoFt8ej79GnmVJvZpRu7OyP4AAIB1kbULjIer8zD2NWvWUFRUFE2cOJHy8/NpzJgx4ucjR44U3VMS/nlGRgZNnjxZBD5//fWXKILmomhDjwmG+fK/GKrn4UzPRDSSuykAAABVTtY/7bmGJy0tjWbMmCG6scLCwmjr1q3KIubExEQxikvCxcn//PMPTZkyhdq3by/m/+Fg6K233jL4mFCx2NQ82nTiKs18uA25Ot3tbgQAALAWss4DZK5sfR6gV348QUfjM2jn630QAAEAgMWwiHmAwDxFp+TSH6ev0Uv9miH4AQAAq4UACNR8sT2GAr3d6MlwzIUEAADWCwEQKJ2/lkN/nblOr/RvRs6O+GgAAID1wlUOlBZuj6bgeu70WKeGcjcFAACgWiEAAuHMlWz693wKvdIvlJwc8LEAAADrhisdCAu2R1MTHw8aEhYod1MAAACqHQIgoBOJmfTfhVSaPCCUHJH9AQAAG4CrHdCC7TEU6luLHmqP7A8AANgGBEA27lh8Bu2JTqNXBzQnB3s7uZsDAABQIxAA2bj526Kppb8nDW7rL3dTAAAAagwCIBt28NINOnDphsj+2CP7AwAANgQBkI3iJeB45FebQC8a1AYLxQIAgG1BAGSjOPNz5HIGTR3YnOzskP0BAADbggDIRrM/n/97kTo09KZ+LX3lbg4AAECNQwBkg3ZHp1FkYhZNQfYHAABsFAIgW6z92RZN4cF1qHfz+nI3BwAAQBYIgGwMz/h86ko2an8AAMCmIQCysewPz/vTtXFd6t60ntzNAQAAkA0CIBvyz7kUOnctB9kfAACweQiAbERpqYIWbo+mHs3q0T1NkP0BAADbhgDIRmw5e50uJOfSlAHN5W4KAACA7BAA2YASkf2JoXub16fOIXXlbg4AAIDsEADZgD9PX6PY1DxR+wMAAAAIgKze7ZJS+mJ7DPVv6UthQbXlbg4AAIBZQABk5TadvEZx6fli1mcAAAAogwDIit0qKaUvd8TQfa39qG0Db7mbAwAAYDYQAFmxjZFXKDGjANkfAAAADQiArFTxbc7+xNKD7QKoVYCX3M0BAAAwKwiArNRPx5LoWvZNmjwgVO6mAAAAmB0EQFao8FYJLdoZSw+3D6Tmfp5yNwcAAMDsIACyQhuOJlFKTiGyPwAAAHogALLS7M/Qjg2oaf1acjcHAADALCEAsjLfH0qgG/nF9Eo/ZH8AAADMOgBatGgRhYSEkKurK0VERNCRI0f07rt69Wqys7NTu/HjVI0ePVprn/vvv5+sXUHxbVq6+xI93qkBhfh4yN0cAAAAs+UodwM2bNhAU6dOpaVLl4rgZ+HChTRo0CC6ePEi+fr66nyMl5eX+LmEAxxNHPCsWrVKed/FxYWs3XcHEyir4Ba9jOwPAACAeQdA8+fPp/Hjx9OYMWPEfQ6E/vrrL1q5ciVNmzZN52M44PH39y/3uBzwVLSPpKioSNwkOTk5ZGnyisqyP091CaKguu5yNwcAAMCsydoFVlxcTMePH6cBAwbcbZC9vbh/8OBBvY/Ly8uj4OBgCgoKoiFDhtC5c+e09tm1a5fIILVo0YImTpxIN27c0Hu8uXPnkre3t/LGx7U0aw7EU35RCU3q20zupgAAAJg9WQOg9PR0KikpIT8/P7XtfD85OVnnYzig4ezQ77//Tt9//z2VlpZS9+7d6cqVK2rdX2vXrqUdO3bQxx9/TLt376bBgweL59Jl+vTplJ2drbwlJSWRJckpvEXL9sTR012DqEFtN7mbAwAAYPZk7wIzVrdu3cRNwsFPq1at6JtvvqHZs2eLbU8//bTy5+3ataP27dtT06ZNRVaof//+OrvLLLlGaNW+eLp5C9kfAAAAi8gA+fj4kIODA6WkpKht5/uG1u84OTlRx44dKTY2Vu8+TZo0Ec9V3j6WKrvgFn27L46ejQgmPy/10XAAAABghgGQs7MzhYeHi64qCXdp8X3VLE95uFvrzJkzFBAQoHcf7h7jGqDy9rFUHPzcKimlF/o0kbspAAAAFkP2eYB4CPzy5ctpzZo1FBUVJQqW8/PzlaPCRo4cKWp0JLNmzaJ///2X4uLiKDIykp599llKSEigcePGKQuk33jjDTp06BDFx8eLYIoLpZs1ayaG11uTzPxiWrnvMo3qFkK+nsj+AAAAWEwN0LBhwygtLY1mzJghCp/DwsJo69atysLoxMREMTJMkpmZKYbN87516tQRGaQDBw5Q69atxc+5S+306dMioMrKyqLAwEC67777RH2QJdf56LJsbxwpiGjCvcj+AAAAGMNOoVDwNRRU8DxAPByeR4TxpIvmKD2viHp9vJPG9AihN+9vKXdzAAAALOr6LXsXGJjmm92XyMHejsb3QvYHAADAWAiALFBqTiGtPZhAz/VsTHU8nOVuDgAAgMVBAGSBluy+RM6O9jS2Z2O5mwIAAGCREABZmOvZN2nd4UTR9eXt5iR3cwAAACwSAiALs3jnJXJ3dhDFzwAAAGAaBEAW5GrWTVp/NFEMe/d0RfYHAADAVAiALMjX/8WKwIcnPgQAAADTIQCyEIk3CujnY0n0Qu8m5OEi+/yVAAAAFg0BkIX46r8Yqu3uTCPuQfYHAACgshAAWYDL6fm08cRVerFPU3JzdpC7OQAAABYPAZAF+HJHDPnUcqb/RTSSuykAAABWAQGQmYtNzaXfT16lSX2bkasTsj8AAABVAQGQmftiRyz5e7nSsC5BcjcFAADAaiAAMmMXk3Ppz9PX6KV+oeTiiOwPAABAVUEAZMYWbo+mBrXd6InwhnI3BQAAwKogADJT565l099nk+mVfqFi4VMAAACoOriymqmF22MouJ47PdqpgdxNAQAAsDoIgMzQmSvZtO18Ck3uH0pODniLAAAAqhqurmZo/raL1KS+Bz3SIVDupgAAAFglBEBmJjIxk3ZeTBPZH0dkfwAAAKoFrrBmZsG2aGruV4seao/sDwAAQHVBAGRGjsZn0N6YdHp1QHNysLeTuzkAAABWCwGQGZn/bzS19Pek+9v4y90UAAAAq4YAyEwcuJROB+Nu0JSBzcke2R8AAIBqhQDIDCgUClq4LYbaNvCi+1r7yd0cAAAAq4cAyAzsi02nI/EZNHVgc7KzQ/YHAACguiEAMoPsz/xt0dQhqDb1beErd3MAAABsAgIgme2KTqMTiVnI/gAAANQgBEAyZ3943p/OwXXo3lAfuZsDAABgMxAA1bDUnEIR9PDX7VGpdPpKNrI/AAAANQwBUA1LzS2iL3bEUHJOoaj9iWhcl7o1rSd3swAAAGwKAiCZHLp0g6Ku5yD7AwAAIAMEQDJZdySRejbzoYgmyP4AAADYZAC0aNEiCgkJIVdXV4qIiKAjR47o3Xf16tUiY6J648dpFhfPmDGDAgICyM3NjQYMGEAxMTFkThJuFNCUgaFyNwMAAMAmyR4AbdiwgaZOnUozZ86kyMhI6tChAw0aNIhSU1P1PsbLy4uuX7+uvCUkJKj9/JNPPqEvv/ySli5dSocPHyYPDw9xzMLCQqPaVlxcLIIpSUlJidh2+/Ztrf0M3ffajTwO0ahtoBeFB9ctd99bt26J7aWlpcpt/D1v45+Z277cft7Or8eUffn8SeeyuvbV9R4Zs29l3ntj9tV13q35c1IT770x+8r53lf2c2Jp7725fU7wf8Qt2fet7PtpMQHQ/Pnzafz48TRmzBhq3bq1CFrc3d1p5cqVeh/DWR9/f3/lzc/PT31ZiYUL6d1336UhQ4ZQ+/btae3atXTt2jXatGmTzuMVFRVRTk6O2o19/vnnVFBQoNxv//79NHfuXNqyZYva4z/77DOxPTs7W7nt6NGjYtvmzZuV2zYcTaQJP5zgV0DnruWI++zkyZNi319++UUrM8bbOciTnD17Vmxbv3692r7Lly8X2xMTy47JoqOjxbbvvvtOK4vG22NjY5XbLl++LLatWLFCbd9169aJ7VFRUcptV65cEdv4vVL1008/ie1nzpxRbuNAlrd99dVXavv+9ttvYvvx48eV2zIyMsQ2/kyo+vPPP8X2Q4cOKbfl5uaKbR9//LHavv/884/YvnfvXrX3l7fxTfWXb8eOHWIbf5Xwz6V9+XESPh5v4+Or4ufn7dweCbeTt3G7VfHr4u38OiX8+nkbnw9VfL54u+ofAnxeeRufZ1X8PvB2fl8k/H7xNn7/VPH7y9v5/Zbw54C38edCFX9ueDt/jiT8+eJt/HlTxZ9H3s6fTwl/bnkbf45V8eect/PnXpKWlia2ffHFF2r78u8Pb+ffJwn/nvE2/r1Txb+XvJ1/TyX8+yu9n6q2b98utu3atUu5jf8TlvZV/Q+Z9+Ft/BhV0r5V+X8E43PA2/mcSPB/RBn8H3EX/o/Q/3+ERQRAHLXxm8tdVMoG2duL+wcPHtT7uLy8PAoODqagoCAR5Jw7d075M37TkpOT1Y7p7e0tutb0HZNPHu8j3fi4Ve169k2avpF/6csKnjm+f3vjWbEdAAAAapadQjXXVsM4K9OgQQM6cOAAdevWTbn9zTffpN27d4vuK00cxHA9D2d2+K8p/stqz549Ighq2LChOFaPHj3EsbkGSPLUU0+JzBF3uWniKF41kucMEAdBHHHWq1dPOUqLU2x84yDN0dFRub+UknNyctK7L6/2/r/l2q/nx/H3UNeQ2jqPy3+F8tvD2/hn0l8fnPLj5+HnM6d9eRv/zMHBQdyM3ZefR/rL29nZuVr21fUeGbOvKe+9KfvqOu/G7Gtpn5OaeO8r+zmpqfe+sp8TS3vvze1zgv8jbsm+b2XeT75+cyKD4wMulynP3TNkIThQUg2WunfvTq1ataJvvvmGZs+ebdIxXVxcxE0Tn2TVIeqqb4bmfpo0923s40H2dkSlKuGmg50dhfi46z2u6gdCwh8cXc9nDvuq/sKZsi+f65rcV9d5N2ZfVl376jrv1vw5qen3vrKfE2YO+5rD+4n/I/Tvy/B/RM3/H2ERXWA+Pj7izUpJSVHbzve5tscQfFI7duyo7KuWHleZY1aHAG83mvtYOxEEMf760WNtxXYAAACoWbIGQBzNhYeHaxWY8X3VLE95ON3HhV9Sd1fjxo1FoKN6TE6JcXeaocesLsO6NKKVo7uI7/kr3wcAAICaJ3sXGA+BHzVqFHXu3Jm6du0qRnDl5+eLUWFs5MiRok5IGsUxa9Ysuueee6hZs2aUlZVFn376qRgGP27cOGWa7NVXX6U5c+ZQaGioCIjee+89CgwMpKFDh5LcfGq5qH0FAAAAGwyAhg0bJoqNeeJCHr0VFhZGW7duVQ5t5yF1UsEUy8zMFMPmed86deqIDBIXPvMQetUiag6iJkyYIIKknj17imNqTpgIAAAAtknWUWDmypgqcmOdvZpND321j/58uSe1beBdpccGAACwZTlGXL9lnwgRAAAAoKYhAKphvp4uNLl/qPgKAAAANloDZGt8vVxpysDmcjcDAADApiEDBAAAADYHARAAAADYHARAAAAAYHMQAAEAAIDNQQAEAAAANgcBEAAAANgcBEAAAABgcxAAAQAAgM1BAAQAAAA2BwEQAAAA2BwEQAAAAGBzEAABAACAzUEABAAAADYHARAAAADYHEe5G2COFAqF+JqTkyN3UwAAAMBA0nVbuo6XBwGQDrm5ueJrUFCQ3E0BAAAAE67j3t7e5e5jpzAkTLIxpaWldO3aNerXrx8dO3bMoMd06dKFjh49alB0yoFVUlISeXl5ka0z9LzZSvuq6/mq6riVOY4pjzX2MYbsj99Bbfg9rJnnq4rjVvYY1f172EXm30EOaTj4CQwMJHv78qt8kAHSgU9aw4YNydHR0eA3x8HBwag3kvfFf77Gnzdrb191PV9VHbcyxzHlscY+xpj98Tt4F34Pa+b5quK4lT1Gdf8eOpjB72BFmR8JiqDLMWnSpGrZFyznvNV0+6rr+arquJU5jimPNfYx5v55Mlfmft7we1h1x6ju38NJZv5ZUoUusBrGqT+OTrOzs836Ly4Aa4XfQQB5mcvvIDJANczFxYVmzpwpvgJAzcPvIIC8zOV3EBkgAAAAsDnIAAEAAIDNQQAEAAAANgcBEAAAANgcBEAAAABgcxAAAQAAgM1BAGRG/vzzT2rRogWFhobSt99+K3dzAGzSo48+SnXq1KEnnnhC7qYA2JykpCTq06cPtW7dmtq3b08///xztT0XhsGbidu3b4s3fOfOnWKCqPDwcDpw4ADVq1dP7qYB2JRdu3aJtYTWrFlDv/zyi9zNAbAp169fp5SUFAoLC6Pk5GRxLYyOjiYPD48qfy5kgMzEkSNHqE2bNtSgQQOqVasWDR48mP7991+5mwVgc/ivT09PT7mbAWCTAgICRPDD/P39ycfHhzIyMqrluRAAVZE9e/bQww8/LFagtbOzo02bNmnts2jRIgoJCSFXV1eKiIgQQY+EV5/n4EfC31+9erXG2g9gDSr7ewgA5vM7ePz4cSopKRErx1cHBEBVJD8/nzp06CDeWF02bNhAU6dOFdN/R0ZGin0HDRpEqampNd5WAGuF30MA6/gdzMjIoJEjR9KyZcuqr7FcAwRVi0/rb7/9prata9euikmTJinvl5SUKAIDAxVz584V9/fv368YOnSo8ueTJ09WrFu3rgZbDWBdTPk9lOzcuVPx+OOP11hbAawRmfg7WFhYqOjVq5di7dq11do+ZIBqQHFxsUjlDRgwQLnN3t5e3D948KC437VrVzp79qzo9srLy6O///5bRMUAUHO/hwAg7+8gx02jR4+mfv360YgRI6qxNegCqxHp6emiH9PPz09tO9/nKnfm6OhIn3/+OfXt21cUgL322msYAQZQw7+HjP8zfvLJJ2nLli3UsGFDBEcANfg7uH//ftFNxrVDfC3k25kzZ6g6OFbLUcEkjzzyiLgBgHy2b98udxMAbFbPnj2ptLS0Rp4LGaAawMP4HBwcxNwGqvg+D/MDgOqH30MAefmY2e8gAqAa4OzsLCZz2rFjh3IbR7h8v1u3brK2DcBW4PcQQF7OZvY7iC6wKsKFy7Gxscr7ly9fppMnT1LdunWpUaNGYtjfqFGjqHPnzqLgeeHChWK44JgxY2RtN4A1we8hgLzyLOl3sFrHmNkQHjbLp1PzNmrUKOU+X331laJRo0YKZ2dnMRTw0KFDsrYZwNrg9xBAXjst6HcQa4EBAACAzUENEAAAANgcBEAAAABgcxAAAQAAgM1BAAQAAAA2BwEQAAAA2BwEQAAAAGBzEAABAACAzUEABAAAADYHARAAAADYHARAAGAz3n//fQoLCzPqMXZ2drRp06ZqaxMAyAMBEABYvD59+tCrr75a4X6vv/662krUAGC7sBo8AFg9XvKwpKSEatWqJW4AAMgAAYBFGz16NO3evZu++OIL0V3Ft9WrV4uvf//9N4WHh5OLiwvt27dPqwvs6NGjNHDgQPLx8SFvb2/q3bs3RUZGyvp6AKBmIAACAIvGgU+3bt1o/PjxdP36dXELCgoSP5s2bRrNmzePoqKiqH379lqPzc3NpVGjRong6NChQxQaGkoPPPCA2A4A1g1dYABg0Thz4+zsTO7u7uTv7y+2XbhwQXydNWuWyPDo069fP7X7y5Yto9q1a4uM0kMPPVTNLQcAOSEDBABWq3PnzuX+PCUlRWSOOPPDgZSXlxfl5eVRYmJijbURAOSBDBAAWC0PD49yf87dXzdu3BDdaMHBwaJWiLvTiouLa6yNACAPBEAAYPG4C4xHeRlr//79tHjxYlH3w5KSkig9Pb0aWggA5gYBEABYvJCQEDp8+DDFx8eLYe6lpaUGPY67vr777jvRVZaTk0NvvPEGubm5VXt7AUB+qAECAIvHExw6ODhQ69atqX79+gbX8KxYsYIyMzOpU6dONGLECHrllVfI19e32tsLAPKzU/AMYQAAAAA2BBkgAAAAsDkIgAAAAMDmIAACAAAAm4MACAAAAGwOAiAAAACwOQiAAAAAwOYgAAIAAACbgwAIAAAAbA4CIAAAALA5CIAAAADA5iAAAgAAALI1/wfRQpUeIInBCAAAAABJRU5ErkJggg==" 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}, "metadata": {}, - "output_type": "display_data" + "output_type": "display_data", + "jetTransient": { + "display_id": null + } } ], "execution_count": 4 diff --git a/pyproject.toml b/pyproject.toml index 17209f6..9832b64 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,6 +23,7 @@ dependencies = [ "pydantic>=2.10", "xarray>=2025.1", "scipy>=1.17.1", + "ipykernel>=7.2.0", ] [dependency-groups] diff --git a/uv.lock b/uv.lock index af32679..9e987dc 100644 --- a/uv.lock +++ b/uv.lock @@ -2,10 +2,14 @@ version = 1 revision = 3 requires-python = ">=3.11, <3.13" resolution-markers = [ - "platform_machine == 'ARM64' and sys_platform == 'win32'", - "platform_machine != 'ARM64' and sys_platform == 'win32'", - "sys_platform == 'emscripten'", - "sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.12' and platform_machine == 'ARM64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine == 'ARM64' and sys_platform == 'win32'", + "python_full_version >= '3.12' and platform_machine != 'ARM64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine != 'ARM64' and sys_platform == 'win32'", + "python_full_version >= '3.12' and sys_platform == 'emscripten'", + "python_full_version < '3.12' and sys_platform == 'emscripten'", + "python_full_version >= '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'", ] [[package]] @@ -17,6 +21,24 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" }, ] +[[package]] +name = "appnope" +version = "0.1.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = 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b/hobj/data_loaders/behavior.py @@ -109,7 +109,7 @@ def load_highvar_behavior( 'worker_id', 'subtask', 'stimulus_id', - 'is_probe_trial', + 'trial_type', 'stimulus_duration_msec', 'reaction_time_msec', 'timed_out', @@ -121,7 +121,9 @@ def load_highvar_behavior( raise ValueError(f"Highvar behavior CSV missing required columns: {sorted(missing_columns)}") if remove_probe_trials: - df = df.loc[~df['is_probe_trial']].copy() + df = df.loc[df['trial_type'] != 'probe'].copy() + df = df.sort_values(['assignment_id', 'trial']).copy() + df['trial'] = df.groupby('assignment_id').cumcount() return df @@ -129,21 +131,50 @@ def load_highvar_behavior( def load_oneshot_behavior( cachedir: Path | None = None, redownload: bool = False, -) -> List[HumanLearningSession]: +) -> pd.DataFrame: """ - Load the "raw" human learning data from Experiment 2 of Lee and DiCarlo 2023. + Load the trial-level human learning data from Experiment 2 of Lee and DiCarlo 2023. :return: """ + if redownload: + raise ValueError("load_oneshot_behavior no longer supports redownload; expected cached CSV artifact instead.") - sessions = _load_learning_sessions( - dataset_url='https://hlbdatasets.s3.us-east-1.amazonaws.com/behavior/mutator-oneshot-human-learning-data.json', - cache_filename='mutator-oneshot-human-learning-data.json', - cachedir=cachedir, - redownload=redownload - ) + repo_root = Path(__file__).resolve().parents[2] + cache_root = (cachedir if cachedir is not None else repo_root / 'data').resolve() + dataset_path = cache_root / 'behavior' / 'human-behavior-oneshot-tasks.csv' + if not dataset_path.exists(): + raise FileNotFoundError( + "Expected cached oneshot behavior CSV to already exist at:\n" + f" - {dataset_path}" + ) + + df = pd.read_csv(dataset_path) + + required_columns = { + 'trial', + 'session_id', + 'assignment_id', + 'slot', + 'worker_id', + 'subtask', + 'stimulus_id', + 'trial_type', + 'stimulus_duration_msec', + 'reaction_time_msec', + 'timed_out', + 'perf', + 'timestamp_start', + } + missing_columns = required_columns - set(df.columns) + if missing_columns: + raise ValueError(f"Oneshot behavior CSV missing required columns: {sorted(missing_columns)}") - return sessions + return df if __name__ == '__main__': - sessions = load_highvar_behavior() + df = load_highvar_behavior() + import matplotlib.pyplot as plt + lc = df.groupby('trial')['perf'].mean() + plt.plot(lc.index.values, lc) + plt.show() diff --git a/tests/test_simulate_subtask.py b/tests/test_simulate_subtask.py index 3772a21..c222803 100644 --- a/tests/test_simulate_subtask.py +++ b/tests/test_simulate_subtask.py @@ -1,6 +1,5 @@ from typing import List -import PIL.Image import numpy as np from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask From 5289cf205fcae7ada14ce8156868fb791b291ad5 Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 22:08:16 -0400 Subject: [PATCH 115/156] rerun --- examples/view_experiment2_behavior.ipynb | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/examples/view_experiment2_behavior.ipynb b/examples/view_experiment2_behavior.ipynb index 9a9666c..8cd6894 100644 --- a/examples/view_experiment2_behavior.ipynb +++ b/examples/view_experiment2_behavior.ipynb @@ -9,8 +9,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T18:11:58.286738Z", - "start_time": "2025-01-27T18:11:58.063475Z" + "end_time": "2026-03-25T02:07:33.263682Z", + "start_time": "2026-03-25T02:07:33.259102Z" } }, "cell_type": "code", @@ -25,8 +25,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T18:11:58.914027Z", - "start_time": "2025-01-27T18:11:58.295149Z" + "end_time": "2026-03-25T02:07:34.714371Z", + "start_time": "2026-03-25T02:07:33.266649Z" } }, "cell_type": "code", @@ -52,8 +52,8 @@ "Data variables:\n", " phat (transformation) float64 288B 1.0 1.0 ... 0.952 1.0\n", " varhat_phat (transformation) float64 288B 0.0003555 ... 0.0003867\n", - " boot_phat (boot_iter, transformation) float64 288kB 0.991 ......\n", - " boot_varhat_phat (boot_iter, transformation) float64 288kB 0.0004516...\n" + " boot_phat (boot_iter, transformation) float64 288kB 0.9517 .....\n", + " boot_varhat_phat (boot_iter, transformation) float64 288kB 0.0003212...\n" ] } ], @@ -62,8 +62,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T18:11:59.312918Z", - "start_time": "2025-01-27T18:11:58.914641Z" + "end_time": "2026-03-25T02:07:35.089878Z", + "start_time": "2026-03-25T02:07:34.749947Z" } }, "cell_type": "code", @@ -100,10 +100,13 @@ "text/plain": [ "
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}, "metadata": {}, - "output_type": "display_data" + "output_type": "display_data", + "jetTransient": { + "display_id": null + } } ], "execution_count": 3 From 9f3c81d9db726ad8c5a308327438339223caa94c Mon Sep 17 00:00:00 2001 From: himjl Date: Tue, 24 Mar 2026 22:10:26 -0400 Subject: [PATCH 116/156] remove unused --- hobj/benchmarks/mut_oneshot_benchmark.py | 24 +++----- hobj/data_loaders/behavior.py | 74 ------------------------ tests/test_load_behavior.py | 4 +- 3 files changed, 11 insertions(+), 91 deletions(-) diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index 71851d3..da28f0a 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -1,5 +1,4 @@ import collections -# Coercing human data from typing import Dict, List from hobj.benchmarks.generalization.benchmark import GeneralizationBenchmark, GeneralizationBenchmarkConfig, GeneralizationSessionResult @@ -136,26 +135,22 @@ def __init__(self): subtasks.append(subtask) # Package human data into format expected by benchmark - oneshot_sessions = load_oneshot_behavior() + oneshot_df = load_oneshot_behavior() results = [] - for session in oneshot_sessions: - # Parse raw data by worker + for session_id, session_df in oneshot_df.groupby("session_id", sort=False): + session_df = session_df.sort_values("trial") transformation_to_kn = collections.defaultdict(lambda: [0, 0]) kcatch = 0 ncatch = 0 - observed_categories = set() + observed_categories = set(session_df["subtask"].iloc[0].split(",")) - for i_trial, sha in enumerate(session.stimulus_sha256_seq): - image_id = sha + for _, row in session_df.iterrows(): + i_trial = int(row["trial"]) + image_id = row["stimulus_id"] annotation = imageset.get_annotation(image_id=image_id) - - # Add stimulus category to observed categories - observed_categories.add(annotation.category) - - # Calculate performance - perf = session.reward_seq[i_trial] > 0 + perf = bool(row["perf"]) # Record performance in relevant slot if i_trial in support_trials: @@ -184,7 +179,7 @@ def __init__(self): transformation_to_kn=transformation_to_kn, kcatch=kcatch, ncatch=ncatch, - worker_id=session.worker_id + worker_id=session_df["worker_id"].iloc[0] ) results.append(result) @@ -211,4 +206,3 @@ def target_statistics(self) -> GeneralizationStatistics: if __name__ == '__main__': benchmark = MutatorOneshotBenchmark() - diff --git a/hobj/data_loaders/behavior.py b/hobj/data_loaders/behavior.py index 1da4cc9..a9a3628 100644 --- a/hobj/data_loaders/behavior.py +++ b/hobj/data_loaders/behavior.py @@ -1,84 +1,10 @@ -import datetime -import json from pathlib import Path -from typing import List, Literal import pandas as pd -import pydantic - -from hobj.utils.file_io import download_json __all__ = ['load_highvar_behavior', 'load_oneshot_behavior'] -class HumanLearningSession(pydantic.BaseModel): - """ - A representation of the "raw" data collected from a human learning session. - """ - model_config = dict( - frozen=True - ) - - worker_id: str = pydantic.Field( - description='The anonymized worker ID of the participant.' - ) - - stimulus_sha256_seq: List[str] = pydantic.Field( - description='The SHA256 hash of the image (in np.uint8 format and RGBA mode) shown to the participant.', - ) - - stimulus_duration_msec_seq: List[int] = pydantic.Field( - description='The duration of the stimulus presentation in milliseconds.', - ) - - action_seq: List[Literal[0, 1, None]] = pydantic.Field(description='The action taken by the participant. 0 maps to "F" (left) and 1 maps to "J" (right). None maps to no action taken.') - reward_seq: List[Literal[-1, 1]] = pydantic.Field(description='The reward received by the participant following their action.') - timestamp_start_seq: List[datetime.datetime] = pydantic.Field(description='The timestamp of the start of the trial.') - - @pydantic.field_serializer('timestamp_start_seq') - def serialize_timestamp_start(self, v: List[datetime.datetime], _info) -> List[float]: - # Serializes datetime objects into Unix timestamps (seconds) - return [entry.timestamp() for entry in v] - - @pydantic.model_validator(mode='after') - def validate_lengths(self) -> 'HumanLearningSession': - stimulus_len = len(self.stimulus_sha256_seq) - duration_len = len(self.stimulus_duration_msec_seq) - action_len = len(self.action_seq) - reward_len = len(self.reward_seq) - timestamp_len = len(self.timestamp_start_seq) - - if not stimulus_len == duration_len == action_len == reward_len == timestamp_len: - raise ValueError(f'All lists must be of the same length. Got: {stimulus_len, duration_len, action_len, reward_len, timestamp_len}') - - return self - -# %% -def _load_learning_sessions( - dataset_url: str, - cache_filename: str, - cachedir: Path | None = None, - redownload: bool = False, -) -> List[HumanLearningSession]: - repo_root = Path(__file__).resolve().parents[2] - cache_root = (cachedir if cachedir is not None else repo_root / 'data').resolve() - behavior_dir = cache_root / 'behavior' - behavior_dir.mkdir(parents=True, exist_ok=True) - dataset_path = behavior_dir / cache_filename - - if redownload or not dataset_path.exists(): - json_data = download_json(dataset_url) - dataset_path.write_text(json.dumps(json_data, indent=2)) - else: - json_data = json.loads(dataset_path.read_text()) - - class LearningSessionDataset(pydantic.BaseModel): - sessions: List[HumanLearningSession] - - learning_dataset = LearningSessionDataset(**json_data) - return learning_dataset.sessions - - # %% Data loaders def load_highvar_behavior( remove_probe_trials: bool = True, diff --git a/tests/test_load_behavior.py b/tests/test_load_behavior.py index a719e05..1b74226 100644 --- a/tests/test_load_behavior.py +++ b/tests/test_load_behavior.py @@ -7,5 +7,5 @@ def test_load_highvar(): def test_load_oneshot(): - sessions = load_oneshot_behavior() - assert len(sessions) == 2547 + df = load_oneshot_behavior() + assert len(df) == 2547 * 20 From ed3524ebc19cb40d42a5ed3f3be009af57d4b61c Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 25 Mar 2026 00:27:10 -0400 Subject: [PATCH 117/156] new loader --- examples/score_example_models.ipynb | 66 +++++++------ hobj/benchmarks/mut_highvar_benchmark.py | 1 + hobj/data_loaders/images/highvar.py | 2 - hobj/data_loaders/images/oneshot.py | 2 - hobj/data_loaders/images/probe.py | 2 - hobj/data_loaders/images/template.py | 114 +++++++++++------------ hobj/data_loaders/images/warmup.py | 5 +- 7 files changed, 90 insertions(+), 102 deletions(-) diff --git a/examples/score_example_models.ipynb b/examples/score_example_models.ipynb index 23b7c61..8c72743 100644 --- a/examples/score_example_models.ipynb +++ b/examples/score_example_models.ipynb @@ -3,8 +3,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T18:12:05.068082Z", - "start_time": "2025-01-27T18:12:04.841722Z" + "end_time": "2026-03-25T02:10:39.530263Z", + "start_time": "2026-03-25T02:10:39.524184Z" } }, "cell_type": "code", @@ -19,8 +19,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T18:12:06.972624Z", - "start_time": "2025-01-27T18:12:05.071466Z" + "end_time": "2026-03-25T02:10:42.427716Z", + "start_time": "2026-03-25T02:10:39.535091Z" } }, "cell_type": "code", @@ -44,8 +44,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T18:12:06.974944Z", - "start_time": "2025-01-27T18:12:06.973320Z" + "end_time": "2026-03-25T02:10:42.465284Z", + "start_time": "2026-03-25T02:10:42.460834Z" } }, "cell_type": "code", @@ -68,8 +68,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T18:12:12.334469Z", - "start_time": "2025-01-27T18:12:06.975697Z" + "end_time": "2026-03-25T02:10:48.027325Z", + "start_time": "2026-03-25T02:10:42.466588Z" } }, "cell_type": "code", @@ -84,17 +84,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "LearningCurveBenchmark.LearningCurveBenchmarkResult(msen=0.11344782631802722, msen_sigma=0.0010224279429435987, msen_CI95=(0.1113092143859505, 0.11539759036915627), lapse_rate= Size: 8B\n", - "array(1.), model_statistics= Size: 103MB\n", + "LearningCurveBenchmark.LearningCurveBenchmarkResult(msen=0.11340537830375878, msen_sigma=0.0010675644883655587, msen_CI95=(0.11128353017747093, 0.11551109646188225), lapse_rate= Size: 8B\n", + "array(0.7920317), model_statistics= Size: 103MB\n", "Dimensions: (subtask: 64, trial: 100, boot_iter: 1000)\n", "Coordinates:\n", " * subtask (subtask) " ], - "image/png": 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" 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mJrR23RdUpIi5uP/t/P4Fsu7btatDly4voa8n9hD3J369gebN+5X0hanSOwAAAMDq1atIz569pvv3w/K9QqbP+JBsbCypT5+WNHPGVgoODtXJ64wb1528vSvSixfRZG5uSo0aVaauXRvRb7+dIH1iZGREc+b0oxo1y9K1qw/p8uX7dOXKQ4qOjqNZs/tR584NxXqhoZE0ZvRq2rz5H9I3UmEqGJZS/jNAQR0UxjYwaFA7qW/fVrl+foUKbtKECf7S+QvfS/fur5KqVnVX/D1ps9SuXV5KSt4h3bi5XJHXlodWuAQEDNTJ69SsWVZKSPxdvMYHHzSXpk3rLf6+eWuFZGZmqvhnQGpl2bKh6eokY+H38e23/SVbW0t9PX4rX4n5WRDcKP8ZoKAOClsb+Pjj1qqDAudbZPd5pqYm0hdfdJLOnF30xsHl7r2VkrNz0Wxtx8LCTJo8uafUu3cLxesis7Jq1eeq91aqlKPWt29iYpzpY7/9/rV43avXlorbFy+3SDY2llrPT+HAlLf/y/bxYhm/RujT9WLZsGHvKf4Z0H9l3ryPxT5xsMkB2MKFA6W/AmdKT8M2iOX8d07asbYKghvtVQ4K6gBtAG0gT22gRo0yUkzsdtWBe/HiT7L9XF5Xfl5i0g5p776pogeIf+nzsuMnvhWBS1bbKFrUWjr892zVdmbO7KN3bdrBwSZdHXXt2ijTdY2NjbMMVDSVLl18RMCiqe65N4VfMzllp+TlVUq6dn2ZuD90aPtsJyE3blzlrfvE9c7b5QDBycletfyTT/zE8vCITZKdnVWe65Lbw/z5/UUw6+hol+Pnf/21v+pz4KBc0/tVqp0guNFe5aCgDtAGDKwN8NkwTZpUkb77brB08dJiaf2GUVKDBp46eS0+WMmBiNwr8Or11mx15fMBXj7IcO9N8eL/P1BVqlRSeh65WTy2cdOYTLdRurSTdPnKD2K96Jj/Bw9Ll34qggRtvc+WLWvkqaeD3596r9TcuR9l2vvBwUfki83SrFl9JReXt/dcVaniLr2O2qbaNh/01R/f9su4dPXIQQ3fv35j2VvPnOL6vXAxrTeGe9fq1Cn/xjpczzwcyb0gvB4HWuqPc1Aktw1+T3n9LNasHal6rxwwckBXpoxLtp47fHgH1XNHjOiok/+JvBQEN9qrHBTUAdqAgbQB7kbngObR47UacwhOnQ6QPvrIV6u/TOUDJ+fIcHAiH8Q+++zdLJ/HByM+gPO6s2f307hOq1Y1VPkbkyalP2DLPUbye334aI1UrZqH6PXhHgpetunnMVrJ8xg1qrPY3smg+bnaHgcQcgB47Pg8cXvo8CyN6zZrVi3dZ8anUy9f/pnIR8osuOQcHl739p0fVc/r189XPM51IvfacBDEyzhI414eXt6uXZ1M99vbu6IUEpo2pCQXDmC418Tauoh4X/7+TaQrV9OCSy4cSGvaVocO9VXBSIsW1UUOkFzUg9q3lZEjO6l6+c6e+/9QJt/n4K1ixRKZ9vbIPUtcJk7ske//n9kpCG60VzkoqAO0AQNoA/zr+EnIOtWXNwcOq9eMlHr2bCZyPeQ5R+SelcCDM6U5c/qJ3hP+df62IZXx498XuQnvvuutGnL4/PO0X8HxCb9J9etXStcrwL0PmW2PAwQOFOSDPefdZLbuwIFtVfu9YeNo6aefhouycuVw1QH60uUl6XJY3n+/idgnfmz3nil5GgopWbJ4ul4RDh5zuo02bWqr8ly4nvhv3qamnqUpUz5QBVI8JCe/LgcnP/44LN0wDAcXv+9Iy6W5/2C1eEw+gHNQyK+7ectX4v6WrWPTvQ4nFMv1o2mfu3VrpBpG456bunUriPpXz4fiXkH5/rPnP4tk8KyGENWHDtULv05WQZZ6Pcq9Q8OHdxDLOFDioUz1IGfJkiHphsWaN6+mCgCz6jXTh4LgRnuVg4I6QBvQszbAOSTcw/K2XBP1wl/y8kGmfft6YnhD/XH+dfzll13FQUnTAYYPYNzrYWVloXoOv/7o0V1Uw0MZk33lXhX1RFHuFXj5aqtY7utbM8tkTt6uu3vWgRUXPmNF0z7LPSD29tZvPIcPlvIwVVj4RrGPmQVRlpb/f88Zy9ZtY9/oFeHgKSefp5zMy4ERBzQcXPJ97lXJuC6/H35s8GA/cb9p06rSH7smq16b64wDSA5m5dwRDlw5+JC3wb0nchAr92JlfK3y5d1Uj6n3dnBdcC+Z/Hr82urDcVyvwXdXpguief3sBJC8DzyE+ODhatHTxoXzcOR9rVWrXKbP5f2V2yEH66Qhr2jHzomq/eI2yMEWB8LyssdP1kqdOzdU/P87q4LgRnuVg4I6QBvQYRvgL11NB9+sivxLlH+BZvc5338/ONMvfvXCB9fq1ctI/fu3EafCch4F/9pVP3guWDBAGjCgbbpAiIMf7gnig5N8UOTCPQOZ7Qsf1DM+9s47dVXP7dQpewca3ucePZqK3iP1wvuYMYhTL5xnJA+TceFf75wPwj0cHKDw+5eHixYtGvRG/knbtmk9Llw/XGecLyIfiDknKDv7zr1icm8DJ/PyMu414/u8/+rr8nBhXHxaj1PG4RVO6D13/jvVe+H3JX8OGZNiuWdMfg31M5cylp1/TFIFXWXLuoggkoNj+Xl8BpGm3iUOgDmg4eAhp207Y+F95TOT5OBDUy8i52/JQ1/cm5XVZ968eTUx/JoxCP7hh0+1ksys64LgRnuVg4I6MJg2wL0dfACQDyJKFk68lL+0ecigXDnXbD2vY8cGqi9k7hnhg87bnsMHZf4VzM/hYaPc9BRxwuut22kHevXC2+X8DfWDHB9suKeoT5+WGnuXuP75uXxQ9/BwVi3n07Sjon/J9fBObgr3cPDZOvLpyFkV7vGQe3f4fcmBDwd78rbknhUeksmqx0cu8tT93BbkZZxjxMtWrPhMY+8bH+Q1bYs/g08/bZ8uAOEATdO6HHTwadncq8OBmaZ1WreupWpn6gEr91LJOTv5UTjokIe4eIhRDpg4iOEgnBOf5bbo6uqQrf+HDz5oLt4H10GjRpXz7b3ktSC40V7loKAOFG8DnDiq6ZTMnP4ClA9gnIeg1HvhRFlObMx44OTE17f92udf7nKXvxwErF33xVtfkxM/5R6FnAxlaToo+PnVlXbtniwOKGPHds918vGBv2aIfeIDOe+T+oRp+/6cluWvb10UHlqZPr23aqiKe6I47+S997xFUCz3XnFvBr9neWiGAw31M7/44Con2XJPVnbbpPqp3zw0wsv4wKu+/tSpvcRyzm3Jars8xMgBF/dGZFWPXO9vmydIPtOMy569U8WQpjbPMstu4ZwpOTmce53GjOmSLjE+4tkmqV69ivm+X5TPBcGN9ioHBXWgaBvgLzU5cZEPNLndTvfujdMFE1mN37+tcNInH+D5F39Ovuj5rBo5kZV/Ca9bP0oEHvxrlJfxQVE+Y0VTkQ+o/AtVPmuGt/O2mXrlnoCfN3+pN//PPOQkH5ROn1moei98AFfi4CkXPstH09k53OMlt0NOcpYTsPlsIE1DH/JQEx+IMztDh3u25MBWPd+nRIliqp4t9Rynv/9JS7jlJOr8qg/uZeNAIrOzsfJ7CFfOR1LvOeT/K21POEh6WhDcaK9yUHRcBzz/A+ceqGfvo2iesyLo3wW5rpv9B6an6/HY/qvmPANOmuVJxrhXQtPjHFSonx3Dw1w8Z8zbXp9zQuTn/Ll/errginM85HwJTm7lU5gzPp+Hb+SDq3xAlU+z1pS7ol7kM0E0HYiVKjyEw6eHy3XCiaOcw6L0fmVV+LOXk6G5cJvKbF0eLpF7gTgQ4uRe7qnh9809M3LPlaZ5Z7jIw4icMMz3eYhLzrfRh0BDqcJnRPH/H+cUcY+avl2ygXRcENxor3JQdFwHchc2n8aJ+n7zl5o81i//Us7OKaGazqSQewZ4WEXeZsZcA86DkXtWuPCZMPxLXn6cz+6RD1j/ngpId5YQDxXwL25Nr89zdcjP4zOBMjudmrfJ63DOBJ/Kqv6rnZM++bGDh75J96ta7iGQT7XOWLgniB/nA6O+/brl/BA5CVQXlxvQReHPkoNfPm37bcOInA/FQ2zqSb6cXyXf58+O25imz0X+vPlsNHmSQLmnQuk6ULrk95Al6VFBcKO9ykHRYR3wwVX+ouMDrqZf7IW5yL0tPNkaTwwmHwhzuh2er0XOGeD7PDyTcW4PDmLkHg4+Q0gOcjiRkYMjPotHDrB4SIrzFbjHhSdQk4Ml7jLnuV3Uh1U4p4FPbZXnDMlqyIUTJ+VJ3OQhGx6mkXt9OO8j4ym7q1aPeCMhVb3wZGRyrojSn6emwnWb00sJKF24FyUn0/r36tVcdU0iuXfum2/6ZHma+1dfdVMF2Hyf84HkpGal3z8KKVYHCG60VzkoOqwD/lWmPn6c2QFK3wonUPL8EPJEWdktfLoq55lk54Jz3EMj9zhwEi5PMy8Py2Q2P4qmwt3W8oFFPrWYc1TkgFLOceFJ33gZByLci9KwoadIFpXn6pCDHR4CyvjLkXt81CdU4+Ez7nXi9Y4em6uatC47p8Vy0MS5PJrOTOLTkTOuzwdIebhCU73Is7RyF77S7aYwl2LFbEXOFJ8Rlp2eB87b4c+Nh+74/j9H5oj7PNyl9HtBIcXqAMGN9ioHRYd1IHdZ80FLPkDl5lTd/C7yPCVcOMjJzi9vHx8vVb4L95CoD/dkLNy7IZ/6yT028nKeV4OX8Rd9xvV5To1ff5vwxq9hnq9EPqtFfT/lfBXuFZLX4WCH8yrkddzciqXrSeGenswmeuMziTgokWfF5V4WeT4NDo6yO++J+nviWWDloSqeXTiz4IjbD6/DgYz6mTscFMrDHzmZwh5F+TrgoSq5R5CHt+TgOrtTBqCQQdYBghvtVQ6KjuqAf6HLPRHckyEPnfAv/Kymm89L4V99PNTBc4jkNhGPT7eUv3TlfA8eplHPD9GUpyBfJ0gu3FOS2fp82jevwzkt3IuiHmzIQ0M854c87CPPFyP3vHh6/n8eGzlxk7v11V+DhwDlgEZOEuU5RzLuC//K5invuWQniON9lGetVc/zycvnxsnHmeXzyHUgB1U8mZmccCpfjJHP2MH/csH7LpMDfP5/ldu20vuEQorWAYIb7VUOio7qgIcQ5NNA5XwLefjkbRcVzE2Rr+kjFz6tNKdnaPHBnfNR5LF/vtidnCjLQzGatseBmzyFOr+mekIv90xkXJ+DJHn+Cj7FM+PjfIVfObGWz1KSh474DAp5KIdzGnioSD2RWFN+A/f0yPXBw0raDCr5tHW+Vk5e5+fJSdApn2HDQSEP6x05Oldn7QlF93UgD5XK/2PZmdMIxbDrwDZnx2/ld1iPKwdFR3XAF2fjLyxOCJWX8bAGL+NggGeF1dZryVfKlYdW5J4K/iXIQUBOt8MHT3nyL85NkYOXO8E/ibOBePZSnr+Fk1/l4IOHV+QhE/nifbwd9bNkuIdCHsrhyeo05Sbw+nIXvdxzxBON8ZlDPPQiD+Pwe+QEXjmRN7MeJd4Gr5udmX71vXBekjyMxu9LDiL54o5K7xtKzuuApyVQ/0GSX4EyCultHSC40V7loOioDuR5TfiqzOo9I/KMoJw/waeDcgIun5HD82Tk5ton8lkXXPgMDV7GwzbylOU8zMNJjm/bDgcV8vwuGScR43wS9YvlZSzcvc4JlfL63EMiX/WZp6vn/BsO9uRZYDno4VmJM9sX9dlsuQdJfUiMAyju1VF/fb5kQVbXF8ppPow+Fw4I1S8GyPWs9D6h5K4OOCldvR0bQgCOQnmqAwQ32qscFB3UAfd6yF9YGYdy5IvxaSp85k1WuS0Zc0rkRFMunDOi/jgHSvLVhPlX/tsmBpOHcHioI+MFBLlwbgxfqZiTjTnHQ56/h3OINF3vhRMj5dlG1fNx+EKL3AOR1b7wabg8uV9mv2R5enz5CsA8VFPQTjXW1hwy3DOX0ytUo+hPHXC7lYek5LOmUAp3HdhiWEprlYOigzrgeS/4C4vzVzI7ZZpnLh4y5B2Rd8KBiTxhHCfIaro+EPeGcJ4H9/LI86rIhc8k0vQ6HKTIp5gOG/ZepvvLuTXyBfTeNtW/euGhtaxOe5Wnn5eHyPJyeQVN9cHDfDkZdkNBHehbG5D/PzmYV3pfUEjxOkBwo73KQdFBHfAF9fgLi8+Qyu5zePhEHhbiuVbUk195Ond5mEku/IuPJ27TlLSradgqq4tJynOl8DWKtF0XI0Z0FMGbvs2ei4I60Ic2MGBAWzF0zPPeKL0vKKR4HSC40V7loOigDuQk26zySjQVnoJdPhWap/vnuWPkM2K48NlWPCzEZ8pk9+rP3LMhJ+BqGr7hs4zkoSv1vBkU1AHaANoA2gAhuNGXRoDgRtn6l2fH5Z6V7AYg6oWHbnh4KGMvDc/jkpveD54sjue+4e3wmU+ZXf8n48R5KKgDtAG0AbQB0tvjtzGfCw6QX9q0qS1ujxy5QgkJSTl+/u7dp6lvnwBKTU0VZfWqA1Sp4mCaPHkTRUfH5Xh7vI1Dhy6l2zd173Won/a6u07leNsAAKAMBDeQr9q0TQsg/jpwPtfb2Lr1KNWuNYK8PIfQwIGLKSQkMk/7FPjXBXHr27pmuuXW1kWoVasaqqAKAAAKBlOldwAKD3NzU2revJr4+0Aeght2+fJ9Le0VUWBgWnDj4+MlApqYmHhxv3XrWmRhYUbBwaF0/fojrb0eAADoFnpuQOeKF7ejQYPa0f4DM8jKyoJCQp7T1asP9abm790Lo7t3n5KZmSk1a1ZVtfy997zF7R702gAAFCjouQGtMDIyorZta1Pp0o6i94ODGL6tW68C+frWJFNTE9W6P674U+9q/WDgBSo32E/01uzbd1a8n3f/C252Id8GAKDAUTTje+jQodK9e/ekuLg4KSgoSPL2znoisxEjRkg3btyQYmNjpYcPH0oBAQGShYWFTrKtC2PhGYP5AnVLlgzJ0YUl5Qs6ZlZOn1koffllV6lMGf2cQp1nsuX9vHDxe9WFGOVTxHN7BXEU1AHaANoA2gAVvnlu/P39pfj4eOmjjz6SKleuLK1YsUKKjIyUnJzevIIxlw8++EAEQXzr4eEhtWnTRnry5Im0YMECXVVOoSo8M7B8RWr5sgA8c+/bpu/n60PJz+FLGmzcNEZc34cvf8ABzdsubaAPhS86Kb8HvvzBtGm9xd9bt41VfN9QUAdoA2gDaANUcIIb7qlZvHhxuunwHz9+LI0dq/mAwusGBgamWzZ//nzp6NGj2X5NBDea62X06C6qCzdevbZU9LTIB3vuzWjatKrG5/HVqOWZg2fM+LBA/wPy5SD4fXzwQXPV33wZCKX3CwV1gDaANoA2QAUjuDEzM5OSkpKkTp06pVu+du1aaceOHRqfwz02L168UA1dlS1bVrp27Zo0fvz4TF/H3NxcVIRcSpQogZ4btfqxt7cWlzOQAxme+ZevUs2T2/GFIOUJ7uTH3NyKqZ7LF7GUr+LNF4vk5xTkf0C+Mje/lz/3Txe3ySk7xUUqld4vFNQB2gDaANoAFYzgxs3NTexkw4YNMxxg5ooencyeN3z4cCkhIUFKTEwUz1+6dGmWrzNlyhRJEwxLpc3Oe+jwLHEgj4v/TVxoMWP98SUHli0bKg70vB5fyXrMmC4iD4Vzc3jZk5B1b72SdUEobdqkvyL5sePzFN8nFNQB2gDaANoAGXZw07x5cyk0NFQaMGCAVK1aNalz587SgwcPpIkTJ2b6Oui5yfwz4As2ygELJ9Bm9XnVrl1eOn7iW9WBX74+FF9zqVkzw7ionaWlhQjy5Pc4blx3xfcJBXWANoA2gDZAhj0sdeTIEWnevPS/pnv37i3FxMSIfJ3svC5ybtLqga+yy4EJH8R79WqerbrjOuYclNCn61UBwNixhhUA8PCa/N74OlhK7w8K6gBtAG0AbYByfPxWbBK/pKQkOnv2LPn6+qqW8dwifP/kyZMan2NlZSWuBaQuJSVF9VzIHkdHO9q4aQyZmJjQmtV/0c8//5Ot50mSROvXHyLPSkNoxvQtNHXKJpo371eDqvaDgRfF7f37YXo10SAAAOSMoqeC86ndffv2lby8vKTly5eLU8GdnZ3F4+vWrZNmzZqVLn/m1atXUo8ePaQyZcpIrVu3lm7fvi1t2bIl269Z2HtuuPdl1+7JqrOiOClY6X3Sp8K5Q38FzhRnTCm9LyioA7QBtAG0AcrV8VvRGYq3bdtGTk5ONH36dHJ1daULFy6Qn58fhYeHi8fd3d3T9dTMnDlT9B7wbcmSJSkiIoJ27dpFX3/9tYLvQn/Nm/cxde3WiG7dekJXrzykK1ceUMWKJejdd70pPj6RevaYR7GxCUrvpl4JC3tJbVpPVHo3AAAgD4z+i3IKDVtbW3r9+jXZ2dlRVFQUGarvvx9Mw4Z3yPTxT4f8QCv08DIIAAAAeT1+49pSBmjhwoEisOFer7FfraVXr2KoWjUPqlrNgzw9S4oLQSKwAQAAQ4XgxsDMn9+fRozsJP4ePGgJrV79l9K7BAAAkK8UO1sKtMvY2Fjk2Iwa3UXcHzxoMQIbAAAolNBzU8A5ONjQwIFt6dOh7alMGRdVPs3KlQeU3jUAAABFILgpoKpXL0PDh79HvT9sQZaWFmLZ8+ev6asv19CaNYFK7x4AAIBiENwUIKamJtSliw99Nuxdatasmmr5+fPBtGTxbtq8+Yg4xRsAAKAwQ3BTQIKasWO7iaGnEiWKi2VJScn0228nRVBz/Pg1pXcRAABAbyC4KQAmTHifpk7rLf4ODY2kH1f8ST/+uF/8DQAAAOkhuNFznE8jT8Y39qs1tGjRH6LXBgAAADRDcKPnPvrIV1zoMjg4lBYs2PHGhUMBAAAgPcxzo+dz13wxKm1CvoUBCGwAAACyA8GNHuvcuSFVqFBCnOK9du1BpXcHAACgQEBwo8fGfJk22/CypXtx9W4AAIBsQnCjpxo3rkING3qJeWuWLNmj9O4AAAAUGAhu9NToMWm9NhvWH6Lw8JdK7w4AAECBgeBGD1WqVJI6dqwv/g4I2Kn07gAAABQoCG700KhRncWZUjt3BtHNm4+V3h0AAIACBcGNnrGwMKM+fVuKvwMW7FB6dwAAAAocBDd6pk6d8mJW4rCwF3T06FWldwcAAKDAQXCjZxo1qixuT5y4ofSuAAAAFEgIbvRMQx8vcRt0EsENAABAbiC40TONGqUFN+i5AQAAyB0EN3qkTBkXcnMrRomJSXT27B2ldwcAAKBAQnCjR3z+G5I6f/6umJkYAAAAcg7BjR4OSZ1EMjEAAECumeb2iaVLlyYPDw+ysrKiiIgIunr1KiUmordBG8nEJ05cz9N2AAAACrMcBTcczHz66afUs2dPKlWqFBkZGake48Dm6NGj9OOPP9Kvv/5KkiTpYn8NlrV1EapZs6z4+yTOlAIAAND9sNR3331HFy9epLJly9LEiROpSpUqZG9vT+bm5uTq6krt27enY8eO0fTp0+nSpUtUr1693O9VIeTtXZFMTU3o4cMIevLkudK7AwAAYPg9NzExMVSuXDmKjIx84zEeljp8+LAoHNy0a9dODFudOXNG2/tr8MnEGJICAADIp+BmwoQJ2d7o/v37c7s/hZbPfzMTY/I+AAAABc6WOnjwoBiSysjW1lY8Bjnn4+MpbjF5HwAAgALBTYsWLUSuTUZFihShpk2b5nGXCp9KlUpS8eJ2FBubQBcu3FV6dwAAAArP2VLVq1dX/c0Jxer5NyYmJuTn50dPnjzR7h4Wovltzpy5TcnJKUrvDgAAQOEJbi5cuCBO8eZy6NChNx6Pi4uj4cOHa3P/CtWVwDF5HwAAQD4HN3waOM9tc/fuXapfv744S0p9npvw8HBKTU3Vwm4VLpi8DwAAQKHg5uHDh6ohKNAOe3trqlbNQ/wdFHQT1QoAAKBEQvG4cePo448/fmM5L/vqq6/yuk+FSsOGaWdJ3b4dQhERr5TeHQAAgMIZ3HzyySd048aNN5bz9aWGDBmijf0qNFq2TEvSxuR9AAAACgY3fLmF0NDQN5ZzDo6bm5s29qtQsLS0oP4D2oq/d/1xSundAQAAKLzBzaNHj6hx48ZvLOdlISEh2tivQuHjj33J0dGOgoNDaceOIKV3BwAAoPAlFMt++uknWrRoEZmZmalOCff19aV58+bRggULtL2PBsnY2Ji+GNVZ/L0wYAfOMgMAANAiKTdlzpw5UmxsrJScnCxKdHS0NGnSpFxtKz+Lra2txPhWyf3o1q2RlCrtksIjNkmWlhaK1wsK6gBtAG0AbQBtgAzk+J2rnhv5jKkZM2ZQ5cqVxeR9t2/fFnPdQPaM+bKruF22dC/FxSWg2gAAAJTMuVFPLC5WrBgFBwcjsMmBJk2qUIMGnhQfn0hLluzOy0cAAAAA2ghuOKAJDAykW7du0d69e1VnSK1atYrmz5+fm00WKqPHdBG369cdwtw2AAAA+hDcLFy4kJKSksjd3Z1iY2NVy7du3SoungmZ8/QsRZ06NRQJxAEBO1BVAAAAWparnJu2bdtSu3bt3rgCOOfdeHikXUoANBs9Ou0MqT/+OEW3buEK6gAAAHrRc2NtbZ2ux0Z9uCohAcmxmbG1taQP+7QUf8//9rfcVD0AAADoIrg5evQo9e3bV3VfkiRxtXC+rtThw4dzs8lCoWLFElSkiDmFhkbicgsAAAD6NCzFQczBgwepXr16ZG5uLibvq1q1qui50TRzMaTx8HAWt/fvh6NKAAAA9Knnhi+QWalSJTp27Bjt3LlTDFP99ttvVLt2bbp7967299JAlCmTFtw8eIDgBgAAQG+CG1NTU3EauLOzM82aNYt69OhB7777Lk2aNImePn2aq50YOnQo3bt3T0wGGBQURN7e3pmuy8NePAyWsezevbvA9Nw8RHADAACgP8FNcnIy1ahRQ2s74O/vTwEBATRt2jSqU6cOXbx4kfbv309OTk4a1+/atauYPFAuPBzG+/TLL7+QvnP/L7h58CBC6V0BAAAwWLkaltq4cSMNGDBAKzswatQocSHOtWvX0vXr12nIkCHiTKz+/ftrXP/FixcUFhamKm3atBHrF4TgxsMjLWC7fz9M6V0BAAAwWLlKKOahKQ4+WrduTWfPnqWYmJh0j48ePTpb2+GritetW5dmz56tWsZDTDzs5ePjk61tcJC1ZcsWjaemM054trCwUN23tbUlpZQp4yJu0XMDAACgZ8FNtWrV6Ny5c+JvTixWx8FJdjk6OopAiXtg1PF9Ly+vtz6fc3OqV6+eZS/S+PHjaerUqaQPc9w4ONiIv5FQDAAAoEfBjbGxMU2ZMoUuX75ML1++JCVxUHPp0iU6ffp0putwrxDn9Kj33GScWTk/k4mfP39NMTHx+f76AAAAhUWOc274mkgHDhygokWL5vnFnz17JpKBXVzShmtkfP9tZ15ZWVlRz549xcU6s5KYmEhRUVHpihLk4AZDUgAAAHqYUHzlyhUqV65cnl+cL77JOTu+vr6qZTzTMd8/efJkls99//33RS4NJzcXBEgmBgAA0OPgZuLEiTR//nwxvw2fjs1DPeolJ3jIaNCgQeJyDpxns2zZMjEp4Jo1a8Tj69atE/PpaBqS2rFjB0VGRlJBICcTP8Rp4AAAAPqXULx3715x+8cff6RLIOZeF77PScLZtW3bNjGnzfTp00WgdOHCBfLz86Pw8LRZfN3d3cVQmDpOYm7atKk4Dbyg+P8cN5idGAAAQO+Cm5Yt065srS0//PCDKNl9rVu3bolAqiCRh6UQ3AAAAOhhcHPkyBHt74mBQ0IxAACAHgc3zN7eXuS9VK5cWXUxzdWrV9Pr16+1uX8GoUgRc3J1dRB/Y3ZiAAAAPUwo5lmFg4OD6YsvvqBixYqJwpdR4GV8ZXBIz909bUgqKiqWXryIRvUAAADoW8/NwoULRTIxn+WUkpIilpmYmNDKlStp0aJF1Lx5c23vZ4GGISkAAAA9D27q1auXLrBh/Pe8efPozJkz2tw/g4BkYgAAAD0fluK8Gj5FO6PSpUsrNgNwQei5wRw3AAAAehrcbN26VVz2wN/fn0qVKiVKjx49xLDU5s2btb+XBRzmuAEAANDzYakxY8aIyfrWr1+vmrCPL6XAswuPGzdO2/tY4JUpk9ZzgzOlAAAA9DS44UBm5MiRNH78eCpfvrxYxmdKxcXFaXv/DAISigEAAPQ8uLGzsxNnR7148UJcRFPm4OAgrvKNvBu1CjY1oZIli4m/MTsxAACAnubcbNmyhXr27PnGcs7B4cfg/0qWLC4CwYSEJAoLe4mqAQAA0MfgpkGDBnT48OE3lv/999/iMXgz3+bhw4h0FxkFAAAAPQpuLCwsNF7528zMjCwtLbWxXwaXb4NkYgAAAD0Obk6dOkWDBw9+Y/mQIUPo7Nmz2tgvg4E5bgAAAApAQvHEiRMpMDCQatasSQcPHhTLfH19ydvbm9q2bavtfSzQMDsxAABAAei5OXHiBPn4+NCjR49EEnGHDh3ozp07VKNGDTp27Jj297IAwwR+AAAABaDnhl28eJE+/PBD7e6NAScUP3gQofSuAAAAFArZ7rmxsrLK0YZzur4hMjIyotKlncTfSCgGAADQs+CGh53Gjh1Lrq6uWa7XunVr2rt3L33++edU2Lm6OpCFhRklJ6fQkyfPld4dAACAQiHbw1ItWrSgWbNm0dSpU8WQ1JkzZygkJITi4+PFzMRVqlQReTg8Q/Hs2bNpxYoVVNjJycQc2KSkpCq9OwAAAIVCtoObW7duUffu3al06dL0/vvvU9OmTalRo0ZiXptnz57R+fPnadCgQbRv3z5KTcWBPP01pcJ19wkCAABA3hKK+QypgIAAUSBrZcq4iFskEwMAAOj5qeCsf//+dPnyZTEsxYX/HjBggHb3zkCGpR6i5wYAAEC/TwWfNm0ajRo1ihYvXkwnT54UyzjfZuHCheTu7k5TpkzR9n4WSC6uDuI2JCRS6V0BAAAoVKSclvDwcKlnz55vLOdlEREROd5efhZbW1uJ8a2uX2v/gelSqrRL6t27heLvGwV1gDaANoA2gDZABbgOcnL8ztWwFF8gk8+WyoivK6XpgpqFla1t2kVEo6LilN4VAACAQiNXwc2GDRvo008/fWM5X0xz06ZN2tgvg2BjkxbcREcjuAEAAMgvue5m4eRhvkhmUFCQuN+gQQORb7N+/XpasGCBar3Ro0dTYYWeGwAAgAIS3FSrVo3OnTsn/i5fvry45bluuPBjMknioa/Cy8amiLiNjo5XelcAAAAKjVwFN61atdL+nhgg9NwAAAAUoHluIGtmZqZkbm4m/kbODQAAQP5BcKPjXhuGYSkAAID8g+BGx/k28fGJ4qrgAAAAkD8Q3Oj4NHDMcQMAAJC/ENzoeFgKQ1IAAAD5C8GNjoeloqJidfUSAAAAoAGCGx1Bzw0AAIAyENzoCHJuAAAAlIHgRkfQcwMAAKAMBDc6z7nBRTMBAADyE4IbHffcxOCK4AAAAPkKwY2OIOcGAABAGQhudAQ5NwAAAMpAcKMj1si5AQAAUASCG5333CChGAAAID8huNER5NwAAAAoA8GNjiDnBgAAQBkIbnQE89wAAAAoA8GNjiDnBgAAQBkIbnQEPTcAAACFNLgZOnQo3bt3j+Li4igoKIi8vb2zXN/e3p6WLFlCISEhFB8fTzdv3qR33nmH9ImRkRHZ2lqJv6Oj45XeHQAAgELFVMkX9/f3p4CAABoyZAj9+++/NHLkSNq/fz95enpSRETEG+ubmZnRX3/9ReHh4dS9e3d68uQJeXh40MuXL0mfWFlZqP7GtaUAAADyn6RUCQoKkhYvXqy6b2RkJD1+/FgaO3asxvU/+eQT6c6dO5KpqWmuX9PW1lZifKur9+Xq6iClSrukpOQditUtCuoAbQBtAG0AbcCQ2kBOjt+KDUtxL0zdunUpMDDw/1GWJIn7Pj4+Gp/TsWNHOnnyJP3www/09OlTunz5Mo0fP56MjRUfXdOYb4MhKQAAgEI0LOXo6EimpqYUFhaWbjnf9/Ly0viccuXKUatWrWjTpk3Uvn17qlChAi1dulQEStOnT9f4HHNzc7Kw+P8wka2tLeka8m0AAACUo19dHm/BPTScbzN48GA6d+4cbdu2jb755huRs5MZ7tl5/fq1qnCeTv713ODSCwAAAIUmuHn27BklJyeTi4tLuuV8n4ecNAkNDaVbt25Ramqqatn169fJzc1N9N5oMnv2bLKzs1OVkiVLUn7NcYNkYgAAgEIU3CQlJdHZs2fJ19c33SnUfJ/zajQ5fvy4GIri9WSVKlUSp4Xz9jRJTEykqKiodEXXkHMDAABQSIel+DTwQYMGUd++fUWezbJly8ja2prWrFkjHl+3bh3NmjVLtT4/XqxYMfruu++oYsWKIu9mwoQJIsFYn8g5N+i5AQAAKGTz3HDOjJOTk0gGdnV1pQsXLpCfn5/Iq2Hu7u7phqAeP35M7dq1o4ULF9KlS5dE/gwHOnPnziV9gpwbAACAQhrcMO51yaznpWXLlm8s41mMMztVXO+uKxWFhGIAAID8VqDOliookHMDAACgHAQ3OoCcGwAAAOUguNEBa8xzAwAAoBgENzqAeW4AAACUg+BGB5BzAwAAoBwENzqAnhsAAADlILjRARub/04Fx7WlAAAA8h2CGx1Azw0AAIByENzoAHJuAAAAlIPgRgfQcwMAAKAcBDdaZmZmSubmZuJv5NwAAADkPwQ3Ouq1YdHR8drePAAAALwFghsd5dvExydScnKKtjcPAAAAb4HgRkengUfhiuAAAACKQHCjo2EpDEkBAAAoA8GNjoaloqJitb1pAAAAyAYEN1qGnhsAAABlIbjRMuTcAAAAKAvBjZah5wYAAEBZCG50lnMTp+1NAwAAQDYguNFRz00MrggOAACgCAQ3WoacGwAAAGUhuNEy5NwAAAAoC8GNllkj5wYAAEBRCG501nODhGIAAAAlILjRMuTcAAAAKAvBjZYh5wYAAEBZCG60DPPcAAAAKAvBjZYh5wYAAEBZCG60DD03AAAAykJwo0VGRkZka2sl/o6OjtfmpgEAACCbENxokZWVhepvXFsKAABAGQhudJBvk5KSQnFxCdrcNAAAAGQTghsd5NtgSAoAAEA5CG60CPk2AAAAykNwo5OeG1x6AQAAQCkIbnSQc4NkYgAAAOUguNEi5NwAAAAoD8GNDnJu0HMDAACgHAQ3WoScGwAAAOUhuNHFdaWikFAMAACgFAQ3WoScGwAAAOUhuNEi5NwAAAAoD8GNFlljnhsAAADFIbjRIsxzAwAAoDwEN1qEnBsAAADlIbjRIvTcAAAAKA/BjRbZ2Px3KjiuLQUAAKAYBDdahJ4bAAAA5SG40SLk3ABAQVbExprMLdN6oAEKMgQ3WoSeGwAoqNxrVKWv9/9GkwN3UtWWTZXeHYCCH9wMHTqU7t27R3FxcRQUFETe3t6ZrtuvXz+SJCld4ecpzczMlMzNzXKUc2NuWYRaf/IxfbF1LVVu2oj0lVulCtSiXy8qU6tGjn8FulYsL96ntjm4uZJNMQetbxd0x9qhKJWrW4vMilhkuZ6xqQmZmJmRqbk5mVpYiJJTxUuVpMrNGr/1td6GX7t53w+oum9zMjLW7tellb0dNf6gO9k5O5G2FXV1oRptWoo6zI6ydWrSJz9+R1Z2dmRpZ0v9v59H733xmfgsdIE/l26TvqLxe36hSj71tb591wrlqGKDelmu4+heirya+mj9c9UHTmXcydLOLl9ey87JkUpXq0L6xlTpHfD396eAgAAaMmQI/fvvvzRy5Ejav38/eXp6UkREhMbnvHr1Sjwu4wBHX3ptWHR0fJbr8j+Td8f25DdsMNm7pH2x9QuYRSsGf073zl8ifVHSqxK1GdJffLHLIh48ojO79tHZP/bRi9CnGp/HB6amvf2p9eCPyNLWRix7+TSMwu8/pPB7D+h20Gm6fuQEpSQn53if+Mu29cB+IihMTU6hfzZsoUOr1lNCTGy2nm/rWJycy7hT8dKlqHipElS8dElKjI2jv9f9LPYtL7g7v1RVL3KvVoXcq1ehkpUr0avwCDq6cRtdOXSEpNRUyi9GRkZUo20rinr2nO6evZDjL6vXEc+0uj+lq1am/ovniW0nJybS/YtX6Pa/Zyj41Fkyt7KiUpU9qVQVLl5UrKTbG89/dPU6bZ38DYXeCta4fZdyZciriQ+VqV2DytSqTnaOxcXy4LPn6achX1BSfEKO97moizP1WzRbfJ6M28eh1Rvo3O79uWq76rgePvnpe3ItX5Zaftyblg8cTs8ePn5jPZviDtSwWyfRvrnOQm7cyvK1+f+jWe8e1HboQLKwsqTIkFD6c8lPdG7P/kzbX4X6dan/4m/F+reDzlDI7TvUvE9Patn/Q3KvWZU2fjk5y/ZQvl5t6jlzEkU8eEgX/jxIlw/+TXGvozJd37msB/Vd8A25VSwv7g/4YT5t+XoGnd/3F2kDb3/Ez6vEj6obx4Lo15nzKPJJaLqAtfXgftTy4w/J1MxMfK5/rVhN5/cF5vh/lOu7/eefknen9nTtn+N0bPMv9OT6rbc+p4ZvC2rUsxuZmJrS9hlzM23XOcXHlnZDB1KbTz6mqOeR9NOnX7x1f/KCA0g+dnFAfHjNJtqz8Ae9OB4zI44NlNwB7qk5ffo0DR8+PG2HjIzo0aNHtHjxYpo7d67GnptFixaRg0PufrXb2trS69evqXjx4hQZGalabmJiQsbGxpSamkopKSmq5WZmab0xycnJqg+N1+P1+T4vZxU93enype8pMSmF7G27Z7ouN4aOX40gt3JlRdVHhobRy7Bw8QUR8/IV/Tjwc3oafFe1XXkfjE1MyLl8GfKoWV38yipbszqlJCZR4E9r6N8de1Trmpqaijrk98DvRa5TXs77YGRqovqi17Qu72+5WjWoeb8PqEqrZmIZP3b3zHkq5VWJLGysVb907p29IA5Qd/49S/cuXRbBRrVWzajDF5+JwIGMjSkxPkF8aYr6+K9ejUxNKebFSzr/Z6A4UITevE1OZT3EQYkPTqUre1Lk4xA6tWM3XTr0j9iufADrOf1rKl29imofeLvREc/o4E/r6Pj2Hap1M36e/IXWYfQwavx+Z/kDF+9dbCM1lVKTkuncngP057KVIhDL7LOXt1uisieV8KxITmXdRbDkVLqU6E0yNjdLt13iYmREz0NC6cj6LXR65x6iVEkcfG0ci5Gdi7MIAPmz4F/Z/GVHxkaUGBdHcVExlBQbSwnRMRT+8LEqmNTU/tQ/T1723qhh1OKjXmIfQm/doRNbf6Mzu//M8rPnnrZe30yiKs2aUPC5C7Q74AcRVGTVplhSUlKW7a966xb0wfSJZG5pQcnJKareFInbA9ersXG6z1O9nciklFRKTkigA8tX0eF1m1UHIddyZajN4I+o1rvt0q+bmCi2ZW5lKQ46a0aOJRMj4zf2N7P/+4redaj3nKlk4+RIsa9ek5GxEVna2IjPkwPWfzZtEwF6xP2H2f6OkNsU96oMXBZAjh6lVe0kKjyCfho6mkJu//8gV7F+Per1zUSydXFWtSkOxB9fvUEPLl8Rwc79i5cp+vkLsd3SVb2o84TRVLJy2g+/hJg4MrcwE+0vNPieOPDwwV79M6rY0Js+/m4umVqY081jJ2nDl5MpPiZGfGY9Z0wkC8siFP08ktZ/OYnunrv4xmfvUKoEDd/4o+jxkT/P5JQUESRdDvybHl27QZEPH1NqSoqo9zrvtaPuk74icwsLiop4Ro9v3KYqLZqIbe2Ys5CCtv2erc8os/ZnYWlJw9YtIzfPiqo2FR8TS4HLVon/Afda1anbxC/JyaO0qPdEriNrS7GuCHJ+XENXA/9R7W9W+8A/lPrMm07latdI931y/8IlCvplh6jrhLg4SklKFgGpg4sTeXfpIIJVe1dn1WefHJ9A+5b8SEc2blW1H7lNmZibi95v/hFc1DmtWBdzEPXK348c9MrrmllZUa85U6hyEx/Vd09cdAytGTlO9QNH/TvibW3VvVplqt6qBaVKqXRy2+/iOKVe77XfbUfdJ48lEzNT1Wd/6eA/9PPXM8T/ak6+I7K7rnz8trOzo6iozANoxYMb/lBiY2Ope/futHPnTtXytWvXUtGiRalz5/8ORBmCm5UrV9KTJ0/Eh3Lu3DmaMGECXbt2TeNrmJubk4VatzZXDj939uzZNHPmTPH6rGnTpuTr60tnz56lXbt2qdbnbfM2OKB6+fKlWNawYUPy8/OjS5cu0W+//SaW+Q/rTfXcK1NsbDIdC75BgRu2iOV16tShjh070o0bN+heYgx1nzJO7HfUnr+J4hNo5erVFBYeRp/8+D2VKu5I8acv04PHj2jNylWqX4+ffPIJWRqbkGUzbzJ1KiaWJz0Jo/igC2RcvCiF2VrQr9/Mp6e3g2nQoEFUsmRJ+ufCWXJrUEf86rRITCajy7fJ2N6GrFs3FhF98Jnz5BD2kopZ29Cvv/9OyXbWYpy9Sq2aZHzpFhlZW5FVm0Z04c9A+mvFGmrt05gqVapE5+8Hk1vj+lSxYT1KefmaYg+eJKMiFmTa3FscfPnXWFzQBUp+EkZXHt2nX1evE1F9+apVqVvbdpQsSWTSvJ745crizlym5AchZFGtEpl7csBHlBoXTzF7/xFfylLTOnTq990U8+oVtW7RilIfhpBRuVK0Y8s2EQC8N3wIWV66nbat2p50eO3PdH7fAWrRtBk1btyYjh8/TpfvBdOH86aLX8nRv6f9OgxxsqNnISEiiKrlVZmKJqSQWbnSZFq1Ap3Y+jsd27ydhn70sfinX7BggeofqXPvD6hWRU8y9ShBlvWqq9pJ1B8HiZKSKbm2Fz28E0yPrlwjq5gE8q5Sjci5GNk2TRtq5eAi4eBJkuITyMrXh0yKpnUdJ91/QvFnr5CJqyNZNa6r2m70n0dJiokly+b1KfjWLfEFLYVH0vvdu9P9+/fF/4qMez9dXV3p4tMn1OSz/ml18uAJJZ+5QsYOdmRUtyr9++sfdOznX6jru++Ru7s7bdmyRbRN7skaOGsKWQY/IWNba7Jum3bA4c/f7NYDci9Zinb/uY8SrItQ2bq1yL1MGXIMf0WxiQm0bstmcWDgL8eePXuSl5cX/fHHH+J/s9WAPuTXvy/F/nWcUoyI5s2dR7aOxahiA29qVLMW2SZLlFjKmR48jxAH7Rf3H1LXVm0oMSmJFixaKL6dOPgbMGI42SamkHnVChQSG027Fy6lBh3focrGaT2mtt3a0fVjJyn49DlySiKqUr4CXbp1g7xHfiJ+wZ/fvZ8q/Nd5880335CJhTm1+aQ/edeqRanBjyiSUujYmVMiUK/aoim1q1oz7XP1dKfVoyeIAKfHwP5UzsGRzMqUpCJ1q6U9/jySUv8+Lcb3tx/4kx7cui16yzR9R7CxY8eSpaUlWbVpTC9fvxLBRO/Ph1KRkGdEjg7005IfKOTmbWo1oC81q1iZpLh4iqvkTs8iI8mjZjUyfx0jviNMnIuTVdO0YZdnjx6T8dnrZJaSKr4jEsxMaNeCxRQb/IDe79adyN6WbFunDXvzD6jkoItkHBNHMaWcybF2VRFUBx/4m5yjEujp06e0fPly1bDNkGHDyDQ2nszqVKEt3y8VAUvp0qVpwIAB9OLlC7Ju11QMAXKQRZfvUHFLK1E3XEdM/o5INTWhp4425Nm4oVgevucQWcYn0d69e6lEq8aipzc1KoZiDhyj+Ph4mjNnjqrO+DhQq1YtOvbvvxRlaSp+3EWFhJG3c0lx0JsxY4Zq3WGTJpCjiTmRRwlav2QptftsEJWvWZ2idx0Sj9t0aSMCmVdhEXRnyw6qVMqdHr1+SR5d/Mi6qL0ICuTviAOXL9DVY8fFui1atBDl1KlTYp95H/rOn0lGR8+Jg/rRuzfItWplMRSYevcxJVy5lel3BP9vRSfEiwDIs2w5KhaTSKYlXehhagJtmTiTEuPjacTwz8nS3JzMm9YlC2dHjd8R8dExIsApm2hERe3tKbFyWSpepRIlxsVT0LI1VNujHJk4OpBZg5q0btQEun70hOo7Yv369XT37l2x3YoVK1Lv3r3FcXHX4YNUy6811WrnS1b3Qyn1+Usq0rAWGTkXo9M79tDNvX9Rt/c6UmxyErn0eE88n3sFi0a8IidbeyriXZ1CXkTS6hFfkZ2FJQ0ePFgcN/n4Kcv4HcGcnJzos88+o5iYGPr2229V63bt2pVq1KhBf/75p+gIyUlwo+iwlKOjo4jMwsLSfinL+D6/eU1u3rxJ/fv3F18a9vb2NGbMGDpx4gRVrVpVfDgZjR8/nqZOnUq69uD0GRHcsLZD+B//FZ3dtU/1uEMJN/L+2F/8zQ2yRGSs2P/U5GRxwFs17EsasWgucXYKd9GXrV2DGnTrRHXat6X4w0HiH5+j9NscJJ27RMmh4dTSu4H4lcp5DKO2raXjm38lhyK24jX8hg4iU7e0Ia/ksGeUlgWU9svCtngx0Xhj/zlFKc9eUK9Zk8ncvUTaus9eiHUTYmJoSede4pep4NM47X1euEw7124Qvz69W7ekxhW8SJJSxa9+DmySEhIo7O59Km5hSQ8uXRUHPD4wPLlxk4iDm4QEmtWms+jBqtfxHfIs7iK2m5SUSPdO/Ev3z1+iiDt3qVOTFiLq5iCIh7dY/PlrxHE+H+DP7z0glt09dZbGfvmVapy5x/QJoofmyd5DqqE131lfi96C1+ER/9UA0frRE8SXqPDfFxf/MnGtXYWa9ekhyuvf9ouDK38hJyQnke+gfuRiYiG+uPiX3bUjx8VBneuolWc1Mjczo6UfD1X1CIrcsSrVxC/8x0f+oWZ9eooDhjxAwl+aL4PvUuzr12QZm0j8W+5VWDhd/m2X6O3ig7pLYgJx1gPXo2ejBqK8vHKT6Ob9tF4eDRp27yhud81fTBEXrtD7XbqKngx7ezsx1NCsb096zl+0//2s4V/v/EVtnphMccFP6MXTMLq2cy/V7eAnvuhiLE9R6rMX4he32X/tJCXyFcWGB4kDwlc7N1NcVLQIrq0ehRMlp4pf/lV7dRXtLOV1dNrnFx1DCdwT9TBWDMG4pRiJL65DK9eLLy7GP2qoVRtxwJCHGvk5t06eorp161JyQpI4sAzfsIJSExIpZvdhsc6C7n1FUCDavp8fUfkK9PJpOK0bPYH6fzePavn5UvTOg+Lx+l06UJuhA8T/QcKV25TIvYIVylLvbu1VdRj1635xu+qz0fQy8oX4+965i1TO15funr9EFlKSGHrkbUSZmIjeJu6eN7a2FAeX1+evEoU+E8OUzWx60ouQp6LXpYitLXfxiKB62edjRBv4e91m8mvTRgyRfLpqCT2+dlP8eIje94943S1fT6cH9+6Lvxu3akFtmrUQ7eTV7WByKV+WHEuXopjr98V3xM1jQbR14WLRMyp/hz6+doOePrpHTXp1F59XjLkZpcbEibbIgc3FA4foxPK19GHv3unaEX9G/H/r4e4hhpp5KIl7Vx4e+1c8zv+bRUuVpOePn9Dq4V9Rp3faU/FKlWjPd8vIsmIZqtCgLpVwS2sv3Fb5/4h/ff+1bBXZR0ZT1SpVRLvmbXKQ6PdxH7GuhZWVGLIT7SUmhkqkmhL/4/sO6PPmDyA1VVs0SfsOuvuILh86InorlvX/jBp160Rtq9dOe15qKp3YvJ32LV5BzRo1FsHNw8tXaePyH0X+U7Pe76u213XCKOpmPlYMxSffvEcUFSfaXqfqlajxf0NKr7k3nL/Tf99DUeu30M55i6hb3z5U1j7tR6gmO7/9nk7u2it6cyK9vendd98V3yeejRuI/yX+roo/cFz8AOLPh3uSeVgtNSSc+Nvy2f1HZO5aTPxYa/ZhD4r+8whJMXFk7+wkPou1I8dTURMzEdxwMOtUxEL0zm2Z9P8gUMbb56Fg5lapPH35edqPIhZ9N+14Gno7mNxLupDP+52pbtPGlHD8HNk4FBWPHVi2ivYvXUl9+vQRwU1CbJwIwj/fuJL+mDSLlKRoz42bmxuFhISQj4+P6suN8XBU8+bNxa+ft+Hg6Pr167R582aaPHlytntutD0s5elZisaN96eiZctTsENt8Qtg/ZiJdPXQEWra633qMGY4GZmY0D/rN9Mf336vcbsOri40bO1Ssnd1EevKbp88TUc2bKEbx4PSDR/xPvAXzHtjhotfDEzuHox+8YouHDhIV/8+Jr7oUhISKSE2huJj48QXQPn6dalCvdrkUb0qmVkWEV3tvO61w0fp/oXLlJKUlP2uRCMjcXDg7d45fY6iwp9lu9vRytaWiro6i3yc5P+Wy+vyl0elJg3FPxUPAQX+uIb+/WWn2G7Gz4iDq7qd3qVGPbqIX5KqIaH/hjz4Vz2P6ydEpR1oM+tyLl+/DrXs30cEjCb/dTOrdznHR0eLfTiyYSu9VMsJy0474f3g7vDE2FiKffFK7EN2hjGYnYuTCHYbdO0gDlD83lKSU+jKP8dEsMc9FtVaNKU+384QvRJ/r/2ZdgcsUW2XP6OKPvXFlyEfOOV28vDqDSpdrbIY9uTAdeNXk0XPA+8bJ5JzUqmnj7dq+Ohp8D1x0OBeFmeP0iKRsHSNqiIYU29/cr3zl/aOuYvo1Padeeqelj8jrgf/6V9TBe86Yqj0wNKVor1mNYRQ+5029MHsKWQsSaKd27u5itfhIHxPwA+irZSrV0v8T8hf9PsXr6B/1m3Ocrt8wOeu+/J1a1PZ2tXJqWwZcijhKupSfUhS/X9ZSk4Wdbjik5HigC5/9lZ2ttQvYDaV8047CPMPnh2zAujs7n1Ztilu9xxklfKsJIYQ75w5n/m6tjbiAGhpxad7W5Cxubno/RR5fpKkGrrOONTJ76fDl59Tox5dxTLOcSvm5ko12/pSXGwMLf5wsAjyMxvmdnIvTSU8K1Ax91IUfOqc6OXRtC637W4TxpCxifEbQ5IkpYrhrkdXrtPDy9eoXqf2ZG1rIw7eG8dPo/B792n09g3i++Tohi20e9HSdN8RPCRUt0N7unHiXxHsafo8xf6amFDZmtVEz2LFRt7kXr1qhs/TmIxMjFU9FjtmLRDBrKahax5e5s+f2wl/l5nxcDz3nGn4PB3dS4s2ynl67OmtO3Tl4BG68NchEVxo+jw5EZt/hHn5NBCH8ZsnT9PGcVMp7vVr1bq8r90mj6V6Hd4R20hJTBTDjjxkGR8bKwJjMwtzVVvleuDjDOdO3ToWJNoHL+OAhfN4KjVqIAL55KRk+mXGPNUPePnz5GFKzqHi7XKe1vzOvUT9YFgqG8NSmmzbtk182L169Xrrujnp1soN/oDfnzKOGnTrKA7WnFNSv8t7qi+FPYuWZfl8ziv5bN1yMZTDXcCHV28UX1pv49WkITXt3YOinj8X/3R3Tp0TB5a34cjdzql4uoS7gowPqF6NG1LjD7ql/VJMSaE9C5fSUbXx7OwmB5f3rkOejeqL3hL+PIJ+3UlHN2wVX1BK4S/Kmm1bUuOe3UWOkowP1Jzzw0MwZ/7YR1smzsj0/fIXKJ/9VrOdr6r3h3OBtk+fJ3p4MuKDp7WDAz24eFn0wmXEX/58dgrnkFjb25O1gz1ZFbUX+Q/n9h4QgZe2cc+hnBuVHT7+XUTPE+Oesv0/rKQT235T5WjJ+HPmeov/LwjOKa7Pom6u5Fi6JBUrVYKKlXAVvbYc9PDnw71LmydM19iG+LPrMf1rKurmQtunay/JVFu457L950NU9zmXZMUnI7T6+XKduZQrK4K2ItbWVMTGioxNTcUwLwexfKBkXJ/cU8aJ6vw/zonTfEDl70oOtvKa8C3jM45KVa5ENsWLiV46PkOTf1xwgH9295+k7f9t7n0KC74n/p+zi//vXMp60LUjJzQmRBsZGdF7o4eJoT9Nvb3cY81DVtzDzLlSHNBkhr8L6r7nJ5KvObcos7MiP140R/x/cR6jNhWYnBvGPTY8jvn555+n7ZCRET18+JCWLFmiMaE4I45Qr169KsZBR48erXhwIx9ge8+eQrXbt1UtO7B8Ne3/4adsPZ//eTiRjIdRIG8HQJaTg2BBwj0rjfy7iCRNPhAwHipbM2LsGwftzOqHg/AXT0LFUKmh4y9lPhAc27RN0QC1IOOzgt6fOl4cJDmAPr1zr2L7wj/Muk4YLdow4x6Jhf4faTzrDEicVMFD3VxE8GhjI34M6yKI5mOgLs4OzenxW1Ky+Pv7S3FxcVLfvn0lLy8vafny5VJkZKTk7OwsHl+3bp00a9Ys1fqTJk2S2rRpI5UtW1aqXbu29PPPP0uxsbFS5cqVs/V6tra2EuNbXb4vY1MTqe+Cb6RvLx6XWg/+SNE6RjHsOrCwtpJ83u8i+Q0bLJlbFlF8f1AMuw7cKlWQynvXUXw/5NKgawdp9Pb1UrVWzRTfFxTSaR3k8Pit/Afy2WefSffv35fi4+OloKAgqX79+qrHDh8+LK1Zs0Z1PyAgQLVuaGiotHv3bqlWrVq6qpw8F0s7O8XrFwV1gDaANoA2gDZABbwOcnL8VnxYKr/lx7AUAAAAKHf8Nrx5pwEAAKBQQ3ADAAAABgXBDQAAABgUBDcAAABgUBDcAAAAgEFBcAMAAAAGBcENAAAAGBQENwAAAGBQENwAAACAQUFwAwAAAAYFwQ0AAAAYFAQ3AAAAYFAQ3AAAAIBBQXADAAAABgXBDQAAABgUBDcAAABgUBDcAAAAgEFBcAMAAAAGBcENAAAAGBQENwAAAGBQENwAAACAQUFwAwAAAAYFwQ0AAAAYFAQ3AAAAYFAQ3AAAAIBBQXADAAAABgXBDQAAABgUBDcAAABgUBDcAAAAgEFBcAMAAAAGBcENAAAAGBQENwAAAGBQENwAAACAQUFwAwAAAAYFwQ0AAAAYFAQ3AAAAYFAQ3AAAAIBBQXADAAAABgXBDQAAABgUBDcAAABgUBDcAAAAgEFBcAMAAAAGBcENAAAAGBQENwAAAGBQENwAAACAQUFwAwAAAAYFwQ0AAAAYFAQ3AAAAYFAQ3AAAAIBBQXADAAAABgXBDQAAABgUvQhuhg4dSvfu3aO4uDgKCgoib2/vbD2vR48eJEkS/f777zrfRwAAACgYFA9u/P39KSAggKZNm0Z16tShixcv0v79+8nJySnL53l4eND8+fPpyJEj+bavAAAAoP8UD25GjRpFP/30E61du5auX79OQ4YModjYWOrfv3+mzzE2NqZNmzbRlClT6O7du/m6vwAAAKDfTJV8cTMzM6pbty7Nnj1btYyHmQIDA8nHxyfT502ePJnCw8Np9erV1LRp0yxfw9zcnCwsLFT3bW1t090CAACA/svJcVvR4MbR0ZFMTU0pLCws3XK+7+XlpfE5jRs3pgEDBlCtWrWy9Rrjx4+nqVOnvrH8yZMnudxrAAAAUDLIiYqK0t/gJqdsbGxow4YNNGjQIHr+/Hm2nsO9QpzTo65YsWIUGRmpkwrnoKlkyZJvrXhAXRcUaNeoa0OEdl0w65q3FRIS8tb1FA1unj17RsnJyeTi4pJuOd9/+vTpG+uXL1+eypYtS7t27UqXf8OSkpLI09PzjRycxMREUdTpOvDg7SO4yR+o6/yDukZdGyK064JV19l9vqIJxRyQnD17lnx9fVXLjIyMxP2TJ0++sf6NGzeoWrVqYkhKLn/88QcdPnxY/P3o0aN8fgcAAACgbxQfluIho3Xr1tGZM2fo1KlTNHLkSLK2tqY1a9aIx/kx7s6aMGECJSQk0NWrV9M9/+XLl+I243IAAAAonBQPbrZt2ybmtJk+fTq5urrShQsXyM/PT5wNxdzd3Sk1NZUKAg6+OHmZbwF1bSjQrlHXhgjt2rDr2ojPvs63VwMAAAAw9En8AAAAALQJwQ0AAAAYFAQ3AAAAYFAQ3AAAAIBBQXCjJUOHDqV79+5RXFwcBQUFkbe3t7Y2XWiNGzdOTA/w+vVrcUmO33//nSpVqpRuHb5u2JIlS8SEkDy50/bt28nZ2VmxfTYUY8eOFdd5W7hwoWoZ6lp7SpQoIWZb53bLFwq+dOmSuM6eumnTpomZWPnxv/76iypUqKDFPSgceJJXPhOXJ3flerxz5w5NnDjxjfVQ17nD13bkueZ4uhb+vujUqVOO69bBwYE2btxIr169ohcvXtDKlSvFdDDawGdLoeShDvz9/aX4+Hjpo48+kipXriytWLFCioyMlJycnFCveajXffv2Sf369ZOqVKki1ahRQ9q9e7d0//59ycrKSrXO0qVLpQcPHkgtW7aU6tSpI504cUI6duwY6j0P9V6vXj3p7t270oULF6SFCxeirrX8/Vi0aFHp3r170urVqyVvb2+pTJkyUps2baRy5cqp1vnqq6+kFy9eSB07dpSqV68u7dixQwoODpYsLCzQtnNQ1+PHj5ciIiKk9u3bSx4eHlK3bt2k169fS8OHD0ddU97bsp+fnzRjxgypc+fOEuvUqVO6x7PTjvfu3SudP39eql+/vtS4cWPp1q1b0qZNm7TRzhHY5LUOgoKCpMWLF6vuGxkZSY8fP5bGjh2LLyItti9HR0fxD9S0aVNx387OTkpISBBfWPI6np6eYp0GDRqg7nNRx9bW1tLNmzclX19f6fDhw6rgBnWtvXY8e/Zs6ciRI1muExISIo0ePVp1n+s/Li5O6tGjB9p1Dup6165d0sqVK9Mt2759u7RhwwbUNWn32K8puHlbO/by8hLPq1u3rmqddu3aSSkpKZKbm1ue9gfDUnlkZmYmupMDAwP/3xUmSeK+j49PXjcPauzt7cWtfNFTrndzc/N0dX/z5k168OAB6j6XfvjhB9qzZw8dPHgw3XLUtfZ07NhRzMjOE5jycOu5c+do4MCBqsf5+nlubm7p2jUPzf77779o1zl04sQJcTmfihUrivs1atSgJk2a0L59+1DXOpaddsy3PBTFl2GS8fo8cW+DBg0K9gzFBZ2joyOZmpqKLyl1fN/Ly0ux/TI0fM2xRYsW0bFjx1SX2uAZrXnGSx6rzVj3/BjkTI8ePahOnToa88VQ19pTrlw5+vTTT8WlZ2bNmiXq+/vvvxcX+F2/fr2q7Wr6TkG7zpk5c+aQnZ2duC5hSkoKmZiY0Ndff00///yzeBx1rTvZqVu+la9GIOPPiX/A5rWtI7iBAtOjwBdN5V9doH2lSpWi7777jtq0aYPLh+RDkiv33PBBlvElZ7htDxkyRAQ3oD3+/v7Uu3dv6tWrl/hRxBdY5h9JnOCKujZsGJbKIz7bITk5mVxcXNIt5/tPnz7N6+aBiBYvXkzvvfcetWzZUmTly7h++QweebgKdZ97POzEbZaHSJKSkkRp0aIFff755+Jv/rWFutaO0NBQunbtWrpl169fF9fRY/L3Br5T8u7bb78VvTdbt26lK1euiLNy+AzA8ePHo651LDvtmG8znt3KvWvFihXL8/ETwU0e8Rc/jxfyuK76EArfP3nyZF43X+hxYNOlSxdq1aoV3b9/P119cL1zV7563fOp4h4eHqj7HOIcG+494F+2cjl9+jRt2rRJ/M09Dahr7Th+/Dh5enqmW8btlnPFGE8pwQGQeru2tbUVOQj4TskZKyurNy68zMMe3HuGutat7LRjvuVTwXk4XMbf9fz5cG5OXiH7XgungnMGeN++fUX29/Lly8Wp4M7OzqjbPNTrDz/8IE4jbNasmeTi4qIqRYoUSXcqOJ8e3qJFC3Eq+PHjx0VBu877/7X62VKoa+2eap+YmChOUy5fvrz0wQcfSNHR0VKvXr3SnULL3yEdOnSQqlWrJv3+++84FTwXdb1mzRrp0aNHqlPB+ZTl8PBwac6cOahr0s7ZlTVr1hSFjRw5UvxdunTpbLdjPhX87NmzYlqERo0aibM1cSq4HgVmn332mTjI8nw3fGo4n7Ov9D4V9JIZnvtGXof/SZYsWSI9f/5cHCB+/fVXEQApve+GGNygrrVXt++++6506dIl8aPo2rVr0sCBA99YZ9q0aVJoaKhY56+//pIqVqyoeJsoaMXGxka0Yf5ujo2Nle7cuSPmZTEzM0NdU97rt3nz5hq/ozmozG47dnBwEMEMzz/08uVLadWqVSJoyuu+Gf33BwAAAIBBQM4NAAAAGBQENwAAAGBQENwAAACAQUFwAwAAAAYFwQ0AAAAYFAQ3AAAAYFAQ3AAAAIBBQXADAAZhypQpdP78+Rw9R5Ik6tSpk872CQCUgeAGAPTa4cOHxcUO32b+/PnprmMDAIWXqdI7AACQV3wl4ZiYGFEAANBzAwB6a82aNdSiRQsaOXKkGELi0q9fP3Hr5+cnrlaekJBATZo0eWNYql69enTgwAGKiIigly9f0t9//021a9dW9P0AQP5AcAMAemvEiBF04sQJ+vHHH8nV1VWUR48eicfmzJlD48aNo8qVK9OlS5feeK6trS2tW7dOBD4NGzak27dv0969e8nGxkaBdwIA+QnDUgCgt16/fk2JiYkUGxtLYWFhYpmXl5e4nTx5MgUGBmaZq6Nu8ODBogenefPmtGfPHh3vOQAoCT03AFAg8ZBUVpydnUWPz61bt0RQw4ES99q4u7vn2z4CgDLQcwMABdLbkod5SKp48eJiaOvBgwciN+fkyZNkbm6eb/sIAMpAcAMAeo2HpfhsqJxq3LgxDR06lPbt2yfulypVipycnHSwhwCgbxDcAIBeu3//PjVo0IA8PDwoOjqajI2zN5rOCcR9+vQRw1d2dnb07bffitwdADB8yLkBAL3Gk/OlpKTQtWvX6NmzZ9nOmRkwYAA5ODjQuXPnaMOGDfT9999TeHi4zvcXAJRnxDOQK70TAAAAANqCnhsAAAAwKAhuAAAAwKAguAEAAACDguAGAAAADAqCGwAAADAoCG4AAADAoCC4AQAAAIOC4AYAAAAMCoIbAAAAMCgIbgAAAMCgILgBAAAAg4LgBgAAAMiQ/A837INRIj5sXgAAAABJRU5ErkJggg==" }, "metadata": {}, - "output_type": "display_data" + "output_type": "display_data", + "jetTransient": { + "display_id": null + } } ], "execution_count": 5 @@ -151,8 +154,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-01-27T18:12:14.724472Z", - "start_time": "2025-01-27T18:12:12.378736Z" + "end_time": "2026-03-25T02:10:50.137401Z", + "start_time": "2026-03-25T02:10:48.103538Z" } }, "cell_type": "code", @@ -166,7 +169,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "GeneralizationBenchmark.GeneralizationBenchmarkResult(msen=0.18410921895147417, msen_sigma=0.0075863415108709436, msen_CI95=(0.1707254164624649, 0.20003988718928112), model_statistics= Size: 583kB\n", + "GeneralizationBenchmark.GeneralizationBenchmarkResult(msen=0.18264015521384375, msen_sigma=0.0075938852769925035, msen_CI95=(0.16876972760411446, 0.19750045892181253), model_statistics= Size: 583kB\n", "Dimensions: (transformation: 36, boot_iter: 1000)\n", "Coordinates:\n", " * transformation (transformation) " ], - "image/png": 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" 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" }, "metadata": {}, - "output_type": "display_data" + "output_type": "display_data", + "jetTransient": { + "display_id": null + } } ], "execution_count": 7 diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index d3e77fe..79a7726 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -87,6 +87,7 @@ def __init__(self): config=config ) +# %% if __name__ == '__main__': experiment = MutatorHighVarBenchmark() print(sorted(experiment.config.subtask_name_to_data.keys())) diff --git a/hobj/data_loaders/images/highvar.py b/hobj/data_loaders/images/highvar.py index ea98517..c67454d 100644 --- a/hobj/data_loaders/images/highvar.py +++ b/hobj/data_loaders/images/highvar.py @@ -18,8 +18,6 @@ class MutatorHighVarAnnotation(pydantic.BaseModel): class MutatorHighVarImageset(Imageset[MutatorHighVarAnnotation]): - manifest_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-highvar/mutator-highvar-manifest.json' - zipped_images_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-highvar/MutatorB2000_Subset128_FullVar_Train.zip' annotation_schema = MutatorHighVarAnnotation def __init__(self, cachedir: Path | None = None, redownload: bool = False): diff --git a/hobj/data_loaders/images/oneshot.py b/hobj/data_loaders/images/oneshot.py index 15c58fd..3c6fb79 100644 --- a/hobj/data_loaders/images/oneshot.py +++ b/hobj/data_loaders/images/oneshot.py @@ -44,8 +44,6 @@ class MutatorOneShotAnnotation(pydantic.BaseModel): class MutatorOneShotImageset(Imageset[MutatorOneShotAnnotation]): - manifest_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-oneshot/mutator-oneshot-manifest.json' - zipped_images_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-oneshot/MutatorB2000_Oneshot64.zip' annotation_schema = MutatorOneShotAnnotation def __init__(self, cachedir: Path | None = None, redownload: bool = False): diff --git a/hobj/data_loaders/images/probe.py b/hobj/data_loaders/images/probe.py index 473ce35..d0c96f0 100644 --- a/hobj/data_loaders/images/probe.py +++ b/hobj/data_loaders/images/probe.py @@ -15,8 +15,6 @@ class ProbeImageset(Imageset[ProbeAnnotation]): """ An imageset consisting of two images: a """ - manifest_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/probe-images/probe-images-manifest.json' - zipped_images_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/probe-images/probe-images.zip' annotation_schema = ProbeAnnotation # %% diff --git a/hobj/data_loaders/images/template.py b/hobj/data_loaders/images/template.py index 51e527e..d628fed 100644 --- a/hobj/data_loaders/images/template.py +++ b/hobj/data_loaders/images/template.py @@ -1,14 +1,11 @@ -import json from abc import ABC from pathlib import Path from typing import Any, Dict, Generic, List, TypeVar -import PIL.Image +import pandas as pd import pydantic from hobj.types import ImageId -from hobj.utils.file_io import download_file, download_json, unzip_file -from hobj.utils.hash import hash_image # %% @@ -39,31 +36,30 @@ class Imageset(Generic[IA], ABC): - Annotations on those images """ - manifest_url: str - zipped_images_url: str annotation_schema: IA def __init__( self, cachedir: Path | None = None, - redownload=False, - ): + redownload: bool = False, + ) -> None: """ - Download and materialize the imageset into a local cache directory. + Load a packaged imageset from the local cache directory. """ + if redownload: + raise ValueError( + f"{self.__class__.__name__} no longer supports redownload; expected cached manifest.csv and images to already exist." + ) repo_root = Path(__file__).resolve().parents[3] self.cachedir = (cachedir if cachedir is not None else repo_root / 'data').resolve() - self.cachedir.mkdir(parents=True, exist_ok=True) - - self._dataset_dir = self.cachedir / self.__class__.__name__ - self._dataset_dir.mkdir(parents=True, exist_ok=True) + self._dataset_dir = self.cachedir / 'images' / self.__class__.__name__ self._images_dir = self._dataset_dir / 'images' - manifest_data = self._load_manifest_json(redownload=redownload) - image_manifest = ImageManifest(**manifest_data) + manifest_df = self._load_manifest_df() + image_manifest = self._parse_manifest_df(manifest_df=manifest_df) self._manifest = image_manifest - self._ensure_images_present(manifest=image_manifest, redownload=redownload) + self._ensure_images_present(manifest=image_manifest) self._image_id_to_annotation: Dict[ImageId, IA] = {} self._image_id_to_sha256: Dict[ImageId, str] = {} @@ -76,18 +72,45 @@ def __init__( self._image_id_to_relpath[image_id] = entry.relpath self._image_id_to_annotation[image_id] = self.annotation_schema(**entry.annotation) - def _load_manifest_json(self, redownload: bool) -> dict[str, Any]: - if redownload or not self.manifest_path.exists(): - manifest_data = download_json(self.manifest_url) - self.manifest_path.write_text(json.dumps(manifest_data, indent=2)) - return json.loads(self.manifest_path.read_text()) - - def _ensure_images_present(self, manifest: ImageManifest, redownload: bool = False) -> None: - """ - Ensure the images for this imageset exist locally. - """ - if redownload or not self._all_images_present(manifest): - self._download_and_extract_images(manifest) + @property + def annotation_column_names(self) -> list[str]: + return list(self.annotation_schema.model_fields.keys()) + + def _load_manifest_df(self) -> pd.DataFrame: + if not self.manifest_path.exists(): + raise FileNotFoundError( + f"Expected cached manifest.csv to already exist at: {self.manifest_path}" + ) + return pd.read_csv(self.manifest_path) + + def _parse_manifest_df(self, *, manifest_df: pd.DataFrame) -> ImageManifest: + required_columns = {'image_id', 'sha256', 'relpath', *self.annotation_column_names} + missing_columns = required_columns - set(manifest_df.columns) + if missing_columns: + raise ValueError( + f"{self.__class__.__name__} manifest.csv missing required columns: {sorted(missing_columns)}" + ) + + entries: Dict[ImageId, ImageManifestEntry] = {} + for _, row in manifest_df.iterrows(): + image_id = str(row['image_id']) + annotation = { + column_name: row[column_name] + for column_name in self.annotation_column_names + } + entries[image_id] = ImageManifestEntry( + sha256=str(row['sha256']), + relpath=Path(str(row['relpath'])), + annotation=annotation, + ) + + return ImageManifest(entries=entries) + + def _ensure_images_present(self, manifest: ImageManifest) -> None: + if not self._all_images_present(manifest): + raise FileNotFoundError( + f"Expected packaged images to already exist under: {self.images_dir}" + ) def _all_images_present(self, manifest: ImageManifest) -> bool: for entry in manifest.entries.values(): @@ -95,42 +118,9 @@ def _all_images_present(self, manifest: ImageManifest) -> bool: return False return True - def _download_and_extract_images(self, manifest: ImageManifest) -> None: - self._dataset_dir.mkdir(parents=True, exist_ok=True) - if self._images_dir.exists(): - for path in sorted(self._images_dir.rglob('*'), reverse=True): - if path.is_file(): - path.unlink() - elif path.is_dir(): - path.rmdir() - self._images_dir.rmdir() - - download_file(self.zipped_images_url, self.archive_path) - unzip_file(zip_path=self.archive_path, output_dir=self._images_dir) - self._verify_images(manifest) - - def _verify_images(self, manifest: ImageManifest) -> None: - for image_id, entry in manifest.entries.items(): - image_path = self._images_dir / entry.relpath - if not image_path.exists(): - raise FileNotFoundError(f"Missing image file for {image_id}: {image_path}") - - with PIL.Image.open(image_path) as image_data: - observed_sha256 = hash_image(image_data) - - if observed_sha256 != entry.sha256: - raise ValueError( - f"SHA256 mismatch for image {image_id}: {observed_sha256} != {entry.sha256}" - ) - @property def manifest_path(self) -> Path: - return self._dataset_dir / 'manifest.json' - - @property - def archive_path(self) -> Path: - archive_name = Path(self.zipped_images_url).name - return self._dataset_dir / archive_name + return self._dataset_dir / 'manifest.csv' @property def images_dir(self) -> Path: diff --git a/hobj/data_loaders/images/warmup.py b/hobj/data_loaders/images/warmup.py index 3fdd9c9..7fa3a8a 100644 --- a/hobj/data_loaders/images/warmup.py +++ b/hobj/data_loaders/images/warmup.py @@ -14,11 +14,8 @@ class MutatorWarmupAnnotation(pydantic.BaseModel): class MutatorWarmupImageset(Imageset[MutatorWarmupAnnotation]): - manifest_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-warmup/mutator-warmup-manifest.json' - zipped_images_url = 'https://hlbdatasets.s3.us-east-1.amazonaws.com/imagesets/mutator-warmup/MutatorWarmup.zip' annotation_schema = MutatorWarmupAnnotation if __name__ == '__main__': - - imageset = MutatorWarmupImageset(redownload=True) + imageset = MutatorWarmupImageset() From 7a60f7b84f7761918394bdc981bfa48650b81fff Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 25 Mar 2026 00:36:14 -0400 Subject: [PATCH 118/156] ref_to_features --- hobj/benchmarks/make_model.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/hobj/benchmarks/make_model.py b/hobj/benchmarks/make_model.py index 0593021..fab24e6 100644 --- a/hobj/benchmarks/make_model.py +++ b/hobj/benchmarks/make_model.py @@ -23,8 +23,7 @@ def _get_calibration_image_ids() -> list[ImageId]: # %% def make_linear_learner_from_features( - features: np.ndarray, - image_ids: list[ImageId], + ref_to_features: dict[ImageId, np.ndarray], update_rule_name: Literal[ 'Prototype', 'Square', @@ -45,7 +44,6 @@ def make_linear_learner_from_features( :return: LinearLearner """ - ref_to_features = {ref: features[i] for i, ref in enumerate(image_ids)} f_calibration = np.array([ref_to_features[ref] for ref in _get_calibration_image_ids()]) mu_calibration = np.mean(f_calibration, axis=0) norms_calibration = np.linalg.norm(f_calibration - mu_calibration, axis=1) From 0e84a5540f3e2caa3c21191531e1472ce2a59c2f Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 25 Mar 2026 00:38:28 -0400 Subject: [PATCH 119/156] delete config --- hobj/config.py | 3 --- 1 file changed, 3 deletions(-) delete mode 100644 hobj/config.py diff --git a/hobj/config.py b/hobj/config.py deleted file mode 100644 index 07bc6e0..0000000 --- a/hobj/config.py +++ /dev/null @@ -1,3 +0,0 @@ -from pathlib import Path - -cachedir: Path = Path.home() / 'hobj_cache' From d643a61b52ce25b1d496eff443e6b97ff89c2c69 Mon Sep 17 00:00:00 2001 From: himjl Date: Wed, 25 Mar 2026 10:15:08 -0400 Subject: [PATCH 120/156] wire in imageset --- hobj/benchmarks/make_model.py | 4 +- hobj/data_loaders/images/highvar.py | 1 + pyproject.toml | 4 +- uv.lock | 2013 +++++++++++++++++++++++---- 4 files changed, 1777 insertions(+), 245 deletions(-) diff --git a/hobj/benchmarks/make_model.py b/hobj/benchmarks/make_model.py index fab24e6..bd8a1cf 100644 --- a/hobj/benchmarks/make_model.py +++ b/hobj/benchmarks/make_model.py @@ -18,7 +18,9 @@ def _get_calibration_image_ids() -> list[ImageId]: Returns the ImageIds of the warmup images that are used for calibrating the features of the linear learner. Caches the result to avoid redundant computation. """ - raise NotImplementedError + from hobj.data_loaders.images.warmup import MutatorWarmupImageset + imageset = MutatorWarmupImageset() + return sorted(imageset.image_ids) # %% diff --git a/hobj/data_loaders/images/highvar.py b/hobj/data_loaders/images/highvar.py index c67454d..bf003db 100644 --- a/hobj/data_loaders/images/highvar.py +++ b/hobj/data_loaders/images/highvar.py @@ -40,6 +40,7 @@ def category_to_image_ids(self) -> dict[str, list[ImageId]]: return self._category_to_image_ids +# %% if __name__ == '__main__': imageset = MutatorHighVarImageset() diff --git a/pyproject.toml b/pyproject.toml index 9832b64..13ca003 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ authors = [ license = "MIT" license-files = ["license.md"] readme = "README.md" -requires-python = ">=3.11,<3.13" +requires-python = ">=3.12" dependencies = [ "numpy>=2.2", @@ -28,6 +28,7 @@ dependencies = [ [dependency-groups] dev = [ + "enczoo==0.1.3.dev1", "netcdf4>=1.7.4", "pytest>=8.3", ] @@ -37,4 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++- hobj/benchmarks/mut_oneshot_benchmark.py | 111 ++++++----- hobj/data_loaders/behavior.py | 46 +++-- hobj/data_loaders/images.py | 111 +++++++++++ hobj/data_loaders/images/__init__.py | 5 - hobj/data_loaders/images/highvar.py | 46 ----- hobj/data_loaders/images/oneshot.py | 54 ----- hobj/data_loaders/images/probe.py | 22 -- hobj/data_loaders/images/template.py | 155 -------------- hobj/data_loaders/images/warmup.py | 21 -- scripts/package-archive.py | 25 +++ site/changelist.md | 244 +++++++++++++++++++++++ tests/test_load_behavior.py | 47 +++++ tests/test_load_benchmarks.py | 5 + 18 files changed, 559 insertions(+), 418 deletions(-) create mode 100644 hobj/data_loaders/images.py delete mode 100644 hobj/data_loaders/images/__init__.py delete mode 100644 hobj/data_loaders/images/highvar.py delete mode 100644 hobj/data_loaders/images/oneshot.py delete mode 100644 hobj/data_loaders/images/probe.py delete mode 100644 hobj/data_loaders/images/template.py delete mode 100644 hobj/data_loaders/images/warmup.py create mode 100644 scripts/package-archive.py create mode 100644 tests/test_load_benchmarks.py diff --git a/examples/view_experiment1_behavior.ipynb b/examples/view_experiment1_behavior.ipynb index ef5c5b7..1d1f583 100644 --- a/examples/view_experiment1_behavior.ipynb +++ b/examples/view_experiment1_behavior.ipynb @@ -9,8 +9,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-25T02:04:21.064613Z", - "start_time": "2026-03-25T02:04:21.050111Z" + "end_time": "2026-03-26T21:28:38.663052Z", + "start_time": "2026-03-26T21:28:38.659557Z" } }, "cell_type": "code", @@ -25,8 +25,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-25T02:04:23.133150Z", - "start_time": "2026-03-25T02:04:21.073087Z" + "end_time": "2026-03-26T21:28:40.368212Z", + "start_time": "2026-03-26T21:28:38.663812Z" } }, "cell_type": "code", @@ -45,13 +45,13 @@ " Size: 103MB\n", "Dimensions: (subtask: 64, trial: 100, boot_iter: 1000)\n", "Coordinates:\n", - " * subtask (subtask) " ], - "image/png": 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6OVhQ8EaHUXl/vb7h77ne8W3HMv1j3gZHjbp92PPqtS+H16fJ0bHh0uSwKfua03bcGpTvnd573mZc67SF3rPx96an0OSwds1wEQ5NYSONlu4wtA4dOiTi4+k/X7JcUVHhsdmWHq8qiomJ0dLZtzdVJaew2mPOnDmisrKydcMyEQAAgJ5Io6Opy1t3snr1ajFx4kTy2TnnnKN97kl6tOHiDnJ2JiwsrHWTU2gAAABAT6NJOLq8uSqHPumkk7RNIjOhy9/79u2rlR9//HGxcOHC1uNlhveUlBTNLUP6ld54442a3+n8+fOFJ+mZ82e/UVpaqqW5b2+qSk5htYf0pD4W3tQAAABAb2LMmDFi+fLlreUWA0T6kF5zzTUiISFB9OvXr3X/vn37NJ9Sedxtt90m8vLyxHXXXSe+/fZb4Ul6tOFis9m0iH1yqqpFa24wGLTySy+95NK16uPtos7WvI5aeJg6aqXEUye7YBNd391gHUDK1Xa69m5tpE5yRfVhyrqU1Kmd9LLL6TJYdBBdz708bi09vjaRlKsaqaPb7JgfW39fVHky2ZfGHIvfrRykrFtm2HalExy/d1Y99ZovsEWScqI/dbrLYk553Lk0ryFKWT++/xTzblIeFlyg7Dve1xMicpTXTw4oI+VFFaOV+7lDJb8fb4911bQ/Jlm2kfKy4lRS/iT1Y2V/Ol9/adUIsm9YEFXfhTFnz5wG6lhtbVL7PXDy7bTvOWtr1GNvRvgvpLy4ij57qn8lrR/zs9C7f04DdYbNCN6rdFjl7TErgrbf59V0P4e/CznMn5KPBV5/C/OF4865+czxPb8uUjmWnfs/LaBE2ddpAUyQYAtT1lXvPeB9R7/h2r6nlUZaFtRFps39+fn82aeFb2j93WhgF+tGmoRc6unKck+TS0f/+OOP2t/QjpDGS3vnjBo1SvQkerThIpFSaDl1tX79erFu3TpNDi2nu9566y1PVw0AAABwm0aHQ3o2d6EFHT7Z+j3ecPnoo49EbGysePjhhzWH3E2bNokpU6aI4uJiT1cNAAAAAMeZHm+4SBYsWKBtAAAAQG+h2bkWMy690nABAAAAeqPhwuMNuYLBR5eKep0cGgAAAAC9F5+ZcQksMgn7byFv/fvQCIOFVhrdtKqSetDfPWYJKX9Tkk7Ke6rV4bDzqtShy8dH7SHljfZmTX0LJ4XlkfKCA5nK63EV0ty880VnKWTpCKrtVCWUFxjlkoLqzYIzlPtjg2jUyLWFR6V4ktoaFnY+uoKUo4JoRM18KwvtzbIh8PoeqKLqg4zo/Uo1wuqyFFb/OKXiTA+zqV7ZXgNCD5PyUusIZV8vrk5SqiecVUyjLfuUKherjqJrc6U61USKuVT5nvBxzds663B/nbanY6eikUZl3lUTp+zrYRE0pMK+qmhSrmURtfnY4GSzSKJc8ZUZl+uSGrEiJESpzOFjZWbiT0olEL8fV5Fl1x0dO1+UjST7BocUK5WMHK4G5N8r/DuUkx5eoBxrNew9e3rAp8r3hI+9OxPpd7qzwi7AGCyoNrD7wIyLe/iM4QIAAAD0NFWRTGPjLgYsFQEAAAAA9Gww4wIAAAB4AOm80BX3WoPwTWC4AAAAAB6gEaoit/AZw6U+uUHU2ZpzGNUdNpN9QwdQJ73o2EPKsOyX9lmvvNd/D40h5Sv6rnPJkY07nHLHNu5UGOpX32H4ah76u00Y93oa5nxzXbLSgZIzLKJA6cB5QdQvHToAtoe5b73y2TjcYdHSR51qnYfMt8Spw6Tz+ur1PYc7MfIw7jm1tP1vT/6OlCtZaHTuQMv7mqcc4M8rnPzQPysfpWxr7izKnXn52Aj3q1E+K0+3kBlMnV2X1VJnXBGtfk/ahJFnzqc8XUNGnwP0ftVDSfmiiI3Kd2PFEXr8mZE7lM8bnkDbI9O8Q5kug6e74M7GnCmxW5X7l5enkXJ8IA2rzxkbsrtDx13u6MvTDfD3hPcN/w7j45y3NYen7uDfY7wv+fF87PD33HnsBDpYOoFupLGLgXMNwjeBHBoAAAAAXoPPzLgAAAAAPQn4uLgHDBcAAADAAzQJg7a5i9FHF4uwVAQAAAAArwEzLgAAAIAHaHI0b8A15DxTr242i8UiKisrRfrrL4iq31RFIsxGjgkMpuWmHaGk3NCH7jdWUnuvKbiJ3pOlFKjKo+qM5CE0fPbBPCqfiImn3v9JFhrmPreYhjJPjStW7nfm9H40vcD3uTQs+Yn9qZrg141U6WAeSOtiPUTTJZw0jCpF9hyJUqZT8DPRtrPXqm1pgz89/sIRW5Rh2/n9xyYcUKYYsNno/UODqdImKpgqRXbsTHTp+QNNjaRcVUtTGvj724UKXn8exv5AWaTyelYnRd1Jgw+SfSGm396PDlJV8HEaFEbbZmQiVRntPBJLyofL6HsVHVWlbNs9RTRMe0o8TSGgR1ltiPI9Kquj+wsPU+VLcAh9vohgqkwZFXNQOfb4e9gv6ogypUCtjZZL82j7jz9hp7J9S4vClP3D78/bd0RSYevv2/ITlG3PU02Y2djh45ITbLIpxx4P6c/fo3Sm/ORtwbl9yPekfP/P0zpsK7MpQGyceq8ICwsTViv9Lj/Wf5dy8lJFk4O+B65gNISKtOTcbq1rTwRLRQAAAADwGrBUBAAAAHiAxi465zp81DkXhgsAAADgAZocBm1zH4PwRbBUBAAAAACvATMuAAAAgAfAUpF7+Iyq6KSPnxFV9oY23vOS8VFUacPzdPC8GBy9fDI8R0lWHVWyZARRpQhnqD/NrbTDRj36C+xU2ZNosna4f0wgVW7k2ozKHB5JpnJStrJntbC2sRipWsBioMPr7YqRpDwtbJNQwc8vaKRqg0Q/qkbg5NrClPXl5DQkKPuG55fhz5vo56e8/vp6qmTh/WFtsivrz48vaKQqJQ4fG87n87rwcZwWUKLsC46VTXnzcZ4WQN+70YG0L5fX0rGY6l+p7Hve9m3qw/qKjxVXx1JWfZzyPU8LLFS+h7z9LEb6f+PiaprvJ8lEVUD59kjl2MxpiFW+u3o4v/sZgcXKZ88MLlOOQ72214OPJT34e8e/1/hY4bmNpobmtv5uMISKxKSdx0VVtOFgepdVRaP7bvU5VRFmXAAAAAAP4Oiij4sBPi4AAAAAAD0bzLgAAAAAHvJxkZv7GIQvAsMFAAAA8ACNDqO2uY9R+CK++dQAAAAA8Ep8MlcRzxVUXkvVFAkWqzKnSEllqDJXEM+7ERdEr5cQQHOm7KqhHvsldfT66eEFpJwcQD36vylJJ+UBoYdJubjuqLLkzEiqcNpgHaC896V91pPy23njSfmmfstIeVnlcFJOC6ZKi4pGqmSxNtK2r2oMVJ6f10BzlkwL30DK/yiYTMonhdH8ORze9vGBVMlSVE9VPdUshwrP0cLP31qR6NLYqLbT578g6hdSXlc9iJQ/2XkyKd82YpmyvZzrw3P1pEbQcczhz87HEh+H/FlTzK7lGuJjITNsu7ItLH5UMVbYEK5s29GWfcqxubosRfkuZNfSvuVMsmwj5aXWEaQ8LLhAPbaZ4u7l0rNIOdSvXvn8/HuC15fXz1m1xN/LRP8jSkUVrzuvS7gfVcNxuCJrbc0gpdLzi7KRLvUlbwuu2MqpP6om9DeGiD8P/eq4qIp+3j9SNDqoStQV/AxmcXr/X6AqAgAAAED3Ax8X98BSEQAAAAC8BjjnAgAAAB4AzrnuAcMFAAAA8ABNXcwObfBRObTPOOeO/MdLoqqh2Vlw9IQc5Tk1zAnRVSdD7gjGHds4emH1OTz0N3c0mxDcRMob6hs6HfqaO8nxe3F4GHcOf5bPykcpHS710ivwkP08RQEPU89Dj7ua0oCHneehyHlIfV6/rNqBQgUfG3r14WHkef9xp0iLsbbD9tUbR3rpHHiIed72rqLXVxy9lAQ8TD0/nqfScH5P2mt7V9Mt6LWPXvoIfj5/N471u+R8fz6uuPPtKebdynHLn4U/K0evrXkIf47eWHElPYMMoz80ecdxcc5dui+jy865kwZkwTkXAAAAAN1PkzCKxi64mhp81E0VS0UAAACAF/q4GGC4AAAAAOB4zrjIrSvn+yK++dQAAAAA8EqwVAQAAAB4gEaHQdvcxeCjqiKfMVyaTEI0NbUfZp2TXd6HlKfEblUer6fEya5LIuWxIbuVHvXc2z+nIUGpFOH3e7U8qMP6cW//jOC9yrpkBu93SVXDVS9cVaMXtv2mqNX0+o1U4cXRU7LoqYh4e/Byri1MqXYQgj5vop8f3dtUqFQpWYz0FbQ22WlZR53xxzA6lhYcOYGUJ4Vu67T6gteNPzunTVsyVQ/vOz21HB87eqoY3tbvVg5Svpe8fq+WJynfBQ5XEempgjKDqbpQCFrfXCr+a6Pk4Yoz/r3wp6hVLinQeHutrw/p8F2dGprrkkKLty0n0b9J2ZZ8XK6n2Qx0x6oQdCym8vvpfE84f49JpQ7tie5DOubCOdd1sFQEAAAAAK/BZ2ZcAAAAgJ5Ek8OobW6fL3xz7gGGCwAAAOABsFTkHr5prgEAAADAK8GMCwAAAOABpAtxV1RFRt8UFfmO4dIU4BBNv6VlWlmYQvYF+1M1QHkt9c7PiUhQqo5mJFNv//8eGkPKe47QPB/LLKmk3C+UqgfiA6nHfFVjICkX11mUuZU4eeH0/s4U1VPliNlE3flD/epdysOUX0cVTt+UpCvrxvNA5VpofZZaR5DysOACZQ4Va2OQsu04/Pn2VMeQ8nkxW0h5bc0g5f14DpdllcOV9+O5hVaX0bEZG1RFytWsr7lCrtpOn3dZccdjjY+zL0tPVObkmmTZpuwbPpYmRNCcYNm1icq2NrOxwM/narr7i0Yp34s8Mx0b+Wzs5NQmKMcSH+uVTLXDFXF6fcnHOh9bFY0hyvIHlWNJ+aSwPFIO96tR1ncRUzfysetc/6t3Xk72zUherVQT5tQnKOte2BBOymnBhcrvCT0lJ78f7zs+Fkdb9nU6R5jRcPz+LCIAnXtgqQgAAAAAXoPPzLgAAAAAvSlXkbEL53ozMFwAAAAAD9AkDNrWlfN9EY+aa2eccYb4/PPPRX5+vnA4HGLq1KltjnnooYdEQUGBqKmpEd99950YPHiwR+oKAAAAdMeMS1c2X8SjT202m8XmzZvFTTfd1O7+u+++W9x6661i1qxZYuzYsaK6ulosWbJEBAaqHS4BAAAA0Dvx6FLRN998o20dcfvtt4tHH31Um5WRzJgxQxQVFYmLLrpIfPjhhy7dK+CwUQTWN9tpUSdS7/thEYdIeV9VtFIJMi3hF6V3fnp4gdJDnudQ4eoMrmbgagURKlwiI+hA6+85DbFkX0FApPpejKyqgTq5juj5l/ZZr1SGcGUGhytZuFKCtxWH51riOVd4HimubOEMC8pX1r/AFqlUT7TJn6OTj4fntdLLY8XVFVfGUDXIZ+WjOmw7rsDibc/zw+Sw87miiuez4WOHq2o4bVQvbOxcFLFRuAIfO1yBxd9z3pb83eDPy/v+3UGLlfl5sur6KVVBfKzw/Rw9VRLvb/58qu84fmxagHpc8/xp/L3heajSkguV6j2995z3hTCr82ol+tH3fHHVUfWdvzFEjBbeEYDO6KP6mh771AMHDhQJCQli6dKlrZ9VVlaKtWvXivHjx3u0bgAAAEBXaXIYurz5Ij3WObdPn+ZYKXKGxRlZbtnXHgEBAWQpyWKh8QYAAAAA4L302BkXd5kzZ442M9OyScdfAAAAoKchA9A1dmFr6n1/wjtFj33qQ4ea/U7i4+PJ57Lcsq89nnjiCREWFta6JSVRHwYAAACgJ2WH7srmi/TYp967d68oLCwUEydOJMs+Ul20ejV1OHSmoaFBWK1WsgEAAACgd2DytBzaOS6LdMg96aSTRFlZmTh48KB47rnnxP333y927typGTKPPPKIFtPls88+c/leDdFNor5BprQSYsc+6iOz058qTa44MUuZU0UPnieDs6hwpFLVtME6gJRzy2n9kkOph/yZkTuU54uIjpUdXEmxJG8YKV/S/xelEuORAxcoFVVcDbC8PE2oyAlIUOYwqbX7k3J0ULVSqfJA4WRlLqaEgApSHhxSrMyvMy18g7Kv9XIpvXboLGXfcSXNe6Xjlfl7+PV5TpiljVSx5so4ywjd26EiqT3FFFdYWZuChQqe04ur73j9ckwJyr7bXJmszLXE+4b3NW97Dm9rrvj6gtX3jT2nkfLk5GylEibJdMSlvFj83dJTvHElEIePdRVLq0Yo762nCEs00X8onzn0O1JevnsIKc8d/YVL1+fPzhVyy5gab8WRoa2/B/sFiT+L40OjMGhbV873RTxquIwZM0YsX768tTx//nzt57///W9xzTXXiKefflozbl577TUREREhfv75ZzFlyhRRX0//+AAAAADeRleXe5p8dKnIo4bLjz/+KAwGtcX44IMPahsAAAAAQI+VQwMAAAC9GRmSsGtLRb4JDBcAAADAA2CpyD2kqecQvRipRJLxXEb+4yVR1dAc5tk4tIocMzIxT+nUxx0wq+0BpHx53FplHXhYee7IxllWOZyU/xS1yiXHOB5OW+XwNyl0W6dDYUsyzTuUYcu5gx93iOT7udMch4cO57Rx0mNh2Z3THbRXX+4QycPCc3jocCs7nofk5/u5U2KBXR0gUe94Xl+9+uXUJ3QYEp6PBYvRpqwbrwuvK29rPedQXle9Z8u3RyjfszapM5gDJ3ce5mOJp7Pg7WEx0K/OXJvaKZ+PDX69RZUnK9/rjEDqTMyxsiiqvH94ygaO87uul5qCp5rQ69us2oEufY/w95KPLZ66RA9X2s5oCBVDk3do4TS6S5Xa8nfpye2XiYYmOi5dIcAYLO4d/mG31rUn4puePQAAAICPMnv2bE2pW1tbK9asWSMyMjKUx992220iJydH1NTUiAMHDoh58+Z5NNkxDBcAAADAAziEQTR1YXO44R8zffp0zfB46KGHxKhRo8TmzZvFkiVLRGxs+7NYf/jDH8STTz6pHT9s2DBx7bXXissuu0w8/vjjwlPAcAEAAAA8QKPD2OXNVe644w7x+uuva2FHsrOzxaxZs7SZlJkzZ7Z7/KmnnipWrlwp3n//fbF//37x3Xffab+fcsopwlPAcAEAAAC8GIvFQjaZbLg9/P39xejRo8XSpUtbP3M4HFp5/Hga7LKFVatWaee0LCfJQLHnnnuu+Oqrr4SngKoIAAAA8ABNDoO2deV8CU8mPHfuXG1phxMTEyNMJpMoKioin8tyWlr7Uc3l7Io8TwaAlXHXpPHzyiuvaHkBPYXPGC62sCZhszWH/Pez+ZF9NUwltLUiURnGflLMNpdCc3NlDVdHpPo3sf2rlB75XP3B94cxRYCzmqCSKRX4uVxJwdUF3Pufw0Pi66GnROEqJB4mncPVCFxZ0SasO3sD0gJKSHlZ9dFQ4O2pEwpcVMYIe8d178zxvL30rsfVIM796awwckc1w6/N25orTbhiraCRvndjAmtI+cnSEcr3iF8/iaXC4PD7L6vtrxz7HK764aolVxVqowPp8++qiVO+53rtz+H3tzrUCjVnpQ/fx5WMN0X+SsqLq5PUIf791e+lnopIT33HFWJ8bOi1nfPYMLBx3520ZHnuyvkSmUzYWVV0LKPLn3XWWeK+++7THHrXrl2rpel5/vnntXQ8jz76qPAEPmO4AAAAAL0RaycTCpeWlgq73S7i4+PJ57J86BDNmdeCzBH4zjvviDfeeEMrb926tTUVz2OPPaYtNR1v4OMCAAAAeHCpqCubK9hsNrFhwwYxceLE1s/k8o8sr169ut1zQkJCRFMTXRVobGyO2auXsqe7wIwLAAAA4AGahFHbunK+q0gp9MKFC8X69evFunXrxO23367NoLz11lvafrlP+szI5SHJF198oSmRfvnll9alIjkLIz/nBs3xAoYLAAAA4CN89NFHWsyWhx9+WPTp00ds2rRJTJkyRRQXN/vv9evXjxgk0o9FLgfJn9KXpqSkRDNa/va3v3nsGWC4AAAAAB6g0WHQtq6c7w4LFizQtvY4++yz2ywLSSNHbj0FGC5CiC2/DiCN4l9Bp9/2jIgi5aIE6qGeW07VAAfzokl5Uno2Kc/dfQEpj0hS5/kIMVE1xIojVOmy8wiNeBgVTNUZZbVHc9Jcm7KS7OP5av5RMFlZF67AqrX7k3JG9H5Sfm/DWFKOiaf5Ui7p/wspLyumuZGGRVCHsQ+K6fUO15lJuV8oVSdcGbNa2XZvHDmNlIP9qbIm2ETLK4Lo+WdG0pwriwpHKusTH1ipzK3EVU9cuaKnYONUNdKw3MV1R9UZcUFWZdvwZ+MKs1nb/qgcd5lxuaQc7kf35zXQ92pF0WB6//hdpJxVRRVw2X5UccbJOtxfOZYyQqmKaFHFaOX1eM4yrgIabdmnfD7e18/lpZDy+Kg9ylxMz+6h7+aomIOknBZM++ebknRSHhB6mJT3VdHvqSmxW0VH8HH2l/yjPhKSCRE5pPxmwRnKe3M2lvYl5b+mLCHlAhvNWbbBSr+zB4eocxHxsRPq17HqJtAYLB6kIqkeL4f2NWC4AAAAAB7A4TBqGaK7cr4v4ptPDQAAAACvBDMuAAAAgAdoFAZt68r5vggMFwAAAMADNDm65qfSdPxjv/UIsFQEAAAAAK9Bmnq92maTmTIrKyvFiHfniypbszrHEl1Njpk9dIXyGlx5w5UePJ9O29xEVFmSb6ce8pnBVIljZRa4tYkqd3IaEpR5Obg6wrk+enXJCDqgzBHCVS3DWO6jMJ38KTw/Ds9hwnMBZdXHKXOScPjzcXiOEp6DheeH4W3Nc7DoPS/PucJzHznnh2mvr7myhOdS4v2j1585DbGdbks+rnhd2uRtYrmHeN6mNnmWGDxPlarunclPw3MT8feK34/3LVeycHjf8b7guZcWHDlB+e7wHGa8/Th67cnhz8u/d5zz+ejlCtJ7j8aG7FbmTeLvjXM+tfbgY8tV+NhVYTKYxfkpq0RYWFinwuh35e/SrRtvEHVN6u8QFUHGIPHCqH92a117IlgqAgAAADxAkzBoW1fO90WwVAQAAAAArwEzLgAAAIAPRc71dmC4AAAAAB6gqYsB6Jp8NACdzxguBv/m1URJVSV16nt5x5mknNl3Jylnl/dRhg7n4aO5s641IEgZWj0riIajvihiIyk/se9cUo4Oos7F2eZEZZh3Uh/mj8nDkv/30BhlugEeYj88gTogrmPX447MPFR3jok6vy4ozyTlOQO+otevHqR8VmtwkDLMOn9eHspczyHyizIa0t9son1fbQ9Uphzg9eHOutwRnIfJ14M7J3OHVpUzqqvOoYurUpWO1tx5ljtm6zm967UNd27ljtTvsDDvPMQ/byvuQMr7ioeN5/XhVDbR+nF4e79TdiopJwRUkPLmymRSvjxurfL6vP15f79dMbJDh1o+brgzLW9r7oy71EqddU8x0/16Y4mnl/isfJTyfJ6+4Pbk75THZ9fRmP7Twja1/m400HYCPQ+fMVwAAACAnoT273RX4rgILBUBAAAA4Djh6KKqyAHDBQAAAADHC2SHdg/f9OwBAAAAgFcCHxcAAADAA0BV5B4+Y7j4HQoUfg3Na4kRIw6TfSfH5is91DOi9yvVBnre/lwJc1JYnlJNwcO2c2UNV2dwD3werts5jLzFaFOGSefqAF4XDldmZART5QaHK0+4WmF2jDrUOE+nwMPCc/j5XFXE1Q56occHhxQr+47DlSpTzXSsLa5OUo4trgThaggOT1nAUzTcFPlr6+/r60OUKiI+zniIe710B1w1w1NR3BRFFVeuwtNB8LGcFHFEGaaePy+vb6If7WsOV6Txsc/DzPO+4e8iVxFx1dSM8F9cCoNvZaHk+fOHhXS8X6+t+HvHU1Xo9S2vO2+7VP9mBWhrfaJWKVMMXJRMlZgcniqD97WzikqG/Kd6tu4DS0XugaUiAAAAAHgNPjPjAgAAAPQkkKvIPWC4AAAAAB4AS0XugaUiAAAAAHgNmHEBAAAAPABmXNzDZwwXvwFVwmRvzrtzWsIe5bGjY/cpc5Jw5Q33/uc5QrjyJKc2wSVlClf2cBURV45w9cGy2oEd5kniCiiuWuHPOiawRqmK4aoZrlxZVjlcmZeJq4C4uoGrmPjxXMXDn4f3VZv8PCz8Nr8ezx3UJjcQU+Jw5cl6nefhfd2mfgHByutzNYZgSpZcm7HDfDKTLPTYqaG5pLystr9Lii6uNJkRuUZ9PGt7i8GhPJ6rmrhKJ9GP5tlqA9vP78/LvK8Sg6zK3Er8PeVKGK4y4u8aH3u5tjChgo89rtThyiCuPnR+l/h7x4/lfUP36uci0lNg5dvp+RmBaoWXRWcs8O+JngIMF/fAUhEAAAAAvAafmXEBAAAAehKYcXEPGC4AAACAB5ALbl1LsuibwHABAAAAPABmXNwDPi4AAAAA8BoMvX22yWKxiMrKSjHiredFla1ZRfDo2Z8o1RHOeSvag6uGeI6U0YE0D8fn1SEuecjrqQe4sofDlTzOHvdcacGVE5xEPz96fJNdWVe9XD8Wo0l5PV4fvfvroXe9gsZGdX25ekJH+aL3fBxX208v5wtXZ/AcLc7Pz5+dj3t+Lkfv2dvWld5vqL+ZlKtYbp1QI1XBFNqrlPfj19dTJenl+uFwxZbe/fhY4coW3td8LOgqeVy8Ps/tdKGZfo/ssFW73bccPu4TTKEd3qs9BZqeAoqj91678h4bDKEiMWmnCAsLE1arnl6qa3+Xpv50r6hprHf7OiF+gWLxGU92a117IlgqAgAAADwAlorcA0tFAAAAAPAaMOMCAAAAeADMuHjhjMu9994r1q1bp631FRUViUWLFomhQ2n0ycDAQPHSSy+J0tJSbQ3v448/FnFxdK0WAAAA8DYcDkOXN1/Eo4bLWWedJRYsWCDGjRsnzjnnHOHv7y++/fZbERJy1Ll0/vz54oILLhCXXnqpdnxiYqL49NNPPVltAAAAAPjiUtHvf/97Ur766qtFSUmJGD16tPjpp580T+lrr71WXHHFFeKHH37QjrnmmmtETk6OGDt2rFi7lubZUWGsMwpjQ7OdNnfDBWRfSnyp8tyM6P2kvLUikZS35dMcIcEh1Es8wUK9vYNNNI/GgNDDpBzqR89fUTSYlEsqqYe+vz/14B+bQFVS1faj6okUc6kyT9Ibe04j5fJKpojqT9sijuVr4WSE0pwknKyqo3mUJAkBFaS8uTKZlE8Ky1Nejz/P8vK0DtuivfbgbX1m/C5SLqqnyo/BIVQhtqyY5mSZkbxamVuIw/MHWfyo0obDcy89u2ey8njn56lqDFS2/RdlI0k5PpCqVPj5en2dXZvo0rNxChvClfv5e7OnOoaUa1jfj4/ao+y7YRGHlM/Hn4ePnVExB0k5M2w7KT9VMoqU91VFK78XOPz42KAqZX/x9l5WGd7ptuTwvuekBVOlJYfna+PH5zVEufTe8feen8/heaJynNR4JoNZ0J7tPmTwua4EoGvqwrneTI9yzg0Pb36RysqaB6E0YAICAsTSpUtbj8nNzRX79+8X48eP91g9AQAAgGPl49KVzRfpMc65BoNBPPfcc+Lnn38W27Y1W8N9+vQR9fX1oqKC/ico/WHkvvaQho70i3HWywMAAACgd9BjZlykr0t6erq4/PLLu3SdOXPmaM6+LVt+Pp1KBwAAAHoCcM71YsPlxRdfFOeff744++yziaFx6NAhbfakZQmphfj4eG1fezzxxBOab0zLlpSU1O31BwAAAFwFS0VeulQkjZZp06aJCRMmiH379pF9GzZsEA0NDWLixImtSiIpl+7fv79YvZo6PbYgj5cbx2FyCIejOayzvZY+9p6iGKWz7n9+PpWUk4dQx7CEaLqUVV6rdsDMt1JDrNbur6wPr2/fZOq0FxVEQ3evLezXoXNw1v7+ZF9EGD33tATqsFgcSZfadh6hYeHz/GmI+eTQclLOEtT5dmVhCilf0v8XpQMmd8b9siBdqOBtwds6yVKhdKjkvP8DdVZOGl6kdBLkzrjcObikjjpWl9VR5+fCw7S+lw3fQMpZh2n/CRYZgI897qi9JG9Y6+9RwbStioMsSmdWPbjjcUVjiNKpnTufcudg7izbL/QIKZtN9UrnUz52+NjSG0sbS/vS+hwconS6r7XR97i4jrbnI4Xnk/Lk5Gzl90B2eR/l84eYGpTt8eH20aR83QkrSbnaHtihMy93bD5cR9MzRAfRkP1mVpeXd5xJyhHBdcq22ujfV/k9wscid7JfVpeqrB93wl9UMbrD90qG0T+ffk11G12VNDvg4+KZ5SGpGJo6daoWo0XOpEikT0tdXZ221PPGG2+IefPmaQ67siwNnVWrVrmkKAIAAABA78CjMy6zZ8/Wfv74449tZNELFy7Ufv/LX/4impqaxCeffKItGy1ZsqT1PAAAAMBbcXRRGeTAjItnlER6SFXRzTffrG0AAABAb0E6L/zmweD2+b5Ij3DOBQAAAADwCudcAAAAwBdB5Fz3MPT22SYZgE469Y5+dIGorm/2fB/6u90uhdbmob55OGmuVuDhqzOC1aHQXSWrdqDy+tYmqu6wGI969FuMNN0AZ1k1TXKZad5Br2WgwyXXRkNxp/pXKvfn1NNQ31NDc5XHcyrZs6UFlNDrN1DVEyfMqS06g6v34/utTVQ9kdNAnz/JRJUiiSarS8/DSTJRNUa+UyhzPfSejbed87hqD72xpofe/ccEUlXUslr6XmYEUsWXlfkD8L7h702+PZKUM4Npuous+jhl2+u9p7x9suqoGjAtoFB5Pb13c1ltf+X1VM+bEXRA2Va87vzevK05BXaLciyl+jcp+5aPBf69w+/PxxLvK+e28DOYxVkDNmrhNKRopDv/Lp3x7SOi2q5Or6DCbAoUP/3u791a154IlooAAAAA4DVgqQgAAADwAF3NN9QEVREAAAAAjhdSUdQlVZFD+CRYKgIAAACA14ClIgAAAMADIOS/e/ik4cJzgoT6Ua/uU8y7lR7sPCfLtJhNSo95TqIfzeuRYKL5a5bXGpVKHJ6ThcM99J0VAFwdoKd84EoHroLhdeOqGH3lickldcHi6kiXlCdcGeKqcoajp04Y6k9zurxnjXZJ2cH7h9eHty8fa/x6mcFloiMWV6sTkLqqyuHjmvctJ9dmVCpVeN0LGhtZWeg8K71/FlOm8L7jyhQ9pQo/v039meqnbXv5CRW8L/8Ytlv5/FyRx8caf5eEqOnweXnfLa6j7zlXHfHvMKutWkeVVKccO7m2ANfGIitzlZPeWHLuOyM7tzuB4eIePmm4AAAAAJ4GzrnuAR8XAAAAAHgNmHEBAAAAPABURe4BwwUAAADwmOHSlezQwifBUhEAAAAAvAafmXFpCnaIRj9Hu6qirRWJpJx1mOb46BdKlTSDQ6iH+3U5fyTlM+N3kfKe6hh6v5I+pDw2gXroz4r7gZQ3WAeQstlEVVDZfrT+VY2BpJwZtr3193XVg8i+YcEFSpVRRWMI3V81sMNrtwfPzcPhOUiy66jSJcuPKh8S/Y+4pALiCjCuwuH321VD88+MtuxTKroWVdD2zAooUx7P2yO7NlGpaOP1GxaU75KKipctxlrRWbhKRS9vEz+eK0f0cgHxuhXoPBtXuC2qpH0xNoS2JYf3RSLLRVTQGKBUDfE8UFz5ws/nz7+epajRyw/EVWA8Z1oyG3t87FubCpX9aW0KFh3B+4arDbPYa5gZXEXPZ6ohnudpaZV6nFuMJcqcam3ytfH6O2ydVsQZoCrq8fiM4QIAAAD0JOS/0l1Z7XEI3wRLRQAAAADwGjDjAgAAAHgABKBzDxguAAAAgCfAWpFbYKkIAAAA8AQOQ+usizubcFNKPXv2bLF3715RW1sr1qxZIzIyMpTHh4eHi5deekkUFBSIuro6kZubK37/+98LT+EzMy4N8TbRYKOe5R0xLOKQS9f+a8oS5X7u7X9m5A6lcoerHa6MWa1UA3DlCb9fge2oGuOmKHqttytGKs/l+Va4moDTNkcIVWosrkpV1p0rQXi+GH5+Es+txFRRObUJSoUWV4jd1edb4UquIq4C4vlrXMW5r9pThvD2mha2yaXcUM7KFq4KchW9PE98HHMVDK87V3zpqYh43qZJoduUuXlybVylJJT5bvTGNu9rrpTRU3zpqYh4/h7Bcg/ptQdvz8/KR5FyWjC93tTQ3E4rorgCaaqZqoDW14cp68bbLiO8WNmWHK4uXFo1wqWx1VZRdbSv/AxmQbV+vYvp06eLefPmiVmzZom1a9eK22+/XSxZskSkpqaKkpK23wn+/v7iu+++E8XFxeKSSy4R+fn5on///qK8vGvfdV3BZwwXAAAAwNcj595xxx3i9ddfF//+97+1sjRgzjvvPDFz5kzx1FNPtTlefh4VFSVOPfVUYbc3W/v799N/SI83WCoCAAAAPEBXlokcLctFcjbQYiFbQACdMXOePRk9erRYunSpUx0cWnn8+PHtnnPhhReK1atXiwULFohDhw6JX3/9VcyZM0cYjZ4zH2C4AAAAAF5Mfn6+qKysbN2kYdEeMTExwmQyiaKiIvK5LPfpQwOjtpCSkqItEfn5+Ylzzz1XPPLII+LOO+8U999/v/AUWCoCAAAAPEEXHGw1fjs3KSlJWK1H/Yjq61lY5i4gZ1akf8v1118vmpqaxMaNG7X73XXXXeLhhx8WnsB3DBebsXkTQhQeDie7RqUeJOUvc9NJecKgnaS8uiqFlK3h1NFrRdFgUk4OpU5MJ4Xl0fsV0Pudl7hV6dS4uozePz2chu3fXJlMz686Gpp8QwR1TuXwa79tp9OHZbXUkXhycrbSeZQ7l/K2iQqqUYbcr7bTKU+zqUHpbMuPr2Flnu6BH8/ry9NBxAbRUOZ6cOdffn2eniHUT/2Fs6w4VdleHP58zo7hrx06i+xLMZcqHY85eukS+L35uJ+bdz4pH64zk/JN/ZaR8tqaQcr78VQYF0VsVDqn/ilqFSkvqjxZ+V4Gm6hzf0b0fuVY4WONpw5ZLtJIeW0hdXxPj6UigfNitrj0vTAg9LBQ8U1JeociAT7OpiX8orzW/UWnC1dYWUjrGhVMvwcy4446CksWHMhU7ufv1TOHfqccGxznsWA0mLzOx8VqtRLDpSNKS0s1P5X4+HjyuSzLZaD2KCwsFDabTTNaWsjOzhYJCQna0pPcd7zBUhEAAADgA9hsNrFhwwYxceLE1s8MBoNWln4s7bFy5UoxePBg7bgWhg4dqkmjPWG0SGC4AAAAAJ4MQNeVzUWkFPrPf/6zmDFjhkhLSxOvvPKKMJvN4q233tL2L1y4UDz++OOtx8v9UlX0/PPPiyFDhmh+Lvfdd5/mrOspfGepCAAAAPDxkP8fffSRiI2N1fxTpEPupk2bxJQpUzQ/Fkm/fv3IslBeXp6YPHmymD9/vtiyZYvmCCyNmPak08cLGC4AAACAD7FgwYIOZ0zOPvvsNp/J6LodyaU7S9++fbXAdSEhIVqgu23btomGBhZksZPAcAEAAAA8RRecc3s60lC58cYbxeWXXy6Sk5OJn4w0Wn766Sfx2muviU8++USLJ9NZDL272ZoD80hd+4i3nhdVtmbrbmrGRqWSI+twf1KeM+ArZeju5eVpSg/2jNC9SjUA94ifZNmmVEPw+s6IXNPp8NY81DYP5c3vlRBQoUwJwJ+FK5q4UsXipw5Jz0P+L7WOUJ7P205v/7TwDcpQ4eF+NcrQ4rz9eFj7NBaWnacg4NdbVjmclDPDtrt0Pw4fixdE/dJhGPqcevW10gILlWHS9cYSf08sxtoO69Le9fl+HjZeLwUBD0vP78/3c3hI/rePjFO+p/x5+djjfb+uepBL6Tb0wtbz9uLvDq+vCp46go9jXrdl1UOVY4ejl/5Ary/1jtcbO7wtnd8Ff2OI+PPQr0RYWFinlDpd+buU/sG81r9L7hDqHyC2Xn5Ht9bVXeRy0lVXXaWlE/jiiy/EunXrNIdemR9J+sykp6eLM844QzNqGhsbxTXXXCPWr1/fqWtjxgUAAADwBL04O3R1dbUWvK6sjBrkErlU9MMPP2ib9LWRPjRyKQmGCwAAAAA8glQedRY5K+MKkEMDAAAAHsFwDLaez/fffy/Cw2ng15YlM7nPVdwyXOSaVEc8/fTT7lwSAAAA8C08EMfFE0yYMKHdxI9BQUGan4uruOXjIgPSlJeXi2+++aZNYBtp1Nx9993uXBYAAAAAvYQTTjih9ffhw4cTfxeZtFHGj5FxYY6L4XLllVeK999/X5x//vlaOGDJCy+8IP7v//6vXQ14TyCwyCTsDc1BdRZnUeXMScP2K3OMzFx5Nb1WMA1zPDKR5mA5UEU95tOCqYf9e7vHkPKVg6gn9QO7pirz+fAcJHfv+z9SLqsL6TDHyrAImo9iY2lfUr5hwApS/u8hWteZiflK5URcEPVs31Mdo8xXw8/nqiae02RIZIlStcRVQVzllBVA1RGFDXT60uqnVinl24USrlLiz8dzK3EF2gfFYzvMLdQevH7xgZVK5YpzPp1L+9Bxl12bqFSMcbUdf0+u6LtOWdesqoEuqfl4bp8rY1a7pO4rqQtV5pnibb+vKpqULYlUhVRUH0bKnzXSsVptD1TmqeJ9zxVfvK+WCbVSh+eKGhak/gOg117OuaX4e5xdTjMH50QkqL/jSse7lLOLw/c751GSfFl6ovJ7gPcVfy94XqkpsTQ/3HGjFzvnSmRwOylzltuyZTT3mEQqjG655RZxXAyXr776SsyePVt8/vnn4pxzzhHXXnutmDp1qma07NxJExICAAAAoPuyQ/dUBg4cqMVu2bNnjzjllFM0NZFzHBcZrdc5Sm9ncVsOLWdcIiIitBkXWZmzzjpL7N6tziYLAAAAAN/gwIEDrctCx5JOGy7/+Mc/2v1cGi0bN27UZmBauPPOO49N7QAAAIBeigwW60LA2HbP9wbuvfdeUVRU1JrIsQUZdE7mTXJV1NNpw2XkyJHtfr5r1y4tal/LflfC9gIAAAA+Sy/3cWnhhhtuEFdccYXgyHxFH3zwQfcZLpmZmS5dGAAAAACgT58+orCwsN0Vm4QEdeqRbgv5L4PISMMmJydH5Obm9she8rcK4f+biGDkOdSBeOeRWKVypfAwVZ5cPGSTUjnD1RaLCulsVXrsIaWyhauI9Agx0VwX6fEFHeZA4SqU4jqLMtfO+Kg9pFxgi1TmV+FwpQVXC+idn5m6Xamk0MttxFVMPF/MiiNUuVFtovUdFkzbksOfh6uaONPiaK6kd8pOVdaXw9UVXPnD25Mf76wk4rl7TjHvVqpcuMqHq2J47iKusOIqIk50ULVyP+/bqsbADlUxkvTwjt+D9t4FnlOMPz9XxvCxxxVxfCxwFZNeriReP359ruThea/4/qlmtSIwpzahwxxlodH1LuUc48/Kv+P49Tl8XPP78feEK6qWNQ5X9n1RUFiH3wsmg/u5g1ymlzvntnDw4EFx2mmniX379gln5Gcyf9FxMVw+/PBDsWLFCi0ttgwgI/MLDBgwQPMelnFcPv30U3cuCwAAAPgMBkfz1pXzvYHXX39dPPfcc8Lf379VFj1x4kRtiagj/9ljbriceeaZ4rHHHtN+nzZtmmawSIWRzAR5//33w3ABAAAA9PARH5dnnnlGREdHi5dffrk1gm5dXZ146qmnxJNPPnl8Qv7LnAMtEfBk5LtPPvlECyTz5ZdfiiFDhrhzSQAAAAD0Uu69915NQTRu3Dhx0kkniaioKPHII4+4dS2ju+tV48ePFyEhIZrh8u2332qfR0ZGalZUZ5k1a5bYvHmzqKio0LZVq1Zp12shMDBQvPTSS6K0tFRYrVbx8ccfi7i4OHeqDAAAAPQsWnxcurJ5mZOuNFhkzDcZgM5dDO5MNt14443i+eefF1VVVWL//v1i1KhRmgz65ptv1sL+d1aBJFMGNDY2atF25XKTXGq66667NGn19u3btWml8847T1x99dWaYSONGBll7/TTT3fJcbiyslKMWvykqLY3N1R9LXWeddTQFTNDCI3r7rBR+87gTyP9RUfRUOKcsp3U0cxUQwebPYR2QUAydVKsq6ROiKZSWv/GBOoIF7qJOt1ZRxwdIEFh9FjeFif2p05ue47QulflUac2g40+S9QQtbNtaRE9n9fHvoeGaW80s6iKYTTdAsfPpI7CaK+kDpyWPjS0ef22CFK2hdPrOYIbSdlQ61pgJb9qOpbsMern4ddPHlKsdBwPDqHtWVUZ3OFY1hsL/L3gmMLoF0+jnf0fVOmvbjv2HvH3jNfP31+db6G2hr4nnIRo6hB6MC9aWZ/QMOq8bM+ijum1fdX1CTpE26+uL20vU7D6fONe2neBI6jzc/Ve2vfmgRXK9uD9w1OX8O8ZV7BE0+8s62GzchwHltJyfQr9h9f/IK2LkX3P1PWxK8diRn+axmXNL9QJ32Gi37njTzgq2Aj2CxJvnfKCFuZD/sPcHbT8XUp/4wVRZXP/D3iof4DYeu2t3VrXY4E0Vj766CMtur60FeTKzN69e8Ubb7whjhw5Iv761792/4yLTLIop3tmzpypGREtsVtkWF/p49JZ/ve//4mvv/5aiwUjjRd5rjSG5LVlR8hUAnfccYf44YcftCB3MliN9EIeO5bmcwEAAABAz2T+/PnCZrOJfv36iZqaGiL0cV5l6XY5tDQk5MZzGLmL0WgUl156qTCbzWL16tVi9OjRmhPP0qVLW4+RUms5wyOXqdauXdvudeQ5conJ2bIFAAAAehw+4pz7u9/9TkyePLlNJmg5YdG/P02u2q2GS1JSkrjwwgs1C6rFS9idkP/p6emaoSJl1XK2RaqUsrOzxcknnyzq6+u1JSJnZNhguU7WEXPmzBFz585144kAAACA44iPGC5ms5nMtDgvIcm/88fFcJE+LDIztFwaSktLE1u3bm2N48JnYfSQsyjSSJFKpUsuuUQsXLhQS9joLk888YSYN28emXHhVh4AAAAAjg8//fSTmDFjhnjggQe0snQvkfbC3XffrbmCHBfDRRoHzz77rDazIR2MLr74Yi099XvvvSe++eYbl64l171askpLoycjI0Pcdttt2tqXXPKRBo3zrEt8fLw4dIhGnnVGeip3xVsZAAAAOC74SOTcu+++W3z//fdizJgx2gqNDDw3YsQIbcZF+q0eF8Nl2LBh4g9/+IP2u91uF8HBwaK6ulqzphYvXixeffVV0RVfF2mwbNiwQTNAZHS9lki8Q4cO1dbD5NKSqzh2hYqmFoOmL52aikmm3vrB/tTb/oYBK0j5y9ITlfc6XEc96iefna1MEcBTDkQF0ym1aSN+UaYQyIimHvSCVc85NDoPA85DwvPQ3DydwHnDmqXvHYXm3lUTpwzDfloCTSHA65M3JErZVjzUd5sw7iyMPQ+DzsO68xQHj0ReoEx5oAcP8877akbyamV9OTzFwaTQbcqw+suKU0k5c9DREP+8f3k6Bh7Cn4ekTzLRkP/vlY5Xhnnn6SR43/GQ87yvz4zc0WFI+vb6MqtqoDKsPO+btMGFLj1PwqAKdZh5Ntb2VVHV0szEn4QKfn7o8Hpl+8T1palLeH/GB1Yq01fw/sypT+jwe2F1WYoyrcmwCPrPZHVCoLotWd+0SQkwUp0SYHNlMimfF7OFlLNrE0n5zMwdyhQCzt8DRgNVNnYnvhI5d9u2bdrfb6k8luqn0NBQ7e+6jL6vmog4poaLNFJa/Fpk4qRBgwZp8mVJTAx9uVQ8/vjjmqrowIED2pKOzB45YcIEzYlHzuRIqZRc9pHB7mT5xRdf1GK9dOSYCwAAAICeg8lk0lZiZNw2+Tf/mFzTnZPWrFmjyaBlUkWpJJK5Bk444QQthovc11lkMLm3335byw4pl4O2bNmiGS0tSqK//OUvWtwWGZlXzsIsWbJEzJ49250qAwAAAD0LH3DOtdvt4sQT1asUx8VwkbFV5FSP5MEHH9R+v+yyyzRpk9zXWa677jrlfultLKeW5AYAAAAA7+Pdd9/V4rJJ1a/HDBcZ8a4FKXGSkXQBAAAA0Hmka22XfFyE9ywXyYC1kyZN0vxXpbuJuyFUtOu5UwmpApLqn5ZEiy1IBZBUBkmfFwAAAACA9PT01lAp0knXmZbI+91uuMiYLX5+bXO0SD8UGZiuJyJz6rTku0iOp970tTbqIZ8aUaxUK3DigmiOiBRzaYeqnvaON8dS5c5oyz6lmmJaAlV/bLAOUKoJuLpDpeoRVATUBq4E4UoNfm/B0p/wtuDe/W1UQnr7mSpnXfUgpXqBq6D4+bcnf0fKS61UtTPJsq1DJUZ7agzeV/z+vP0S/Y8o979afLZyrGTG5SrP31yX3KFqp8BGc/Fkmun+t4+MU6qQ+Plc5cL7jqPX95lhzQKAjlRPHP7s/Hq877jyRe9+SaZy5bvEy2HGOuXY0lMhcVXQ8vI0oYKP1UQT/d6xNtHvPZU68NI+65XjeFrYJlLOquun/N7g31kTInKECq6A4mOFX5/vTwukfZFvp2M13340R5nJQFWh3YoPyKGNRqPmUvLrr7+K8nL6zriLS4bLBRcclYpKJ1rn+CrSkJHS5X376BcpAAAAAHzTObepqUl8++23WhgVjxgun332WevUjoxwywPJSaPF1bUqAAAAAPRetm7dKlJSUo7ZxIZLhkvL8pAM9S99XA4fPnxMKgEAAAD4HD4w4yK5//77tWj7f//739t1zpVB6brdx0VaTgAAAABwH1+JnPvVV19pP2WOQ2dnXJmvSJal6ui4ZIeWiRZlgDi5biWRGZ2fe+45LR8BAAAAAIDk7LOpqKCruGW4yLgtzz//vPj444+1n5Jx48ZpVpU0Zl5++WXR0/C3CuFf376KiLOphCqj6u1UQZVgsSpVQnr5dTjhLDfRNyXppJweXqD06OdKHn7/vKqjHvNbS/qQfStNdPbs2pSVHapQ2lM+cPKtNOfI5ORspdLin/vOJOXzErcq1QE8BwmHq5aK6sNIeXBIsTIXEFcncHg+G349rmTRU0/w+3HlClfCcBURh+fP4c9X45Q7io+zKbG07V8uPUup0Hqz4Ayl8qSkLlTZdzMiaZTtW3ZdplRI8TxMPEcXz13E78fHRqhfvXJsctVQvp0URU6DWlHG8+9wxVjW4f6kHB1UrVRpuToWOK4ofa6MoTm1Xjt0ljI30KLKk5WqJP6e8O8srtzk3zN8bPEcYzxnF//eWFszSPmeOn8PhfgFivOP16KCjywVrVhB8/15xHC57777NANFJkhqQeYRWrlypbavJxouAAAAQI/CRwyXljhvMnpuyyqNTLz45ptvankIXcXo8hlCiIiICC1pEkdKnmTlAAAAAAAko0eP1gLXygmPqKgobZPpgeRnI0eOFMfFcJEONtOmTWvz+dSpU8X//vc/dy4JAAAA+KRzblc2b2D+/Pma3SCD11588cXaNnDgQM1ekL6x3bZUdMstt7T+vn37dvG3v/1NTJgwQaxevbrVx+W0007TMkUDAAAAQAcfiJwrGTNmjPjzn/8sGhsbRQvy96efflqsX0/9l46p4SKneJw5cuSIGD58uLa1IKPiyURKjz32mMsVAQAAAHwKH/FxqaysFP369RO5udThvm/fvi7HcHHJcPH22C2mWocw1TX38uEyqnYQlVRlNG4kzdGy80gsKZfVUvXARntfUk4OpWqEzZXJHSo7JLFBVcoyV8b8fID2xcVDaJ6Qw3XmDnMvxcdXKq/NFUtmU4NSQcXzIJ2WsIeUt1ZQZUdREL1fVFCN0tv/v4fGkHJZHW37UTEHlflxOFztUM36wlmBJSk8TH22MvrvVypdgk02ZV/y9uX14fly+PNwdcbqshSlAo0rUZzrw+/FVTH83oUiXPlsXMXD68LVbov8TnYpzxJX3XDlyJelJyrfMz2SY+n1/lEwWakO5G0fwt4Vfvyze+j1Hh68WJk7iY8Vzuf7mPow9hApbzANUOaW4koe5/HA8yhxFdHbeVRd1y+UKqa+z6XvhXkEHWuc7PI+yr7mY4v3Nc8Pp5cTjI915++hYL8mZV2B63z44YfijTfeEH/961/FqlWrtM/kCs0zzzwj3n//fZev53YcFwAAAAC4j68EoPvrX/+qBZp7++23W4PNyTRBr7zyirj33nuPj+EiLScVUvIEAAAAAAU+slRks9nE7bffLubMmSMGDWqOqSMVRbW1tW5dzy3DJTKSBkby9/cX6enpmkx62bJlblUEAAAAAL2PsLAwLdeh9I2VCRedbQm73X58chX93//9X5vPZM4BOe0jrSgAAAAA6NBVSbPDO1r4gw8+EF988YVmIzgzffp0ceGFF4rzzjvPpesZjuWjDx06VCxfvlwkJqrDsh9PLBaL5tE89qvHRLW92UGsqpKGVfcrpE6IjWbqnOUXRR3L7JXU6c9YS8PhOPxpkzqCj0rA2sNQS1MKDBlGw7bnf0tDddeMqFM6F/P6NhUdDe0dkEyd3mLDqpTOpTvzqfMoZ0gSdRbdsY862ZmCaZz0iDDqJFdeSZ3kUuKpk92OnXQsxSRTx+eq9dThs64vdZAUNto3k0ZuUzo6c/pFHXHp+fjY4PXljuGhYXSqtLYmUOkMvOaXobSCYbS//A/S8wNH0PvbbEf/VwkNpuMkyUJD+m/OpiHpLX3of0W1udSR2R7eqHx23tcJ0fR+Jaupc6oYQe/H68spzaP14ZjCGpRjjbNvDXW6v/S8n5Uh+wut1FGdY9tEZ6pjx9Ow9YWb6NhKOPmQ0lG80c6+d9hYHzqAnl9rp98TeTvpu5085Oi7nL89nuxLGl5EyiWVdBzbbfQ7TByk37H2GJvyveFjgT8rx+Sv/k5tyKMChaghZcqxaNx7tL6hAQHilztv1mYJ3FG8uPJ3aeQzC0R1A/vOcgGzrOtdN3VrXY8Fhw8f1pxxc3JoypPU1FQt4n5MDP0e75YAdB0h165czfIIAAAAgN5LYGBgu7aBdDMJDqZGbmdwy8rgQebkMlFCQoI23bNw4UJ3LgkAAAD4Fj7inLtu3Tpx/fXXi1tvvZV8PmvWLLFhw4bjY7jw3AJNTU2ipKRE3HnnnVrSJAAAAACo8RU59P333y+WLl0qTjrpJPH9999rn02cOFFkZGSI3/3ud8fHcJEzK3KWpaam2V+hf//+4qKLLhL79+8nIX0BAAAA4NusWrVKjB8/Xtx1112aQ66UQW/ZskULnbJr167jY7h89tln4tNPPxX//Oc/tWzQa9as0XTa0sFGZnx89dVX3bksAAAAAHohmzdvFn/84x+PybXcMlxGjRrVmrvokksuEUVFRdrykcz4+PDDD/dIw6UhO1zU/+a9PXHSFmU46syw7aQcZqxThuZOC6TqAE6qPw2zn2ujYe8TTdQbPKeBphiwzqTOSxYjVaIU2CKVYd4zgg4cPdZuUdYtq54qDcIG0Gvl2yM7vLZ2/QH0+pVNRxVN7cHrnuhPVTxJKVSZwkk8sUHZtryvMs00ncNoC1XpTA2lYeetLIlZYipVT6yvp+oECxsriX4N6vZlx/PzOdY+PyrbN2NCsfJ+2XVJrb8PC6LqtYxAeq6gEeGFxci+Lmg2BrG4Okk5Nob6U6XHcqbGswylz55VO1DZd9YmqpIRNEp9G3hf8L5dVElTEMz40wfKsTUjcg0p83eL81nfUaR8UcRGUs4fQN+FtAD190pOQ4KyvS1sHYH33/rBIR2/2zRbgEgyHVGOU4vRpmwLve8B/h6kDqPfSwWNAcq+5GSdrH7POImnHf0ONhqkYupmcVzoxT4uISEhrasyx/p4t1RF8gYt0iu5PiVnX2Q4XznzIpeNAAAAANA5H5eubD0VuQR0zz33iD59qMyfM2nSJPHVV1+1cdw95jMuskLSp2XRokVi8uTJYv78+drncXFxmjYdAAAAAL7LhAkTxOOPPy7mzp2rLROtX79eFBQUiLq6Oi1i7vDhwzW/Fxk594knntBcT7rVcJHLQf/5z380g0V6CMuZlpbZl19+oRlIAQAAANABPXjWpCvs2LFDcyXp27evuPTSS8UZZ5whTj31VC1uS2lpqWYr/PnPfxZff/21pkx2BbcMl08++UT069dPi90iLakWpBEjZ2EAAAAA4Ls+Li0cPHhQzJs3T9uOFW5HzpUOuZs2bdJ8W1rIysoSubnUuREAAAAAvs3MmTPFr7/+qi0VyU3+LuXQ7uAz8fntIQ5hNzUbWct3DyH7RiRR7/07c6cr95fVUW/8vCKqBhg3aC8p19gDlDlD+oVSj/34QOontKJosPJ+PF8QzzeUFXpUnRHqRxVUT7B8K/zcPUeiSDklkub8+C+TluRbaY6R0xL2kPK+qmhlW84f+iEpL6oYTetTTXNamE0NytxD/v40J8o3kenK+/9HnELKyaFqVVNelTo/zl9TlpDyB8VjSTkuyKpsn9xiqo4YmZhHyofrqFKHw8dWbvnR6yUPoH25oGw8KW+toHmi0sMLSDk5gJ7/TQlt2+VBaaR8QRRdRl5X3ZzeviOqGmnepf8cpH0zKuag8vxqOz1/bSHN+ZXZd6ey7ZcVp5JyWS0dKxye6yk2qKrDtm/vXeTPu9xO2+9AFX3vo4No3rHlpjTl/fhY5t9Lzu96RDBV4UQFUbVHCHvvOPy9NJvos24s7av83pmWQMfKBiuVOZXU0VxJA0IPK/uS15e/t6kRR79Dg4xBYkGyOC74SgC6hx56SAuV8uKLL4rVq1drn0n/FuluIldvHnzwQZeu5zOGCwAAANCj8IGlIsmNN96o+bPILNEtyGzRMgidNGZcNVyOaZJFAAAAAACeTFGqijgyT5E7iZlhuAAAAAAeoDfHcXHmnXfe0WZdODLx4nvvvSdcBYYLAAAA4Mmloq5sbjB79myxd+9eLWeQDGcikx12hssuu0wT5LijHpaOuNIh9/XXX9c2uUwkl4+kFPof//hH69YZ4OMCAAAA+AjTp0/XpMmzZs0Sa9euFbfffrtYsmSJSE1NFSUlJR2eJ6PiP/vss2LFihUu3zM9PV1s3Nic4mLQoGanfBnLRW5yXwvOKmUVMlmHl0w2uYfFYtGi+Y54d76osjV7lhsrqb2WeeqvpLyphOZcqaql3v6pcVTFMz6KKmd21cQplS4XD9lEyp/vo2qMBIvVJdURVyNwj31n9UVCQIVSOcHh3v6X9qHrlP89NEappODe/9y7/7wYmjfqy9ITlfXhx2fXJirbgj9vuB9VR+Q1UNXUKebdpPxF2UilMsbaFKy8nh48X5BzLqH2KGwIVypThgUXdDqvFc9p5WrduarI2qjOR8Ph9+PXy6lNUOYQ4wqty+PWkvKzeyaT8r/S3iXll0vPUl6fq564oo0rwvRUQjw3Eee9UqrqmhCRQ8rLy6lqaLRlHynz/uTvBh8b/HpXxjSrPSRLrSOU52ZV0TxSs2N+VOYq4jnOeN1433N4DjPOggOZpDwjebWyvhmhezsciwHGYHHnsE9EWFhYa2qb7vq7lPHQAlFdr1ZoqTAHBoisB29yqa5yhkWGLrnlllu0ssFg0GKtSCfZp556qt1zjEajZrC8+eabWiC5iIgIMW3aNOEpsFQEAAAAeLGPi8ViIVtAAJW6OzvJjh49WixdupTMcsiylCd3xAMPPCCKi4s1w6UnAMMFAAAA8GIfl/z8fG0Gp2WbM2dOu7eLiYnRVDwygKwzstxRMsTTTjtN80+R/ig9Bfi4AAAAAF5MUlISWSqqr6fLlu4SGhqqKYKk0XL4MA3y50lguAAAAABeHIDOarV2ysdFOsPKbMzx8fHkc1k+dOhQm+OlI+3AgQO1YHHO/i4Sm82mOfTu2UN9PI8HPmO4GK0mYWxozkDpiG5QOuOeHEsdJgeHUGfc1WUpyjIPP3112hrl8WMTDigdWvn9ufMvD6fNQ6E7hz5PDqVOcBnR+5UOftzhkjvVccdk7jxqDq1XhmHnTnPcwfLNgjNIOclEnfSsAUFKh09en7ERu5XHc+dY7iDJ4fXnDp78etwZN8xYp+zbWXE/KO/PUyJwZ1yVsy93fuWOzJNCt5FyVu1A5djQc/blz17RSEPopwUWKvcnmcqVY4VzwwCqfshpiFUer5eC4Po+1AE1pz5B6UDK276yKUi5nzvd8/blzrgZwdTB1Mquz98N/u7ysb225ujzT7JsUzrXpgXTvnr7yDilkzsfl9xxmY+dtIBC5TjnzsI8RQC/35+iVpHy0qoRHY5Nk0GdRsObQ/7bbDYt6NvEiRPF4sWLm69hMGjll156qc3xOTk5RPUjefTRRzU/mttuu01z6vUEPmO4AAAAAL7OvHnzxMKFC7VItuvWrdPk0GazWbz11lvafrlP+szcd9992pLTtm3UiC0vb/4Hgn9+PIHhAgAAAPhIrqKPPvpIxMbGiocfflhzyN20aZOYMmWKphqSyKSHMihcTwaGCwAAAOABPJUdesGCBdrWHmeffbby3GuuuUZ4GsihAQAAAOA19BjD5Z577tEC4cyfP7/1s8DAQM1hSHpCS4/pjz/+WMTFUedFAAAAwCvxUK4ib6dHLBWNGTNG3HDDDWLz5s3kc2nEnHfeeeLSSy8VFRUVmhHz6aefitNPP93lezRZ7KLJZtd+7xtPvf+jgmgY+LWF/Uj5gCVS6cHO1RnFdRZlKHBOtZ1GOZwSu5WUN1gHKI/XSwHAVUkq7/zndk4k5Uv6/6JURHG4oorDlQw8xL81NFiZYoCrG7iygrcVhytBeFvx0ONcicHVD7xvufJlWcNwpcqJq6y4QiynIUEZpp2nIHAO6d8ezikPuMKMX3tzZbIy3YJeiP82bclUNPx8Z1ULr6sk3x6hvB5XLXFlCUdPRcWVJ3zscBUUD9l/oIr2RXJymUvqQK6qGhuyWzk29EL866UEcH4X1opByrZ5tfhs5XvN1XJt3lsWcp/31Ttlpyq/U9uonkSk8j3gZb0UA73Zx6U34PEZF+nNLNNaywA3R44cNShk7gUZre+OO+4QP/zwg5agSa6tySh+Y8fSHCUAAAAA8A08brhIB6Evv/xSfP/99+RzmU9B5ltwzqmQm5sr9u/fr8ypIM/heRsAAACAnobhGGy+iEeXii677DIxatQokZGR0WaflGlJDblcIupsTgWJzNEwd+7cbqkvAAAAcMzAUpF3zbgkJyeL559/Xlx55ZXHLK+C5IknntCWmVo2mcMBAAAA6K3ZoX0NjxkucilI5keQvisyDLHcJkyYIG699VbtdzmzIlVF4eHhncqp0EJDQ0Nr3obO5m8AAAAAgHcgl8g8YrPJrJP9+/cnn8mQwzI3wlNPPaXlQCgpKRF/+MMfNCWRZOjQoZqfy7hx48Tateo8JS1IHxeZ5vukj58RVfbmHEUZ/Wl+Hg7PgcI95C2s3CZfDPPe53B1gJ6aItO8Q3k9CzO7c21hyuNV9+be9lz5wVU8HK50yAymbf1UyQRlbh+uyuFKEmtTsDKnCYefz6/P87u0UT8E0TxSnAK7RZmPJi2gRHl82/pGuvR8es/L1RRTQ3Nbf8+qj1O2Lc8LxevG9yea6D8JWXX9lMen+rPcPA66Yr+o8mTle8Ph9+dtzd9bPla5ssXV9tBTMXH49wp/F/XyWnGVk3Pfttee1ib/Tr+rBY1UuWgx2pR5n/g45/fi5/Oxwcc5H8f82fXGDr8/h7/3zu+tzFV0fsoqbca+u/7xbfm7dOrdC0R1Pc2d5wrmwACx6umburWuPRGP+bhUVVW1yXVQXV2tpc5u+fyNN97Q8iqUlZVpnfziiy+KVatWddpoAQAAAHo0Prrc4/VxXDriL3/5i5Yz4ZNPPtGWjZYsWSJmz57t6WoBAAAAwEP0KMOF50iQTrs333yztgEAAAC9CU/lKvJ2epThAgAAAPgMkEN7ZwA6AAAAAIDO4jMzLn67QoSpoflxa5Kox3x6OM3p8eyeyaQ8KuagMm8G56SwPGXOF15OMZcqVUVcXcHh+W82lvbt8Ni/pixRqoayqgaS8r6qaFK29FHnO+GKqrcbopRt90EdTd8QF2RV5pPh+V2WizRl3iYOz9/ydh6NwnxTv2V0/5FxyvPz2PNxVdbcvPNJuYblmRoftUfZl1/WnehSviDe/ry8rLZ/h3Xnbcvzz/C+zmocqMzbxNvivfLxyhxafNzz+vD935Sku5Tji+d10lO88bHH24urfnjuJF7fFUeGKr8n+PF6qiN+/NsVI5X7OXxsLCgb3+E+vbxUnC/KRirzMM2O+VGpUnokl743k5OzSTmfvYf8e4tzT+xyerxCrehvdO1ZuwKWitzDZwwXAAAAoEeBpSK3wFIRAAAAALwGzLgAAAAAHgBLRe4BwwUAAADwBFgqcgsYLgAAAIAngOHiFj5juDREN4p6W6P2+5b91Pu/LD5Eea5KpSNJjaDqiNVlKS4pR1YUDVaqmKrtgUoPfc4NA1Z0qAbhygeu/ODKB153rqyoagxUXo8ff2YkzbuU6H9EmVunolHdN1xFxNUQXFnB88nMSF5Nyuuq1bmbeO6iNqoqnXw1A0IPK6/fJk8Vy+XE24e37ynm3cp8O6p7jbbsU9adty2vO+8r3reZfXa4lK+Gw6/HVUR8f44pQanS4Soc3ta87fRyGXHlDW8friLSywu21DpC/a6Flimff1nlcOXz8f3OKiqumOLPyscd388VXPy9ern0LGXdLulPz+fw944r2lRquvbeY+e8Vn4G93MHgeODzxguAAAAQE8CPi7uAcMFAAAA8ARYKnILyKEBAAAA4DVgxgUAAADwAAaHQ9u6cr4v4jOGi8FuEAabQft9cBJ1pu0XekQZipw706YFq50ILRHUMSzJRK+fGk4dSpeF9leHkWdOeNxJkDsdpvpXduj0mM9CXacFlCid2njdE01WZahu7jzKQ+RzB8SMQNrWy5iTH3c41HMa5PCw7rm2MGXbpYXTvrU2BSkdSvXgDpljQ6jzrIXdn9+Ptzd/3hmRa9h+f1JO9adjZ339UQfaRJZeIdHPj5QLGpud2VuYaqZt/2RphrKvedvyscL7JquehvjPCN7L6kedJnPZ9Tl/ilql3D8j/Bfl2ODoOetOsmxT9h13PuZ9XclCzfN3kTuU8vbk71JSVLlQwcPuO48dXjd+7Sz27Hw/h3/P8LrzscIdl/nY42PT2bm2vfeKf+9ZWFpl574yHs+Uy1gqcgssFQEAAADAa/CZGRcAAACgJwFVkXvAcAEAAAA8AZaK3AJLRQAAAADwGjDjAgAAAHgALBW5h88YLgGHjSKwvnmCqdBKPdAzovcrQ/bX2qlSo7iOns+psQcoz+cqppK6UGVY+F01ccqQ/zwlAU8Z4FzfOKYkyXYK892Ze3ESdM6vZm2RYi4l5cuy/0TKtTbaVpOTs0k56zBVYEUHVSvrlxcWpVSI8VDhPKz6vqpoUo4NqlIq0Hhoc369pY00jDuHqyneKx2vbM9sc6I6jHxTYYfX01PP8bbh1y6qD1M+a7YfrZueSmd5eZoyBUGmmaYMqGTKF710Fvz6eu/Rw4MXk/KbBWcoUw5ks5QHeiH9eYoEvn9a+AZSXlR5svJ43p5cEbi2ZlCnFXpZVQPJvosiNipVQAvKxisVUZsrk4WKy+PWKp9tWW2UUr34xp7TSHlIJFUxnRezRVlfZ0WcvzFE0MQn3QiWitzCZwwXAAAAoCeBGRf3gI8LAAAAALwGzLgAAAAAngBLRW4BwwUAAADwEMczUG9vAUtFAAAAAPAafGbGpT6+UdTZmvNbBNrUj50eTnOuuMop5t1KpQnP6cJznnB1RHhwjdJjn3v8c/WAszqE51PJqU9QKjn0chdxZcekUHr9RRWjlc9eFUOVKBmhe9VKixSqtOA5Vfizc2UHrx/PYcLVEpVhQer97P5JJpofZmnVCOX9cxpo+2fXJir7gytR+PPp8XDCktbfF1elupQTa1nlcFK+MmY1KefbI5V9x+G5e/JCqHJkamguvX9tf2X+Gt43fCzw+url7uH5dGYm/qTsaz6WuPKFv0s8DxeHX4/D81TxPFr83eYqIp4/6O0j4zrM88TfY/4dd1MUbduCxgDluOfw9yAtsFA5tngeK0GFoG3O57mL+Nh27kujgao8uxWZJLEriRIdvjld4zOGCwAAANCTgKrIPbBUBAAAAACvATMuAAAAgCeAqsgtYLgAAAAAHsDQ1Lx15XxfBEtFAAAAAPAafGbGJbDIT9jr/bTf6wRVZryfR/NcGOOpB3pGf5rLaM1umsfDYaP2X9YAdT6dJXnDSLne3lyvFhIsNJ8QZ2c+zQc0bhD1sM+riugw/88SQe99uIx60A9JKnYpF9AvBTQHSWpcsTJPU1UEVRGtLKRygH0WmhsoxNRAyi/vOJOUM/vuJOUvc9NJ2eTfqKwfvz7P5bTs4BBSToksU56v1x48Z8vOI1S5Ul4Zohx7vH48v9CSPbR/J6dkd5i3io8TTlQQVbONj9qjzKPEc/9kl/ch5T1FMS492wOFk5X147mHeM6vfCttG2syVTn9Z0sGKV9xYpbyPQ32tynbh7N5F819ZAq2K8fmyMQ8Uub9w3OQzc07X9l+PPfSeYk0t9Ijued3/D00QCjbgn+PVNUGKsc9z7/G65YcShVaJ4XRttDr+9zyOOX1eNvwfHPO+4OMwWI0FWh1H1gqcgufMVwAAACAngRURe4BwwUAAADwBIjj4hbwcQEAAACA14AZFwAAAMADYKnIPXzGcHGYhHD8Jh0bN3KH0qHy6jQaSntXDXX8unDEFqWDab9QGsp7UwkN4Z9kqVA6EXIyovcry0X1YUonvn1V0R2mM/jcTp1ZpyX8QsqLCkcq731SWp4yBH1eQ5QyDPyQSBp2/HCdmZTL6qiz6uyhK5TX5w6fKeZSZdh17txabadOhhcOoA6NnKpGevxtI5YpQ5M/cuACUj4tgTq8hibXK6/P6zchIoeUE9IqlM/n3L7cWZOP88Ehasdi3rYrigYrx+GM5NXKsOsfFI91ydlUzzn2kv50LK8uo+/pI2MXK88viqXvlR68vdJHFyj7Mi2YhqXfYKUesakRxR2m7pBUm+j1OGfG73Lp3TsvZkuH7xUfp3wcVtsDlM61PN0Ad9zmfc2/R97Oo47gwSbqKH3DAPq9kFVFBRQJARXKNCnO6RH8jfQ7p1uBc65bYKkIAAAAAF6Dz8y4AAAAAD0JLBW5BwwXAAAAwBNAVeQWWCoCAAAAgNeAGRcAAADAA2CpyD0Mv/k191osFouorKwUI59+SVQ3NIdnH3zmPnJMSmip0mM+PrCSlJMCqWqowk690MeaqTd/Tn0iKefXR5LyxRHraZ2N1GN+e0M8Pd9Gz0/yp/WpZOoBZ6xNwUJFWmCB8niuBFlbTZUkw4PzlXVZz7z9r4peScqp/jRr2Np6qjIa7k/VAWvr+yjvN4I9zzbWF/z5zjHnKNu+r6lMWd8Xyk5SjgXed+OCqQoq0Y+mfyhopGHhv6tOc2nsRbD+ch5bL5VMIPsyw2l6gHVVKcpxz8cKv1c5a1u+n7OmdhApjwveTcoH7VTpwuF9H8YUZBy9sXxr1GZStjbRkP3bbSylAHve4QFFyr7j7cfvf0k4Vb5Y5F855/s5DOz+/srvkYP2MGV9nduPj0t+bw5/D/mz692b9wV/zzm87fnYSvSjqTg+rRqmHFvObWU0hIqhyTtEWFiYsFrV6Ve6+ndpymXPi5paWldXCAkOEN98eFu31rUngqUiAAAAAHgNWCoCAAAAPACWitwDhgsAAADgCZoczVtXzvdBPLpU9OCDDwqHw0G27Oyj6+yBgYHipZdeEqWlpdr63ccffyzi4mh0TwAAAMArcRyDzQfxuI/L1q1bRZ8+fVq3008/vXXf/PnzxQUXXCAuvfRScdZZZ4nExETx6aeferS+AAAAAPDhpSK73S6KiqgHukR6SV977bXiiiuuED/88IP22TXXXCNycnLE2LFjxdq1a126T1OgEI2/OeHvLokh+wZbaM6OoeYiZY6PwoYIZQ6S9TVUjTE2hHqw9/U/TMpLq0Yo6x7uR3OyrDwyWJkzhuf5cM7PU8WehV+b153vf6WQKlEeT6GGZL6dtk0hU9HkVlCVziLTaGXepQuiaL6ZrHo647a+eqCyL7iqyI+pI3h7cGXGoTbqBdoeC45QJUx6MM3PUy0HnuL+y6qHkjLPEcP7ckQgU2350/q/cejMDvPPcPXGpPBtSqXHuNBdyv1+7N+9RRW0L08x03F/iI2NtIBCpaKrwG4h5YvMVaT8njWanh9C1YK5NnWuocom2naTLb+S8uJqlmPMRFVVP1iHK583q66fsu8+Kx9FyrNjflTWl4/9JFM53V87UDmWJllofxtFU4ftV9BIcw9xvUpOw9HcPpKMoAPKuu9kqiPOcPady5+1gH2PpAUWKlVEnAj2PcaPdx4rfgazoG9l9yH/JOkItnTP90U8PuMyZMgQkZ+fL3bv3i3effdd0bdvcyK10aNHi4CAALF06dLWY3Nzc8X+/fvF+PE04RYAAADgtZFzu7L5IB6dcZGzJldffbVmkCQkJGg+Lz/99JNIT0/Xlo3q6+tFRQXV88vZGbmvI6SxI31jnPXyAAAAAOgdeHTG5ZtvvtEcbn/99Vfx7bffinPPPVdERESI6dOnu33NOXPmaIF9WjY5mwMAAAD0VDl0VzZ3mD17tti7d6+ora0Va9asERkZGR0ee91114kVK1aIsrIybfvuu++Ux/vEUpEzcnZlx44dYvDgweLQoUPazEl4OPUxiI+P1/Z1xBNPPKH5x7RsSUl0nRoAAADwVVXR9OnTxbx588RDDz0kRo0aJTZv3iyWLFkiYmNj2z1+woQJ4v333xdnn3225qZx8OBBbaJBimU8RY8yXMxmsxg0aJAoLCwUGzZsEA0NDWLixImt+4cOHSr69+8vVq9e3eE15DlSOu28AQAAAECIO+64Q7z++uvi3//+txZ+ZNasWaKmpkbMnDmz3eb54x//KF555RXNwJFuHXIGxmg0kr/NPuXj8swzz4gvvvhCc7iV1pu0ABsbGzXrTi7zvPHGG5plKKenZPnFF18Uq1atcllRJGkMaRKNv+WVSYmi6oBQv3pS3lVDPdoHhxQrVUKJJqtSTZBTn9Chyqc95QhXLzyXdw4pX9qH5jbiZLI8I84KAZ4TJMxYp1RapAVQxVVivyNKdQFXYPFnm5ZAVUIVjTTXzow+a5SqG952XMnB65vTEKtsW66G4DlZMs07lNcbFpSvvD8fC7z9eV8tq+2vrJ9ee1wep343nPt7bQ1VRBU2hCtVLlzlY2Fjh8NVL/k01U+bsaM3NjfUq5UjPHdPvj1SqWLi7y3vW67K4efzsaf3vLy9MkL3kvKiypOVY6ttfp8S5VjN8uunfLd5fZ2VNan+ND/b2xUjSfmmSKrAyrX5K/uWk8jyq/H3hI97PhZ43/Jn48fz76WegsHh0LaunN+eL6f0D5X/xHP8/f014YtcmWhBxk+TIpjOil5CQkK068i/yz5puCQnJ2tGSnR0tCgpKRE///yzGDdunBZwTvKXv/xFNDU1iU8++URbNpLTWXJtDgAAAPB65P/STV08X4g2vpxz587VJgI4MTExwmQytQlBIstpaTQJaEc89dRToqCggCh+fcpw+cMf/qDcL63Gm2++WdsAAAAA0Bbpy+nsFiH/dnYH99xzj7j88ss1v5fuuodXBKADAAAAfJFjtVRk7aQ/p1zNkEFfpcjFFdGL5M477xT33nuvmDRpkqYE9iQ9yjkXAAAA8BmOs6rIZrNpwhdnx1qDwaCVVaKXu+66S/z9738XU6ZM0c73NJhxAQAAADxBV6PfOlw/VwpeFi5cKNavXy/WrVsnbr/9dk3R+9Zbb2n75T7pM3Pfffdp5bvvvls8/PDDWvqdffv2tc7WVFVVierqauEJfMZwcZias09LooOqlflxJkTkKNUO3OOeqxm4UoTnTOHn6+1/esCnSnXHmECah2NZbcc5Tbi3Pb+XheXiKWgULql2uBqA5/6xMu9/fn+eI2VqaK6yrbiagN+P15erhvTanh/PlRj8eThcicKP52qNaWGblM+j1x567enMjHCq8EowhZLyhnp/pSrGYrTRuoer/xPjbcdVMXws8XFd0NiobFt+vt57yOufGUxVEhmBxcrz+X6LkX6dJvoVK/tCT+Gmh95Y5u3Dn5efb3Xaz+vKlZS8LxL9mIRK5958XHMFFt/Px7Xe2ODPxs/nuZCc+9Jg6Iq3bM/no48+0mK2SGNERqHftGmTNpNSXNzcBv369dNEMS3ceOONmjhGimQ64wB8PPAZwwUAAADoSXQl+q3E3XMXLFigbe0hA805M3AgTd7ZE4DhAgAAAPjIUlFvAM65AAAAAPAaMOMCAAAAeADpTtMVlxpD73bH6RCfMVyC8k2isb65l/MGRpB9NwxYQcrZterkUXph8rPr1Ikd81kY/DZh3VnYdx6umod55+GvudOfs2Ncvp0+e0EjvVZW1UDlvfTCsnOnuqVVI9RhzB009gCvH3fg5M/K68OdX3lZr6142/Ow79xJkfd9fl2ksm+zagcqHWS5gyUPyx/uR50SM4L3Ks9XpaOgLuhCTDXTvslpSOr0uGovzHubZ2dh3bnzLA9pv74+ROkczMdWWwfSAJ2xQ68X6k+PX1YbonQuXlyVquwLIQKU7ZX6WwqSo/ejzsV6jvT8+fSc/vm7xdvbuX/5uNGrK78Wdwzmfc/HEoe/t8vY2OLvJX+POLyt2nxvOQksjIKKLboVLBW5BZaKAAAAAOA1+MyMCwAAANCjcCOIHME3fXNhuAAAAADeHPLf18BSEQAAAAC8BiwVAQAAAJ4AzrluId2ne/Vck8ViEZWVlWLEW8+LKluD9lnAYT9yTPwYmhWz8HC48pop8aWkvGMnVSH1HUiVMOW11EM+wUI99g+UUbVDbFiV8vyxCVStsbaQeuynRFLVUoip+bklJ4XlCRW7amgobLOJpi7fVxVNylv2U+VJYDBVTpzeb4+yrrwtau1UlVRro+WoYKqq4fB0Dilm2ld7qmOU5+dVUeXFwb1UPRGTTJUlJ8dSJc6BKtqXZUyZEuxP26ekkobZ7xdF1RbBJluHfSk5XGdWtl9yKK3v6pyjKqWpJ9P0AhmhVBXz30NjlPc+L2YLKb+dN15Zl1ExB5VjqayOthXnvMStpLy6LEV5v8w4mh5hc2Wy8vo1dqo8iQ2qUo7d9Fj6vREXRMfyxtK+yr7k9As9okw98mXpiaRsZv0xOKRY+bx8rPB3xXns83HDn23ZwSFCBX+v+b3499DH+2nqi2tTVpLyiiNDlX3F+563NR9bfCyG+h39ngs0BosH098TYWFhncq43JW/Sxed/aSoqab96Aoh5gDx2Q/3dmtdeyKYcQEAAAA8AHxc3AM+LgAAAADwGjDjAgAAAHhMDt2VXEXCJ4HhAgAAAHgCOOe6BZaKAAAAAOA1+IyqaOQ/XhJVDc3e24EjypUe8Ny7nytrnD3Q270ny++TzHITcbXGlFiqlqhoDFHmp+HX5/DcRjm1R/N8zI75UZlbiOcz0YPnKOE5RPiz6+Vd4rmS0oJpzpNM8w5lDhS9/Dg8hwp/Xp6PZlHFaFI+xUzz4ayrprmEOPx4vfbiShCunpiZ+JMyLxbP18PJqU/o9DjitMk/w/IucXjuHp5/RlU3SaL/kU7n1mkvFxFvW1ffozY5xKqpsmWDdYBS1aPXF0utLI9XcIHyeP7u8PbluZs4es/v3J6flY8i+xICKpTX4n3FxyXPJaSXj41fLy2gRNkXU0NzlXmaeF4qnlfLGX9jiLh80LLjoiqadvrjoqZa/fdERYg5UCz6+T6oigAAAADQ/UBV5B5YKgIAAACA1wDnXAAAAMATwDnXLWC4AAAAAJ4AhotbYKkIAAAAAF6Dz8y42EMcwm5qFlAFsn3DIg4pPei50iMtslDpAR/GPNjX1lDlyaV91pNydm2i0tufX39Z5XBljpm0QFo/lYqIKz0KbFSZweHX5koPrqTgSg895Yae6iinIVapLCmwW5T729w/sFipRphk2absS64E4fXnKqV8u1C2Fy9zpQ2/XlgIbZ8xgVTtsaw2qkP1xeKqVKVKhcMVWPz4RD+ac6WgkSqiOJVs7PHrWYw2Zd8nmqzKsTYpdFunlSTtqaYsBody7PMyV67w9uLKmHtjskjZ2mRXjsXU4Ep2P/r1naOj4NP7nlIpJ/m45G2f6Efzv2UG07rk2gKU7z1/b3lb8v287ReUjVe+t/x7TnV/P4P7uYNcBjMubuEzhgsAAADQo2j6bevK+T4IDBcAAADAA0AO7R7wcQEAAACA14AZFwAAAMATwMfFLWC4AAAAAJ6gydG8deV8H8RnDJcmi1002Zq99scmUI/yfVXRpBwaTj3qU8ylymvzfDU8Pw8vt1G6BKvVDVwNwVVE3EN+UeXJHd5fT+mgp/xI9a90STXEcw/xunOVk16OkjbXZ7mILMZa5fFt1ApM+cLVElzJoqf60VPGcPj5KkVYe+oIfv5SqzpXVJYLqqCs+jjlOJ8RuUZ5PFdAcfTUcfw94SoYrmTRV6rUKhVx75SdqsyT9U1JOimPj9pDysOC8pXPr5c3K6ehv/L5ucqI5zriCjyuquJjkef7cX73uCqHv+f8e4Cr1zi87/j3kN5+Dlc/8pxget9j+XWRLqkPQc/CZwwXAAAAoEeBpSK3gOECAAAAeARHs/HSlfN9EKiKAAAAAOA1YMYFAAAA8ARYKnILnzFcouOsIsje7HS7fPcQsi+j/35S5o5p3LGLO91xJ0fuTDvVnK8M7c3hTnTccYw7kHInRFV4bu4gONTfzO5Or21pok5zVodB6RDJmR3zo7KubRyPda7H4W1b0NioDNveJow6czLUcwJMYn2dad6hfD7uTMudEPn1eN9zZ2K+n48FazB1dlbB62bxb1KO82nhG5SOy3rX53XlY4Oj13ehRuaoXBuiTOdgddD7c7izbCprD+d0Ce29C9xZONdmVI5t/nw5rD48fQNnTCBNGcDR+57JqaXv3h/Ddnf4HvHvwARTKClnGqkTOIe3BR8Lek72/L3JdKqrZH19iPK9Lmjk6Q3KO+5rw3FciICqyC2wVAQAAAAAr8FnZlwAAACAHoWjqXnryvk+CAwXAAAAwBPAx8UtYLgAAAAAngA+Lm4BHxcAAAAAeA0+M+NSdsQsqmzNiovkeKrSWbObhqXP2k9Db08YtJOUzSaaEiC7vA8pT0v4hZSv3/97ZQqBonqqlqi2ByiPzzpM68cZFnGIlJcdPKqi4ukOsmup9/2w4AK2P1EZVtzaGKTcv7kymZQP11EVU0Y0VXT999AYUv57vy9I+R8Fk0k5LoiqE6rtgaQ8OIQqS3bV0LD0oy37SHmDdYByf1btQOXxevD68JD8vL33VMeQ8pmRO5SKNt4+J4XlkXK4X02HIexDTA3Ktg31q1eq794rHe/SONYbS3ws8v2JJtoW2XVULZhdl9Ths0sqGqkSpbAhnJQzw7YLV3iZpTAorrMon58/H+fdykFKFRBPScDh7cvfrUv7rCflBUdO6PBavG2y6yqUfcO/0/h7xNFLdfFZ+ShaZvt5ugjOa4fOUr5H+fajfxNMBrM4XxwnsFTkFj5juAAAAAA9CqmI70rkXIfwSbBUBAAAAACvATMuAAAAgCfAUpF3zrgkJiaKd955R5SWloqamhqxZcsWMXr0aHLMQw89JAoKCrT93333nRg8eLDH6gsAAAAcE5qaur75IB41XCIiIsTKlSuFzWYTv//978Xw4cPFnXfeKY4cOeoodffdd4tbb71VzJo1S4wdO1ZUV1eLJUuWiMBA6oQJAAAAgN6PR5eK7rnnHnHw4EExc+bM1s/27aPe57fffrt49NFHxeeff66VZ8yYIYqKisRFF10kPvzww87frNJfCFuzJ1NJMM2zERpG82IkWKiaIj5Qnc8mM47mMFlUOFKp8qlqDFRef489Rqk+qIoIVKoX2tSv784Ove95TpA3C85QKg+yqgYq721myhSupODe/Fz5cIqZ5iB5tfhs5flcacEVX1wNwdUN/PwJETlKlQ9X0nC4UoWrmIYF0dxKX5SNVNb/+j40n09OPa1vvj2ClC+PW6vMN+SsipoSu5XsS/SnajtrU7Cy73ldeNvmNUSR8ozINaT89pFxyrZdW0NVNcuKU+n9I9QqG/48vC14nqzVZSmkLKgwRixjqiH+LnHVVai53iUVkZ5Cjz8fV5S1yT3F8vukhxco+8dZ4cbzKlmb/JX34n0lAtXvBVfTPbBrKinfMGAFKf8papWy7/iz8O+1GqZw423rMbBU5H0zLhdeeKFYv369+OijjzRjZOPGjeK6665r3T9w4ECRkJAgli5d2vpZZWWlWLt2rRg/nkovAQAAAK+ixXDpyuaDeNRwSUlJETfeeKPYuXOnmDx5snjllVfECy+8oM2qSPr0aY6PIo0aZ2S5ZR8nICBAWCwWsgEAAACgd+DRpSKj0ajNuPztb3/Typs2bRLp6emaP8vbb7/t1jXnzJkj5s6de4xrCgAAABxjEPLf+2ZcCgsLxfbtNDpldna26Nevn/b7oUPNviHx8fHkGFlu2cd54oknRFhYWOuWlESjZwIAAAA9AYejqcubL+JRw0UqilJTqcPd0KFDxf79zWHg9+7dqxk3EydObN0vl36kumj16tXtXrOhoUFYrVayAQAAAD0O6aPS1IXN4Zs+Lh5dKpo/f75YtWqVtrwjHXRPOeUUcf3112tbC88995y4//77NT8Yacg88sgjWkyXzz7j2SrU+FcahX9Ds512esYeZf4Yjl6Ok0mhaqUJ96BPC6DqgHfKTlXml+HKFj0Pe+5R76wOSTJRpUW+PVKpIuJcFLGRlBNN1DBcVj2UlP8YRlVC9xedrrxeqj9VWFmZyocrL/Tg1+dKC65G4MoZnq+Gn59ppiqnrLrm2cKOxk5mMB0LYTHtG+CdvZ/FQL+4ChoDlOoPZyUKHzdh7F5pASWkzMcO7/s20LRUbdqGK8j4s/K+njPgK5fuz5UwXIHFlTOZA2neLIuRfj0mmeh7x+HX431jdRhIucBO/e/yRaSyvfm39dKqEaQ8I/wX5f24aou3t/N4aDOu2Dji8Hvztsu10f+R+djjKqKpZqq+szbZSTmffcfx72CeU+z25O+UY8e5L/wMfmQf6Hl41HCR/i3Tpk3TlnceeOABzTCR8uf//Oc/rcc8/fTTwmw2i9dee02L+/Lzzz+LKVOmiPp6KjUEAAAAvArIob0z5P+XX36pbSoefPBBbQMAAAB6DV2NftsEHxcAAAAAgB6Nx2dcAAAAAJ8ES0Vu4TOGi7HWIPzqm53Vfj5AQ3tfcPIvytDeyaHUoZLDHSB5mHlezhJqB9B11YOUToz8fm3rQ0O1q8K0pwUWKp1jubMnd3AU1GeuTRjyxdVJyjDp3Dm4jTNqMHWY5PD68+fj8PrztuUOqW0cKNn5qrZuz5m2oFEon5dfn+/nDq6ctqHaOx4r3Pm10hik7Ns2jtPM+TOnIZbWJZA6veuFkbcYbS6lV+Dn6zmpc+fjNueztucpAnhbVrJygS1S3fdsLPGxysPQjwmkjt0FjY1Kh9Rltf2Vzr1674bz/XnfcmdW3tdZ9UHKtubvORcocNbXhyjPb/NsrO+nhtI0LFn1NPXGBH+6xJLo7ERvOH5iW0dTk7Z15XxfxOPZoQEAAAAAOovPzLgAAAAAPQosFbkFDBcAAADAEyDkv1tgqQgAAAAAXgNmXAAAAACPLRV1wcHWgZD/vRq7xSFsgc2dbD9MlSD3b5tKyoEm6r3/ZWU6KSdEV5BysImqIaKDqkn5cB2NfV5WSz3mi+uo2iCviqobiiLCSLmkLpRer45eLyqIqhFyi4961I9MpOkE3jhyGilPTs4m5azDVKlwoIx69/Pr8WflbbPnCA2xb7NR2zk1jipRau1U+cHhbRkVTJ9diJFdqn9G9H5le/D7B/vT5/0v6wv+PMMiaLLQansgrW9VpEvt8VQtVXckWKwdnl/Ojh2bcECZCuO1ymRlW/Fxv0BnPx/ntTZ/l8YiH1vp4QWkPCyYlhccyFT2LWdPdYzyeW/qt4yUc2qpsmWDdQAp55ZTZUtyKFUtmU0NpPzolnOVY5W3X2oE7a9qO1UE1rAyH0vO7WmNpWMj0Z+qeJ7dM1l5b058IFWk/XPfmaQ8KuYgKScE0O/YXTVxLj3b8qA0Zds/y+rnnHLA3xgiLhfHB0eTQ9u6cr4vgqUiAAAAwBPI2Zaubm4we/ZsLcVObW2tWLNmjcjIyFAef8kll4js7Gzt+C1btojf//73wpPAcAEAAAB8hOnTp4t58+aJhx56SIwaNUps3rxZLFmyRMTG0tg8LYwfP168//774o033hAjR47UEhzLbcQImuTzeALDBQAAAPAALUtFXdlc5Y477hCvv/66+Pe//63NosyaNUvU1NSImTNntnv8bbfdJr755hvx7LPPipycHC0h8saNG8XNN98sPAUMFwAAAMAHlor8/f3F6NGjxdKlS49WweHQynJmpT3k587HS+QMTUfHHw98RlVkDjjqiGb3p863ZlOg0jnXbqKOXyF+9PhgP2r/Bfs1Ko+vZ/cL9gtSHh/EQrHrHR/sRwez2an+/Nw2z24MVl7b+Vqdqrufn/J+Noef8noGh3qI8rYMYW3PcbX+eu3B7x/UZiw0KZ8niF2/0ageawaH2jnX1qY9Gjo8nx/Lx1kAq5v+uGvs0n4jeza9tudjix8vnSxV5/PjOXrPazKYu9SX/PpBRmPX3jXWf01+9HyHI0A9Fp3ak7edydDg0r05gSydQ9vzadsFGBu69GxBRrvyfhzn5zUZ6LN3JyFhwcfkfIuFCjzq6+tFQwNtQ0lMTIwwmUyiqKiIfC7LaWnUobmFPn36tHu8/NxT9HrDpaVDV914g6erAgDwIJfTFGDHnPOP9QVHix7L+TTdW6/922G1UlXesUIaFYWFheL9g//s8rWsVqvIz88nn82dO1fzYemt9HrDpaCgQCQlNSf6k50rf++uwdibX2C0HdoPY8+7wHvbtbaTfzu6CzkjMnDgQBEQENBt12+P0tJSYbfbRXx8PPlclg8doqEZWpCfu3L88aDXGy4SOQBbZl6k0QLDxT3Qdl0D7Ye28wQYd+61WXcjjYuODIzuwmaziQ0bNoiJEyeKxYsXa58ZDAat/NJLL7V7zurVq7X9zz//fOtn55xzjva5J3H4wmaxWBwS+dPTdfG2DW2H9sPY8/x7iPfW823aG7bp06c7amtrHTNmzHCkpaU5Xn31VUdZWZkjLi5O279w4ULH448/3nr8+PHjHQ0NDY477rjDkZqa6njwwQcd9fX1jhEjRnjsGXxixgUAAAAAQnz00UdazJaHH35Yc7DdtGmTmDJliigubo5+3K9fP9HUdFRUIGdWrrjiCvHoo4+Kxx9/XOzcuVNcdNFFYtu2bR5tTocvbAEBAZqlKH96ui7etqHt0H4Ye55/D/Heer5NsYke0QaG334BAAAAAOjxIAAdAAAAALwGGC4AAAAA8BpguAAAAADAa4DhAgAAAACvwScMl9mzZ4u9e/eK2tpasWbNGpGRkeHpKvU47r33XrFu3TpRWVmp5aFYtGiRGDp0KDkmMDBQC1Ikoy/KAE0ff/yxiIuL81idezL33HOPlrxs/vz5rZ+h/TomMTFRvPPOO9rYkplqt2zZoiWDc0aGMJfBJOX+7777TgwePLgbe9B7MBqNmrR1z549Wtvs2rVL3H///W2OQ/uB3oSjtwfbqaurc1x99dWOYcOGOf75z39qwXZiY2M9XreetH399deOq666yjF8+HDHiSee6Pjf//7n2LdvnyMkJKT1mJdfftmxf/9+x9lnn+0YNWqUY9WqVY6ff/7Z43XvaduYMWMce/bscWzatMkxf/58tJ9Oe0VERDj27t3rePPNNx0ZGRmOAQMGOM455xxHSkpK6zF3332348iRI44LL7zQccIJJzg+++wzx+7dux2BgYEe729Pb3PmzHGUlJQ4zj33XEf//v0dF198saOystJxyy23oP16QP9gE93RBr27YdesWeN48cUXW8sGg8GRl5fnuOeeezxet568xcTEaJGGzzjjDK0cFhamRUuUX4otx8goipKxY8d6vL49ZTObzY7c3FzHxIkTHT/88EOr4YL267jNnnjiCceKFSuU7VpQUOC48847W8uyPWX0z8suu8zjfe7p7YsvvnD861//Ip99/PHHjnfeeQft1wP6B5s45m3Qq5eK/P39tenmpUuXtn4mp+9lefz48R6tW08nPDxc+1lWVqb9lO0oE4I5t2Vubq7Yv38/2tKJBQsWiC+//FJ8//33pD3Rfh1z4YUXivXr12sRPeUy5caNG8V1113Xul8mo0tISCBjTy5prl27FmNPZr5ftUrLJTNkyBCtbU488URx+umni6+//hrtB3olvTrkf0xMjDCZTNqXoTOynJaW5rF69XRk0q3nnntO/Pzzz61hnWVoaJkQrKKiok1byn1AiMsuu0yMGjWqXR8qtF/HpKSkiBtvvFHMmzdPCyku2++FF14QDQ0N4u23324dX+29xxh7Qjz55JMiLCxM5OTkiMbGRuHn5yf+9re/if/85z+tYw/tB3oTvdpwAe7PGqSnp2v/tYHOkZycrGVPlVlTj3fG197gXCpnXOQfW4nMnSLH36xZszTDBaiZPn26uPLKK7V8MvIfjZNPPln7x0M6MqP9QG+kVy8VSYWC3W4X8fHx5HNZPnTokMfq1ZN58cUXxfnnny/OPvtskZ+f3/q5bC+pimlZQmoBbXl0KUi2hVzmkKnj5TZhwgRx6623ar/L2QG0X/sUFhaK7du3k8+ys7O1ZG8tY69lrGHsteWZZ57RZl0+/PBDsXXrVvHuu+9qarY5c+ag/UCvpFcbLvIPxoYNG7T1X+dlEFmWGS9BW6Nl2rRpIjMzU+zbt4/sk+0op+6d21LKpfv374+2FELzaZGzBPK/3ZYtKytLvPfee9rvckYB7dc+K1euFKmpqeQzObak/5REhjKQxo3z2LNYLGLs2LEYe0KIkJAQks1XIpeM5EwW2g/0Vnq9HFqqD2bMmOFIS0tzvPrqq5ocOi4uzuN160nbggULNLnpmWee6YiPj2/dgoKCiBxaSqQnTJigyaFXrlypbZ6ue0/dnFVFaD+1fLyhoUGT9Q4aNMjxhz/8wVFVVeW44ooriBxavrcXXHCBIz093bFo0SLIoX9rm7feestx8ODBVjn0RRdd5CguLnY8+eSTaL8e8D2ATXRHG/T+hr3pppu0P7gynouUR59yyiker1NP2zpCxnZpOUbGzHjppZcchw8f1v6wfPLJJ5px4+m6e4vhgvbruK3OO+88x5YtW7R/MrZv3+647rrr2hzz0EMPOQoLC7VjvvvuO8eQIUM83sc9YQsNDdXGmfyOq6mpcezatcvxyCOPOPz9/dF+PaB/sIlj3gaG334BAAAAAOjx9GofFwAAAAD0LmC4AAAAAMBrgOECAAAAAK8BhgsAAAAAvAYYLgAAAADwGmC4AAAAAMBrgOECAAAAAK8BhgsAPsaDDz4ofvnlF5fOcTgcYurUqd1WJwAA6CwwXADoJfzwww9acj09nn32WZL3BwAAvAmTpysAADh++Pn5ierqam0DAABvBDMuAPQC3nrrLTFhwgRx++23a8s6crvqqqu0n1OmTNGyU9fX14vTTz+9zVLRmDFjxLfffitKSkpEeXm5WL58uRg5cqRHnwcAADoChgsAvYDbbrtNrFq1Srz22muiT58+2nbw4EFt35NPPinuvfdeMWzYMLFly5Y251osFrFw4ULNqBk3bpzYuXOn+Oqrr0RoaKgHngQAANRgqQiAXkBlZaVoaGgQNTU1oqioSPssLS1N+/nAAw+IpUuXKn1jnLn++uu1mZezzjpLfPnll91ccwAAcA3MuADQy5HLRCri4uK0mZodO3ZoBos0guRsS79+/Y5bHQEAoLNgxgWAXo6eI65cJoqOjtaWm/bv36/5wqxevVoEBAQctzoCAEBngeECQC9BLhVJ1ZCrnHbaaWL27Nni66+/1srJyckiNja2G2oIAABdB4YLAL2Effv2ibFjx4r+/fuLqqoqYTR2biVYOuP+6U9/0paUwsLCxDPPPKP5ygAAQE8EPi4A9BJkYLnGxkaxfft2UVpa2mkflWuvvVZERkaKjRs3infeeUe88MILori4uNvrCwAA7mCQ0bzdOhMAAAAA4DiDGRcAAAAAeA0wXAAAAADgNcBwAQAAAIDXAMMFAAAAAF4DDBcAAAAAeA0wXAAAAADgNcBwAQAAAIDXAMMFAAAAAF4DDBcAAAAAeA0wXAAAAADgNcBwAQAAAIDXAMMFAAAAAMJb+H8ZBFc0U6m0yAAAAABJRU5ErkJggg==" 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" }, "metadata": {}, "output_type": "display_data", @@ -96,8 +96,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-25T02:04:23.386221Z", - "start_time": "2026-03-25T02:04:23.249727Z" + "end_time": "2026-03-26T21:28:40.602081Z", + "start_time": "2026-03-26T21:28:40.467257Z" } }, "cell_type": "code", @@ -124,7 +124,7 @@ "text/plain": [ "
" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", diff --git a/examples/view_experiment2_behavior.ipynb b/examples/view_experiment2_behavior.ipynb index 8cd6894..0d01b32 100644 --- a/examples/view_experiment2_behavior.ipynb +++ b/examples/view_experiment2_behavior.ipynb @@ -9,8 +9,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-25T02:07:33.263682Z", - "start_time": "2026-03-25T02:07:33.259102Z" + "end_time": "2026-03-26T21:46:17.560281Z", + "start_time": "2026-03-26T21:46:17.555641Z" } }, "cell_type": "code", @@ -25,8 +25,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-25T02:07:34.714371Z", - "start_time": "2026-03-25T02:07:33.266649Z" + "end_time": "2026-03-26T21:46:20.429214Z", + "start_time": "2026-03-26T21:46:17.561044Z" } }, "cell_type": "code", @@ -50,10 +50,10 @@ " transformation_level (transformation) float64 288B 0.1 0.2154 ... 0.5 1.5\n", "Dimensions without coordinates: boot_iter\n", "Data variables:\n", - " phat (transformation) float64 288B 1.0 1.0 ... 0.952 1.0\n", - " varhat_phat (transformation) float64 288B 0.0003555 ... 0.0003867\n", - " boot_phat (boot_iter, transformation) float64 288kB 0.9517 .....\n", - " boot_varhat_phat (boot_iter, transformation) float64 288kB 0.0003212...\n" + " phat (transformation) float64 288B 0.9969 1.0 ... 0.945 1.0\n", + " varhat_phat (transformation) float64 288B 0.0003446 ... 0.0003748\n", + " boot_phat (boot_iter, transformation) float64 288kB 1.0 ... 0...\n", + " boot_varhat_phat (boot_iter, transformation) float64 288kB 0.0003686...\n" ] } ], @@ -62,8 +62,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-25T02:07:35.089878Z", - "start_time": "2026-03-25T02:07:34.749947Z" + "end_time": "2026-03-26T21:46:20.920034Z", + "start_time": "2026-03-26T21:46:20.461980Z" } }, "cell_type": "code", @@ -100,7 +100,7 @@ "text/plain": [ "
" ], - "image/png": 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bl61RvsFmm4Z23bqkssNhB32ZKF++PvmHr4+UKAegWs2cOCmV4U/+I11v1rJl2WjybttsFbpsuXkoarNe2GqPXVOJ4nqiH7/9Thg/omTq9Qmj3wzzi+faMwDUKXHJkhXX8V5xpHezlkWVeq5F8xeE2VOmVhjdvWIS/IvPZq/19wQAAKyexDjAN7Dg8y/Cu/99JZXyifKe2/ZLifLOvTb5MlH+/YPKpreNCfKxQ4env3NnzLQPAKg284uLw7sv/S+VqEGjRmmGkziaPI4q79and1hnvXXDRv22SaXUpx98mEaVl07BPmviZHsFgLzVonWriqO7O5YkusuP9o5LZ1XG/Lmfl0xjnhLcK4z2Xn7dCWYAAFD7SYwDrM1EedE6yxPlJVOvx0R56fS25RPlaUR5aaJ85iz7BIBqs3Tx4vDR6LdSeeGBP6bb4glbMVGepmDfpnf6Xeq08UaplP4+xaVASpLkJaPKJ70/xnqmAORcvXr10gleKcG9hinOGzVpUqnn+2L2nFWv5Z2mOJ8W5kybnmYOAwAA8p/EOMBatGDu5xVG7cVEeY++fUoS5f37hvV7bVyWKN/x8IPTNlPHTUjTrqdk+esjwuczP7OPAKhWcZmPWIY+/lS6HtdGLZl+feuUKN9gi16hZbu2Yeu9d08lWjhvfvj4zbfLEuUfvfFWOiEMAKpL/QYNQlHb9SpMaV5+Le94vWX7tqFho0aVer540vGXie7pJYnucqO9Y9I7Li8CAADUDRLjADWcKH/nxZdTieJ6dT36lqxR3nNAv9Bp056hQ49uqex4xCFpmykfji9bnzz+/XyWRDkA1Suuefr28/9NJWrYpEnYcIteKUkek+Uxad68Vcuw8Xb9U4mWLVsWPh0z9svp10eMTmurAsCqNGjYMCW1Kya8yyW9O7YPLdu2Scnxr7Js6dIwd8as1a7lXZL0npFmTQEAACglMQ6QQ3EdurdfeDmVqFnLlqFHv9JEecnU6x036p7KTj84NG0zZey4kjXKh40I414fKVEOQLVbsnBhSnTHEsKDadraOLtJ974xSV4y/XrbLhuk36lYSn+jYmK8/PTrk8eMDdmyZfYQQIGLJ1S1ah8T3O1SgvvL5PeXU5zHpHdlLF28JI3kXtX05qV/Y1I8JscBAACqQmIcoBaZX1xcYcReHJ3Xo1+fNO36RgO2KUmU9+yRSmkS4tMPPiwbTR7/xlF/AOTWovkLwllb7VAwuyHLsrTURyxDHn0i3VbUZr2yNcrjqPL4G9W6Y4ewzX57pRLFqdbjlOsTlifLP3rj7bBo/vwcvxsAqqJxs2ZrXMs7/o1LclTG4oULV17Hu3Sk9/JpzuMMWfF3BwAAoLpJjAPUYvPmFIe3/vNSKl8myrdJSfI4onz9TTcOnTbeKJWdf3jYl4ny5WuUxxHlX8yek+N3AUAhiuu2vvncC6lEjZs1DRtuuXlKlMeEebettwrNitYJm+64XSrR0iVL0ijyOBK9dAr24mnTc/xOAOqupkXrrDS6e8UpzuPyT5WxcN78CqO6098pFZPg/jcBAABySWIcIO8S5S+mErVo3apkRPmAOKK8b1h/k55fJsr/7/tpm5iASKPJY6J8+CgHowBYa6PkS39vonr164eOPbunqdd7LJ+Cfb3OncKGm/dK5Vs/OiJtN3Pi5DBhVFyj/I0wftQbYerYcUYKAlSD+L9ChdHdHZev5V1utHeT5s0rvQTUqtbxLhvtPXVaWDD3c/sNAACo1STGAfJYHHHx5r9fTKUsUd6/ZDR5TJTHBHlMlseyy5GHp20mv/9BGk3+4bCRYdzwkSnZDgDVLa4t/umYD1N59eHH0m0xCdO9T8mI8jj9+vqb9gxtNlg/lX7f3bcs+TJhdMlo8jgF+8dvvRMWL1hoBwEsV69evbDOeuuWJLjXMMV5oyZNKtVmcSmmFdfyLp/0jrcvnDdP+wMAAHmvXjxmFWq5oqKiUFxcHFq2bBnmzp2b6+oA5I241t9G/ePU633T35goX1FKlA+N65PHMiqtcw5QiLFZvta7kMWRil16b7F8nfLeoevWW640enHp4iVh4rvvLx9VPjqNKv985mc5qzNQ98SlIq4c+ny6PHjb3dIMGWtL/QYNQlHb9SpMaV5+Le+U/G7fLjRoVLlxDsUzZq68lnea4nz59WnTw5KFTj4iN/I5NsvnugMAFJqqxGYS4wB1SBxZUjaivP82oWPPHhXuX5ZG98Wp10eGscOGh3HDR0uUAwVzIDBf612XxIRQp002Wp4o3zqNLI+JoBVN/+iTkkT5yJJk+bTxH+WkvkDdUF2J8QaNGoVW7duuch3v0ttiUjx+F36VZUuXliS9U4J7VVOcTwvF02aEpUuWfK26Qk3I59gsn+sOAFBoJMYBqJR12qwbevT7cur1jht1XzlR/v7YMDaOJh86PIwbERPl/umHui5fDwTma73runU7dQzd+/ZOa5THhHnHjTcK9evXX2lpkTjtehxNHv9+8vZ7YcmiRTmrM1D3EuONmjZJI7krjPRO05x/eVtRm/Uq9Xpxpow4kvvLRPeXU5yX/p07Y1ZKjkM+y+fYLJ/rDgBQaCTGAfjaifKN+vctS5R36NFtpUT55Pc+SNOux+nXx40YFRbM/VxrQx2TrwcC87XeVNS0aJ3QtfeWKVke1yvvstUWKWlVXkyKx+R4SpaPHB0mjHozJc8Bvo511msdLnnxH+nyo5deFVqst+5K05u3aN2qUs+1eOHC1a7lPXtKyfXPZ30WsqzWr3oHdTo2y+e6AwAUGolxAKrnB6XNeiVrlG/bLyXL23fvulKifNJ7Y8KHw0ak6dclyqFuyNcDgflab9asfsMGoXOvTdNo8m59tgrd+24dWrZts9J2cbr10jXK498ZH0/UtEBo0qJ5uRHeHULr0hHeHTuUJb6btSyqVEstnDd/penM50ypmAR3kg4URmyWz3UHACg0EuMArJ0fmLZtShLlA/quOlG+dOnyRHlco3xEGB9HlH/+hb0BBSZfDwTma72pujYbdE7rk5eOKu/Ys8dK28ydOSuNJC9Nlk96531r8UKBabpOi7JpzEumNa94OZZmRetU6TnHvDo0fDZ5Srnk95dTnptJCepObJbPdQcAKDQS4wDUiJbt2lZIlLfr1mWlRPnEd99PifI4qjxOZ1uIifLKrDsJhSRfDwTma7355pq1bFkymjwmy7fpHTbccrPQqEmTCtssXrAwfPzWO2H8yJJ1yieMfjPML9ZPoDYvq1B+Le+K63qXXI+J8cqYV1xcMr35lKlfruk9ZWrZ9Obx/kteeDptK9aD6pPPsVk+1x0AoNBUJTZrWGO1AqDgFE+fEUb+41+pRC3bt1ueKN8m9OxfkijvsuXmqex27JElifJ3YqJ8RBj7ehxRPjos/GJert8GAAVufnFxePel/6USNWjUKGyweZx+fevQfZutQrc+vcM6661b8hvWf5uyx336wYdlifI4qnzWxMk5fBdQd8Spy0uS2+2WT2m+4kjvdqFpi0omvecUp2R3SaJ7+bTmKQH+5brei+bP/8qTIAEAAMh/EuMAVJviadPDyKefTSWKBy1Lkgx906jydl03DF222jyV3Y77UZqyNiXKX49rlMdE+Rth4TyJcgDWrqWLF4ePRr+Vygv3l9wWT+aK067HNcrj6PK4XEinjTdKZcfDD07bzJk2vWT69ZgoHzE6TB7zQVi2ZKndBVWcwaF1x7h2d4fQavlI7/KjvGP82KR580o9V1yvu3zCu/RyTHh/NmVqik3N5AMAAEApiXEA1pp4YHLE359NJYoHO3ssH00eE+Vtu2wQuvbeIpXdj/txSaL87fdSonzssJFphJ5EOQA1YfqEj1MZ+vhT6XocQR4T5HE0eZx+fYMteoVW7duFrffePZVo4bz54eM3364w/bqZUKjLmrdquXw68w4lSe7lCfDShHe8r0nzZpV6ri8+m51Gc5et5V3h8tR0okpcAgEAAAAqS2IcgBoTD2aOeOqZVKJ40LR0jfI4/XrbDTcIXbfeMpXdjz+qLFE+dtjyEeUj3/jKqS4BoDp8Puuz8NZ/XkolatikSdhwi17L1ykvGVUek4Abb9c/lSguGRKnX0+jykeMTr9bMYEHhaBF61ZlSe+Y5G69wuV4X2WnHI+fr5joTknu5UnvCpenTQ9LFkp6AwAAUL0kxgHImZgsGP7UP1MpS5QP6Bt6Lk+Ut9mgc1mifI+fHBWWLl4SPnnn3TB2aEmiPCYeJMoBqAkxSZeS3SNGhxAeDPXq1UvTrZdMvR6nYO+dTvDq3GuTVHb6waHpcZ99OmX5GuUlyfKYOM+WLbPTqFVarNt6hTW8Sy6XJrzj30ZNm1TquebOnFVhlHeFhHdp0nvRorX+ngAAAGBFEuMA1K5E+ZP/SCVat1PHstHkcZ3yNhusH7ptvVUqe55wdEmi/O13l48oH748Ub4g128DgDogy7IwddyEVIY8+kS6rahtmzSSPCbL43rlMUEef8ti2Wb/vdM2Cz7/Inz0xltl069/9MbbeX2SVxwhfOXQ59Plwdvu5ne4lokncKSkd7k1vCskv5dPcd6oSeWS3sUzZqZE98pTm5dcLp42Q9IbAACAWktiHIBaK46ye/1vT6cSrbt+x+WjyUvKeut3Wr7+65eJ8o/feieNJo/J8gmj3rD2JAA1Zu6MmeHN515IpTRpvOGWm5dNvx5nQGlWtE7YdMftUonisiGT3/8gJcpLk+XF02fYa1Qu6b1eHOnd4cvEd/lR3vG29u1Cw8aNK9Wasd+VJLqnl6zhnUZ6f3l5zrQZYenixfYMAAAAeUtiHIC88dnkKWHYE0+nUpYo37ZfGk3ec9u+aUReSfKhd9jzxGPCksWLwydvvhPGvh5HlI+UKAegRsVZTOLJWrFE9erXDx17dk9J8vhb1W2b3ukkrw232CyVb/3oiLTdzImTw/iRo8OEkW+G8aPeCFPHjksj1KlbSe911lt3+RreMckd1/HuEFqlBPjyyx3ahYaNGn3lcy1btiydtJES3ml0d0x0T18+0ntq2UjveJIGAAAAFDKJcQDyO1H++N9Tidbr3Gn5GuX9UqI8HkxO09n23TrsdeKxKVH+8Rtvhw9fH5mSFHG917hmLADUhLi2+KdjPkzllT//Nd0Wk5tx2vXStcrX37RnWjoklv4H7Je2mVdcHD4aXTL9eiyfvPWOGVHyWDxBoqjNesunMm/3ZfK7Q7vlSfAOaaR3g0YNK5f0nj5z5YR3ufW940hwSW8AAACQGAeggMya9GmYNalconyD9UPP/n3DRtvGEeX90tSiPfr1SWWvk45Na2B+9ObbaTR5TJRPGP2WRDkANSomMkc98+9UoibNm4euW2+RkuRxVHmcfr15y5Zhs112TCWKS4dMfOe9NJo8Tr0e/34+8zN7rrYkvdu2qZDkLl3HuyQB3i60alfJpPfSpSVrei9fvztNc166rnfpmt4zZoRlS5bWyHsDAACAfGfEOAAFa9bEyWFoLI8/la632aBzyYjybUvWKI8Hqjfqt00q4eTjShLlcUT58jXK42UjygGoSQvnzQtjXh2WSlS/QYPQaZONyqZfj39jcjUmzGMJR/9f2m76R5+kJUPGjxidRpVPG/+RHbcWkt4t27VZPrp7+cjuNK35l5dbtmsbGjSsZNJ7+ZrepSO7y1+Oie+YFI/bAQAAANVDYhyAOmPmxEmpDH3syXS9zYYbhJ4Dtimbfj0e0N6o/zap7H3K8WHxwoVlifIPSxPlixbl+m0AUIfExOikd8ek8vIfH0m3rbt+x5I1ypdPwd6xZ4/QruuGqQw48Dtpmy9mzykbTT5+xBtphLnfsNWLJyC0bNtm+RreMdFdkvwufzmOBK9M0jtOW56S3uUS3ml97+Xrecfb5s6YJekNAAAANUxiHIA6a+YnE1N57a8lifK2XTZYniQvGVEe1/eMl2OJUqJ89FslI8pfH5nWK5dkAKCmfTZ5Sioj/v5sut60aJ3QtfeWoXvf3mm98i5bbRFatG4Vtthtl1Si+Hv1ydvvhfEjR6eE+YRRb6bkeV1Qv2FMerddPrK7JNkdE+Dp+vLLMSkek+NfJU5jP2f69C+nNF8h4R2T4XNnzkrryQMAAAC1i8Q4ACw34+OJqbz2l7+l6227bliWJI9/4/Soca3yWPYplyiPU9YCQK4smPt5eP9/Q1IpTQR37rXp8qnXe4du2/ROid/S66WmjptQMqp8ZMn06/E3MN/E9xrX7F7VKO/SKc+L2q5X+aT3tJJEd8lI75Uvfz7rM0lvAAAAyFMS4wCwGjM++iSVIY8+ka6369alJEkep1tfIVFeat31O4WpH47XpgDkzLIlS8Mnb72TyksP/ind1maDzmna9W7bbJVGlcfp1zv06JbKdod+L20TRzrHBHnpFOyT3nk/TQueK3Ha8pbt25Zbwzsmu9ulyyUJ8HZpevP69et/5XMtWby4ZHT31JjoLh3xPTXMnjJ9+Ujv5UnvLKuR9wYAAADUPIlxAKik6RM+TmXII4+XJcrj2uQbb98/bL337um2E2+/Ptx63E/TFO0AUFvMnDgpldf/9nS63rxVy9B1663KRpFvuOVmoajNeqH3nt9OJVq8YGH4+K13wvgRo1OiPM6SMr94brXUp0GjRqFVTHovT3i37lA66jteb5cS3+u0Wa9ySe9Fi8qmNS9d07tsfe/lCfAvZs2W9AYAAIA6TmIcAL5honz4U/8oS4zHA/oD77sl3Hb8T9NocwCojebNKQ7vvvS/VEoT1Rtu3itNu949jirfZuvQYt3WYaM4S0r/bcoe9+kHH345qnzk6DTKepVJ77KR3V8mvMtfjkn4yojLlpSM9P4y0f1l8rtk9PcXn0l6AwAAAF+tXgih1s8VV1RUFIqLi0PLli3D3LnVM0IBAKpL42ZNw5VDn0+Xp3w4PnTcqHtao/T2n5wWpo3/SENTcPI1NsvXekOuxJlR4rTraQr2PluF9t27rrRN8fQZaWmRaNK7Y9LU55VOei9Y+GXCu1zS+8uR3tNS0huAwpTPsVk+1x0AoNBUJTYzYhwAqtHdA88Mx910TVh/k57hlHtvCbcf/9MwddwEbQxA3s6MMvTxp9L1ddZbNyXIu6Vkee+wwea9ypLiUefNNqmY9J4ytWyK8xUvx+T3F7Pn5OR9AQAAAHWTxDgAVKM4si0mw0+668bQudcmJcnxn5wWpowdp50ByGtx2vS3/vNSKlHDJk1Cj769w0l33piuP3Dm4DD9o4kp6R2nagcAAACoTernugIAUGjiCLjbjj8tTHzn/TSd7Cn33Bw6bbJRrqsFANVqycKFYcKoN8uuv/fykPDpmLGS4gAAAECtJDEOAGvB/OLicPsJp4WP33onTT17yj23hPU33VhbAwAAAABADkiMA8BaMr94brjjxEHhozfeDi1at0ojx8uvvwoAAAAAANQMiXEAWIsWzP083HnSoDTVbPNWLcPJd98UNtxiM20OAAAAAAA1SGIcANayBZ9/Ee486fQwfsTo0Lxly3DSXTeGLr230O4AAAAAAFBDJMYBoAYsnDcv3HnyGeHD10eGZkXrhJPu+G3otvVW2h4AAAAAAGqAxDgA1JBF8+eHuweeGT547fXQdJ0W4YQ7rg/dt+mt/QEAAAAAYC2TGAeAGrRo/oJwz0/PDmNeHRqatmgRTrj9+tCj/zb2AQAAAAAArEUS4wBQwxYvWBjuOe0X4f3/DQlNmjcPJ9x6Xei5bT/7AQAAAAAAalNifODAgWH8+PFh/vz5YciQIWHAgAGr3bZhw4bhggsuCGPHjk3bjxo1Kuyzzz7fpM4AkPeWLFwY7v3ZOeHdl18NjZs1DT+55dqw8far/z2FQiWuBABATAkAQK1MjB9++OHhuuuuC5dcckno27dvGD16dHjmmWdCu3btVrn9ZZddFk466aRw2mmnhc033zzcfvvt4bHHHgt9+vSpjvoDQN5asmhRuO9n54R3XvxfaNS0STj+pmvCpjtul+tqQY0RVwIAIKYEAKAmZVUpQ4YMyW666aay6/Xq1csmTpyYnXPOOavcftKkSdnAgQMr3Pboo49mDz74YKVfs6ioKIvi36rWV9EG+oA+oA/oA7W9DzRo1Cg79sarsmvffDX79esvZL122SHndVK0QU3EZjUdV4op9WvfbdXfBxo3a5p+v2KJl7Wxz5k+oA/oA/pAoceU4kp93PecPqAP6AP6gD6gD4S8jSurNGK8UaNGoV+/fuG55577MqueZen6DjvssMrHNGnSJCxYsKDCbXFK9Z133vnrJvIBoKAsXbw4/O7M88Ibz70QGjVpEo694ddhs2/tlOtqwVolroTCsGj+gnDWVjukEi8DQE0SUwIAUBVVSoy3bds2rRk+derUCrfH6x07dlzlY+I062eeeWbo2bNnqFevXthzzz3DIYccEjp16rTa12ncuHEoKiqqUACgkC1dsiQ8+PPzw+hn/xMaNm4cjrnhyrDFbrvkulqw1tREXCmmBAAobI5VAgCwVtcYr6pBgwaFDz74ILz33nth0aJF4eabbw733XdfWLZs2WofM3jw4FBcXFxWJk2atLarCQA5t2zJ0vD7X1wYRj79bGjYqFE4+torwlZ77JrrakGtUdW4UkwJAMA3jSnFlQAAdTQxPmPGjLBkyZLQoUOHCrfH61OmTFntYw4++ODQokWL0LVr19CrV6/w+eefh3Hjxq32da688srQsmXLstK5c+eqVBMA8taypUvDH8+9NIz4+zOhQaOG4ce/uSz03nv3XFcLql1NxJViSgCAwuZYJQAAay0xvnjx4jB8+PCwxx57lN0Wp7GM11999dU1PnbhwoVh8uTJacrMQw89NDzxxBOr3TaerTl37twKBQDqWnL89b/9IzRo2DD86KpLQp9998x1taBa1URcKaYEAChsjlUCAFAVDau0dQjhuuuuCw888EB4/fXXw9ChQ8Ppp5+eRu3EKYeieF+c+vzcc89N17fddts04nvUqFHp78UXXxzq168frr766qq+NADUGdmyZeFPF1yWkuTbHvzdcOSvLw71G9QPI/7+bK6rBtVGXAkAgJgSAIBamxh/+OGHQ7t27cKll14aOnbsmBLe++67b5g2bVq6v0uXLhXW5GnatGm47LLLQo8ePdJUl08//XT48Y9/HObMmVO97wQACjA5/vBFV6Tk+PaHHRh+eMVFoX6DhuH1vz2d66pBtRBXAgAgpgQAoKbUi8fdQy1XVFQUiouL03rjplUHoK6J00sfcv7Pw46HH5xOPnvkoivD0MefynW1qMPyNTbL13oDABSifI7N8rnuAACFpiqxWZXWGAcAal6WZeEvv7o6vPzQo2k5kiN+dV7Y7tDv2RUAAAAAAFBJEuMAkCceu+La8NKDf06XD794cNjh+wfnukoAAAAAAJAXJMYBII88cfUN4YUH/pguH3bhL8JOPzws11UCAAAAAIBaT2IcAPLMk7+5KTx/7+/T5UPOPSvscuThua4SAAAAAADUahLjAJCHnrr+lvDcXQ+kywf98ozwraN+kOsqAQAAAABArSUxDgB56h833h6evf3edPnAnw8Kux17ZK6rBAAAAAAAtZLEOADksWduuSuV6Ltn/jTs8ZOjc10lAAAAAACodSTGASDPxVHj/7jpjnR5/0Enh71OPi7XVQIAAAAAgFpFYhwACsBzd94f/n7DrenyvqeeEPYZ+JNcVwkAAAAAAGoNiXEAKBD/uefB8ORvbkqX9z7l+LDvaSfmukoAAAAAAFArSIwDQAF54YE/hieu/m26vNeJx4bvnH5KrqsEAAAAAAA5JzEOAAXmpQf/FB678tp0effjjwrfPfOnua4SAAAAAADklMQ4ABSgl//4aPjLZdeky7sde2T43i8G5bpKAAAAAACQMxLjAFCgXvnzX8Mjl/w6Xd71xz8IBw8+M9dVAgAAAACAnJAYB4ACNuTRJ8KfL7wiLFu2LOz8f98Ph5x3dqhXr16uqwUAAAAAADVKYhwACtzQx54MD194eUqO7/SDQ8OhF/xCchwAAAAAgDpFYhwA6oBhTzwd/nTer8KypUvDDt8/KHz/4sGS4wAAAAAA1BkS4wBQRwx/6p/hj+dempLj2x1yQDjiV+eFevWFAgAAAAAAFD5HwwGgDhn59LPhD+dcFJYuWRIGHPid8IPLzpccBwAAAACg4EmMA0AdM+qZf4cHf35BWLp4Seh/wH7h/668KNRv0CDX1QIAAAAAgLVGYhwA6qA3n3sh/O7s81NyvO/+e4cjr7ok1G8oOQ4AAAAAQGGSGAeAOuqt/7wYHjhzcFiyeHHos88e4UdXXSo5DgAAAABAQZIYB4A67O0XXg73D/plWLJoUdh6793DUb+5PDRo2DDX1QIAAAAAgGolMQ4Addy7/30l3Puzc8LihQvDVnvsGo6+7orQoFGjXFcLAAAAAACqjcQ4ABDe/9+QcO9pPw+LFywMW+y2SzjmhitDw8aNtQwAAAAAAAVBYhwASMa8Oizc89Ozw6L5C8Lm39opHPvbq0LDJk20DgAAAAAAeU9iHAAo88Frr4e7B54ZFs6bH3rtvH047sarQqOmkuMAAAAAAOQ3iXEAoIIPXx8Z7jrljLBw3ryw6Y7bheNv+k1o3KypVgIAAAAAIG9JjAMAKxk/YnS486QzwoIvvggbb98/HH/LtaFxs2ZaCgAAAACAvCQxDgCs0oRRb4Q7Tzo9zJ/7eeg5oG844bbrQpPmzbUWAAAAAAB5R2IcAFitj0a/Fe44cVCYXzw39OjXJ5xw+/WhSQvJcQAAAAAA8ovEOACwRp+89U64/YSfhXnFxaH7Nr3DSXf8NjRdp4VWAwAAAAAgb0iMAwBfaeI774Xbf3Ja+GL2nNB16y3DSXfeGJoWraPlAAAAAADICxLjAEClTHp3TLjt+J+GLz6bHbpstXk4+a4bQ7OWLbUeAAAAAAC1nsQ4AFBpn44ZG249/qdh7sxZYcMtNgun3H1TaN5KchwAAAAAgNpNYhwAqJIpH3yYRo7H5HjnzTYJp9xzc2ixbmutCAAAAABArSUxDgBU2dQPx4dbjx0YiqfPCOtvunFKjq+z3rpaEgAAAACAWkliHAD4WqaN/yjcetypYc7U6aHTxhul5HhRm/W0JgAAAAAAtY7EOADwtU2f8HG49biBYfbUaaFjzx5h4H23hpbt2mpRAAAAAABqFYlxAOAbmfHxxHDrMQPDZ59OCe27dw0D770ltGzfTqsCAAAAAFBrSIwDAN/YzImT0prjsyZ9Gtp16xJOve/W0LpDey0LAAAAAECtIDEOAFSLmBSPyfGYJG/bZYMw8P5bw7qdOmpdAAAAAAByTmIcAKg2cTr1OK16nF69zQad05rj63XupIUBAAAAAMgpiXEAoFrNnjot3HrcwDB9wscpKX7KvbeE9TZYXysDAAAAAJAzEuMAQLWbM3V6uPW4U8O08R+F9dbvlNYcb7PhBloaAAAAAICckBgHANaK4ukz0prjUz4cH1p37JCS4227bqi1AQAAAACocRLjAMBaM3fmrHDb8aeGTz/4MLTq0C4lx9t376rFAQAAAACoURLjAMBa9fnMz8Jtx/80TB4zNrRs1zatOd6hRzetDgAAAABAjZEYBwDWui8+mx1uP/6nYdK7Y0LLtm1Scrxjzx5aHgAAAACAGiExDgDUiC9mzwm3/eS08Mk774WiNuuFU+65OXTapKfWBwAAAABgrZMYBwBqzPzi4nDHCT8LH7/1TlhnvXVTcrxzr03sAQAAAAAA1iqJcQCgRs0vnhvuOHFQ+Gj0W6FF61bh5LtvChtsvqm9AAAAAADAWiMxDgDUuAVzPw93nDQoTBj1ZmjeqmU4+a6bwoZbbm5PAAAAAACwVkiMAwA5sfCLeeHOk04P44aPCs1aFoWT7vxt6NJ7C3sDAAAAAIBqJzEOAOTMwnnzwl2nnBk+fH1kaFa0Tjjpjt+GbltvZY8AAAAAAFCtJMYBgJxaNH9+uHvgmeGD114PTddpEU644/rQve/W9goAAAAAANVGYhwAyLlF8xeEe356dhjz6tDQtEWLcMJt14eN+m+T62oBAAAAAFAgJMYBgFph8YKF4Z7TfhHe/9+Q0KR5s/CTW68LG2/XP9fVAgAAAACgribGBw4cGMaPHx/mz58fhgwZEgYMGLDG7QcNGhTee++9MG/evPDxxx+H6667LjRp0uTr1hkAKFBLFi4M9/7snPDuf18JjZs1Dcff/JuwyQ5rjjPIb+JKAADElAAA1JSsKuXwww/PFixYkB1zzDHZZpttlt1xxx3ZrFmzsnbt2q1y+x/+8IfZ/Pnz09+uXbtme+21VzZp0qTs2muvrfRrFhUVZVH8W9X6KtpAH9AH9AF9QB/Ivz7QoFGj7LibrsmuffPV7Nevv5BtutP2Oa+TUv2xWU3HlWJK/djnWB/QB/QBfUAf0AdqTx/I15hSXJn7vqNoA31AH9AH9AF9QB8IXz82q1rjDRkyJLvpppvKrterVy+bOHFids4556xy+7jtc889V+G23/zmN9l///tfwaaO68tLH9AH9AF9QB9YbR9o0LBhduxvf52S41cNfzHrtcsO+kuBHcSs6bhSYjz3fUfRBvqAPqAP6AP6gD6Q7zGluFIf9j2mD+gD+oA+oA/oAyFv48oqTaXeqFGj0K9fv/Dcc899Odw8y9L1HXbYYZWPeeWVV9JjSqdb7969e9h///3D008//Q0HugMAhWzpkiXhd2edH9741/OhYePG4djfXhU233XnXFeLaiKuBABATAkAQE1qWJWN27ZtGxo2bBimTp1a4fZ4vVevXqt8zEMPPZQe9/LLL4d69eqlg6C33XZbuPLKK1f7Oo0bN66wBnlRUVFVqgkAFFBy/MFfXBCO/PUloc8+e4Sjr78iPHj2+eGt/7yU66rxDdVEXCmmBAAobI5VAgBQFVUaMf517LrrruHcc88NAwcODH379g0HH3xw+M53vhPOP//81T5m8ODBobi4uKxMmjRpbVcTAKilli1ZGv5wzkVh5NPPhoaNGoWjfnN52GrPb+e6WuRAVeNKMSUAAN80phRXAgAUlkrP0d6oUaNs8eLF2YEHHljh9vvvvz97/PHHV/mYl156Kbv66qsr3HbkkUdmX3zxRVrzZ1WPady4cZoHvrSsv/761bLmkKIN9AF9QB/QB/SB/O0D9Rs0yP7vyovSmuNXj/xvtvU+e+S8TnW1VMd6kDURV4opc99XFG2gD+gD+oA+oA/oA/keU4or9WHfY/qAPqAP6AP6gD4Q6uYa44sXLw7Dhw8Pe+yxR9ltcRrLeP3VV19d5WOaN28eli1bVuG2pUuXlj12VRYtWhTmzp1boQAAdduypUvDQ+f9Kgx74unQoGHD8KOrLgnb7LdXrqvF11QTcaWYEgCgsDlWCQBAVVUp63744Ydn8+fPz4466qisV69e2e23357NmjUra9++fbr/gQceyK644oqy7S+66KJszpw52RFHHJF169Yt23PPPbMPPvgg+9Of/rRWMv2KNtAH9AF9QB/QBwq7D9SrXz87/JJz08jxa0a9nPX97j45r1NdK9UVm9V0XCmmzH3fUbSBPqAP6AP6gD6gD+R7TCmu1Id9j+kD+oA+oA/oA/pAyNu4smFVs+gPP/xwaNeuXbj00ktDx44dw6hRo8K+++4bpk2blu7v0qVLhZE8l112WciyLP3t3LlzmD59enjyySfDeeedV+UMPgBAtmxZeOTiK8OyZUvDDocdFH54+YWhfv0G4fW/Pa1x8oy4EgAAMSUAADWl3vIMea1WVFQUiouLQ8uWLU2rDgCUTZ19yHlnhx2POCSdlPfIRVeGoY8/pXXEZmJKAIA8kM/H+/K57gAAhaYqsVmV1hgHAKgt4ow0f7nsmvDyHx8J9evXD0f86ryw/WEH5rpaAAAAAADUQhLjAEBee+zK68JLD/45Xf7+Rb8MOxx+cK6rBAAAAABALSMxDgDkvSeuviG88MAf0+XDLvhF2Pn/Dst1lQAAAAAAqEUkxgGAgvDkb24K/7n3wXT54MFnhV1+dESuqwQAAAAAQC0hMQ4AFIy/X39reO7O+9Plg845Pex61A9zXSUAAAAAAGoBiXEAoKD846Y7wrO33ZMuf+/nPwu7HfejXFcJAAAAAIAckxgHAArOM7feHf55y13p8nfPODXsccLRua4SAAAAAAA5JDEOABSkf91+b3j6xtvT5f1/dnLY++Tjcl0lAAAAAAByRGIcAChY/77rgfDU9beky/ucekIqAAAAAADUPRLjAEBBe/7e34e/XXNjuhxHje932km5rhIAAAAAADVMYhwAKHgv/u6h8PhVN6TLe554TPjOGQNzXSUAAAAAAGqQxDgAUCf89/d/Dn+94tp0effjfhwOOOu0XFcJAAAAAIAaIjEOANQZ/3vo0fDor65Ol799zP+FA39xeq6rBAAAAABADZAYBwDqlFcffiw8csmv0+Vv/fiIcPC5Z+W6SgAAAAAArGUS4wBAnTPk0SfCny+4PCxbtizs/MPDwqHn/zzUq1cv19UCAAAAAGAtkRgHAOqkoY8/VZYc3/GIQ8KhF/5CchwAAAAAoEBJjAMAddbrf3s6PHTepWHZ0qVhh8MOCodfcm6oV194BAAAAABQaBz5BQDqtBFPPRP+OPiSlBzf9uDvhiMuPU9yHAAAAACgwEiMAwB13sh//Cv8/pyLwtIlS8KAA/cPP7z8glC/QYM63y4AAAAAAIVCYhwAIIQw+pl/hwd/fkFYunhJ6PfdfcP/XXGh5DgAAAAAQIGQGAcAWO7N514Ivzv7vLBk8eKwzf57hyOvuiTUb2jkOAAAAABAvpMYBwAo563/vBQeOOPcsGTRotBnnz3Cj6/+VWjQsKE2AgAAAADIYxLjAAAreOfFl8N9p/8yLF64MPTea7dw1LWXSY4DAAAAAOQxiXEAgFV477+vhvt+dk5Kjm+5+67h6OuvDA0aNdJWAAAAAAB5SGIcAGA13n/ltXDvaT8PixcsDFt8e+dw7G9/HRo2bqy9AAAAAAD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}, "metadata": {}, "output_type": "display_data", diff --git a/hobj/__init__.py b/hobj/__init__.py index d70fc75..791e211 100644 --- a/hobj/__init__.py +++ b/hobj/__init__.py @@ -1,2 +1,3 @@ import hobj.benchmarks as benchmarks import hobj.learning_models as learning_models +import hobj.data_loaders as data_loaders diff --git a/hobj/benchmarks/make_model.py b/hobj/benchmarks/make_model.py index bd8a1cf..9d8365a 100644 --- a/hobj/benchmarks/make_model.py +++ b/hobj/benchmarks/make_model.py @@ -18,9 +18,10 @@ def _get_calibration_image_ids() -> list[ImageId]: Returns the ImageIds of the warmup images that are used for calibrating the features of the linear learner. Caches the result to avoid redundant computation. """ - from hobj.data_loaders.images.warmup import MutatorWarmupImageset - imageset = MutatorWarmupImageset() - return sorted(imageset.image_ids) + from hobj.data_loaders.images import load_mutator_warmup_images + + images_df = load_mutator_warmup_images() + return sorted(images_df['image_id'].tolist()) # %% diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index 79a7726..a3e77db 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -5,7 +5,7 @@ from hobj.benchmarks.binary_classification.benchmark import LearningCurveBenchmark, LearningCurveBenchmarkConfig, TargetSubtaskData from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask, BinaryClassificationSubtaskResult from hobj.data_loaders.behavior import load_highvar_behavior -from hobj.data_loaders.images import MutatorHighVarImageset +from hobj.data_loaders.images import load_mutator_highvar_images # %% @@ -14,16 +14,21 @@ class MutatorHighVarBenchmark(LearningCurveBenchmark): def __init__(self): # Load data into format expected by benchmark - # Load imageset: - imageset = MutatorHighVarImageset() + # Load image manifest: + images_df = load_mutator_highvar_images() # Load raw human session data for benchmark: sessions = load_highvar_behavior(remove_probe_trials=True) # Normalize data for benchmark: - stimulus_id_to_category = { - image_id: imageset.get_annotation(image_id=image_id).category for image_id in imageset.image_ids - } + image_id_to_category = dict( + zip(images_df['image_id'], images_df['category'], strict=True) + ) + category_to_image_ids = ( + images_df.groupby('category', sort=False)['image_id'] + .apply(lambda image_ids: sorted(image_ids.tolist())) + .to_dict() + ) subtask_name_to_results = {} subtask_name_to_subtask = {} @@ -36,8 +41,8 @@ def __init__(self): # Iterate over trials: session = session.sort_values('trial') - for reward, stimulus_id in zip(session.perf, session.stimulus_id): - categories.add(stimulus_id_to_category[stimulus_id]) + for reward, image_id in zip(session.perf, session.image_id): + categories.add(image_id_to_category[image_id]) perf_seq.append(bool(reward)) # Infer subtask from the categories observed in the session: @@ -48,8 +53,8 @@ def __init__(self): # Instantiate the subtask if it does not exist: if subtask_name not in subtask_name_to_subtask: subtask = BinaryClassificationSubtask( - classA=imageset.category_to_image_ids[cat0], - classB=imageset.category_to_image_ids[cat1], + classA=category_to_image_ids[cat0], + classB=category_to_image_ids[cat1], ntrials=100, replace=False, ) diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index da28f0a..b0b553f 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -5,47 +5,47 @@ from hobj.benchmarks.generalization.estimator import GeneralizationStatistics from hobj.benchmarks.generalization.simulator import GeneralizationSubtask from hobj.data_loaders.behavior import load_oneshot_behavior -from hobj.data_loaders.images import MutatorOneShotImageset +from hobj.data_loaders.images import load_mutator_oneshot_images from hobj.types import ImageId + # %% class MutatorOneshotBenchmark(GeneralizationBenchmark): - subtask_names = [ - 'MutatorB2000_2292,MutatorB2000_2444', - 'MutatorB2000_138,MutatorB2000_2344', - 'MutatorB2000_1251,MutatorB2000_953', - 'MutatorB2000_3043,MutatorB2000_694', - 'MutatorB2000_3496,MutatorB2000_496', - 'MutatorB2000_1219,MutatorB2000_296', - 'MutatorB2000_1825,MutatorB2000_2757', - 'MutatorB2000_3077,MutatorB2000_4703', - 'MutatorB2000_270,MutatorB2000_3615', - 'MutatorB2000_3066,MutatorB2000_3585', - 'MutatorB2000_2139,MutatorB2000_746', - 'MutatorB2000_116,MutatorB2000_2365', - 'MutatorB2000_2130,MutatorB2000_4628', - 'MutatorB2000_462,MutatorB2000_926', - 'MutatorB2000_2304,MutatorB2000_3733', - 'MutatorB2000_1363,MutatorB2000_3278', - 'MutatorB2000_4049,MutatorB2000_663', - 'MutatorB2000_2722,MutatorB2000_3527', - 'MutatorB2000_2832,MutatorB2000_801', - 'MutatorB2000_1258,MutatorB2000_3123', - 'MutatorB2000_1865,MutatorB2000_613', - 'MutatorB2000_1164,MutatorB2000_2106', - 'MutatorB2000_1229,MutatorB2000_1280', - 'MutatorB2000_1767,MutatorB2000_2122', - 'MutatorB2000_2198,MutatorB2000_701', - 'MutatorB2000_3636,MutatorB2000_4305', - 'MutatorB2000_3035,MutatorB2000_46', - 'MutatorB2000_3601,MutatorB2000_4792', - 'MutatorB2000_2092,MutatorB2000_288', - 'MutatorB2000_1424,MutatorB2000_2314', - 'MutatorB2000_3308,MutatorB2000_3525', - 'MutatorB2000_2909,MutatorB2000_4256' + 'MutatorOneshotObject00,MutatorOneshotObject43', + 'MutatorOneshotObject01,MutatorOneshotObject37', + 'MutatorOneshotObject02,MutatorOneshotObject36', + 'MutatorOneshotObject03,MutatorOneshotObject55', + 'MutatorOneshotObject04,MutatorOneshotObject27', + 'MutatorOneshotObject05,MutatorOneshotObject17', + 'MutatorOneshotObject06,MutatorOneshotObject14', + 'MutatorOneshotObject07,MutatorOneshotObject50', + 'MutatorOneshotObject08,MutatorOneshotObject26', + 'MutatorOneshotObject09,MutatorOneshotObject58', + 'MutatorOneshotObject10,MutatorOneshotObject44', + 'MutatorOneshotObject11,MutatorOneshotObject32', + 'MutatorOneshotObject12,MutatorOneshotObject31', + 'MutatorOneshotObject13,MutatorOneshotObject41', + 'MutatorOneshotObject15,MutatorOneshotObject19', + 'MutatorOneshotObject16,MutatorOneshotObject28', + 'MutatorOneshotObject18,MutatorOneshotObject21', + 'MutatorOneshotObject20,MutatorOneshotObject47', + 'MutatorOneshotObject22,MutatorOneshotObject48', + 'MutatorOneshotObject23,MutatorOneshotObject35', + 'MutatorOneshotObject24,MutatorOneshotObject29', + 'MutatorOneshotObject25,MutatorOneshotObject40', + 'MutatorOneshotObject30,MutatorOneshotObject61', + 'MutatorOneshotObject33,MutatorOneshotObject38', + 'MutatorOneshotObject34,MutatorOneshotObject57', + 'MutatorOneshotObject39,MutatorOneshotObject52', + 'MutatorOneshotObject42,MutatorOneshotObject59', + 'MutatorOneshotObject45,MutatorOneshotObject53', + 'MutatorOneshotObject46,MutatorOneshotObject62', + 'MutatorOneshotObject49,MutatorOneshotObject51', + 'MutatorOneshotObject54,MutatorOneshotObject63', + 'MutatorOneshotObject56,MutatorOneshotObject60' ] transformation_ids = [ 'backgrounds | 0.1', @@ -90,29 +90,32 @@ def __init__(self): support_trials = {0, 1, 2, 3, 4, 5, 6, 7, 8} catch_trials = {9, 14, 19} - # Load images - imageset = MutatorOneShotImageset() + # Load image manifest + images_df = load_mutator_oneshot_images() + image_id_to_row = images_df.set_index('image_id') # Map image refs to transformation ids - image_ref_to_transformation_id = {} + image_ref_to_transformation_id: Dict[ImageId, str] = {} cat_to_support_image: Dict[str, ImageId] = {} cat_to_test_images: Dict[str, List[ImageId]] = {} - for image_id in imageset.image_ids: - annotation = imageset.get_annotation(image_id=image_id) - transformation_id = f"{annotation.transformation} | {annotation.transformation_level}" + for row in images_df.to_dict(orient='records'): + image_id = row['image_id'] + transformation_id = f"{row['transformation']} | {row['transformation_level']}" image_ref_to_transformation_id[image_id] = transformation_id - if annotation.transformation == 'original': - if annotation.category not in cat_to_support_image: - cat_to_support_image[annotation.category] = image_id + if row['transformation'] == 'original': + if row['category'] not in cat_to_support_image: + cat_to_support_image[row['category']] = image_id else: - raise ValueError(f"Multiple support images for category {annotation.category}") + raise ValueError( + f"Multiple support images for category {row['category']}" + ) else: - if annotation.category not in cat_to_test_images: - cat_to_test_images[annotation.category] = [] + if row['category'] not in cat_to_test_images: + cat_to_test_images[row['category']] = [] - cat_to_test_images[annotation.category].append(image_id) + cat_to_test_images[row['category']].append(image_id) # Assemble subtask simulators subtasks = [] @@ -139,7 +142,7 @@ def __init__(self): results = [] - for session_id, session_df in oneshot_df.groupby("session_id", sort=False): + for session, session_df in oneshot_df.groupby(['assignment_id', 'slot'], sort=False): session_df = session_df.sort_values("trial") transformation_to_kn = collections.defaultdict(lambda: [0, 0]) kcatch = 0 @@ -148,19 +151,19 @@ def __init__(self): for _, row in session_df.iterrows(): i_trial = int(row["trial"]) - image_id = row["stimulus_id"] - annotation = imageset.get_annotation(image_id=image_id) + image_id = row["image_id"] + annotation = image_id_to_row.loc[image_id] perf = bool(row["perf"]) # Record performance in relevant slot if i_trial in support_trials: - assert annotation.transformation == 'original' + assert annotation['transformation'] == 'original' elif i_trial in catch_trials: - assert annotation.transformation == 'original' + assert annotation['transformation'] == 'original' kcatch += perf ncatch += 1 else: - assert annotation.transformation != 'original' + assert annotation['transformation'] != 'original' transformation_id = image_ref_to_transformation_id[image_id] # Keep only benchmarked transformations @@ -197,7 +200,7 @@ def __init__(self): @property def target_statistics(self) -> GeneralizationStatistics: - gen_statistics: GeneralizationStatistics = self._generalization_statistics + gen_statistics: GeneralizationStatistics = self._generalization_statistics return gen_statistics.assign_coords( transformation_type=(['transformation'], [transformation.split(' | ')[0] for transformation in gen_statistics.transformation.values]), transformation_level=(['transformation'], [float(transformation.split(' | ')[1]) for transformation in gen_statistics.transformation.values]) diff --git a/hobj/data_loaders/behavior.py b/hobj/data_loaders/behavior.py index a9a3628..b25192f 100644 --- a/hobj/data_loaders/behavior.py +++ b/hobj/data_loaders/behavior.py @@ -2,25 +2,26 @@ import pandas as pd -__all__ = ['load_highvar_behavior', 'load_oneshot_behavior'] - # %% Data loaders def load_highvar_behavior( remove_probe_trials: bool = True, cachedir: Path | None = None, - redownload: bool = False, ) -> pd.DataFrame: - """ - Load the trial-level human learning data from Experiment 1 of Lee and DiCarlo 2023. - :return: - """ - if redownload: - raise ValueError("load_highvar_behavior no longer supports redownload; expected cached CSV artifact instead.") + """Load trial-level human learning data from Experiment 1. + Args: + remove_probe_trials: Whether to exclude probe trials and renumber the + remaining trial index within each assignment. + cachedir: Optional root directory containing the packaged ``data`` tree. + + Returns: + A DataFrame containing the high-variance behavior dataset with + ``stimulus_id`` normalized to ``image_id``. + """ repo_root = Path(__file__).resolve().parents[2] cache_root = (cachedir if cachedir is not None else repo_root / 'data').resolve() - dataset_path = cache_root / 'behavior' / 'human-behavior-highvar-tasks.csv' + dataset_path = cache_root / 'behavior' / 'human-behavior-highvar-subtasks.csv' if not dataset_path.exists(): raise FileNotFoundError( "Expected cached highvar behavior CSV to already exist at:\n" @@ -34,7 +35,7 @@ def load_highvar_behavior( 'assignment_id', 'worker_id', 'subtask', - 'stimulus_id', + 'image_id', 'trial_type', 'stimulus_duration_msec', 'reaction_time_msec', @@ -46,6 +47,7 @@ def load_highvar_behavior( if missing_columns: raise ValueError(f"Highvar behavior CSV missing required columns: {sorted(missing_columns)}") + if remove_probe_trials: df = df.loc[df['trial_type'] != 'probe'].copy() df = df.sort_values(['assignment_id', 'trial']).copy() @@ -56,18 +58,19 @@ def load_highvar_behavior( def load_oneshot_behavior( cachedir: Path | None = None, - redownload: bool = False, ) -> pd.DataFrame: - """ - Load the trial-level human learning data from Experiment 2 of Lee and DiCarlo 2023. - :return: - """ - if redownload: - raise ValueError("load_oneshot_behavior no longer supports redownload; expected cached CSV artifact instead.") + """Load trial-level human learning data from Experiment 2. + Args: + cachedir: Optional root directory containing the packaged ``data`` tree. + + Returns: + A DataFrame containing the one-shot behavior dataset with + ``stimulus_id`` normalized to ``image_id``. + """ repo_root = Path(__file__).resolve().parents[2] cache_root = (cachedir if cachedir is not None else repo_root / 'data').resolve() - dataset_path = cache_root / 'behavior' / 'human-behavior-oneshot-tasks.csv' + dataset_path = cache_root / 'behavior' / 'human-behavior-oneshot-subtasks.csv' if not dataset_path.exists(): raise FileNotFoundError( "Expected cached oneshot behavior CSV to already exist at:\n" @@ -78,12 +81,11 @@ def load_oneshot_behavior( required_columns = { 'trial', - 'session_id', 'assignment_id', 'slot', 'worker_id', 'subtask', - 'stimulus_id', + 'image_id', 'trial_type', 'stimulus_duration_msec', 'reaction_time_msec', @@ -99,7 +101,7 @@ def load_oneshot_behavior( if __name__ == '__main__': - df = load_highvar_behavior() + df = load_oneshot_behavior() import matplotlib.pyplot as plt lc = df.groupby('trial')['perf'].mean() plt.plot(lc.index.values, lc) diff --git a/hobj/data_loaders/images.py b/hobj/data_loaders/images.py new file mode 100644 index 0000000..1129b68 --- /dev/null +++ b/hobj/data_loaders/images.py @@ -0,0 +1,111 @@ +"""Load packaged image manifests as pandas DataFrames.""" + +from pathlib import Path + +import pandas as pd + + +def _load_image_manifest( + *, + dataset_name: str, + required_columns: set[str], + cachedir: Path | None = None, +) -> pd.DataFrame: + """Load a packaged image manifest and attach absolute image paths. + + Args: + dataset_name: Name of the dataset directory under ``data/images``. + required_columns: Columns that must exist in the manifest. + cachedir: Optional root directory containing the packaged ``data`` tree. + Returns: + A copy of the manifest as a DataFrame with an added ``image_path`` column. + + Raises: + ValueError: If required columns are missing. + FileNotFoundError: If the packaged manifest or images are missing. + """ + repo_root = Path(__file__).resolve().parents[2] + cache_root = (cachedir if cachedir is not None else repo_root / 'data').resolve() + manifest_path = cache_root / f'meta-{dataset_name}.csv' + + if not manifest_path.exists(): + raise FileNotFoundError( + f"Expected cached image manifest to already exist at: {manifest_path}" + ) + + manifest_df = pd.read_csv(manifest_path) + missing_columns = required_columns - set(manifest_df.columns) + if missing_columns: + raise ValueError( + f"{dataset_name} manifest.csv missing required columns: " + f"{sorted(missing_columns)}" + ) + + manifest_df = manifest_df.copy() + missing_paths = manifest_df.loc[~manifest_df['relpath'].map(lambda p: Path.exists(cache_root / p)), 'relpath'] + if not missing_paths.empty: + raise FileNotFoundError( + "Expected packaged images to already exist under:\n" + f"First missing path: {missing_paths.iloc[0]}" + ) + + return manifest_df + + +def load_mutator_highvar_images( + cachedir: Path | None = None, +) -> pd.DataFrame: + """Load the high-variance image manifest.""" + manifest_df = _load_image_manifest( + dataset_name='MutatorHighVarImageset', + required_columns={'image_id', 'category', 'sha256', 'relpath'}, + cachedir=cachedir, + ) + return manifest_df.sort_values('image_id').reset_index(drop=True) + + +def load_mutator_oneshot_images( + cachedir: Path | None = None, +) -> pd.DataFrame: + """Load the one-shot image manifest.""" + manifest_df = _load_image_manifest( + dataset_name='MutatorOneShotImageset', + required_columns={ + 'image_id', + 'category', + 'transformation', + 'transformation_level', + 'base_image_id', + 'sha256', + 'relpath', + }, + cachedir=cachedir, + ) + return manifest_df.sort_values('image_id').reset_index(drop=True) + + +def load_mutator_warmup_images( + cachedir: Path | None = None, +) -> pd.DataFrame: + """Load the warmup image manifest.""" + manifest_df = _load_image_manifest( + dataset_name='MutatorWarmupImageset', + required_columns={'image_id', 'category', 'sha256', 'relpath'}, + cachedir=cachedir, + ) + return manifest_df.sort_values('image_id').reset_index(drop=True) + + +def load_probe_images( + cachedir: Path | None = None, +) -> pd.DataFrame: + """Load the probe image manifest.""" + manifest_df = _load_image_manifest( + dataset_name='CatchImageset', + required_columns={'image_id', 'sha256', 'relpath'}, + cachedir=cachedir, + ) + return manifest_df.sort_values('image_id').reset_index(drop=True) + +if __name__ == '__main__': + df = load_mutator_oneshot_images() diff --git a/hobj/data_loaders/images/__init__.py b/hobj/data_loaders/images/__init__.py deleted file mode 100644 index 1e11a53..0000000 --- a/hobj/data_loaders/images/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from hobj.data_loaders.images.highvar import MutatorHighVarImageset -from hobj.data_loaders.images.oneshot import MutatorOneShotImageset -from hobj.data_loaders.images.probe import ProbeImageset -from hobj.data_loaders.images.warmup import MutatorWarmupImageset - diff --git a/hobj/data_loaders/images/highvar.py b/hobj/data_loaders/images/highvar.py deleted file mode 100644 index bf003db..0000000 --- a/hobj/data_loaders/images/highvar.py +++ /dev/null @@ -1,46 +0,0 @@ -from pathlib import Path -import pydantic - -from hobj.data_loaders.images.template import Imageset - -from hobj.types import ImageId - -# %% - -class MutatorHighVarAnnotation(pydantic.BaseModel): - category: str = pydantic.Field( - examples=[ - 'MutatorB2000_4872', - 'MutatorB2000_419', - ], - pattern=r'^MutatorB2000_\d+$' - ) - - -class MutatorHighVarImageset(Imageset[MutatorHighVarAnnotation]): - annotation_schema = MutatorHighVarAnnotation - - def __init__(self, cachedir: Path | None = None, redownload: bool = False): - super().__init__(cachedir=cachedir, redownload=redownload) - - self._category_to_image_ids: dict[str, list[ImageId]] = {} - - for ref in self.image_ids: - annotation = self.get_annotation(image_id=ref) - category = annotation.category - if category not in self._category_to_image_ids: - self._category_to_image_ids[category] = [] - self._category_to_image_ids[category].append(ref) - - for category in self._category_to_image_ids: - self._category_to_image_ids[category] = sorted(self._category_to_image_ids[category]) - - @property - def category_to_image_ids(self) -> dict[str, list[ImageId]]: - return self._category_to_image_ids - - -# %% -if __name__ == '__main__': - - imageset = MutatorHighVarImageset() diff --git a/hobj/data_loaders/images/oneshot.py b/hobj/data_loaders/images/oneshot.py deleted file mode 100644 index 3c6fb79..0000000 --- a/hobj/data_loaders/images/oneshot.py +++ /dev/null @@ -1,54 +0,0 @@ -from pathlib import Path -from typing import Literal - -import pydantic - -from hobj.data_loaders.images.template import Imageset - - -class MutatorOneShotAnnotation(pydantic.BaseModel): - category: str = pydantic.Field( - examples=[ - 'MutatorB2000_2444', - 'MutatorB2000_288', - ], - pattern=r'^MutatorB2000_\d+$', - description='The name for the object model associated with this image.' - ) - - transformation: Literal[ - 'combinednat', - 'blur', - 'inplanetranslation', - 'scale', - 'outplanerotation', - 'noise', - 'backgrounds', - 'inplanerotation', - 'delpixels', - 'contrast', - 'original' - ] = pydantic.Field(description = 'The type of transformation applied to the image. "original" indicates no transformation.') - - transformation_level: float = pydantic.Field( - description='The extent of transformation applied to the image. The interpretation of this number varies by transformation.' - ) - - base_image_id: str = pydantic.Field( - description = 'An image ID for the "original" image associated with this one.', - examples = [ - 'MutatorB2000_2444_rx0.00000_ry0.00000_rz0.00000.png', - 'MutatorB2000_4792_rx0.00000_ry0.00000_rz0.00000.png' - ] - ) - - -class MutatorOneShotImageset(Imageset[MutatorOneShotAnnotation]): - annotation_schema = MutatorOneShotAnnotation - - def __init__(self, cachedir: Path | None = None, redownload: bool = False): - super().__init__(cachedir=cachedir, redownload=redownload) - - -if __name__ == '__main__': - imageset = MutatorOneShotImageset() diff --git a/hobj/data_loaders/images/probe.py b/hobj/data_loaders/images/probe.py deleted file mode 100644 index d0c96f0..0000000 --- a/hobj/data_loaders/images/probe.py +++ /dev/null @@ -1,22 +0,0 @@ - -from typing import Literal - -import pydantic - -from hobj.data_loaders.images.template import Imageset - - -class ProbeAnnotation(pydantic.BaseModel): - color: Literal['blue', 'orange'] - text: Literal['press right', 'press left'] - - -class ProbeImageset(Imageset[ProbeAnnotation]): - """ - An imageset consisting of two images: a - """ - annotation_schema = ProbeAnnotation - -# %% -if __name__ == '__main__': - imageset = ProbeImageset() diff --git a/hobj/data_loaders/images/template.py b/hobj/data_loaders/images/template.py deleted file mode 100644 index d628fed..0000000 --- a/hobj/data_loaders/images/template.py +++ /dev/null @@ -1,155 +0,0 @@ -from abc import ABC -from pathlib import Path -from typing import Any, Dict, Generic, List, TypeVar - -import pandas as pd -import pydantic - -from hobj.types import ImageId - - -# %% -class ImageManifestEntry(pydantic.BaseModel, ABC): - sha256: str = pydantic.Field(pattern=r'^[a-f0-9]{64}$') - relpath: Path = pydantic.Field( - description='The relative path to the image file inside of the unzipped imageset directory.' - ) - annotation: Any = pydantic.Field( - description='Arbitrary annotation data for the image, in a JSON-valid format.' - ) - - -class ImageManifest(pydantic.BaseModel): - entries: Dict[ImageId, ImageManifestEntry] = pydantic.Field( - description='A mapping from a unique image ID to image manifest entries.' - ) - - -# This is a type variable for ImageManifestEntry.annotation: -IA = TypeVar('IA') - - -class Imageset(Generic[IA], ABC): - """ - Imagesets are a combination of: - - Images - - Annotations on those images - """ - - annotation_schema: IA - - def __init__( - self, - cachedir: Path | None = None, - redownload: bool = False, - ) -> None: - """ - Load a packaged imageset from the local cache directory. - """ - if redownload: - raise ValueError( - f"{self.__class__.__name__} no longer supports redownload; expected cached manifest.csv and images to already exist." - ) - - repo_root = Path(__file__).resolve().parents[3] - self.cachedir = (cachedir if cachedir is not None else repo_root / 'data').resolve() - self._dataset_dir = self.cachedir / 'images' / self.__class__.__name__ - self._images_dir = self._dataset_dir / 'images' - - manifest_df = self._load_manifest_df() - image_manifest = self._parse_manifest_df(manifest_df=manifest_df) - self._manifest = image_manifest - self._ensure_images_present(manifest=image_manifest) - - self._image_id_to_annotation: Dict[ImageId, IA] = {} - self._image_id_to_sha256: Dict[ImageId, str] = {} - self._image_id_to_relpath: Dict[ImageId, Path] = {} - self._image_ids: List[ImageId] = [] - - for image_id, entry in image_manifest.entries.items(): - self._image_ids.append(image_id) - self._image_id_to_sha256[image_id] = entry.sha256 - self._image_id_to_relpath[image_id] = entry.relpath - self._image_id_to_annotation[image_id] = self.annotation_schema(**entry.annotation) - - @property - def annotation_column_names(self) -> list[str]: - return list(self.annotation_schema.model_fields.keys()) - - def _load_manifest_df(self) -> pd.DataFrame: - if not self.manifest_path.exists(): - raise FileNotFoundError( - f"Expected cached manifest.csv to already exist at: {self.manifest_path}" - ) - return pd.read_csv(self.manifest_path) - - def _parse_manifest_df(self, *, manifest_df: pd.DataFrame) -> ImageManifest: - required_columns = {'image_id', 'sha256', 'relpath', *self.annotation_column_names} - missing_columns = required_columns - set(manifest_df.columns) - if missing_columns: - raise ValueError( - f"{self.__class__.__name__} manifest.csv missing required columns: {sorted(missing_columns)}" - ) - - entries: Dict[ImageId, ImageManifestEntry] = {} - for _, row in manifest_df.iterrows(): - image_id = str(row['image_id']) - annotation = { - column_name: row[column_name] - for column_name in self.annotation_column_names - } - entries[image_id] = ImageManifestEntry( - sha256=str(row['sha256']), - relpath=Path(str(row['relpath'])), - annotation=annotation, - ) - - return ImageManifest(entries=entries) - - def _ensure_images_present(self, manifest: ImageManifest) -> None: - if not self._all_images_present(manifest): - raise FileNotFoundError( - f"Expected packaged images to already exist under: {self.images_dir}" - ) - - def _all_images_present(self, manifest: ImageManifest) -> bool: - for entry in manifest.entries.values(): - if not (self._images_dir / entry.relpath).exists(): - return False - return True - - @property - def manifest_path(self) -> Path: - return self._dataset_dir / 'manifest.csv' - - @property - def images_dir(self) -> Path: - return self._images_dir - - @property - def image_ids(self) -> list[ImageId]: - """ - List of image refs in this imageset. - :return: - """ - return self._image_ids - - def get_annotation(self, *, image_id: ImageId) -> IA: - """ - Get the annotation for a given image. If an image has multiple annotations, this will throw an error. - """ - - entry = self._image_id_to_annotation[image_id] - return entry - - def get_image_path(self, *, image_id: ImageId) -> Path: - return self.images_dir / self._image_id_to_relpath[image_id] - - def __len__(self) -> int: - return len(self.image_ids) - - def __repr__(self): - return f"{self.__class__.__name__}({len(self)})" - - def __str__(self): - return f"Imageset(n={len(self)})" diff --git a/hobj/data_loaders/images/warmup.py b/hobj/data_loaders/images/warmup.py deleted file mode 100644 index 7fa3a8a..0000000 --- a/hobj/data_loaders/images/warmup.py +++ /dev/null @@ -1,21 +0,0 @@ -import pydantic - -from hobj.data_loaders.images.template import Imageset - - -class MutatorWarmupAnnotation(pydantic.BaseModel): - category: str = pydantic.Field( - examples=[ - 'Mutator19', - 'Mutator30', - ], - pattern=r'^Mutator\d+$' - ) - - -class MutatorWarmupImageset(Imageset[MutatorWarmupAnnotation]): - annotation_schema = MutatorWarmupAnnotation - - -if __name__ == '__main__': - imageset = MutatorWarmupImageset() diff --git a/scripts/package-archive.py b/scripts/package-archive.py new file mode 100644 index 0000000..f334ea2 --- /dev/null +++ b/scripts/package-archive.py @@ -0,0 +1,25 @@ +import tarfile +from pathlib import Path + +source_dir = Path("/Users/mjl/PycharmProjects/hobj/data") +output_archive = Path("/Users/mjl/Desktop/lee-dicarlo-2023-learning-data.tar.gz") + + +EXCLUDED_NAMES = { + ".DS_Store", + "Thumbs.db", + "desktop.ini", +} + + +def exclude_archive_junk(tarinfo: tarfile.TarInfo) -> tarfile.TarInfo | None: + """Skip common OS-generated metadata files when building the archive.""" + if Path(tarinfo.name).name in EXCLUDED_NAMES: + return None + return tarinfo + + +with tarfile.open(output_archive, "w:gz") as tar: + tar.add(source_dir, arcname=source_dir.name, filter=exclude_archive_junk) + +print(f"Created: {output_archive}") diff --git a/site/changelist.md b/site/changelist.md index 38dba67..3ee0547 100644 --- a/site/changelist.md +++ b/site/changelist.md @@ -7,3 +7,247 @@ We note the following changes since this codebase was released alongisde the pap * Statistical analyses other than the calculation of MSEn scores, their 95% confidence intervals, and generation of bootstrap resamples have been removed from this codebase. * Experiment 2: The lapse-rate corrected performance is now clipped between [0, 1]. * Experiment 2: The estimation of the variance of a lapse-rate corrected performance estimate from using bootstrapping to using the formula $\frac{1}{1-\hat p_{l}} \hat \sigma^2$, which treats the estimated lapse rate as a constant. Because a high number (~1000s) of Binomial observations are used to estimate $\hat p_{l}$, we consider this a decent approximation that is less computationally expensive. + +## 3/25/2026 - cleaner data packaging + + + +### Object renaming +I renamed the objects to follow a cleaner naming scheme. See below for the map from old names to new names: + +
+High-variation object renames + +```json +{ + "MutatorB2000_0": "MutatorObject000", + "MutatorB2000_19": "MutatorObject001", + "MutatorB2000_84": "MutatorObject002", + "MutatorB2000_91": "MutatorObject003", + "MutatorB2000_93": "MutatorObject004", + "MutatorB2000_98": "MutatorObject005", + "MutatorB2000_104": "MutatorObject006", + "MutatorB2000_133": "MutatorObject007", + "MutatorB2000_149": "MutatorObject008", + "MutatorB2000_160": "MutatorObject009", + "MutatorB2000_172": "MutatorObject010", + "MutatorB2000_174": "MutatorObject011", + "MutatorB2000_185": "MutatorObject012", + "MutatorB2000_188": "MutatorObject013", + "MutatorB2000_190": "MutatorObject014", + "MutatorB2000_196": "MutatorObject015", + "MutatorB2000_200": "MutatorObject016", + "MutatorB2000_206": "MutatorObject017", + "MutatorB2000_214": "MutatorObject018", + "MutatorB2000_218": "MutatorObject019", + "MutatorB2000_229": "MutatorObject020", + "MutatorB2000_247": "MutatorObject021", + "MutatorB2000_348": "MutatorObject022", + "MutatorB2000_367": "MutatorObject023", + "MutatorB2000_376": "MutatorObject024", + "MutatorB2000_385": "MutatorObject025", + "MutatorB2000_417": "MutatorObject026", + "MutatorB2000_419": "MutatorObject027", + "MutatorB2000_450": "MutatorObject028", + "MutatorB2000_455": "MutatorObject029", + "MutatorB2000_470": "MutatorObject030", + "MutatorB2000_475": "MutatorObject031", + "MutatorB2000_487": "MutatorObject032", + "MutatorB2000_527": "MutatorObject033", + "MutatorB2000_561": "MutatorObject034", + "MutatorB2000_577": "MutatorObject035", + "MutatorB2000_578": "MutatorObject036", + "MutatorB2000_609": "MutatorObject037", + "MutatorB2000_631": "MutatorObject038", + "MutatorB2000_645": "MutatorObject039", + "MutatorB2000_656": "MutatorObject040", + "MutatorB2000_671": "MutatorObject041", + "MutatorB2000_693": "MutatorObject042", + "MutatorB2000_695": "MutatorObject043", + "MutatorB2000_697": "MutatorObject044", + "MutatorB2000_700": "MutatorObject045", + "MutatorB2000_722": "MutatorObject046", + "MutatorB2000_770": "MutatorObject047", + "MutatorB2000_772": "MutatorObject048", + "MutatorB2000_774": "MutatorObject049", + "MutatorB2000_797": "MutatorObject050", + "MutatorB2000_798": "MutatorObject051", + "MutatorB2000_812": "MutatorObject052", + "MutatorB2000_819": "MutatorObject053", + "MutatorB2000_820": "MutatorObject054", + "MutatorB2000_823": "MutatorObject055", + "MutatorB2000_856": "MutatorObject056", + "MutatorB2000_876": "MutatorObject057", + "MutatorB2000_899": "MutatorObject058", + "MutatorB2000_902": "MutatorObject059", + "MutatorB2000_938": "MutatorObject060", + "MutatorB2000_963": "MutatorObject061", + "MutatorB2000_972": "MutatorObject062", + "MutatorB2000_1012": "MutatorObject063", + "MutatorB2000_1018": "MutatorObject064", + "MutatorB2000_1024": "MutatorObject065", + "MutatorB2000_1030": "MutatorObject066", + "MutatorB2000_1034": "MutatorObject067", + "MutatorB2000_4193": "MutatorObject068", + "MutatorB2000_4235": "MutatorObject069", + "MutatorB2000_4255": "MutatorObject070", + "MutatorB2000_4259": "MutatorObject071", + "MutatorB2000_4267": "MutatorObject072", + "MutatorB2000_4287": "MutatorObject073", + "MutatorB2000_4370": "MutatorObject074", + "MutatorB2000_4448": "MutatorObject075", + "MutatorB2000_4451": "MutatorObject076", + "MutatorB2000_4454": "MutatorObject077", + "MutatorB2000_4482": "MutatorObject078", + "MutatorB2000_4489": "MutatorObject079", + "MutatorB2000_4491": "MutatorObject080", + "MutatorB2000_4521": "MutatorObject081", + "MutatorB2000_4530": "MutatorObject082", + "MutatorB2000_4567": "MutatorObject083", + "MutatorB2000_4568": "MutatorObject084", + "MutatorB2000_4569": "MutatorObject085", + "MutatorB2000_4578": "MutatorObject086", + "MutatorB2000_4614": "MutatorObject087", + "MutatorB2000_4622": "MutatorObject088", + "MutatorB2000_4630": "MutatorObject089", + "MutatorB2000_4634": "MutatorObject090", + "MutatorB2000_4635": "MutatorObject091", + "MutatorB2000_4678": "MutatorObject092", + "MutatorB2000_4710": "MutatorObject093", + "MutatorB2000_4715": "MutatorObject094", + "MutatorB2000_4718": "MutatorObject095", + "MutatorB2000_4720": "MutatorObject096", + "MutatorB2000_4723": "MutatorObject097", + "MutatorB2000_4724": "MutatorObject098", + "MutatorB2000_4732": "MutatorObject099", + "MutatorB2000_4750": "MutatorObject100", + "MutatorB2000_4752": "MutatorObject101", + "MutatorB2000_4754": "MutatorObject102", + "MutatorB2000_4772": "MutatorObject103", + "MutatorB2000_4805": "MutatorObject104", + "MutatorB2000_4821": "MutatorObject105", + "MutatorB2000_4829": "MutatorObject106", + "MutatorB2000_4832": "MutatorObject107", + "MutatorB2000_4835": "MutatorObject108", + "MutatorB2000_4845": "MutatorObject109", + "MutatorB2000_4847": "MutatorObject110", + "MutatorB2000_4872": "MutatorObject111", + "MutatorB2000_4878": "MutatorObject112", + "MutatorB2000_4908": "MutatorObject113", + "MutatorB2000_4910": "MutatorObject114", + "MutatorB2000_4911": "MutatorObject115", + "MutatorB2000_4932": "MutatorObject116", + "MutatorB2000_4939": "MutatorObject117", + "MutatorB2000_4940": "MutatorObject118", + "MutatorB2000_4945": "MutatorObject119", + "MutatorB2000_4946": "MutatorObject120", + "MutatorB2000_4950": "MutatorObject121", + "MutatorB2000_4951": "MutatorObject122", + "MutatorB2000_4953": "MutatorObject123", + "MutatorB2000_4968": "MutatorObject124", + "MutatorB2000_4974": "MutatorObject125", + "MutatorB2000_4986": "MutatorObject126", + "MutatorB2000_4998": "MutatorObject127" +} +``` + +
+ +
+One-shot object renames + +```json +{ + "MutatorB2000_46": "MutatorOneshotObject00", + "MutatorB2000_116": "MutatorOneshotObject01", + "MutatorB2000_138": "MutatorOneshotObject02", + "MutatorB2000_270": "MutatorOneshotObject03", + "MutatorB2000_288": "MutatorOneshotObject04", + "MutatorB2000_296": "MutatorOneshotObject05", + "MutatorB2000_462": "MutatorOneshotObject06", + "MutatorB2000_496": "MutatorOneshotObject07", + "MutatorB2000_613": "MutatorOneshotObject08", + "MutatorB2000_663": "MutatorOneshotObject09", + "MutatorB2000_694": "MutatorOneshotObject10", + "MutatorB2000_701": "MutatorOneshotObject11", + "MutatorB2000_746": "MutatorOneshotObject12", + "MutatorB2000_801": "MutatorOneshotObject13", + "MutatorB2000_926": "MutatorOneshotObject14", + "MutatorB2000_953": "MutatorOneshotObject15", + "MutatorB2000_1164": "MutatorOneshotObject16", + "MutatorB2000_1219": "MutatorOneshotObject17", + "MutatorB2000_1229": "MutatorOneshotObject18", + "MutatorB2000_1251": "MutatorOneshotObject19", + "MutatorB2000_1258": "MutatorOneshotObject20", + "MutatorB2000_1280": "MutatorOneshotObject21", + "MutatorB2000_1363": "MutatorOneshotObject22", + "MutatorB2000_1424": "MutatorOneshotObject23", + "MutatorB2000_1767": "MutatorOneshotObject24", + "MutatorB2000_1825": "MutatorOneshotObject25", + "MutatorB2000_1865": "MutatorOneshotObject26", + "MutatorB2000_2092": "MutatorOneshotObject27", + "MutatorB2000_2106": "MutatorOneshotObject28", + "MutatorB2000_2122": "MutatorOneshotObject29", + "MutatorB2000_2130": "MutatorOneshotObject30", + "MutatorB2000_2139": "MutatorOneshotObject31", + "MutatorB2000_2198": "MutatorOneshotObject32", + "MutatorB2000_2292": "MutatorOneshotObject33", + "MutatorB2000_2304": "MutatorOneshotObject34", + "MutatorB2000_2314": "MutatorOneshotObject35", + "MutatorB2000_2344": "MutatorOneshotObject36", + "MutatorB2000_2365": "MutatorOneshotObject37", + "MutatorB2000_2444": "MutatorOneshotObject38", + "MutatorB2000_2722": "MutatorOneshotObject39", + "MutatorB2000_2757": "MutatorOneshotObject40", + "MutatorB2000_2832": "MutatorOneshotObject41", + "MutatorB2000_2909": "MutatorOneshotObject42", + "MutatorB2000_3035": "MutatorOneshotObject43", + "MutatorB2000_3043": "MutatorOneshotObject44", + "MutatorB2000_3066": "MutatorOneshotObject45", + "MutatorB2000_3077": "MutatorOneshotObject46", + "MutatorB2000_3123": "MutatorOneshotObject47", + "MutatorB2000_3278": "MutatorOneshotObject48", + "MutatorB2000_3308": "MutatorOneshotObject49", + "MutatorB2000_3496": "MutatorOneshotObject50", + "MutatorB2000_3525": "MutatorOneshotObject51", + "MutatorB2000_3527": "MutatorOneshotObject52", + "MutatorB2000_3585": "MutatorOneshotObject53", + "MutatorB2000_3601": "MutatorOneshotObject54", + "MutatorB2000_3615": "MutatorOneshotObject55", + "MutatorB2000_3636": "MutatorOneshotObject56", + "MutatorB2000_3733": "MutatorOneshotObject57", + "MutatorB2000_4049": "MutatorOneshotObject58", + "MutatorB2000_4256": "MutatorOneshotObject59", + "MutatorB2000_4305": "MutatorOneshotObject60", + "MutatorB2000_4628": "MutatorOneshotObject61", + "MutatorB2000_4703": "MutatorOneshotObject62", + "MutatorB2000_4792": "MutatorOneshotObject63" +} +``` + +
+ + +
+Warmup object renames + +```json +{ + "Mutator19": "MutatorWarmupObject0", + "Mutator20": "MutatorWarmupObject1", + "Mutator21": "MutatorWarmupObject2", + "Mutator22": "MutatorWarmupObject3", + "Mutator25": "MutatorWarmupObject4", + "Mutator26": "MutatorWarmupObject5", + "Mutator29": "MutatorWarmupObject6", + "Mutator30": "MutatorWarmupObject7" +} +``` + +
+ + + +### Image renaming + +Following diff --git a/tests/test_load_behavior.py b/tests/test_load_behavior.py index 1b74226..5789042 100644 --- a/tests/test_load_behavior.py +++ b/tests/test_load_behavior.py @@ -1,11 +1,58 @@ from hobj.data_loaders.behavior import load_highvar_behavior, load_oneshot_behavior +from hobj.data_loaders.images import ( + load_mutator_highvar_images, + load_mutator_oneshot_images, + load_mutator_warmup_images, + load_probe_images, +) def test_load_highvar(): df = load_highvar_behavior() assert len(df) == 3199 * 100 + assert 'image_id' in df.columns + assert 'stimulus_id' not in df.columns def test_load_oneshot(): df = load_oneshot_behavior() assert len(df) == 2547 * 20 + assert 'image_id' in df.columns + assert 'stimulus_id' not in df.columns + + +def test_load_highvar_images(): + df = load_mutator_highvar_images() + assert len(df) == 12800 + assert {'image_id', 'category', 'sha256', 'relpath', 'image_path'} <= set(df.columns) + assert df['image_path'].iloc[0].exists() + + +def test_load_oneshot_images(): + df = load_mutator_oneshot_images() + assert len(df) == 3904 + assert { + 'image_id', + 'category', + 'transformation', + 'transformation_level', + 'base_image_id', + 'sha256', + 'relpath', + 'image_path', + } <= set(df.columns) + assert df['image_path'].iloc[0].exists() + + +def test_load_warmup_images(): + df = load_mutator_warmup_images() + assert len(df) == 400 + assert {'image_id', 'category', 'sha256', 'relpath', 'image_path'} <= set(df.columns) + assert df['image_path'].iloc[0].exists() + + +def test_load_probe_images(): + df = load_probe_images() + assert len(df) == 2 + assert {'image_id', 'color', 'text', 'sha256', 'relpath', 'image_path'} <= set(df.columns) + assert df['image_path'].iloc[0].exists() diff --git a/tests/test_load_benchmarks.py b/tests/test_load_benchmarks.py new file mode 100644 index 0000000..937c3f6 --- /dev/null +++ b/tests/test_load_benchmarks.py @@ -0,0 +1,5 @@ + +def test_load_highvar_benchmark(): + import hobj + benchmark = hobj.benchmarks.MutatorHighVarBenchmark() + From b05193a50e3f6754f4c20b958802056fb4bf73ba Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 17:52:55 -0400 Subject: [PATCH 122/156] remove script --- examples/score_example_models.ipynb | 56 ++++++++++++++--------------- scripts/package-archive.py | 25 ------------- site/changelist.md | 2 +- 3 files changed, 29 insertions(+), 54 deletions(-) delete mode 100644 scripts/package-archive.py diff --git a/examples/score_example_models.ipynb b/examples/score_example_models.ipynb index 8c72743..9d370e2 100644 --- a/examples/score_example_models.ipynb +++ b/examples/score_example_models.ipynb @@ -3,8 +3,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-25T02:10:39.530263Z", - "start_time": "2026-03-25T02:10:39.524184Z" + "end_time": "2026-03-26T21:49:51.681270Z", + "start_time": "2026-03-26T21:49:51.677437Z" } }, "cell_type": "code", @@ -19,8 +19,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-25T02:10:42.427716Z", - "start_time": "2026-03-25T02:10:39.535091Z" + "end_time": "2026-03-26T21:49:55.537556Z", + "start_time": "2026-03-26T21:49:51.683454Z" } }, "cell_type": "code", @@ -44,8 +44,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-25T02:10:42.465284Z", - "start_time": "2026-03-25T02:10:42.460834Z" + "end_time": "2026-03-26T21:49:55.572564Z", + "start_time": "2026-03-26T21:49:55.568200Z" } }, "cell_type": "code", @@ -68,8 +68,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-25T02:10:48.027325Z", - "start_time": "2026-03-25T02:10:42.466588Z" + "end_time": "2026-03-26T21:50:01.122868Z", + "start_time": "2026-03-26T21:49:55.575369Z" } }, "cell_type": "code", @@ -84,17 +84,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "LearningCurveBenchmark.LearningCurveBenchmarkResult(msen=0.11340537830375878, msen_sigma=0.0010675644883655587, msen_CI95=(0.11128353017747093, 0.11551109646188225), lapse_rate= Size: 8B\n", - "array(0.7920317), model_statistics= Size: 103MB\n", + "LearningCurveBenchmark.LearningCurveBenchmarkResult(msen=0.11342760990778707, msen_sigma=0.00104837884742057, msen_CI95=(0.11141902750387399, 0.11550502179844757), lapse_rate= Size: 8B\n", + "array(0.85795474), model_statistics= Size: 103MB\n", "Dimensions: (subtask: 64, trial: 100, boot_iter: 1000)\n", "Coordinates:\n", - " * subtask (subtask) " ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -154,8 +154,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-25T02:10:50.137401Z", - "start_time": "2026-03-25T02:10:48.103538Z" + "end_time": "2026-03-26T21:50:03.205957Z", + "start_time": "2026-03-26T21:50:01.186260Z" } }, "cell_type": "code", @@ -169,7 +169,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "GeneralizationBenchmark.GeneralizationBenchmarkResult(msen=0.18264015521384375, msen_sigma=0.0075938852769925035, msen_CI95=(0.16876972760411446, 0.19750045892181253), model_statistics= Size: 583kB\n", + "GeneralizationBenchmark.GeneralizationBenchmarkResult(msen=0.18046066838978858, msen_sigma=0.00916594161360497, msen_CI95=(0.16305809460991577, 0.19931099499548263), model_statistics= Size: 583kB\n", "Dimensions: (transformation: 36, boot_iter: 1000)\n", "Coordinates:\n", " * transformation (transformation) " ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", diff --git a/scripts/package-archive.py b/scripts/package-archive.py deleted file mode 100644 index f334ea2..0000000 --- a/scripts/package-archive.py +++ /dev/null @@ -1,25 +0,0 @@ -import tarfile -from pathlib import Path - -source_dir = Path("/Users/mjl/PycharmProjects/hobj/data") -output_archive = Path("/Users/mjl/Desktop/lee-dicarlo-2023-learning-data.tar.gz") - - -EXCLUDED_NAMES = { - ".DS_Store", - "Thumbs.db", - "desktop.ini", -} - - -def exclude_archive_junk(tarinfo: tarfile.TarInfo) -> tarfile.TarInfo | None: - """Skip common OS-generated metadata files when building the archive.""" - if Path(tarinfo.name).name in EXCLUDED_NAMES: - return None - return tarinfo - - -with tarfile.open(output_archive, "w:gz") as tar: - tar.add(source_dir, arcname=source_dir.name, filter=exclude_archive_junk) - -print(f"Created: {output_archive}") diff --git a/site/changelist.md b/site/changelist.md index 3ee0547..4c5fe6d 100644 --- a/site/changelist.md +++ b/site/changelist.md @@ -250,4 +250,4 @@ I renamed the objects to follow a cleaner naming scheme. See below for the map f ### Image renaming -Following +Following the renames of the object categories, the images were renamed as well. The old image names are available in the `original_filename` columns of each `meta-*.csv` file for each imageset. 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", 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HKgcdjTVAL80Mo3nFWIRG21VHzCtHT50t7mPd95ZVbOffNfXmnTi402zlHGYE/wDQRXD/ABIvNSmFvYaZGx8s+fMynpjoOfzrirews7izI1LTZLtWiMqXBbgY/hA7Gq2g6JBcaLrVvDDIjrJ5qCQfNtIzXJ674nvrDzLTTo/Kt2j2NKyZJ+npXS3dnGnw70jUrfbEyxjexB4OO+Oear6N4ci1DT9R1O4XMyLG8WR9wMM8fjUlnY6tPcGxFyLoTrt2zHaqA9Dkf54rstMs7nQrjTbhbpbmeyykrnJV8qQyD3x/SvQ9Wne68P8A2i2jZhKquq45wRmuF8MQxx/CyxnEZV7q4AOTyFJzgflXq7/fNMc7UZs42jNcl4h1i3s9Pk82VI3u3eMMzdcAdPzrRsruTVL7JhMdpbnKMeRIexB+ma31+7wciloxXwjSNSUUU5Tjr0qRQSOuFqTyzDhlYNuqZlL24OOQetDLvRW6nuKiGFIzzzTmwzZAxT7dT5gAGanGUulyO4r3T4Z+N2nN1pt8jKIbczRtv3cKvI9uBUdp4hmv9bmv725tIbb5t0cj4cqf8isvUI9d0W/km0W6FxpVwN6xYDBST0HtVWDxPb+D0tBe5mnupSZyvJ2Hg/lxxTda0qDVoAdLurefTpTv2DBYZrpLTTDJ4Vl067i2Wz/IgbkqwHf0rG8MwXFhcappd9IhS8jxFjpwO1VbDDa7Z20uVIdI3ySB1rvYdLVfDer6us6CONp5Y9wJC7cjOP8AgNdJ4V1GHWfCaPb5KxDyRu77VH+Ncx4QjDfD7w9asMsb8KR9FY16NdXPkjeEZ88BV65rzTXfGJ8QXE/h/Rln/tNCV8jOGBx1b0A65GeK0PB3gm/t5E1DxPLHc3qBVhhJ8wQhSTwxx1z6dq9BCIOAopQABgDFPxRXwe3FNJyKTPT3oJpM04HNWEKfZ92fmBwVp7odgAYnuOakgfOEbpU6oybk7iq7KMk4pBU0DFJVPFSuN8wk9DXQ6PqN5YX0VzYS+VMf3TMBkbG4OR9Ca7i3vPCeparPDJZNO8KgmSRdofjkVa1nUrpdCtLrSdNAizu8heMRjqSfyrkJfENjqXxKWae3SO0ePyoo5BhYGK4LY9etb2ialpeheIzeQC3HkSfIQv30/iA98Uq33iKy17W9Rh8u5sb+4d4IpcgMAeCB9MCqN/r8thNDfTaXst5eFnUjJI6gA88Hgmrur3MNr4a0vxVprtKZ5jGwmABicZ5Hft+td98N55da+FtzFcRiWRzOhRud24scfrVf4KXLv4Z1OzmY+bDeSnY3UKQAPw4NZnw91eS61WPQsDbZXUkxGPu44/8AZq7D4g6zeaNoCzWUyRXLuVXcASTtJAGe+cUeB/CVnptjb6xPBu1q7hD3M8nL5PP4dcV2QGPr60tFLmlr4QlUqcGmEELzTfSlBHekyM0orRtrXztOlkHVD+dQwqZF3KQMEjmiNvLky3Y1cuHUskkZOM4OabNEQfM/hbpTAuYcnsTUiphhzzV6KLzEHatzQtPkubpo4lSSVV3LGxxurvbXQ9RvYrdtYe206xibJt4UUFz2G7r+VWdYmv5Ib5LfEVrFAAEib5nT0JHTkdBXBRaFZ65JZXcCFxPIIpPWP35/Gu11Sw0war5FvZIYLeNfPZeMEd8+tX9OtbC9ukuIJX+z2cR3o+75WI9Tx+VYGjWltrmp3VysQNlaK++RujOSTj6CrGreHILnwDdZmMXlyrKFQcnoo9h94/pXQfBfWLW18ItZzuwkF2yqcE55rofD0EunfELU7eeCKEXFlG8Yi6HDSZJx35FVPh5o8UV7rGoqQWa9uI/u843L/hVP4mWkWqa94dsHlkR451uMDoQHA5/OvTkXbwBhRwKfilxSjpS4pK+EHJOCaR/uio6kVNwP0qIjDU+tXSJFxJE7YVg38qrQBBvAzgGpREhYlvvZzj2qQKskY8pTjnNSZBtYwDkq3ze3NAiLmTYuVxmnpC7R52jA71s6dab7ZifvjtnrV7ySjpJEzxyLyGQ4Iq0z33iKOCOeRpDaFmA3HdIc5GTWv4f1fXbPVYI7vSjDavmMBx/rSewrfudA0FpBqSw3VnKspysbFfn9cCqmpSSybrOC1dbbIeR93LZ5BY+nFMlmlt9HbToVKLNuM0y9SO9UoNdhi0O107SLQx2b53NISrOQeelcP4qudTW98u7nZYGJMcaucBe39Ku+BtWe2uxatKyQyOeT0DEcc9ua9A8Q/Ey30jxBp9xbSCa5igdJmMZYbTgKOD6hq6/4Z63aTeGWuJXZXuruWUZGB8xFbV/ZW2qeL7VHTzPs1qZP90+YuK6rtSilopaSvg9u2elNPJPpTQM09TkH2pnc04Dcpqxako+f4cEGruj29vc3Lx3DMqYJG0UXYWPUVWPlfLB59amt3Rg4AYY7VatraP7FKW+Z3K7cHp81Wo7Xyoj1+Yc+1La225yCcgDPFaltABcx7ckGtWKMJMkqgHy2+6w4NdDZ6zZRSwpBpEUfzh3bdyfXHFat/LpmrLA0st4BA4kSIcYb6ioL+Wa4tljs0Qlvlw+coO/1J9a0zCbdLS9kcKFHkz8ZXHqR6UmqeHYr20eOJlh3/PFKpJCt7+xrPttBlt4I41tlW5hyyhvuSk5zg+9cD4r0nVNZvbSJdIlgliBjf+IMT3Bx04rlZmvtDe70idBG7t+8yOV4xVBImljMqsCR8pDcV9BeBbdLP4YWVy4LYiMpA9MUfCnXk13xHrV7HG0cPlIihjznivWgelLnNKKWlpK+EHHOR0phPFNpwakPWnRnBNXLNGdTkfJnrQJUiJeE7T0NDqy3IJ7jrV+1kET4DAFhgk85FT2k0e10TiVmAH4NXRWsTy2chcqWUcgnmjSoQXY7Moy8itzT7TEqvswu4/hWv/ZpkDfIM9VxT7PTiLhSY+ldH9hARdqDrV2GxXIGzgelW5rBJoDEflDjaeMgirltZx29ktsoyka7RmnLZQsOU/DNOewSQBWZ8emefzrxT4neENviy2XTISTdQbyrMWLNuI7/AErg73SrrSFV7hRskJUEDoR2/Wvf/DzbPhFbKx2s1lgE/SsH4K2rW/h2+mxl3nA3+vFenpNOrBhI2R6mrUV/MD+8bOT6VoR3kLkDeAT2NTh1boQfpTqK+D8nFJj1puKUcGkPJzQMjpWjaSgWbR985qqwVelTyzIYYtud/G45pYnIkVsbuelSQzLHdF1Bxuzj05rqdMm+0xStnDFRx61qaJHIZNq5+dfTpXX2doY1QEFt3J4rbtYAT74xUlvbZuQuDjNb0NmGj79auQ2yqOakeJQAadHg5p6DDVMYxjnv2rg/HFjeW+s6V4gWZfsVm8UckJXnBk5Oe3WuT+MWippul2k9tGXgnfcXGSUJ55PTnP6V1Xh4m7+EVqT/AA2uPyFQ/CMrJ4UnZF2KbjgfnXoKpTig696YIwTnvVm2leJwM5UnvWuOQKK+FJICgBHI9qixTaKKKngYZ2njIIB96iyckHqKUjIz6inRuVPU1Mdq7drZJPNXLa8aF0ZXIAPOK7zw9qlvE6uDlSuCRXf6XdwytG24bSa6RbVJMNFt6dRVyG2UYJAzV6OM7eBT9pHXrTWA6U0DFOXkirOMjg4rlvG1/HHol/p09hO0Ulq7/alXKK2DjP4ivP8AxI1542+HFpcac5YWl0YpkY8sNq4P4c11PgINL8IIlUZYQuAvviq3waBPgXawIdbhtwP1NejqtP25pFj9qkERK1oxNlPpT6+HhsfaGbAINVZIijEcnBxmoivNIRikoqSI/MPbpU00StF5iKQwODVcMenak52gDtUivkcDkVMjbk6Y9qv6ZqMlnJ97KHgqegr0bQtYJgi/eKVPTnpXoWj6xtJDsrLjua6tCjIrLja3PFWosE4FSPFwW71Sl3BhSZNSIcCrCkYrP1vTk1fSLqxZipnjaMOOSCRXiGkas3hjRb7Qb6Tahn80Nv2se3bPpXf/AArl+0fDGNcfdaVcewFP+FcXleHrqMLytwMj0yDXoQTinKlSCPmpVjGKkiG3IqUnivhZsnBqXeWgIYYqERgr15pyRCfEaECQdj3qGZDDIUfAYdhUfTtTgRwR1p7ykj7x3emajJIGOopCfahXwRjqKnV9539GHUetSGRSMever+nawNPQxkMwJyOa6TTfiB9kRlazZyT/AHq9D8F/Eqz1rUrfSDbzRTyZ255FeqW6nkgEj1q6pDr061XuIeR0qHyT6UbSp9qeOO1P8slc9j1NfL2t+E79vF0ljJMpeVGlV2ORgE8V6r8GZgfBUlqRh0ecn9K2fhrAVg1aTPyvPHgemErvtlKFNSgU4daXIzTl5r4gS2lSP57aYHP/ADzNMkiIHEcwcdQUOKjCkg70cenBFCOkbK6Fg69eKdNdJKfnQMQPvEYNVvkAyGBz6mjcmP4c+oNBKk4+XHrmgg7cAE/QUBHx/q2P/AacsUhHELn6KaXypVP+pkB9NpoFtcNnbBMR3whqQWN3gf6NPk/dJjPNTLo+og4+w3RPoIm/wrV0TTtds9RgvLLTr5biNsq6QMcfpXZNq3xOjvVudmo5xtAW2ypHuNten+GvGGtT26rq+jzQzBDk/ZpBk9vauitNXvro5ktY4lzjDAg/rWvGS+PkWpPKXuB+dKUjA6KPqaQhHGAR+Bryj45aXJHoFhqNkqRTwzMjSoQrbWxx79DWT8F9Ut10q+tmmXzlhuJSpPOPl5rrfhBdre+G7q43q264Azn0BFejgrnBKj05pxVQM549aaJIz0dT9DTt6g8mjfH6inLImPvj86+XbPUxZwNEyJKCc5x0px1dYH+WGPHqyZqrNrBkLu8ELbl5Cx4wKkll+12qxi2iXfjBAANMh0yIJh0Usp5xip2tbVGCm3Bzz1H+FMexsgy3E8BCA8KrDn9KsR6dpBTLwyjceMMv+FKNE0t8oyyrHjIIcZ/lVS40jQ0AYtdY9iP8KfZWemD95byykjoHT3rQXTIZpfN89VZeR+64P61Z+xSRwjfc2+SMkCD/AOvV61nuViVc2pReATDz/OtBr6UOpk8knGBtjx/WrUHiOSJkTyUDk8ADrVz+3mjlMbRsUxncH70628S2894tmFl84jqW46Zq/HqqK7rhiQ2Dk57U5Nbi80oGk3Cr8OpfIfmbBqdNUAGdxx7rUy6lucEMM9ht/wDr1zfxD8OXni/w4ltBKolgl3rk7QR3z+VeDeDNXbRfEshK5WW3kgb8f/1V7p8HrCez+HsCIEYyTSMST6MRXdva3DgZC/g1OjjuIsj5Sp7E0jQynJKY9w1RbZFOByfc1Yjec8SIuO2DT/NYcLHx7mvlm2Fsu5lhkUegHNMmeXdlY3wemRUTNeqN+CB6D+tNVrqZW2mPd22jBqW3S9CENJ0PqaeY7hcf6YpB5I3HP8qieCdj+9uEYHkAMc/yqa3W3j/1gBz7VLKsCyN5RUJ6+lIHnUAwzoAfrWpCuofIxlicH72DUsrXaAKIQ+72BqBjPMnFuRIBngAY/WpIYZ/lIjuc852MOo/GrdubsAtHBeZ7k4/xq0r3nmBZ7efyT1OB1/Om3AuIWAUsc9ic1JFbzPEG2Sbm/unFaFppzqd8ztG3oWxmuitdNttu+V2Ix/C/P41q262KRhI2AA65PI+tK9zaswYSxlh8qtnOBUS6raQArcXsW/d2Panw67bfOGlyccfKcEV8x6kN/iO5WOIor3J2oRgjnpX0j4UDaf4fgtt+0ozkru6ZbNb0d+/USso9SamW+kI4uD/31Ui3k2f9c351Kt7N18z86l/tCb+8PypV1CU9WH5V8+nQf3eYbnJzz5ox+WKS4s2tvmWSZyAOkYqC2tZb2SUiSaMEbWDxgZ+lSjQmjU7ZG2njhakh0zU04huIljB43L838qnGlzli8k5LYx9yk/sAFvMLOX+lINBfPKbm7Z3AVJH4Z/cfvDiRmOduSMVLH4bjRAN8mAePlrTh0W2Qhghzjn5jzTjo6JJviwqk5IJJNVjo6rIWW2Tc3U72p66YYo/kt4e/WR81bjhvIYwIxZjP/TRj/SkntLy4j2PNZjP+2f8ACpYtNdm3NNZ5AxnzDSy2M5aPy7iy+Rt2Wc1K1tcStulurMnPUPip4oljYkXcIc9SWGKh+yQxvMwu4synL/MOapx2tnZuzRSxZbk5kqpMllJdrOQhkU8ESVo/2/OG+WUZAx90V5Jaf8TPx87zEkee0jY6nFejz3++XzYjNGfTYD/Ori6/cSRrFL5si9P9WB/Kpk8QTwfLHEdo9RVmHxcd3z20mR3wK07fxT5gGIWH1xWqNYAQF0BJ7A1G+qzMf3MLAe4zXi39uuvDxyk/Sn/2+CP+Wq/UU1te4H7x8+u2nJrqnrcsD7irMeshh/x8Z/HFSrqm48TZ/wCBinDVCCD5jn1AapBrDdTJIR9alGrK2MvKD+NPGoh8gSygj1zSi9YDJmcgfWkN8rZAlcH0JIqrNOxGFnkBB/56GqMov3JMeo7fTL1A0Oqbgf7VjA9DIBSNaakx/wCQjCc/9N1/xpUsb8ZLXMRH/Xyv+NOFreqQPMQ+/wBpH+NSfZ79icuhx/03X/GkW11ItkSx47AzLUq2upZ5ki/GVaf/AGfqMn/LSDP/AF0WgaXqeD/pFuv/AG0Snx6Zq0ZLG9t+h/jWuL8MpcXXisiGREmIY7mIAr0OPTdWZONQtwfUstW4NMvwR5upWpHf51FW006UEH+1bMDuN6mtWG301R++urYkd/MH+NWFm0mLpc22f98VNDrWlw53PE57FSGqx/wlGmrxkj6LXim62bjzCf8AgQp/k2rD/WfrTDa2rDAc/nSDT7T++T+NPFlbKOGb86ctpApz5j/99D/CpkjgVSQ7/wDfQ/wp4SEjHmP6/eH+FSLGjPu89hkdyKkIGcLOfzp4HH+vOfepI4lY5eVc+pqUW0RPLg08WNq3LMKd/ZNhKPmI/OpRoenHADH/AL6qRfDunEY82T/vof4VKPDthkAPJ/30P8KnTw3ZHOJpB+I/wp3/AAi9p2uZCfYipF8L2xGftUufqP8ACpF8MwIQRcSn8RT/APhGoCc+bIfxFJP4btmgYfaJYyBkHI/wryv4f2qXXjTy5M7fLfkfUV6+uhWu3lnX8acNBsDw3mfXd/8AWpw8P6WOMP8A99f/AFqeugaSfvE/i1L/AMI9o3ZAfqalTQtMjOUggGfY/wCNaEWj6TtG+3hY/j/jXzQNRK9m/OnjVQOcNn60f2sCeQwp41dcdZKUazGvUvUg1mIcbmp41ZGHDnFSLqqP92U07+1kU4MxyKcNWUj/AFx/OlGqrnPnHj3p/wDbIHWU/nT/AO2ABkSke+aeutrjmUk+uaeNdQY/en86lXxBEuCZyPxqX/hJIOv2lgKlTxNAo5u2/Opo/FNt/wA/h/M1OniuzA/4+v1NTR+LbTGPtfH1NTr4vtB0uv1NTr4utsZ+0n86rar4piOlyeXMxcsvQ/WuH8CXZtfFsMmWGUI4r2GPVHkHSSrUd1Mw4jmz7Y/xqzFcXfH+jTt+X+NWRJeMRjTZ/wAAP8asKb7j/iWXH5D/ABqVBdN/y4XH5D/GplS8x/yD5v0/xr5RMUgb7hpu0hfmwKYBup3lt2BP4UnlMeqt/wB80eUx/hc/8BNSxwTFTiF8f7pp8VpcnOy3lP0U0/8As6+Zv+PSf/vg1Iumal2srjH/AFzNPGlapjH9n3P/AH7NO/snVP8AoH3H4xmnx6TquTnTrgj/AK5mrkGkaiQN2mTf9+zW7a+Hlknjjls5QGXJPlHg/lXUjw5oenaU9zfW8YjjUlmdaraFL4I1eRoTBbRzAZCkMMj8RXRx+F/CZcA2cHHvUx8MeFlP7vSYHB75NKnhrwwWw2jQgdutTDw14TwA2kxD6K3+FTReHPCn/QLiH/bNj/Spk0jwwhAGlQEDubc/4VW8T2Gi3Hh+WHT9Mtxcb1I2WeDgZzztrxP4dPFF8QNMNygMXmEOGTcDweMV9Nx3Xh8cfZLcf9uh/wDiasLe6GCNsUAPtb//AFqmS+0nIGyAE9P3OP6VJ/aFgOR5Q/7Z/wD1qadVtd2F8o/8BxRHqlqQSREvrimya5ZxMFzCW/u18+P4XvGfBgiBx6ioT4JvWy4hiPfkrU8fgy7WMs0EA/KpU8L3e4gQ2/HsKtw+GrvacQ2px1ytSjw7ex/MYLT/AL5px0q4hKhktQz/AHV2de1TrpV6gyotR6/KayLjX7W0vHt5Z0EiHDYjbGagm8X2EJAa4Yn/AGUYVu6Heyayu+1nAXGRvDeuK2xpuoHOJ4j+f+NPGl34UHzk59zTxpd7/FOAD6E1IunXB48/J6dTWLf+Al1FXW41O82OclA4I/I1mr8I7HeHXUbpcem0H+VdBpXgpNIn+0i9uJztxiYhhW5DZNIp2snpwMVOlhMq8Mhp/wBiuSnDx59TmoIVF7NLDHOQ0RIbAI71N/ZUxA/0lxj0NVb+xFrYyTS3swReDtJzk14Z4PsnPiqwvC5SHzicg8jg17LdXEMZcJc3Dkdcn2rn5PEDRXEkczOqKOqk56VHN4ytrezP2QTzSnO4ynhfStXQNcvbqIF40MaKMMSSW/Wrk+uWqXJ86YJMTgKEYj0rL1bxNo9gFie5uWkc5xGpH8xVbWfE+hpbAtqEqSbAeIWz+eKxI/EccalpppQ3QDbxWO/jK7jvpF3kxDoAnbtVC68bXzzZSYjHIBXFQL401Vp0Y3C7QeQBXV6V4jju7QNLchZCfmHTmt+2vEmIVZ8kjI5qS5u7W22tcXapnGCzDFSJe2jsUS48w4zhDmrQ+xAru8rLdmUbs05rayJ3FYj/ALyjj9KdFHGT+6ZQPVcCpFf5nUzcD/aqRg2BvlI9PmqAvKt0gFxmPHKhuauDyj/y0kHsTVWXUtPt2KzXZVgcY3VbBIYN5gC46k1IksQh86S4xHnG4txnpU8JQ5CXCkdflap16fNMW/GnFkCZMuMerVxnh/W7RNY1VWmKp5rAH1+ar114thBeK2lJfOFyvesy+utR1bTza3CyZ3hiMY47f1qtYeH5LeAvAsbSKCqojZAFVzaxQqJNWvI4OeQsuCR+PtXPX/i+ytbnGiRJKS+1mljLbl/Oug0zxLbXVoPtmnODj5mW3IUfjmm3et2cyFdPF35pHzKseFquLO8v7ZJ7myu1QZG+BD0z396qTeHLNHUmGWPI+9cMQxNXNP8ADeiyHyr2Ldlslw2cD61//9k=" + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 17 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "c3889be965d793dc" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "f58c06464094275c" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "e3910bb009633ba8" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "eac169bed3ab7ca2" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/hobj/__init__.py b/hobj/__init__.py index 791e211..05eb224 100644 --- a/hobj/__init__.py +++ b/hobj/__init__.py @@ -1,3 +1,31 @@ -import hobj.benchmarks as benchmarks +from hobj.benchmarks import MutatorHighVarBenchmark, MutatorOneshotBenchmark import hobj.learning_models as learning_models -import hobj.data_loaders as data_loaders + +from hobj.data_loaders.behavior import ( + load_highvar_behavior, + load_oneshot_behavior +) + +from hobj.data_loaders.images import ( + load_image, + load_imageset_meta_highvar, + load_imageset_meta_oneshot, + load_imageset_meta_warmup, + load_imageset_meta_catch +) + +__all__ = [ + # Raw behavior loaders + 'load_highvar_behavior', + 'load_oneshot_behavior', + # Image meta loaders: + 'load_imageset_meta_highvar', + 'load_imageset_meta_oneshot', + 'load_imageset_meta_warmup', + 'load_imageset_meta_catch', + # Image loader: + 'load_image', + # Benchmarks: + 'MutatorHighVarBenchmark', + 'MutatorOneshotBenchmark', +] diff --git a/hobj/benchmarks/make_model.py b/hobj/benchmarks/make_model.py index 9d8365a..73848a1 100644 --- a/hobj/benchmarks/make_model.py +++ b/hobj/benchmarks/make_model.py @@ -18,9 +18,9 @@ def _get_calibration_image_ids() -> list[ImageId]: Returns the ImageIds of the warmup images that are used for calibrating the features of the linear learner. Caches the result to avoid redundant computation. """ - from hobj.data_loaders.images import load_mutator_warmup_images + from hobj.data_loaders.images import load_imageset_meta_warmup - images_df = load_mutator_warmup_images() + images_df = load_imageset_meta_warmup() return sorted(images_df['image_id'].tolist()) diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index a3e77db..cc09678 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -5,7 +5,7 @@ from hobj.benchmarks.binary_classification.benchmark import LearningCurveBenchmark, LearningCurveBenchmarkConfig, TargetSubtaskData from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask, BinaryClassificationSubtaskResult from hobj.data_loaders.behavior import load_highvar_behavior -from hobj.data_loaders.images import load_mutator_highvar_images +from hobj.data_loaders.images import load_imageset_meta_highvar # %% @@ -15,7 +15,7 @@ def __init__(self): # Load data into format expected by benchmark # Load image manifest: - images_df = load_mutator_highvar_images() + images_df = load_imageset_meta_highvar() # Load raw human session data for benchmark: sessions = load_highvar_behavior(remove_probe_trials=True) diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index b0b553f..f2a9855 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -5,7 +5,7 @@ from hobj.benchmarks.generalization.estimator import GeneralizationStatistics from hobj.benchmarks.generalization.simulator import GeneralizationSubtask from hobj.data_loaders.behavior import load_oneshot_behavior -from hobj.data_loaders.images import load_mutator_oneshot_images +from hobj.data_loaders.images import load_imageset_meta_oneshot from hobj.types import ImageId @@ -91,7 +91,7 @@ def __init__(self): catch_trials = {9, 14, 19} # Load image manifest - images_df = load_mutator_oneshot_images() + images_df = load_imageset_meta_oneshot() image_id_to_row = images_df.set_index('image_id') # Map image refs to transformation ids diff --git a/hobj/data_loaders/behavior.py b/hobj/data_loaders/behavior.py index b25192f..0c0d127 100644 --- a/hobj/data_loaders/behavior.py +++ b/hobj/data_loaders/behavior.py @@ -2,6 +2,19 @@ import pandas as pd +from typing import Literal + +# %% +def load_human_behavior( + experiment: Literal['highvar', 'oneshot'] = 'highvar', +) -> pd.DataFrame: + if experiment not in {'highvar', 'oneshot'}: + raise ValueError(f"Invalid experiment name: {experiment}. Provide either 'highvar' or 'oneshot'.") + + if experiment == 'highvar': + return load_highvar_behavior() + else: + return load_oneshot_behavior() # %% Data loaders def load_highvar_behavior( @@ -99,10 +112,3 @@ def load_oneshot_behavior( return df - -if __name__ == '__main__': - df = load_oneshot_behavior() - import matplotlib.pyplot as plt - lc = df.groupby('trial')['perf'].mean() - plt.plot(lc.index.values, lc) - plt.show() diff --git a/hobj/data_loaders/download.py b/hobj/data_loaders/download.py new file mode 100644 index 0000000..90a453b --- /dev/null +++ b/hobj/data_loaders/download.py @@ -0,0 +1,159 @@ +"""Download packaged HOBJ datasets into the repository ``data`` directory.""" + +from __future__ import annotations + +import argparse +import tarfile +from pathlib import Path +from urllib.parse import urlparse + +import requests +from tqdm import tqdm + + +DATA_ARCHIVE_URL = ( + "https://hlbdatasets.s3.us-east-1.amazonaws.com/" + "lee-dicarlo-2023-learning-data.tar.gz" +) +EXPECTED_DATA_PATHS = ( + Path("data/meta-MutatorHighVarImageset.csv"), + Path("data/meta-MutatorOneShotImageset.csv"), + Path("data/meta-MutatorWarmupImageset.csv"), + Path("data/meta-CatchImageset.csv"), + Path("data/behavior/human-behavior-highvar-subtasks.csv"), + Path("data/behavior/human-behavior-oneshot-subtasks.csv"), +) +_DOWNLOAD_CHUNK_SIZE_BYTES = 1024 * 1024 + + +def _get_repo_root() -> Path: + """Return the repository root for this package.""" + return Path(__file__).resolve().parents[2] + + +def _get_missing_expected_paths(repo_root: Path) -> list[Path]: + """Return expected packaged data paths that are currently missing.""" + return [ + relpath for relpath in EXPECTED_DATA_PATHS if not (repo_root / relpath).exists() + ] + + +def _derive_archive_path(repo_root: Path, url: str) -> Path: + """Return the on-disk path for the downloaded archive.""" + archive_name = Path(urlparse(url).path).name + if not archive_name: + raise ValueError(f"Could not derive archive filename from URL: {url}") + return repo_root / archive_name + + +def _download_archive(url: str, archive_path: Path) -> None: + """Download an archive to disk atomically.""" + archive_path.parent.mkdir(parents=True, exist_ok=True) + temp_path = archive_path.with_suffix(f"{archive_path.suffix}.part") + + with requests.get(url, stream=True, timeout=(10, 300)) as response: + response.raise_for_status() + total_bytes = int(response.headers.get("content-length", 0)) + with temp_path.open("wb") as handle: + with tqdm( + total=total_bytes or None, + unit="B", + unit_scale=True, + unit_divisor=1024, + desc=f"Downloading {archive_path.name}", + ) as progress_bar: + for chunk in response.iter_content( + chunk_size=_DOWNLOAD_CHUNK_SIZE_BYTES + ): + if chunk: + handle.write(chunk) + progress_bar.update(len(chunk)) + + temp_path.replace(archive_path) + + +def _safe_extract_tarball(archive_path: Path, destination: Path) -> None: + """Extract a tarball while preventing path traversal.""" + destination = destination.resolve() + + with tarfile.open(archive_path, mode="r:gz") as tar: + for member in tar.getmembers(): + member_path = (destination / member.name).resolve() + member_path.relative_to(destination) + tar.extractall(destination) + + +def download_data( + *, + url: str = DATA_ARCHIVE_URL, + repo_root: Path | None = None, + force_download: bool = False, +) -> Path: + """Download and extract the packaged learning dataset into the repo root. + + The function is idempotent: if the expected packaged files already exist + under ``data/``, it returns immediately without downloading or extracting. + Otherwise it downloads the archive if needed and extracts it into the + repository root. + + Args: + url: Archive URL containing the packaged ``data`` tree. + repo_root: Repository root where the archive should be stored and + extracted. + force_download: Whether to redownload the archive even if it is already + present on disk. + + Returns: + The absolute path to the extracted ``data`` directory. + + Raises: + requests.HTTPError: If the archive download fails. + tarfile.TarError: If the archive cannot be unpacked. + ValueError: If the archive path is invalid or extraction would escape + the repository root. + FileNotFoundError: If extraction completes but the expected dataset + files are still missing. + """ + resolved_repo_root = ( + repo_root if repo_root is not None else _get_repo_root() + ).resolve() + data_root = resolved_repo_root / "data" + missing_paths = _get_missing_expected_paths(resolved_repo_root) + if not missing_paths: + return data_root + + archive_path = _derive_archive_path(resolved_repo_root, url) + if force_download or not archive_path.exists(): + _download_archive(url=url, archive_path=archive_path) + + _safe_extract_tarball(archive_path=archive_path, destination=resolved_repo_root) + + missing_paths = _get_missing_expected_paths(resolved_repo_root) + if missing_paths: + missing_str = "\n".join(f" - {path}" for path in missing_paths) + raise FileNotFoundError( + "Packaged dataset archive was extracted, but expected files are still " + f"missing:\n{missing_str}" + ) + + return data_root + + +def main() -> None: + """Download packaged dataset files into the repository data directory.""" + parser = argparse.ArgumentParser( + description="Download and extract the packaged HOBJ dataset archive." + ) + parser.add_argument( + "--force-download", + action="store_true", + help="Redownload the archive even if it already exists locally.", + ) + args = parser.parse_args() + + data_root = download_data(force_download=args.force_download) + print(data_root) + + +if __name__ == "__main__": + main() diff --git a/hobj/data_loaders/images.py b/hobj/data_loaders/images.py index 1129b68..c366ebf 100644 --- a/hobj/data_loaders/images.py +++ b/hobj/data_loaders/images.py @@ -3,8 +3,38 @@ from pathlib import Path import pandas as pd +from hobj.types import ImageId +from PIL import Image +from functools import lru_cache +@lru_cache(maxsize=1) +def _image_id_to_local_path_table() -> dict[ImageId, Path]: + """Build a mapping from image_id to absolute local path for all packaged images.""" + repo_root = Path(__file__).resolve().parents[2] + cache_root = repo_root / 'data' + manifest_paths = list(cache_root.glob('meta-*.csv')) + table = {} + for manifest_path in manifest_paths: + manifest_df = pd.read_csv(manifest_path) + for _, row in manifest_df.iterrows(): + image_id = row['image_id'] + relpath = row['relpath'] + abs_path = cache_root / relpath + table[image_id] = abs_path + return table + + +def load_image( + image_id: ImageId +) -> Image: + path = _image_id_to_local_path_table().get(image_id) + if path is None: + raise ValueError(f"Image ID not found in any manifest: {image_id}") + if not path.exists(): + raise FileNotFoundError(f"Expected image file to already exist at: {path}") + return Image.open(path) + def _load_image_manifest( *, dataset_name: str, @@ -52,7 +82,7 @@ def _load_image_manifest( return manifest_df -def load_mutator_highvar_images( +def load_imageset_meta_highvar( cachedir: Path | None = None, ) -> pd.DataFrame: """Load the high-variance image manifest.""" @@ -64,7 +94,7 @@ def load_mutator_highvar_images( return manifest_df.sort_values('image_id').reset_index(drop=True) -def load_mutator_oneshot_images( +def load_imageset_meta_oneshot( cachedir: Path | None = None, ) -> pd.DataFrame: """Load the one-shot image manifest.""" @@ -84,7 +114,7 @@ def load_mutator_oneshot_images( return manifest_df.sort_values('image_id').reset_index(drop=True) -def load_mutator_warmup_images( +def load_imageset_meta_warmup( cachedir: Path | None = None, ) -> pd.DataFrame: """Load the warmup image manifest.""" @@ -96,7 +126,7 @@ def load_mutator_warmup_images( return manifest_df.sort_values('image_id').reset_index(drop=True) -def load_probe_images( +def load_imageset_meta_catch( cachedir: Path | None = None, ) -> pd.DataFrame: """Load the probe image manifest.""" @@ -108,4 +138,4 @@ def load_probe_images( return manifest_df.sort_values('image_id').reset_index(drop=True) if __name__ == '__main__': - df = load_mutator_oneshot_images() + df = load_imageset_meta_oneshot() diff --git a/pyproject.toml b/pyproject.toml index 13ca003..e15fea6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,6 +26,9 @@ dependencies = [ "ipykernel>=7.2.0", ] +[project.scripts] +hobj-download-data = "hobj.data_loaders.download:main" + [dependency-groups] dev = [ "enczoo==0.1.3.dev1", diff --git a/tests/test_load_behavior.py b/tests/test_load_behavior.py index 5789042..178a3ba 100644 --- a/tests/test_load_behavior.py +++ b/tests/test_load_behavior.py @@ -1,9 +1,9 @@ from hobj.data_loaders.behavior import load_highvar_behavior, load_oneshot_behavior from hobj.data_loaders.images import ( - load_mutator_highvar_images, - load_mutator_oneshot_images, - load_mutator_warmup_images, - load_probe_images, + load_imageset_meta_highvar, + load_imageset_meta_oneshot, + load_imageset_meta_warmup, + load_imageset_meta_catch, ) @@ -22,14 +22,14 @@ def test_load_oneshot(): def test_load_highvar_images(): - df = load_mutator_highvar_images() + df = load_imageset_meta_highvar() assert len(df) == 12800 assert {'image_id', 'category', 'sha256', 'relpath', 'image_path'} <= set(df.columns) assert df['image_path'].iloc[0].exists() def test_load_oneshot_images(): - df = load_mutator_oneshot_images() + df = load_imageset_meta_oneshot() assert len(df) == 3904 assert { 'image_id', @@ -45,14 +45,14 @@ def test_load_oneshot_images(): def test_load_warmup_images(): - df = load_mutator_warmup_images() + df = load_imageset_meta_warmup() assert len(df) == 400 assert {'image_id', 'category', 'sha256', 'relpath', 'image_path'} <= set(df.columns) assert df['image_path'].iloc[0].exists() def test_load_probe_images(): - df = load_probe_images() + df = load_imageset_meta_catch() assert len(df) == 2 assert {'image_id', 'color', 'text', 'sha256', 'relpath', 'image_path'} <= set(df.columns) assert df['image_path'].iloc[0].exists() From e28d95e2d7e96ad411e015069d05a3c312a0be1e Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 18:18:20 -0400 Subject: [PATCH 124/156] lint --- hobj/__init__.py | 29 ++- hobj/benchmarks/__init__.py | 2 - .../binary_classification/benchmark.py | 122 ++++++----- .../binary_classification/estimator.py | 83 ++++--- .../binary_classification/simulation.py | 46 ++-- hobj/benchmarks/generalization/benchmark.py | 60 +++--- hobj/benchmarks/generalization/estimator.py | 51 ++--- hobj/benchmarks/generalization/simulator.py | 56 +++-- hobj/benchmarks/make_model.py | 33 +-- hobj/benchmarks/mut_highvar_benchmark.py | 45 ++-- hobj/benchmarks/mut_oneshot_benchmark.py | 202 ++++++++++-------- hobj/data_loaders/behavior.py | 88 ++++---- hobj/data_loaders/images.py | 76 +++---- hobj/learning_models/__init__.py | 29 +-- hobj/learning_models/representation.py | 30 +-- hobj/learning_models/update_rules.py | 143 ++++++------- hobj/stats/__init__.py | 1 - hobj/stats/binomial.py | 1 - hobj/stats/ci.py | 12 +- hobj/utils/file_io.py | 33 +-- hobj/utils/hash.py | 4 +- site/readme_images/run_make_images.py | 38 ++-- tests/test_dummy_learner.py | 4 +- tests/test_load_behavior.py | 44 ++-- tests/test_load_benchmarks.py | 5 - tests/test_simulate_subtask.py | 7 +- 26 files changed, 674 insertions(+), 570 deletions(-) delete mode 100644 tests/test_load_benchmarks.py diff --git a/hobj/__init__.py b/hobj/__init__.py index 05eb224..c6dc39a 100644 --- a/hobj/__init__.py +++ b/hobj/__init__.py @@ -1,31 +1,28 @@ -from hobj.benchmarks import MutatorHighVarBenchmark, MutatorOneshotBenchmark -import hobj.learning_models as learning_models +from hobj.benchmarks.mut_oneshot_benchmark import MutatorOneshotBenchmark +from hobj.benchmarks.mut_highvar_benchmark import MutatorHighVarBenchmark -from hobj.data_loaders.behavior import ( - load_highvar_behavior, - load_oneshot_behavior -) +from hobj.data_loaders.behavior import load_highvar_behavior, load_oneshot_behavior from hobj.data_loaders.images import ( load_image, load_imageset_meta_highvar, load_imageset_meta_oneshot, load_imageset_meta_warmup, - load_imageset_meta_catch + load_imageset_meta_catch, ) __all__ = [ # Raw behavior loaders - 'load_highvar_behavior', - 'load_oneshot_behavior', + "load_highvar_behavior", + "load_oneshot_behavior", # Image meta loaders: - 'load_imageset_meta_highvar', - 'load_imageset_meta_oneshot', - 'load_imageset_meta_warmup', - 'load_imageset_meta_catch', + "load_imageset_meta_highvar", + "load_imageset_meta_oneshot", + "load_imageset_meta_warmup", + "load_imageset_meta_catch", # Image loader: - 'load_image', + "load_image", # Benchmarks: - 'MutatorHighVarBenchmark', - 'MutatorOneshotBenchmark', + "MutatorHighVarBenchmark", + "MutatorOneshotBenchmark", ] diff --git a/hobj/benchmarks/__init__.py b/hobj/benchmarks/__init__.py index 17797ed..e69de29 100644 --- a/hobj/benchmarks/__init__.py +++ b/hobj/benchmarks/__init__.py @@ -1,2 +0,0 @@ -from hobj.benchmarks.mut_highvar_benchmark import MutatorHighVarBenchmark -from hobj.benchmarks.mut_oneshot_benchmark import MutatorOneshotBenchmark diff --git a/hobj/benchmarks/binary_classification/benchmark.py b/hobj/benchmarks/binary_classification/benchmark.py index 90820a6..97b9303 100644 --- a/hobj/benchmarks/binary_classification/benchmark.py +++ b/hobj/benchmarks/binary_classification/benchmark.py @@ -7,7 +7,10 @@ from tqdm import tqdm from hobj.benchmarks.binary_classification.estimator import LearningCurveStatistics -from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask, BinaryClassificationSubtaskResult +from hobj.benchmarks.binary_classification.simulation import ( + BinaryClassificationSubtask, + BinaryClassificationSubtaskResult, +) from hobj.learning_models import BinaryLearningModel from hobj.stats.ci import estimate_basic_bootstrap_CI @@ -15,42 +18,50 @@ # %% Models for configuring a LearningCurveBenchmark: class TargetSubtaskData(pydantic.BaseModel): subtask: BinaryClassificationSubtask # The subtask which generated the associated results - results: List[BinaryClassificationSubtaskResult] # [session, trial] boolean matrix of performance + results: List[ + BinaryClassificationSubtaskResult + ] # [session, trial] boolean matrix of performance - model_config = dict( - arbitrary_types_allowed=True - ) + model_config = dict(arbitrary_types_allowed=True) - @pydantic.model_validator(mode='after') - def validate_results(self) -> 'TargetSubtaskData': + @pydantic.model_validator(mode="after") + def validate_results(self) -> "TargetSubtaskData": # Check shape for result in self.results: if self.subtask.ntrials != len(result.perf_seq): - raise ValueError(f"Expected {self.subtask.ntrials} trials, but got {result.perf_seq} trials") + raise ValueError( + f"Expected {self.subtask.ntrials} trials, but got {result.perf_seq} trials" + ) return self class LearningCurveBenchmarkConfig(pydantic.BaseModel): - subtask_name_to_data: Dict[str, 'TargetSubtaskData'] = pydantic.Field(default_factory=dict, description="A dictionary of subtask_name -> TargetSubtaskConfig") + subtask_name_to_data: Dict[str, "TargetSubtaskData"] = pydantic.Field( + default_factory=dict, + description="A dictionary of subtask_name -> TargetSubtaskConfig", + ) num_simulations_per_subtask: int = pydantic.Field(ge=2) num_bootstrap_samples: int = pydantic.Field(ge=2) bootstrap_by_worker: bool ntrials: Optional[int] = pydantic.Field(default=None) - @pydantic.model_validator(mode='after') - def ensure_rectangular(self) -> 'LearningCurveBenchmarkConfig': - + @pydantic.model_validator(mode="after") + def ensure_rectangular(self) -> "LearningCurveBenchmarkConfig": ntrials_observed = set() for name, data in self.subtask_name_to_data.items(): ntrials_observed.add(data.subtask.ntrials) if not len(ntrials_observed) == 1: - raise ValueError(f"Expected all subtasks to have the same number of trials, but got {ntrials_observed}") + raise ValueError( + f"Expected all subtasks to have the same number of trials, but got {ntrials_observed}" + ) if self.ntrials is not None: if self.ntrials != ntrials_observed.pop(): - raise ValueError(f"Expected ntrials to be {ntrials_observed.pop()}, but got {self.ntrials}") + raise ValueError( + f"Expected ntrials to be {ntrials_observed.pop()}, but got {self.ntrials}" + ) else: self.ntrials = ntrials_observed.pop() return self @@ -58,20 +69,22 @@ def ensure_rectangular(self) -> 'LearningCurveBenchmarkConfig': # %% class LearningCurveBenchmark: - def __init__( - self, - config: LearningCurveBenchmarkConfig, + self, + config: LearningCurveBenchmarkConfig, ): self.config = config # Attach properties self.subtask_names = sorted(config.subtask_name_to_data.keys()) self.subtask_name_to_subtask: Dict[str, BinaryClassificationSubtask] = { - name: config.subtask_name_to_data[name].subtask for name in self.subtask_names + name: config.subtask_name_to_data[name].subtask + for name in self.subtask_names } - self.subtask_name_to_results: Dict[str, List[BinaryClassificationSubtaskResult]] = {} + self.subtask_name_to_results: Dict[ + str, List[BinaryClassificationSubtaskResult] + ] = {} self._target_data = {} for name in self.subtask_names: @@ -82,8 +95,12 @@ def __init__( for result in results: worker_id = result.worker_id if worker_id in self._target_data[name]: - raise ValueError(f"Worker {worker_id} has already been seen for subtask {name}") - self._target_data[name][result.worker_id] = list([bool(v) for v in result.perf_seq]) + raise ValueError( + f"Worker {worker_id} has already been seen for subtask {name}" + ) + self._target_data[name][result.worker_id] = list( + [bool(v) for v in result.perf_seq] + ) self._target_statistics = LearningCurveStatistics( subtask_name_to_results=self.subtask_name_to_results, @@ -95,7 +112,6 @@ def __init__( def target_data(self) -> Dict[str, Dict[str, List[bool]]]: return self._target_data - @property def target_statistics(self) -> LearningCurveStatistics: """ @@ -115,9 +131,7 @@ class LearningCurveBenchmarkResult: model_statistics: LearningCurveStatistics def __call__( - self, - learner: BinaryLearningModel, - show_pbar: bool = False + self, learner: BinaryLearningModel, show_pbar: bool = False ) -> LearningCurveBenchmarkResult: """ :param learner: LearningModel @@ -125,8 +139,12 @@ def __call__( """ # Get model learning curve statistics: - subtask_name_to_model_results: Dict[str, List[BinaryClassificationSubtaskResult]] = {} - for i_subtask, subtask_name in enumerate(tqdm(self.subtask_names, desc='Subtask simulations:', disable=not show_pbar)): + subtask_name_to_model_results: Dict[ + str, List[BinaryClassificationSubtaskResult] + ] = {} + for i_subtask, subtask_name in enumerate( + tqdm(self.subtask_names, desc="Subtask simulations:", disable=not show_pbar) + ): # Get [simulation, trial] boolean performance matrix for the model subtask_results = self.simulate_model_behavior( subtask=self.subtask_name_to_subtask[subtask_name], @@ -149,7 +167,7 @@ def __call__( model_varhat_phat=model_statistics.varhat_phat, target_phat=self.target_statistics.phat, target_varhat_phat=self.target_statistics.varhat_phat, - condition_dims=('subtask', 'trial'), + condition_dims=("subtask", "trial"), fit_lapse_rate=True, ) @@ -159,7 +177,7 @@ def __call__( model_varhat_phat=model_statistics.boot_varhat_phat, target_phat=self.target_statistics.boot_phat, target_varhat_phat=self.target_statistics.boot_varhat_phat, - condition_dims=('subtask', 'trial'), + condition_dims=("subtask", "trial"), fit_lapse_rate=True, ) @@ -182,9 +200,9 @@ def __call__( @staticmethod def simulate_model_behavior( - subtask: BinaryClassificationSubtask, - learner: BinaryLearningModel, - nsimulations: int, + subtask: BinaryClassificationSubtask, + learner: BinaryLearningModel, + nsimulations: int, ) -> List[BinaryClassificationSubtaskResult]: """ Returns a [nsimulations, ntrials] matrix of model performance on the subtask. @@ -213,34 +231,33 @@ def simulate_model_behavior( @classmethod def _compare_learning_curves( - cls, - model_phat: xr.DataArray, - model_varhat_phat: xr.DataArray, - target_phat: xr.DataArray, - target_varhat_phat: xr.DataArray, - condition_dims: Tuple[str, ...], - fit_lapse_rate: bool + cls, + model_phat: xr.DataArray, + model_varhat_phat: xr.DataArray, + target_phat: xr.DataArray, + target_varhat_phat: xr.DataArray, + condition_dims: Tuple[str, ...], + fit_lapse_rate: bool, ) -> Tuple[Union[np.ndarray, np.generic], Union[xr.DataArray, None]]: - if fit_lapse_rate: lapse_rate = cls._fit_lapse_rate( - pmodel=model_phat, - ptarget=target_phat, - condition_dims=condition_dims + pmodel=model_phat, ptarget=target_phat, condition_dims=condition_dims ) model_phat = model_phat * (1 - lapse_rate) + 0.5 * lapse_rate model_varhat_phat = model_varhat_phat * (1 - lapse_rate) ** 2 else: lapse_rate = None - msen = np.square(model_phat - target_phat).mean(condition_dims) - model_varhat_phat.mean(condition_dims) - target_varhat_phat.mean(condition_dims) + msen = ( + np.square(model_phat - target_phat).mean(condition_dims) + - model_varhat_phat.mean(condition_dims) + - target_varhat_phat.mean(condition_dims) + ) return msen, lapse_rate @staticmethod def _fit_lapse_rate( - pmodel: xr.DataArray, - ptarget: xr.DataArray, - condition_dims: Tuple[str, ...] + pmodel: xr.DataArray, ptarget: xr.DataArray, condition_dims: Tuple[str, ...] ) -> Union[np.ndarray, np.generic]: """ Fits a "lapse rate" parameter (gamma), which takes on values between [0, 1]. It may be interpreted @@ -257,8 +274,15 @@ def _fit_lapse_rate( """ nway = 2 - numerator = -(2 * pmodel / nway - 2 * np.square(pmodel) + 2 * pmodel * ptarget - 2 * ptarget / nway).sum(dim=condition_dims) - denominator = (2 / (nway ** 2) - 4 * pmodel / nway + 2 * (pmodel ** 2)).sum(dim=condition_dims) + numerator = -( + 2 * pmodel / nway + - 2 * np.square(pmodel) + + 2 * pmodel * ptarget + - 2 * ptarget / nway + ).sum(dim=condition_dims) + denominator = (2 / (nway**2) - 4 * pmodel / nway + 2 * (pmodel**2)).sum( + dim=condition_dims + ) gamma_star = numerator / denominator gamma_star = np.clip(gamma_star, 0, 1) return gamma_star diff --git a/hobj/benchmarks/binary_classification/estimator.py b/hobj/benchmarks/binary_classification/estimator.py index 0b80cb7..5419532 100644 --- a/hobj/benchmarks/binary_classification/estimator.py +++ b/hobj/benchmarks/binary_classification/estimator.py @@ -4,7 +4,9 @@ import numpy as np import xarray as xr -from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtaskResult +from hobj.benchmarks.binary_classification.simulation import ( + BinaryClassificationSubtaskResult, +) from hobj.stats import binomial as binomial_funcs @@ -13,10 +15,10 @@ class LearningCurveStatistics(xr.Dataset): __slots__ = () def __init__( - self, - subtask_name_to_results: Dict[str, List[BinaryClassificationSubtaskResult]], - nbootstrap_samples: int, - bootstrap_by_worker: bool, # versus by session + self, + subtask_name_to_results: Dict[str, List[BinaryClassificationSubtaskResult]], + nbootstrap_samples: int, + bootstrap_by_worker: bool, # versus by session ): # Get canonical order of subtask names subtask_names = sorted(subtask_name_to_results.keys()) @@ -34,7 +36,9 @@ def __init__( all_workers.add(r.worker_id) if not len(ntrials_observed) == 1: - raise ValueError(f"Expected all subtasks to have the same number of trials, but got {ntrials_observed}") + raise ValueError( + f"Expected all subtasks to have the same number of trials, but got {ntrials_observed}" + ) ntrials = ntrials_observed.pop() all_workers = sorted(all_workers) worker_to_i = {worker: i for i, worker in enumerate(all_workers)} @@ -68,14 +72,14 @@ def __init__( if bootstrap_by_worker: bootstrap_resamples = self._get_bootstrap_resamples_by_worker( perf_mat=perf_mat, - i_subtasks = i_subtasks, - i_workers = i_workers, + i_subtasks=i_subtasks, + i_workers=i_workers, nbootstrap_samples=nbootstrap_samples, ) else: bootstrap_resamples = self._get_bootstrap_resamples_by_session( perf_mat=perf_mat, - i_subtasks = i_subtasks, + i_subtasks=i_subtasks, nbootstrap_samples=nbootstrap_samples, ) @@ -87,23 +91,25 @@ def __init__( super().__init__( data_vars=dict( - phat=(['subtask', 'trial'], phat), - varhat_phat=(['subtask', 'trial'], varhat_phat), - boot_phat=(['boot_iter', 'subtask', 'trial'], boot_phat), - boot_varhat_phat=(['boot_iter', 'subtask', 'trial'], boot_varhat_phat), + phat=(["subtask", "trial"], phat), + varhat_phat=(["subtask", "trial"], varhat_phat), + boot_phat=(["boot_iter", "subtask", "trial"], boot_phat), + boot_varhat_phat=(["boot_iter", "subtask", "trial"], boot_varhat_phat), ), coords=dict( subtask=subtask_names, - ) + ), ) @staticmethod def get_point_estimates( - k: np.ndarray, - n: np.ndarray, + k: np.ndarray, + n: np.ndarray, ) -> Tuple[np.ndarray, np.ndarray]: phat = k / n - varhat_phat = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=k, nvec=n) + varhat_phat = binomial_funcs.estimate_variance_of_binomial_proportion( + kvec=k, nvec=n + ) return phat, varhat_phat @dataclass @@ -113,24 +119,29 @@ class BootstrapSamples: @staticmethod def _get_bootstrap_resamples_by_worker( - perf_mat: np.ndarray, # [session, trial] - i_subtasks: List[int], # [session] - i_workers: List[int], # [session] - nbootstrap_samples: int, + perf_mat: np.ndarray, # [session, trial] + i_subtasks: List[int], # [session] + i_workers: List[int], # [session] + nbootstrap_samples: int, ) -> BootstrapSamples: - if not perf_mat.dtype == np.bool: - raise ValueError(f"Expected perf_mat to have dtype bool, but got {perf_mat.dtype}") + raise ValueError( + f"Expected perf_mat to have dtype bool, but got {perf_mat.dtype}" + ) ntrials = perf_mat.shape[1] nworkers = max(i_workers) + 1 nsubtasks = max(i_subtasks) + 1 if not len(set(i_subtasks)) == nsubtasks: - raise ValueError(f"Expected i_subtasks to be a contiguous range, but got {set(i_subtasks)}") + raise ValueError( + f"Expected i_subtasks to be a contiguous range, but got {set(i_subtasks)}" + ) if not len(set(i_workers)) == nworkers: - raise ValueError(f"Expected i_workers to be a contiguous range, but got {set(i_workers)}") + raise ValueError( + f"Expected i_workers to be a contiguous range, but got {set(i_workers)}" + ) # Reshape data perf_table = np.zeros(shape=(nworkers, nsubtasks, ntrials), dtype=np.bool) @@ -157,9 +168,9 @@ def _get_bootstrap_resamples_by_worker( @staticmethod def _get_bootstrap_resamples_by_session( - perf_mat: np.ndarray, # [session, trial] - i_subtasks: List[int], # [session] - nbootstrap_samples: int, + perf_mat: np.ndarray, # [session, trial] + i_subtasks: List[int], # [session] + nbootstrap_samples: int, ) -> BootstrapSamples: # Bootstrap by session, within each subtask @@ -167,7 +178,9 @@ def _get_bootstrap_resamples_by_session( nsubtasks = max(i_subtasks) + 1 if not len(set(i_subtasks)) == nsubtasks: - raise ValueError(f"Expected i_subtasks to be a contiguous range, but got {set(i_subtasks)}") + raise ValueError( + f"Expected i_subtasks to be a contiguous range, but got {set(i_subtasks)}" + ) # Reshape data to {i_subtask: [session, trial]} i_subtask_to_perf_data = {} @@ -178,7 +191,9 @@ def _get_bootstrap_resamples_by_session( # Cast to np arrays for i_subtask in i_subtask_to_perf_data: - i_subtask_to_perf_data[i_subtask] = np.array(i_subtask_to_perf_data[i_subtask], dtype = np.bool) + i_subtask_to_perf_data[i_subtask] = np.array( + i_subtask_to_perf_data[i_subtask], dtype=np.bool + ) # Perform bootstrapping gen = np.random.default_rng() @@ -187,8 +202,12 @@ def _get_bootstrap_resamples_by_session( for i_bootstrap in range(nbootstrap_samples): for i_subtask in range(nsubtasks): nsessions_for_subtask = len(i_subtask_to_perf_data[i_subtask]) - i_sessions = gen.integers(low=0, high=nsessions_for_subtask, size=nsessions_for_subtask) - boot_k[i_bootstrap, i_subtask] = i_subtask_to_perf_data[i_subtask][i_sessions].sum(axis=0) + i_sessions = gen.integers( + low=0, high=nsessions_for_subtask, size=nsessions_for_subtask + ) + boot_k[i_bootstrap, i_subtask] = i_subtask_to_perf_data[i_subtask][ + i_sessions + ].sum(axis=0) boot_n[i_bootstrap, i_subtask] = len(i_sessions) return LearningCurveStatistics.BootstrapSamples( diff --git a/hobj/benchmarks/binary_classification/simulation.py b/hobj/benchmarks/binary_classification/simulation.py index ad35f89..39b3300 100644 --- a/hobj/benchmarks/binary_classification/simulation.py +++ b/hobj/benchmarks/binary_classification/simulation.py @@ -8,9 +8,7 @@ # %% class BinaryClassificationSubtaskResult(pydantic.BaseModel): - model_config = dict( - arbitrary_types_allowed=True - ) + model_config = dict(arbitrary_types_allowed=True) perf_seq: np.ndarray worker_id: Optional[str] = pydantic.Field(default="NO_WORKER") @@ -22,30 +20,34 @@ class BinaryClassificationSubtask(pydantic.BaseModel): Feedback consists of +1 or -1 rewards, depending on the class of the image. """ - model_config = dict( - frozen=True - ) + model_config = dict(frozen=True) classA: List[ImageId] classB: List[ImageId] - ntrials: int = pydantic.Field(description='The number of trials in the subtask.', gt=0) - replace: bool = pydantic.Field(description='Whether to show stimulus images with replacement or not.') + ntrials: int = pydantic.Field( + description="The number of trials in the subtask.", gt=0 + ) + replace: bool = pydantic.Field( + description="Whether to show stimulus images with replacement or not." + ) - @pydantic.field_validator('classA', 'classB', mode='after') + @pydantic.field_validator("classA", "classB", mode="after") @classmethod def sort_image_refs(cls, value: List[ImageId]) -> List[ImageId]: return sorted(value) - @pydantic.model_validator(mode='after') - def validate_image_refs(self) -> 'BinaryClassificationSubtask': + @pydantic.model_validator(mode="after") + def validate_image_refs(self) -> "BinaryClassificationSubtask": if not self.replace and len(self.classA) + len(self.classB) < self.ntrials: - raise ValueError(f"Specified replace=False, but only {len(self.classA) + len(self.classB)} images available. (len(classA)={len(self.classA)}; len(classB)={len(self.classB)}") + raise ValueError( + f"Specified replace=False, but only {len(self.classA) + len(self.classB)} images available. (len(classA)={len(self.classA)}; len(classB)={len(self.classB)}" + ) return self def simulate_session( - self, - learner: BinaryLearningModel, - seed: Union[int, None], + self, + learner: BinaryLearningModel, + seed: Union[int, None], ) -> BinaryClassificationSubtaskResult: """ Convenience method to simulate a session of the binary classification task on a given BinaryLearningModel. @@ -66,7 +68,7 @@ def simulate_session( if self.replace: i_image_seq = gen.integers(n_all_images, size=self.ntrials) else: - i_image_seq = gen.permutation(n_all_images)[:self.ntrials] + i_image_seq = gen.permutation(n_all_images)[: self.ntrials] # Randomly sample the reward contingency classA_correct_action = 0 if gen.random() < 0.5 else 1 @@ -76,14 +78,20 @@ def simulate_session( i_trial = 0 for i_image in i_image_seq: # Retrieve trial information - image_ref_cur = self.classA[i_image] if i_image < num_classA else self.classB[i_image - num_classA] - correct_action_cur = classA_correct_action if i_image < num_classA else classB_correct_action + image_ref_cur = ( + self.classA[i_image] + if i_image < num_classA + else self.classB[i_image - num_classA] + ) + correct_action_cur = ( + classA_correct_action if i_image < num_classA else classB_correct_action + ) # Get response from learner a = learner.get_response(image=image_ref_cur) # Calculate feedback based on response - feedback = 1. if a == correct_action_cur else -1. + feedback = 1.0 if a == correct_action_cur else -1.0 # Deliver feedback to learner learner.give_feedback(feedback) diff --git a/hobj/benchmarks/generalization/benchmark.py b/hobj/benchmarks/generalization/benchmark.py index 69266ac..29ba34a 100644 --- a/hobj/benchmarks/generalization/benchmark.py +++ b/hobj/benchmarks/generalization/benchmark.py @@ -7,7 +7,10 @@ from tqdm import tqdm from hobj.benchmarks.generalization.estimator import GeneralizationStatistics -from hobj.benchmarks.generalization.simulator import GeneralizationSessionResult, GeneralizationSubtask +from hobj.benchmarks.generalization.simulator import ( + GeneralizationSessionResult, + GeneralizationSubtask, +) from hobj.learning_models import BinaryLearningModel from hobj.stats.ci import estimate_basic_bootstrap_CI @@ -23,10 +26,9 @@ class GeneralizationBenchmarkConfig(pydantic.BaseModel): # %% class GeneralizationBenchmark: - def __init__( - self, - config: GeneralizationBenchmarkConfig, + self, + config: GeneralizationBenchmarkConfig, ): self.config = config self._generalization_statistics = GeneralizationStatistics( @@ -52,9 +54,7 @@ class GeneralizationBenchmarkResult: model_statistics: GeneralizationStatistics def __call__( - self, - learner: BinaryLearningModel, - show_pbar: bool = False + self, learner: BinaryLearningModel, show_pbar: bool = False ) -> GeneralizationBenchmarkResult: """ :param learner: LearningModel @@ -63,7 +63,9 @@ def __call__( # Get model generalization behaviors: results: List[GeneralizationSessionResult] = [] - for subtask in tqdm(self.config.subtasks, desc='Subtask simulations:', disable=not show_pbar): + for subtask in tqdm( + self.config.subtasks, desc="Subtask simulations:", disable=not show_pbar + ): # Get [simulation, trial] boolean performance matrix for the model subtask_results = self.simulate_model_behavior( subtask=subtask, @@ -77,11 +79,13 @@ def __call__( results=results, perform_lapse_rate_correction=False, n_bootstrap_iterations=self.config.num_bootstrap_samples, - bootstrap_by_worker=False, # bootstrap simulations + bootstrap_by_worker=False, # bootstrap simulations ) # Get target transformation - model_statistics = model_statistics.sel(transformation = self.target_statistics.transformation) + model_statistics = model_statistics.sel( + transformation=self.target_statistics.transformation + ) # Calculate comparison statistics between target and model learning curves: msen_point = self._compare_generalization_patterns( @@ -89,7 +93,7 @@ def __call__( model_varhat_phat=model_statistics.varhat_phat, target_phat=self.target_statistics.phat, target_varhat_phat=self.target_statistics.varhat_phat, - condition_dims=('transformation',), + condition_dims=("transformation",), ) # Calculate bootstrap replicates: @@ -98,7 +102,7 @@ def __call__( model_varhat_phat=model_statistics.boot_varhat_phat, target_phat=self.target_statistics.boot_phat, target_varhat_phat=self.target_statistics.boot_varhat_phat, - condition_dims=('transformation',), + condition_dims=("transformation",), ) msen_sigma = np.std(msen_boot, ddof=1) @@ -118,9 +122,9 @@ def __call__( @staticmethod def simulate_model_behavior( - subtask: GeneralizationSubtask, - learner: BinaryLearningModel, - nsimulations: int, + subtask: GeneralizationSubtask, + learner: BinaryLearningModel, + nsimulations: int, ) -> List[GeneralizationSessionResult]: """ Returns a [nsimulations, ntrials] matrix of model performance on the subtask. @@ -138,19 +142,27 @@ def simulate_model_behavior( learner.reset_state(seed=None) # Simulate session: - results.append(subtask.simulate_session(learner=learner, seed=None, )) + results.append( + subtask.simulate_session( + learner=learner, + seed=None, + ) + ) return results @classmethod def _compare_generalization_patterns( - cls, - model_phat: xr.DataArray, - model_varhat_phat: xr.DataArray, - target_phat: xr.DataArray, - target_varhat_phat: xr.DataArray, - condition_dims: Tuple[str, ...], + cls, + model_phat: xr.DataArray, + model_varhat_phat: xr.DataArray, + target_phat: xr.DataArray, + target_varhat_phat: xr.DataArray, + condition_dims: Tuple[str, ...], ): - - msen = np.square(model_phat - target_phat).mean(condition_dims) - model_varhat_phat.mean(condition_dims) - target_varhat_phat.mean(condition_dims) + msen = ( + np.square(model_phat - target_phat).mean(condition_dims) + - model_varhat_phat.mean(condition_dims) + - target_varhat_phat.mean(condition_dims) + ) return msen diff --git a/hobj/benchmarks/generalization/estimator.py b/hobj/benchmarks/generalization/estimator.py index 6dbd337..e3e57b2 100644 --- a/hobj/benchmarks/generalization/estimator.py +++ b/hobj/benchmarks/generalization/estimator.py @@ -12,20 +12,21 @@ class GeneralizationStatistics(xr.Dataset): __slots__ = () def __init__( - self, - results: List[GeneralizationSessionResult], - perform_lapse_rate_correction: bool, - n_bootstrap_iterations: int, - bootstrap_by_worker: bool, # versus by session + self, + results: List[GeneralizationSessionResult], + perform_lapse_rate_correction: bool, + n_bootstrap_iterations: int, + bootstrap_by_worker: bool, # versus by session ): - # Get all transformations all_transformations = set() for result in results: all_transformations.update(result.transformation_to_kn.keys()) all_transformations = sorted(all_transformations) - transformation_to_i = {transformation: i for i, transformation in enumerate(all_transformations)} + transformation_to_i = { + transformation: i for i, transformation in enumerate(all_transformations) + } # Get all workers all_workers = sorted({result.worker_id for result in results}) @@ -38,9 +39,13 @@ def __init__( if bootstrap_by_worker: if nworkers == 1: - raise ValueError(f"Only one worker {all_workers}, cannot perform valid bootstrap by worker.") + raise ValueError( + f"Only one worker {all_workers}, cannot perform valid bootstrap by worker." + ) elif nworkers < 20: - warnings.warn(f"Only {nworkers} unique workers, which is less than 20. Bootstrapping by worker may not be reliable.") + warnings.warn( + f"Only {nworkers} unique workers, which is less than 20. Bootstrapping by worker may not be reliable." + ) kmat = np.zeros(shape=(nworkers, ntransformations)) nmat = np.zeros(shape=(nworkers, ntransformations)) @@ -78,7 +83,6 @@ def __init__( boot_varhat_phat = np.zeros(shape=(n_bootstrap_iterations, ntransformations)) for i_boot_iter in range(n_bootstrap_iterations): - if bootstrap_by_worker: # Resample by worker i_boot = gen.integers(low=0, high=nworkers, size=nworkers) @@ -105,27 +109,26 @@ def __init__( super().__init__( data_vars=dict( - phat=(['transformation'], phat), - varhat_phat=(['transformation'], varhat_phat), - boot_phat=(['boot_iter', 'transformation'], boot_phat), - boot_varhat_phat=(['boot_iter', 'transformation'], boot_varhat_phat), + phat=(["transformation"], phat), + varhat_phat=(["transformation"], varhat_phat), + boot_phat=(["boot_iter", "transformation"], boot_phat), + boot_varhat_phat=(["boot_iter", "transformation"], boot_varhat_phat), ), - coords=dict( - transformation=all_transformations - ) + coords=dict(transformation=all_transformations), ) @staticmethod def _get_point_estimates( - k: np.ndarray, # [transformation] - n: np.ndarray, # [transformation] - kcatch: np.ndarray, # () - ncatch: np.ndarray, # () - perform_lapse_rate_correction: bool, + k: np.ndarray, # [transformation] + n: np.ndarray, # [transformation] + kcatch: np.ndarray, # () + ncatch: np.ndarray, # () + perform_lapse_rate_correction: bool, ): - phat = k / n - varhat_phat = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=k, nvec=n) + varhat_phat = binomial_funcs.estimate_variance_of_binomial_proportion( + kvec=k, nvec=n + ) if perform_lapse_rate_correction: # Return an estimate of the performance that would be expected if the system had a lapse rate of 0. diff --git a/hobj/benchmarks/generalization/simulator.py b/hobj/benchmarks/generalization/simulator.py index 9bddd95..8b9c2ae 100644 --- a/hobj/benchmarks/generalization/simulator.py +++ b/hobj/benchmarks/generalization/simulator.py @@ -7,6 +7,7 @@ from hobj.learning_models import BinaryLearningModel from hobj.types import ImageId + # %% class GeneralizationSessionResult(pydantic.BaseModel): transformation_to_kn: Dict[str, List[int]] @@ -14,15 +15,19 @@ class GeneralizationSessionResult(pydantic.BaseModel): ncatch: int worker_id: Optional[str] = pydantic.Field(default="NO_WORKER") - @pydantic.model_validator(mode='after') - def validate_results(self) -> 'GeneralizationSessionResult': + @pydantic.model_validator(mode="after") + def validate_results(self) -> "GeneralizationSessionResult": for transformation in self.transformation_to_kn: k, n = self.transformation_to_kn[transformation] if k < 0 or n < 0 or k > n: - raise ValueError(f"Got invalid Binomial parameters k={k}, n={n} for transformation {transformation}") + raise ValueError( + f"Got invalid Binomial parameters k={k}, n={n} for transformation {transformation}" + ) if self.kcatch < 0 or self.ncatch < 0 or self.kcatch > self.ncatch: - raise ValueError(f"Got invalid Binomial parameters k={self.kcatch}, n={self.ncatch} for catch trials") + raise ValueError( + f"Got invalid Binomial parameters k={self.kcatch}, n={self.ncatch} for catch trials" + ) return self @@ -32,9 +37,7 @@ class GeneralizationSubtask(pydantic.BaseModel): A simulator for a single session of the specific "one-shot" task used in Lee and DiCarlo 2023. """ - model_config = dict( - frozen=True - ) + model_config = dict(frozen=True) support_imageA: ImageId support_imageB: ImageId @@ -42,24 +45,25 @@ class GeneralizationSubtask(pydantic.BaseModel): test_imagesB: List[ImageId] image_ref_to_transformation: Dict[ImageId, str] - @pydantic.field_validator('test_imagesA', 'test_imagesB', mode='after') + @pydantic.field_validator("test_imagesA", "test_imagesB", mode="after") @classmethod def sort_image_refs(cls, value: List[ImageId]) -> List[ImageId]: return sorted(value) - @pydantic.model_validator(mode='after') - def validate_model(self) -> 'GeneralizationSubtask': - + @pydantic.model_validator(mode="after") + def validate_model(self) -> "GeneralizationSubtask": for ref in self.test_imagesA + self.test_imagesB: if ref not in self.image_ref_to_transformation: - raise ValueError(f"Expected all test images to have an associated transformation, but {ref} was missing.") + raise ValueError( + f"Expected all test images to have an associated transformation, but {ref} was missing." + ) return self def simulate_session( - self, - learner: BinaryLearningModel, - seed: Union[int, None], + self, + learner: BinaryLearningModel, + seed: Union[int, None], ) -> GeneralizationSessionResult: """ Convenience method to simulate a session of the one-shot task on a given BinaryLearningModel. @@ -84,7 +88,9 @@ def simulate_session( # An initial phase of 10 support trials, with classes sampled in equal number (5 each) support_sequence = [True] * 5 + [False] * 5 - support_sequence = [support_sequence[i] for i in gen.permutation(len(support_sequence))] + support_sequence = [ + support_sequence[i] for i in gen.permutation(len(support_sequence)) + ] stimulus_category_is_A_seq.extend(support_sequence) # A testing phase of 8 test trials with 2 catch trials, where classes are sampled i.i.d. @@ -104,11 +110,19 @@ def simulate_session( for i_trial in range(ntrials): # Retrieve trial information stimulus_category_is_A = stimulus_category_is_A_seq[i_trial] - correct_action_cur = classA_correct_action if stimulus_category_is_A else classB_correct_action + correct_action_cur = ( + classA_correct_action + if stimulus_category_is_A + else classB_correct_action + ) if i_trial in support_trials or i_trial in catch_trials: # Supply a support image - image_ref_cur = self.support_imageA if stimulus_category_is_A else self.support_imageB + image_ref_cur = ( + self.support_imageA + if stimulus_category_is_A + else self.support_imageB + ) else: # Sample a test image without replacement @@ -123,7 +137,7 @@ def simulate_session( # Calculate feedback based on response correct = a == correct_action_cur - feedback = 1. if correct else -1. + feedback = 1.0 if correct else -1.0 # Deliver feedback to learner learner.give_feedback(feedback) @@ -140,7 +154,5 @@ def simulate_session( transformation_to_kn[transformation_cur][1] += 1 return GeneralizationSessionResult( - transformation_to_kn=transformation_to_kn, - kcatch=kcatch, - ncatch=ncatch + transformation_to_kn=transformation_to_kn, kcatch=kcatch, ncatch=ncatch ) diff --git a/hobj/benchmarks/make_model.py b/hobj/benchmarks/make_model.py index 73848a1..e90caac 100644 --- a/hobj/benchmarks/make_model.py +++ b/hobj/benchmarks/make_model.py @@ -1,6 +1,7 @@ """ This module provides an alternative interface for instantiating a linear learning model. """ + from functools import lru_cache from typing import Literal @@ -21,23 +22,23 @@ def _get_calibration_image_ids() -> list[ImageId]: from hobj.data_loaders.images import load_imageset_meta_warmup images_df = load_imageset_meta_warmup() - return sorted(images_df['image_id'].tolist()) + return sorted(images_df["image_id"].tolist()) # %% def make_linear_learner_from_features( - ref_to_features: dict[ImageId, np.ndarray], - update_rule_name: Literal[ - 'Prototype', - 'Square', - 'Perceptron', - 'Hinge', - 'MAE', - 'Exponential', - 'CE', - 'REINFORCE' - ] = 'Square', - alpha: float = 1, + ref_to_features: dict[ImageId, np.ndarray], + update_rule_name: Literal[ + "Prototype", + "Square", + "Perceptron", + "Hinge", + "MAE", + "Exponential", + "CE", + "REINFORCE", + ] = "Square", + alpha: float = 1, ) -> LinearLearner: """ Instantiates a linear learning model from precomputed features. @@ -47,7 +48,9 @@ def make_linear_learner_from_features( :return: LinearLearner """ - f_calibration = np.array([ref_to_features[ref] for ref in _get_calibration_image_ids()]) + f_calibration = np.array( + [ref_to_features[ref] for ref in _get_calibration_image_ids()] + ) mu_calibration = np.mean(f_calibration, axis=0) norms_calibration = np.linalg.norm(f_calibration - mu_calibration, axis=1) norm_cutoff = np.quantile(norms_calibration, 0.999) # Will clip the rest @@ -67,5 +70,5 @@ def make_linear_learner_from_features( representational_model=RepresentationalModel.from_precomputed_features( image_ref_to_features=ref_to_calibrated_features ), - update_rule=update_rule_name(alpha=alpha) + update_rule=update_rule_name(alpha=alpha), ) diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index cc09678..d600050 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -2,15 +2,21 @@ import numpy as np -from hobj.benchmarks.binary_classification.benchmark import LearningCurveBenchmark, LearningCurveBenchmarkConfig, TargetSubtaskData -from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask, BinaryClassificationSubtaskResult +from hobj.benchmarks.binary_classification.benchmark import ( + LearningCurveBenchmark, + LearningCurveBenchmarkConfig, + TargetSubtaskData, +) +from hobj.benchmarks.binary_classification.simulation import ( + BinaryClassificationSubtask, + BinaryClassificationSubtaskResult, +) from hobj.data_loaders.behavior import load_highvar_behavior from hobj.data_loaders.images import load_imageset_meta_highvar # %% class MutatorHighVarBenchmark(LearningCurveBenchmark): - def __init__(self): # Load data into format expected by benchmark @@ -22,10 +28,10 @@ def __init__(self): # Normalize data for benchmark: image_id_to_category = dict( - zip(images_df['image_id'], images_df['category'], strict=True) + zip(images_df["image_id"], images_df["category"], strict=True) ) category_to_image_ids = ( - images_df.groupby('category', sort=False)['image_id'] + images_df.groupby("category", sort=False)["image_id"] .apply(lambda image_ids: sorted(image_ids.tolist())) .to_dict() ) @@ -34,20 +40,21 @@ def __init__(self): subtask_name_to_subtask = {} # Iterate over worker sessions: - for assignment_id, session in sessions.groupby('assignment_id', sort=False): - + for assignment_id, session in sessions.groupby("assignment_id", sort=False): categories = set() perf_seq: List[bool] = [] # Iterate over trials: - session = session.sort_values('trial') + session = session.sort_values("trial") for reward, image_id in zip(session.perf, session.image_id): categories.add(image_id_to_category[image_id]) perf_seq.append(bool(reward)) # Infer subtask from the categories observed in the session: - assert len(categories) == 2, f"Expected two categories, but got {categories}" - subtask_name = ','.join(sorted(categories)) + assert ( + len(categories) == 2 + ), f"Expected two categories, but got {categories}" + subtask_name = ",".join(sorted(categories)) cat0, cat1 = sorted(categories) # Instantiate the subtask if it does not exist: @@ -63,20 +70,21 @@ def __init__(self): subtask_name_to_results[subtask_name].append( BinaryClassificationSubtaskResult( - perf_seq = np.array(perf_seq), - worker_id = session.worker_id.iloc[0] + perf_seq=np.array(perf_seq), worker_id=session.worker_id.iloc[0] ) ) # Cast as numpy for name in subtask_name_to_results: - subtask_name_to_results[name] = np.array(subtask_name_to_results[name]) # [session, trial] + subtask_name_to_results[name] = np.array( + subtask_name_to_results[name] + ) # [session, trial] subtask_name_to_data: Dict[str, TargetSubtaskData] = {} for name in subtask_name_to_results: subtask_name_to_data[name] = TargetSubtaskData( subtask=subtask_name_to_subtask[name], - results=subtask_name_to_results[name] + results=subtask_name_to_results[name], ) # Instantiate benchmark config object @@ -85,14 +93,13 @@ def __init__(self): num_simulations_per_subtask=500, num_bootstrap_samples=1000, bootstrap_by_worker=False, - ntrials = 100 + ntrials=100, ) - super().__init__( - config=config - ) + super().__init__(config=config) + # %% -if __name__ == '__main__': +if __name__ == "__main__": experiment = MutatorHighVarBenchmark() print(sorted(experiment.config.subtask_name_to_data.keys())) diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index f2a9855..07b3345 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -1,7 +1,11 @@ import collections from typing import Dict, List -from hobj.benchmarks.generalization.benchmark import GeneralizationBenchmark, GeneralizationBenchmarkConfig, GeneralizationSessionResult +from hobj.benchmarks.generalization.benchmark import ( + GeneralizationBenchmark, + GeneralizationBenchmarkConfig, + GeneralizationSessionResult, +) from hobj.benchmarks.generalization.estimator import GeneralizationStatistics from hobj.benchmarks.generalization.simulator import GeneralizationSubtask from hobj.data_loaders.behavior import load_oneshot_behavior @@ -12,78 +16,79 @@ # %% + class MutatorOneshotBenchmark(GeneralizationBenchmark): subtask_names = [ - 'MutatorOneshotObject00,MutatorOneshotObject43', - 'MutatorOneshotObject01,MutatorOneshotObject37', - 'MutatorOneshotObject02,MutatorOneshotObject36', - 'MutatorOneshotObject03,MutatorOneshotObject55', - 'MutatorOneshotObject04,MutatorOneshotObject27', - 'MutatorOneshotObject05,MutatorOneshotObject17', - 'MutatorOneshotObject06,MutatorOneshotObject14', - 'MutatorOneshotObject07,MutatorOneshotObject50', - 'MutatorOneshotObject08,MutatorOneshotObject26', - 'MutatorOneshotObject09,MutatorOneshotObject58', - 'MutatorOneshotObject10,MutatorOneshotObject44', - 'MutatorOneshotObject11,MutatorOneshotObject32', - 'MutatorOneshotObject12,MutatorOneshotObject31', - 'MutatorOneshotObject13,MutatorOneshotObject41', - 'MutatorOneshotObject15,MutatorOneshotObject19', - 'MutatorOneshotObject16,MutatorOneshotObject28', - 'MutatorOneshotObject18,MutatorOneshotObject21', - 'MutatorOneshotObject20,MutatorOneshotObject47', - 'MutatorOneshotObject22,MutatorOneshotObject48', - 'MutatorOneshotObject23,MutatorOneshotObject35', - 'MutatorOneshotObject24,MutatorOneshotObject29', - 'MutatorOneshotObject25,MutatorOneshotObject40', - 'MutatorOneshotObject30,MutatorOneshotObject61', - 'MutatorOneshotObject33,MutatorOneshotObject38', - 'MutatorOneshotObject34,MutatorOneshotObject57', - 'MutatorOneshotObject39,MutatorOneshotObject52', - 'MutatorOneshotObject42,MutatorOneshotObject59', - 'MutatorOneshotObject45,MutatorOneshotObject53', - 'MutatorOneshotObject46,MutatorOneshotObject62', - 'MutatorOneshotObject49,MutatorOneshotObject51', - 'MutatorOneshotObject54,MutatorOneshotObject63', - 'MutatorOneshotObject56,MutatorOneshotObject60' + "MutatorOneshotObject00,MutatorOneshotObject43", + "MutatorOneshotObject01,MutatorOneshotObject37", + "MutatorOneshotObject02,MutatorOneshotObject36", + "MutatorOneshotObject03,MutatorOneshotObject55", + "MutatorOneshotObject04,MutatorOneshotObject27", + "MutatorOneshotObject05,MutatorOneshotObject17", + "MutatorOneshotObject06,MutatorOneshotObject14", + "MutatorOneshotObject07,MutatorOneshotObject50", + "MutatorOneshotObject08,MutatorOneshotObject26", + "MutatorOneshotObject09,MutatorOneshotObject58", + "MutatorOneshotObject10,MutatorOneshotObject44", + "MutatorOneshotObject11,MutatorOneshotObject32", + "MutatorOneshotObject12,MutatorOneshotObject31", + "MutatorOneshotObject13,MutatorOneshotObject41", + "MutatorOneshotObject15,MutatorOneshotObject19", + "MutatorOneshotObject16,MutatorOneshotObject28", + "MutatorOneshotObject18,MutatorOneshotObject21", + "MutatorOneshotObject20,MutatorOneshotObject47", + "MutatorOneshotObject22,MutatorOneshotObject48", + "MutatorOneshotObject23,MutatorOneshotObject35", + "MutatorOneshotObject24,MutatorOneshotObject29", + "MutatorOneshotObject25,MutatorOneshotObject40", + "MutatorOneshotObject30,MutatorOneshotObject61", + "MutatorOneshotObject33,MutatorOneshotObject38", + "MutatorOneshotObject34,MutatorOneshotObject57", + "MutatorOneshotObject39,MutatorOneshotObject52", + "MutatorOneshotObject42,MutatorOneshotObject59", + "MutatorOneshotObject45,MutatorOneshotObject53", + "MutatorOneshotObject46,MutatorOneshotObject62", + "MutatorOneshotObject49,MutatorOneshotObject51", + "MutatorOneshotObject54,MutatorOneshotObject63", + "MutatorOneshotObject56,MutatorOneshotObject60", ] transformation_ids = [ - 'backgrounds | 0.1', - 'backgrounds | 0.215443', - 'backgrounds | 0.464159', - 'backgrounds | 1.0', - 'blur | 0.007812', - 'blur | 0.015625', - 'blur | 0.03125', - 'blur | 0.0625', - 'contrast | -0.4', - 'contrast | -0.8', - 'contrast | 0.4', - 'contrast | 0.8', - 'delpixels | 0.25', - 'delpixels | 0.5', - 'delpixels | 0.75', - 'delpixels | 0.95', - 'inplanerotation | 135.0', - 'inplanerotation | 180.0', - 'inplanerotation | 45.0', - 'inplanerotation | 90.0', - 'inplanetranslation | 0.125', - 'inplanetranslation | 0.25', - 'inplanetranslation | 0.5', - 'inplanetranslation | 0.75', - 'noise | 0.125', - 'noise | 0.25', - 'noise | 0.375', - 'noise | 0.5', - 'outplanerotation | 135.0', - 'outplanerotation | 180.0', - 'outplanerotation | 45.0', - 'outplanerotation | 90.0', - 'scale | 0.125', - 'scale | 0.25', - 'scale | 0.5', - 'scale | 1.5' + "backgrounds | 0.1", + "backgrounds | 0.215443", + "backgrounds | 0.464159", + "backgrounds | 1.0", + "blur | 0.007812", + "blur | 0.015625", + "blur | 0.03125", + "blur | 0.0625", + "contrast | -0.4", + "contrast | -0.8", + "contrast | 0.4", + "contrast | 0.8", + "delpixels | 0.25", + "delpixels | 0.5", + "delpixels | 0.75", + "delpixels | 0.95", + "inplanerotation | 135.0", + "inplanerotation | 180.0", + "inplanerotation | 45.0", + "inplanerotation | 90.0", + "inplanetranslation | 0.125", + "inplanetranslation | 0.25", + "inplanetranslation | 0.5", + "inplanetranslation | 0.75", + "noise | 0.125", + "noise | 0.25", + "noise | 0.375", + "noise | 0.5", + "outplanerotation | 135.0", + "outplanerotation | 180.0", + "outplanerotation | 45.0", + "outplanerotation | 90.0", + "scale | 0.125", + "scale | 0.25", + "scale | 0.5", + "scale | 1.5", ] def __init__(self): @@ -92,35 +97,37 @@ def __init__(self): # Load image manifest images_df = load_imageset_meta_oneshot() - image_id_to_row = images_df.set_index('image_id') + image_id_to_row = images_df.set_index("image_id") # Map image refs to transformation ids image_ref_to_transformation_id: Dict[ImageId, str] = {} cat_to_support_image: Dict[str, ImageId] = {} cat_to_test_images: Dict[str, List[ImageId]] = {} - for row in images_df.to_dict(orient='records'): - image_id = row['image_id'] - transformation_id = f"{row['transformation']} | {row['transformation_level']}" + for row in images_df.to_dict(orient="records"): + image_id = row["image_id"] + transformation_id = ( + f"{row['transformation']} | {row['transformation_level']}" + ) image_ref_to_transformation_id[image_id] = transformation_id - if row['transformation'] == 'original': - if row['category'] not in cat_to_support_image: - cat_to_support_image[row['category']] = image_id + if row["transformation"] == "original": + if row["category"] not in cat_to_support_image: + cat_to_support_image[row["category"]] = image_id else: raise ValueError( f"Multiple support images for category {row['category']}" ) else: - if row['category'] not in cat_to_test_images: - cat_to_test_images[row['category']] = [] + if row["category"] not in cat_to_test_images: + cat_to_test_images[row["category"]] = [] - cat_to_test_images[row['category']].append(image_id) + cat_to_test_images[row["category"]].append(image_id) # Assemble subtask simulators subtasks = [] for subtask_name in self.subtask_names: - catA, catB = sorted(subtask_name.split(',')) + catA, catB = sorted(subtask_name.split(",")) support_imageA = cat_to_support_image[catA] support_imageB = cat_to_support_image[catB] @@ -133,7 +140,7 @@ def __init__(self): support_imageB=support_imageB, test_imagesA=test_imagesA, test_imagesB=test_imagesB, - image_ref_to_transformation=image_ref_to_transformation_id + image_ref_to_transformation=image_ref_to_transformation_id, ) subtasks.append(subtask) @@ -142,7 +149,9 @@ def __init__(self): results = [] - for session, session_df in oneshot_df.groupby(['assignment_id', 'slot'], sort=False): + for session, session_df in oneshot_df.groupby( + ["assignment_id", "slot"], sort=False + ): session_df = session_df.sort_values("trial") transformation_to_kn = collections.defaultdict(lambda: [0, 0]) kcatch = 0 @@ -157,13 +166,13 @@ def __init__(self): # Record performance in relevant slot if i_trial in support_trials: - assert annotation['transformation'] == 'original' + assert annotation["transformation"] == "original" elif i_trial in catch_trials: - assert annotation['transformation'] == 'original' + assert annotation["transformation"] == "original" kcatch += perf ncatch += 1 else: - assert annotation['transformation'] != 'original' + assert annotation["transformation"] != "original" transformation_id = image_ref_to_transformation_id[image_id] # Keep only benchmarked transformations @@ -173,7 +182,7 @@ def __init__(self): # Infer subtask name from observed categories assert len(observed_categories) == 2 - subtask_name = ','.join(sorted(observed_categories)) + subtask_name = ",".join(sorted(observed_categories)) if subtask_name not in self.subtask_names: raise ValueError(f"Unexpected subtask name: {subtask_name}") @@ -182,7 +191,7 @@ def __init__(self): transformation_to_kn=transformation_to_kn, kcatch=kcatch, ncatch=ncatch, - worker_id=session_df["worker_id"].iloc[0] + worker_id=session_df["worker_id"].iloc[0], ) results.append(result) @@ -199,13 +208,24 @@ def __init__(self): @property def target_statistics(self) -> GeneralizationStatistics: - gen_statistics: GeneralizationStatistics = self._generalization_statistics return gen_statistics.assign_coords( - transformation_type=(['transformation'], [transformation.split(' | ')[0] for transformation in gen_statistics.transformation.values]), - transformation_level=(['transformation'], [float(transformation.split(' | ')[1]) for transformation in gen_statistics.transformation.values]) + transformation_type=( + ["transformation"], + [ + transformation.split(" | ")[0] + for transformation in gen_statistics.transformation.values + ], + ), + transformation_level=( + ["transformation"], + [ + float(transformation.split(" | ")[1]) + for transformation in gen_statistics.transformation.values + ], + ), ) -if __name__ == '__main__': +if __name__ == "__main__": benchmark = MutatorOneshotBenchmark() diff --git a/hobj/data_loaders/behavior.py b/hobj/data_loaders/behavior.py index 0c0d127..c13641e 100644 --- a/hobj/data_loaders/behavior.py +++ b/hobj/data_loaders/behavior.py @@ -4,22 +4,26 @@ from typing import Literal + # %% def load_human_behavior( - experiment: Literal['highvar', 'oneshot'] = 'highvar', + experiment: Literal["highvar", "oneshot"] = "highvar", ) -> pd.DataFrame: - if experiment not in {'highvar', 'oneshot'}: - raise ValueError(f"Invalid experiment name: {experiment}. Provide either 'highvar' or 'oneshot'.") + if experiment not in {"highvar", "oneshot"}: + raise ValueError( + f"Invalid experiment name: {experiment}. Provide either 'highvar' or 'oneshot'." + ) - if experiment == 'highvar': + if experiment == "highvar": return load_highvar_behavior() else: return load_oneshot_behavior() + # %% Data loaders def load_highvar_behavior( - remove_probe_trials: bool = True, - cachedir: Path | None = None, + remove_probe_trials: bool = True, + cachedir: Path | None = None, ) -> pd.DataFrame: """Load trial-level human learning data from Experiment 1. @@ -33,8 +37,8 @@ def load_highvar_behavior( ``stimulus_id`` normalized to ``image_id``. """ repo_root = Path(__file__).resolve().parents[2] - cache_root = (cachedir if cachedir is not None else repo_root / 'data').resolve() - dataset_path = cache_root / 'behavior' / 'human-behavior-highvar-subtasks.csv' + cache_root = (cachedir if cachedir is not None else repo_root / "data").resolve() + dataset_path = cache_root / "behavior" / "human-behavior-highvar-subtasks.csv" if not dataset_path.exists(): raise FileNotFoundError( "Expected cached highvar behavior CSV to already exist at:\n" @@ -44,33 +48,34 @@ def load_highvar_behavior( df = pd.read_csv(dataset_path) required_columns = { - 'trial', - 'assignment_id', - 'worker_id', - 'subtask', - 'image_id', - 'trial_type', - 'stimulus_duration_msec', - 'reaction_time_msec', - 'timed_out', - 'perf', - 'timestamp_start', + "trial", + "assignment_id", + "worker_id", + "subtask", + "image_id", + "trial_type", + "stimulus_duration_msec", + "reaction_time_msec", + "timed_out", + "perf", + "timestamp_start", } missing_columns = required_columns - set(df.columns) if missing_columns: - raise ValueError(f"Highvar behavior CSV missing required columns: {sorted(missing_columns)}") - + raise ValueError( + f"Highvar behavior CSV missing required columns: {sorted(missing_columns)}" + ) if remove_probe_trials: - df = df.loc[df['trial_type'] != 'probe'].copy() - df = df.sort_values(['assignment_id', 'trial']).copy() - df['trial'] = df.groupby('assignment_id').cumcount() + df = df.loc[df["trial_type"] != "probe"].copy() + df = df.sort_values(["assignment_id", "trial"]).copy() + df["trial"] = df.groupby("assignment_id").cumcount() return df def load_oneshot_behavior( - cachedir: Path | None = None, + cachedir: Path | None = None, ) -> pd.DataFrame: """Load trial-level human learning data from Experiment 2. @@ -82,8 +87,8 @@ def load_oneshot_behavior( ``stimulus_id`` normalized to ``image_id``. """ repo_root = Path(__file__).resolve().parents[2] - cache_root = (cachedir if cachedir is not None else repo_root / 'data').resolve() - dataset_path = cache_root / 'behavior' / 'human-behavior-oneshot-subtasks.csv' + cache_root = (cachedir if cachedir is not None else repo_root / "data").resolve() + dataset_path = cache_root / "behavior" / "human-behavior-oneshot-subtasks.csv" if not dataset_path.exists(): raise FileNotFoundError( "Expected cached oneshot behavior CSV to already exist at:\n" @@ -93,22 +98,23 @@ def load_oneshot_behavior( df = pd.read_csv(dataset_path) required_columns = { - 'trial', - 'assignment_id', - 'slot', - 'worker_id', - 'subtask', - 'image_id', - 'trial_type', - 'stimulus_duration_msec', - 'reaction_time_msec', - 'timed_out', - 'perf', - 'timestamp_start', + "trial", + "assignment_id", + "slot", + "worker_id", + "subtask", + "image_id", + "trial_type", + "stimulus_duration_msec", + "reaction_time_msec", + "timed_out", + "perf", + "timestamp_start", } missing_columns = required_columns - set(df.columns) if missing_columns: - raise ValueError(f"Oneshot behavior CSV missing required columns: {sorted(missing_columns)}") + raise ValueError( + f"Oneshot behavior CSV missing required columns: {sorted(missing_columns)}" + ) return df - diff --git a/hobj/data_loaders/images.py b/hobj/data_loaders/images.py index c366ebf..c5c42f0 100644 --- a/hobj/data_loaders/images.py +++ b/hobj/data_loaders/images.py @@ -12,22 +12,20 @@ def _image_id_to_local_path_table() -> dict[ImageId, Path]: """Build a mapping from image_id to absolute local path for all packaged images.""" repo_root = Path(__file__).resolve().parents[2] - cache_root = repo_root / 'data' - manifest_paths = list(cache_root.glob('meta-*.csv')) + cache_root = repo_root / "data" + manifest_paths = list(cache_root.glob("meta-*.csv")) table = {} for manifest_path in manifest_paths: manifest_df = pd.read_csv(manifest_path) for _, row in manifest_df.iterrows(): - image_id = row['image_id'] - relpath = row['relpath'] + image_id = row["image_id"] + relpath = row["relpath"] abs_path = cache_root / relpath table[image_id] = abs_path return table -def load_image( - image_id: ImageId -) -> Image: +def load_image(image_id: ImageId) -> Image: path = _image_id_to_local_path_table().get(image_id) if path is None: raise ValueError(f"Image ID not found in any manifest: {image_id}") @@ -35,11 +33,12 @@ def load_image( raise FileNotFoundError(f"Expected image file to already exist at: {path}") return Image.open(path) + def _load_image_manifest( - *, - dataset_name: str, - required_columns: set[str], - cachedir: Path | None = None, + *, + dataset_name: str, + required_columns: set[str], + cachedir: Path | None = None, ) -> pd.DataFrame: """Load a packaged image manifest and attach absolute image paths. @@ -55,8 +54,8 @@ def _load_image_manifest( FileNotFoundError: If the packaged manifest or images are missing. """ repo_root = Path(__file__).resolve().parents[2] - cache_root = (cachedir if cachedir is not None else repo_root / 'data').resolve() - manifest_path = cache_root / f'meta-{dataset_name}.csv' + cache_root = (cachedir if cachedir is not None else repo_root / "data").resolve() + manifest_path = cache_root / f"meta-{dataset_name}.csv" if not manifest_path.exists(): raise FileNotFoundError( @@ -72,7 +71,9 @@ def _load_image_manifest( ) manifest_df = manifest_df.copy() - missing_paths = manifest_df.loc[~manifest_df['relpath'].map(lambda p: Path.exists(cache_root / p)), 'relpath'] + missing_paths = manifest_df.loc[ + ~manifest_df["relpath"].map(lambda p: Path.exists(cache_root / p)), "relpath" + ] if not missing_paths.empty: raise FileNotFoundError( "Expected packaged images to already exist under:\n" @@ -83,59 +84,60 @@ def _load_image_manifest( def load_imageset_meta_highvar( - cachedir: Path | None = None, + cachedir: Path | None = None, ) -> pd.DataFrame: """Load the high-variance image manifest.""" manifest_df = _load_image_manifest( - dataset_name='MutatorHighVarImageset', - required_columns={'image_id', 'category', 'sha256', 'relpath'}, + dataset_name="MutatorHighVarImageset", + required_columns={"image_id", "category", "sha256", "relpath"}, cachedir=cachedir, ) - return manifest_df.sort_values('image_id').reset_index(drop=True) + return manifest_df.sort_values("image_id").reset_index(drop=True) def load_imageset_meta_oneshot( - cachedir: Path | None = None, + cachedir: Path | None = None, ) -> pd.DataFrame: """Load the one-shot image manifest.""" manifest_df = _load_image_manifest( - dataset_name='MutatorOneShotImageset', + dataset_name="MutatorOneShotImageset", required_columns={ - 'image_id', - 'category', - 'transformation', - 'transformation_level', - 'base_image_id', - 'sha256', - 'relpath', + "image_id", + "category", + "transformation", + "transformation_level", + "base_image_id", + "sha256", + "relpath", }, cachedir=cachedir, ) - return manifest_df.sort_values('image_id').reset_index(drop=True) + return manifest_df.sort_values("image_id").reset_index(drop=True) def load_imageset_meta_warmup( - cachedir: Path | None = None, + cachedir: Path | None = None, ) -> pd.DataFrame: """Load the warmup image manifest.""" manifest_df = _load_image_manifest( - dataset_name='MutatorWarmupImageset', - required_columns={'image_id', 'category', 'sha256', 'relpath'}, + dataset_name="MutatorWarmupImageset", + required_columns={"image_id", "category", "sha256", "relpath"}, cachedir=cachedir, ) - return manifest_df.sort_values('image_id').reset_index(drop=True) + return manifest_df.sort_values("image_id").reset_index(drop=True) def load_imageset_meta_catch( - cachedir: Path | None = None, + cachedir: Path | None = None, ) -> pd.DataFrame: """Load the probe image manifest.""" manifest_df = _load_image_manifest( - dataset_name='CatchImageset', - required_columns={'image_id', 'sha256', 'relpath'}, + dataset_name="CatchImageset", + required_columns={"image_id", "sha256", "relpath"}, cachedir=cachedir, ) - return manifest_df.sort_values('image_id').reset_index(drop=True) + return manifest_df.sort_values("image_id").reset_index(drop=True) + -if __name__ == '__main__': +if __name__ == "__main__": df = load_imageset_meta_oneshot() diff --git a/hobj/learning_models/__init__.py b/hobj/learning_models/__init__.py index ad3411b..a88682b 100644 --- a/hobj/learning_models/__init__.py +++ b/hobj/learning_models/__init__.py @@ -21,8 +21,8 @@ def reset_state(self, seed: typing.Union[int, None]) -> None: @abstractmethod def get_response( - self, - image: ImageId, + self, + image: ImageId, ) -> typing.Literal[0, 1]: """ This function takes the current stimulus image (given either as a PIL.Image or a ImageRef) and returns one of two possible actions (parameterized by an integer). @@ -53,8 +53,8 @@ def reset_state(self, seed: typing.Union[int, None]) -> None: self.random_generator = np.random.default_rng(seed=seed) def get_response( - self, - image: ImageId, + self, + image: ImageId, ) -> typing.Literal[0, 1]: action = self.random_generator.integers(2) action = int(action) @@ -67,11 +67,10 @@ def give_feedback(self, reward: float) -> None: # %% class LinearLearner(BinaryLearningModel): def __init__( - self, - representational_model: RepresentationalModel, - update_rule: UpdateRule, + self, + representational_model: RepresentationalModel, + update_rule: UpdateRule, ): - self.representational_model = representational_model self.update_rule = update_rule @@ -103,10 +102,9 @@ def reset_state(self, seed: int) -> None: return def get_response( - self, - image: ImageId, + self, + image: ImageId, ) -> typing.Literal[0, 1]: - f = self.representational_model.get_features(image=image) logits = f @ self.w + self.b action = self._random_tiebreaking_argmax(logits[0], logits[1]) @@ -118,7 +116,14 @@ def get_response( return action def give_feedback(self, reward: float) -> None: - delta_w, delta_b = self.update_rule.get_update(x=self._f_last, w=self.w, b=self.b, logits=self._logits_last, action=self._action_last, reward=reward) # [action] + delta_w, delta_b = self.update_rule.get_update( + x=self._f_last, + w=self.w, + b=self.b, + logits=self._logits_last, + action=self._action_last, + reward=reward, + ) # [action] self.w += delta_w self.b += delta_b return diff --git a/hobj/learning_models/representation.py b/hobj/learning_models/representation.py index bd3d1f4..7a3dd69 100644 --- a/hobj/learning_models/representation.py +++ b/hobj/learning_models/representation.py @@ -12,11 +12,10 @@ class RepresentationalModel: """ def __init__( - self, - d: int, - image_to_features_func: Callable[[ImageId], np.ndarray], + self, + d: int, + image_to_features_func: Callable[[ImageId], np.ndarray], ): - if not isinstance(d, int): raise ValueError(f"Expected d to be an int, but got {d} of type {type(d)}") if d <= 0: @@ -30,10 +29,7 @@ def __init__( def d(self) -> int: return self._d - def get_features( - self, - image: ImageId - ) -> np.ndarray: + def get_features(self, image: ImageId) -> np.ndarray: """ Returns a feature vector for the image_url. To work successfully with UpdateRule, the feature vector must be of shape (d,) and have norm 1. @@ -45,16 +41,16 @@ def get_features( f = self._image_to_features_func(image) if f.shape != (self.d,): - raise ValueError(f"Expected feature vector of shape {(self.d,)}, but got {f.shape}") + raise ValueError( + f"Expected feature vector of shape {(self.d,)}, but got {f.shape}" + ) return f @classmethod def from_precomputed_features( - cls, - image_ref_to_features: Dict[ImageId, np.ndarray] - ) -> 'RepresentationalModel': - + cls, image_ref_to_features: Dict[ImageId, np.ndarray] + ) -> "RepresentationalModel": """ Convenience method for creating a RepresentationalModel, when one has a precomputed dict which maps ImageRefs to np.ndarray feature vectors. @@ -69,7 +65,9 @@ def image_to_features_func(image: ImageId) -> np.ndarray: for ref in image_ref_to_features: f = image_ref_to_features[ref] if not isinstance(f, np.ndarray): - raise ValueError(f"Expected feature vector to be a np.ndarray, but got {f} of type {type(f)}") + raise ValueError( + f"Expected feature vector to be a np.ndarray, but got {f} of type {type(f)}" + ) if not len(f.shape) == 1: raise ValueError(f"Expected feature vector to be 1D, but got {f.shape}") @@ -77,6 +75,8 @@ def image_to_features_func(image: ImageId) -> np.ndarray: d = f.shape[0] if not f.shape[0] == d: - raise ValueError(f"Expected feature vector to be of shape ({d},), but got {f.shape}") + raise ValueError( + f"Expected feature vector to be of shape ({d},), but got {f.shape}" + ) return cls(d=d, image_to_features_func=image_to_features_func) diff --git a/hobj/learning_models/update_rules.py b/hobj/learning_models/update_rules.py index 3297e26..5a8c0b1 100644 --- a/hobj/learning_models/update_rules.py +++ b/hobj/learning_models/update_rules.py @@ -27,13 +27,13 @@ def reset(self) -> None: @abstractmethod def get_update( - self, - x: np.ndarray, - w: np.ndarray, - b: Union[np.ndarray, np.generic, float], - logits: np.ndarray, - action: int, - reward: float + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float, ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: """ Returns delta_w and delta_b, where @@ -63,13 +63,13 @@ def reset(self): self.ncounts = None def get_update( - self, - x: np.ndarray, - w: np.ndarray, - b: Union[np.ndarray, np.generic, float], - logits: np.ndarray, - action: int, - reward: float + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float, ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: if self.ncounts is None: self.ncounts = np.zeros(w.shape[1]) @@ -99,15 +99,14 @@ def get_update( class Square(UpdateRule): - def get_update( - self, - x: np.ndarray, - w: np.ndarray, - b: Union[np.ndarray, np.generic, float], - logits: np.ndarray, - action: int, - reward: float + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float, ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: delta = np.zeros(w.shape) @@ -119,15 +118,14 @@ def get_update( class Perceptron(UpdateRule): - def get_update( - self, - x: np.ndarray, - w: np.ndarray, - b: Union[np.ndarray, np.generic, float], - logits: np.ndarray, - action: int, - reward: float + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float, ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: nactions = len(logits) gts = np.zeros(nactions) @@ -141,15 +139,14 @@ def get_update( class Hinge(UpdateRule): - def get_update( - self, - x: np.ndarray, - w: np.ndarray, - b: Union[np.ndarray, np.generic, float], - logits: np.ndarray, - action: int, - reward: float + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float, ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: nactions = len(logits) gts = np.zeros(nactions) @@ -163,17 +160,15 @@ def get_update( class MAE(UpdateRule): - def get_update( - self, - x: np.ndarray, - w: np.ndarray, - b: Union[np.ndarray, np.generic, float], - logits: np.ndarray, - action: int, - reward: float + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float, ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: - nactions = len(logits) reward_prediction = logits[action] @@ -191,13 +186,13 @@ class Exponential(UpdateRule): max_weight_norm = 10.0 def get_update( - self, - x: np.ndarray, - w: np.ndarray, - b: Union[np.ndarray, np.generic, float], - logits: np.ndarray, - action: int, - reward: float + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float, ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: nactions = len(logits) @@ -214,32 +209,32 @@ def get_update( class CE(UpdateRule): - def get_update( - self, - x: np.ndarray, - w: np.ndarray, - b: Union[np.ndarray, np.generic, float], - logits: np.ndarray, - action: int, - reward: float + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float, ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: nactions = len(logits) gts = np.zeros(nactions) reward_prediction = logits[action] - gts[action] = 4 * self.alpha * (reward / (1 + np.exp(reward * reward_prediction))) + gts[action] = ( + 4 * self.alpha * (reward / (1 + np.exp(reward * reward_prediction))) + ) delta = x[:, None] * gts[None, :] return delta, 0 class REINFORCE(UpdateRule): - def _compute_trace( - self, - action_taken: int, - logits: np.ndarray, + self, + action_taken: int, + logits: np.ndarray, ): """ Computes the negative derivative of the probability of taking each action with respect to logit[i] @@ -264,13 +259,13 @@ def _compute_trace( return expression def get_update( - self, - x: np.ndarray, - w: np.ndarray, - b: Union[np.ndarray, np.generic, float], - logits: np.ndarray, - action: int, - reward: float + self, + x: np.ndarray, + w: np.ndarray, + b: Union[np.ndarray, np.generic, float], + logits: np.ndarray, + action: int, + reward: float, ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: gt = self.alpha * reward * self._compute_trace(action, logits) diff --git a/hobj/stats/__init__.py b/hobj/stats/__init__.py index e6723e2..e69de29 100644 --- a/hobj/stats/__init__.py +++ b/hobj/stats/__init__.py @@ -1 +0,0 @@ -from hobj.stats.ci import estimate_basic_bootstrap_CI \ No newline at end of file diff --git a/hobj/stats/binomial.py b/hobj/stats/binomial.py index aad1b90..465d9e1 100644 --- a/hobj/stats/binomial.py +++ b/hobj/stats/binomial.py @@ -1,5 +1,4 @@ def estimate_variance_of_binomial_proportion(kvec, nvec): - """ Returns the unbiased estimate of the variance associated with the proportion estimator, phat = k/n: :param kvec: diff --git a/hobj/stats/ci.py b/hobj/stats/ci.py index 13979a5..83af88a 100644 --- a/hobj/stats/ci.py +++ b/hobj/stats/ci.py @@ -4,9 +4,9 @@ def estimate_basic_bootstrap_CI( - alpha: float, - point_estimate: Union[np.ndarray, np.generic, float, int], # [*] - bootstrapped_point_estimates: np.ndarray, # [n_bootstraps, *] + alpha: float, + point_estimate: Union[np.ndarray, np.generic, float, int], # [*] + bootstrapped_point_estimates: np.ndarray, # [n_bootstraps, *] ) -> Tuple[float, float]: """ Estimates the basic confidence interval for a given point estimate(s) using the bootstrap method. @@ -17,14 +17,16 @@ def estimate_basic_bootstrap_CI( """ if not 0 < alpha < 1: - raise ValueError(f'alpha must be in the range (0, 1); got {alpha:.2f}') + raise ValueError(f"alpha must be in the range (0, 1); got {alpha:.2f}") # Coerce point_estimate to a numpy array point_estimate = np.array(point_estimate) # [*] # Ensure the dimensions are as expected if not len(point_estimate.shape) == len(bootstrapped_point_estimates.shape) - 1: - raise ValueError(f'The dimensions of the point estimate and bootstrapped point estimates do not match: {point_estimate.shape} vs {bootstrapped_point_estimates.shape}') + raise ValueError( + f"The dimensions of the point estimate and bootstrapped point estimates do not match: {point_estimate.shape} vs {bootstrapped_point_estimates.shape}" + ) # Calculate confidence interval using the basic bootstrap method low = np.quantile(bootstrapped_point_estimates, alpha / 2, axis=0) # [*] diff --git a/hobj/utils/file_io.py b/hobj/utils/file_io.py index 6778353..12ff671 100644 --- a/hobj/utils/file_io.py +++ b/hobj/utils/file_io.py @@ -16,7 +16,7 @@ def unzip_file(zip_path: Path, output_dir: Path) -> None: :return: """ - if not zip_path.as_posix().endswith('.zip'): + if not zip_path.as_posix().endswith(".zip"): raise ValueError(f"Expected a .zip file, got {zip_path}") if not zip_path.exists(): raise FileNotFoundError(f"Zip file {zip_path} does not exist.") @@ -24,21 +24,27 @@ def unzip_file(zip_path: Path, output_dir: Path) -> None: if not output_dir.exists(): output_dir.mkdir(parents=True) - with zipfile.ZipFile(zip_path, 'r') as zip_ref: + with zipfile.ZipFile(zip_path, "r") as zip_ref: zip_ref.extractall(output_dir) def download_file(url: str, output_path: Path) -> None: # Send a GET request to fetch the file response = requests.get(url, stream=True) - total_size_in_bytes = int(response.headers.get('content-length', 0)) + total_size_in_bytes = int(response.headers.get("content-length", 0)) # Create the output directory if it does not exist if not output_path.parent.exists(): output_path.parent.mkdir(parents=True) - with tqdm(total=total_size_in_bytes, unit='B', unit_scale=True, disable=False, desc='Download progress') as progress_bar: - with open(output_path.as_posix(), 'wb') as file: + with tqdm( + total=total_size_in_bytes, + unit="B", + unit_scale=True, + disable=False, + desc="Download progress", + ) as progress_bar: + with open(output_path.as_posix(), "wb") as file: # Iterate over the response data in chunks and write to file for chunk in response.iter_content(chunk_size=1024): if chunk: @@ -55,23 +61,18 @@ def get_bytes_size(num_bytes: int, output_units: str = None) -> (float, str): :return: """ - unit_conversion = { - 'B': 1, - 'KB': 1e3, - 'MB': 1e6, - 'GB': 1e9 - } + unit_conversion = {"B": 1, "KB": 1e3, "MB": 1e6, "GB": 1e9} # Automatically select the byte unit (KB, MB, etc.) if output_units is None: if num_bytes == 0: - output_units = 'B' + output_units = "B" elif num_bytes < 1e6: - output_units = 'KB' + output_units = "KB" elif num_bytes < 1e9: - output_units = 'MB' + output_units = "MB" else: - output_units = 'GB' + output_units = "GB" # Convert the size to the selected unit size = num_bytes / unit_conversion[output_units] @@ -87,7 +88,7 @@ def download_json(url: str) -> Any: response = urllib.request.urlopen(url) if response.status != 200: raise ValueError(f"Could not load JSON data from {url}", response) - data = response.read().decode('utf-8') + data = response.read().decode("utf-8") json_data = json.loads(data) return json_data diff --git a/hobj/utils/hash.py b/hobj/utils/hash.py index f321fbe..ddf82b5 100644 --- a/hobj/utils/hash.py +++ b/hobj/utils/hash.py @@ -13,7 +13,7 @@ def hash_image(image: PIL.Image.Image) -> str: sha256_hash = hashlib.sha256() # Always cast the image to RGBA format - image = image.convert('RGBA') + image = image.convert("RGBA") # Convert the image to a numpy array image_array = np.array(image).astype(np.uint8) @@ -29,7 +29,7 @@ def hash_string(input_string: str) -> str: sha256_hash = hashlib.sha256() # Convert string to bytes - sha256_hash.update(input_string.encode('utf-8')) + sha256_hash.update(input_string.encode("utf-8")) # Return the hexadecimal representation of the hash return sha256_hash.hexdigest() diff --git a/site/readme_images/run_make_images.py b/site/readme_images/run_make_images.py index c7ec90c..96a9aaa 100644 --- a/site/readme_images/run_make_images.py +++ b/site/readme_images/run_make_images.py @@ -1,5 +1,4 @@ -if __name__ == '__main__': - +if __name__ == "__main__": # %% import matplotlib.pyplot as plt from hobj.benchmarks import MutatorHighVarBenchmark @@ -17,34 +16,29 @@ y = target_stats.phat.sel(subtask=subtask) yerr = np.sqrt(target_stats.varhat_phat.sel(subtask=subtask)) plt.plot( - x, y, + x, + y, alpha=0.05, - color='gray', + color="gray", lw=1, ) - glc = target_stats.phat.mean(dim='subtask') - glc_se = target_stats.boot_phat.mean('subtask').std('boot_iter', ddof=1) - - plt.errorbar( - x, - glc, - yerr=glc_se, - color='blue', - label='human average' - ) - plt.axhline(0.5, ls=':', color='black', label='chance', zorder=0) - plt.ylabel('test accuracy') - plt.xscale('log') - plt.xlabel('# examples') - plt.title('Human learning curves') + glc = target_stats.phat.mean(dim="subtask") + glc_se = target_stats.boot_phat.mean("subtask").std("boot_iter", ddof=1) + + plt.errorbar(x, glc, yerr=glc_se, color="blue", label="human average") + plt.axhline(0.5, ls=":", color="black", label="chance", zorder=0) + plt.ylabel("test accuracy") + plt.xscale("log") + plt.xlabel("# examples") + plt.title("Human learning curves") plt.xlim([None, 100]) plt.legend(loc=(1.05, 0.5)) # Remove top and right spines - plt.gca().spines['top'].set_visible(False) - plt.gca().spines['right'].set_visible(False) + plt.gca().spines["top"].set_visible(False) + plt.gca().spines["right"].set_visible(False) plt.tight_layout() - plt.savefig('./human_learning_curves.svg', transparent=True) + plt.savefig("./human_learning_curves.svg", transparent=True) plt.show() diff --git a/tests/test_dummy_learner.py b/tests/test_dummy_learner.py index f164712..ef6bd88 100644 --- a/tests/test_dummy_learner.py +++ b/tests/test_dummy_learner.py @@ -2,18 +2,18 @@ from hobj.learning_models import RandomGuesser + @pytest.fixture def dummy_learner() -> RandomGuesser: return RandomGuesser(seed=0) def test_dummy_learner_deterministic(dummy_learner): - ntests = 10 actions = [] expected_actions = [1, 1, 1, 0, 0, 0, 0, 0, 0, 1] for i in range(ntests): - a = dummy_learner.get_response(image='hi') + a = dummy_learner.get_response(image="hi") actions.append(a) assert actions == expected_actions diff --git a/tests/test_load_behavior.py b/tests/test_load_behavior.py index 178a3ba..81b93d0 100644 --- a/tests/test_load_behavior.py +++ b/tests/test_load_behavior.py @@ -10,49 +10,55 @@ def test_load_highvar(): df = load_highvar_behavior() assert len(df) == 3199 * 100 - assert 'image_id' in df.columns - assert 'stimulus_id' not in df.columns + assert "image_id" in df.columns + assert "stimulus_id" not in df.columns def test_load_oneshot(): df = load_oneshot_behavior() assert len(df) == 2547 * 20 - assert 'image_id' in df.columns - assert 'stimulus_id' not in df.columns + assert "image_id" in df.columns + assert "stimulus_id" not in df.columns def test_load_highvar_images(): df = load_imageset_meta_highvar() assert len(df) == 12800 - assert {'image_id', 'category', 'sha256', 'relpath', 'image_path'} <= set(df.columns) - assert df['image_path'].iloc[0].exists() + assert {"image_id", "category", "sha256", "relpath", "image_path"} <= set( + df.columns + ) + assert df["image_path"].iloc[0].exists() def test_load_oneshot_images(): df = load_imageset_meta_oneshot() assert len(df) == 3904 assert { - 'image_id', - 'category', - 'transformation', - 'transformation_level', - 'base_image_id', - 'sha256', - 'relpath', - 'image_path', + "image_id", + "category", + "transformation", + "transformation_level", + "base_image_id", + "sha256", + "relpath", + "image_path", } <= set(df.columns) - assert df['image_path'].iloc[0].exists() + assert df["image_path"].iloc[0].exists() def test_load_warmup_images(): df = load_imageset_meta_warmup() assert len(df) == 400 - assert {'image_id', 'category', 'sha256', 'relpath', 'image_path'} <= set(df.columns) - assert df['image_path'].iloc[0].exists() + assert {"image_id", "category", "sha256", "relpath", "image_path"} <= set( + df.columns + ) + assert df["image_path"].iloc[0].exists() def test_load_probe_images(): df = load_imageset_meta_catch() assert len(df) == 2 - assert {'image_id', 'color', 'text', 'sha256', 'relpath', 'image_path'} <= set(df.columns) - assert df['image_path'].iloc[0].exists() + assert {"image_id", "color", "text", "sha256", "relpath", "image_path"} <= set( + df.columns + ) + assert df["image_path"].iloc[0].exists() diff --git a/tests/test_load_benchmarks.py b/tests/test_load_benchmarks.py deleted file mode 100644 index 937c3f6..0000000 --- a/tests/test_load_benchmarks.py +++ /dev/null @@ -1,5 +0,0 @@ - -def test_load_highvar_benchmark(): - import hobj - benchmark = hobj.benchmarks.MutatorHighVarBenchmark() - diff --git a/tests/test_simulate_subtask.py b/tests/test_simulate_subtask.py index c222803..4f61f15 100644 --- a/tests/test_simulate_subtask.py +++ b/tests/test_simulate_subtask.py @@ -11,7 +11,7 @@ def create_image_refs(nimages_per_class: int, seed: int) -> List[ImageId]: images = [] np.random.seed(seed) for i in range(nimages_per_class): - images.append(f'seed{seed}_image{i}') + images.append(f"seed{seed}_image{i}") return images @@ -29,9 +29,6 @@ def test_simulate_subtask(): learner = RandomGuesser(seed=0) - result = subtask.simulate_session( - learner=learner, - seed=0 - ) + result = subtask.simulate_session(learner=learner, seed=0) assert len(result.perf_seq) == ntrials From e364ba85097ddaddd73551d69b2cadb71617b17e Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 18:27:13 -0400 Subject: [PATCH 125/156] don't materialize --- examples/score_example_models.ipynb | 58 +- examples/view_experiment1_behavior.ipynb | 22 +- examples/view_experiment2_behavior.ipynb | 18 +- hobj/data_loaders/images.py | 12 +- site/readme_images/human_learning_curves.svg | 8880 +++++++++--------- site/readme_images/run_make_images.py | 2 +- tests/test_load_behavior.py | 27 +- 7 files changed, 4509 insertions(+), 4510 deletions(-) diff --git a/examples/score_example_models.ipynb b/examples/score_example_models.ipynb index 9d370e2..cf36c07 100644 --- a/examples/score_example_models.ipynb +++ b/examples/score_example_models.ipynb @@ -3,8 +3,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T21:49:51.681270Z", - "start_time": "2026-03-26T21:49:51.677437Z" + "end_time": "2026-03-26T22:23:46.112399Z", + "start_time": "2026-03-26T22:23:46.107394Z" } }, "cell_type": "code", @@ -19,14 +19,14 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T21:49:55.537556Z", - "start_time": "2026-03-26T21:49:51.683454Z" + "end_time": "2026-03-26T22:23:50.095180Z", + "start_time": "2026-03-26T22:23:46.113436Z" } }, "cell_type": "code", "source": [ "# Load benchmarks\n", - "from hobj.benchmarks import MutatorHighVarBenchmark, MutatorOneshotBenchmark\n", + "from hobj import MutatorHighVarBenchmark, MutatorOneshotBenchmark\n", "\n", "benchmark1 = MutatorHighVarBenchmark()\n", "benchmark2 = MutatorOneshotBenchmark()" @@ -44,8 +44,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T21:49:55.572564Z", - "start_time": "2026-03-26T21:49:55.568200Z" + "end_time": "2026-03-26T22:23:50.132492Z", + "start_time": "2026-03-26T22:23:50.127829Z" } }, "cell_type": "code", @@ -68,8 +68,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T21:50:01.122868Z", - "start_time": "2026-03-26T21:49:55.575369Z" + "end_time": "2026-03-26T22:23:55.719115Z", + "start_time": "2026-03-26T22:23:50.134712Z" } }, "cell_type": "code", @@ -84,17 +84,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "LearningCurveBenchmark.LearningCurveBenchmarkResult(msen=0.11342760990778707, msen_sigma=0.00104837884742057, msen_CI95=(0.11141902750387399, 0.11550502179844757), lapse_rate= Size: 8B\n", - "array(0.85795474), model_statistics= Size: 103MB\n", + "LearningCurveBenchmark.LearningCurveBenchmarkResult(msen=0.11344782631802723, msen_sigma=0.001029320465701059, msen_CI95=(0.11137540880220907, 0.11541750060967891), lapse_rate= Size: 8B\n", + "array(1.), model_statistics= Size: 103MB\n", "Dimensions: (subtask: 64, trial: 100, boot_iter: 1000)\n", "Coordinates:\n", " * subtask (subtask) " ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -154,8 +154,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T21:50:03.205957Z", - "start_time": "2026-03-26T21:50:01.186260Z" + "end_time": "2026-03-26T22:23:57.954802Z", + "start_time": "2026-03-26T22:23:55.786205Z" } }, "cell_type": "code", @@ -169,7 +169,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "GeneralizationBenchmark.GeneralizationBenchmarkResult(msen=0.18046066838978858, msen_sigma=0.00916594161360497, msen_CI95=(0.16305809460991577, 0.19931099499548263), model_statistics= Size: 583kB\n", + "GeneralizationBenchmark.GeneralizationBenchmarkResult(msen=0.17902651379876888, msen_sigma=0.008827749299170323, msen_CI95=(0.1632750537580675, 0.19707395861786794), model_statistics= Size: 583kB\n", "Dimensions: (transformation: 36, boot_iter: 1000)\n", "Coordinates:\n", " * transformation (transformation) " ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", diff --git a/examples/view_experiment1_behavior.ipynb b/examples/view_experiment1_behavior.ipynb index 1d1f583..0dca904 100644 --- a/examples/view_experiment1_behavior.ipynb +++ b/examples/view_experiment1_behavior.ipynb @@ -9,8 +9,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T21:28:38.663052Z", - "start_time": "2026-03-26T21:28:38.659557Z" + "end_time": "2026-03-26T22:23:26.063761Z", + "start_time": "2026-03-26T22:23:26.059240Z" } }, "cell_type": "code", @@ -25,13 +25,13 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T21:28:40.368212Z", - "start_time": "2026-03-26T21:28:38.663812Z" + "end_time": "2026-03-26T22:23:27.770785Z", + "start_time": "2026-03-26T22:23:26.064934Z" } }, "cell_type": "code", "source": [ - "from hobj.benchmarks import MutatorHighVarBenchmark\n", + "from hobj import MutatorHighVarBenchmark\n", "benchmark1 = MutatorHighVarBenchmark()\n", "target_statistics = benchmark1.target_statistics\n", "print(target_statistics)" @@ -50,7 +50,7 @@ "Data variables:\n", " phat (subtask, trial) float64 51kB 0.44 0.68 0.62 ... 0.9 0.94\n", " varhat_phat (subtask, trial) float64 51kB 0.005029 ... 0.001151\n", - " boot_phat (boot_iter, subtask, trial) float64 51MB 0.54 ... 0.98\n", + " boot_phat (boot_iter, subtask, trial) float64 51MB 0.46 ... 0.92\n", " boot_varhat_phat (boot_iter, subtask, trial) float64 51MB 0.005069 ... 0...\n" ] } @@ -60,8 +60,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T21:28:40.466372Z", - "start_time": "2026-03-26T21:28:40.401967Z" + "end_time": "2026-03-26T22:23:27.905063Z", + "start_time": "2026-03-26T22:23:27.805804Z" } }, "cell_type": "code", @@ -96,8 +96,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T21:28:40.602081Z", - "start_time": "2026-03-26T21:28:40.467257Z" + "end_time": "2026-03-26T22:23:28.055608Z", + "start_time": "2026-03-26T22:23:27.905949Z" } }, "cell_type": "code", @@ -124,7 +124,7 @@ "text/plain": [ "
" ], - "image/png": 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data", diff --git a/examples/view_experiment2_behavior.ipynb b/examples/view_experiment2_behavior.ipynb index 0d01b32..20ac169 100644 --- a/examples/view_experiment2_behavior.ipynb +++ b/examples/view_experiment2_behavior.ipynb @@ -9,8 +9,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T21:46:17.560281Z", - "start_time": "2026-03-26T21:46:17.555641Z" + "end_time": "2026-03-26T22:23:36.412Z", + "start_time": "2026-03-26T22:23:36.407827Z" } }, "cell_type": "code", @@ -25,13 +25,13 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T21:46:20.429214Z", - "start_time": "2026-03-26T21:46:17.561044Z" + "end_time": "2026-03-26T22:23:39.053698Z", + "start_time": "2026-03-26T22:23:36.412905Z" } }, "cell_type": "code", "source": [ - "from hobj.benchmarks import MutatorOneshotBenchmark\n", + "from hobj import MutatorOneshotBenchmark\n", "benchmark2 = MutatorOneshotBenchmark()\n", "target_statistics = benchmark2.target_statistics\n", "print(target_statistics)" @@ -52,8 +52,8 @@ "Data variables:\n", " phat (transformation) float64 288B 0.9969 1.0 ... 0.945 1.0\n", " varhat_phat (transformation) float64 288B 0.0003446 ... 0.0003748\n", - " boot_phat (boot_iter, transformation) float64 288kB 1.0 ... 0...\n", - " boot_varhat_phat (boot_iter, transformation) float64 288kB 0.0003686...\n" + " boot_phat (boot_iter, transformation) float64 288kB 0.9797 .....\n", + " boot_varhat_phat (boot_iter, transformation) float64 288kB 0.0004078...\n" ] } ], @@ -62,8 +62,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T21:46:20.920034Z", - "start_time": "2026-03-26T21:46:20.461980Z" + "end_time": "2026-03-26T22:23:39.471935Z", + "start_time": "2026-03-26T22:23:39.087799Z" } }, "cell_type": "code", diff --git a/hobj/data_loaders/images.py b/hobj/data_loaders/images.py index c5c42f0..8f6b1fa 100644 --- a/hobj/data_loaders/images.py +++ b/hobj/data_loaders/images.py @@ -1,4 +1,4 @@ -"""Load packaged image manifests as pandas DataFrames.""" +"""Load packaged image manifests and images.""" from pathlib import Path @@ -25,7 +25,8 @@ def _image_id_to_local_path_table() -> dict[ImageId, Path]: return table -def load_image(image_id: ImageId) -> Image: +def load_image(image_id: ImageId) -> Image.Image: + """Load an image by ``image_id`` from the packaged dataset.""" path = _image_id_to_local_path_table().get(image_id) if path is None: raise ValueError(f"Image ID not found in any manifest: {image_id}") @@ -40,14 +41,15 @@ def _load_image_manifest( required_columns: set[str], cachedir: Path | None = None, ) -> pd.DataFrame: - """Load a packaged image manifest and attach absolute image paths. + """Load a packaged image manifest after validating packaged image files. Args: - dataset_name: Name of the dataset directory under ``data/images``. + dataset_name: Name of the packaged manifest. required_columns: Columns that must exist in the manifest. cachedir: Optional root directory containing the packaged ``data`` tree. + Returns: - A copy of the manifest as a DataFrame with an added ``image_path`` column. + A copy of the manifest as a DataFrame. Raises: ValueError: If required columns are missing. diff --git a/site/readme_images/human_learning_curves.svg b/site/readme_images/human_learning_curves.svg index 24ac401..8b2d032 100644 --- a/site/readme_images/human_learning_curves.svg +++ b/site/readme_images/human_learning_curves.svg @@ -6,11 +6,11 @@ - 2025-01-27T13:47:46.663343 + 2026-03-26T18:22:59.609218 image/svg+xml - Matplotlib v3.9.2, https://matplotlib.org/ + Matplotlib v3.9.0, https://matplotlib.org/ @@ -42,18 +42,18 @@ z +" clip-path="url(#pee8cec0876)" style="fill: none; stroke-dasharray: 1.5,2.475; stroke-dashoffset: 0; stroke: #000000; stroke-width: 1.5"/> - - + @@ -105,7 +105,7 @@ z - + @@ -120,7 +120,7 @@ z - + @@ -161,131 +161,131 @@ z - - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -516,12 +516,12 @@ z - - + @@ -564,7 +564,7 @@ z - + @@ -606,7 +606,7 @@ z - + @@ -653,7 +653,7 @@ z - + @@ -680,7 +680,7 @@ z - + @@ -736,7 +736,7 @@ z - + @@ -783,7 +783,7 @@ z - + @@ -1015,522 +1015,522 @@ L 287.827931 60.476176 L 288.340456 72.368628 L 288.847777 72.368628 L 289.35 72.368628 -" clip-path="url(#pc940f8c749)" style="fill: none; stroke: #808080; stroke-opacity: 0.05; stroke-linecap: square"/> +" clip-path="url(#pee8cec0876)" style="fill: none; stroke: #808080; stroke-opacity: 0.05; stroke-linecap: square"/> - 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- + - - - + + + - - - +L 288.340456 60.476176 +L 288.847777 66.422402 +L 289.35 54.52995 +" clip-path="url(#pee8cec0876)" style="fill: none; stroke: #808080; stroke-opacity: 0.05; stroke-linecap: square"/> - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +" clip-path="url(#pee8cec0876)" style="fill: none; stroke: #0000ff; stroke-width: 1.5; stroke-linecap: square"/> + diff --git a/site/readme_images/run_make_images.py b/site/readme_images/run_make_images.py index 96a9aaa..f827a3b 100644 --- a/site/readme_images/run_make_images.py +++ b/site/readme_images/run_make_images.py @@ -1,7 +1,7 @@ if __name__ == "__main__": # %% import matplotlib.pyplot as plt - from hobj.benchmarks import MutatorHighVarBenchmark + from hobj import MutatorHighVarBenchmark import numpy as np benchmark = MutatorHighVarBenchmark() diff --git a/tests/test_load_behavior.py b/tests/test_load_behavior.py index 81b93d0..05e7292 100644 --- a/tests/test_load_behavior.py +++ b/tests/test_load_behavior.py @@ -1,9 +1,10 @@ from hobj.data_loaders.behavior import load_highvar_behavior, load_oneshot_behavior from hobj.data_loaders.images import ( + load_image, + load_imageset_meta_catch, load_imageset_meta_highvar, load_imageset_meta_oneshot, load_imageset_meta_warmup, - load_imageset_meta_catch, ) @@ -24,10 +25,7 @@ def test_load_oneshot(): def test_load_highvar_images(): df = load_imageset_meta_highvar() assert len(df) == 12800 - assert {"image_id", "category", "sha256", "relpath", "image_path"} <= set( - df.columns - ) - assert df["image_path"].iloc[0].exists() + assert {"image_id", "category", "sha256", "relpath"} <= set(df.columns) def test_load_oneshot_images(): @@ -41,24 +39,23 @@ def test_load_oneshot_images(): "base_image_id", "sha256", "relpath", - "image_path", } <= set(df.columns) - assert df["image_path"].iloc[0].exists() def test_load_warmup_images(): df = load_imageset_meta_warmup() assert len(df) == 400 - assert {"image_id", "category", "sha256", "relpath", "image_path"} <= set( - df.columns - ) - assert df["image_path"].iloc[0].exists() + assert {"image_id", "category", "sha256", "relpath"} <= set(df.columns) def test_load_probe_images(): df = load_imageset_meta_catch() assert len(df) == 2 - assert {"image_id", "color", "text", "sha256", "relpath", "image_path"} <= set( - df.columns - ) - assert df["image_path"].iloc[0].exists() + assert {"image_id", "color", "text", "sha256", "relpath"} <= set(df.columns) + + +def test_load_image(): + df = load_imageset_meta_highvar() + image = load_image(df["image_id"].iloc[0]) + assert image.size[0] > 0 + assert image.size[1] > 0 From 5b3849ad2cfab0af879120cbc5b9b2ae1bb2a53c Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 18:27:43 -0400 Subject: [PATCH 126/156] adjust test --- tests/test_load_behavior.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_load_behavior.py b/tests/test_load_behavior.py index 05e7292..bfdaa66 100644 --- a/tests/test_load_behavior.py +++ b/tests/test_load_behavior.py @@ -51,7 +51,7 @@ def test_load_warmup_images(): def test_load_probe_images(): df = load_imageset_meta_catch() assert len(df) == 2 - assert {"image_id", "color", "text", "sha256", "relpath"} <= set(df.columns) + assert {"image_id", "sha256", "relpath"} <= set(df.columns) def test_load_image(): From f9efcdb373d2c65147c5d779bb6f209e370144bb Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 18:31:18 -0400 Subject: [PATCH 127/156] skip if runner --- tests/test_load_behavior.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/tests/test_load_behavior.py b/tests/test_load_behavior.py index bfdaa66..5383b6e 100644 --- a/tests/test_load_behavior.py +++ b/tests/test_load_behavior.py @@ -1,3 +1,7 @@ +import os + +import pytest + from hobj.data_loaders.behavior import load_highvar_behavior, load_oneshot_behavior from hobj.data_loaders.images import ( load_image, @@ -7,6 +11,11 @@ load_imageset_meta_warmup, ) +pytestmark = pytest.mark.skipif( + os.environ.get("GITHUB_ACTIONS") == "true", + reason="Packaged dataset is not available on GitHub Actions runners.", +) + def test_load_highvar(): df = load_highvar_behavior() @@ -51,7 +60,7 @@ def test_load_warmup_images(): def test_load_probe_images(): df = load_imageset_meta_catch() assert len(df) == 2 - assert {"image_id", "sha256", "relpath"} <= set(df.columns) + assert {"image_id", "sha256", "relpath"} <= set(df.columns) def test_load_image(): From 3cc356a03ac0e0b2f1ac43a524611e0360687055 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 18:31:49 -0400 Subject: [PATCH 128/156] trigger --- .github/workflows/ci.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 00db357..c9e275f 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -5,6 +5,7 @@ on: paths: # Changes to source code - hobj/** + - test/**.py # Changes to workflows - .github/workflows/ci.yml # Changes to project/dependency metadata From 70ccf51d6c93a5afdf863cda2a58ef1e902a5b77 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 18:46:02 -0400 Subject: [PATCH 129/156] auto fetching --- README.md | 4 + hobj/__init__.py | 2 + hobj/benchmarks/make_model.py | 36 ++++--- hobj/benchmarks/mut_highvar_benchmark.py | 10 +- hobj/benchmarks/mut_oneshot_benchmark.py | 7 +- hobj/data_loaders/behavior.py | 32 +++--- hobj/data_loaders/download.py | 131 ++++++++++++++++++----- hobj/data_loaders/images.py | 56 +++++++--- hobj/learning_models/create_model.py | 1 + 9 files changed, 204 insertions(+), 75 deletions(-) create mode 100644 hobj/learning_models/create_model.py diff --git a/README.md b/README.md index 9b4cab4..579d59f 100644 --- a/README.md +++ b/README.md @@ -28,6 +28,10 @@ result = benchmark(model) print(result.msen, result.msen_CI95) ``` +On first use, the packaged dataset is downloaded automatically into `./data`. +To use a different location, pass `cachedir=...` to a data loader or benchmark +constructor, or prefetch manually with `hobj-download-data --cachedir /path/to/data`. + For more details, check out `examples/`. ### Installation diff --git a/hobj/__init__.py b/hobj/__init__.py index c6dc39a..719aa95 100644 --- a/hobj/__init__.py +++ b/hobj/__init__.py @@ -25,4 +25,6 @@ # Benchmarks: "MutatorHighVarBenchmark", "MutatorOneshotBenchmark", + # Learning model loader: + ..., ] diff --git a/hobj/benchmarks/make_model.py b/hobj/benchmarks/make_model.py index e90caac..4e9da7f 100644 --- a/hobj/benchmarks/make_model.py +++ b/hobj/benchmarks/make_model.py @@ -3,6 +3,7 @@ """ from functools import lru_cache +from pathlib import Path from typing import Literal import numpy as np @@ -13,15 +14,19 @@ # %% -@lru_cache(maxsize=1) -def _get_calibration_image_ids() -> list[ImageId]: - """ - Returns the ImageIds of the warmup images that are used for calibrating the features of the linear learner. - Caches the result to avoid redundant computation. +@lru_cache(maxsize=None) +def _get_calibration_image_ids(cachedir: Path | None = None) -> list[ImageId]: + """Return the warmup image IDs used for feature calibration. + + Args: + cachedir: Optional directory containing the packaged ``data`` tree. + + Returns: + Sorted image IDs used for calibration. """ from hobj.data_loaders.images import load_imageset_meta_warmup - images_df = load_imageset_meta_warmup() + images_df = load_imageset_meta_warmup(cachedir=cachedir) return sorted(images_df["image_id"].tolist()) @@ -39,17 +44,22 @@ def make_linear_learner_from_features( "REINFORCE", ] = "Square", alpha: float = 1, + cachedir: Path | None = None, ) -> LinearLearner: - """ - Instantiates a linear learning model from precomputed features. - :param ref_to_features: Dict[mref.ImageRef, np.ndarray], the features to use. - :param update_rule_name: str, the name of the update rule to use. - :param alpha: float, the learning rate. - :return: LinearLearner + """Instantiate a linear learner from precomputed features. + + Args: + ref_to_features: Precomputed features keyed by image ID. + update_rule_name: Name of the update rule to use. + alpha: Learning-rate parameter for the update rule. + cachedir: Optional directory containing the packaged ``data`` tree. + + Returns: + A calibrated linear learner. """ f_calibration = np.array( - [ref_to_features[ref] for ref in _get_calibration_image_ids()] + [ref_to_features[ref] for ref in _get_calibration_image_ids(cachedir=cachedir)] ) mu_calibration = np.mean(f_calibration, axis=0) norms_calibration = np.linalg.norm(f_calibration - mu_calibration, axis=1) diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index d600050..64c77cc 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -1,3 +1,4 @@ +from pathlib import Path from typing import Dict, List import numpy as np @@ -17,14 +18,17 @@ # %% class MutatorHighVarBenchmark(LearningCurveBenchmark): - def __init__(self): + def __init__(self, cachedir: Path | None = None): # Load data into format expected by benchmark # Load image manifest: - images_df = load_imageset_meta_highvar() + images_df = load_imageset_meta_highvar(cachedir=cachedir) # Load raw human session data for benchmark: - sessions = load_highvar_behavior(remove_probe_trials=True) + sessions = load_highvar_behavior( + remove_probe_trials=True, + cachedir=cachedir, + ) # Normalize data for benchmark: image_id_to_category = dict( diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index 07b3345..91964d4 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -1,4 +1,5 @@ import collections +from pathlib import Path from typing import Dict, List from hobj.benchmarks.generalization.benchmark import ( @@ -91,12 +92,12 @@ class MutatorOneshotBenchmark(GeneralizationBenchmark): "scale | 1.5", ] - def __init__(self): + def __init__(self, cachedir: Path | None = None): support_trials = {0, 1, 2, 3, 4, 5, 6, 7, 8} catch_trials = {9, 14, 19} # Load image manifest - images_df = load_imageset_meta_oneshot() + images_df = load_imageset_meta_oneshot(cachedir=cachedir) image_id_to_row = images_df.set_index("image_id") # Map image refs to transformation ids @@ -145,7 +146,7 @@ def __init__(self): subtasks.append(subtask) # Package human data into format expected by benchmark - oneshot_df = load_oneshot_behavior() + oneshot_df = load_oneshot_behavior(cachedir=cachedir) results = [] diff --git a/hobj/data_loaders/behavior.py b/hobj/data_loaders/behavior.py index c13641e..1e00b99 100644 --- a/hobj/data_loaders/behavior.py +++ b/hobj/data_loaders/behavior.py @@ -1,23 +1,33 @@ from pathlib import Path +from typing import Literal import pandas as pd -from typing import Literal +from hobj.data_loaders.download import resolve_data_root # %% def load_human_behavior( experiment: Literal["highvar", "oneshot"] = "highvar", + cachedir: Path | None = None, ) -> pd.DataFrame: + """Load trial-level human behavior for one of the packaged experiments. + + Args: + experiment: Name of the experiment to load. + cachedir: Optional directory containing the packaged ``data`` tree. + + Returns: + The requested behavior dataset. + """ if experiment not in {"highvar", "oneshot"}: raise ValueError( f"Invalid experiment name: {experiment}. Provide either 'highvar' or 'oneshot'." ) if experiment == "highvar": - return load_highvar_behavior() - else: - return load_oneshot_behavior() + return load_highvar_behavior(cachedir=cachedir) + return load_oneshot_behavior(cachedir=cachedir) # %% Data loaders @@ -36,13 +46,12 @@ def load_highvar_behavior( A DataFrame containing the high-variance behavior dataset with ``stimulus_id`` normalized to ``image_id``. """ - repo_root = Path(__file__).resolve().parents[2] - cache_root = (cachedir if cachedir is not None else repo_root / "data").resolve() + cache_root = resolve_data_root(cachedir=cachedir) dataset_path = cache_root / "behavior" / "human-behavior-highvar-subtasks.csv" if not dataset_path.exists(): raise FileNotFoundError( - "Expected cached highvar behavior CSV to already exist at:\n" - f" - {dataset_path}" + "Expected highvar behavior CSV to exist after resolving packaged data " + f"at:\n - {dataset_path}" ) df = pd.read_csv(dataset_path) @@ -86,13 +95,12 @@ def load_oneshot_behavior( A DataFrame containing the one-shot behavior dataset with ``stimulus_id`` normalized to ``image_id``. """ - repo_root = Path(__file__).resolve().parents[2] - cache_root = (cachedir if cachedir is not None else repo_root / "data").resolve() + cache_root = resolve_data_root(cachedir=cachedir) dataset_path = cache_root / "behavior" / "human-behavior-oneshot-subtasks.csv" if not dataset_path.exists(): raise FileNotFoundError( - "Expected cached oneshot behavior CSV to already exist at:\n" - f" - {dataset_path}" + "Expected oneshot behavior CSV to exist after resolving packaged data " + f"at:\n - {dataset_path}" ) df = pd.read_csv(dataset_path) diff --git a/hobj/data_loaders/download.py b/hobj/data_loaders/download.py index 90a453b..78aa9cb 100644 --- a/hobj/data_loaders/download.py +++ b/hobj/data_loaders/download.py @@ -1,10 +1,12 @@ -"""Download packaged HOBJ datasets into the repository ``data`` directory.""" +"""Download packaged HOBJ datasets into a local ``data`` directory.""" from __future__ import annotations import argparse +import shutil import tarfile from pathlib import Path +from tempfile import TemporaryDirectory from urllib.parse import urlparse import requests @@ -15,13 +17,13 @@ "https://hlbdatasets.s3.us-east-1.amazonaws.com/" "lee-dicarlo-2023-learning-data.tar.gz" ) -EXPECTED_DATA_PATHS = ( - Path("data/meta-MutatorHighVarImageset.csv"), - Path("data/meta-MutatorOneShotImageset.csv"), - Path("data/meta-MutatorWarmupImageset.csv"), - Path("data/meta-CatchImageset.csv"), - Path("data/behavior/human-behavior-highvar-subtasks.csv"), - Path("data/behavior/human-behavior-oneshot-subtasks.csv"), +EXPECTED_DATA_RELATIVE_PATHS = ( + Path("meta-MutatorHighVarImageset.csv"), + Path("meta-MutatorOneShotImageset.csv"), + Path("meta-MutatorWarmupImageset.csv"), + Path("meta-CatchImageset.csv"), + Path("behavior/human-behavior-highvar-subtasks.csv"), + Path("behavior/human-behavior-oneshot-subtasks.csv"), ) _DOWNLOAD_CHUNK_SIZE_BYTES = 1024 * 1024 @@ -31,10 +33,20 @@ def _get_repo_root() -> Path: return Path(__file__).resolve().parents[2] -def _get_missing_expected_paths(repo_root: Path) -> list[Path]: +def _get_default_data_root(repo_root: Path | None = None) -> Path: + """Return the default packaged data directory.""" + resolved_repo_root = ( + repo_root if repo_root is not None else _get_repo_root() + ).resolve() + return resolved_repo_root / "data" + + +def _get_missing_expected_paths(data_root: Path) -> list[Path]: """Return expected packaged data paths that are currently missing.""" return [ - relpath for relpath in EXPECTED_DATA_PATHS if not (repo_root / relpath).exists() + relpath + for relpath in EXPECTED_DATA_RELATIVE_PATHS + if not (data_root / relpath).exists() ] @@ -80,26 +92,77 @@ def _safe_extract_tarball(archive_path: Path, destination: Path) -> None: for member in tar.getmembers(): member_path = (destination / member.name).resolve() member_path.relative_to(destination) - tar.extractall(destination) + tar.extractall(destination, filter="data") + + +def _find_extracted_data_root(extraction_root: Path) -> Path: + """Return the packaged data directory from an extracted archive tree. + + Args: + extraction_root: Temporary directory containing the extracted archive. + + Returns: + The directory that contains the packaged data files. + + Raises: + FileNotFoundError: If the extracted archive does not contain the + expected packaged dataset layout. + """ + nested_data_root = extraction_root / "data" + if not _get_missing_expected_paths(nested_data_root): + return nested_data_root + + if not _get_missing_expected_paths(extraction_root): + return extraction_root + + raise FileNotFoundError( + "Packaged dataset archive did not contain the expected data directory." + ) + + +def resolve_data_root( + *, + cachedir: Path | None = None, + repo_root: Path | None = None, +) -> Path: + """Return a populated packaged data directory, downloading it on demand. + + Args: + cachedir: Optional directory to use instead of the repository + ``data`` directory. + repo_root: Optional repository root used only when ``cachedir`` is not + provided. + + Returns: + The absolute path to the packaged data directory. + """ + data_root = ( + cachedir if cachedir is not None else _get_default_data_root(repo_root) + ).resolve() + if _get_missing_expected_paths(data_root): + return download_data(cachedir=data_root, url=DATA_ARCHIVE_URL) + return data_root def download_data( *, url: str = DATA_ARCHIVE_URL, repo_root: Path | None = None, + cachedir: Path | None = None, force_download: bool = False, ) -> Path: - """Download and extract the packaged learning dataset into the repo root. + """Download and extract the packaged learning dataset into a cache root. The function is idempotent: if the expected packaged files already exist - under ``data/``, it returns immediately without downloading or extracting. - Otherwise it downloads the archive if needed and extracts it into the - repository root. + under the resolved data directory, it returns immediately without + downloading or extracting. Otherwise it downloads the archive if needed and + extracts it into the target cache. Args: url: Archive URL containing the packaged ``data`` tree. - repo_root: Repository root where the archive should be stored and - extracted. + repo_root: Repository root used when ``cachedir`` is not provided. + cachedir: Optional directory to use instead of the repository + ``data`` directory. force_download: Whether to redownload the archive even if it is already present on disk. @@ -110,25 +173,30 @@ def download_data( requests.HTTPError: If the archive download fails. tarfile.TarError: If the archive cannot be unpacked. ValueError: If the archive path is invalid or extraction would escape - the repository root. + the extraction directory. FileNotFoundError: If extraction completes but the expected dataset files are still missing. """ - resolved_repo_root = ( - repo_root if repo_root is not None else _get_repo_root() + data_root = ( + cachedir if cachedir is not None else _get_default_data_root(repo_root) ).resolve() - data_root = resolved_repo_root / "data" - missing_paths = _get_missing_expected_paths(resolved_repo_root) + missing_paths = _get_missing_expected_paths(data_root) if not missing_paths: return data_root - archive_path = _derive_archive_path(resolved_repo_root, url) + archive_root = data_root.parent + archive_root.mkdir(parents=True, exist_ok=True) + archive_path = _derive_archive_path(archive_root, url) if force_download or not archive_path.exists(): _download_archive(url=url, archive_path=archive_path) - _safe_extract_tarball(archive_path=archive_path, destination=resolved_repo_root) + with TemporaryDirectory(dir=archive_root) as extraction_root_str: + extraction_root = Path(extraction_root_str) + _safe_extract_tarball(archive_path=archive_path, destination=extraction_root) + extracted_data_root = _find_extracted_data_root(extraction_root) + shutil.copytree(extracted_data_root, data_root, dirs_exist_ok=True) - missing_paths = _get_missing_expected_paths(resolved_repo_root) + missing_paths = _get_missing_expected_paths(data_root) if missing_paths: missing_str = "\n".join(f" - {path}" for path in missing_paths) raise FileNotFoundError( @@ -140,10 +208,16 @@ def download_data( def main() -> None: - """Download packaged dataset files into the repository data directory.""" + """Download packaged dataset files into a local data directory.""" parser = argparse.ArgumentParser( description="Download and extract the packaged HOBJ dataset archive." ) + parser.add_argument( + "--cachedir", + type=Path, + default=None, + help="Optional directory to use instead of the repository data directory.", + ) parser.add_argument( "--force-download", action="store_true", @@ -151,7 +225,10 @@ def main() -> None: ) args = parser.parse_args() - data_root = download_data(force_download=args.force_download) + data_root = download_data( + cachedir=args.cachedir, + force_download=args.force_download, + ) print(data_root) diff --git a/hobj/data_loaders/images.py b/hobj/data_loaders/images.py index 8f6b1fa..083393f 100644 --- a/hobj/data_loaders/images.py +++ b/hobj/data_loaders/images.py @@ -1,19 +1,26 @@ """Load packaged image manifests and images.""" +from functools import lru_cache from pathlib import Path import pandas as pd -from hobj.types import ImageId from PIL import Image -from functools import lru_cache + +from hobj.data_loaders.download import resolve_data_root +from hobj.types import ImageId -@lru_cache(maxsize=1) -def _image_id_to_local_path_table() -> dict[ImageId, Path]: - """Build a mapping from image_id to absolute local path for all packaged images.""" - repo_root = Path(__file__).resolve().parents[2] - cache_root = repo_root / "data" - manifest_paths = list(cache_root.glob("meta-*.csv")) +@lru_cache(maxsize=None) +def _image_id_to_local_path_table(cache_root: Path) -> dict[ImageId, Path]: + """Build a mapping from image ID to absolute local path. + + Args: + cache_root: Directory containing the packaged image manifests. + + Returns: + A mapping from image ID to absolute image path. + """ + manifest_paths = sorted(cache_root.glob("meta-*.csv")) table = {} for manifest_path in manifest_paths: manifest_df = pd.read_csv(manifest_path) @@ -25,13 +32,27 @@ def _image_id_to_local_path_table() -> dict[ImageId, Path]: return table -def load_image(image_id: ImageId) -> Image.Image: - """Load an image by ``image_id`` from the packaged dataset.""" - path = _image_id_to_local_path_table().get(image_id) +def load_image( + image_id: ImageId, + cachedir: Path | None = None, +) -> Image.Image: + """Load an image by ``image_id`` from the packaged dataset. + + Args: + image_id: ID of the image to load. + cachedir: Optional directory containing the packaged ``data`` tree. + + Returns: + The requested image. + """ + cache_root = resolve_data_root(cachedir=cachedir) + path = _image_id_to_local_path_table(cache_root).get(image_id) if path is None: raise ValueError(f"Image ID not found in any manifest: {image_id}") if not path.exists(): - raise FileNotFoundError(f"Expected image file to already exist at: {path}") + raise FileNotFoundError( + f"Expected image file to exist after resolving packaged data at: {path}" + ) return Image.open(path) @@ -55,13 +76,13 @@ def _load_image_manifest( ValueError: If required columns are missing. FileNotFoundError: If the packaged manifest or images are missing. """ - repo_root = Path(__file__).resolve().parents[2] - cache_root = (cachedir if cachedir is not None else repo_root / "data").resolve() + cache_root = resolve_data_root(cachedir=cachedir) manifest_path = cache_root / f"meta-{dataset_name}.csv" if not manifest_path.exists(): raise FileNotFoundError( - f"Expected cached image manifest to already exist at: {manifest_path}" + "Expected image manifest to exist after resolving packaged data at: " + f"{manifest_path}" ) manifest_df = pd.read_csv(manifest_path) @@ -74,11 +95,12 @@ def _load_image_manifest( manifest_df = manifest_df.copy() missing_paths = manifest_df.loc[ - ~manifest_df["relpath"].map(lambda p: Path.exists(cache_root / p)), "relpath" + ~manifest_df["relpath"].map(lambda p: (cache_root / p).exists()), "relpath" ] if not missing_paths.empty: raise FileNotFoundError( - "Expected packaged images to already exist under:\n" + "Expected packaged images to exist after resolving packaged data " + "under:\n" f"First missing path: {missing_paths.iloc[0]}" ) diff --git a/hobj/learning_models/create_model.py b/hobj/learning_models/create_model.py new file mode 100644 index 0000000..2f3baf2 --- /dev/null +++ b/hobj/learning_models/create_model.py @@ -0,0 +1 @@ +def create_model(): ... From 0e8b66426f8cfa7a2117d65745dddd8c23044056 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 18:46:28 -0400 Subject: [PATCH 130/156] readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 579d59f..6643f66 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,7 @@ import hobj model = hobj.learning_models.RandomGuesser() # A subclass of BinaryLearningModel # Load benchmark: -benchmark = hobj.benchmarks.MutatorHighVarBenchmark() # Try benchmark 2: hobj.benchmarks.MutatorOneshotBenchmark() +benchmark = hobj.MutatorHighVarBenchmark() # Try benchmark 2: hobj.MutatorOneshotBenchmark() result = benchmark(model) # Print the score and its CI: From 55d18b32c80c6bd11489811d950457c2f9efb5a9 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 19:02:57 -0400 Subject: [PATCH 131/156] image loading --- hobj/__init__.py | 6 +- hobj/data_loaders/images.py | 132 +++++++++++------- hobj/learning_models/create_model.py | 1 - .../make_model.py | 0 4 files changed, 89 insertions(+), 50 deletions(-) delete mode 100644 hobj/learning_models/create_model.py rename hobj/{benchmarks => learning_models}/make_model.py (100%) diff --git a/hobj/__init__.py b/hobj/__init__.py index 719aa95..876aa60 100644 --- a/hobj/__init__.py +++ b/hobj/__init__.py @@ -5,6 +5,8 @@ from hobj.data_loaders.images import ( load_image, + list_image_ids, + get_image_path, load_imageset_meta_highvar, load_imageset_meta_oneshot, load_imageset_meta_warmup, @@ -21,10 +23,10 @@ "load_imageset_meta_warmup", "load_imageset_meta_catch", # Image loader: + "list_image_ids", "load_image", + "get_image_path", # Benchmarks: "MutatorHighVarBenchmark", "MutatorOneshotBenchmark", - # Learning model loader: - ..., ] diff --git a/hobj/data_loaders/images.py b/hobj/data_loaders/images.py index 083393f..4cde8ae 100644 --- a/hobj/data_loaders/images.py +++ b/hobj/data_loaders/images.py @@ -10,52 +10,6 @@ from hobj.types import ImageId -@lru_cache(maxsize=None) -def _image_id_to_local_path_table(cache_root: Path) -> dict[ImageId, Path]: - """Build a mapping from image ID to absolute local path. - - Args: - cache_root: Directory containing the packaged image manifests. - - Returns: - A mapping from image ID to absolute image path. - """ - manifest_paths = sorted(cache_root.glob("meta-*.csv")) - table = {} - for manifest_path in manifest_paths: - manifest_df = pd.read_csv(manifest_path) - for _, row in manifest_df.iterrows(): - image_id = row["image_id"] - relpath = row["relpath"] - abs_path = cache_root / relpath - table[image_id] = abs_path - return table - - -def load_image( - image_id: ImageId, - cachedir: Path | None = None, -) -> Image.Image: - """Load an image by ``image_id`` from the packaged dataset. - - Args: - image_id: ID of the image to load. - cachedir: Optional directory containing the packaged ``data`` tree. - - Returns: - The requested image. - """ - cache_root = resolve_data_root(cachedir=cachedir) - path = _image_id_to_local_path_table(cache_root).get(image_id) - if path is None: - raise ValueError(f"Image ID not found in any manifest: {image_id}") - if not path.exists(): - raise FileNotFoundError( - f"Expected image file to exist after resolving packaged data at: {path}" - ) - return Image.open(path) - - def _load_image_manifest( *, dataset_name: str, @@ -162,6 +116,90 @@ def load_imageset_meta_catch( ) return manifest_df.sort_values("image_id").reset_index(drop=True) +# %% Image loaders: + +@lru_cache(maxsize=None) +def _image_id_to_local_path_table(cache_root: Path) -> dict[ImageId, Path]: + """Build a mapping from image ID to absolute local path. + + Args: + cache_root: Directory containing the packaged image manifests. + + Returns: + A mapping from image ID to absolute image path. + """ + manifest_paths = sorted(cache_root.glob("meta-*.csv")) + table = {} + for manifest_path in manifest_paths: + manifest_df = pd.read_csv(manifest_path) + for _, row in manifest_df.iterrows(): + image_id = row["image_id"] + relpath = row["relpath"] + abs_path = cache_root / relpath + table[image_id] = abs_path + return table + + +def list_image_ids(cachedir: Path | None = None) -> list[ImageId]: + """Iterate over all packaged image IDs. + + Args: + cachedir: Optional directory containing the packaged ``data`` tree. + + Yields: + Image IDs from the packaged dataset. + """ + cache_root = resolve_data_root(cachedir=cachedir) + table = _image_id_to_local_path_table(cache_root) + return sorted(table.keys()) + +def load_image( + image_id: ImageId, + cachedir: Path | None = None, +) -> Image.Image: + """Load an image by ``image_id`` from the packaged dataset. + + Args: + image_id: ID of the image to load. + cachedir: Optional directory containing the packaged ``data`` tree. + + Returns: + The requested image. + """ + path = get_image_path(image_id=image_id, cachedir=cachedir) + return Image.open(path) + + + +def get_image_path( + image_id: ImageId, + cachedir: Path | None = None, +) -> Path: + """Return the local path for an image in the packaged dataset. + + Args: + image_id: ID of the image to resolve. + cachedir: Optional directory containing the packaged ``data`` tree. + + Returns: + The absolute path to the requested image. + + Raises: + ValueError: If the image ID is not present in any packaged manifest. + FileNotFoundError: If the resolved image file is missing on disk. + """ + cache_root = resolve_data_root(cachedir=cachedir) + path = _image_id_to_local_path_table(cache_root).get(image_id) + if path is None: + raise ValueError(f"Image ID not found in any manifest: {image_id}") + if not path.exists(): + raise FileNotFoundError( + f"Expected image file to exist after resolving packaged data at: {path}" + ) + return path + + +# %% if __name__ == "__main__": - df = load_imageset_meta_oneshot() + df = list_image_ids() diff --git a/hobj/learning_models/create_model.py b/hobj/learning_models/create_model.py deleted file mode 100644 index 2f3baf2..0000000 --- a/hobj/learning_models/create_model.py +++ /dev/null @@ -1 +0,0 @@ -def create_model(): ... diff --git a/hobj/benchmarks/make_model.py b/hobj/learning_models/make_model.py similarity index 100% rename from hobj/benchmarks/make_model.py rename to hobj/learning_models/make_model.py From c11d029477ac0cfc21f6ef297efde4644e2bd68a Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 19:14:24 -0400 Subject: [PATCH 132/156] data; getter --- README.md | 6 ++- examples/score_example_models.ipynb | 56 ++++++++++++------------ hobj/__init__.py | 27 ++++++++---- hobj/benchmarks/mut_highvar_benchmark.py | 4 +- hobj/benchmarks/mut_oneshot_benchmark.py | 4 +- hobj/{data_loaders => data}/__init__.py | 0 hobj/{data_loaders => data}/behavior.py | 2 +- hobj/{data_loaders => data}/download.py | 0 hobj/{data_loaders => data}/images.py | 2 +- hobj/learning_models/__init__.py | 21 --------- hobj/learning_models/make_model.py | 17 ++++--- hobj/learning_models/random_guesser.py | 29 ++++++++++++ tests/test_dummy_learner.py | 2 +- tests/test_load_behavior.py | 4 +- tests/test_simulate_subtask.py | 2 +- 15 files changed, 99 insertions(+), 77 deletions(-) rename hobj/{data_loaders => data}/__init__.py (100%) rename hobj/{data_loaders => data}/behavior.py (98%) rename hobj/{data_loaders => data}/download.py (100%) rename hobj/{data_loaders => data}/images.py (99%) create mode 100644 hobj/learning_models/random_guesser.py diff --git a/README.md b/README.md index 6643f66..af7a210 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,5 @@ +from hobj.learning_models import random_guesser + # `hobj`: human object learning benchmarks [![CI](https://github.com/himjl/hobj/actions/workflows/ci.yml/badge.svg)](https://github.com/himjl/hobj/actions/workflows/ci.yml) @@ -18,10 +20,10 @@ Then, an instance of that subclass may be passed in as an argument to a benchmar import hobj # Load model: -model = hobj.learning_models.RandomGuesser() # A subclass of BinaryLearningModel +model = random_guesser.RandomGuesser() # A subclass of BinaryLearningModel # Load benchmark: -benchmark = hobj.MutatorHighVarBenchmark() # Try benchmark 2: hobj.MutatorOneshotBenchmark() +benchmark = hobj.MutatorHighVarBenchmark() # Try benchmark 2: hobj.MutatorOneshotBenchmark() result = benchmark(model) # Print the score and its CI: diff --git a/examples/score_example_models.ipynb b/examples/score_example_models.ipynb index cf36c07..6c84698 100644 --- a/examples/score_example_models.ipynb +++ b/examples/score_example_models.ipynb @@ -3,8 +3,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T22:23:46.112399Z", - "start_time": "2026-03-26T22:23:46.107394Z" + "end_time": "2026-03-26T23:13:43.475576Z", + "start_time": "2026-03-26T23:13:43.468622Z" } }, "cell_type": "code", @@ -19,8 +19,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T22:23:50.095180Z", - "start_time": "2026-03-26T22:23:46.113436Z" + "end_time": "2026-03-26T23:13:47.357510Z", + "start_time": "2026-03-26T23:13:43.477479Z" } }, "cell_type": "code", @@ -44,18 +44,18 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T22:23:50.132492Z", - "start_time": "2026-03-26T22:23:50.127829Z" + "end_time": "2026-03-26T23:13:47.395356Z", + "start_time": "2026-03-26T23:13:47.389676Z" } }, "cell_type": "code", "source": [ "# Load example model – a random guesser\n", - "from hobj.learning_models import RandomGuesser\n", + "from hobj import RandomGuesser\n", "\n", "learner = RandomGuesser()" ], - "id": "41c8eaea44ac8454", + "id": "bdc5bcb78d79e755", "outputs": [], "execution_count": 3 }, @@ -68,8 +68,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T22:23:55.719115Z", - "start_time": "2026-03-26T22:23:50.134712Z" + "end_time": "2026-03-26T23:13:52.978137Z", + "start_time": "2026-03-26T23:13:47.396747Z" } }, "cell_type": "code", @@ -84,17 +84,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "LearningCurveBenchmark.LearningCurveBenchmarkResult(msen=0.11344782631802723, msen_sigma=0.001029320465701059, msen_CI95=(0.11137540880220907, 0.11541750060967891), lapse_rate= Size: 8B\n", + "LearningCurveBenchmark.LearningCurveBenchmarkResult(msen=0.11344782631802723, msen_sigma=0.0009983236192838236, msen_CI95=(0.1114400003423075, 0.11529258551129108), lapse_rate= Size: 8B\n", "array(1.), model_statistics= Size: 103MB\n", "Dimensions: (subtask: 64, trial: 100, boot_iter: 1000)\n", "Coordinates:\n", " * subtask (subtask) " ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -154,8 +154,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T22:23:57.954802Z", - "start_time": "2026-03-26T22:23:55.786205Z" + "end_time": "2026-03-26T23:13:55.095280Z", + "start_time": "2026-03-26T23:13:53.070036Z" } }, "cell_type": "code", @@ -169,7 +169,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "GeneralizationBenchmark.GeneralizationBenchmarkResult(msen=0.17902651379876888, msen_sigma=0.008827749299170323, msen_CI95=(0.1632750537580675, 0.19707395861786794), model_statistics= Size: 583kB\n", + "GeneralizationBenchmark.GeneralizationBenchmarkResult(msen=0.18251220351974604, msen_sigma=0.00911146923718916, msen_CI95=(0.16616627500845524, 0.2024641217606151), model_statistics= Size: 583kB\n", "Dimensions: (transformation: 36, boot_iter: 1000)\n", "Coordinates:\n", " * transformation (transformation) " ], - "image/png": 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data", diff --git a/hobj/__init__.py b/hobj/__init__.py index 876aa60..f33905b 100644 --- a/hobj/__init__.py +++ b/hobj/__init__.py @@ -1,9 +1,9 @@ from hobj.benchmarks.mut_oneshot_benchmark import MutatorOneshotBenchmark from hobj.benchmarks.mut_highvar_benchmark import MutatorHighVarBenchmark -from hobj.data_loaders.behavior import load_highvar_behavior, load_oneshot_behavior +from hobj.data.behavior import load_highvar_behavior, load_oneshot_behavior -from hobj.data_loaders.images import ( +from hobj.data.images import ( load_image, list_image_ids, get_image_path, @@ -13,7 +13,23 @@ load_imageset_meta_catch, ) +from hobj.learning_models.make_model import ( + create_linear_learner, +) + +from hobj.learning_models.random_guesser import RandomGuesser + __all__ = [ + # Image loader: + "list_image_ids", + "get_image_path", + "load_image", + # Learning Models: + "create_linear_learner", + "RandomGuesser", + # Benchmarks: + "MutatorHighVarBenchmark", + "MutatorOneshotBenchmark", # Raw behavior loaders "load_highvar_behavior", "load_oneshot_behavior", @@ -22,11 +38,4 @@ "load_imageset_meta_oneshot", "load_imageset_meta_warmup", "load_imageset_meta_catch", - # Image loader: - "list_image_ids", - "load_image", - "get_image_path", - # Benchmarks: - "MutatorHighVarBenchmark", - "MutatorOneshotBenchmark", ] diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index 64c77cc..4975150 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -12,8 +12,8 @@ BinaryClassificationSubtask, BinaryClassificationSubtaskResult, ) -from hobj.data_loaders.behavior import load_highvar_behavior -from hobj.data_loaders.images import load_imageset_meta_highvar +from hobj.data.behavior import load_highvar_behavior +from hobj.data.images import load_imageset_meta_highvar # %% diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index 91964d4..06026f3 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -9,8 +9,8 @@ ) from hobj.benchmarks.generalization.estimator import GeneralizationStatistics from hobj.benchmarks.generalization.simulator import GeneralizationSubtask -from hobj.data_loaders.behavior import load_oneshot_behavior -from hobj.data_loaders.images import load_imageset_meta_oneshot +from hobj.data.behavior import load_oneshot_behavior +from hobj.data.images import load_imageset_meta_oneshot from hobj.types import ImageId diff --git a/hobj/data_loaders/__init__.py b/hobj/data/__init__.py similarity index 100% rename from hobj/data_loaders/__init__.py rename to hobj/data/__init__.py diff --git a/hobj/data_loaders/behavior.py b/hobj/data/behavior.py similarity index 98% rename from hobj/data_loaders/behavior.py rename to hobj/data/behavior.py index 1e00b99..15d6875 100644 --- a/hobj/data_loaders/behavior.py +++ b/hobj/data/behavior.py @@ -3,7 +3,7 @@ import pandas as pd -from hobj.data_loaders.download import resolve_data_root +from hobj.data.download import resolve_data_root # %% diff --git a/hobj/data_loaders/download.py b/hobj/data/download.py similarity index 100% rename from hobj/data_loaders/download.py rename to hobj/data/download.py diff --git a/hobj/data_loaders/images.py b/hobj/data/images.py similarity index 99% rename from hobj/data_loaders/images.py rename to hobj/data/images.py index 4cde8ae..afb8e37 100644 --- a/hobj/data_loaders/images.py +++ b/hobj/data/images.py @@ -6,7 +6,7 @@ import pandas as pd from PIL import Image -from hobj.data_loaders.download import resolve_data_root +from hobj.data.download import resolve_data_root from hobj.types import ImageId diff --git a/hobj/learning_models/__init__.py b/hobj/learning_models/__init__.py index a88682b..95c78ef 100644 --- a/hobj/learning_models/__init__.py +++ b/hobj/learning_models/__init__.py @@ -41,27 +41,6 @@ def give_feedback(self, reward: float) -> None: # %% -class RandomGuesser(BinaryLearningModel): - """ - A dummy model which simply returns random outputs, independent of the given image. - """ - - def __init__(self, seed: int = 0): - self.random_generator = np.random.default_rng(seed=seed) - - def reset_state(self, seed: typing.Union[int, None]) -> None: - self.random_generator = np.random.default_rng(seed=seed) - - def get_response( - self, - image: ImageId, - ) -> typing.Literal[0, 1]: - action = self.random_generator.integers(2) - action = int(action) - return action - - def give_feedback(self, reward: float) -> None: - return # %% diff --git a/hobj/learning_models/make_model.py b/hobj/learning_models/make_model.py index 4e9da7f..c464817 100644 --- a/hobj/learning_models/make_model.py +++ b/hobj/learning_models/make_model.py @@ -24,15 +24,15 @@ def _get_calibration_image_ids(cachedir: Path | None = None) -> list[ImageId]: Returns: Sorted image IDs used for calibration. """ - from hobj.data_loaders.images import load_imageset_meta_warmup + from hobj.data.images import load_imageset_meta_warmup images_df = load_imageset_meta_warmup(cachedir=cachedir) return sorted(images_df["image_id"].tolist()) # %% -def make_linear_learner_from_features( - ref_to_features: dict[ImageId, np.ndarray], +def create_linear_learner( + image_id_to_features: dict[ImageId, np.ndarray], update_rule_name: Literal[ "Prototype", "Square", @@ -49,7 +49,7 @@ def make_linear_learner_from_features( """Instantiate a linear learner from precomputed features. Args: - ref_to_features: Precomputed features keyed by image ID. + image_id_to_features: Precomputed features keyed by image ID. update_rule_name: Name of the update rule to use. alpha: Learning-rate parameter for the update rule. cachedir: Optional directory containing the packaged ``data`` tree. @@ -58,16 +58,19 @@ def make_linear_learner_from_features( A calibrated linear learner. """ + if alpha < 0 or alpha > 1: + raise ValueError("alpha must be in the interval [0, 1]") + f_calibration = np.array( - [ref_to_features[ref] for ref in _get_calibration_image_ids(cachedir=cachedir)] + [image_id_to_features[ref] for ref in _get_calibration_image_ids(cachedir=cachedir)] ) mu_calibration = np.mean(f_calibration, axis=0) norms_calibration = np.linalg.norm(f_calibration - mu_calibration, axis=1) norm_cutoff = np.quantile(norms_calibration, 0.999) # Will clip the rest ref_to_calibrated_features = {} - for ref in ref_to_features: - f = ref_to_features[ref] + for ref in image_id_to_features: + f = image_id_to_features[ref] fc = f - mu_calibration fcn = fc / norm_cutoff norm_cur = np.linalg.norm(fcn) diff --git a/hobj/learning_models/random_guesser.py b/hobj/learning_models/random_guesser.py new file mode 100644 index 0000000..fdebed7 --- /dev/null +++ b/hobj/learning_models/random_guesser.py @@ -0,0 +1,29 @@ +import typing + +import numpy as np + +from hobj.learning_models import BinaryLearningModel +from hobj.types import ImageId + + +class RandomGuesser(BinaryLearningModel): + """ + A dummy model which simply returns random outputs, independent of the given image. + """ + + def __init__(self, seed: int = 0): + self.random_generator = np.random.default_rng(seed=seed) + + def reset_state(self, seed: typing.Union[int, None]) -> None: + self.random_generator = np.random.default_rng(seed=seed) + + def get_response( + self, + image: ImageId, + ) -> typing.Literal[0, 1]: + action = self.random_generator.integers(2) + action = int(action) + return action + + def give_feedback(self, reward: float) -> None: + return diff --git a/tests/test_dummy_learner.py b/tests/test_dummy_learner.py index ef6bd88..38937b9 100644 --- a/tests/test_dummy_learner.py +++ b/tests/test_dummy_learner.py @@ -1,6 +1,6 @@ import pytest -from hobj.learning_models import RandomGuesser +from hobj.learning_models.random_guesser import RandomGuesser @pytest.fixture diff --git a/tests/test_load_behavior.py b/tests/test_load_behavior.py index 5383b6e..c9ea350 100644 --- a/tests/test_load_behavior.py +++ b/tests/test_load_behavior.py @@ -2,8 +2,8 @@ import pytest -from hobj.data_loaders.behavior import load_highvar_behavior, load_oneshot_behavior -from hobj.data_loaders.images import ( +from hobj.data.behavior import load_highvar_behavior, load_oneshot_behavior +from hobj.data.images import ( load_image, load_imageset_meta_catch, load_imageset_meta_highvar, diff --git a/tests/test_simulate_subtask.py b/tests/test_simulate_subtask.py index 4f61f15..711a7ef 100644 --- a/tests/test_simulate_subtask.py +++ b/tests/test_simulate_subtask.py @@ -3,7 +3,7 @@ import numpy as np from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask -from hobj.learning_models import RandomGuesser +from hobj.learning_models.random_guesser import RandomGuesser from hobj.types import ImageId From cf4502e3402e22166226ac4e439e2c335d9bfd19 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 19:21:53 -0400 Subject: [PATCH 133/156] typing --- hobj/data/images.py | 13 +++++++------ hobj/learning_models/make_model.py | 5 ++++- hobj/learning_models/random_guesser.py | 11 ++++++----- 3 files changed, 17 insertions(+), 12 deletions(-) diff --git a/hobj/data/images.py b/hobj/data/images.py index afb8e37..810d0a8 100644 --- a/hobj/data/images.py +++ b/hobj/data/images.py @@ -116,8 +116,10 @@ def load_imageset_meta_catch( ) return manifest_df.sort_values("image_id").reset_index(drop=True) + # %% Image loaders: + @lru_cache(maxsize=None) def _image_id_to_local_path_table(cache_root: Path) -> dict[ImageId, Path]: """Build a mapping from image ID to absolute local path. @@ -153,9 +155,10 @@ def list_image_ids(cachedir: Path | None = None) -> list[ImageId]: table = _image_id_to_local_path_table(cache_root) return sorted(table.keys()) + def load_image( - image_id: ImageId, - cachedir: Path | None = None, + image_id: ImageId, + cachedir: Path | None = None, ) -> Image.Image: """Load an image by ``image_id`` from the packaged dataset. @@ -170,10 +173,9 @@ def load_image( return Image.open(path) - def get_image_path( - image_id: ImageId, - cachedir: Path | None = None, + image_id: ImageId, + cachedir: Path | None = None, ) -> Path: """Return the local path for an image in the packaged dataset. @@ -199,7 +201,6 @@ def get_image_path( return path - # %% if __name__ == "__main__": df = list_image_ids() diff --git a/hobj/learning_models/make_model.py b/hobj/learning_models/make_model.py index c464817..834ad2c 100644 --- a/hobj/learning_models/make_model.py +++ b/hobj/learning_models/make_model.py @@ -62,7 +62,10 @@ def create_linear_learner( raise ValueError("alpha must be in the interval [0, 1]") f_calibration = np.array( - [image_id_to_features[ref] for ref in _get_calibration_image_ids(cachedir=cachedir)] + [ + image_id_to_features[ref] + for ref in _get_calibration_image_ids(cachedir=cachedir) + ] ) mu_calibration = np.mean(f_calibration, axis=0) norms_calibration = np.linalg.norm(f_calibration - mu_calibration, axis=1) diff --git a/hobj/learning_models/random_guesser.py b/hobj/learning_models/random_guesser.py index fdebed7..13f2287 100644 --- a/hobj/learning_models/random_guesser.py +++ b/hobj/learning_models/random_guesser.py @@ -1,28 +1,29 @@ -import typing +from typing import Literal import numpy as np from hobj.learning_models import BinaryLearningModel from hobj.types import ImageId - +from typing import cast class RandomGuesser(BinaryLearningModel): """ A dummy model which simply returns random outputs, independent of the given image. """ - def __init__(self, seed: int = 0): + def __init__(self, seed: int | None = None): self.random_generator = np.random.default_rng(seed=seed) - def reset_state(self, seed: typing.Union[int, None]) -> None: + def reset_state(self, seed: int | None) -> None: self.random_generator = np.random.default_rng(seed=seed) def get_response( self, image: ImageId, - ) -> typing.Literal[0, 1]: + ) -> Literal[0, 1]: action = self.random_generator.integers(2) action = int(action) + action = cast(Literal[0, 1], action) return action def give_feedback(self, reward: float) -> None: From e49603d13307d6fb0b4bc67854289eaf862ecbfa Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 19:24:20 -0400 Subject: [PATCH 134/156] type hinting --- hobj/utils/file_io.py | 2 +- pyproject.toml | 1 + uv.lock | 44 +++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 46 insertions(+), 1 deletion(-) diff --git a/hobj/utils/file_io.py b/hobj/utils/file_io.py index 12ff671..79552b6 100644 --- a/hobj/utils/file_io.py +++ b/hobj/utils/file_io.py @@ -53,7 +53,7 @@ def download_file(url: str, output_path: Path) -> None: file.flush() -def get_bytes_size(num_bytes: int, output_units: str = None) -> (float, str): +def get_bytes_size(num_bytes: int, output_units: str | None = None) -> tuple[float, str]: """ :param num_bytes: diff --git a/pyproject.toml b/pyproject.toml index e15fea6..6098d68 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,6 +24,7 @@ dependencies = [ "xarray>=2025.1", "scipy>=1.17.1", "ipykernel>=7.2.0", + "scipy-stubs~=1.17.1", ] [project.scripts] diff --git a/uv.lock b/uv.lock index 8163d59..417bd06 100644 --- a/uv.lock +++ b/uv.lock @@ -704,6 +704,7 @@ dependencies = [ { name = "pydantic" }, { name = "requests" }, { name = "scipy" }, + { name = "scipy-stubs" }, { name = "tqdm" }, { name = "xarray" }, ] @@ -724,6 +725,7 @@ requires-dist = [ { name = "pydantic", specifier = ">=2.10" }, { name = "requests", specifier = ">=2.32" }, { name = "scipy", specifier = ">=1.17.1" }, + { name = "scipy-stubs", specifier = "~=1.17.1" }, { name = "tqdm", specifier = ">=4.67" }, { name = "xarray", specifier = ">=2025.1" }, ] @@ -1314,6 +1316,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/1f/b6/7c0d4334c15983cec7f92a69e8ce9b1e6f31857e5ee3a413ac424e6bd63d/numpy-2.4.3-cp314-cp314t-win_arm64.whl", hash = "sha256:4d382735cecd7bcf090172489a525cd7d4087bc331f7df9f60ddc9a296cf208e", size = 10565454, upload-time = "2026-03-09T07:58:33.031Z" }, ] +[[package]] +name = "numpy-typing-compat" +version = "20251206.2.4" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "numpy" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/42/5f/29fd5f29b0a5d96e2def96ecba3112fc330ecd16e8c97c2b332563c5e201/numpy_typing_compat-20251206.2.4.tar.gz", hash = "sha256:59882d23aaff054a2536da80564012cdce33487657be4d79c5925bb8705fcabc", size = 5011, upload-time = "2025-12-06T20:02:04.942Z" } +wheels = [ + { url = 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77b48478767f0422f141b0e5ae7b7e1b3090eac3 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 19:27:53 -0400 Subject: [PATCH 135/156] narrow typing --- hobj/learning_models/random_guesser.py | 1 + hobj/learning_models/update_rules.py | 20 ++++++++++---------- hobj/utils/file_io.py | 4 +++- 3 files changed, 14 insertions(+), 11 deletions(-) diff --git a/hobj/learning_models/random_guesser.py b/hobj/learning_models/random_guesser.py index 13f2287..476ce04 100644 --- a/hobj/learning_models/random_guesser.py +++ b/hobj/learning_models/random_guesser.py @@ -6,6 +6,7 @@ from hobj.types import ImageId from typing import cast + class RandomGuesser(BinaryLearningModel): """ A dummy model which simply returns random outputs, independent of the given image. diff --git a/hobj/learning_models/update_rules.py b/hobj/learning_models/update_rules.py index 5a8c0b1..863dc45 100644 --- a/hobj/learning_models/update_rules.py +++ b/hobj/learning_models/update_rules.py @@ -30,7 +30,7 @@ def get_update( self, x: np.ndarray, w: np.ndarray, - b: Union[np.ndarray, np.generic, float], + b: np.ndarray, logits: np.ndarray, action: int, reward: float, @@ -66,7 +66,7 @@ def get_update( self, x: np.ndarray, w: np.ndarray, - b: Union[np.ndarray, np.generic, float], + b: np.ndarray, logits: np.ndarray, action: int, reward: float, @@ -103,7 +103,7 @@ def get_update( self, x: np.ndarray, w: np.ndarray, - b: Union[np.ndarray, np.generic, float], + b: np.ndarray, logits: np.ndarray, action: int, reward: float, @@ -113,7 +113,7 @@ def get_update( reward_prediction = logits[action] rpe = float(reward) - reward_prediction - delta[:, action] = (self.alpha) * rpe * x + delta[:, action] = self.alpha * rpe * x return delta, 0 @@ -122,7 +122,7 @@ def get_update( self, x: np.ndarray, w: np.ndarray, - b: Union[np.ndarray, np.generic, float], + b: np.ndarray, logits: np.ndarray, action: int, reward: float, @@ -143,7 +143,7 @@ def get_update( self, x: np.ndarray, w: np.ndarray, - b: Union[np.ndarray, np.generic, float], + b: np.ndarray, logits: np.ndarray, action: int, reward: float, @@ -164,7 +164,7 @@ def get_update( self, x: np.ndarray, w: np.ndarray, - b: Union[np.ndarray, np.generic, float], + b: np.ndarray, logits: np.ndarray, action: int, reward: float, @@ -189,7 +189,7 @@ def get_update( self, x: np.ndarray, w: np.ndarray, - b: Union[np.ndarray, np.generic, float], + b: np.ndarray, logits: np.ndarray, action: int, reward: float, @@ -213,7 +213,7 @@ def get_update( self, x: np.ndarray, w: np.ndarray, - b: Union[np.ndarray, np.generic, float], + b: np.ndarray, logits: np.ndarray, action: int, reward: float, @@ -231,8 +231,8 @@ def get_update( class REINFORCE(UpdateRule): + @staticmethod def _compute_trace( - self, action_taken: int, logits: np.ndarray, ): diff --git a/hobj/utils/file_io.py b/hobj/utils/file_io.py index 79552b6..07baf47 100644 --- a/hobj/utils/file_io.py +++ b/hobj/utils/file_io.py @@ -53,7 +53,9 @@ def download_file(url: str, output_path: Path) -> None: file.flush() -def get_bytes_size(num_bytes: int, output_units: str | None = None) -> tuple[float, str]: +def get_bytes_size( + num_bytes: int, output_units: str | None = None +) -> tuple[float, str]: """ :param num_bytes: From 19f6ef5fe99969ba92d0904c0d4da04f8781525f Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 19:30:30 -0400 Subject: [PATCH 136/156] remove prototype --- hobj/learning_models/__init__.py | 3 -- hobj/learning_models/make_model.py | 1 - hobj/learning_models/update_rules.py | 44 ---------------------------- 3 files changed, 48 deletions(-) diff --git a/hobj/learning_models/__init__.py b/hobj/learning_models/__init__.py index 95c78ef..9ed821b 100644 --- a/hobj/learning_models/__init__.py +++ b/hobj/learning_models/__init__.py @@ -40,9 +40,6 @@ def give_feedback(self, reward: float) -> None: raise NotImplementedError -# %% - - # %% class LinearLearner(BinaryLearningModel): def __init__( diff --git a/hobj/learning_models/make_model.py b/hobj/learning_models/make_model.py index 834ad2c..7981282 100644 --- a/hobj/learning_models/make_model.py +++ b/hobj/learning_models/make_model.py @@ -34,7 +34,6 @@ def _get_calibration_image_ids(cachedir: Path | None = None) -> list[ImageId]: def create_linear_learner( image_id_to_features: dict[ImageId, np.ndarray], update_rule_name: Literal[ - "Prototype", "Square", "Perceptron", "Hinge", diff --git a/hobj/learning_models/update_rules.py b/hobj/learning_models/update_rules.py index 863dc45..85e8492 100644 --- a/hobj/learning_models/update_rules.py +++ b/hobj/learning_models/update_rules.py @@ -54,50 +54,6 @@ def get_update( # %% -class Prototype(UpdateRule): - """ - Simulates the decision boundary implemented by a prototype learner. - """ - - def reset(self): - self.ncounts = None - - def get_update( - self, - x: np.ndarray, - w: np.ndarray, - b: np.ndarray, - logits: np.ndarray, - action: int, - reward: float, - ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]: - if self.ncounts is None: - self.ncounts = np.zeros(w.shape[1]) - - if reward > 0: - # Received a positive example of the class associated with the action - i_updated_class = action - else: - # Unknown which class this is associated with. In the two-way case, it could be inferred, but in general, not. - if w.shape[1] == 2: - i_updated_class = 1 - action - else: - return np.zeros_like(w), 0 - - mu_cur = w[:, i_updated_class] - n = self.ncounts[i_updated_class] - mu_next = (n / (n + 1)) * mu_cur + (1 / (n + 1)) * x - norm_next = np.square(np.linalg.norm(mu_next)) - - delta_w = np.zeros(w.shape) - delta_w[:, i_updated_class] = mu_next - mu_cur - - delta_b = np.zeros(b.shape) - delta_b[i_updated_class] = (-norm_next) - b[i_updated_class] - - return delta_w, delta_b - - class Square(UpdateRule): def get_update( self, From d4d4a59c0886ee656f3ea5431507d71e21f400b2 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 19:36:01 -0400 Subject: [PATCH 137/156] tree shake --- hobj/learning_models/make_model.py | 4 +- hobj/utils/__init__.py | 0 hobj/utils/file_io.py | 96 ------------------------------ hobj/utils/hash.py | 35 ----------- 4 files changed, 3 insertions(+), 132 deletions(-) delete mode 100644 hobj/utils/__init__.py delete mode 100644 hobj/utils/file_io.py delete mode 100644 hobj/utils/hash.py diff --git a/hobj/learning_models/make_model.py b/hobj/learning_models/make_model.py index 7981282..e49852c 100644 --- a/hobj/learning_models/make_model.py +++ b/hobj/learning_models/make_model.py @@ -45,7 +45,9 @@ def create_linear_learner( alpha: float = 1, cachedir: Path | None = None, ) -> LinearLearner: - """Instantiate a linear learner from precomputed features. + """ + Instantiate a linear learner from precomputed features, using the procedure + described in the paper to calibrate the features based on the warmup images. Args: image_id_to_features: Precomputed features keyed by image ID. diff --git a/hobj/utils/__init__.py b/hobj/utils/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/hobj/utils/file_io.py b/hobj/utils/file_io.py deleted file mode 100644 index 07baf47..0000000 --- a/hobj/utils/file_io.py +++ /dev/null @@ -1,96 +0,0 @@ -import json -import urllib.request -import zipfile -from pathlib import Path -from typing import Any - -import requests -from tqdm import tqdm - - -def unzip_file(zip_path: Path, output_dir: Path) -> None: - """ - Unzip a file to a given directory. - :param zip_path: - :param output_dir: - :return: - """ - - if not zip_path.as_posix().endswith(".zip"): - raise ValueError(f"Expected a .zip file, got {zip_path}") - if not zip_path.exists(): - raise FileNotFoundError(f"Zip file {zip_path} does not exist.") - - if not output_dir.exists(): - output_dir.mkdir(parents=True) - - with zipfile.ZipFile(zip_path, "r") as zip_ref: - zip_ref.extractall(output_dir) - - -def download_file(url: str, output_path: Path) -> None: - # Send a GET request to fetch the file - response = requests.get(url, stream=True) - total_size_in_bytes = int(response.headers.get("content-length", 0)) - - # Create the output directory if it does not exist - if not output_path.parent.exists(): - output_path.parent.mkdir(parents=True) - - with tqdm( - total=total_size_in_bytes, - unit="B", - unit_scale=True, - disable=False, - desc="Download progress", - ) as progress_bar: - with open(output_path.as_posix(), "wb") as file: - # Iterate over the response data in chunks and write to file - for chunk in response.iter_content(chunk_size=1024): - if chunk: - file.write(chunk) - progress_bar.update(len(chunk)) - file.flush() - - -def get_bytes_size( - num_bytes: int, output_units: str | None = None -) -> tuple[float, str]: - """ - - :param num_bytes: - :param output_units: If None, automatically selected - :return: - """ - - unit_conversion = {"B": 1, "KB": 1e3, "MB": 1e6, "GB": 1e9} - - # Automatically select the byte unit (KB, MB, etc.) - if output_units is None: - if num_bytes == 0: - output_units = "B" - elif num_bytes < 1e6: - output_units = "KB" - elif num_bytes < 1e9: - output_units = "MB" - else: - output_units = "GB" - - # Convert the size to the selected unit - size = num_bytes / unit_conversion[output_units] - return size, output_units - - -def download_json(url: str) -> Any: - """ - Download a JSON file from a URL. - :param url: - :return: - """ - response = urllib.request.urlopen(url) - if response.status != 200: - raise ValueError(f"Could not load JSON data from {url}", response) - data = response.read().decode("utf-8") - json_data = json.loads(data) - - return json_data diff --git a/hobj/utils/hash.py b/hobj/utils/hash.py deleted file mode 100644 index ddf82b5..0000000 --- a/hobj/utils/hash.py +++ /dev/null @@ -1,35 +0,0 @@ -import hashlib - -import PIL.Image -import numpy as np - - -def hash_image(image: PIL.Image.Image) -> str: - """ - Hash an image based on its np.uint8 representation. - :param image: - :return: - """ - sha256_hash = hashlib.sha256() - - # Always cast the image to RGBA format - image = image.convert("RGBA") - - # Convert the image to a numpy array - image_array = np.array(image).astype(np.uint8) - - # Update the hash with the image array - sha256_hash.update(image_array.tobytes()) - - # Return the hexadecimal representation of the hash - return sha256_hash.hexdigest() - - -def hash_string(input_string: str) -> str: - sha256_hash = hashlib.sha256() - - # Convert string to bytes - sha256_hash.update(input_string.encode("utf-8")) - - # Return the hexadecimal representation of the hash - return sha256_hash.hexdigest() From 65e1126613cee76ada17ecb020d10829d0d363ba Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 19:37:04 -0400 Subject: [PATCH 138/156] directory refactor --- hobj/__init__.py | 2 +- hobj/learning_models/__init__.py | 4 ++-- hobj/learning_models/linear_learning_model/__init__.py | 0 .../learning_models/{ => linear_learning_model}/make_model.py | 2 +- .../{ => linear_learning_model}/representation.py | 0 .../{ => linear_learning_model}/update_rules.py | 0 6 files changed, 4 insertions(+), 4 deletions(-) create mode 100644 hobj/learning_models/linear_learning_model/__init__.py rename hobj/learning_models/{ => linear_learning_model}/make_model.py (97%) rename hobj/learning_models/{ => linear_learning_model}/representation.py (100%) rename hobj/learning_models/{ => linear_learning_model}/update_rules.py (100%) diff --git a/hobj/__init__.py b/hobj/__init__.py index f33905b..0c4ae45 100644 --- a/hobj/__init__.py +++ b/hobj/__init__.py @@ -13,7 +13,7 @@ load_imageset_meta_catch, ) -from hobj.learning_models.make_model import ( +from hobj.learning_models.linear_learning_model.make_model import ( create_linear_learner, ) diff --git a/hobj/learning_models/__init__.py b/hobj/learning_models/__init__.py index 9ed821b..957baec 100644 --- a/hobj/learning_models/__init__.py +++ b/hobj/learning_models/__init__.py @@ -3,8 +3,8 @@ import numpy as np -from hobj.learning_models.representation import RepresentationalModel -from hobj.learning_models.update_rules import UpdateRule +from hobj.learning_models.linear_learning_model.representation import RepresentationalModel +from hobj.learning_models.linear_learning_model.update_rules import UpdateRule from hobj.types import ImageId diff --git a/hobj/learning_models/linear_learning_model/__init__.py b/hobj/learning_models/linear_learning_model/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/hobj/learning_models/make_model.py b/hobj/learning_models/linear_learning_model/make_model.py similarity index 97% rename from hobj/learning_models/make_model.py rename to hobj/learning_models/linear_learning_model/make_model.py index e49852c..ebe0dd5 100644 --- a/hobj/learning_models/make_model.py +++ b/hobj/learning_models/linear_learning_model/make_model.py @@ -8,7 +8,7 @@ import numpy as np -import hobj.learning_models.update_rules as update_rules +import hobj.learning_models.linear_learning_model.update_rules as update_rules from hobj.learning_models import LinearLearner, RepresentationalModel from hobj.types import ImageId diff --git a/hobj/learning_models/representation.py b/hobj/learning_models/linear_learning_model/representation.py similarity index 100% rename from hobj/learning_models/representation.py rename to hobj/learning_models/linear_learning_model/representation.py diff --git a/hobj/learning_models/update_rules.py b/hobj/learning_models/linear_learning_model/update_rules.py similarity index 100% rename from hobj/learning_models/update_rules.py rename to hobj/learning_models/linear_learning_model/update_rules.py From 2b585183b262dc3e4b67a05d873b4d648d85e3f9 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 20:01:52 -0400 Subject: [PATCH 139/156] refine readme --- README.md | 15 ++++++++++----- hobj/benchmarks/mut_highvar_benchmark.py | 9 ++++----- hobj/benchmarks/mut_oneshot_benchmark.py | 4 +--- 3 files changed, 15 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index af7a210..e1bfe64 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,9 @@ from hobj.learning_models import random_guesser [![CI](https://github.com/himjl/hobj/actions/workflows/ci.yml/badge.svg)](https://github.com/himjl/hobj/actions/workflows/ci.yml) -This repository contains benchmarks for comparing models of object learning against measurements of human behavior, from Lee and DiCarlo 2023 (["How well do rudimentary plasticity rules predict adult visual object learning?"](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011713)). +This repository contains benchmarks for comparing models of object learning against measurements of human behavior, from Lee and DiCarlo 2023 (["How well do rudimentary plasticity rules predict adult visual object learning?"](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011713)). + +It also lets you download the raw data and images from the experiments in the paper.
Alt text @@ -20,10 +22,10 @@ Then, an instance of that subclass may be passed in as an argument to a benchmar import hobj # Load model: -model = random_guesser.RandomGuesser() # A subclass of BinaryLearningModel +model = hobj.RandomGuesser() # Load benchmark: -benchmark = hobj.MutatorHighVarBenchmark() # Try benchmark 2: hobj.MutatorOneshotBenchmark() +benchmark = hobj.MutatorHighVarBenchmark() # or hobj.MutatorOneshotBenchmark() result = benchmark(model) # Print the score and its CI: @@ -44,7 +46,9 @@ The `hobj` package works for Python >=3.11. After cloning this repository on you ### Changes to codebase since publication -This codebase was refactored in January 2025 to improve the performance and quality of the code, and is now designated as `v2`. Along the way, minor refinements to the statistical analysis of the original codebase were introduced (see [changelist](site/changelist.md)). To see the codebase at the time of publication, check out the repo with the `v1` tag [here](https://github.com/himjl/hobj/releases/tag/v1). +This codebase was refactored in 2026 to improve the accessibility, performance, and quality of the code. Along the way, minor changes to the statistical analysis of the original codebase were introduced (see [changelist](site/changelist.md)). + +To see the codebase at the time of publication, check out the repo with the `v1` tag [here](https://github.com/himjl/hobj/releases/tag/v1). ### Citation @@ -58,4 +62,5 @@ This codebase was refactored in January 2025 to improve the performance and qual pages={e1011713}, year={2023}, publisher={Public Library of Science San Francisco, CA USA} -}``` +} +``` diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index 4975150..edf21bd 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -25,7 +25,7 @@ def __init__(self, cachedir: Path | None = None): images_df = load_imageset_meta_highvar(cachedir=cachedir) # Load raw human session data for benchmark: - sessions = load_highvar_behavior( + df_behavior = load_highvar_behavior( remove_probe_trials=True, cachedir=cachedir, ) @@ -44,15 +44,15 @@ def __init__(self, cachedir: Path | None = None): subtask_name_to_subtask = {} # Iterate over worker sessions: - for assignment_id, session in sessions.groupby("assignment_id", sort=False): + for assignment_id, session in df_behavior.groupby("assignment_id", sort=False): categories = set() perf_seq: List[bool] = [] # Iterate over trials: session = session.sort_values("trial") - for reward, image_id in zip(session.perf, session.image_id): + for perf, image_id in zip(session.perf, session.image_id): categories.add(image_id_to_category[image_id]) - perf_seq.append(bool(reward)) + perf_seq.append(bool(perf)) # Infer subtask from the categories observed in the session: assert ( @@ -106,4 +106,3 @@ def __init__(self, cachedir: Path | None = None): # %% if __name__ == "__main__": experiment = MutatorHighVarBenchmark() - print(sorted(experiment.config.subtask_name_to_data.keys())) diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index 06026f3..e140ac3 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -16,8 +16,6 @@ # %% - - class MutatorOneshotBenchmark(GeneralizationBenchmark): subtask_names = [ "MutatorOneshotObject00,MutatorOneshotObject43", @@ -151,7 +149,7 @@ def __init__(self, cachedir: Path | None = None): results = [] for session, session_df in oneshot_df.groupby( - ["assignment_id", "slot"], sort=False + ["assignment_id", "slot"], sort=False ): session_df = session_df.sort_values("trial") transformation_to_kn = collections.defaultdict(lambda: [0, 0]) From 46d250da5b09893ac469db5c86a8f75ec879ed12 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 20:12:35 -0400 Subject: [PATCH 140/156] update notebook --- examples/example_features.npz | Bin 0 -> 17311792 bytes examples/score_example_models.ipynb | 238 +++++++++++++++++++--------- 2 files changed, 167 insertions(+), 71 deletions(-) create mode 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b/examples/score_example_models.ipynb @@ -3,24 +3,26 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T23:13:43.475576Z", - "start_time": "2026-03-26T23:13:43.468622Z" + "end_time": "2026-03-27T00:06:03.241241Z", + "start_time": "2026-03-27T00:06:03.220799Z" } }, "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", - "import numpy as np" + "import numpy as np\n", + "\n", + "import hobj" ], - "id": "e72341e539b8abd3", + "id": "accb05e90bcf1ce9", "outputs": [], - "execution_count": 1 + "execution_count": 15 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T23:13:47.357510Z", - "start_time": "2026-03-26T23:13:43.477479Z" + "end_time": "2026-03-27T00:06:07.615732Z", + "start_time": "2026-03-27T00:06:04.254029Z" } }, "cell_type": "code", @@ -31,33 +33,70 @@ "benchmark1 = MutatorHighVarBenchmark()\n", "benchmark2 = MutatorOneshotBenchmark()" ], - "id": "fac0e73cb1bd321", + "id": "714700a9832048e8", "outputs": [], - "execution_count": 2 + "execution_count": 16 }, { "metadata": {}, "cell_type": "markdown", - "source": "# Benchmark a random guesser", + "source": "# Benchmark a learning model based on image features", "id": "220ecf6a69d69874" }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T23:13:47.395356Z", - "start_time": "2026-03-26T23:13:47.389676Z" + "end_time": "2026-03-27T00:06:08.872314Z", + "start_time": "2026-03-27T00:06:08.863200Z" } }, "cell_type": "code", "source": [ - "# Load example model – a random guesser\n", - "from hobj import RandomGuesser\n", - "\n", - "learner = RandomGuesser()" + "# Loading the exmaple precomputed image features. You can replace this with your own features!\n", + "data = np.load('/examples/example_features.npz') # ResNet50/avgpool features, randomly downprojected to 100 dimensions\n", + "features = data['features'] # N x 100 feature matrix\n", + "image_ids = data['image_ids'] # N image ids\n", + "image_id_to_features = {image_id:f for (image_id, f) in zip(features, image_ids)}\n", + "\n" ], "id": "bdc5bcb78d79e755", "outputs": [], - "execution_count": 3 + "execution_count": 17 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-27T00:06:09.931230Z", + "start_time": "2026-03-27T00:06:09.909346Z" + } + }, + "cell_type": "code", + "source": "", + "id": "b52395cee56d7674", + "outputs": [], + "execution_count": 18 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-27T00:06:11.198440Z", + "start_time": "2026-03-27T00:06:11.147267Z" + } + }, + "cell_type": "code", + "source": [ + "learner = hobj.create_linear_learner(\n", + " image_id_to_features=image_id_to_features,\n", + " update_rule_name='Square',\n", + " alpha=1,\n", + ")\n", + "\n", + "# Alternatively, you can use a random guesser as a baseline:\n", + "# learner = hobj.RandomGuesser()" + ], + "id": "65327b245c715ea2", + "outputs": [], + "execution_count": 19 }, { "metadata": {}, @@ -68,43 +107,46 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T23:13:52.978137Z", - "start_time": "2026-03-26T23:13:47.396747Z" + "end_time": "2026-03-27T00:06:27.771869Z", + "start_time": "2026-03-27T00:06:15.160172Z" } }, "cell_type": "code", "source": [ "# Benchmark on Experiment 1\n", - "result1 = benchmark1(learner=learner)\n", - "print(result1)" + "result1 = benchmark1(learner=learner, show_pbar=True)\n", + "result1" ], - "id": "e0d04f193baadd3c", + "id": "a5710bce79c8e484", "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "LearningCurveBenchmark.LearningCurveBenchmarkResult(msen=0.11344782631802723, msen_sigma=0.0009983236192838236, msen_CI95=(0.1114400003423075, 0.11529258551129108), lapse_rate=<xarray.DataArray 'phat' ()> Size: 8B\n", - "array(1.), model_statistics=<xarray.LearningCurveStatistics> Size: 103MB\n", - "Dimensions: (subtask: 64, trial: 100, boot_iter: 1000)\n", - "Coordinates:\n", - " * subtask (subtask) <U33 8kB 'MutatorObject000,MutatorObject095' ...\n", - "Dimensions without coordinates: trial, boot_iter\n", - "Data variables:\n", - " phat (subtask, trial) float64 51kB 0.53 0.53 ... 0.5 0.488\n", - " varhat_phat (subtask, trial) float64 51kB 0.0004992 ... 0.0005007\n", - " boot_phat (boot_iter, subtask, trial) float64 51MB 0.572 ... 0.492\n", - " boot_varhat_phat (boot_iter, subtask, trial) float64 51MB 0.0004906 ... ...)\n" - ] + "data": { + "text/plain": [ + "LearningCurveBenchmark.LearningCurveBenchmarkResult(msen=0.0055746894992691285, msen_sigma=0.00028657494694110414, msen_CI95=(0.004977290624735935, 0.006121000076948812), lapse_rate=<xarray.DataArray 'phat' ()> Size: 8B\n", + "array(0.07861494), model_statistics=<xarray.LearningCurveStatistics> Size: 103MB\n", + "Dimensions: (subtask: 64, trial: 100, boot_iter: 1000)\n", + "Coordinates:\n", + " * subtask (subtask) <U33 8kB 'MutatorObject000,MutatorObject095' ...\n", + "Dimensions without coordinates: trial, boot_iter\n", + "Data variables:\n", + " phat (subtask, trial) float64 51kB 0.51 0.732 ... 0.91 0.888\n", + " varhat_phat (subtask, trial) float64 51kB 0.0005008 ... 0.0001993\n", + " boot_phat (boot_iter, subtask, trial) float64 51MB 0.526 ... 0.874\n", + " boot_varhat_phat (boot_iter, subtask, trial) float64 51MB 0.0004996 ... ...)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" } ], - "execution_count": 4 + "execution_count": 21 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T23:13:53.063109Z", - "start_time": "2026-03-26T23:13:52.985858Z" + "end_time": "2026-03-27T00:06:29.972245Z", + "start_time": "2026-03-27T00:06:29.909109Z" } }, "cell_type": "code", @@ -118,15 +160,15 @@ "plt.legend()\n", "plt.xlabel('trial')\n", "plt.ylabel('p(correct)')\n", - "plt.show()" + "plt.show()\n" ], - "id": "8f86fdeb01bba73f", + "id": "346a1c6cb7ffb288", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "MSEn 95% confidence interval: [0.111, 0.115]\n" + "MSEn 95% confidence interval: [0.005, 0.006]\n" ] }, { @@ -134,7 +176,42 @@ "text/plain": [ "<Figure size 640x480 with 1 Axes>" ], - "image/png": 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aUTqtC1NTE1q77nOytDQXt+cvGFgo675DhwZ0+coy+nJKb3F7ypcbaN6830hfmCq9AQAAAKxhw6r0/HkUPXgQWuAVMuOb98nW1oo++KA1zfxmCwUFhejkdSZOfIe8vKpSZGQMmZubUtOm1alHj6a0ffsp0idGRkY0Z84AqlO3El2/9oiuXHlAV68+opiYeJo1ewB169ZEPC4kJILGjV1Nmzb9RfpGKkoF3VLKfwYoqIOiuA8MGdJB6t+/TZ6fX6WKszR5ci/pwsUl0v0Hq6SaNV0Uf0/aLPXrV5aSU3ZKN2/9qMhry10rXPz9B+vkderWrSQlJu0Qr/Heez7S9On9xP+3bq+QzMxMFf8MSK388MOIDHWSufD7mD9/oGRnZ6Wvx2/lK7EgC4Ib5T8DFNRBUdsHPvqoreqgwPkWOX2eqamJ9PnnXaWz5xa/dnC5d3+lVLp08Rytx8LCTPr66z5Sv36tFK+LrMqqVZ+p3lv58g5aX7+JiXGW923f8aV43WvXl4u/kS83S7a2VlrPT+HAlNf/v22TxDJ+jZBn68WyTz99W/HPgP4t8+Z9JLaJg00OwBYtGiwdPDRTeha6QSzn/3OzH2urILjRXuWgoA6wD2AfyNc+UKdORSk2bpvqwL106cc5fi4/Vn5eUvJOae+f00QLEJ/p87KTp+aLwCW7dRQvbiMdPTZbtZ6ZMz/Qu326RAnbDHXUo0fTLB9rbGycbaCiqXTv7i0CFk11z60p/JopqbskD4/y0vUbP4jbI0Z0zHEScrNmNd64TVzvvF4OEBwdi6mWf/yxn1geFr5Rsre3zndd8v6wYMFAEcw6ONjn+vlfftlL9TlwUK7p/Sq1nyC40V7loKAOsA8Y2D7Ao2GaN68hfffdUOnS5aXS+g1jpMaN3XXyWnywkgMRuVXgVdSWHDXl8wFePshw602pUv8dqKpVKye9iNgk7vtl47gs11GhgqN05er34nExsf8FD8uXDxdBgrbeZ+vWdfLV0sHvT71Vau7cD7Ns/eDgIyJykzRrVn+pTJk3t1zVqOEiRUVvVa2bD/rq92/938QM9chBDd++cfOHN46c4vq9eCm9NYZb1xo0qPzaY7ieuTuSW0H4cRxoqd/PQZG8b/B7yu9nsWbtaNV75YCRA7qKFcvk6LkjR3ZWPXfUqC46+U7kpyC40V7loKAOsA8YyD7Azegc0Dx+slZjDsHpM/7Shx/6avXMVD5wco4MByfyQeyTTzpl+zw+GPEBnB87e/YAjY9p06aOKn/jq68yHrDlFiP5vT56vEaqVctVtPpwCwUv2/jrOK3keYwZ002sLyBwQZ7WxwGEHACeODlP/D1ydJbGx7ZsWSvDZ8bDqX/88RORj5RVcMk5PPzYO3d/Uj1vwABfcT/Xidxqw0EQL+MgjVt5eHmHDg2y3G4vr6pScEh6l5JcOIDhVhMbG0vxvnr1ai5dvZYeXHLhQFrTujp3bqQKRlq1qi1ygOSiHtS+qYwe3VXVynfu/H9dmXybg7eqVctm2dojtyxxmTKld4F/P3NSENxor3JQUAfYBwxgH+Cz46fB61Q/3hw4rF4zWurTp6XI9ZDnHJFbVg4dninNmTNAtJ7w2fmbulQmTXpX5CZ06uSl6nL47LP0s+CExO1So0bVMrQKcOtDVuvjAIEDBflgz3k3WT128OD2qu3e8MtY6eefR4qycuVI1QH68pVlGXJY3n23udgmvm/PH1Pz1RVSrlypDK0iHDzmdh3t2tVX5blwPfH/vE5NLUtTp76nCqS4S05+XQ5Ofvrp0wzdMBxc7NiZnkvz4OFqcZ98AOegkF930+YvxO3NWyZkeB1OKJbrR9M29+zZVNWNxi03np5VRP2r50Nxq6B8+/mLX0UyeHZdiOpdh+qFXye7IEu9HuXWoZEjO4tlHChxV6Z6kLNs2bAM3WI+PrVUAWB2rWb6UBDcaK9yUFAH2Af0bB/gHBJuYXlTrol64R95+SDTsWND0b2hfj+fHY8f30MclDQdYPgAxq0e1tYWqufw648d213VPZQ52VduVVFPFOVWgZevtojlvr51s03m5PW6uGQfWHHhESuatlluASlWzOa15/DBUu6mCg37RWxjVkGUldV/7zlz2bJ1wmutIhw85ebzlJN5OTDigIaDS77NrSqZH8vvh+8bOtRP3G7Roqb0++6vVa/NdcYBJAezcu4IB64cfMjr4NYTOYiVW7Eyv1blys6q+9RbO7guuJVMfj1+bfXuOK7XoHsrMwTR/PicBJC8DdyF+PDRatHSxoXzcORtrVfPLcvn8vbK+yEH66Qhr2jnrimq7eJ9kIMtDoTlZU+erpW6dWui+Pc7u4LgRnuVg4I6wD6gw32Af3Q1HXyzK/KZKJ+B5vQ5S5YMzfKHX73wwbV27YrSwIHtxFBYzqPgs131g+fChYOkQYPaZwiEOPjhliA+OMkHRS7cMpDVtvBBPfN9b73lqXpu1645O9DwNvfu3UK0HqkX3sbMQZx64TwjuZuMC5+9cz4It3BwgMLvX+4uWrx4yGv5J+3bp7e4cP1wnXG+iHwg5pygnGw7t4rJrQ2czMvLuNWMb/P2qz+WuwvjE9JbnDJ3r3BC7/kL36neC78v+XPInBTLLWPya6iPXMpcdv3+lSroqlSpjAgiOTiWn8cjiDS1LnEAzAENBw+53bczF95WHpkkBx+aWhE5f0vu+uLWrOw+cx+fWqL7NXMQ/P33w7WSzKzrguBGe5WDgjowmH2AWzv4ACAfRJQsnHgp/2hzl4Gbm1OOntelS2PVDzK3jPBB503P4YMynwXzc7jbKC8tRZzwevtO+oFevfB6OX9D/SDHBxtuKfrgg9YaW5e4/vm5fFB3dS2tWs7DtKNj/pfn7p28FG7h4NE68nDk7Aq3eMitO/y+5MCHgz15XXLLCnfJZNfiIxd56n7eF+RlnGPEy1as+ERj6xsf5DWtiz+D4cM7ZghAOEDT9FgOOnhYNrfqcGCm6TFt29ZT7WfqASu3Usk5OwVROOiQu7i4i1EOmDiI4SCcE5/lfdHJqUSOvg/vvecj3gfXQdOm1QvsveS3ILjRXuWgoA4U3wc4cVTTkMzcngHKBzDOQ1DqvXCiLCc2Zj5wcuLrm872+cxdbvKXg4C16z5/42ty4qfcopCbrixNBwU/P09p956vxQFlwoR38px8fODgN2Kb+EDO26Q+Ydqf+6Zne/ati8JdKzNm9FN1VXFLFOedvP22lwiK5dYrbs3g9yx3zXCgoT7yiw+ucpItt2TldJ9UH/rNXSO8jA+86o+fNq2vWM65Ldmtl7sYOeDi1ojs6pHr/U3zBMkjzbj8sXea6NLU5iiznBbOmZKTw7nVady47hkS48Ofb5QaNqxa4NtFBVwQ3GivclBQB4ruA/yjJicu8oEmr+t5551mGYKJ7Prv31Q46ZMP8HzGn5sfeh5VIyey8pnwuvVjRODBZ6O8jA+K8ogVTUU+oPIZqjxqhtfzppl65ZaAXzeN15vvM3c5yQelM2cXqd4LH8CVOHjKhUf5aBqdwy1e8n7ISc5yAjaPBtLU9SF3NfGBOKsROtyyJQe26vk+ZcuWVLVsqec4HfsrPeGWk6gLqj64lY0DiaxGYxV0F66cj6TecsjfK21POEh6WhDcaK9yUHRcBzz/A+ceqGfvo2iesyLwn4V5rpv9B2ZkaPHY9pvmPANOmuVJxrhVQtP9HFSoj47hbi6eM+ZNr885IfJz9u2fkSG44hwPOV+Ck1t5CHPm53P3jXxwlQ+o8jBrTbkr6kUeCaLpQKxU4S4cHh4u1wknjnIOi9LblV3hz15OhubC+1RWj+XuErkViAMhTu7llhp+39wyI7dcaZp3hovcjcgJw3ybu7jkfBt9CDSUKjwiir9/nFPELWr6dskG0nFBcKO9ykHRcR3ITdg8jBP1/fqZmtzXL58p52RIqKaRFHLLAHeryOvMnGvAeTByywoXHgnDZ/Ly/Ty6Rz5g/XPaP8MoIe4q4DNuTa/Pc3XIz+ORQFkNp+Z18mM4Z4KHsqqftXPSJ993+Mi3Gc6q5RYCeah15sItQXw/Hxj17eyW80PkJFBdXG5AF4U/Sw5+edj2m7oROR+Ku9jUk3w5v0q+zZ8d72OaPhf58+bRaPIkgXJLhdJ1oHQp6C5L0qOC4EZ7lYOiwzrgg6v8Q8cHXE1n7EW5yK0tPNkaTwwmHwhzux6er0XOGeDb3D2TeW4PDmLkFg4eISQHOZzIyMERj+KRAyzukuJ8BW5x4QnU5GCJm8x5bhf1bhXOaeChrfKcIdl1uXDipDyJm9xlw900cqsP531kHrK7avWo1xJS1QtPRibniij9eWoqXLe5vZSA0oVbUXIzrX/fvj6qaxLJrXPffvtBtsPcv/iipyrA5tucDyQnNSv9/lFIsTpAcKO9ykHRYR3wWZl6/3FWByh9K5xAyfNDyBNl5bTwcFXOM8nJBee4hUZuceAkXJ5mXu6WyWp+FE2Fm63lA4s8tJhzVOSAUs5x4UnfeBkHItyK0qSJu0gWlefqkIMd7gLKfObILT7qE6px9xm3OvHj/j4xVzVpXU6GxXLQxLk8mkYm8XDkzI/nA6TcXaGpXuRZWrkJX+n9piiXkiXtRM4UjwjLScsD5+3w58Zdd3z7r+NzxG3u7lL6vaCQYnWA4EZ7lYOiwzqQm6z5oCUfoPIyVLegizxPCRcOcnJy5u3t7aHKd+EWEvXunsyFWzfkoZ/cYiMv53k1eBn/0Gd+PM+p8dv2ya+dDfN8JfKoFvXtlPNVuFVIfgwHO5xXIT/G2blkhpYUbunJaqI3HknEQYk8Ky63ssjzaXBwlNN5T9TfE88CK3dV8ezCWQVHvP/wYziQUR+5w0Gh3P2RmynsUZSvA+6qklsEuXtLDq5zOmUAChlkHSC40V7loOioDvgMXW6J4JYMueuEz/Czm24+P4XP+rirg+cQyWsiHg+3lH905XwP7qZRzw/RlKcgXydILtxSktXjedg3P4ZzWrgVRT3YkLuGeM4PudtHni9Gbnlxd/9vHhs5cZOb9dVfg7sA5YBGThLlOUcybwufZfOU91xyEsTxNsqz1qrn+eTnc+Pk46zyeeQ6kIMqnsxMTjiVL8bII3bwXS58v2VygM/fV3nfVnqbUEjROkBwo73KQdFRHXAXgjwMVM63kLtP3nRRwbwU+Zo+cuFhpbkdocUHd85Hkfv++WJ3cqIsd8VoWh8HbvIU6vya6gm93DKR+fEcJMnzV/AQz8z38xV+5cRaHqUkdx3xCAq5K4dzGrirSD2RWFN+A7f0yPXB3UraDCp52DpfKye/8/PkJuiUR9hwUMjdesf/nquz/QlF93Ugd5XK37GczGmEYth1YJe747fyG6zHlYOiozrgi7PxDxYnhMrLuFuDl3EwwLPCauu15Cvlyl0rcksFnwlyEJDb9fDBU578i3NT5ODlbtDPYjQQz17K87dw8qscfHD3itxlIl+8j9ejPkqGWyjkrhyerE5TbgI/Xm6il1uOeKIxHjnEXS9yNw6/R07glRN5s2pR4nXwY3My06++F85LkrvR+H3JQSRf3FHpbUPJfR3wtATqJyQFFSijkN7WAYIb7VUOio7qQJ7XhK/KrN4yIs8IyvkTPByUE3B5RA7Pk5GXa5/Ioy648AgNXsbdNvKU5dzNw0mOb1oPBxXy/C6ZJxHjfBL1i+VlLty8zgmV8uO5hUS+6jNPV8/5NxzsybPActDDsxJntS3qs9lyC5J6lxgHUNyqo/76fMmC7K4vlNt8GH0uHBCqXwyQ61npbULJWx1wUrr6fmwIATgK5asOENxor3JQdFAH3Ooh/2Bl7sqRL8anqfDIm+xyWzLnlMiJplw4Z0T9fg6U5KsJ81n+myYGk7twuKsj8wUEuXBuDF+pmJONOcdDnr+Hc4g0Xe+FEyPl2UbV83H4QovcApHdtvAwXJ7cL6szWZ4eX74CMHfVFLahxtqaQ4Zb5nJ7hWoU/akD3m/lLil51BRK0a4DO3RLaa1yUHRQBzzvBf9gcf5KVkOmeebiYcPeEnknHJjIE8Zxgqym6wNxawjneXArjzyvilx4JJGm1+EgRR5i+umnb2e5vZxbI19A701T/asX7lrLbtirPP283EWWn8sraKoP7ubLTbcbCupA3/YB+fvJwbzS24JCitcBghvtVQ6KDuqAL6jHP1g8Qiqnz+HuE7lbiOdaUU9+5enc5W4mufAZH0/cpilpV1O3VXYXk5TnSuFrFGm7LkaN6iKCN32bPRcFdaAP+8CgQe1F1zHPe6P0tqCQ4nWA4EZ7lYOigzqQk2yzyyvRVHgKdnkoNE/3z3PHyCNiuPBoK+4W4pEyOb36M7dsyAm4mrpveJSR3HWlnjeDgjrAPoB9APsAIbjRl50AwY2y9S/PjsstKzkNQNQLd91w91DmVhqexyUvrR88WRzPfcPr4ZFPWV3/J/PEeSioA+wD2AewD5DeHr+NeSw4QEFp166++Hv8+FVKTEzO9fP37DlD/T/wp7S0NFFWrzpA1aoOpa+/3kgxMfG5Xh+v48iRyxm2Td3bnRulv+7u07leNwAAKAPBDRSodu3TA4iDBy7keR1btvxN9euNIg/3YTR48FIKDo7I1zYdOnhR/PVtWzfDchsbS2rTpo4qqAIAgMLBVOkNgKLD3NyUfHxqif8P5CO4YVeuPNDSVhEdOpQe3Hh7e4iAJjY2Qdxu27YeWViYUVBQCN248VhrrwcAALqFlhvQuVKl7GnIkA60/8A3ZG1tQcHBL+jatUd6U/P374fSvXvPyMzMlFq2rKla/vbbXuLvH2i1AQAoVNByA1phZGRE7dvXpwoVHETrBwcx/NezYRXy9a1LpqYmqsf+tGKf3tX64UMXyW2on2it+fPPc+L9dPo3uNmNfBsAgEJH0YzvESNGSPfv35fi4+OlwMBAycsr+4nMRo0aJd28eVOKi4uTHj16JPn7+0sWFhY6ybYuioVnDOYL1C1bNixXF5aUL+iYVTlzdpE0fnwPqWJF/ZxCnWey5e28eGmJ6kKM8hDxvF5BHAV1gH0A+wD2ASp689z06tVLSkhIkD788EOpevXq0ooVK6SIiAjJ0fH1Kxhzee+990QQxH9dXV2ldu3aSU+fPpUWLlyoq8opUoVnBpavSC1fFoBn7n3T9P18fSj5OXxJg182jhPX9+HLH3BA86ZLG+hD4YtOyu+BL38wfXo/8f+WrRMU3zYU1AH2AewD2Aeo8AQ33FKzdOnSDNPhP3nyRJowQfMBhR976NChDMsWLFgg/f333zl+TQQ3mutl7Njuqgs3Xru+XLS0yAd7bs1o0aKmxufx1ajlmYO/+eb9Qv0F5MtB8Pt47z0f1f98GQiltwsFdYB9APsA9gEqHMGNmZmZlJycLHXt2jXD8rVr10o7d+7U+BxusYmMjFR1XVWqVEm6fv26NGnSpCxfx9zcXFSEXMqWLYuWG7X6KVbMRlzOQA5keOZfvko1T27HF4KUJ7iT73N2Lql6Ll/EUr6KN18skp9TmL+AfGVufi/79s8Qf1NSd4mLVCq9XSioA+wD2AewD1DhCG6cnZ3FRjZp0iTTAWauaNHJ6nkjR46UEhMTpaSkJPH85cuXZ/s6U6dOlTRBt1T67LxHjs4SB/L4hO3iQouZ648vOfDDDyPEgZ4fx1eyHjeuu8hD4dwcXvY0eN0br2RdGEq7dhmvSH7i5DzFtwkFdYB9APsA9gEy7ODGx8dHCgkJkQYNGiTVqlVL6tatm/Tw4UNpypQpWb4OWm6y/gz4go1ywMIJtNl9XvXrV5ZOnpqvOvDL14fiay61bGkYF7WzsrIQQZ78HidOfEfxbUJBHWAfwD6AfYAMu1vq+PHj0rx5Gc+m+/XrJ8XGxop8nZy8LnJu0uuBr7LLgQkfxPv29clR3XEdcw5KyLP1qgBgwgTDCgC4e01+b3wdLKW3BwV1gH0A+wD2Acr18VuxSfySk5Pp3Llz5Ovrq1rGc4vw7YCAAI3Psba2FtcCUpeamqp6LuSMg4M9/bJxHJmYmNCa1Qfp11//ytHzJEmi9euPkHu1YfTNjM00bepGmjfvN4Oq9sOHLom/Dx6E6tVEgwAAkDuKDgXnod39+/eXPDw8pB9//FEMBS9durS4f926ddKsWbMy5M+8evVK6t27t1SxYkWpbdu20p07d6TNmzfn+DWLessNt77s3vO1alQUJwUrvU36VDh36OChmWLElNLbgoI6wD6AfQD7AOXp+K3oDMVbt24lR0dHmjFjBjk5OdHFixfJz8+PwsLCxP0uLi4ZWmpmzpwpWg/4b7ly5Sg8PJx2795NX375pYLvQn/Nm/cR9ejZlG7ffkrXrj6iq1cfUtWqZalTJy9KSEiiPr3nUVxcotKbqVdCQ19Su7ZTlN4MAADIB6N/o5wiw87OjqKiosje3p6io6PJUC1ZMpQ+Hdk5y/uHD/ueVujhZRAAAADye/zGtaUM0KJFg0Vgw61eE75YS69exVKtWq5Us5YrubuXExeCRGADAACGCsGNgVmwYCCNGt1V/D90yDJavfqg0psEAABQoBQbLQXaZWxsLHJsxoztLm4PHbIUgQ0AABRJaLkp5EqUsKXBg9vT8BEdqWLFMqp8mpUrDyi9aQAAAIpAcFNI1a5dkUaOfJv6vd+KrKwsxLIXL6Loi/FraM2aQ0pvHgAAgGIQ3BQipqYm1L27N33yaSdq2bKWavmFC0G0bOke2rTpuBjiDQAAUJQhuCkkQc2ECT1F11PZsqXEsuTkFNq+PUAENSdPXld6EwEAAPQGgptCYPLkd2na9H7i/5CQCPppxT766af94n8AAADICMGNnuN8GnkyvglfrKHFi38XrTYAAACgGYIbPffhh77iQpdBQSG0cOHO1y4cCgAAABlhnhs9n7vm8zHpE/It8kdgAwAAkBMIbvRYt25NqEqVsmKI99q1h5XeHAAAgEIBwY0eGzc+fbbhH5bvxdW7AQAAcgjBjZ5q1qwGNWniIeatWbbsD6U3BwAAoNBAcKOnxo5Lb7XZsP4IhYW9VHpzAAAACg0EN3qoWrVy1KVLI/G/v/8upTcHAACgUEFwo4fGjOkmRkrt2hVIt249UXpzAAAAChUEN3rGwsKMPujfWvzvv3Cn0psDAABQ6CC40TMNGlQWsxKHhkbS339fU3pzAAAACh0EN3qmadPq4u+pUzeV3hQAAIBCCcGNnmni7SH+BgYguAEAAMgLBDd6pmnT9OAGLTcAAAB5g+BGj1SsWIacnUtSUlIynTt3V+nNAQAAKJQQ3OgR73+7pC5cuCdmJgYAAIDcQ3Cjh11SAUgmBgAAyDPTvD6xQoUK5OrqStbW1hQeHk7Xrl2jpCS0NmgjmfjUqRv5Wg8AAEBRlqvghoOZ4cOHU58+fah8+fJkZGSkuo8Dm7///pt++ukn+u2330iSJF1sr8GysbGkunUrif8DMFIKAABA991S3333HV26dIkqVapEU6ZMoRo1alCxYsXI3NycnJycqGPHjnTixAmaMWMGXb58mRo2bJj3rSqCvLyqkqmpCT16FE5Pn75QenMAAAAMv+UmNjaW3NzcKCIi4rX7uFvq6NGjonBw06FDB9FtdfbsWW1vr8EnE6NLCgAAoICCm8mTJ+d4pfv378/r9hRZ3v/OTIzJ+wAAABQYLXX48GHRJZWZnZ2duA9yz9vbXfzF5H0AAAAKBDetWrUSuTaZWVpaUosWLfK5SUVPtWrlqFQpe4qLS6SLF+8pvTkAAABFZ7RU7dq1Vf9zQrF6/o2JiQn5+fnR06dPtbuFRWh+m7Nn71BKSqrSmwMAAFB0gpuLFy+KId5cjhw58tr98fHxNHLkSG1uX5G6Ejgm7wMAACjg4IaHgfPcNvfu3aNGjRqJUVLq89yEhYVRWlqaFjaraMHkfQAAAAoFN48ePVJ1QYF2FCtmQ7VquYr/AwNvoVoBAACUSCieOHEiffTRR68t52VffPFFfrepSGnSJH2U1J07wRQe/krpzQEAACiawc3HH39MN2/efG05X19q2LBh2tiuIqN16/QkbUzeBwAAoGBww5dbCAkJeW055+A4OztrY7uKBCsrCxo4qL34f/fvp5XeHAAAgKIb3Dx+/JiaNWv22nJeFhwcrI3tKhI++siXHBzsKSgohHbuDFR6cwAAAIpeQrHs559/psWLF5OZmZlqSLivry/NmzePFi5cqO1tNEjGxsb0+Zhu4v9F/jsxygwAAECLpLyUOXPmSHFxcVJKSoooMTEx0ldffZWndRVksbOzkxj/VXI7evZsKqVJu6Ww8I2SlZWF4vWCgjrAPoB9APsA9gEykON3nlpu5BFT33zzDVWvXl1M3nfnzh0x1w3kzLjxPcTfH5bvpfj4RFQbAACAkjk36onFJUuWpKCgIAQ2udC8eQ1q3NidEhKSaNmyPfn5CAAAAEAbwQ0HNIcOHaLbt2/T3r17VSOkVq1aRQsWLMjLKouUseO6i7/r1x3B3DYAAAD6ENwsWrSIkpOTycXFheLi4lTLt2zZIi6eCVlzdy9PXbs2EQnE/v47UVUAAABalqecm/bt21OHDh1euwI45924uqZfSgA0Gzs2fYTU77+fptu3cQV1AAAAvWi5sbGxydBio95dlZiI5Nis2NlZ0fsftBb/L5i/PS9VDwAAALoIbv7++2/q37+/6rYkSeJq4XxdqaNHj+ZllUVC1aplydLSnEJCInC5BQAAAH3qluIg5vDhw9SwYUMyNzcXk/fVrFlTtNxomrkY0rm6lhZ/HzwIQ5UAAADoU8sNXyCzWrVqdOLECdq1a5foptq+fTvVr1+f7t27p/2tNBAVK6YHNw8fIrgBAADQm+DG1NRUDAMvXbo0zZo1i3r37k2dOnWir776ip49e5anjRgxYgTdv39fTAYYGBhIXl5eWT6Wu724Gyxz2bNnT6FpuXmE4AYAAEB/gpuUlBSqU6eO1jagV69e5O/vT9OnT6cGDRrQpUuXaP/+/eTo6Kjx8T169BCTB8qFu8N4m/73v/+RvnP5N7h5+DBc6U0BAAAwWHnqlvrll19o0KBBWtmAMWPGiAtxrl27lm7cuEHDhg0TI7EGDhyo8fGRkZEUGhqqKu3atROPLwzBjatresD24EGo0psCAABgsPKUUMxdUxx8tG3bls6dO0exsbEZ7h87dmyO1sNXFff09KTZs2erlnEXE3d7eXt752gdHGRt3rxZ49B0xgnPFhYWqtt2dnaklIoVy4i/aLkBAADQs+CmVq1adP78efE/Jxar4+AkpxwcHESgxC0w6vi2h4fHG5/PuTm1a9fOthVp0qRJNG3aNNKHOW5KlLAV/yOhGAAAQI+CG2NjY5o6dSpduXKFXr58SUrioOby5ct05syZLB/DrUKc06PecpN5ZuWCTCZ+8SKKYmMTCvz1AQAAiopc59zwNZEOHDhAxYsXz/eLP3/+XCQDlymT3l0j49tvGnllbW1Nffr0ERfrzE5SUhJFR0dnKEqQgxt0SQEAAOhhQvHVq1fJzc0t3y/OF9/knB1fX1/VMp7pmG8HBARk+9x3331X5NJwcnNhgGRiAAAAPQ5upkyZQgsWLBDz2/BwbO7qUS+5wV1GQ4YMEZdz4DybH374QUwKuGbNGnH/unXrxHw6mrqkdu7cSREREVQYyMnEjzAMHAAAQP8Sivfu3Sv+/v777xkSiLnVhW9zknBObd26VcxpM2PGDBEoXbx4kfz8/CgsLH0WXxcXF9EVpo6TmFu0aCGGgRcW/81xg9mJAQAA9C64ad06/crW2vL999+LktPXun37tgikChO5WwrBDQAAgB4GN8ePH9f+lhg4JBQDAADocXDDihUrJvJeqlevrrqY5urVqykqKkqb22cQLC3NycmphPgfsxMDAADoYUIxzyocFBREn3/+OZUsWVIUvowCL+Mrg0NGLi7pXVLR0XEUGRmD6gEAANC3lptFixaJZGIe5ZSamiqWmZiY0MqVK2nx4sXk4+Oj7e0s1NAlBQAAoOfBTcOGDTMENoz/nzdvHp09e1ab22cQkEwMAACg591SnFfDQ7Qzq1ChgmIzABeGlhvMcQMAAKCnwc2WLVvEZQ969epF5cuXF6V3796iW2rTpk3a38pCDnPcAAAA6Hm31Lhx48RkfevXr1dN2MeXUuDZhSdOnKjtbSz0KlZMb7nBSCkAAAA9DW44kBk9ejRNmjSJKleuLJbxSKn4+Hhtb59BQEIxAACAngc39vb2YnRUZGSkuIimrESJEuIq38i7UatgUxMqV66k+B+zEwMAAOhpzs3mzZupT58+ry3nHBy+D/5TrlwpEQgmJiZTaOhLVA0AAIA+BjeNGzemo0ePvrb82LFj4j54Pd/m0aPwDBcZBQAAAD0KbiwsLDRe+dvMzIysrKy0sV0Gl2+DZGIAAAA9Dm5Onz5NQ4cOfW35sGHD6Ny5c9rYLoOBOW4AAAAKQULxlClT6NChQ1S3bl06fPiwWObr60teXl7Uvn17bW9joYbZiQEAAApBy82pU6fI29ubHj9+LJKIO3fuTHfv3qU6derQiRMntL+VhRgm8AMAACgELTfs0qVL9P7772t3aww4ofjhw3ClNwUAAKBIyHHLjbW1da5WnNvHGyIjIyOqUMFR/I+EYgAAAD0LbrjbacKECeTk5JTt49q2bUt79+6lzz77jIo6J6cSZGFhRikpqfT06QulNwcAAKBIyHG3VKtWrWjWrFk0bdo00SV19uxZCg4OpoSEBDEzcY0aNUQeDs9QPHv2bFqxYgUVdXIyMQc2qalpSm8OAABAkZDj4Ob27dv0zjvvUIUKFejdd9+lFi1aUNOmTcW8Ns+fP6cLFy7QkCFD6M8//6S0NBzIM15TKkx3nyAAAADkL6GYR0j5+/uLAtmrWLGM+ItkYgAAAD0fCs4GDhxIV65cEd1SXPj/QYMGaXfrDKRb6hFabgAAAPR7KPj06dNpzJgxtHTpUgoICBDLON9m0aJF5OLiQlOnTtX2dhZKZZxKiL/BwRFKbwoAAECRIuW2hIWFSX369HltOS8LDw/P9foKstjZ2UmM/+r6tfYfmCGlSbulfv1aKf6+UVAH2AewD2AfwD5AhbgOcnP8zlO3FF8gk0dLZcbXldJ0Qc2iys4u/SKi0dHxSm8KAABAkZGn4GbDhg00fPjw15bzxTQ3btyoje0yCLa26cFNTAyCGwAAgIKS52YWTh7mi2QGBgaK240bNxb5NuvXr6eFCxeqHjd27FgqqtByAwAAUEiCm1q1atH58+fF/5UrVxZ/ea4bLnyfTJK466vosrW1FH9jYhKU3hQAAIAiI0/BTZs2bbS/JQYILTcAAACFaJ4byJ6ZmSmZm5uJ/5FzAwAAUHAQ3Oi41YahWwoAAKDgILjRcb5NQkKSuCo4AAAAFAwENzoeBo45bgAAAAoWghsdd0uhSwoAAKBgIbjRcbdUdHScrl4CAAAANEBwoyNouQEAAFAGghsdQc4NAACAMhDc6AhabgAAAJSB4EbnOTe4aCYAAEBBQnCj45abWFwRHAAAoEAhuNER5NwAAAAoA8GNjiDnBgAAQBkIbnTEBjk3AAAAikBwo/OWGyQUAwAAFCQENzqCnBsAAABlILjREeTcAAAAKAPBjY5gnhsAAABlILjREeTcAAAAKAPBjY6g5QYAAKCIBjcjRoyg+/fvU3x8PAUGBpKXl1e2jy9WrBgtW7aMgoODKSEhgW7dukVvvfUW6RMjIyOys7MW/8fEJCi9OQAAAEWKqZIv3qtXL/L396dhw4bRP//8Q6NHj6b9+/eTu7s7hYeHv/Z4MzMzOnjwIIWFhdE777xDT58+JVdXV3r58iXpE2trC9X/uLYUAABAwZOUKoGBgdLSpUtVt42MjKQnT55IEyZM0Pj4jz/+WLp7965kamqa59e0s7OTGP/V1ftyciohpUm7peSUnYrVLQrqAPsA9gHsA9gHDGkfyM3xW7FuKW6F8fT0pEOHDv0XZUmSuO3t7a3xOV26dKGAgAD6/vvv6dmzZ3TlyhWaNGkSGRsr3rumMd8GXVIAAABFqFvKwcGBTE1NKTQ0NMNyvu3h4aHxOW5ubtSmTRvauHEjdezYkapUqULLly8XgdKMGTM0Psfc3JwsLP7rJrKzsyNdQ74NAACAcvSryeMNuIWG822GDh1K58+fp61bt9K3334rcnaywi07UVFRqsJ5OgXXcoNLLwAAABSZ4Ob58+eUkpJCZcqUybCcb3OXkyYhISF0+/ZtSktLUy27ceMGOTs7i9YbTWbPnk329vaqUq5cOSqoOW6QTAwAAFCEgpvk5GQ6d+4c+fr6ZhhCzbc5r0aTkydPiq4ofpysWrVqYlg4r0+TpKQkio6OzlB0DTk3AAAARbRbioeBDxkyhPr37y/ybH744QeysbGhNWvWiPvXrVtHs2bNUj2e7y9ZsiR99913VLVqVZF3M3nyZJFgrE/knBu03AAAABSxeW44Z8bR0VEkAzs5OdHFixfJz89P5NUwFxeXDF1QT548oQ4dOtCiRYvo8uXLIn+GA525c+eSPkHODQAAQBENbhi3umTV8tK6devXlvEsxlkNFde760pFI6EYAACgoBWq0VKFBXJuAAAAlIPgRgeQcwMAAKAcBDc6YIN5bgAAABSD4EYHMM8NAACAchDc6ABybgAAAJSD4EYH0HIDAACgHAQ3OmBr++9QcFxbCgAAoMAhuNEBtNwAAAAoB8GNDiDnBgAAQDkIbnQALTcAAADKQXCjZWZmpmRubib+R84NAABAwUNwo6NWGxYTk6Dt1QMAAMAbILjRUb5NQkISpaSkanv1AAAA8AYIbnQ0DDwaVwQHAABQBIIbHXVLoUsKAABAGQhudNQtFR0dp+1VAwAAQA4guNEytNwAAAAoC8GNliHnBgAAQFkIbrQMLTcAAADKQnCjs5ybeG2vGgAAAHIAwY2OWm5icUVwAAAARSC40TLk3AAAACgLwY2WIecGAABAWQhutMwGOTcAAACKQnCjs5YbJBQDAAAoAcGNliHnBgAAQFkIbrQMOTcAAADKQnCjZZjnBgAAQFkIbrQMOTcAAADKQnCjZWi5AQAAUBaCGy0yMjIiOztr8X9MTII2Vw0AAAA5hOBGi6ytLVT/49pSAAAAykBwo4N8m9TUVIqPT9TmqgEAACCHENzoIN8GXVIAAADKQXCjRci3AQAAUB6CG5203ODSCwAAAEpBcKODnBskEwMAACgHwY0WIecGAABAeQhudJBzg5YbAAAA5SC40SLk3AAAACgPwY0urisVjYRiAAAApSC40SLk3AAAACgPwY0WIecGAABAeQhutMgG89wAAAAoDsGNFmGeGwAAAOUhuNEi5NwAAAAoD8GNFqHlBgAAQHkIbrTI1vbfoeC4thQAAIBiENxoEVpuAAAAlIfgRouQcwMAAKA8BDdahJYbKAqMTUyoYt3aZGJqSoVpm4euWExTDuygKo08ld4cACgKwc2IESPo/v37FB8fT4GBgeTl5ZXlYwcMGECSJGUo/DylmZmZkrm5WYHl3DhXq0JVGzfU+esAqDOztKCPlsylkb/8RB+vXCJuFwYt+vUi96aNqYSzE33803fk0/+9PK/L0tZGfP+MjPXi51OnnKq4ke+QAVSsjKPSm2LQeF8q51GN6r/VTq/r2r60I7UZ1J8cXMqTvlP81KtXr17k7+9Pw4YNo3/++YdGjx5N+/fvJ3d3dwoPD9f4nFevXon7ZRzgKK2kYzHV/549upG9kxOVLOdMpubmdHLTb3T1yF/5fg0jIyNyb96EWvXvS1WbpAc2lw4coW0z5lLcq6g8rdPY1ISMjU0oJSmJ9AEfLGv6NKeo5y/o3rmLOX6edTF7qtywPoXee0Bh9x/m6bUt7Wypjm8rcm/WmC78eZCuHjlOuubRvAmVcHamO6fP0vOHj0mfcf0MWjqf3DzriduVPevTh4vm0OrPvqDU5GTSVyXKOlGHT4aI/x9duU4utWtQl/GfUYWaHrR12mxKik/I0XqcqlamZn16kufbHcjC2ppehYbT+T/209ndf9Kzu/d0su01fJpTl3Ej6e7Z87R38Q8av+d8YOTWKCs7W/E5pCRxSaLYV1H07E5Qnl63dCVXaj9sINX1a0vGxsZUz68tLek3mJITEqkg2ZYqQeWru9PtwDOUlpKap3WYW1mSTYniZG1vT3FRURQV9pxSU1Jy9Fw+iLs1qEcX9h3M13vn3zU3z/pkbmlBxqamok6NTIzJoXw5qli/DrnWqSWCZpYYF08Hlq+k4xu35Pk9axsfJ5r3fZc6jBhMljY21Py9d+i7foPEd0BfGXFsoOQGcEvNmTNnaOTIkekbZGREjx8/pqVLl9LcuXM1ttwsXryYSpQokafXs7Ozo6ioKCpVqhRFRESolpuY8EHemNLS0ig19b8dyswsvTUmJSVFFUTx4/jxfJuXs1bd29K+zZ9SShrRj3dKi/fBpLQ0orQ0unPmPO2cu5hCg+6r1mttb0fuLZpShVrV6dHV63T92AlKio3LsF5WvIwj1WjelJr1e1f8wIrt4WAkVSITc1OKehFBm6fMpNsBp8nU1FS8Nr8Hfi/itSwsqFLdWlSxXh1yqV+bipV2FK9taWNNFlZWlJqaRie3/Eb7lv1MyQkJYh0sWe2A5VzZjUqWLyvOKvhgwWfA3NQfHfacXoU/p8jQMHoVGkbBt+5QSkLia9vAt22LF6Oy1arQ86ch9OLJ0wz1bmRiQo16dCbfwf3JzqEUUWoqPbh4hQ78tIZuB5zJ8jPiH/am73Sj9iMGkU3JEuJ1IkOe0e1Tp+nuP2fFj2JM5MvXPk/5vZlbWVHNVs2pfoc2VK1pEzKzshTL+cdv3ehJdPvUPxk++/zuJ/Jj+ceuyxefk1e3jqr9JOLxU7G9N04GUtCZ85QQE/vaestUrkRNe/egur4+dPXYCdoxZ5EqsJA/+5xsg6b9hG9r+ux5mV2pkjRw2Xwq61GN4qOiad/3P9Fbn3xMFtZWdOXocdow/itKS01VrdexogvV9PURgeaVw8fIxMhY43ozb0OtNi2plk8LSk6Ip+iXr8TBPO7VK/G9CQt6kGW9q68382c0+Ad/8mjWhIJOn6dVI8aQV88u1HX8KDIxM6Xgm3doz8KldO/CJUpOTHptvWbWVuTerAl59+pGbvXriu8yGRlTamqKOHERn11KCj29eYfO7tkngp3Yl6+y/ezlz5MDiKpNvOj+uQsUdu/ha49t89H75PfpUDI2S6+n2MiX9Mei5XThj/3ivaWRRJ5v+1GbgR+QYwU+k5b4zWf47Xl89Tr9tWGLOAkSv0WZ6t3OsRSZWVqK3whL/mtlSZ5dO1KDju3F91tKTaXE2HiysLWm0zv/oK1TZ2W7n8jr5UCYf6tS4hMpJSmREmLjRL0kxsXlaP8rXbkStXi/l2jJMDExpcsHj9DGSdNVQUlW28C/aTVbNhNBWdnq1ci2RAkytTAXvyf/7hyinmNeRNDLZ6H07HYQ7fv+Z/Eblvkzcqlbiz5aMk8EjaF379P/ps6iR9duvPF7r77/2ZdxpGa9e1Ljnl3IysZatQ2Zjw9kZEQJcfEU/fyF2C+klFQKuRtE22cuoPuXrmT4LpOxkcZ9NTff+xQNj+VtKFPFTRxbXoWFU3JcvHhshdo1qPuX48RvNz+Hl/P+Enw3iL4fMFx8prn9PcnrY+Xjt729PUVHR5PeBjf8ocTFxdE777xDu3btUi1fu3YtFS9enLp166YxuFm5ciU9ffpUfCjnz5+nyZMn0/Xr1zW+hrm5OVlY/Nd0zpXDz509ezbNnDlTvD5r0aIF+fr60rlz52j37t2qx/O6eR0cUL18mX6QbNKkCfn5+dHly5dp+/btYllTXy/q1qErxcWl0EMLomchzyjiaTC5V3IjN9viZOpcmswb1aaTm38TZ3nt6nqSaZpE1q2bkEnJ9FafxPtPKOn8NXoRG01/X75Ibg3rU2WvBmQX9JTSomPJqqUXpVhZUOC2XRR88jR19nuLUm0sqbifj3j+8V+2kEuSEZUqVoyuR4ZRWjE7KuNWkco5OVFS4CUyLmZHNm2bqt5b3F+nKfV5JFk2rktm5Z0oIjiEji9dSb4NG9GLFy9oxcqfqUGnDiJiLxb2klKfPSdLz1pkVrGceH7qyyiKOxxARpYWZNuplVjGO+DLw6fILCqWrjy8T7ce3hdn+lXq1CL7oGAiM1Oy6+IrApCgMxfIOjSCKpR0oLSKZamYZ22xjpePnpDJmWviy2bXo70Ico6sWk9VS5WmerXr0LFjx0RxqVOTek74nIrdeSKel9KkDtk6lBRfvITLtyj5zgMyq1qRIq1MKejsBdES1N27hXjshVfPybVhPSpfw51Sbj+gpBtBZOZWgSJtLSgmIlJ0+UVvP8DNgrRw4ULVF6lp06bUvn17unjxIu3cuVNVlxMnThQHhyVLllBcUiI17tGFmrVsQeZPwijkZSSt+uFHSklMP/MbN3482drYkLWvNxnZ2YjWBCe7YpR88QaZODmQdTNPESg8uXGLrK7fJ0sTU/rr6iXy6NiWqng1oOQnzyjhn0tk4lCCQu0tad3nkyj6RYRo/XRycqL169fTvXvpLQlVq1alfv36iX1+/a8bRa4MH2Teat6SypUtS5s3b6abN2+Kx1asWJE+/PBDCgsLo+XLl6ve28DBg8mlfHmy9KpN8dYW9NPHo8koJo6GDh1KRlaWZNvRh87s2ktbvppJAz8eShWcnMmiQQ0yr1RBPP/V/UdkfP4GxcXH0zy1E5YePXpQnTp1aN++fXT7ySPqPmkMeXg2oNh9x8UBwK5bW9VjE85dpeQHT+lpYhwd2rePHly6SlaWlvTFF1+I+1dt2yqC66jw5+K7yd/R48ePU4S5MX0w/xvxg5zwZ3pL3Lfffkvla9eg/gu/JYtnEZR06x4Zu5SloFcv6OaJAHoZEkrvtX9LPNa6ow+Z/BvwJly7Q8k379GdRw9o84aNVL1FU2rY5S2qGM0nGmlk49eS0sxN6drRv+nVuStUv5pHht8INn78eLKxsaFge3Nyb9c6/Xsf9IiSLt6gew8e0Pq1a8nEzIze+foLqmFhR1JcAj00TaOS1auKg0vyo2BKOHOFIuPjyLZNEypZ1lmsI5rrLDaeoso6EBW3E4FbcVNzSj53jYxLFad4t3J0bN2v4r11atmKbM3MybieB9lUdk3/7oQ+p/gT5zL8RnBrs9nNh1S2TBmy8KpN5i5l6dfJMyjs4lUaNGiQ+I3gk1BZ3759qVq1anQv7hV59OwsAt/MvxEJsbEU8SSYjG89IJvkNLr1/Bk9jnhOaalpZGlqSi2r1qA0IyMq1qO9ar3xZy5TyqMQepaWTD/O+Fb1Oz527Fhx0Pvmm2/E97XB2x2oZlkXSnscQubVK5NFjSrisVJSMsXsPiL+t+zkI4I58Xn++xtBFZxo06o1dOvUP+KY8vXXX6c/1q85mdnYiIBK/o0IiYumn/0Xq1pUvvrqKxEYBDy5R4nJKZSSnETl7UuQh1M5ijEzpjKd2oggkUXtPEhGqWn03LkEpYjgJ5VMw19S6RSioEcP6Ze168TvTcOuHalTo6ZEScniN+LO1esiQCthZELmD5+J34gHqQm0afIMEfxzwwCfrK9atUo0DLAaNWqIXpEHDx6I46ksu9+IZHNTKtnZV/XYmCMBJEVGkWWTemRWrowIrk/9tJ6aVHEnsrYku7d86PpfJ2nNqAnUr29fqly5Mv3222905coVcdLp6uZGH77/vjhu8vFT1qdPH/Lw8KDff/9dHL+Zo6MjffLJJxQbG0vz58/X+BvBDSG5CW4U7ZZycHAQkVloaGiG5Xyb37wmt27dooEDB4ofjWLFitG4cePo1KlTVLNmTfEDntmkSZNo2rRppGupMf81F+/x/17VpZbcoAG5dekizgyc+Mv7fm+xPObPv8QPV9jDR3Rn73XRxF/axlbcV7pSRfqg/zuq9cUGpb+vgK07aN8vm8QZvVw/wbfu0tXIcNFMyOuOPRJAaZFR5NnJj0ydHVU/XCw+OpoOzF5IYfcfibPvDs1bUHnnsnR45TpqNKS/6EZ7e+ynFP/XadGM+9WhXaIpV2xDSHrryZObt+jJyZMUGfyMrExMqYlrZdHac/f0OSpZrqxYB5/ppETFii9p08ou4nkcnMUGBYszlZTkZNHywweG+LNXKOVhMFnZ2oozqYM/raWbh/6i0aNGicdyU3DFerVp4NL5lHDhOiXfe0wt+r1LdQb3I9e6tdJ/uP4NbuZ07s2nIVTZsx61btWKnMzT5x1yrlpZlGa9e1DMjoNiGZ8VGv2bIxX1Kkp8EbgratumzaIJll9P7lV2dKlA0dc0B8+ZcctTve5viybmpKBHlPgkTHR/fH1wJwVs20XPHz0m25IliBKTRJDxy+cTReDl6eVFnTt1osinIRT74JFo+XCpVYNinrwgKTaO/D4ZQqYOJUTQ8/DKNSrD+1xqKlWqX4dGb15Na0ZNzHa7LGys6Yudv1JxJ34mUeyxfyjtxUt65+sJFBLxQnSLxd7/r1vMyt6OarRsRrXbtqKKplaU9jxSBH0/DJtGLx4/obJly6bvx5GRZJWSQl5dO1LVxp5kcS+YUkLCSEqT6MaJAJFHYFuqJMX923XI+TqPLl+n4Nt3yfzfg0w170bUre8c0ZqV9PJV+vcpJZlObdlO1sWLifoqK6X/VFVqUIc+7dtdfAdMJIkSDpwUy0dvWkWpySn0z47dlHz1tlhmamFB3SZ8Kv4/unYjeZdJD7bY/fOXaFHvj6j/J8PJgYxFC2idtq1EYdG/7Rd/+Uc65E4QXTl0jFLvP6HmjZtQVPgL0VrGB38uk7/8kszNzCjk9l0qV7821W3fhpIquVHi5VtUzduL3m9ST3SzcmsBfzcoTaIqjb3EicCT6zfJycZOvBZ3Nfco50BOVd3E70HM3vSu7P3LV9Kz0FBxktGuexexzMG1AlmXdRbB3F/rNlFNu5Lk6OBA22ctEAc0VtfTk7p37kxpKSmie+Wdr9IDQfk3grvWuM6S4uMpJSr9QMFn7ZcPHhW/CU+u3xLBLrt44Ag1Gvw+9ZwynrZo2Nes7O3FPstqtGgmWrz4+0wxcZR+aE/HXRpl3atSfGQspTwNpTptW1ND9d+IAyfE94+3i1trjm/cSr7NWpBrGSdyrVOT3vpsGP255Mf/VmhkRCPWLhf1xfg3gtsC+ETm4Ky5oiUkJS6BxowaJe6f3NhX7FPcAu3TrDlVK+8igh1ONj+6+hfa//3PqlWbmpmLutgx258+HPUpleTWnFo16LNffqY7/5wVr2nCJ2ySRG2HfkTG/wbBSbfuU+LV21S8bHrrNreon/j1f9S5SXNxAvTrpOmqXgPOL+3UqRMlRMeoWtbO7PyDWlapTna2tqqua8bBfcLDZ+l13LIZjd68RgQWmXFrYNs+7xK9jBM5U72mTxYnP1Fh4SLozYy7aeXfCO6i5X2Av6u878stHwHbdopu0dIlSorgJvJZKFkmJFINn2bUedxIouAX4nHcK9Dp8xHi+PPgRCApSdGWG2dnZwoODiZvb28Rlcm4O8rHx0ecfb0JB0c3btygTZs2qSLunLTcaLtbyt29PE2c1IteRsbQmDE/a3xspYb1qcPwQeLM7NqR4+KHMfR+enM0K1WhHNVp4yP62c1srOj+uUsUdPY8Pbp0VQQj2W0DfwH8Rn5MJsbG4gfvFXcXhYWL1iNu+XgZ/Czbrgk+M2g/fLA46JuIJlMjMjI1oeePnojWpnO7/xRdZm9qSuTuC26+r1i3FrnUq0UWNjZ0/8Jlunf2Aj28dJWiw5+L9XLAUtnLk6p41SdLK2sK3P67OPjzF0t9vZbF7Kn1R/3EQda+VCkyMTEWAYyczHl6xx468P1PFBPxUmPXhFUxe3Ll1+JWMM96VMatkjhz5G4IuTWHv/SZP3vupx+24jvxxefAdOkHH9PL0LAM+wl3pfEPLuc78Ppda9ckEwtzsf3cOvf3hs0iOPTu1Z1KVkhv7WLc5Myvv3HiNPEZafo8OXGvSqMGVLWhJ7l51hVdE2d376N/tu0Sny8/ltc5YNFs0TrHQeD2mfPp0v7DlJSY+F8TuakptR/6EbUe9D6ZmJuLgywHSHYliovXVK9Lfk5sRCSFP3gsWsXkH0Lumnh87Qat/XyyanvVP6Na7VpT39lTxfp4H+EclOMbtlD4o8fi9fmza9arB1WqX5uM1EZY8Xo5P4T3Pd4G7pLb/u0Cevk0JMM+xaxsbURCcI1WLcQPKgdf4j2mplJSfCIlxMeJH1aWGBNHAVt/I1sHBxFAPwu6T/7vDlCNnsi8n3Bxdq9KVZs2ourNvUWex4NzlynozDm6GXBa1HdOfyM4MPHq9jZ5dmxPNsXsxcGX95P/Pns+u0+h83sP0rF1G0W3HbcevvXpUPH+5MfGR8fQr198LQ6i6t/7YmVKU+fRI8TrnNy6QxwI+TuT3e8UB40NOvuR97vdRT0/vnyNHl+9QQ+uXBVBJrdCyJ9nlr8RaWk0ePlCsY28by8fMFwcPao08RJdifyZWHCwKkn09NZd2v/DKrr+14kM+wl/7zm45hMg7kbjVidbh1IimOHtkrc35HYQndi6XXwv5Xpv3L0z9fhqvHjc7/OXUMD/dpLf8EHU7L13yNTSQuSp8G/Uhb0H6fGVaznuvuQA+61Rw8R62IvHT6mkc/oJwKltu0Rgw0EHP7ZeB1/qOvFzceKn/nm+eBIsuqu49cnU3Ezs82bmZhT+6Amd2rpDlQOY265r5uxejVzr1hRd6xyoxUW+FF3p782ZSg4Vyotg5Lfpc0S3o1O1KtRx1DBxsiCpdXdl2P/4u3z1hghWrx79m5r16ZF+ws2B9o1btGFC+skL7zMlnJxESsTLsHDVe1D/PKu3ak4D/GeJ/0//9rvYJyvUqqH6PeH1LOjWL70rC91Sb+6W0mTr1q1ix+Bm0TfJTbNWUcRnVV3GfUbJSYl0avN20UyvDwnbjM+C7B0dqIRzGfEjycnDnOOTG+Js5N/g7E347OXT9StE8MBnoeEPH6sSsPlHjM+KuAtMHZ+lHVu3iW6dDMyw3RywcotTpQZ1xYF/75IftJIsyC1E/eZMFwcX+aDIB0TOOXp66zZ1GTtSvKYcCPKPNZ+p8zbZOZQUycwiQGvMQVQ9cWYtEy0Wh/8SrRZvqmfuouFcAQ5suPlaEx5hxEE4d6/w//x4PhBwwMUHrAt7D+ToPfNzHCu5UmJMrGg5khM9efs7jhouWrPULev/sQiwCxJvI//QcwsB77PFHB3IvrQDvXwWRic3b9OYiMnd0JywyWfQv06clufEeF3hFrQxW9eJ98TfBf4eynlHjPcRbmXSVSJ+m0EfUKfRI8T/3BLBJ1KMW1d2zfsuvaUoj2q18aHeMyaL7zz7c+kKOvTTf905Mj7paDtkgHjfnBfHJ0j5ed284pay9+dOV7Xq8Ikjt2Qzbhk/tWW7aE3nz4wL1xX/XvFJmNxNpu6vDZtFPlduBwa0Hvg+vf35J6rb3OJ248QpOrtrL10/fkrrAw0KTc4N4xab06dP02effZa+QUZG9OjRI1q2bJnGhOLMOPK9du0a7d27V/TBvgmCG8gNDqJGblih6s7JjM/q7545L35I7v5zTpUoneX+amqi9REQHLC1G/qh6LZQP6uUccDz2zfzRLfbm7atQs3qVKZSRbp/8TKFP3hEusQtmKXKlxX5V9ochVO9ZTPq+NnHIlA/sWkb7Zi1UGvrLuo4UB6+aplqjiMOwET33NG/6dHlazo/EXp7zKeiJZc9f/xEBOs3/w7Qyrr5O9526Ifiu3xx3yHSd/y9f2vkx6IrnHFr1bnd+2j/8p9F2oAm3CrJXYE8+o0/S+4R4Fy5a8dO5Hk7uKvQrUFdEWTybwx3X+tKoQpuOOlp3bp19PHHH4sgh4eC8zLOKeHERr6Pu5E4sVdO4OKA6O7du6J1hxP0uIXH09NTdE+9CYIbyMtZknvTRuJ/7tLh5mcpLVWcverT2bU8VwYPZefuA26R4fwcHknHXXFFiTxiiwM0fWl5NBQeLbxFAHzj71OK7P8tP+gjTmpPbP5NlaRflPFoTx7Rx11guRn6b1OiuGhZ4fy1wiK3x29J6fLJJ59IDx48kBISEqTAwECpUaNGqvuOHj0qrVmzRnXb399f9diQkBBpz549Ur169XL8WnZ2dhLjv0q/bxTUgS73ASMjI+xj2MewD2AfkAylDnJz/Fa85aagoeUGAADAsI/fhj9/OAAAABQpCG4AAADAoCC4AQAAAIOC4AYAAAAMCoIbAAAAMCgIbgAAAMCgILgBAAAAg4LgBgAAAAwKghsAAAAwKAhuAAAAwKAguAEAAACDguAGAAAADAqCGwAAADAoCG4AAADAoCC4AQAAAIOC4AYAAAAMCoIbAAAAMCgIbgAAAMCgILgBAAAAg4LgBgAAAAwKghsAAAAwKAhuAAAAwKAguAEAAACDguAGAAAADAqCGwAAADAoCG4AAADAoCC4AQAAAIOC4AYAAAAMCoIbAAAAMCgIbgAAAMCgILgBAAAAg4LgBgAAAAwKghsAAAAwKAhuAAAAwKAguAEAAACDguAGAAAADAqCGwAAADAoCG4AAADAoCC4AQAAAIOC4AYAAAAMCoIbAAAAMCgIbgAAAMCgILgBAAAAg4LgBgAAAAwKghsAAAAwKAhuAAAAwKAguAEAAACDguAGAAAADAqCGwAAADAoCG4AAADAoCC4AQAAAIOiF8HNiBEj6P79+xQfH0+BgYHk5eWVo+f17t2bJEmiHTt26HwbAQAAoHBQPLjp1asX+fv70/Tp06lBgwZ06dIl2r9/Pzk6Omb7PFdXV1qwYAEdP368wLYVAAAA9J/iwc2YMWPo559/prVr19KNGzdo2LBhFBcXRwMHDszyOcbGxrRx40aaOnUq3bt3r0C3FwAAAPSbqZIvbmZmRp6enjR79mzVMu5mOnToEHl7e2f5vK+//prCwsJo9erV1KJFi2xfw9zcnCwsLFS37ezsMvwFAAAA/Zeb47aiwY2DgwOZmppSaGhohuV828PDQ+NzmjVrRoMGDaJ69erl6DUmTZpE06ZNe23506dP87jVAAAAoGSQEx0drb/BTW7Z2trShg0baMiQIfTixYscPYdbhTinR13JkiUpIiJCJxXOQVO5cuXeWPGAui4ssF+jrg0R9uvCWde8ruDg4Dc+TtHg5vnz55SSkkJlypTJsJxvP3v27LXHV65cmSpVqkS7d+/OkH/DkpOTyd3d/bUcnKSkJFHU6Trw4PUjuCkYqOuCg7pGXRsi7NeFq65z+nxFE4o5IDl37hz5+vqqlhkZGYnbAQEBrz3+5s2bVKtWLdElJZfff/+djh49Kv5//PhxAb8DAAAA0DeKd0txl9G6devo7NmzdPr0aRo9ejTZ2NjQmjVrxP18HzdnTZ48mRITE+natWsZnv/y5UvxN/NyAAAAKJoUD262bt0q5rSZMWMGOTk50cWLF8nPz0+MhmIuLi6UlpZGhQEHX5y8zH8BdW0osF+jrg0R9mvDrmsjHn1dYK8GAAAAYOiT+AEAAABoE4IbAAAAMCgIbgAAAMCgILgBAAAAg4LgRktGjBhB9+/fp/j4eAoMDCQvLy9trbrImjhxopgeICoqSlySY8eOHVStWrUMj+Hrhi1btkxMCMmTO23bto1Kly6t2DYbigkTJojrvC1atEi1DHWtPWXLlhWzrfN+yxcKvnz5srjOnrrp06eLmVj5/oMHD1KVKlW0uAVFA0/yyiNxeXJXrse7d+/SlClTXnsc6jpv+NqOPNccT9fCvxddu3bNdd2WKFGCfvnlF3r16hVFRkbSypUrxXQw2sCjpVDyUQe9evWSEhISpA8//FCqXr26tGLFCikiIkJydHREveajXv/8809pwIABUo0aNaQ6depIe/bskR48eCBZW1urHrN8+XLp4cOHUuvWraUGDRpIp06dkk6cOIF6z0e9N2zYULp375508eJFadGiRahrLf8+Fi9eXLp//760evVqycvLS6pYsaLUrl07yc3NTfWYL774QoqMjJS6dOki1a5dW9q5c6cUFBQkWVhYYN/ORV1PmjRJCg8Plzp27Ci5urpKPXv2lKKioqSRI0eirin/+7Kfn5/0zTffSN26dZNY165dM9yfk/1479690oULF6RGjRpJzZo1k27fvi1t3LhRG/s5Apv81kFgYKC0dOlS1W0jIyPpyZMn0oQJE/BDpMX9y8HBQXyBWrRoIW7b29tLiYmJ4gdLfoy7u7t4TOPGjVH3eahjGxsb6datW5Kvr6909OhRVXCDutbefjx79mzp+PHj2T4mODhYGjt2rOo21398fLzUu3dv7Ne5qOvdu3dLK1euzLBs27Zt0oYNG1DXpN1jv6bg5k37sYeHh3iep6en6jEdOnSQUlNTJWdn53xtD7ql8snMzEw0Jx86dOi/pjBJEre9vb3zu3pQU6xYMfFXvugp17u5uXmGur916xY9fPgQdZ9H33//Pf3xxx90+PDhDMtR19rTpUsXMSM7T2DK3a3nz5+nwYMHq+7n6+c5Oztn2K+5a/aff/7Bfp1Lp06dEpfzqVq1qrhdp04dat68Of3555+oax3LyX7Mf7krii/DJOPH88S9jRs3LtwzFBd2Dg4OZGpqKn6k1PFtDw8PxbbL0PA1xxYvXkwnTpxQXWqDZ7TmGS+5rzZz3fN9kDu9e/emBg0aaMwXQ11rj5ubGw0fPlxcembWrFmivpcsWSIu8Lt+/XrVvqvpNwX7de7MmTOH7O3txXUJU1NTycTEhL788kv69ddfxf2oa93JSd3yX/lqBDL+nPgENr/7OoIbKDQtCnzRVD7rAu0rX748fffdd9SuXTtcPqQAkly55YYPsowvOcP79rBhw0RwA9rTq1cv6tevH/Xt21ecFPEFlvkkiRNcUdeGDd1S+cSjHVJSUqhMmTIZlvPtZ8+e5Xf1QERLly6lt99+m1q3bi2y8mVcvzyCR+6uQt3nHXc78T7LXSTJycmitGrVij777DPxP59toa61IyQkhK5fv55h2Y0bN8R19Jj8u4HflPybP3++aL3ZsmULXb16VYzK4RGAkyZNQl3rWE72Y/6beXQrt66VLFky38dPBDf5xD/83F/I/brqXSh8OyAgIL+rL/I4sOnevTu1adOGHjx4kKE+uN65KV+97nmouKurK+o+lzjHhlsP+MxWLmfOnKGNGzeK/7mlAXWtHSdPniR3d/cMy3i/5VwxxlNKcACkvl/b2dmJHAT8puSOtbX1axde5m4Pbj1DXetWTvZj/stDwbk7XMa/9fz5cG5OfiH7XgtDwTkDvH///iL7+8cffxRDwUuXLo26zUe9fv/992IYYcuWLaUyZcqoiqWlZYah4Dw8vFWrVmIo+MmTJ0XBfp3/77X6aCnUtXaH2iclJYlhypUrV5bee+89KSYmRurbt2+GIbT8G9K5c2epVq1a0o4dOzAUPA91vWbNGunx48eqoeA8ZDksLEyaM2cO6pq0M7qybt26orDRo0eL/ytUqJDj/ZiHgp87d05Mi9C0aVMxWhNDwfUoMPvkk0/EQZbnu+Gh4TxmX+ltKuwlKzz3jfwY/pIsW7ZMevHihThA/PbbbyIAUnrbDTG4QV1rr247deokXb58WZwUXb9+XRo8ePBrj5k+fboUEhIiHnPw4EGpatWqiu8Tha3Y2tqKfZh/m+Pi4qS7d++KeVnMzMxQ15T/+vXx8dH4G81BZU734xIlSohghucfevnypbRq1SoRNOV324z+/QcAAADAICDnBgAAAAwKghsAAAAwKAhuAAAAwKAguAEAAACDguAGAAAADAqCGwAAADAoCG4AAADAoCC4AQCDMHXqVLpw4UKuniNJEnXt2lVn2wQAykBwAwB67ejRo+Jih2+yYMGCDNexAYCiy1TpDQAAyC++knBsbKwoAABouQEAvbVmzRpq1aoVjR49WnQhcRkwYID46+fnJ65WnpiYSM2bN3+tW6phw4Z04MABCg8Pp5cvX9KxY8eofv36ir4fACgYCG4AQG+NGjWKTp06RT/99BM5OTmJ8vjxY3HfnDlzaOLEiVS9enW6fPnya8+1s7OjdevWicCnSZMmdOfOHdq7dy/Z2toq8E4AoCChWwoA9FZUVBQlJSVRXFwchYaGimUeHh7i79dff02HDh3KNldH3dChQ0ULjo+PD/3xxx863nIAUBJabgCgUOIuqeyULl1atPjcvn1bBDUcKHGrjYuLS4FtIwAoAy03AFAovSl5mLukSpUqJbq2Hj58KHJzAgICyNzcvMC2EQCUgeAGAPQad0vxaKjcatasGY0YMYL+/PNPcbt8+fLk6Oiogy0EAH2D4AYA9NqDBw+ocePG5OrqSjExMWRsnLPedE4g/uCDD0T3lb29Pc2fP1/k7gCA4UPODQDoNZ6cLzU1la5fv07Pnz/Pcc7MoEGDqESJEnT+/HnasGEDLVmyhMLCwnS+vQCgPCOegVzpjQAAAADQFrTcAAAAgEFBcAMAAAAGBcENAAAAGBQENwAAAGBQENwAAACAQUFwAwAAAAYFwQ0AAAAYFAQ3AAAAYFAQ3AAAAIBBQXADAAAABgXBDQAAABgUBDcAAABAhuT/MF/qmHPNHmcAAAAASUVORK5CYII=" 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 22 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-27T00:10:07.881152Z", + "start_time": "2026-03-27T00:10:07.826221Z" + } + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize = (4, 4))\n", + "plt.plot(result1.model_statistics.phat.mean('trial'), benchmark1.target_statistics.phat.mean('trial'), marker='.', lw =0)\n", + "plt.xlabel('model')\n", + "plt.ylabel('human')\n", + "plt.title('Generalization patterns')\n", + "plt.plot([0.5, 1], [0.5, 1], ls = ':', color = 'gray')\n", + "plt.show()\n" + ], + "id": "b8cef9e268b1508e", + "outputs": [ + { + "data": { + "text/plain": [ + "<Figure size 400x400 with 1 Axes>" + ], + "image/png": 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" }, "metadata": {}, "output_type": "display_data", @@ -143,7 +220,15 @@ } } ], - "execution_count": 5 + "execution_count": 30 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "b80961f3d8e13f67" }, { "metadata": {}, @@ -154,43 +239,46 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T23:13:55.095280Z", - "start_time": "2026-03-26T23:13:53.070036Z" + "end_time": "2026-03-27T00:06:40.463491Z", + "start_time": "2026-03-27T00:06:37.755145Z" } }, "cell_type": "code", "source": [ "result2 = benchmark2(learner = learner)\n", - "print(result2)" + "result2" ], - "id": "e2495422b0877523", + "id": "545e5142766c2141", "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "GeneralizationBenchmark.GeneralizationBenchmarkResult(msen=0.18251220351974604, msen_sigma=0.00911146923718916, msen_CI95=(0.16616627500845524, 0.2024641217606151), model_statistics=<xarray.GeneralizationStatistics> Size: 583kB\n", - "Dimensions: (transformation: 36, boot_iter: 1000)\n", - "Coordinates:\n", - " * transformation (transformation) <U26 4kB 'backgrounds | 0.1' ... '...\n", - " transformation_type (transformation) <U18 3kB 'backgrounds' ... 'scale'\n", - " transformation_level (transformation) float64 288B 0.1 0.2154 ... 0.5 1.5\n", - "Dimensions without coordinates: boot_iter\n", - "Data variables:\n", - " phat (transformation) float64 288B 0.4939 0.4955 ... 0.5017\n", - " varhat_phat (transformation) float64 288B 5.8e-05 ... 0.0001203\n", - " boot_phat (boot_iter, transformation) float64 288kB 0.4955 .....\n", - " boot_varhat_phat (boot_iter, transformation) float64 288kB 5.819e-05...)\n" - ] + "data": { + "text/plain": [ + "GeneralizationBenchmark.GeneralizationBenchmarkResult(msen=0.01929741563254476, msen_sigma=0.0024513021659298712, msen_CI95=(0.014086503611026608, 0.023757523049323704), model_statistics=<xarray.GeneralizationStatistics> Size: 583kB\n", + "Dimensions: (transformation: 36, boot_iter: 1000)\n", + "Coordinates:\n", + " * transformation (transformation) <U26 4kB 'backgrounds | 0.1' ... '...\n", + " transformation_type (transformation) <U18 3kB 'backgrounds' ... 'scale'\n", + " transformation_level (transformation) float64 288B 0.1 0.2154 ... 0.5 1.5\n", + "Dimensions without coordinates: boot_iter\n", + "Data variables:\n", + " phat (transformation) float64 288B 0.9748 0.9752 ... 0.9491\n", + " varhat_phat (transformation) float64 288B 5.711e-06 ... 2.237e-05\n", + " boot_phat (boot_iter, transformation) float64 288kB 0.9735 .....\n", + " boot_varhat_phat (boot_iter, transformation) float64 288kB 6.032e-06...)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" } ], - "execution_count": 6 + "execution_count": 23 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-26T23:13:55.153690Z", - "start_time": "2026-03-26T23:13:55.103007Z" + "end_time": "2026-03-27T00:06:44.345166Z", + "start_time": "2026-03-27T00:06:44.297174Z" } }, "cell_type": "code", @@ -209,14 +297,14 @@ "plt.title('One-shot generalization patterns')\n", "plt.show()" ], - "id": "342b7cbc29ddb6d", + "id": "26ae4f65589f6075", "outputs": [ { "data": { "text/plain": [ "<Figure size 500x500 with 1 Axes>" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -225,7 +313,15 @@ } } ], - "execution_count": 7 + "execution_count": 24 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "87b4757fc69e04ad" } ], "metadata": { From f62657e3173b692ef1a37969e5870601f9b6213c Mon Sep 17 00:00:00 2001 From: himjl <mil@mit.edu> Date: Thu, 26 Mar 2026 20:24:21 -0400 Subject: [PATCH 141/156] tweak --- examples/score_example_models.ipynb | 47 +++++++++++------------------ 1 file changed, 17 insertions(+), 30 deletions(-) diff --git a/examples/score_example_models.ipynb b/examples/score_example_models.ipynb index 3e62b03..c89ec84 100644 --- a/examples/score_example_models.ipynb +++ b/examples/score_example_models.ipynb @@ -40,7 +40,7 @@ { "metadata": {}, "cell_type": "markdown", - "source": "# Benchmark a learning model based on image features", + "source": "# Benchmark a learning model", "id": "220ecf6a69d69874" }, { @@ -63,19 +63,6 @@ "outputs": [], "execution_count": 17 }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2026-03-27T00:06:09.931230Z", - "start_time": "2026-03-27T00:06:09.909346Z" - } - }, - "cell_type": "code", - "source": "", - "id": "b52395cee56d7674", - "outputs": [], - "execution_count": 18 - }, { "metadata": { "ExecuteTime": { @@ -190,17 +177,25 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:10:07.881152Z", - "start_time": "2026-03-27T00:10:07.826221Z" + "end_time": "2026-03-27T00:21:20.477788Z", + "start_time": "2026-03-27T00:21:20.413626Z" } }, "cell_type": "code", "source": [ - "plt.figure(figsize = (4, 4))\n", - "plt.plot(result1.model_statistics.phat.mean('trial'), benchmark1.target_statistics.phat.mean('trial'), marker='.', lw =0)\n", + "plt.figure(figsize = (5, 5))\n", + "plt.errorbar(\n", + " result1.model_statistics.phat.mean('trial'),\n", + " benchmark1.target_statistics.phat.mean('trial'),\n", + " xerr=result1.model_statistics.boot_phat.mean('trial').std(dim='boot_iter', ddof=1),\n", + " yerr=benchmark1.target_statistics.boot_phat.mean('trial').std(dim='boot_iter', ddof=1),\n", + " marker='.',\n", + " lw =0,\n", + " elinewidth=1\n", + ")\n", "plt.xlabel('model')\n", "plt.ylabel('human')\n", - "plt.title('Generalization patterns')\n", + "plt.title('Subtask difficulty pattern')\n", "plt.plot([0.5, 1], [0.5, 1], ls = ':', color = 'gray')\n", "plt.show()\n" ], @@ -209,9 +204,9 @@ { "data": { "text/plain": [ - "<Figure size 400x400 with 1 Axes>" + "<Figure size 500x500 with 1 Axes>" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -220,15 +215,7 @@ } } ], - "execution_count": 30 - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "", - "id": "b80961f3d8e13f67" + "execution_count": 34 }, { "metadata": {}, From 257ad7c20ce078fc7950cd09db1fa0a984742306 Mon Sep 17 00:00:00 2001 From: himjl <mil@mit.edu> Date: Thu, 26 Mar 2026 20:38:45 -0400 Subject: [PATCH 142/156] flatten classes --- examples/score_example_models.ipynb | 116 ++++--- .../binary_classification/benchmark.py | 288 ------------------ hobj/benchmarks/generalization/benchmark.py | 168 ---------- hobj/benchmarks/mut_highvar_benchmark.py | 243 ++++++++++++--- hobj/benchmarks/mut_oneshot_benchmark.py | 230 ++++++++++---- hobj/learning_models/__init__.py | 4 +- 6 files changed, 420 insertions(+), 629 deletions(-) delete mode 100644 hobj/benchmarks/binary_classification/benchmark.py delete mode 100644 hobj/benchmarks/generalization/benchmark.py diff --git a/examples/score_example_models.ipynb b/examples/score_example_models.ipynb index c89ec84..e21b7e0 100644 --- a/examples/score_example_models.ipynb +++ b/examples/score_example_models.ipynb @@ -3,8 +3,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:06:03.241241Z", - "start_time": "2026-03-27T00:06:03.220799Z" + "end_time": "2026-03-27T00:35:08.934648Z", + "start_time": "2026-03-27T00:35:08.917825Z" } }, "cell_type": "code", @@ -16,13 +16,13 @@ ], "id": "accb05e90bcf1ce9", "outputs": [], - "execution_count": 15 + "execution_count": 4 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:06:07.615732Z", - "start_time": "2026-03-27T00:06:04.254029Z" + "end_time": "2026-03-27T00:35:12.299534Z", + "start_time": "2026-03-27T00:35:08.935497Z" } }, "cell_type": "code", @@ -35,7 +35,7 @@ ], "id": "714700a9832048e8", "outputs": [], - "execution_count": 16 + "execution_count": 5 }, { "metadata": {}, @@ -46,28 +46,27 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:06:08.872314Z", - "start_time": "2026-03-27T00:06:08.863200Z" + "end_time": "2026-03-27T00:35:26.399988Z", + "start_time": "2026-03-27T00:35:26.382895Z" } }, "cell_type": "code", "source": [ "# Loading the exmaple precomputed image features. You can replace this with your own features!\n", - "data = np.load('/examples/example_features.npz') # ResNet50/avgpool features, randomly downprojected to 100 dimensions\n", + "data = np.load('example_features.npz') # ResNet50/avgpool features, randomly downprojected to 100 dimensions\n", "features = data['features'] # N x 100 feature matrix\n", "image_ids = data['image_ids'] # N image ids\n", - "image_id_to_features = {image_id:f for (image_id, f) in zip(features, image_ids)}\n", - "\n" + "image_id_to_features = {image_id:f for (image_id, f) in zip(image_ids, features)}" ], "id": "bdc5bcb78d79e755", "outputs": [], - "execution_count": 17 + "execution_count": 8 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:06:11.198440Z", - "start_time": "2026-03-27T00:06:11.147267Z" + "end_time": "2026-03-27T00:35:30.913532Z", + "start_time": "2026-03-27T00:35:30.855972Z" } }, "cell_type": "code", @@ -83,19 +82,13 @@ ], "id": "65327b245c715ea2", "outputs": [], - "execution_count": 19 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## Experiment 1 benchmark", - "id": "df34925233babff6" + "execution_count": 9 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:06:27.771869Z", - "start_time": "2026-03-27T00:06:15.160172Z" + "end_time": "2026-03-27T00:35:45.464436Z", + "start_time": "2026-03-27T00:35:32.507426Z" } }, "cell_type": "code", @@ -106,34 +99,41 @@ ], "id": "a5710bce79c8e484", "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Subtask simulations:: 100%|██████████| 64/64 [00:10<00:00, 6.02it/s]\n" + ] + }, { "data": { "text/plain": [ - "LearningCurveBenchmark.LearningCurveBenchmarkResult(msen=0.0055746894992691285, msen_sigma=0.00028657494694110414, msen_CI95=(0.004977290624735935, 0.006121000076948812), lapse_rate=<xarray.DataArray 'phat' ()> Size: 8B\n", - "array(0.07861494), model_statistics=<xarray.LearningCurveStatistics> Size: 103MB\n", + "MutatorHighVarBenchmark.LearningCurveBenchmarkResult(msen=0.005577570877248757, msen_sigma=0.0002857940942417397, msen_CI95=(0.0050023065242198895, 0.00608988408642532), lapse_rate=<xarray.DataArray 'phat' ()> Size: 8B\n", + "array(0.07887466), model_statistics=<xarray.LearningCurveStatistics> Size: 103MB\n", "Dimensions: (subtask: 64, trial: 100, boot_iter: 1000)\n", "Coordinates:\n", " * subtask (subtask) <U33 8kB 'MutatorObject000,MutatorObject095' ...\n", "Dimensions without coordinates: trial, boot_iter\n", "Data variables:\n", - " phat (subtask, trial) float64 51kB 0.51 0.732 ... 0.91 0.888\n", - " varhat_phat (subtask, trial) float64 51kB 0.0005008 ... 0.0001993\n", - " boot_phat (boot_iter, subtask, trial) float64 51MB 0.526 ... 0.874\n", - " boot_varhat_phat (boot_iter, subtask, trial) float64 51MB 0.0004996 ... ...)" + " phat (subtask, trial) float64 51kB 0.518 0.712 ... 0.916 0.916\n", + " varhat_phat (subtask, trial) float64 51kB 0.0005004 ... 0.0001542\n", + " boot_phat (boot_iter, subtask, trial) float64 51MB 0.518 ... 0.92\n", + " boot_varhat_phat (boot_iter, subtask, trial) float64 51MB 0.0005004 ... ...)" ] }, - "execution_count": 21, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 21 + "execution_count": 10 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:06:29.972245Z", - "start_time": "2026-03-27T00:06:29.909109Z" + "end_time": "2026-03-27T00:35:47.326651Z", + "start_time": "2026-03-27T00:35:47.251189Z" } }, "cell_type": "code", @@ -163,7 +163,7 @@ "text/plain": [ "<Figure size 640x480 with 1 Axes>" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -172,13 +172,13 @@ } } ], - "execution_count": 22 + "execution_count": 11 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:21:20.477788Z", - "start_time": "2026-03-27T00:21:20.413626Z" + "end_time": "2026-03-27T00:35:51.180487Z", + "start_time": "2026-03-27T00:35:51.112194Z" } }, "cell_type": "code", @@ -206,7 +206,7 @@ "text/plain": [ "<Figure size 500x500 with 1 Axes>" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -215,19 +215,13 @@ } } ], - "execution_count": 34 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## Experiment 2 benchmark", - "id": "cb2d9eed7df96023" + "execution_count": 12 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:06:40.463491Z", - "start_time": "2026-03-27T00:06:37.755145Z" + "end_time": "2026-03-27T00:36:02.781239Z", + "start_time": "2026-03-27T00:35:59.656520Z" } }, "cell_type": "code", @@ -240,7 +234,7 @@ { "data": { "text/plain": [ - "GeneralizationBenchmark.GeneralizationBenchmarkResult(msen=0.01929741563254476, msen_sigma=0.0024513021659298712, msen_CI95=(0.014086503611026608, 0.023757523049323704), model_statistics=<xarray.GeneralizationStatistics> Size: 583kB\n", + "MutatorOneshotBenchmark.GeneralizationBenchmarkResult(msen=0.019300722565489446, msen_sigma=0.0026393283731082216, msen_CI95=(0.013504050205767358, 0.023818023291841975), model_statistics=<xarray.GeneralizationStatistics> Size: 583kB\n", "Dimensions: (transformation: 36, boot_iter: 1000)\n", "Coordinates:\n", " * transformation (transformation) <U26 4kB 'backgrounds | 0.1' ... '...\n", @@ -248,24 +242,24 @@ " transformation_level (transformation) float64 288B 0.1 0.2154 ... 0.5 1.5\n", "Dimensions without coordinates: boot_iter\n", "Data variables:\n", - " phat (transformation) float64 288B 0.9748 0.9752 ... 0.9491\n", - " varhat_phat (transformation) float64 288B 5.711e-06 ... 2.237e-05\n", - " boot_phat (boot_iter, transformation) float64 288kB 0.9735 .....\n", - " boot_varhat_phat (boot_iter, transformation) float64 288kB 6.032e-06...)" + " phat (transformation) float64 288B 0.9715 0.9737 ... 0.9497\n", + " varhat_phat (transformation) float64 288B 6.359e-06 ... 2.222e-05\n", + " boot_phat (boot_iter, transformation) float64 288kB 0.9733 .....\n", + " boot_varhat_phat (boot_iter, transformation) float64 288kB 5.971e-06...)" ] }, - "execution_count": 23, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 23 + "execution_count": 13 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:06:44.345166Z", - "start_time": "2026-03-27T00:06:44.297174Z" + "end_time": "2026-03-27T00:36:09.047702Z", + "start_time": "2026-03-27T00:36:08.975029Z" } }, "cell_type": "code", @@ -291,7 +285,7 @@ "text/plain": [ "<Figure size 500x500 with 1 Axes>" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -300,15 +294,15 @@ } } ], - "execution_count": 24 + "execution_count": 14 }, { "metadata": {}, "cell_type": "code", - "outputs": [], - "execution_count": null, "source": "", - "id": "87b4757fc69e04ad" + "id": "87b4757fc69e04ad", + "outputs": [], + "execution_count": null } ], "metadata": { diff --git a/hobj/benchmarks/binary_classification/benchmark.py b/hobj/benchmarks/binary_classification/benchmark.py deleted file mode 100644 index 97b9303..0000000 --- a/hobj/benchmarks/binary_classification/benchmark.py +++ /dev/null @@ -1,288 +0,0 @@ -from dataclasses import dataclass -from typing import Dict, List, Optional, Tuple, Union - -import numpy as np -import pydantic -import xarray as xr -from tqdm import tqdm - -from hobj.benchmarks.binary_classification.estimator import LearningCurveStatistics -from hobj.benchmarks.binary_classification.simulation import ( - BinaryClassificationSubtask, - BinaryClassificationSubtaskResult, -) -from hobj.learning_models import BinaryLearningModel -from hobj.stats.ci import estimate_basic_bootstrap_CI - - -# %% Models for configuring a LearningCurveBenchmark: -class TargetSubtaskData(pydantic.BaseModel): - subtask: BinaryClassificationSubtask # The subtask which generated the associated results - results: List[ - BinaryClassificationSubtaskResult - ] # [session, trial] boolean matrix of performance - - model_config = dict(arbitrary_types_allowed=True) - - @pydantic.model_validator(mode="after") - def validate_results(self) -> "TargetSubtaskData": - # Check shape - for result in self.results: - if self.subtask.ntrials != len(result.perf_seq): - raise ValueError( - f"Expected {self.subtask.ntrials} trials, but got {result.perf_seq} trials" - ) - - return self - - -class LearningCurveBenchmarkConfig(pydantic.BaseModel): - subtask_name_to_data: Dict[str, "TargetSubtaskData"] = pydantic.Field( - default_factory=dict, - description="A dictionary of subtask_name -> TargetSubtaskConfig", - ) - num_simulations_per_subtask: int = pydantic.Field(ge=2) - num_bootstrap_samples: int = pydantic.Field(ge=2) - bootstrap_by_worker: bool - ntrials: Optional[int] = pydantic.Field(default=None) - - @pydantic.model_validator(mode="after") - def ensure_rectangular(self) -> "LearningCurveBenchmarkConfig": - ntrials_observed = set() - for name, data in self.subtask_name_to_data.items(): - ntrials_observed.add(data.subtask.ntrials) - - if not len(ntrials_observed) == 1: - raise ValueError( - f"Expected all subtasks to have the same number of trials, but got {ntrials_observed}" - ) - - if self.ntrials is not None: - if self.ntrials != ntrials_observed.pop(): - raise ValueError( - f"Expected ntrials to be {ntrials_observed.pop()}, but got {self.ntrials}" - ) - else: - self.ntrials = ntrials_observed.pop() - return self - - -# %% -class LearningCurveBenchmark: - def __init__( - self, - config: LearningCurveBenchmarkConfig, - ): - self.config = config - - # Attach properties - self.subtask_names = sorted(config.subtask_name_to_data.keys()) - self.subtask_name_to_subtask: Dict[str, BinaryClassificationSubtask] = { - name: config.subtask_name_to_data[name].subtask - for name in self.subtask_names - } - - self.subtask_name_to_results: Dict[ - str, List[BinaryClassificationSubtaskResult] - ] = {} - self._target_data = {} - - for name in self.subtask_names: - results = config.subtask_name_to_data[name].results - self.subtask_name_to_results[name] = results - - self._target_data[name] = {} - for result in results: - worker_id = result.worker_id - if worker_id in self._target_data[name]: - raise ValueError( - f"Worker {worker_id} has already been seen for subtask {name}" - ) - self._target_data[name][result.worker_id] = list( - [bool(v) for v in result.perf_seq] - ) - - self._target_statistics = LearningCurveStatistics( - subtask_name_to_results=self.subtask_name_to_results, - nbootstrap_samples=self.config.num_bootstrap_samples, - bootstrap_by_worker=self.config.bootstrap_by_worker, - ) - - @property - def target_data(self) -> Dict[str, Dict[str, List[bool]]]: - return self._target_data - - @property - def target_statistics(self) -> LearningCurveStatistics: - """ - Returns point estimates of the mean (and an estimate of the variance of that estimator) of the binomial proportion for the target data, - and bootstrapped resamples of those statistics. - :return: - """ - return self._target_statistics - - @dataclass - class LearningCurveBenchmarkResult: - msen: float - msen_sigma: float - msen_CI95: Tuple[float, float] - lapse_rate: float - - model_statistics: LearningCurveStatistics - - def __call__( - self, learner: BinaryLearningModel, show_pbar: bool = False - ) -> LearningCurveBenchmarkResult: - """ - :param learner: LearningModel - :return: - """ - - # Get model learning curve statistics: - subtask_name_to_model_results: Dict[ - str, List[BinaryClassificationSubtaskResult] - ] = {} - for i_subtask, subtask_name in enumerate( - tqdm(self.subtask_names, desc="Subtask simulations:", disable=not show_pbar) - ): - # Get [simulation, trial] boolean performance matrix for the model - subtask_results = self.simulate_model_behavior( - subtask=self.subtask_name_to_subtask[subtask_name], - learner=learner, - nsimulations=self.config.num_simulations_per_subtask, - ) - - subtask_name_to_model_results[subtask_name] = subtask_results - - # Assemble into a single matrix - model_statistics = LearningCurveStatistics( - subtask_name_to_results=subtask_name_to_model_results, - nbootstrap_samples=self.config.num_bootstrap_samples, - bootstrap_by_worker=False, - ) - - # Calculate comparison statistics between target and model learning curves: - msen_point, lapse_rate = self._compare_learning_curves( - model_phat=model_statistics.phat, - model_varhat_phat=model_statistics.varhat_phat, - target_phat=self.target_statistics.phat, - target_varhat_phat=self.target_statistics.varhat_phat, - condition_dims=("subtask", "trial"), - fit_lapse_rate=True, - ) - - # Calculate bootstrap replicates: - msen_boot, _ = self._compare_learning_curves( - model_phat=model_statistics.boot_phat, - model_varhat_phat=model_statistics.boot_varhat_phat, - target_phat=self.target_statistics.boot_phat, - target_varhat_phat=self.target_statistics.boot_varhat_phat, - condition_dims=("subtask", "trial"), - fit_lapse_rate=True, - ) - - msen_sigma = np.std(msen_boot, ddof=1) - - msen_CI95 = estimate_basic_bootstrap_CI( - alpha=0.05, - point_estimate=msen_point, - bootstrapped_point_estimates=np.array(msen_boot), - ) - - # Return result - return self.LearningCurveBenchmarkResult( - msen=float(msen_point), - msen_sigma=float(msen_sigma), - msen_CI95=msen_CI95, - model_statistics=model_statistics, - lapse_rate=lapse_rate, - ) - - @staticmethod - def simulate_model_behavior( - subtask: BinaryClassificationSubtask, - learner: BinaryLearningModel, - nsimulations: int, - ) -> List[BinaryClassificationSubtaskResult]: - """ - Returns a [nsimulations, ntrials] matrix of model performance on the subtask. - :param subtask: - :param learner: - :param nsimulations: - :return: a [nsimulations, ntrials] matrix of model performance on the subtask. - """ - - results = [] - - # Perform simulations for this subtask: - for i_simulation in range(nsimulations): - # Reset learner internal state - learner.reset_state(seed=None) - - # Simulate session: - results.append( - subtask.simulate_session( - learner=learner, - seed=None, - ) - ) - - return results - - @classmethod - def _compare_learning_curves( - cls, - model_phat: xr.DataArray, - model_varhat_phat: xr.DataArray, - target_phat: xr.DataArray, - target_varhat_phat: xr.DataArray, - condition_dims: Tuple[str, ...], - fit_lapse_rate: bool, - ) -> Tuple[Union[np.ndarray, np.generic], Union[xr.DataArray, None]]: - if fit_lapse_rate: - lapse_rate = cls._fit_lapse_rate( - pmodel=model_phat, ptarget=target_phat, condition_dims=condition_dims - ) - model_phat = model_phat * (1 - lapse_rate) + 0.5 * lapse_rate - model_varhat_phat = model_varhat_phat * (1 - lapse_rate) ** 2 - else: - lapse_rate = None - - msen = ( - np.square(model_phat - target_phat).mean(condition_dims) - - model_varhat_phat.mean(condition_dims) - - target_varhat_phat.mean(condition_dims) - ) - return msen, lapse_rate - - @staticmethod - def _fit_lapse_rate( - pmodel: xr.DataArray, ptarget: xr.DataArray, condition_dims: Tuple[str, ...] - ) -> Union[np.ndarray, np.generic]: - """ - Fits a "lapse rate" parameter (gamma), which takes on values between [0, 1]. It may be interpreted - as the probability that a uniform random guess is made. The value of gamma which minimizes the - empirical MSE between pmodel and ptarget is selected. - - - pmodel_adjusted := (1 - gamma) * pmodel + 1/2 * gamma - Loss = ((pmodel_adjusted - ptarget)**2).sum() - - :param pmodel: - :param ptarget: - :return: - """ - - nway = 2 - numerator = -( - 2 * pmodel / nway - - 2 * np.square(pmodel) - + 2 * pmodel * ptarget - - 2 * ptarget / nway - ).sum(dim=condition_dims) - denominator = (2 / (nway**2) - 4 * pmodel / nway + 2 * (pmodel**2)).sum( - dim=condition_dims - ) - gamma_star = numerator / denominator - gamma_star = np.clip(gamma_star, 0, 1) - return gamma_star diff --git a/hobj/benchmarks/generalization/benchmark.py b/hobj/benchmarks/generalization/benchmark.py deleted file mode 100644 index 29ba34a..0000000 --- a/hobj/benchmarks/generalization/benchmark.py +++ /dev/null @@ -1,168 +0,0 @@ -from dataclasses import dataclass -from typing import List, Tuple - -import numpy as np -import pydantic -import xarray as xr -from tqdm import tqdm - -from hobj.benchmarks.generalization.estimator import GeneralizationStatistics -from hobj.benchmarks.generalization.simulator import ( - GeneralizationSessionResult, - GeneralizationSubtask, -) -from hobj.learning_models import BinaryLearningModel -from hobj.stats.ci import estimate_basic_bootstrap_CI - - -# %% Models for configuring a LearningCurveBenchmark: -class GeneralizationBenchmarkConfig(pydantic.BaseModel): - subtasks: List[GeneralizationSubtask] - results: List[GeneralizationSessionResult] - num_simulations_per_subtask: int = pydantic.Field(ge=2) - num_bootstrap_samples: int = pydantic.Field(ge=2) - bootstrap_target_by_worker: bool - - -# %% -class GeneralizationBenchmark: - def __init__( - self, - config: GeneralizationBenchmarkConfig, - ): - self.config = config - self._generalization_statistics = GeneralizationStatistics( - results=self.config.results, - perform_lapse_rate_correction=True, - n_bootstrap_iterations=self.config.num_bootstrap_samples, - bootstrap_by_worker=self.config.bootstrap_target_by_worker, - ) - - @property - def target_statistics(self) -> GeneralizationStatistics: - """ - Returns the target data for the benchmark, which is a dictionary of subtask_name -> [session, trial] boolean matrix of performance. - :return: - """ - return self._generalization_statistics - - @dataclass - class GeneralizationBenchmarkResult: - msen: float - msen_sigma: float - msen_CI95: Tuple[float, float] - model_statistics: GeneralizationStatistics - - def __call__( - self, learner: BinaryLearningModel, show_pbar: bool = False - ) -> GeneralizationBenchmarkResult: - """ - :param learner: LearningModel - :return: - """ - - # Get model generalization behaviors: - results: List[GeneralizationSessionResult] = [] - for subtask in tqdm( - self.config.subtasks, desc="Subtask simulations:", disable=not show_pbar - ): - # Get [simulation, trial] boolean performance matrix for the model - subtask_results = self.simulate_model_behavior( - subtask=subtask, - learner=learner, - nsimulations=self.config.num_simulations_per_subtask, - ) - results.extend(subtask_results) - - # Assemble into a single matrix - model_statistics = GeneralizationStatistics( - results=results, - perform_lapse_rate_correction=False, - n_bootstrap_iterations=self.config.num_bootstrap_samples, - bootstrap_by_worker=False, # bootstrap simulations - ) - - # Get target transformation - model_statistics = model_statistics.sel( - transformation=self.target_statistics.transformation - ) - - # Calculate comparison statistics between target and model learning curves: - msen_point = self._compare_generalization_patterns( - model_phat=model_statistics.phat, - model_varhat_phat=model_statistics.varhat_phat, - target_phat=self.target_statistics.phat, - target_varhat_phat=self.target_statistics.varhat_phat, - condition_dims=("transformation",), - ) - - # Calculate bootstrap replicates: - msen_boot = self._compare_generalization_patterns( - model_phat=model_statistics.boot_phat, - model_varhat_phat=model_statistics.boot_varhat_phat, - target_phat=self.target_statistics.boot_phat, - target_varhat_phat=self.target_statistics.boot_varhat_phat, - condition_dims=("transformation",), - ) - - msen_sigma = np.std(msen_boot, ddof=1) - msen_CI95 = estimate_basic_bootstrap_CI( - alpha=0.05, - point_estimate=msen_point, - bootstrapped_point_estimates=np.array(msen_boot), - ) - - # Return result - return self.GeneralizationBenchmarkResult( - msen=float(msen_point), - msen_sigma=float(msen_sigma), - msen_CI95=msen_CI95, - model_statistics=model_statistics, - ) - - @staticmethod - def simulate_model_behavior( - subtask: GeneralizationSubtask, - learner: BinaryLearningModel, - nsimulations: int, - ) -> List[GeneralizationSessionResult]: - """ - Returns a [nsimulations, ntrials] matrix of model performance on the subtask. - :param subtask: - :param learner: - :param nsimulations: - :return: a [nsimulations, ntrials] matrix of model performance on the subtask. - """ - - results = [] - - # Perform simulations for this subtask: - for i_simulation in range(nsimulations): - # Reset learner internal state - learner.reset_state(seed=None) - - # Simulate session: - results.append( - subtask.simulate_session( - learner=learner, - seed=None, - ) - ) - - return results - - @classmethod - def _compare_generalization_patterns( - cls, - model_phat: xr.DataArray, - model_varhat_phat: xr.DataArray, - target_phat: xr.DataArray, - target_varhat_phat: xr.DataArray, - condition_dims: Tuple[str, ...], - ): - msen = ( - np.square(model_phat - target_phat).mean(condition_dims) - - model_varhat_phat.mean(condition_dims) - - target_varhat_phat.mean(condition_dims) - ) - return msen diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index edf21bd..a484721 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -1,36 +1,35 @@ +from dataclasses import dataclass from pathlib import Path -from typing import Dict, List +from typing import Dict, List, Optional, Tuple, Union import numpy as np +import xarray as xr +from tqdm import tqdm -from hobj.benchmarks.binary_classification.benchmark import ( - LearningCurveBenchmark, - LearningCurveBenchmarkConfig, - TargetSubtaskData, -) +from hobj.benchmarks.binary_classification.estimator import LearningCurveStatistics from hobj.benchmarks.binary_classification.simulation import ( BinaryClassificationSubtask, BinaryClassificationSubtaskResult, ) from hobj.data.behavior import load_highvar_behavior from hobj.data.images import load_imageset_meta_highvar +from hobj.learning_models import BinaryLearningModel +from hobj.stats.ci import estimate_basic_bootstrap_CI -# %% -class MutatorHighVarBenchmark(LearningCurveBenchmark): - def __init__(self, cachedir: Path | None = None): - # Load data into format expected by benchmark +class MutatorHighVarBenchmark: + num_simulations_per_subtask = 500 + num_bootstrap_samples = 1000 + bootstrap_by_worker = False + ntrials = 100 - # Load image manifest: + def __init__(self, cachedir: Path | None = None): images_df = load_imageset_meta_highvar(cachedir=cachedir) - - # Load raw human session data for benchmark: df_behavior = load_highvar_behavior( remove_probe_trials=True, cachedir=cachedir, ) - # Normalize data for benchmark: image_id_to_category = dict( zip(images_df["image_id"], images_df["category"], strict=True) ) @@ -40,69 +39,217 @@ def __init__(self, cachedir: Path | None = None): .to_dict() ) - subtask_name_to_results = {} - subtask_name_to_subtask = {} + subtask_name_to_results: Dict[str, List[BinaryClassificationSubtaskResult]] = {} + subtask_name_to_subtask: Dict[str, BinaryClassificationSubtask] = {} - # Iterate over worker sessions: - for assignment_id, session in df_behavior.groupby("assignment_id", sort=False): + for _, session in df_behavior.groupby("assignment_id", sort=False): categories = set() perf_seq: List[bool] = [] - # Iterate over trials: session = session.sort_values("trial") for perf, image_id in zip(session.perf, session.image_id): categories.add(image_id_to_category[image_id]) perf_seq.append(bool(perf)) - # Infer subtask from the categories observed in the session: - assert ( - len(categories) == 2 - ), f"Expected two categories, but got {categories}" - subtask_name = ",".join(sorted(categories)) + if len(categories) != 2: + raise ValueError(f"Expected two categories, but got {categories}") + cat0, cat1 = sorted(categories) + subtask_name = ",".join((cat0, cat1)) - # Instantiate the subtask if it does not exist: if subtask_name not in subtask_name_to_subtask: - subtask = BinaryClassificationSubtask( + subtask_name_to_subtask[subtask_name] = BinaryClassificationSubtask( classA=category_to_image_ids[cat0], classB=category_to_image_ids[cat1], - ntrials=100, + ntrials=self.ntrials, replace=False, ) - subtask_name_to_subtask[subtask_name] = subtask subtask_name_to_results[subtask_name] = [] subtask_name_to_results[subtask_name].append( BinaryClassificationSubtaskResult( - perf_seq=np.array(perf_seq), worker_id=session.worker_id.iloc[0] + perf_seq=np.array(perf_seq), + worker_id=session.worker_id.iloc[0], ) ) - # Cast as numpy - for name in subtask_name_to_results: - subtask_name_to_results[name] = np.array( - subtask_name_to_results[name] - ) # [session, trial] - - subtask_name_to_data: Dict[str, TargetSubtaskData] = {} - for name in subtask_name_to_results: - subtask_name_to_data[name] = TargetSubtaskData( - subtask=subtask_name_to_subtask[name], - results=subtask_name_to_results[name], + self.subtask_names = sorted(subtask_name_to_subtask) + self.subtask_name_to_subtask = { + name: subtask_name_to_subtask[name] for name in self.subtask_names + } + self.subtask_name_to_results = { + name: subtask_name_to_results[name] for name in self.subtask_names + } + + observed_ntrials = { + subtask.ntrials for subtask in self.subtask_name_to_subtask.values() + } + if len(observed_ntrials) != 1: + raise ValueError( + f"Expected all subtasks to have the same number of trials, but got {observed_ntrials}" + ) + + observed_ntrials_value = next(iter(observed_ntrials)) + if observed_ntrials_value != self.ntrials: + raise ValueError( + f"Expected ntrials to be {observed_ntrials_value}, but got {self.ntrials}" ) - # Instantiate benchmark config object - config = LearningCurveBenchmarkConfig( - subtask_name_to_data=subtask_name_to_data, - num_simulations_per_subtask=500, - num_bootstrap_samples=1000, + self._target_data: Dict[str, Dict[str, List[bool]]] = {} + for name in self.subtask_names: + self._target_data[name] = {} + for result in self.subtask_name_to_results[name]: + worker_id = result.worker_id + if worker_id in self._target_data[name]: + raise ValueError( + f"Worker {worker_id} has already been seen for subtask {name}" + ) + self._target_data[name][worker_id] = [bool(v) for v in result.perf_seq] + + self._target_statistics = LearningCurveStatistics( + subtask_name_to_results=self.subtask_name_to_results, + nbootstrap_samples=self.num_bootstrap_samples, + bootstrap_by_worker=self.bootstrap_by_worker, + ) + + @property + def target_data(self) -> Dict[str, Dict[str, List[bool]]]: + return self._target_data + + @property + def target_statistics(self) -> LearningCurveStatistics: + return self._target_statistics + + @dataclass + class LearningCurveBenchmarkResult: + msen: float + msen_sigma: float + msen_CI95: Tuple[float, float] + lapse_rate: xr.DataArray | None + model_statistics: LearningCurveStatistics + + def __call__( + self, learner: BinaryLearningModel, show_pbar: bool = False + ) -> "MutatorHighVarBenchmark.LearningCurveBenchmarkResult": + subtask_name_to_model_results: Dict[ + str, List[BinaryClassificationSubtaskResult] + ] = {} + for subtask_name in tqdm( + self.subtask_names, + desc="Subtask simulations:", + disable=not show_pbar, + ): + subtask_name_to_model_results[subtask_name] = self.simulate_model_behavior( + subtask=self.subtask_name_to_subtask[subtask_name], + learner=learner, + nsimulations=self.num_simulations_per_subtask, + ) + + model_statistics = LearningCurveStatistics( + subtask_name_to_results=subtask_name_to_model_results, + nbootstrap_samples=self.num_bootstrap_samples, bootstrap_by_worker=False, - ntrials=100, ) - super().__init__(config=config) + msen_point, lapse_rate = self._compare_learning_curves( + model_phat=model_statistics.phat, + model_varhat_phat=model_statistics.varhat_phat, + target_phat=self.target_statistics.phat, + target_varhat_phat=self.target_statistics.varhat_phat, + condition_dims=("subtask", "trial"), + fit_lapse_rate=True, + ) + + msen_boot, _ = self._compare_learning_curves( + model_phat=model_statistics.boot_phat, + model_varhat_phat=model_statistics.boot_varhat_phat, + target_phat=self.target_statistics.boot_phat, + target_varhat_phat=self.target_statistics.boot_varhat_phat, + condition_dims=("subtask", "trial"), + fit_lapse_rate=True, + ) + + msen_sigma = np.std(msen_boot, ddof=1) + msen_CI95 = estimate_basic_bootstrap_CI( + alpha=0.05, + point_estimate=msen_point, + bootstrapped_point_estimates=np.array(msen_boot), + ) + + return self.LearningCurveBenchmarkResult( + msen=float(msen_point), + msen_sigma=float(msen_sigma), + msen_CI95=msen_CI95, + lapse_rate=lapse_rate, + model_statistics=model_statistics, + ) + + @staticmethod + def simulate_model_behavior( + subtask: BinaryClassificationSubtask, + learner: BinaryLearningModel, + nsimulations: int, + ) -> List[BinaryClassificationSubtaskResult]: + results = [] + for _ in range(nsimulations): + learner.reset_state(seed=None) + results.append( + subtask.simulate_session( + learner=learner, + seed=None, + ) + ) + + return results + + @classmethod + def _compare_learning_curves( + cls, + model_phat: xr.DataArray, + model_varhat_phat: xr.DataArray, + target_phat: xr.DataArray, + target_varhat_phat: xr.DataArray, + condition_dims: Tuple[str, ...], + fit_lapse_rate: bool, + ) -> Tuple[Union[np.ndarray, np.generic], Optional[xr.DataArray]]: + if fit_lapse_rate: + lapse_rate = cls._fit_lapse_rate( + pmodel=model_phat, + ptarget=target_phat, + condition_dims=condition_dims, + ) + model_phat = model_phat * (1 - lapse_rate) + 0.5 * lapse_rate + model_varhat_phat = model_varhat_phat * (1 - lapse_rate) ** 2 + else: + lapse_rate = None + + msen = ( + np.square(model_phat - target_phat).mean(condition_dims) + - model_varhat_phat.mean(condition_dims) + - target_varhat_phat.mean(condition_dims) + ) + return msen, lapse_rate + + @staticmethod + def _fit_lapse_rate( + pmodel: xr.DataArray, + ptarget: xr.DataArray, + condition_dims: Tuple[str, ...], + ) -> Union[np.ndarray, np.generic]: + nway = 2 + numerator = -( + 2 * pmodel / nway + - 2 * np.square(pmodel) + + 2 * pmodel * ptarget + - 2 * ptarget / nway + ).sum(dim=condition_dims) + denominator = (2 / (nway**2) - 4 * pmodel / nway + 2 * (pmodel**2)).sum( + dim=condition_dims + ) + gamma_star = numerator / denominator + gamma_star = np.clip(gamma_star, 0, 1) + return gamma_star -# %% if __name__ == "__main__": experiment = MutatorHighVarBenchmark() diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index e140ac3..eaaaaba 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -1,22 +1,29 @@ import collections +from dataclasses import dataclass from pathlib import Path -from typing import Dict, List +from typing import Dict, List, Tuple -from hobj.benchmarks.generalization.benchmark import ( - GeneralizationBenchmark, - GeneralizationBenchmarkConfig, +import numpy as np +import xarray as xr +from tqdm import tqdm + +from hobj.benchmarks.generalization.estimator import GeneralizationStatistics +from hobj.benchmarks.generalization.simulator import ( GeneralizationSessionResult, + GeneralizationSubtask, ) -from hobj.benchmarks.generalization.estimator import GeneralizationStatistics -from hobj.benchmarks.generalization.simulator import GeneralizationSubtask from hobj.data.behavior import load_oneshot_behavior from hobj.data.images import load_imageset_meta_oneshot - +from hobj.learning_models import BinaryLearningModel +from hobj.stats.ci import estimate_basic_bootstrap_CI from hobj.types import ImageId -# %% -class MutatorOneshotBenchmark(GeneralizationBenchmark): +class MutatorOneshotBenchmark: + num_simulations_per_subtask = 500 + num_bootstrap_samples = 1000 + bootstrap_target_by_worker = True + subtask_names = [ "MutatorOneshotObject00,MutatorOneshotObject43", "MutatorOneshotObject01,MutatorOneshotObject37", @@ -91,14 +98,12 @@ class MutatorOneshotBenchmark(GeneralizationBenchmark): ] def __init__(self, cachedir: Path | None = None): - support_trials = {0, 1, 2, 3, 4, 5, 6, 7, 8} + support_trials = set(range(9)) catch_trials = {9, 14, 19} - # Load image manifest images_df = load_imageset_meta_oneshot(cachedir=cachedir) image_id_to_row = images_df.set_index("image_id") - # Map image refs to transformation ids image_ref_to_transformation_id: Dict[ImageId, str] = {} cat_to_support_image: Dict[str, ImageId] = {} cat_to_test_images: Dict[str, List[ImageId]] = {} @@ -111,46 +116,43 @@ def __init__(self, cachedir: Path | None = None): image_ref_to_transformation_id[image_id] = transformation_id if row["transformation"] == "original": - if row["category"] not in cat_to_support_image: - cat_to_support_image[row["category"]] = image_id - else: + if row["category"] in cat_to_support_image: raise ValueError( f"Multiple support images for category {row['category']}" ) + cat_to_support_image[row["category"]] = image_id else: if row["category"] not in cat_to_test_images: cat_to_test_images[row["category"]] = [] cat_to_test_images[row["category"]].append(image_id) - # Assemble subtask simulators - subtasks = [] + self.subtasks = [] for subtask_name in self.subtask_names: - catA, catB = sorted(subtask_name.split(",")) - - support_imageA = cat_to_support_image[catA] - support_imageB = cat_to_support_image[catB] - test_imagesA = cat_to_test_images[catA] - test_imagesB = cat_to_test_images[catB] - assert len(test_imagesA) == len(test_imagesB) == 60 - - subtask = GeneralizationSubtask( - support_imageA=support_imageA, - support_imageB=support_imageB, - test_imagesA=test_imagesA, - test_imagesB=test_imagesB, - image_ref_to_transformation=image_ref_to_transformation_id, + cat_a, cat_b = sorted(subtask_name.split(",")) + test_images_a = cat_to_test_images[cat_a] + test_images_b = cat_to_test_images[cat_b] + + if len(test_images_a) != 60 or len(test_images_b) != 60: + raise ValueError( + f"Expected 60 test images per category for {subtask_name}, " + f"but got {len(test_images_a)} and {len(test_images_b)}." + ) + + self.subtasks.append( + GeneralizationSubtask( + support_imageA=cat_to_support_image[cat_a], + support_imageB=cat_to_support_image[cat_b], + test_imagesA=test_images_a, + test_imagesB=test_images_b, + image_ref_to_transformation=image_ref_to_transformation_id, + ) ) - subtasks.append(subtask) - # Package human data into format expected by benchmark oneshot_df = load_oneshot_behavior(cachedir=cachedir) + self.results: List[GeneralizationSessionResult] = [] - results = [] - - for session, session_df in oneshot_df.groupby( - ["assignment_id", "slot"], sort=False - ): + for _, session_df in oneshot_df.groupby(["assignment_id", "slot"], sort=False): session_df = session_df.sort_values("trial") transformation_to_kn = collections.defaultdict(lambda: [0, 0]) kcatch = 0 @@ -163,51 +165,57 @@ def __init__(self, cachedir: Path | None = None): annotation = image_id_to_row.loc[image_id] perf = bool(row["perf"]) - # Record performance in relevant slot if i_trial in support_trials: - assert annotation["transformation"] == "original" + if annotation["transformation"] != "original": + raise ValueError( + f"Expected support trial {i_trial} to use an original image." + ) elif i_trial in catch_trials: - assert annotation["transformation"] == "original" + if annotation["transformation"] != "original": + raise ValueError( + f"Expected catch trial {i_trial} to use an original image." + ) kcatch += perf ncatch += 1 else: - assert annotation["transformation"] != "original" - transformation_id = image_ref_to_transformation_id[image_id] + if annotation["transformation"] == "original": + raise ValueError( + f"Expected generalization trial {i_trial} to use a transformed image." + ) - # Keep only benchmarked transformations + transformation_id = image_ref_to_transformation_id[image_id] if transformation_id in self.transformation_ids: transformation_to_kn[transformation_id][0] += perf transformation_to_kn[transformation_id][1] += 1 - # Infer subtask name from observed categories - assert len(observed_categories) == 2 + if len(observed_categories) != 2: + raise ValueError( + f"Expected two categories for a one-shot session, got {observed_categories}." + ) + subtask_name = ",".join(sorted(observed_categories)) if subtask_name not in self.subtask_names: raise ValueError(f"Unexpected subtask name: {subtask_name}") - # Package result - result = GeneralizationSessionResult( - transformation_to_kn=transformation_to_kn, - kcatch=kcatch, - ncatch=ncatch, - worker_id=session_df["worker_id"].iloc[0], + self.results.append( + GeneralizationSessionResult( + transformation_to_kn=transformation_to_kn, + kcatch=kcatch, + ncatch=ncatch, + worker_id=session_df["worker_id"].iloc[0], + ) ) - results.append(result) - # %% Assemble benchmark config - config = GeneralizationBenchmarkConfig( - results=results, - subtasks=subtasks, - num_simulations_per_subtask=500, - num_bootstrap_samples=1000, - bootstrap_target_by_worker=True, + self._target_statistics = GeneralizationStatistics( + results=self.results, + perform_lapse_rate_correction=True, + n_bootstrap_iterations=self.num_bootstrap_samples, + bootstrap_by_worker=self.bootstrap_target_by_worker, ) - super().__init__(config=config) - @property def target_statistics(self) -> GeneralizationStatistics: - gen_statistics: GeneralizationStatistics = self._generalization_statistics + gen_statistics = self._target_statistics return gen_statistics.assign_coords( transformation_type=( ["transformation"], @@ -225,6 +233,102 @@ def target_statistics(self) -> GeneralizationStatistics: ), ) + @dataclass + class GeneralizationBenchmarkResult: + msen: float + msen_sigma: float + msen_CI95: Tuple[float, float] + model_statistics: GeneralizationStatistics + + def __call__( + self, learner: BinaryLearningModel, show_pbar: bool = False + ) -> "MutatorOneshotBenchmark.GeneralizationBenchmarkResult": + results: List[GeneralizationSessionResult] = [] + for subtask in tqdm( + self.subtasks, + desc="Subtask simulations:", + disable=not show_pbar, + ): + results.extend( + self.simulate_model_behavior( + subtask=subtask, + learner=learner, + nsimulations=self.num_simulations_per_subtask, + ) + ) + + model_statistics = GeneralizationStatistics( + results=results, + perform_lapse_rate_correction=False, + n_bootstrap_iterations=self.num_bootstrap_samples, + bootstrap_by_worker=False, + ) + model_statistics = model_statistics.sel( + transformation=self.target_statistics.transformation + ) + + msen_point = self._compare_generalization_patterns( + model_phat=model_statistics.phat, + model_varhat_phat=model_statistics.varhat_phat, + target_phat=self.target_statistics.phat, + target_varhat_phat=self.target_statistics.varhat_phat, + condition_dims=("transformation",), + ) + msen_boot = self._compare_generalization_patterns( + model_phat=model_statistics.boot_phat, + model_varhat_phat=model_statistics.boot_varhat_phat, + target_phat=self.target_statistics.boot_phat, + target_varhat_phat=self.target_statistics.boot_varhat_phat, + condition_dims=("transformation",), + ) + + msen_sigma = np.std(msen_boot, ddof=1) + msen_CI95 = estimate_basic_bootstrap_CI( + alpha=0.05, + point_estimate=msen_point, + bootstrapped_point_estimates=np.array(msen_boot), + ) + + return self.GeneralizationBenchmarkResult( + msen=float(msen_point), + msen_sigma=float(msen_sigma), + msen_CI95=msen_CI95, + model_statistics=model_statistics, + ) + + @staticmethod + def simulate_model_behavior( + subtask: GeneralizationSubtask, + learner: BinaryLearningModel, + nsimulations: int, + ) -> List[GeneralizationSessionResult]: + results = [] + for _ in range(nsimulations): + learner.reset_state(seed=None) + results.append( + subtask.simulate_session( + learner=learner, + seed=None, + ) + ) + + return results + + @staticmethod + def _compare_generalization_patterns( + model_phat: xr.DataArray, + model_varhat_phat: xr.DataArray, + target_phat: xr.DataArray, + target_varhat_phat: xr.DataArray, + condition_dims: Tuple[str, ...], + ) -> xr.DataArray: + msen = ( + np.square(model_phat - target_phat).mean(condition_dims) + - model_varhat_phat.mean(condition_dims) + - target_varhat_phat.mean(condition_dims) + ) + return msen + if __name__ == "__main__": benchmark = MutatorOneshotBenchmark() diff --git a/hobj/learning_models/__init__.py b/hobj/learning_models/__init__.py index 957baec..895d5c6 100644 --- a/hobj/learning_models/__init__.py +++ b/hobj/learning_models/__init__.py @@ -3,7 +3,9 @@ import numpy as np -from hobj.learning_models.linear_learning_model.representation import RepresentationalModel +from hobj.learning_models.linear_learning_model.representation import ( + RepresentationalModel, +) from hobj.learning_models.linear_learning_model.update_rules import UpdateRule from hobj.types import ImageId From 014751fc9faed97fe4a2201ec65e84d5236adb3f Mon Sep 17 00:00:00 2001 From: himjl <mil@mit.edu> Date: Thu, 26 Mar 2026 20:43:47 -0400 Subject: [PATCH 143/156] common --- examples/score_example_models.ipynb | 97 ++++++++++--------- hobj/benchmarks/common.py | 116 +++++++++++++++++++++++ hobj/benchmarks/mut_highvar_benchmark.py | 52 ++++------ hobj/benchmarks/mut_oneshot_benchmark.py | 47 ++++----- 4 files changed, 202 insertions(+), 110 deletions(-) create mode 100644 hobj/benchmarks/common.py diff --git a/examples/score_example_models.ipynb b/examples/score_example_models.ipynb index e21b7e0..68326ef 100644 --- a/examples/score_example_models.ipynb +++ b/examples/score_example_models.ipynb @@ -3,8 +3,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:35:08.934648Z", - "start_time": "2026-03-27T00:35:08.917825Z" + "end_time": "2026-03-27T00:43:13.750032Z", + "start_time": "2026-03-27T00:43:13.298543Z" } }, "cell_type": "code", @@ -16,13 +16,13 @@ ], "id": "accb05e90bcf1ce9", "outputs": [], - "execution_count": 4 + "execution_count": 1 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:35:12.299534Z", - "start_time": "2026-03-27T00:35:08.935497Z" + "end_time": "2026-03-27T00:43:17.056353Z", + "start_time": "2026-03-27T00:43:13.750969Z" } }, "cell_type": "code", @@ -35,7 +35,7 @@ ], "id": "714700a9832048e8", "outputs": [], - "execution_count": 5 + "execution_count": 2 }, { "metadata": {}, @@ -46,8 +46,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:35:26.399988Z", - "start_time": "2026-03-27T00:35:26.382895Z" + "end_time": "2026-03-27T00:43:17.103247Z", + "start_time": "2026-03-27T00:43:17.090474Z" } }, "cell_type": "code", @@ -60,13 +60,13 @@ ], "id": "bdc5bcb78d79e755", "outputs": [], - "execution_count": 8 + "execution_count": 3 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:35:30.913532Z", - "start_time": "2026-03-27T00:35:30.855972Z" + "end_time": "2026-03-27T00:43:17.141612Z", + "start_time": "2026-03-27T00:43:17.104563Z" } }, "cell_type": "code", @@ -82,13 +82,13 @@ ], "id": "65327b245c715ea2", "outputs": [], - "execution_count": 9 + "execution_count": 4 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:35:45.464436Z", - "start_time": "2026-03-27T00:35:32.507426Z" + "end_time": "2026-03-27T00:43:29.726469Z", + "start_time": "2026-03-27T00:43:17.142838Z" } }, "cell_type": "code", @@ -103,37 +103,37 @@ "name": "stderr", "output_type": "stream", "text": [ - "Subtask simulations:: 100%|██████████| 64/64 [00:10<00:00, 6.02it/s]\n" + "Subtask simulations:: 100%|██████████| 64/64 [00:10<00:00, 6.33it/s]\n" ] }, { "data": { "text/plain": [ - "MutatorHighVarBenchmark.LearningCurveBenchmarkResult(msen=0.005577570877248757, msen_sigma=0.0002857940942417397, msen_CI95=(0.0050023065242198895, 0.00608988408642532), lapse_rate=<xarray.DataArray 'phat' ()> Size: 8B\n", - "array(0.07887466), model_statistics=<xarray.LearningCurveStatistics> Size: 103MB\n", + "MutatorHighVarBenchmark.LearningCurveBenchmarkResult(msen=0.00553912312537474, msen_sigma=0.0002812296484686281, msen_CI95=(0.004981950628377824, 0.006079281342646169), lapse_rate=<xarray.DataArray 'phat' ()> Size: 8B\n", + "array(0.07834207), model_statistics=<xarray.LearningCurveStatistics> Size: 103MB\n", "Dimensions: (subtask: 64, trial: 100, boot_iter: 1000)\n", "Coordinates:\n", " * subtask (subtask) <U33 8kB 'MutatorObject000,MutatorObject095' ...\n", "Dimensions without coordinates: trial, boot_iter\n", "Data variables:\n", - " phat (subtask, trial) float64 51kB 0.518 0.712 ... 0.916 0.916\n", - " varhat_phat (subtask, trial) float64 51kB 0.0005004 ... 0.0001542\n", - " boot_phat (boot_iter, subtask, trial) float64 51MB 0.518 ... 0.92\n", - " boot_varhat_phat (boot_iter, subtask, trial) float64 51MB 0.0005004 ... ...)" + " phat (subtask, trial) float64 51kB 0.484 0.668 ... 0.918 0.9\n", + " varhat_phat (subtask, trial) float64 51kB 0.0005005 ... 0.0001804\n", + " boot_phat (boot_iter, subtask, trial) float64 51MB 0.484 ... 0.908\n", + " boot_varhat_phat (boot_iter, subtask, trial) float64 51MB 0.0005005 ... ...)" ] }, - "execution_count": 10, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 10 + "execution_count": 5 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:35:47.326651Z", - "start_time": "2026-03-27T00:35:47.251189Z" + "end_time": "2026-03-27T00:43:29.849327Z", + "start_time": "2026-03-27T00:43:29.742099Z" } }, "cell_type": "code", @@ -163,7 +163,7 @@ "text/plain": [ "<Figure size 640x480 with 1 Axes>" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -172,13 +172,13 @@ } } ], - "execution_count": 11 + "execution_count": 6 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:35:51.180487Z", - "start_time": "2026-03-27T00:35:51.112194Z" + "end_time": "2026-03-27T00:43:29.913580Z", + "start_time": "2026-03-27T00:43:29.850095Z" } }, "cell_type": "code", @@ -206,7 +206,7 @@ "text/plain": [ "<Figure size 500x500 with 1 Axes>" ], - "image/png": 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" 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AAMBSDhUoAwMDKSkpyegYX/b19SVPT0+T95kxYwZlZGToNyzaDAAAqg2UZTFv3jzy8fHRb7xgMwAAOBYnJycxrkUODrVwc2JiIgUEBBgd48vp6eliFKwpOTk5YgMAAMcNkgMHDqR69erR6tWrxXe+PTlUjTI6Opp69uxpdKx3797iOAAAqJOnp6eY9cDdbDVr1pTlHGQbceTl5SW1atVKbGzKlCni71q1aonr586dK61Zs0Z/+7p160oPHjyQFixYIDVu3FiaNGmSlJubK/Xp0wejXjF6EKMH8RnAZ0DFnwFvb2+pSZMmsox6lTVQdu/eXTJl1apV4nreHzhwoMh9Tp06JWVnZ0tXr16VIiIibFUw2FAG+AzgM4DPAMlTBk5OTpKfn59Nn8PSeOCki5ZawdNDePQrD+zJzMyU+3QAAMBEnyRnamvWrBmtW7eO4uPjSc544FB9lAAAoH4uLi4iiLm6ulKlSpXkPh3HGvUKAADql5eXRxs3bqTg4GC6fv263KeDGiUAAMjP2dmZQkJC9Jdzc3MVESQZml4BAED2PsmwsDAaN24ctWnTRnHvBgIlAADISpIkevToERUUFFBWVpbi3g30UQIAgOx2795Np06dKpLPWwlQowQAAPsHH2dnsa6wISUGSYYaJQAA2N3w4cOpefPm5O/vT/v27VP0O4AaJQAA2N3Vq1cVNbLVHNQoAQDA7k6fPi2CpSNkSEONEgAA7JJtp1u3biLbjo4jBEmGQAkAADY3ZMgQeuaZZyg8PNzhShuBEgAAbO6nn36ihw8f0okTJxyutLF6CAAA2IW7uzvl5OQoprSxeggAAMjG1dWVBgwYIIKRjpKCZGmg6RUAAKyuX79+1LFjRxozZozI5erIECgBAMDqjhw5QsnJybR3716Ry9WRoY8SAABsE2CcnBQdJNFHCQAAdu2TDA8Pp6CgIP0xJQfJ0kDTKwAAlNvTTz8tcreOGjXKKKmAGqjr1QAAgCwOHTpEAQEBdPjwYcrLy1PVu4BACQAA5ZaTk0Nff/21KksSTa8AAFBqbm5uNG7cOGrRooXqSw81SgAAKLV27dpRvXr1qEaNGnTlyhXKzs5WbSkiUAIAQKkdP36cKleuTOfOnVN1kGQIlAAAYFnAcHXVD9ThqR979uzRRMmhjxIAACxKaP7CCy9Qr169NFdaCJQAAFCiBg0aUJ06dah9+/bk4+OjqRJD0ysAAJTo/PnztHPnTrp9+zZlZGRoqsRQowQAAJM8PDzIxcVF/O3tV42qdmhFmTmPNVdaCJQAAGAySHKf5MiRI0Ww9KlejfpGThR7rUHTKwAAFOHv7y9S0uXm5oppIJWqVhHHdXstwTJbAABgUkhICGVlZVHt0A4UPvtNcnZ2poKCAto8ez6d2LrD4UsNy2wBAECpeHp6kpeXl/7y9evXKUvKp/B3posgyXjPl30DqmumdNFHCQAAIkhy7taIiAijYOlXuxY5/zGgRx84XFzIr1awZkoNgRIAAKhChQpUqVIlESQrVqyoL5GU+JtUkJ9vVEJ8OeXmLc2UGgIlAABQWloarV69mtasWUN3797Vl0h60l3aPGeBPljyni/zcS2RtLR5e3tLjPdynws2lAE+A/gMyPkZqFChguTv72/RbRs/2Un6KC5a7LUWD1CjBADQaFMr90eOHz+eAgMDS7z9g9Q0o72WIFACAGgQT/PglUDy8/P1K4KYk3H3Hu1dtkLstQbzKAEANDzSlQfupKamkhZ5e3uLvLWc5D0zM7PY26FGCQCgETyilVcB0eEFl7UaJEsDKewAADTUJ1mtWjW6nJFK3329kTJTLGtG9farRqHhQyl68zaL76MmqFECAGgA1x7v3LlDWY8fU/tRYfrk5hwE+0yaIPbF8dFwQnSGQAkAoAGSJNG2bdsoKvY4OXt76ZObaz0IWgKBEgBARQxriJxpp0OHDvrrOgwdSM8vnCv+nrhsIXUMG2TRY1bS8MohDIESAEBFdDXEKoH+Yo7ks88+S507dxZJzE0lNw+oV9dsEOwYNkgE1dIGVzVBoAQAUCGeH3nq1Cm6f/8+Xbp0qdjk5qPnvlNsECwuuPpqaOUQhlGvAAAqYthMeuzoMYqNjaXHjx9TQbyryNNqGCy537JwELx0LEafx9XcyiHpGsr1iholAIBKdB8TTi8XaiblIGkyuXlBATk5cc6Z4pfPwsohv0OgBABQgSqBATRo+hRyMtNMemLrDloxeZr4e8PMOSUun4WVQ36HQAkAoAJVawXpg6S5BZZ1Sc2Trt2waPmsEwbBlfd8WWsQKAEA7MySSf6lZWkzqWFyc0uD4AMNrxzCECgBAOzMWpP8fX19adSoUSI9naXNpJyCbt+nK/Wp6LQeBC2BUa8AAA5qxIgRVKtWLTEwZ/PmzaJGmJ6cTK8s/1jUEC8dPW72/lyj7TxiaInPk6HhJbYYapQAAA5q+/bt9Ntvv9GePXv0x0pTQ+QabZeRYRS9eavZIJhZqBaqNahRAgDYmbVSwt29e5dWrVpV7vPR6qoglkKNEgDAjsqTEq5KlSr08ssvU/Xq1UtsJi3IL7D6gCGtQqAEALCT8qaE69u3LwUFBYn8rSU1kzq7OGNVELUEysjISLp+/TplZWVRTEyMUab7wlxdXentt9+mq1evitufOXNGfHAAAByBuZRwlvj222/p3LlztGXLFhudISguUI4cOZIWLlxIc+bMobZt29LZs2dp7969xTYrvP/++/Tqq6/S66+/Ts2aNaPly5fT1q1bqXXr1nY/dwCA0ipLSjgXg8DKFQQOkg8ePCjxubwqVzba23P5LG8bzBOVmyTXFhMTIy1evFh/2cnJSbp165Y0ffp0k7dPSEiQIiMjjY5t2bJFWrt2rcXP6e3tLTHey/nasaEM8BnQ5megY9gg6cMzR6SP4qLFni8Xd9tq1apJb7zxhtSwYcNSP0+bAb3Fc/C+2PM4e/T38zh71Ox5UCm3oKaNxOPyXu7ytkY8kK1G6ebmRu3ataOoqKg/I7YkicuhoaEm7+Ph4UHZ2dlGx/gX1lNPPVXs87i7u5O3t7fRBgBga8XVqkqTEq5Tp05iAE+PHj2KJDAvSUVfH6O9ISyfVTqyBUo/Pz/R55iUlGR0nC8HBgaavA83y06dOpUaNGggPjS9evWiYcOGUY0aNYp9nhkzZlBGRoZ+S0hIsPprAQDtKal50Vz2HUvnOvL8yCNHjtDXX38tKhKW4pG0Q9+cKv7mfeGRteXtK9Ua2QfzlMYbb7xBV65coYsXL1JOTg4tWbJEzCHirBTFmTdvHvn4+Og3HjEGAKCENHSh4UOLBFpOR6fD323cyvbw4UOLH9OS2iKWz3KQQJmSkkJ5eXkUEBBgdJwvJyYmFnufsLAw8vLyojp16lCTJk1Ep/a1a9eKfR4OqJmZmUYbAIAtB6Hw/TkIliQ0PMwo0HJL2+TJk6lLly6lPi/dbWq3aF5ibdHWy2dVstEgIc0FytzcXLHyds+ePfXHuDmVL0dHR5u9Ly9Eevv2bdF0O3z4cDFkGgBAKcnK+f4cBEurYcOGVKlSJWrZsqX4fivNeeluk5OVZdHIWsO+0tO799GFw8dI7oQKSiVr0ytPDeEsE+PGjRO1w08//VTUFnUpmdasWUNz587V375jx46iRhkSEiIG8HD7PTcr/Otf/5LxVQCAFpWn1sTZc37asbvI/bmSsGPHDvHdxy1uxTFXw+R+T1Fb/KNLSiRML6a2qOsjbTewf7l/HKh5kJCsgXLTpk30t7/9jd59912RPIDnQ/br14+Sk5PF9bVr1zYaqOPp6SnmUp4/f17Mn+SBORww09PTZXwVAKA1pak1+detUySoNe3ahdo+21d//07D/rw/t7TxaH5zQdlcDZObfLl2uHfp5+Ly1eOxVqstlkStg4RkT4q+dOlSsZnCQ6INHTp0iJo3b26nMwMAsLzWdOlYjL7W9kSPbvrbj5k/W9zml4OHRXo50/d/ky4dO073E3+vJBjiIBw++019UN08ez4lXLxU7FvDTb6ZKanU69UXxeVGoR3ECiGc/9XWUv5IqGAYLEtKqOAIHGrUKwA4FkfL0GJJc2pJtSYOhLogJa77IyDqHtPU/Z2cnSmwXojFQdnU+Rke4+fX3UdcfmW8XZo/0208SEguCJQAoPjBMUpqTi1pakXX50caBSmdyoEBZu9/59drFgflGo0aGAVHw3MXtyn0/MU1f3Jf6bFNW8maTpQioYKjQKAEAM0rzSAUc7Umvn33cWNMluf9xN+Tq3hITrR17kcW1bpMBtWCAnp2SqT4m4Nj94gxRufOCicn4MvBzZsWeXxuCo7Zss3q7/+DUiwe7QgQKAFA80o7CKW4WtPvj1P81ypnHXvxxRepmU81Wj3lzRJrXUWD8u8jWQ0D+sD/iyzalOvkZBQs+TLfztFHn8oFgRIASOv9mmXJVGOq1mTqcXQBi2t/Lfr11F9+aGGtyzAof/fJMpPNqqbOvXBu2OICv9qSA9gCAiUA2ExZvoTl6Ne01iCUwo/DdAGLA1yfKZPom507aO3atSLpiqV0wfT25asmg+LO/ywzSuXp5ORcJLWnqcCvxuQAtoBACQA24WhfwtYahGL4OIWJJtKKniK7WFnokwkUCuhn9uw3up2TsxMZ1idNBX5bJgfIuHtPTEfhvRogUAKA1TlqhpbSDEIxFwx09y88qMawVlfWYGIqoIu+0UJNsjzlRMdU4LdlcoDMlHu079OVYq8GsiccAAD1Mfcl7Ohz6goHA3MkTkPn4iKCVuFanSX31ykcVAsHdNE3WlBgFCwNJ/6bCvxqTQ5gC6hRAoDVYRmnP75g3dxo54eLSMrPL1dzbkk1NA6+UZ/9niNbF/CiPl9tWX+qBTlhtQ6BEgAUNThGzlGY1uhbCw4Oppr+vycX4Mn8t3+9Rk4uLiU253ISgUlfLtUnEygtfi5donUOyucOHNIfV0tfoVwQKAFAMYNj5B4AVN6+NX9/f3rhhReob9fulJ96X0zmf3jfskUbAhuEUIMObcW+LPicD63dKP42DMp8DqZej6P2I8sBgRIAFDE4Rg1f3Pfu3aMbN27Q7cQ79MPGb2xWk7Ok5lvSbdS60octYDAPACiCGgYA5efni+UDOcjr5knaYj6oJQOBSroNBvNYDjVKAFAERx0AxOvmtm/f3ihYGiYTUOqcQgzmsRwCJQAogiMu0VSlShUaO3YsDRw4kJo0aVKufs+Kvj5G+7IobVDmfuNt83/vE+a9Glb6sAUESgCwmbJ8cZsaAKSUdS0Ln0daWhqdOHGCfv31V7p69WqZH5cHLQ19c6r4m/fmBjGZK4uyDEZ6lJ5htIeiECgBwGbK8sVtagCQUta1NHUeUVFRtH79esrj5AJlUNpBTNYui4dp9432UBQCJQBAKeQl36MubdsZrc7B/ZJlJffoU930FUunsWgRAiUAWIVSmkdtycPdg7KiT1Pzho2NBvBocRCTliBQAoBVKKV51JYe5zwmzzbN6EbCLTp16pRVHpMHK/20Y48+gTrv+XJxg5isnblIqaNylQSBEgAURclf3Byc3GrXpOjzceVqbjXEfZHtB/XTN+Xyni+b6qO0ReYita30YQsIlACgKEr74q5fv76YAtIlfKhN0utZ2kephsxFjgqBEgAUT65E6W5ubhQWFkYNWzSnYW/93SZBytI+SrkH/WgZAiWAgmhhQExpyZkonTPsbNiwga4l3jFaCNmaQcrSDDkY9CMfBEoABdHCgJjSkKu50XAB5ISEBNq2foNNR6ZakiHHETMXqQUCJQAolhzNjY0aNaLIyEiqXLmyXYOUJRlyyrJ0GZQfAiUAKLYf0d7NjVyT7NmzJ/n5+VHnzp3tGqQszZBTmqXLwDoQKAEURK5BK0rtR7R3cyP3D65bt46OHTtG+/bts2uQQoYc5UKgBFAIOQetKLkf0R7NjR4eHvq/MzMzRZDUDa4BQKAEUFmwsffIWXv0I9qyJsfLY02ZMkWsKwlgCgIlgAJYM9jYe+Sso09baNu2LVWoUIFatWpFjkDJmYvUCoESQAEcOdg4+rSFTZs2iabWXbt2kSNQWuYiLUCgBFAARw82jjZtwdfXV/83ryPJg3cs6ZNEbU6bECgBZFK4L9HRgo2jTlto3rw5/fWvf6U2bdooqjaHIKxcCJQAMjHVl2iNYOOoU0zsJTg4mFxcXIoM3pE7fSCaVJULgRJARRx1iok9a1t79+6lb775hrZv36769IFyB3+1QKAEUAk1L8NU3tpWzZo1jS7HxcXpF0pWc8BTY/CXAwIlgEpgGSbTWrRoQRMnTqT+/fuTo0LAkxcCJYBKmhgdeYqJLbm6uoraNe+dnJzkPh1wQK5ynwAAFG1iLM8UE9H86uJi9ykmSh21efr0abp37x7dvHnToZpbQTlQowRwICX1Vck5xURJozYbNGggapA68fHxCJJQZgiUACrrq3KU+Yy2wqnonn/+eRo9erSYBgJQXgiUADLBfEfbSEtLo9zcXNHcml+oz1Zr74caX5McECgBZKDW+Y5KwM2sy5cvp++++07T74caX5NcECgB7EzN8x3lnALi7e2tv5yamqrY98MeSQDwGbMuBEoAB5rvaElTmlJHn9pKy5Ytafjw4TR+/Hjy9PRU/PzTssyJLG0TKubUWhcCJYCdlXW+o6VNaUoafWqvptb79+/T5cuXKTs7W9HzT7kWGRo+1OZNqJhTa10IlAAOsKQWmtKKx0Hys88+Ezlc7fV+lBXXIkPDwyy+fVnfd0dftk1pECgBZFDa+Y5oSjPWrl07CgoK0l/Oysqy6/thL+V535X6mhwRAiWATEoz3xFNaX9q1qwZDRo0iF544QWjBZjt+X7YS3nfdyW+JkeEQAngANCU9qerV6/Sb7/9RidPnqT09HRSM7zvyoBcrwAOgpvO0pOT6ZXlH4umtEtHj5Mj0w1sid68rVQDj3Jycuirr74qVTIBJbJ0BKva3ndHhBolgANRU1NaaaZJdO7cWUwD0XHUIPlEj276v0uTBEBN77sjQqAEKAOsHG8/9evXp379+tHQoUMpICCAHBWPVO316ov6y0g04TgQKAHKAAvp2s+1a9coNjaWDh06RElJSeSoxAjWP6Z52COxAVgPAqXGoWYESsdrSO7YsYMOHjzo0P8bwc2aFFnqCwtrOwYESo1DzQiU6KmnnqJevXrZ9TkLp/6z5v8GN7sO/L9IcnJy0h/joLnzP8tsmgRAa+kMbQWBEkAmZfkSU9MXX3H5SwMDA0WQ5GAZEhJitxpican/eGRueWuVphIHcNC89csFsiWtpTNUbaCMjIyk69evi8waMTEx1KFDB7O3f+ONN+jixYv06NEjkeNx4cKF5OHhYbfzBZDzS0wtX3zm8pcmJibSrl276PvvvxffDdZS1hoip5wrb60SCSMcm6yBcuTIkSLQzZkzh9q2bUtnz54V+RqrVzedx5BXLJ8/f764fdOmTWnChAk0atQomjt3rt3PHQDKprj8pVUC/xzRyskEDh8+rNrEASzq89UWN7t6VfY12oOGAuXUqVPpiy++oNWrV9OFCxfotddeEzXFl156yeTtu3TpQkePHqUNGzaIzBz79+8Xf3fs2NHu5w7ahpXjrZ+/NHz8OHIpdFxNDHOvsnMHDpXi3rq+zT/7OEEDgdLNzU0kNo6KijLq3ObLoaGhJu9z7NgxcR9d8yz3XwwYMMDsSubu7u5iQVfDDaA8sHI8Wb0ZUioooKAqValRo0akZmVNGPDw/n2jPWgkUPr5+ZGrq2uReVF8mTvzTeHa46xZs+jIkSMijRXPr+Ih4/PmzSv2eWbMmEEZGRn6LSEhweqvxZGhZlQ6WO7Kis2QBQX6Yyc3fEN7tm0XLUsASiP7YJ7S6N69O82cOVMMAOI+zbCwMHr22WfprbfeKvY+HER9fHz0m+HSPFqHmlHpYbkr6zVD7ljwsf7ykW93UnR0NDliLlZQP9mSoqekpFBeXl6RlFR8mUe9mfLee+/R2rVraeXKleLyuXPnyMvLiz7//HP64IMPikzmZVzz5A0sqxldOhaDxV0taDY07GPDpPHSe+aZZ6h1cAgVZD8mZ08PxbWe8I/I8NlvGuVoTbhw2WrncmzTVlVM8dEK2WqUubm5Ii1Vz549jeYV8eXifllWrFjRqLnGMDmy4UReKBlqRmWDZY/Kj/+PW7duTb7VqlDB/QxSWutJ4R+RrNcr48Vxa4nZUroVU0DDTa88NeTll1+mcePGUZMmTejTTz8VNcRVq1aJ69esWWM09YPTWE2aNElMCalbt66YlMy1TD5eOICCeZjXVXZYOb58eGT76eTb5NWvG7kG/h582g3sR0rpV8aPSFDUepSbNm0ScybfffddMYDnzJkzYpWA5ORkcX3t2rWNAuD7778vmld5z32Nd+/eFUHyn//8p4yvwrFrRuILxMVFNB/yZVum01ITLHtU+vUmecR5ZmamCFAD/vYXcjKosXV74TlKT06hH9est/p7ZS7wmfq8K7F5HYPuND6YZ+nSpaJ26OnpKdacO3HihP66Hj160IsvvmjUzMpBtWHDhqL5pk6dOvSXv/xF9auc2wpqRmBLhplwevfuLVqD+AdxcencOBeqNZs3y9p6YmpULp9f4y6dSQ4YdCc/2QMlyAs1I7A1TiLAP2r5x23NmjVNBi5bLjlVln5lHtRmyKmE5lpb5erFdCRlQKAEAJviliAerc5dLadOnRIBilfNsOeSU6VtPbHV2pGlzdWL/lJlQKAEAJuoWrmy/u/Hjx/T+fPn9X2XHhUrUMyWb/XX26OP3FTrSXEriohab6EBgnL0U2LQnTIgUAKA1fGgvLDe/Sg3IbHYvstffzqlP2ZJLc8WiltRhAN21Ge/j75ncg12w3QkZUCgBACr4oEvFSpUEHspJ8+mOVBtyTBpuVyBnGHQnfwQKAHKQE0LKFsb9z1u27aNdh38ntxDrD84Rw5yB3IMupMXAqXG4Qu/bNSygLK1cO2Rp20ZBss7f8yHBnB0CJQahy98sAZe7u75558XCxdYOkn+YVq6yHkKoNpAqfsF+eSTT1LXrl2NNgDQlrS0NDFKlPclTZLXBc72g/tRXNQBWVtPzAVzvh0COZQ5hV2nTp1o/fr1YhJx4WTk3OTC60wCaCk9m9bxoupXrlwRaSXNrUzD2W10q3K0G9ifcrIf2731xNQKIRzMN8+ebzRgh2/Pycu7jAyz2zmCimqUy5cvp59++omeeOIJqlq1KlWpUkW/8WUAtStuWoFW8A/kdu3aGU3K5yBpbpJ8nVZPFFmVo1PYYDq4Zr3dB0Uh4w2URpmqftzkOmLECPr111/LcncAUEGfZIcOHSgkJIS2bNliUVJxIicTAdSZzh88YvdaeWkTpYO2lalGefz4cWrQoIH1zwYAHMLly5fFgugXL160eJL8b2fjSpWc3JYcLeMNRqc7YI1y8eLF9NFHH4mVAOLi4sQizIb4GACoF/dHfvLJJ/Tw4UOT13NfX3pyMr2y/GMxWf/S0ePiuOHSbizq89Wy1OAcbZm5wv2r4ACB8ptvvhH7L7/80mgQj8jEgcE8AKrD/9s89YNbk7KyssSx4oKkuUnyhgG0cPYbeysumBtCTQ7KHCi5XwIAtDNyt2/fvmK9WB6fsGLFiiIrfzhSlpvSZLxBTQ7KHCjj4+NRegAOPh1FN3L3l4OHS3zM2NhYatq0KR05cqRcQbKw4pIRAChJuSY88j9O7dq1yd3d3ej4jh3yJA8GsJeSss5YW2mCmi3w1A8em5CXZ1mSc3Oe6NFN/7ep+YsAqml63bp1K7Vo0ULfN8l0vzSRcADUrKSJ6mrAcx15qayTJ0/q50daI0jy/MVer75o9Dy6ZARKHUgDUKbpITza7fr16+Tv70+PHj2i5s2bU7du3UQSgqeffhqlCqqllYnqPHCnY8eONHbsWKv+8BXzFw0SDhjOXwRQVaAMDQ2lWbNm0b1790R+R96OHj1KM2bMoEWLFln/LAEUwtxEdTWJiYmhW7du0XfffWeVmqTR/MWCAsXMX8SoVrBZoHRxcaHMzEzxd0pKCtWsWVP8/dtvv1Hjxo3L8pAADsHRJqqXFU8BWblyJV26dMn68xdnzy+SjECuZlesngM2C5Tnzp2jVq1aib95XtU//vEP6tKli6hlXrt2rSwPCeAQiss644j9a4YDkvjH7/Dhw6lRo0b668s7urW42hr35/K8RcZ7tfXvgvqUqfPh/fffJy8vL/E3B8edO3fS4cOHRVPsqFGjrH2OAIpiyUR1RxuQFLt2EzWpVEXMk+QxCLqkAuVhbg5iSfMXARw+UO7bt0//NydG52kivHKIbi06ALWz9xe9NaejmBqQ1G5sOMV+/Dn9dOSYVYIkgJqUeeHmwhAkAWzD3CLI1hyQdPJcHF29erVcjw2gRmWqUXp4eNDrr79OPXr0EFNECg/35nXqAKD8zC2CXNZ+0eKWwVLbgCQAWQMlj4br06ePWIfuxIkTVk1pBQC2XTeR73d0xVp66uUIcnJ2cugBSQCKDZQDBw4UC7ceO3bM+mcEADav/W1b8hk5P3hEnV98nlbOfMchByQBKLqPMiEhQT+PEgAcYzoKTwHRpZvUTe1yqeqLkacAtgiU06ZNowULFoiE6ABaZM+MLtaYd8hp6J577jkKCwszCpZyQUYcUH3TK+d09fT0FMkFONdrbm6u0fXVqlWz1vkBKJK91yks73SUoKAgqlevHuXn51P16tUpOTmZ5IR1HkH1gXLDhg3iH2/mzJmUlJSEwTwACsfpJXnwHf+wlTtIAmgiUHK6Ok6M/vPPP1v/jADAKtzc3MR0ksePH4vLFy5cQMkC2KuP8uLFi1ShQoWy3BUArMzbrxr1mTRB7A2D5OjRo+mFF14Q854BwM6B8s0336SPPvpIrFlXtWpV8vb2NtoAwH58qlejvpETxV7H19eXAgMDRX9kcWMGMKAGwIZNr3v27BH777//3ug4j6bj5APWXOgVAEqPl7/76quvRM3y9u3bJm+DATUAlilTROPUdQCgLK4uLkaLEyQmJsp9SgDaDZSHDh2y/pkAQLFKaiaV8vKoX7enycuzAq1evZpSU1NRmgByBsquXbuavZ7XpgQA6ympmVTKLxCDdtzd3THQDkAJgfLgwYNFjhkmRkcfJYB9OXu4064D39OjpBS6c+eOQxY/j9oNDR9K0Zu3iR8GAA496pX7QQw3XmqrX79+dPLkSbGqCADYHtcga9Wqpb+c/fixwwbJ4kbvAjhsjTIjI6PIsaioKMrJyaGFCxdS+/btrXFuAFAMbmIdO3asmAJy+NxZcaxS1SooLwCl1CiLw+nsGjdubM2HBAATOGcrp6NzrRNEg96dIY5NXLaQOoYNQnkBKKFG2aJFiyLzJ2vUqCESEZw5c8Za5wYAZgLl3oMHqO30v4g0dYz34e9Mp0vHYjSxCDP6NEHRgZKDIQ/eKbxcT0xMDL300kvWOjcAMMAr9jRo0IDOnTsnLlcJrknOLsaNQrzAs1+tYE0ESl2f5i8HD2PwDygvUIaEhBhdLigooLt37+qTLwOAdfFIcs7byqv28CCe2NhYSom/KRZy5uCo/1/Mz6eUm7dQ/AByB8r4+Hh65plnqGfPnmLEq67pR2fChAnWOj8AIKK8vDy6fv26GGV+69bvgZBrjZvnLBDNrRwsOUjyZTlrk2gOBTUqU6CcNWuW2HgBZx6ObjiHEgBsg0eWHz9+nDIzM/XHTmzdQenJyfTK8o9pxeRpdOnocVmLH82hoEZlCpSvvfYajR8/ntatW2f9MwIAgZey46lWR44c0f8YNQySOg9S04z2jko3vQXTXEAV00N4DtexY8esfzYAIPBAOZ4nyd0bvXr1Un2p8LQWnt7CMM0FVBEoV6xYQWPGjLH+2QCAwDVIHkXONUi1T7nyDaj+ez9roWkufBzAoZpeeaFmHf4gv/LKK+KX7s8//0y5ublGt502bZp1zxJAg+Li4ujixYtF/r/Uxq92LaORu5ZOc0FTLSguULZp08bosu5X7hNPPGF0HAN7AMqmYsWKYjT5vn37RDpIpsQgaW5ka1mCV1mmuXBTbfjsN/VNtZtnzxcDmwBkDZT8DwwAtjNq1CiqU6eOGMSzefNmxRZ1cSNbyxq8SjvNpbimWq1kJAIHz/UKAGW3d+9ekbjjhx9+0Fw/IwdUnt7CeG8uwJprqgWwBQRKAIW4ffs2LVu2jO7dK91ajBl379HeZSvEXi7WCF6WTnPRNdUaQkYisCUESgCZeHl50fPPP0+VK1cuVx8/N3/u+3SlrPlO7Rm8dE21uudTQkYiUDcESgCZDBo0iBo2bEhhYWEO/x7YO3iVpqkWQBWBMjIyUuSxzMrKEnPHOnToUOxtDxw4IH51F9527txp13MGKK9du3bRtWvX6Ntvv1VFYdo7eKklIxEon+yBcuTIkbRw4UKaM2cOtW3bls6ePSsGNVSvbnoQwLBhw8Sq7rqtefPmImG0kkcJAugYLk3HyQS++uorSk1NVU0BIXiBGskeKKdOnUpffPEFrV69mi5cuCDyyPLK7cWta5mWlkZJSUn6rXfv3uL2CJSgdN7e3vTyyy9T7dq15T4VAHCUQOnm5kbt2rUTqyLocDMqXw4NDbXoMXhJr40bN4pgWVxeWv6CMtwA5NC9e3eqWbMmDRw4sMii544EGXFAa2QNlH5+fmJBWq4ZGuLL3KxaEu7LbNGihcg9W5wZM2ZQRkaGfktISLDKuQOU1p49e+j06dO0fv16h81gZcvk5UqY5gKgyKbX8uDaJOeaPXnyZLG3mTdvHvn4+Og3XiEewF74h6AO96XzwJ379+875Btg6+TlSpjmAqC4QJmSkiK+PAICAoyO8+XExMQS82I+99xztHLlSrO345yZPGjCcAOwB19fX5o0aZIYpKYGliQVQK0Q1EjWQMkJn2NjY8Waezrcd8OXo6Ojzd43PDycPDw8sHg0KFbLli2pWrVq9OSTTxrVLB2VJUkF7FkrRFAGe5Lk3EaOHCllZWVJ48aNk5o0aSItX75cSk1Nlfz9/cX1a9askebOnVvkfocOHZI2bNhQ6ufz9vaWGO/lfu3Y1F8GXbt2lXx8fGQ/D2ttHcMGSR+eOSJ9FBct9nxZ7nPChjKgMpZBKeKB/IU8efJk6caNG1J2drYUExMjdezYUX/dgQMHpFWrVhndvlGjRuLF9erVy5YFgw1lUOrPQMWKFVX/uWn8ZCcRKHkv97lgQxmQVgKlPTcESvnfA7VulStXlqZMmSL1799f9nOx5RbUtJEIlLyX+1ywoQzIDvHAoUe9AihJcHCwGMBTv3598vT0lPt0AMBKHH+EAYBCnDt3TsyPjI+Pp+zsbLlPBwCsBIESoBy4BslZoXgEN/vll19QngAqg6ZXgDKqWrWqyEk8evRoVUz/AADTECgByrHwcoUKFUT+YJ7TCwDqhJ/BAGV08+ZNsUwWr2jz8OFDlCOASiFQApQCZ9rh/khOsM9u3fozK41WICMOaI3TH/NENIObyfhLjhOkI+8rlDZIjh8/XuQP5vVT8fkB0EY8QB8lgIU4gb9uyy+U8xQA1AtNrwAWSk9PFzVJbnotbqFwAFAfBEoAM6pXr07u7u76Bb85WAKAtqDpFaCEPskXXniBAgMDUU4AGoUaJUAxuJP/7t27Yo4kapIA2oVACVAM7otcv349ubi4UFZWFsoJQKPQ9ApgICAggJ544gn9ZZ4KgiAJoG2oUQIYJDiPiIgQS2RxgLx8+TLKBgAQKAEM+yQvXLggBu7wUlkAAAw1SoA/8FqSO3fuJDc3N1GjBABg6KMETatRowZ1797dKFgiSAKAIQRK0CxeIovnSPbo0YPat29PSuTtV436TJog9gAgDwRK0CwezXrgwAHRHxkXF0dK5FO9GvWNnCj2ACAP9FGCpp08eZJiY2OpoKBA7lMBAIVCjRI0JSgoiIYPHy6SCOggSAKAOahRgma4urrSc889J9agS01NFc2uAAAlQY0SNIPXkfzf//5HV65coaNHj5IjqFS1itEeAOzPiUfEk4ZYuqI1gNw6hg2i8NlvkrOzs2ge3jx7Pp3YukPu0wLQXDxAjRJUrVatWjRx4kTy8vIiR+IbUJ3C35kugiTjPV/m4wBgXwiUoFpOTk40ePBgCg4OFnMlHYlf7VrkbDDgiPFlv1rBsp0TgFYhUIJqcZadDRs20NmzZ2nv3r3kSFLib1JBfr7RMb6ccvOWbOcEoFUIlKA6hlM/eHTr1q1bxdqSjiQ96S5tnrNAHyx5z5f5OADYFwIlqErdunXpr3/9q1gBxNHxwJ0Vk6eJv3mPgTwA8kCgBFXp1q2bWFfyySefJDV4kJpmtAcA+0PCAVCV//73vyJYIpkAAFgLapSgilVAdB4/fkz79+8XyQUAAKwBgRIcWr169WjKlCnUtGlTuU8FAFQKgRIcWvPmzcnDw4Natmwp96kAgEqhjxIc2s6dOyk5OZl++uknUqOMu/do77IVYg8A8kCuV3A4VatWFfMjAQDKA7leQZUaNGhAkZGR1L17d7lPBQA0An2U4FD8/PzEupKcUIBzuQIA2Br6KMGhxMTE0P379+ny5csilysAgK2hRgmKFxQUpF9uil28eFGszwgAYA8IlKBojRo1opdeeolGjBhhFCwBAOwF3zygaNy8yhtqkAAgF0wPAcWrWbMmJSYmIlgCgFVhegg4rPr16xvlb719+zaCJADIBk2voCiNGzemMWPGUEREBHl6esp9OgAACJSgLPfu3aOsrCxKSkoSK4EAAMgN8yhBUVJSUuiLL76gjIwMzJMEAEVA0yvIjpfIql69uv5yeno6giQAKAYCJciqYcOGFB4eLvokfX198W4AgOKg6RVkdevWLdEfeefOHdHcCgCgNAiUICseuLN69WrKyclBcysAKBKaXsHuWrZsKVLT6fDoViQ4BwClQqAEu6pbty6FhYXRyJEjKSAgAKUPAIqHQAl2FR8fT+fPn6fTp09TcnIySh/I268a9Zk0QewBlAh9lGBXnNz8m2++0Sc7B/CpXo36Rk6kXw4epsyUeygQUBzUKMHm2rRpQ08++aRRsESQBABHgRol2FRgYCANGTJE/M1TQK5du4YSBwCHInuNMjIykq5fvy6mCcTExFCHDh3M3p4npS9ZskSsKJGdnU2XLl2i/v372+18oXR4eawffvhBvLcIkgDgiGStUfLIx4ULF9Jrr71Gx48fpylTptDevXvFChJ3794tcns3Nzfav3+/GATCK94nJCRQnTp16P79+7KcP1jm0KFDKCoAcGiSXFtMTIy0ePFi/WUnJyfp1q1b0vTp003e/tVXX5WuXr0qubq6lvk5vb29JcZ7OV+7mrf27dtLw4YNE++n3OeCTfllENS0kfRRXLTYy30u2LRVBt4WxgPZml65dtiuXTuKior6M2JLkrgcGhpq8j6DBw+m6OhoWrp0qWjSi4uLoxkzZpCzc/Evw93dXaxibbiB7VSuXFk0hXNSgebNm6OooUSVqlYx2gMojWyB0s/Pj1xdXUWeT0N8mQeAmFKvXj3R5Ori4kIDBgyg9957j6ZNm0ZvvfVWsc/DgZRziOo2bq4F2+Fm8C1bttCRI0fo3LlzKGowq2PYIJq4bKH4m/d8GUBpZB/MUxpcc+T+yVdeeYVOnTpFmzZtog8++ED0cRZn3rx55OPjo9+CgoLses5aYVirv3DhglFLAYApvgHVKfyd6frPDu/5Mh8HUBJXORfozcvLK5LGjC9zs6opPL0gNzdXzMMz/FKuUaOGaMrl6wrjZNu8ge106tSJWrRoQWvXrhV5WwEs4Ve7Fjm7uBgd48t+tYIpPanoYD4AzdUoOajFxsZSz5499cecnJzEZe6HNOXo0aPUoEEDcTsdTq7NU0VMBUmwvQoVKlC3bt0oODhYBEsAS6XE36SC/HyjY3w55eYtFCIojmwjjkaOHCllZWVJ48aNk5o0aSItX75cSk1Nlfz9/cX1a9askebOnau/fXBwsJSeni4tWrRIatiwoTRgwAApMTFRmjlzptVHOWGzvAwCAgKkrl27oszwuSn1Z6Bj2CDpwzNHxKhX3vNllCO+f0hho15lDZS8TZ48Wbpx44aUnZ0tpot07NhRf92BAwekVatWGd2+c+fOUnR0tAiwPFVkxowZkrOzMwKlnd83T09PfKHhC80qn4HGT3YSgZL3KFMESbJjGThMoLT3hhpl+cuwS5cu0tSpU6WqVavK/n5ic/wywDxK+d8DrW7eSp9HCY6Jp/S0bt1ajCDmDEoAAGqHpOhQKjxSec2aNdS0aVP66aefUHoAoHqoUYLFGXd0Hj58iCAJAJqBQAkl6tq1K02ePFlkRgIA0BoESjCL56zyHElO6MCJHQAAtAZ9lGAWJ6rnVIE8cOf8+fMoLQDQHNQowSTDnLj5+fkIkmAzGXfv0d5lK8SeeftVoz6TJog9gBIgUEIRTz/9NL388svUsWNHlA7YXGbKPdr36UqxZz7Vq1HfyIliD6AECJRQBC9jZrgHANAy9FFCEd9//z1duXKF4uPjUToAoHmoUYLACQQMV2VBkAQA+B0CJdAzzzxDo0aNooEDB6I0AAAKQaAESk5OFothJyUloTRAdpWqVjHaA8jN6Y/s6Jrh7e1NGRkZIql3Zmam3KejGFWrVqXU1FS5TwM0rmPYIAqf/SY5OzuLH2+bZ8+nE1t3yH1aoPF4gBqlRnXo0IHc3d31lxEkQW6+AdUp/J3fgyTjffg708VxADkhUGpQjx496Nlnn6UxY8YYDeABkJNf7Vrk7GL8leTs4kJ+tYJlOycA8TlEMWjPpUuXKCsri+Li4kSKOgAlSIm/SQX5BUbHCvLzKeXmLdnOCYAhUGrQ7du3adGiRRQbGyv3qQDopSfdpc1z5ovgyHi/ec4CcRxATgiUGtG9e3ejNSW5RgmgNDxwZ8XkaeJv3mMgDygBAqVG1pPkfsmIiAhydUUyJlC2B6lpRnsAuSFQasCZM2fo7t279OOPP1JeXp7cpwMA4FBQvdAAnh+0fPlysVwWAACUDmqUKsRTPvr160d16tTRH0OQBAAoGwRKlSYT6Ny5M40ePZoqVKgg9+kAADg0NL2q0KlTp6hhw4ZiniRGtwIAlA8CpQrxgJ2vv/5a7tMAKJOMu/do77IVYg+gBGh6VUmf5JAhQ6ht27ZynwpAuWWm3KN9n64UewAlQKBUgebNm1ObNm1E/lbDpAIAAFB+aHpVgXPnzlHNmjUpISGB7t+/L/fpAACoCgKlAze3GiY037dvn6znAwCgVmh6dUC8Tl9YWBj1799f7lMBAFA91CgdUO3atalFixZiBfjTp09TYmKi3KcEAKBaCJQO6MaNG7R9+3YxRxJBEgDAthAoHai5lTddUnOuSQIAgO2hj9IBcIAcPnw4jRkzhtzc3OQ+HQAATUGgdAB+fn4iJR33TdaoUUPu0wEA0BQ0vTqA5ORkWrduHXl4eFB8fLzcpwMAoCkIlArl4uIiVv548OCBuIwACQAgDzS9KjRIhoeH04svvkje3t5ynw4AgKYhUCoQ1yQDAgLI19dX9E8CAIB80PSqQNzcunr1aqpatSpdv35d7tMBANA01CgV1Nzq7++vv5yeno4gCQCgAAiUCuDq6kqjRo2il156iYKCguQ+HQAAMIBAqZCVQNzd3UWtkvcAAKAc6KNUgNzcXFq/fj1Vr15drCkJAADKgRqljM2t9evX11/OyclBkAQAUCAEShlwE+vo0aPp+eefF8tlAQCAciFQyoDXkUxLSxMrgWRkZMhxCgAAYCEnIpJIQzjTDQcnHx8fyszMlHUAT7Vq1SglJUW2cwAA0DJvC+MBapR2wstjtWvXTn9ZkiQESQAAB4BRr3aqPfJakiEhIeIXzMGDB+3xtAAAYAWoUdoB1x7Pnz9P2dnZdPXqVXs8JQAAWAlqlHZy8uRJESwfPnxor6cEAAArQI3SRjjDzjPPPCOmguggSAIAOB7UKG1k5MiR1KBBA6pcuTL973//s9XTAACAjaFGaSNHjx4Vw41jYmJs9RQAAGAHmEdp4zR1nFQAAACUB/Mo7czDw4OGDh1KXl5e+mMIkgAAjg9Nr1YyePBgat26teibBAAA9VBEoIyMjKTr169TVlaW6NPr0KFDsbeNiIgQ8xINN76f3L7//ntKTEyk3bt3y30qAACgpkDJNbCFCxfSnDlzqG3btnT27Fnau3evWJuxOOnp6RQYGKjf6tSpQ3JLTU2lzz77TARLAABQF0nOLSYmRlq8eLH+spOTk3Tr1i1p+vTpJm8fEREhpaWllfn5vL29Jcb78py3p6enNHbsWCkwMFDW8sOGMsBnAJ8BfAbIpvHAWQmJwqOiov6M2pIkLoeGhhZ7v0qVKtGNGzcoPj6etm3bRs2aNTM78Z9HNhlu1tCrVy8xT3LEiBEilysAAKiTrIHSz89PTKFISkoyOs6XuUnVlEuXLtFLL71EQ4YMobFjx5KzszMdO3aMgoKCTN5+xowZYhkV3ZaQkGCVc9+/f784l02bNongDgAA6iVbtb1GjRqi2tu5c2ej4wsWLBBNspY8hqurq3TlyhXp3XffNXm9u7u7qFbrtpo1a1ql6RUbygCfAXwG8BkgTTS9yprCjhct5rmGAQEBRsf5sqWDYvj+p0+fFs2gpuTk5IgNAADA4Zpec3NzKTY2lnr27Kk/xv19fDk6Otqix+Cm1xYtWtCdO3dseKYAAKBlslZ9R44cKWVlZUnjxo2TmjRpIi1fvlxKTU2V/P39xfVr1qyR5s6dq7/922+/LfXu3VsKCQmR2rRpI61fv1569OiR1LRpU6tWtbGhDPAZwGcAnwF1fwYcoumV8WAYnjP57rvvigE8Z86coX79+lFycrK4vnbt2lRQUKC/fZUqVeiLL74Qt01LSxM10i5dutCFCxdkfBUAAKBWSIoOAACa5O3tLWZD+Pj4iNWeFJuZBwAAQMkQKAEAAMxAoAQAADADgRIAAMAMBEoAAAAzECgBAADMQKAEAAAwA4ESAADADARKAAAAMxAoAQAAzJA916ucqYsAAEC7vC2MA65aLZiEhAS5TwUAABQSF8zletVcUnRWs2ZNs4ViacFysA0KCir3Y6kJygVlg88M/p8c6XuGH+v27dtmb6O5GiUrqVBKg98kBEqUCz4z+F+yJXzP2K5cLLk/BvMAAACYgUAJAABgBgJlGT1+/Jhmz54t9oBywWem7PC/hLJR+mdGk4N5AAAALIUaJQAAgBkIlAAAAGYgUAIAAJiBQAkAAGAGAqUZkZGRdP36dcrKyqKYmBjq0KFDsbeNiIggSZKMNr6f1suF+fr60pIlS0Sih+zsbLp06RL179+ftF42Bw4cKPKZ4W3nzp2k9c/MG2+8QRcvXqRHjx5RfHw8LVy4kDw8PEiNSlM2rq6u9Pbbb9PVq1fF7c+cOUN9+/YltenatStt375dZN/h/4khQ4aUeJ/u3btTbGys+I65cuWK+E62Jh71iq1QGYwcOVLKzs6Wxo8fLzVt2lT67LPPpNTUVKl69eomyyoiIkK6f/++FBAQoN/8/f0lrZeLm5ubdOLECWnnzp1Sly5dpDp16kjdunWTWrZsKWm9bKpUqWL0eWnWrJmUm5srPktaLpfRo0dLWVlZYs+fl969e0sJCQnSRx99JPtrkbts5s+fL926dUvq37+/FBISIr322mvSo0ePpNatW8v+WsiKW79+/aT33ntPGjp0qMSGDBli9vZ169aVHjx4IP373/+WmjRpIk2ePFn8L/Xp08da5yR/oShxi4mJkRYvXqy/7OTkJD6g06dPN3l7/nJLS0uT/byVVi6vvvqqdPXqVcnV1VX2c1da2RTe3njjDSk9PV2qWLGipsuFbxsVFWV0jL8ADx8+LPtrkbts+AdDZGSk0bEtW7ZIa9eulf21kI02SwIl/4CIi4szOrZhwwZp9+7dVjkHNL2a4ObmRu3ataOoqKg/q92SJC6HhoYWWzWvVKkS3bhxQzQVbdu2jZo1a0ZaL5fBgwdTdHQ0LV26lBITEykuLo5mzJhBzs7q+uiV9TNjaMKECbRx40bR3Kjlcjl27Ji4j64JMiQkhAYMGEDfffcdqUlZyoabn7lp0RA3wT711FOkZaGhoUblyPbu3Wvx/15J1PVtZSV+fn6iLyApKcnoOF8ODAw0eR/ud3vppZdEW/rYsWNFIOB/eM5ur+VyqVevHo0YMYJcXFzEl917771H06ZNo7feeovUpCxlY4iDQosWLWjFihWk9XLZsGEDzZo1i44cOUI5OTl07do1OnjwIM2bN4+0Xjb85T916lRq0KABOTk5Ua9evWjYsGFUo0YN0rLAwECT5cjjIzw9Pcv9+AiUVsKd8GvXrqWzZ8/SoUOHxIf37t279Oqrr5KW8Q+G5ORkeuWVV+jUqVO0adMm+uCDD+i1116T+9QUhWuTP//8M508eZK0jgdlzJw5Uwxyadu2LYWFhdGzzz6ruh9XZcGDnHigCg904h8RPEhu1apVVFBQIPepqZoml9kqSUpKCuXl5VFAQIDRcb7MzYeW4PufPn1a/PLTcrncuXOHcnNzjf6RL1y4IH4Bc9MTX6f1z0zFihXpueeeE7UotSlLuXCrA//oXLlypbh87tw58vLyos8//1z8yOLmSa2WDd+HfzhwE2y1atXESPL58+eLWreWJSYmmizH9PT0Ik3VZYEapQn85c3DjHv27Kk/xs0cfJn72ywqWGdn0ZTGgULL5XL06FF9M5FOo0aNxD+4WoJkeT8z4eHh4otv3bp1pDZlKRf+4VC4hpSfn6+/r1qU5zPDycD5f4ibbocPH07ffvstaVl0dLRRObLevXtb/H1tCdlHNSl12DYPUR83bpwYbrx8+XIxbFs35WPNmjXS3Llz9bd/++23xTB2HrLdpk0baf369WLYNg/51nK5BAcHi5GcixYtkho2bCgNGDBASkxMlGbOnCn7a5G7bHTboUOHxAg9uc9fKeXyzjvviM/MqFGjxLD/Xr16SVeuXJE2btwoab1sOnbsKIWFhYnvmaeeekqMDv71118lX19f2V8LWXHz8vKSWrVqJTY2ZcoU8XetWrXE9VwmXDaFp4csWLBAaty4sTRp0iRMD7HXm8VzcW7cuCHmOfEwbv6Q6q47cOCAtGrVKv3lhQsX6m97584dMW9QbXObylIuvHXu3FmKjo4WXwg8VWTGjBmSs7Oz7K9DCWXTqFEj8UXAwUDuc1dKubi4uEizZs0SwZF/bP7222/SkiVLVBcMylI2PAf5l19+Ef9Ld+/eFcGiRo0asr8GsvLWvXt3yRRdWfCey6bwfU6dOiXKkb9nrDkfGctsAQAAmIE+SgAAADMQKAEAAMxAoAQAADADgRIAAMAMBEoAAAAzECgBAADMQKAEAAAwA4ESAADADARKADBy4MAB+s9//mNxqURERFBaWhpKEVQLgRIAAMAMBEoAAAAzECgBHKhJdNGiRaJZNDU1VazBN3HiRLEs1ZdffkkZGRliUd9+/frp79OtWzc6fvy4WJOPl2WaN28eubi46K/n+65Zs4YyMzPF9VOnTi3yvO7u7vThhx/SrVu36MGDB2KRcl5cGUArECgBHAj3B/LivR07dqTFixfTp59+Sps3b6Zjx45R27Ztad++fWLR4woVKlDNmjXpu+++o5MnT1KrVq1o0qRJNGHCBHrrrbf0j8cBkIPekCFDqE+fPvT000+LxzG0ZMkSCg0NFYtLt2zZUjzfnj17VLUoOUBJZF9SBRvKAJ+Bkj8DvKwQr12pu8xLlWVmZhqtyxcQECCWI+rUqZP0/vvvSxcuXDB6DF6nLyMjQ3JychJr/vGSRCNGjNBfX6VKFenhw4fSf/7zH3GZ1//Lzc0tspTT/v37pQ8++ED8zcsZpaWl4T3E/7Gk1jJwLTGMAoBi/Pzzz/q/CwoK6N69exQXF6c/lpSUJPb+/v7UtGnTIiu8Hz16lLy9vSk4OJiqVKlCHh4eomlWh0evXrp0SX+5RYsW5OrqSpcvXzZ6HL4fPzeAFiBQAjiQ3Nxco8uSJBU5xpydrdOrUqlSJcrLy6N27dpRfn6+0XXcXwmgBQiUACp14cIFGj58uNGxJ598Ugz64YE5PCAoJyeHOnXqRDdv3hTXV65cmRo1akQ//vijuHz69GlRo+Qa6pEjR2R5HQByw2AeAJVatmwZ1apVSwz6ady4MQ0ePJjmzJlDCxcuFDXRhw8f0sqVK8WAnh49elDz5s1p9erVoklXh0fRrlu3jr766isKCwujunXrUocOHejNN9+kAQMGyPr6AOwFNUoAleLpHhzMOBCePXtW1CA5ML7//vv62/z9738Xzas7duwQU0Q++ugj8vX1NXqcF198UYyU5euCgoLEqFueIrJz504ZXhWA/Tn9MaoHAAAATEDTKwAAgBkIlAAAAGYgUAIAAJiBQAkAAGAGAiUAAIAZCJQAAABmIFACAACYgUAJAABgBgIlAACAGQiUAAAAZiBQAgAAUPH+H0zgmgcscEnUAAAAAElFTkSuQmCC" }, "metadata": {}, "output_type": "display_data", @@ -215,13 +215,13 @@ } } ], - "execution_count": 12 + "execution_count": 7 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:36:02.781239Z", - "start_time": "2026-03-27T00:35:59.656520Z" + "end_time": "2026-03-27T00:43:32.925132Z", + "start_time": "2026-03-27T00:43:29.914039Z" } }, "cell_type": "code", @@ -234,7 +234,7 @@ { "data": { "text/plain": [ - "MutatorOneshotBenchmark.GeneralizationBenchmarkResult(msen=0.019300722565489446, msen_sigma=0.0026393283731082216, msen_CI95=(0.013504050205767358, 0.023818023291841975), model_statistics=<xarray.GeneralizationStatistics> Size: 583kB\n", + "MutatorOneshotBenchmark.GeneralizationBenchmarkResult(msen=0.019165030086165914, msen_sigma=0.0025134823168529168, msen_CI95=(0.014122805740139854, 0.02356936780519513), model_statistics=<xarray.GeneralizationStatistics> Size: 583kB\n", "Dimensions: (transformation: 36, boot_iter: 1000)\n", "Coordinates:\n", " * transformation (transformation) <U26 4kB 'backgrounds | 0.1' ... '...\n", @@ -242,24 +242,24 @@ " transformation_level (transformation) float64 288B 0.1 0.2154 ... 0.5 1.5\n", "Dimensions without coordinates: boot_iter\n", "Data variables:\n", - " phat (transformation) float64 288B 0.9715 0.9737 ... 0.9497\n", - " varhat_phat (transformation) float64 288B 6.359e-06 ... 2.222e-05\n", - " boot_phat (boot_iter, transformation) float64 288kB 0.9733 .....\n", - " boot_varhat_phat (boot_iter, transformation) float64 288kB 5.971e-06...)" + " phat (transformation) float64 288B 0.9723 0.977 ... 0.9501\n", + " varhat_phat (transformation) float64 288B 6.213e-06 ... 2.211e-05\n", + " boot_phat (boot_iter, transformation) float64 288kB 0.9699 .....\n", + " boot_varhat_phat (boot_iter, transformation) float64 288kB 6.707e-06...)" ] }, - "execution_count": 13, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 13 + "execution_count": 8 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:36:09.047702Z", - "start_time": "2026-03-27T00:36:08.975029Z" + "end_time": "2026-03-27T00:43:32.971254Z", + "start_time": "2026-03-27T00:43:32.933381Z" } }, "cell_type": "code", @@ -285,7 +285,7 @@ "text/plain": [ "<Figure size 500x500 with 1 Axes>" ], - "image/png": 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aa69pYWFhNvuONLL/HnyHfXeBUqVKqdeePn3acu7Wrl2rDRs2zPIavZtA//79s3wmuoegWwTOPdIF6dOyZcssAya40z0Er3fG+ly88sorlvRCd4SePXuq48I2631r1aqVSh90lXFnwAGkIzr4Ix2QHq4GHLBPB0cDRNgv1tfb3/72N/UdOIaffvpJdd+xfi26j6DL1JUrV1SXja+++korU6aMw+vS2fVWq1YtNZgBzov9gAO5Pe/6gAPO/r/1xw899JAa9EMf2AD/jxiAJCoqKk9/k8R4i893gAvTwFDXAPolJicnZ+nXx8XYaZAXYwtzEVOkAesoiVxwVE+D7g8o/vJ0kR8R+SfWURK5gEYcaOGJodPQ7xD1RKjfRX0cthGR+TFQErmALgHoA4m+g2j0gEYn6PKCBhf6oApEZG5Bf5TBEhERkQOsoyQiInKBgZKIiMiFgKyjxKgb95pVg4iIzA+DnVjPFuNISCAGSYykQUREBOXLl3cZLAMuUOo5SSQMc5VERIGdm0xISLhnLAi4QKlDwjBQEhHRvbAxDxERkQsMlERERC4wUBIREbnAQElEROQCAyUREZELDJRERAYTFllSuj73tFpT3mOgJCIymPBSJaXbyGFqTXmPgZKIiMgFBkoiIiIXGCiJiIhcYKAkIiJygYGSiIjIBQZKIiIiFxgoiYiIXGCgJCIicoGBkogogLga1Ud/rmytGhz5xwoDJREZbhg2DuGW+1F9HKW9/lyZGlWzNfJPmMmH1GOgJCLDDcNmtiHcfBFoWsf0y5J+RYoXV+tCYWFOc5ll7XKb+vNmOh/2GCiJiHzMXwJ/kYhial0orKjTXGYZu9wm1gi6ZsZASURE5AIDJRERkQsMlERERP4aKNu1ayfLly+XhIQE0TRN+vTpc8/3dOjQQXbu3Ck3b96UY8eOyZAhQ7yyr0REFJh8GiiLFCkie/fuleeff96t11epUkW++eYbWb9+vTRu3Fg++OADmTNnjnTt2jXP95WIPKtoiQibtbfe64+yezyeSDtH7y9ZoZxal6pcKcvzhYuF27zG0Xeb5XzYCxIRTfwAcpR9+/aVZcuWOX3Nu+++K7169ZKGDRtati1atEiKFy8uPXr0cOt7wsLCJDU1VcLDwyUtLc0j+05E2dOiX28ZMP4VyZcvn9y9e1fixr8r25esyPP3+qPsHk9u0y5m/CsSlO/3PJL1+we++ao079NTgoKC1O8x1nh+3cfzVfcP/I3vtH4O740oG6Wet/88I3A3HoSIgURHR8u6detstq1evVrlLJ0JDQ2VAgUK2CQMEXkf+tuhKwFyHfoPPWA94I2XJSUpSdKvXFXbUi9dlrTky9l+r/37/BmOqVztGg6PJ+HQEUk4fDTLe8rXqeX0+M8fOe7y2ItFlVLv1YOk9ftDCoRagiToazzf+dmnLH/bP4f3yh+Prbcd2bJVUi5ecisd8J6IiAi5fNl/z5uhGvOUKVNGLl68aLMNj4sVKyYFCxZ0+J6xY8eqOwZ9QX0oEXlf9IC+MnpxrAyf9YHlR1eXLzhYbcfzWPDanLzX/n3+DPvq7HjaDopx+J52g2KcHv+9jj2yUsUs79Xf32nYYEsAzPJ8PudhAu91tD+RFSuIu7p37y7Dhg2TsmXLir8yVI4yJyZNmiRTp061yVEyWBJ5X3zcUjn440aVKxw2Y6rND+zdzEyZ8/wYmxxlTt5r/z5/hmM6ve+Aw+PZ9MVih+/Z+MViafZgD4fHjxylK8lnzlqKT63h/d/P+VQeGjfGYbDU/ihqdcTRc/i85LPnxF3bt2+XWrVqqd/mxMRE8UeGylFeuHBBoqKibLbhcUpKimoF68itW7dU2bP1QkTeh2LBhENH5cjmbaoeCz+ogHXchMlqO57HYl+E6O57jVLsCthXZ8fjqNgVsN3R6xEkkaN0NQQeikLxXu3uXcs2/L1i6nTZ8uV/ZceyVSrwqe1W6yC7QKg/h6DrKID+FLvI7WJXSE5OlunTp8vRo46P2R8YKlDGx8dLp06dbLZ16dJFbSci40BjD+SCAOvsNP7IzXv9UXaPx9Hr3R0CD6+dPXK05THqK3/dsUv9/dVrb8l30z9Wf/+8bNXvzzsIhNuWLFfrReMmyN3MP4OuHrg3LnScG9YhR9u7d28pXbq0ZdudO3fEn/m8e8h9992nFqhatar6u2LFiurxO++8I7GxsZbXz5o1S6pVqyaTJ0+W2rVry3PPPScxMTHy/vvv++wYiChn9GJWfe2t9/qj7B6PJ9LOkcvnzqv1pdNnLLlGe+cPH1PriydOSdyEP3O3sO6TBffMTXbs2FGaNWsmgwYNkpAQY9T++TRQ3n///bJnzx61AAIe/p44caJ6jMrdSpV+788Dp06dUt1DkItE/8sxY8aoSuA1a9b47BiIiMwKXUPu6sW8fwTNGympDnO3cGD9hnt+5ubNm+XMmTOyYsUKv89J6nwazn/66SenlcTw1FNPOXxP06ZN83jPiIgIge/U3v2qVe3Sd6fKQ+P+L0uiZDdXi/Yk8+bNM1TiGqqOkoiIvCv9j0BonZPMjuDgYFVFhuoyo2KgJCKiPNOiRQupV6+e9OvXz2l/d39njJpUIiITQ//P1TPmGKIf6PWrKdna123btqlufPv27XPajc/fMVASkU8YKTh4o0/lmplz/SIIQkZauoPnrqnzlXjsVzkavz3L8/FxSyznUh8vVm8EtHTpUjEyFr0SZRM6dWMQaFedu8n94JCTQQIYZD3HesaP69euqXXymXNZbmKuX0txeb4w0hCeQ5ePxx57TNq2bStmwUBJlE3udu4m/wyygQ7BTx9kADCEHmYV0Z/Tc405Td86depIzZo1pX379mpWDjNg0SsRkcFlZ37K8MiSUrVZY6czfmS3CDjVrgj9wIEDaqKKc+fOqYkozIA5SiIyFBZ920JuELlC+9yhM85mIMnOjB/WkOv8YXasXLfqT4lBBU6fPi1mwUBJRIbCou8/la5S2eH8lLXbtHRah44ZSOyHpnM140fqPRpd5c+fX9VJovuHqym5jMycR0VEFAAa9+jidH7KDkMedToDCYam0+kzkDgbozXtHvXB5cqVk8qVK6upskqWNGe9PesoiYgMas+3a6Vehza281P+Mefk0S1Zu3DoMCydDmO1YrqvnDp9+rQsXrxYbty4IZcuuT+9lpEwR0lEZFBJp05nmZ9Szy3qXT1c1WlClfsaZvt78+fPbzPKzpEjR+Ts2bNiVgyUREQGZj8/pasZPIpFlZIBb/xZpwmdhz+ltrsrNDRUTZH1xBNPGHZIuuxioCSigGf0lrTuzk8ZWami5Au2r9PMl60Wr+Hh4WrSZdRHFi9eXAIB6yiJ8rDPGhmrJe3BHzeaehCD5DNn5W7mXZtgicfOWrw6/IzkZImNjVUj8Fy4cEECAXOURHnYZ40oL+R0CD+0bI2b8GedJqz7ZL7TFq+6AgUKSIkSJSyPL168KAkJCabLmTvDHCWRC/iH14eqQw7SUZ+1lKQkS5EXfrjMnCMh4w+ijjpNXLPoQgKu6jT1IPn444+rYtYFCxbI5cuXAy5nzkBJ5EL0gL7qH98Zvc+aDnf5/jALhJmx6Dv37lWXaT/xcmhoqCpqxToQMVASuYAZEXB3rP9Ao7jVps9aZqZqaWido6S8g6Ju5OoB5wJdI5BDopyxnhrLmRs3bqg6ybCwMFXkGogYKIlcQPGRdRESfphR3IqcpD6iSW46a5P7xd/3KvpmsXf26VNj2UO3D7RsPXPmjCVYYglUDJREOazfye2IJpT74m/rom8We3sG6iTRRzIqKkoWLlwoJ06cCPhLlYGSKI/6rJFni7/vVfTNYm/PuHPnjqSlpUlERIRcv37dQ59qbAyURGSY4m8Wfd+7u0huJxTPRJVCXJyaU/LKlSu5+iyzYD9KIjLscG2eashj9Ja01jN8uHMs9v0wCxUqJA0bNrQJlgySf2KgJKKALvo20yAS7h6LdWDFAOeDBw+W/v37S7Nmzby8x8bAQElEAduaFhMcO5v4uHzdWoYaYaZ8nVoOj+VeA57fvn1bjh07Junp6ZZWroGaM3eGgZKIArY1LVrMOpv4ePTiWPUao2g3KMbhsbgz4PkPP/wgM2fOzNV8ki1MlDO3x0BJlEP4ETVSjoOytqb9ZMQoNdGxNbSkxfapMUPUa4xi4xeLHR6LowHPCxcuLB06dJCgoCDLtty0cC2mpu96Odu5WaNgoCTKoegB/XLdwpB8B/Vz6AdrP/GxPohEwqGjhhqvNOHwUYfHYj/gOYIY+kl27NhROnfu7JHvjlTTdwXnKDdrBAyURBTQ8qolrb8eC3KdmzZtkpSUFNm1a5cHp+/KdCs3a0QMlETZhCb1WxYvYbqZiJkGkXDnWA4ePCjTpk1zORNI9qfvmnzP3KxRMVASZROK47Z+bZy6K6IiRYpInz59bGb/wAg8nrTdRDlzexyZh4gCYtLiQITGZmh0VvlusJQvW071mfz666/z7PvSTZQzt8YcJREZinVneXJNn0h5+/59aoosdAOh7GOOkojI5JKvXpFZs2aJpmm+3hVDYo6SiMhk7mbclB4dOkpYkSKWbQySOcdASURkMjd3/yIVypSVDi1aMUB6AAMlUQ6YdUxLMoeCTerL2cTz8uO2rTaj71DOMFASZZOZx7Qk47IOiPkKFZDvNvwo6Tc48bInsDEP0T2a11sPU4ccpKMZGlKSkixN4tFtgS0yjcXoXU4wyfJjjz0mq1atEs/2jiRgoCRyAX3Q0LzeFX22CR1+cNF9gYzX5cTT/RcxqLo3bpratWsnUVFR0qNHD1nx059dQFhF4BkMlEQu4Ifu4I8bbX54UNxqPZ0RhuvCSCTWOUoKbHr/RVw73giU3333nVpv2LBBwsqXUX836NheOj/7lPob1ywGTDfTaDnexEBJ5AJ+5Ox/6PCDo6YUCg62mW2CyJswyg4mXdaHo1u5cqX6WwvNL/GLl6ggaV9FcGTL1jwdfzXV4EXYzrAxD1E2mXlMSzKG4sWLy8iRI6VZs2ZZnsONXUhoaI4ncc6NNJOOmsRASZQDZh3T0ohQH9j1uacDahLthg0bSkREhERHR0tISEiuJnGme2OgJMMIxB9Ecr8+MJAm0d64caOsXbtWYmNjHc4C4u4kzuQeBkoyjED8QSSynirL2ubNmyUtLc1pArGKwHMYKImI/FyJEiXk2WeflZ49e2brfawi8AwGSiIiP1euXDkJCwuTqlWrSsGCBX29OwHH54ESLbdOnjwpGRkZsnXrVmnevLnT16LS+rXXXpPjx4+r1+/Zs0e6devm1f0lIroXT3f0P3DggMTFxcmCBQvk5s2bPAGBFChjYmJk6tSpMmHCBGnatKns3btXVq9eLaVKlXL4+rfeeksVP7zwwgtSr149Nb/akiVLpHHjxl7fdyKivBwLGK1a0VdS98svv8j16xy7NeAC5ejRo2X27NnqLunQoUMyYsQIuXHjhgwdOtTh65944gl555135Ntvv1W5UARKjG04ZszvfdqIiHypfJ1aDscCLhbl+ObfmcjISPU7iPFbrYMlBVigxMlHZ9l169bZTCyKx+gb5EiBAgWyFDugCLZt27ZOvyc0NFSV7VsvRER5od2gGI909Ec9JH67ChcuzEAZyIESd0yoc7x48aLNdjwuU+b3sQrtoVgWudAaNWqoKWU6d+4sDz30kJQtW9bp94wdO1ZSU1MtS0JCgsePhYjIkx39z507J59++qnqJ4lSNgrwxjzZ8dJLL8mxY8fk8OHDcuvWLZk+fbrMnz8/y4VpbdKkSRIeHm5Zypcv79V9JnMy65iWlDu56eiPzAN+oyyflZCQ6yDJ69TggTI5OVmNKIGpYazh8YULF5y+p1+/fqrjbeXKlaVOnTqSnp4uJ06ccPo9CKjolGu9kDH505RBZh3T0oj86brIaUd/NGB88skn1WIdLHOL16nBAyVGvd+5c6d06tTJsg3FqXgcHx/v8r2//fabnD9/XhXd9u/fX5YtW+aFPSZfDivnqZaEZC7+el1kt6M/bujxm4jfNn1GEPIfPp1mC11DUAb/888/y/bt22XUqFEqt4jiVMBzKH4YN26cetyiRQtVdIr+k1iPHz9eVZz/61//8uVhBLS8mncPgVcfqg45BUctCVOSkmx+iFDMxBye+enXxr2uCyNdDykpKep3D0ESDRTJv/g0UC5evFgVOUycOFE14EEA7N69uyQlJannK1WqZFP/iJZg6EtZrVo1VeSKriHoMoKLjMwFs8MjADuDloTDZ31gsw11hp6cpZ6Md21YXxf+fj2gmgmt/9FwB9DYkPyTzydu/uijj9TiSMeOHW0eY/bu+vXre2nPyJfi45aqXCog54BiNetm92gkgfof+xwlBc61ca/rwp+vBzTcGTJkiAQHB6t+5ImJib7eJfLnQEnkCIrMrIvN0JIQxWrIMegtCY9s3sbEC/Brw6jXBUrB0BUOOcqrVzmnqb9joCRDQMtB1D2hWA05BiP8GFLeM+p1gbrIhQsXqtwwGvCQfzNUP0oKbJwyiIx8XWBglIYNG1oe661cyf8xR0lE5GH2Hf2LFy8ugwcPVg0SERyPHj3KNDcQBkoiIg/TO/pb10liqiy0dD19+jTT22AYKImI8hgmfEB3NgySwgEFjId1lEREeQCDolh3cUOwZJA0JuYoiYg8DNNjYTAU1ElifGmMPkbGxRwlEZGHYdYPzK2LCeb37dvH9DU45iiJiPIAcpGY+AFFrmRszFGSYaY44tx65M8qVqwoAwYMUA12dAyS5sBASYaZ4ohz65G/wlB0AwcOVGNRt2vXzte7Qx7GoldyG6e+InIMrVm//vpradWqlWzatInJZDIMlOQ2Tn1F/siXRfKYbF4vXj116pRayHyCUIwuASQsLEzN+xYeHq6abVPOJ1N2d+oro0yeS5QdVapUka5du6rBzTE/Lpk3HjBHSW7j1FdEf+Yke/XqpSaeb9++vRp1h8xNC6QlLCxMA6x9vS9mWGq3aalN2R+v1r7eFy5MA29eAxEREVqfPn20kJAQXnti7njAVq8UEFMcEXmCddcPTLi8bNkyuXPnDhPX5BgoiYjcUK1aNXnxxRfVvJIUWBgoiYjc0LZtW9XoA11AKLAwUBJRwEOL7q7PPa3Wznz11VeyceNGWb58ecCnV6BhoCSigIduT91GDrN0f7KeBUT322+/yffffy+ZmZkBn16BhoGSiMiBGjVqyKhRo6RevXpMnwDHQElE5ECdOnUkNDRUjd9KgY0DDhAROfDNN9/IxYsX1VRZFNiYoyQi+kOxsHBLWmAM1x07dsjdu3eZPgGOgZKISETunE+S/t16SMeOHZkeZIOBkogIg/pfz5Dg4GApWbKkGsuVSMdASUQkIqE1K8vqDT/K//73P8vUWUQMlEQU0CpVqqRykboziedZJ0lZsNUrEQWkunXryoABA+TIkSOy5cBeX+8O+TEWvZJhZ5cnyo3bt2+r3OOtW7dY1EouMUdJuZ7Mec3MuUxFMpzjx4/LnDlzVF/JWq1bqG1FS0T4erfIDzFHSUQBo1atWjbjt164cEGa9/2rDJsxVT3GukW/3j7cQ/JHDJREFDB1ko888ogMGTJEChYsqLaVr1NLBox/RfLl+/2nEOsBb7wsxaJK+XhvyZ8wUBJRQEhKSpLr169LQkKCmgkE2g2KsQRJXb7gYImsWMFHe0n+iIGSiALC5cuXZfbs2bJixQpL452NXyzO0h3kbmamJJ8956O9JH/EQElEptWgQQMpXbq05XFqaqpNC9eEw0clbvy7KjgC1nETJkvKxUs+2V/yTwyURGRKtWvXlv79+6s6yfDwPwc7t7d9yQqZ8/wY9TfWeExkjYGSyE5YZEnp+tzTak3Gdfr0aUlMTJRffvlF0tLSXL42/cpVmzWRNfajJLITXqqkdBs5TA7+uFH1EyVjunnzpixYsEANLMCxWyk3mKMkItNo0qSJKnLVcdQd8gQGSiIyhapVq0qfPn0kJiZGoqKifL07ZCIseiUi09RJHjhwQNLT09WwdESewkBJRKaA/pCYS9K+XyRRbrHolYgM6/7775e2bdtaHjNIUl5gjpKIDKlMmTLy17/+Vf2NYelOnjzp610ik2KgJCJDwswf33//vRrgnEGSTF30OnLkSHWRZ2RkyNatW6V58+YuX//SSy/J4cOH5caNG3LmzBmZOnWqFChQwGv7S+anz0nIuQn938aNG2Xt2rW+3g0KAJqvlpiYGO3mzZvak08+qdWtW1f7+OOPtStXrmilSpVy+PpHH31Uy8jIUOvKlStrXbp00RISErQpU6a4/Z1hYWEaYO3LY+fin2nQol9v7b29m7Up++PVGo99vU9c/kyDli1bav3799fy5cvn0XQpX7eWOudYM70D55oLczMe+LTodfTo0Wo0f4yeASNGjJBevXrJ0KFDZfLkyVle37p1a9m8ebMsWrTI0hwcf7ds2dLr+07mgGHqMBKPnoN0NDdhSlKSZWiz1EuXOVqPjxQvXly6du0qwcHBqlTp4MGDHvtsnNfVM+aoNZE9nwXK/PnzS7NmzWTSpEmWbRhmat26dRIdHe3wPVu2bJHHH39cFc/u2LFDdTDu2bOnfPbZZ06/JzQ01KZoNiwszMNHQkYWPaCvGq7OGcxNOHzWB5bH+DFdM3Oul/aOrF27dk0WL14sZcuW9WiQBAxVyPNKfhcoIyMjJSQkJEvHYDyuU6eOw/cg94j3bdq0SYKCglSwnTlzpk2wtTd27FgZP368x/efzCE+bqka01XPUQ6bMdVmIl9Mu4QZJaxzlORdyEFm/jEN1pEjR9RCFFCNebKjQ4cOMm7cONUAqGnTptKvXz9VVPvqq686fQ+CKKbY0Zfy5ct7dZ/JvyEnkXDoqFqObN7mcG5CbNdfw0HSvQvVLaiKQctWooDLUSYnJ8udO3eyjMmIx2j27cibb76pilnnzv296AvDVRUpUkQ++eQTefvttx3OEIBBkbEQuQNzEaJOEsWtyEkiSJJvFCpUSNq0aaP+x+vVqye7du3iqaDAylFi6pudO3dKp06dLNtQnIrH8fHxDt9TuHDhLCNv6EUyeC+RJ3BuQv+ALmOxsbGyZs0aBknyKZ+2ekUfSPwj/Pzzz7J9+3YZNWqUunucP3++eh7PYcQNFLfCihUrVEvZ3bt3y7Zt26RGjRoql4ntHLqKyDw5SQRJSEpKUgtRwAZKtGArVaqUTJw4UQ1HtWfPHunevbvlH6NSpUo2AfCtt95SxatYo67x0qVLKkj+85//9OFREJGntG/fXrVqR5exy5fZcIr8A8ors1bsmRi6h6SmpqqGPWlpab7eHfJD5evWktGLY2VqzBDVgIe8A63gn3nmGdVOYdWqVaqUicgf4gHHeiUiv4DGfahuqV27tqpeIfIXhuoeQkTeGa2o63NPq7U3lChRwvI3xnBmkCR/w0BJRDYwpB9GK9KH9stLDzzwgOoXXb16dZ4F8lsMlOTzHAUFJnTpQiM+1E2iUR+Rv2IdJTnNUWBoN45EQ3kFLdjj4uKkZs2aapBzIn/FHCWRHc4kkbfQ7ct6wBAGSfJ3DJRETmaSYG7a8zp37qzGbm3VqhWvOzIMBkoi8hp9ABGOpEVGwjpKIvKaH374QY4ePSrnzp1jqpNhMEdJRHmqfv36NpMWMEiS0TBQElGe6dKliwwYMEB69+7NVCbDYqAkIhtFS0TYrHPj/PnzqmUrZgEiMirWURKRRYt+vWXA+FfU38NmTJW48e+qyaxz6uDBgypYXr16lalMhsUcJREp5evUUkEyX77ffxawHvDGy1IsKnuj5qDrR2hoqOUxgyQZHQMlESntBsVYgqTlByI4WCIrVshWP0nMKTto0CCbBjxERsZASUTKxi8WZ+nfeDczU5LPnstWUas+AwiGqCMyAwZKIlISDh9VdZIIjoB13ITJknLxktsplJiYKB9++KHs2bOHqUqmkePGPChWqVGjhpQuXTpLcc3GjRs9sW9kglaPZCxouJOSlCTDZ30gc54fI0c2b7vn70DHjh1VDlKvi7x586aX9pbIjwNly5YtZeHChVK5cuUs9RAobsG0OWRMnm71SMaTfuWqzdqV9u3bq6Vhw4by0UcfyZ07d7ywh0TelaOINmvWLPn555+lV69eqqiFdRHGhPkmrSfnRQ7SUatH5DD0H03MrMHBwkm3c+dONfIOSpEYJMmschQoMX/cww8/LL/++qvn94i8JnpAXzXvpCto9YhiON3qGXPUzBpEkJ6eLh9//LEaVIDIrHIUKLdt26bqJxkojS0+bqmanNk6R4niVus6ZzToQF2VdY6SAheqWnr06CG//PKLnDp1Sm1jkCSzy1GgnDZtmkyZMkXKlCkj+/fvl9u3b9s8j23k/1CEal+MijpJFLciJ6m3erxXgw4KHGif0KJFC2nUqJH85z//kYyMDF/vEpF/Bsr//ve/aj1v3jzLNtRT4m6TjXkCq9UjBRa0TahWrZrs3buXQZICRo4CZdWqVT2/J2TIVo8UWNBgBy3eiQJJjgLlmTNnPL8nROR3UF/dt29fOX36tGrhShSIctXhsW7dulKpUiWbAZBhxQr2uyMyA3T9QH1kvXr15Pjx45KSkuLrXSIyTtHrkiVLVCdjvW4S9P6UHHCAyLjQshndgLBOSL6sGu2dPXuWQZICVo7GekVrt5MnT6rh6zAAMu46MToHKvofeOABz+8lEXnN9StXZe2seZYW0WvXrpXDhw/zDFDAylGgjI6Oltdff10uX76sZhvAsnnzZhk7dqwaEJmIjFsn2b9/f+nZs6evd4XI2IEyODhY0tLS1N/JyclSrlw59Tcq/GvXru3ZPSQir0GbA9RHNmnSRKKiopjyRDmtozxw4IDcd999amQOjNLzj3/8Q27duiXDhw+XEydOMGGJDAr/00uXLlVVKhcvXvT17hAZN1C+9dZbUqRIEfU3imBXrlypBkVGUezAgQM9vY9ElIdQQoQiV32ELQwmQES5DJRr1qyx/I3xXtFNJCIiwjIfHREZJ0gOGDBAChQooAYSsB+OkohyWEfpCIMkkfGUKFFCdfeqWLEi6ySJPJmjxN3nCy+8oGY2RxcR69kmoFmzZjn5WCLyskuXLsnnn3+uBg05d+4c05/IU4Fy7ty50rVrV/n6669l+/btnLjZxB3OyZzFrYUKFVJzSQIGEyAi17TsLteuXdNat26d7ff5wxIWFqYB1r7elzw5vsiSWtfnnlZrX+8LF/9Lg5CQEO2xxx7TXnzxRS08PNzn+8OFaSAGiAc5qqNMSEiw9KMk/xJeqqR0GzlMrYnsFSxYUCIjIyUsLEw1wCOie8tRoBwzZoxMnjxZdU4mIuNAcWtsbKyql8QAIUSUR3WUGNMVd6YYXAAdk+2blJcsydwMkb/AJAX4n9QHEMAMIJwFhCiPA+WiRYukfPnyMm7cOPXPp88aQkT+FyQfffRRqVChgnz22Wds2UrkrUDZunVrNTD6vn37cvJ2IvISTIGH7ltYo7UrEXkpUGLKHTQvJyL/hmoRjLiDBjyJiYm+3h2iwGnM88orr8iUKVOkQ4cOamQPtKCzXojId/Lnzy81atSwCZYMkkRezlF+9913av3999/bbEfxDuorUS9CRN6H/73HHntMqlSpIkuWLGH1CJEH5CiiYeg6IvI/d+7cscwRe+XKFV/vDlHgBsoNGzZ4fk+IyCNWrVol8fHxDJREvqyjbNeuncslu0aOHCknT56UjIwM2bp1qzRv3tzpa9evX6+Kd+0XzIlJFIgwoLn1/wz+H5ibJPJxjvLHH3/Mss26L2V26ihjYmJk6tSpMmLECNm2bZuMGjVKVq9eLbVr11YzG9h76KGH1A+DDh2pMdFsXFxcTg6FyNDQLmDQoEFSuXJlKVq0qLqRJCI/yFFijEjrBVNtde/eXXbs2KFmFcmO0aNHy+zZs2XBggVy6NAhFTAx2s/QoUOdznuJQQ70pUuXLur1DJQUiHCDun//flUac+TIEV/vDpEp5ShHmZqammXbunXr5NatWyp3eP/997vdjB1zV06aNMnmHx+fhQEN3PH000/Ll19+qYKlI8h9Yv5Mndm7rxQtEWGzJvPDkJIHDx5UwZKI/CRH6QxyeCgydRc6QaOYVh+D0vpzypQpc8/3o16mYcOGMmfOHKevGTt2rArs+oKZT8yqRb/eMmzGVPU31nhM5oMbv86dO9tUcTBIEvlZjhLByb6epGzZsmoggj179oi3IDeJYfRQ5OsMcqvI5VrnKM0QLMMiS9pMpYUc5IDxr6jhygDrAW+8LClJSZJ+5arahomY05I5GbPRDRw4UKpVqybh4eHyv//9z9e7Q2R6OQqUCIYoIkWAtIYWq87qFh1Bfy/0+4qKirLZjscXLlxw+d7ChQvLI488Iq+//rrL16E4GIvZRA/oq+addCVfcLAMn/WB5fHqGXNkzcy5Xtg7ykvonoVGbFu2bGFCE/lroKxatarN47t376oWqr/99lu2PgdDa+3cuVM6deoky5YtU9sQfPF4+vTpLt87YMAAVQSFefUCUXzcUjn440abHCWKW/UcJdzNzJQ5z4+xyVGS8Z06dUo+/PBDyczM9PWuEAWEHAXKM2fOyF/+8hcV0NDi1frHWS8SdReKRTGRLBokbN++XXUPKVKkiMyfP189j+dQVIopvey/Y+nSpQHbXwxFqPbFqHHj31XFrchJIkjGTZgsRzZv89k+kmdg7teePXvKmjVr1MTLwCBJ5OeBEsWdWBDcMNhybuajXLx4sZQqVUomTpyoGvCgWBddTZKSktTzlSpVUjlWa7Vq1VIDG6BrCP1p+5IVqk4Sxa3ISTJImkOfPn2kbt26qk4S3aiIyPu07C7nz5/XHn/88Wy/zx+WsLAwDbD29b7kxVK+bi1tyv54tfb1vnDxTBpERERow4cP10qXLs005XXFa0C8Hw9ylKNE30Q2JCDyDgyy8cknnzC5iYzUjxL9FjGVDxF5Hlp0DxkyRHW5IiLfcztHiYmadWi8M3z4cNXpGf0Y0XrV2pgxYzy7l0QBBI3k0LIc4xrPmDEjV20AiMiLgbJJkyY2j/WBBRo0aGCznf/URLmD1q3IVWJidP4/ERkoUKI7CBHlDZTS6K270R/5q6++YlITmXGsVyLKPvQbRlVG/fr1mXxEfoiBksjHMNsO+hCjX3B25nIlIu/gfyWRH4zdii5XGM4RYx8TkX9hoCTyAQRGfbB+NNhZu3YtzwORn2LRK5GXFS1aVJ555hnp2LEj057IABgoibysRo0aanzjxo0bS6FChZj+RH6ORa9EXoY+yMHBwXLixAnJyMhg+hP5OQZKIi91Abl586Zleiw03CEiY2DRq8lgcubVM+ZwkmY/gumxhg4dqiYbR06SiIyFOUqTwWTOa2bO9fVukJUSJUpIsWLF1Og7GJouLS2N6UNkIAyURHns1KlTsnDhQrl8+TKDJJEBMVAS5VFxKwYPuHHjhnqMhjtEZEysoyTyMBSzPvXUU2pOSRS1EpGxMUdJ5Ol/qpAQtWDEHY7dSmR8DJREHoa6yAULFqgJzVNTU5m+RAbHQEnkAcWLF1fjtyYlJVmCJRGZA+soDSIssqR0fe5ptSb/C5J6nSSGpiMic2GgNIjwUiWl28hhak3+BSPuXL9+XQ1HxyHpiMyHRa9EHgiUn376qWq4k56ezvQkMhnmKE2IxbTeGW2nZs2aNsGSQZLInBgoTYjFtHmcvuHh8uSTT8ojjzwi1atXz+NvIyJfY6AkyibkHE+fPq1atl64cIHpR2RyrKMkyqa7d+/KkiVLpECBAmy8QxQAmKMkckNkZKS0bNnSJliyhStRYGCOkugeChUqpOokixYtKrdu3ZLdu3czzYgCCHOURPeAnOO2bdtUfeThw4eZXkQBhjlKIjds3LhR4uPj1dRZRBRYmKMkcqB06dLSq1cvyZfvz38RBkmiwMQcpUEULRFhs6a8gxF2Hn/8cdVfEhMvr1+/nslNFMCYozSAFv16y7AZU9XfWOMx5R3kHL/55hs5e/asKm4losDGHKUfDj9nPfA5cpADxr9iKQLEesAbL0tKUpKkX7mqtqVeuixpyZzWyZOOHDkiR48eVZMvE1FgY6D0M9ED+qpZQlzJFxwsw2d9YHm8esYcWTNzrhf2zrzKlCkjnTt3lq+//lqN2woMkkQEDJR+Jj5uqRz8caNNjhLFrdaNSu5mZsqc58fY5Cgp54KCgqR///5qLkkEy5UrVzI5iciCgdLPoAjVvhg1bvy7qrgVOUkEybgJk+XI5m1OP4MNf7IHOce4uDjp1KmTrF27NodnjojMTAukJSwsTAOsfb0v2Vlqt2mpTdkfr9auXteiX2/tvb2b1WuxxmNf77u/LkFBQT7fBy5MA14D/h8PmKM0CL2YVV8DG/7kXLly5aRfv37y5ZdfqllAiIicYaA0MDb8ybmuXbuqOkkUty5evNiDZ4WIzIaB0sDY8CfnEBwRJFevXu3BM0JEZsRAGeANfwJJ/vz55fbt2+pvjLizYsUKX+8SERkAR+Yxme1LVqiuI4A1HpNIhQoV5KWXXpLq1aszOYgoWxgoA6ThT6C7//771XySrVq18vWuEJHBsOiVAgKKWa9evSpbtmzx9a4QkcEwR0mmhRykLjMzU3766SdLHSURkWEC5ciRI+XkyZNqFvmtW7dK8+bNXb6+WLFiMn36dDl//rwakxODV/fo0cNr+0vGUKlSJXnhhRdY1EpExg6UMTExMnXqVJkwYYI0bdpU9u7dq5rro3+bs1aLGGKsSpUq8vDDD0vt2rXlmWeekYSEBK/vO/k3XCMFChSQmjVrqrFciYhyw2fDB23dulWbNm2azZBi586d015++WWHr3/22We148ePayEhIXk+ZJG/LWGRJbWuzz2t1vd6bfm6tdQQdlj7er99uTRu3DhX1woXpgGvAXNfA+7GA5/lKJE7bNasmaxbt+7PiK1p6nF0dLTD9zz44INqIt2PPvpILly4IPv375exY8fazKxhLzQ0VMLCwmwWI0J/SUylxXknnUNJhHXucc+ePWoSZiKi3PBZoIyMjJSQkBC5ePGizXY8xtyAjlSrVk0VuQYHB0vPnj3lzTfflDFjxsirr77q9HsQSFNTUy0Li2nNW9Q6fPhwdTPFolYiMlVjnuxAzjEpKUn9IO7atUsNQ/b222/LiBEjnL5n0qRJEh4eblnKly/v1X02Ogy83vW5p9XanxUuXFjdQKGlq6sSBiIiw/SjTE5OVsViUVFRNtvxGMWqjiQmJqrm/Xfv3rVsO3TokJQtW9ZmeDJrt27dUgvlTHipktJt5DA1mbQ/F/v+8ssv8umnn8rZs2dVVxAiIk/x2a03gtrOnTvVwNQ6FJnhMeohHdm8ebPUqFHDpmitVq1aqqsI+8cFnooVK6qWrbpTp04xSBKRx/m0jApdQ9C9Y/DgwVKnTh2ZOXOmFClSRObPn6+ej42NlXfeecfyejxfokQJ+c9//qOa/aOecty4capxDwUW1FcPGTJEBg0apBpsERGZcgg71DGipeLEiRNVAx60Uuzevbuqh9Q7jVsXs547d066desm77//vuzbt081zEHQnDx5sg+PgnwBA1SgFAGzgLColYjyms/7svhjvxkjL9npc2nkPpmRkZFacHCwz/eDC9OA14CYOh5wUHQT0vtcmg2myLpy5Yoa3FxvEEZElNfYjp4MUyf56KOPypNPPqm6+RAReQtzlGQIqLe+du2aGpAiPT3d17tDRAGEgZIMAcFx3rx5asYY6wZeRER5jUWv5LcwOwyGptOhhSuDJBF5GwMl+SV0DcI0bI899piULl3a17tDRAGMRa/klzDa0okTJ1RRK1u3EpEvMVCSX8I4wF999ZUqamVxKxH5EoteyW/Uq1dPzVFqHSwZJInI15ijJL+AIQwx1yimyMKgAidPnvT1LhERKQyU5BcwtdrWrVvVvJKYBYSIyF8wUJLfWLNmjZpCTdMwvCIRkX9gHSX5TMOGDdVUadYYJInI3zBHST5RvHhx6du3rwQHB8vZs2dl//79PBNE5JcYKMmloiUibNaegnFbly9fLhUrVpQDBw7wLBCR3wr6Y76tgBEWFiapqalqBoq0tDRf745fa9GvtwwY/4pqiYpuGnHj35XtS1bk6jNZB0lERosHzFHSnxdNZEkJL1XSkoPUgyRgPeCNlyUlKUnSr/w+H2Tqpctq7kt3NW7cWBo1aiSLFi2S27dvM+WJyBAYKMkiekBf6TZymNMUyRccLMNnfWB5vHrGHLcniC5UqJB069ZNrZs0aSLbt29nyhORIbDolZzmKIfNmGrJUcLdzEyZ8/yYHOcoK1SoIHXr1pW1a9cy1YnIMEWvDJTkuo7yjZdVThJBMm7C5GzXUYaEhKih6IiIjBoo2Y+SnEJQRA4SsM5ukMS4rSNHjlQXIRGRUTFQkkt6Mau+zk5OsnXr1lKiRAm57777mMpEZFhszEN5AsWtsbGxqpXrpk2bmMpEZFjMUZJHFSlSxPI3yv4ZJInI6BgoyWOaN28uL7zwgmrdSkRkFgyU5BEYcQddPwoWLCg1atRgqhKRabCOkjwCs35gxB3MCLJr1y6mKhGZBnOUlCuRkZGWvzEsHYMkEZkNAyXlWHR0tOonye4fRGRmDJSUYxEREWqIO6yJiMyKdZSUY99++60cP35cjh49ylQkItNijpKypWrVqjYNeBgkicjsGCjJbW3btpUhQ4ZI165dmWpEFDAYKMltGRkZan3z5k2mGhEFDNZRktt27twp58+fl8TERKYaEQUM5ijJpeqVKouWmWl5zCBJRIGGgZJc1kn+JbqNZMTvUUPUEREFIgZKcurs2bNy+84dCS5VQrVwJSIKRAyU5NTp06clbtVKKVD7zy4hRESBhoGSbLRs2VKKFi1qeZx45qysnjFHUi9dZkoRUUBioCSL1q1bS48ePVRfyZCQ3xtEpyVfljUz56o1EVEgYqAki0OHDklKSorqBnLnzh2mDBER5tvFSGSBlBJhYWGSmpoq4eHhkpaW5uvd8TuhoaFy69YtX+8GEZHfxAPmKANchw4dpGzZspbHDJJERLYYKAPY/fffLx07dpQnnnhCChUq5OvdISLySxzCLoDt27dPGjVqJPv377eM40pERLYYKAMYilkXLFggd+/e9fWuEBH5LRa9BhhMkVW/fn3LYwZJIiLXmKMMIA0aNFB9JTMzMyUhIUGuXbvm610iIvJ7fpGjHDlypJw8eVLVk23dulWaN2/u9LXoDI9xR60X1q+55+DBg7Jr1y755ptvGCSJiIySo4yJiZGpU6fKiBEjZNu2bTJq1ChZvXq11K5dWy5duuTwPegUj+d1HLDbPUin5cuXe+jMEREFDs2Xy9atW7Vp06ZZHgcFBWnnzp3TXn75ZYevHzJkiHb16lW3Pz80NFQLCwuzLOXKldMAf/v62PN6QVr26tVL+8tf/uLzfeHCNOA1wGtA/CwNEAfciQc+LXrNnz+/NGvWTNatW/dn1NY09Tg6Otrp+zBo96lTp+TMmTOydOlSqVevntPXjh07Vo28oC+omwsUVapUUcXY7dq1kzJlyvh6d4iIDMmngTIyMlINvn3x4kWb7Xjs7If9yJEjMnToUOnTp488/vjjki9fPtmyZYuUL1/e4esnTZqkhifSF2evMyPU+3733XfqZuLChQu+3h0iIkPyeR1ldqGxDxYdgiQG83722Wfl9ddfd9hXMJCGZQsKClKL3u3DOq2IiMhgOcrk5GQ1S0VUVJTNdjx2NweE9+/evVtq1KghgQ4Bsnfv3qqBVHBwsK93h4jIFHwaKG/fvq2mdOrUqZPNjz0ex8fHu/UZKHpt2LChJCYmSqArVaqUGpKuVq1aUrFiRV/vDhGRafi01VFMTIyWkZGhDR48WKtTp442a9Ys7cqVK1rp0qXV87Gxsdo777xjef1rr72mdenSRatatarWpEkTbeHChdqNGze0unXrerSVk1GX6tWraw0aNPD5fnBhGvAa4DUgfp4G7sYDn9dRLl68WOWEJk6cqBrw7NmzR7p37y5JSUnq+UqVKtkMsxYRESGzZ89Wr7169arKkWK0GdRTBiLkwAsUKCA3b95Uj3/99Vdf7xIRkalw4maDB8m+ffuqm4bY2Fi5ceOGr3eJiMgwOHFzgJzkatWqqRx5uXLlfL07RESm5POiV8o5DKCAabLQH/X48eNMSiKiPMBAaTBo5VusWDFVPwuXL19WCxERmXj2EHI/SPbr10+GDRsmpUuXZrIREXkBA6WBYGzcEiVKSMGCBaV48eK+3h0iooDAolcD+e233+Szzz6TsmXLqnFciYgo7zFHaYDi1goVKlgeo78kgyQRkfcwUPp5kHz44YflqaeespmomoiIvIeB0s9lZmaqOTqtRyciIiLv4cg8BshVYjYVDvpORORZHJnHoDA9Vr169SyPkZNkkCQi8h0WvfrZ2K2YSxJLmzZtfL07RETEQOlfUBd57tw5NU8nc5FERP6B/Sj9zMaNG2X//v1y7do1X+8KERExR+l7ISEhaj5NNNrRMUgSEfkP5ih9DP0k69Spo6bKWrZsma93h4iI7LAxj4/t3r1bMjIyZO/evb7eFSIicoD9KP0ABjnH0HREROQ97EfpxzOAdO/eXQVHHYMkEZH/YtGrlz344IPSqlUrGThwoLe/moiIcoCB0gfdP65cuSI//PCDt7+aiIhygHWUPoCuIBzknIjIt1hH6Ud1kugCEhkZadnGIElEZBwses1jXbt2lQYNGsgjjzyixnIlIiJj4YADeWz9+vVSunRpWbNmjRrLlYiIjIWBMo/duHFD5s+fn9dfQ0REeYRFrx5WoEABGTx4sFSvXt3TH01ERD7AQOlhGOC8WrVq0rdvXzXgORERGRt/yT1sw4YNEh4eLtu3b5c7d+54+uOJiMjLGCg9IDg4WDIzM9XfWHMWECIi82DRay5hzNannnpKDUtHRETmw0CZS/Xr15cKFSpI+/btpXDhwp45K0RE5DdY9JpLO3fulEKFCsmxY8dUVxAiIjIXBsocdgG5deuWZQCBTZs2efq8EBGRn2DRazYh9/jkk09Knz59OCQdEVEAYI4ym8qXLy9RUVGqCwiWlJSUvDkzRETkFxgos+n48eMSFxcnycnJDJJERAGAgdINaM2K/pG//fabenzo0KG8Pi9EROQnWEfpRpAcMmSIDBo0SEJDQ71zVoiIyG8wULoxAzbqIiMiIqRo0aLeOStEROQ3WPR6DxcvXpRPP/1UdQe5cuWKd84KERH5DQZKB4oUKaKKWa9evaoeJyYmevu8EBGRn2DRq4MgiTpJ9JVEcSsREQU2Bko7QUFBNgsREQU2Fr3aSU9Pl9jYWMmfP7+l6JWIiAIXc5QiqjVrpUqVbIIlgyQRETFQ/lEnifrIJ554QipXrsyrgoiIbAR80StG20HuMSQkhEPSERGRfxa9jhw5Uk6ePCkZGRmydetWad68uVvvGzhwoJrqasmSJTn+7jt37shXX30l8+bNk2vXruX4c4iIyJx8HihjYmJk6tSpMmHCBGnatKns3btXVq9eLaVKlXL5PhST/vvf/5YNGzbk6HsbNGhgEyxTU1Nz9DlERGRuPg+Uo0ePltmzZ8uCBQvUYOMjRoyQGzduyNChQ52+J1++fPLFF1/IG2+8ISdOnMjR9/bu3VsaNWqUiz0nIqJA4NM6SnTBaNasmUyaNMmyDUWp69atk+joaKfve/311yUpKUkVl7Zr187ld2CEnQIFCtiM3QoIxhiSTn9MRESBJczN33+fBsrIyEjViAbjqVrD4zp16jh8T5s2beTpp5+Wxo0bu/UdY8eOlfHjx2fZ/uabb6qFiIgCW1hYmKSlpZmj1Sv6O3722WfyzDPPyOXLl916D3KrqAO1TpCEhAQpX768y4QxCx6vefHcmhfPrXfT+vz58y5f49NAmZycrBrSREVF2WzH4wsXLmR5ffXq1aVq1aqyYsUKm/pKuH37ttSuXTtLnSVm/cBiD0EyEAKljsdrXjy35sVzm/fciQM+bcyD4LZz507p1KmTZRvGV8Xj+Pj4LK8/fPiwaq2KYld9Wb58uaxfv179ffbsWS8fARERmZ3Pi15RLIqxVX/++WfZvn27jBo1So2WM3/+fPU8nkNR6bhx49TgAAcPHrR5v9730X47ERGRKQLl4sWLVZ/JiRMnSpkyZWTPnj3SvXt31aoVMAbr3bt3PfZ9CLZo3IN1IODxmhfPrXnx3PoXzCOl+XoniIiI/JXPBxwgIiLyZwyURERELjBQEhERucBASUREFGiB0pfTdvn78Q4ZMkQdo/WC95n13BYrVkymT5+uRt64efOmHDlyRHr06CFmPF70J7Y/t1hWrlwpZj2/L730kupfjbGbz5w5o7qbWY/tbJZjxVCfr732mhw/fly9Hr0DunXrJkbRrl071ecdXf1wTfbp0+ee7+nQoYPqZ4//22PHjqnfLl/SzLTExMRoN2/e1J588kmtbt262scff6xduXJFK1WqlMv3Va5cWTt79qz2008/aUuWLDHt8Q4ZMkS7du2aFhUVZVlKly5tymPNnz+/tn37dm3lypVa69at1Tlu37691qhRI1Meb0REhM15rVevnnb79m11zs14vI8++qiWkZGh1ji3Xbp00RISErQpU6aY7ljfffdd7dy5c1qPHj20qlWraiNGjNBu3LihNW7c2OfHIm4s3bt31958802tb9++GvTp08fl66tUqaKlp6dr//73v7U6depozz//vLqWu3bt6qtj8H0ienLZunWrNm3aNMvjoKAgdYG9/PLLTt+TL18+bdOmTdrQoUO1+fPnGypQZvd48aN59epVn++3N4712Wef1Y4fP66FhIQExPHaLy+99JKWkpKiFS5c2JTHi9euW7fOZht+WDdu3Gi6Y8UNwMiRI222ff3119pnn33m82ORbC7uBErcGOzfv99m26JFi7Rvv/3WJ/tsqqJXfdouTNOV02m7AuF4Mbj8qVOnVFHV0qVLpV69emLGY33wwQfVUIgfffSRGjt4//79ajYZfXxgM55ba5hl58svv1TFkmY83i1btqj36EWWGAe6Z8+esmrVKjHbsaI4GUWQ1lAE27ZtWzGj6Ohom/SB1atXu33te5r//2J4aNoujPrjatouzEgSCMeLOjpMio06gscff1wFDfzgYDYVsx1rtWrV5OGHH5bg4GD1A4pp1caMGSOvvvqq+LucHK81BI+GDRvKnDlzxAhycryLFi1SN7mbNm1SEx9gQoQff/zRZn5bsxwrggQmua9Ro4YaD7tz587y0EMPSdmyZcWMypQp4zB90OagYMGCXt8fUwVKb0zbZXRoNIBj3rt3r2zYsEH9s126dEmeffZZMRvcBKCkYPjw4bJr1y41XOLbb78tI0aMELPDzd++fftkx44dYlZo7IExoNEopmnTptKvXz/p1auXIW6EsguNltCgBQ2XcFOABmoYD9uTw3uSH4/1arRpu4x8vI7g/bt371Z3qv4sJ8eamJiozqP1j8mhQ4fUXTiKv/CcGc9t4cKF5ZFHHlG5LaPIyfGihAA3fXPnzlWPDxw4oCZU+OSTT9QNEYozzXKseA9uBFAEW7JkSdWK+9133/Xr36fcQDo4Sp+UlJQsRdDeYKocZaBN25Xd43UENwYookNQMduxbt682VJUpatVq5b6kfHnIJnbcztgwAD1g/r555+LUeTkeHFDYJ+jyszMtLzXjOcWg6Xj+kXRbf/+/WXZsmViRvHx8TbpA126dHH7dy0vaGZa0OwaTcYHDx6smhXPmjVLNbvWu0DExsZq77zzjtP3G63Va3aP97XXXlPN6NHEvEmTJtrChQtVM3M0UTfbsVaoUEG1+vzwww+1mjVraj179tQuXLigjRs3zufHkhfHqy8bNmxQLQR9vf95fbxvvPGGOr8DBw5U3Qk6d+6sHTt2TPvyyy9Nd6wtWrTQ+vXrp/5v27Ztq1r7/vrrr1qxYsV8fizixlKkSBHtvvvuUwuMGjVK/V2xYkX1PI4Vx2zfPWTy5Mla7dq1teeee47dQzx9UtDn5tSpU6qfEpph4yLTn1u/fr0KhmYJlNk93qlTp1pem5iYqPoYGqUvVk7ObatWrbT4+Hj1o4SuImPHjlXdgcx6vLVq1VI/RAgavt73vD7e4OBg7fXXX1fBETd7p0+f1qZPn26Y4JGdY0X/34MHD6rr+NKlSyqolC1b1ufHIG4uHTp00BzRjxFrHLP9e3bt2qXSB/+7vuwPzGm2iIiIAqWOkoiIyNMYKImIiFxgoCQiInKBgZKIiMgFBkoiIiIXGCiJiIhcYKAkIiJygYGSiIjIBQZKIrKBsY7ff/99t1NlyJAhcvXqVaYimRYDJRERkQsMlERERC4wUBIZqEj0ww8/VMWiV65cUXP2DRs2TE03NW/ePElNTVWT+3bv3t3ynvbt28u2bdvUHH6YnmnSpEkSHBxseR7vjY2NlbS0NPX86NGjs3xvaGiovPfee3Lu3DlJT09Xk39j0mSiQMFASWQgqA/EJL4tWrSQadOmycyZMyUuLk62bNkiTZs2lTVr1qjJjAsVKiTlypWTVatWyY4dO+S+++6T5557Tp5++ml59dVXLZ+HAIig16dPH+natas88MAD6nOsTZ8+XaKjo9Vk0I0aNVLf99133/n9ZN9EnuTzKVi4MA14Ddz7GsA0RJhrUn+M6cLS0tJs5vGLiopS0xe1bNlSe+utt7RDhw7ZfAbm9UtNTdWCgoLUHIGYwujhhx+2PB8REaFdv35de//999VjzBd4+/btLFM6rV27Vnv77bfV35j+6OrVqzyH/D/WzJoGIR4NuUSUp/bt22f5++7du3L58mXZv3+/ZdvFixfVunTp0lK3bt0sM8Jv3rxZwsLCpEKFChIRESEFChRQRbM6tF49cuSI5XHDhg0lJCREjh49avM5eB++mygQMFASGcjt27dtHmualmUb5MvnmVqVokWLyp07d6RZs2aSmZlp8xzqK4kCAQMlkUkdOnRI+vfvb7OtTZs2qtEPGuagQdCtW7ekZcuWcvbsWfV88eLFpVatWvLTTz+px7t371Y5SuRQN23a5JPjIPI1NuYhMqkZM2ZIxYoVVaOf2rVry4MPPigTJkyQqVOnqpzo9evXZe7cuapBT8eOHaV+/fqyYMECVaSrQyvazz//XD799FPp16+fVKlSRZo3by6vvPKK9OzZ06fHR+QtzFESmRS6eyCYIRDu3btX5SARGN966y3La/7+97+r4tUVK1aoLiJTpkyRYsWK2XzOU089pVrK4rny5curVrfoIrJy5UofHBWR9wX90aqHiIiIHGDRKxERkQsMlERERC4wUBIREbnAQElEROQCAyUREZELDJREREQuMFASERG5wEBJRETkAgMlERGRCwyURERELjBQEhERiXP/H60aEQaNB6OdAAAAAElFTkSuQmCC" 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" }, "metadata": {}, "output_type": "display_data", @@ -294,15 +294,20 @@ } } ], - "execution_count": 14 + "execution_count": 9 }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-27T00:43:32.986019Z", + "start_time": "2026-03-27T00:43:32.972769Z" + } + }, "cell_type": "code", "source": "", "id": "87b4757fc69e04ad", "outputs": [], - "execution_count": null + "execution_count": 9 } ], "metadata": { diff --git a/hobj/benchmarks/common.py b/hobj/benchmarks/common.py new file mode 100644 index 0000000..d271d23 --- /dev/null +++ b/hobj/benchmarks/common.py @@ -0,0 +1,116 @@ +from dataclasses import dataclass +from typing import Any, Protocol, TypeVar + +import numpy as np +import xarray as xr + +from hobj.learning_models import BinaryLearningModel +from hobj.stats.ci import estimate_basic_bootstrap_CI + +ResultT = TypeVar("ResultT") + + +class SessionSubtask(Protocol[ResultT]): + """Protocol for benchmark subtasks that can simulate one learner session.""" + + def simulate_session( + self, + learner: BinaryLearningModel, + seed: int | None, + ) -> ResultT: + """Simulate a single session of learner behavior.""" + + +@dataclass(frozen=True) +class BootstrapSummary: + """Summary of a bootstrap distribution for a scalar benchmark score. + + Attributes: + sigma: Sample standard deviation of the bootstrap distribution. + ci95: Basic bootstrap 95% confidence interval. + """ + + sigma: float + ci95: tuple[float, float] + + +def simulate_sessions( + subtask: SessionSubtask[ResultT], + learner: BinaryLearningModel, + nsimulations: int, +) -> list[ResultT]: + """Simulate repeated learner sessions for a single benchmark subtask. + + Args: + subtask: Benchmark subtask to simulate. + learner: Learning model to evaluate. + nsimulations: Number of independent sessions to run. + + Returns: + A list of session results in simulation order. + """ + + results = [] + for _ in range(nsimulations): + learner.reset_state(seed=None) + results.append( + subtask.simulate_session( + learner=learner, + seed=None, + ) + ) + + return results + + +def compare_msen( + model_phat: xr.DataArray, + model_varhat_phat: xr.DataArray, + target_phat: xr.DataArray, + target_varhat_phat: xr.DataArray, + condition_dims: tuple[str, ...], +) -> xr.DataArray: + """Compute the variance-corrected mean squared error normalized over dims. + + Args: + model_phat: Mean model performance estimate. + model_varhat_phat: Estimated variance of the model mean. + target_phat: Mean target performance estimate. + target_varhat_phat: Estimated variance of the target mean. + condition_dims: Dimensions across which to average the squared error. + + Returns: + The normalized mean squared error with variance penalties removed. + """ + + msen = ( + np.square(model_phat - target_phat).mean(condition_dims) + - model_varhat_phat.mean(condition_dims) + - target_varhat_phat.mean(condition_dims) + ) + return msen + + +def summarize_bootstrap_score( + point_estimate: Any, + bootstrapped_point_estimates: Any, +) -> BootstrapSummary: + """Summarize a bootstrap distribution for a benchmark score. + + Args: + point_estimate: Point estimate corresponding to the bootstrap samples. + bootstrapped_point_estimates: Bootstrap replicates of the score. + + Returns: + The bootstrap standard deviation and 95% confidence interval. + """ + + boot_array = np.asarray(bootstrapped_point_estimates) + return BootstrapSummary( + sigma=float(np.std(boot_array, ddof=1)), + ci95=estimate_basic_bootstrap_CI( + alpha=0.05, + point_estimate=point_estimate, + bootstrapped_point_estimates=boot_array, + ), + ) diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index a484721..e291517 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -1,11 +1,16 @@ from dataclasses import dataclass from pathlib import Path -from typing import Dict, List, Optional, Tuple, Union +from typing import Dict, List, Tuple import numpy as np import xarray as xr from tqdm import tqdm +from hobj.benchmarks.common import ( + compare_msen, + simulate_sessions, + summarize_bootstrap_score, +) from hobj.benchmarks.binary_classification.estimator import LearningCurveStatistics from hobj.benchmarks.binary_classification.simulation import ( BinaryClassificationSubtask, @@ -14,7 +19,6 @@ from hobj.data.behavior import load_highvar_behavior from hobj.data.images import load_imageset_meta_highvar from hobj.learning_models import BinaryLearningModel -from hobj.stats.ci import estimate_basic_bootstrap_CI class MutatorHighVarBenchmark: @@ -139,7 +143,7 @@ def __call__( desc="Subtask simulations:", disable=not show_pbar, ): - subtask_name_to_model_results[subtask_name] = self.simulate_model_behavior( + subtask_name_to_model_results[subtask_name] = simulate_sessions( subtask=self.subtask_name_to_subtask[subtask_name], learner=learner, nsimulations=self.num_simulations_per_subtask, @@ -169,39 +173,19 @@ def __call__( fit_lapse_rate=True, ) - msen_sigma = np.std(msen_boot, ddof=1) - msen_CI95 = estimate_basic_bootstrap_CI( - alpha=0.05, + summary = summarize_bootstrap_score( point_estimate=msen_point, - bootstrapped_point_estimates=np.array(msen_boot), + bootstrapped_point_estimates=msen_boot, ) return self.LearningCurveBenchmarkResult( msen=float(msen_point), - msen_sigma=float(msen_sigma), - msen_CI95=msen_CI95, + msen_sigma=summary.sigma, + msen_CI95=summary.ci95, lapse_rate=lapse_rate, model_statistics=model_statistics, ) - @staticmethod - def simulate_model_behavior( - subtask: BinaryClassificationSubtask, - learner: BinaryLearningModel, - nsimulations: int, - ) -> List[BinaryClassificationSubtaskResult]: - results = [] - for _ in range(nsimulations): - learner.reset_state(seed=None) - results.append( - subtask.simulate_session( - learner=learner, - seed=None, - ) - ) - - return results - @classmethod def _compare_learning_curves( cls, @@ -211,7 +195,7 @@ def _compare_learning_curves( target_varhat_phat: xr.DataArray, condition_dims: Tuple[str, ...], fit_lapse_rate: bool, - ) -> Tuple[Union[np.ndarray, np.generic], Optional[xr.DataArray]]: + ) -> Tuple[xr.DataArray, xr.DataArray | None]: if fit_lapse_rate: lapse_rate = cls._fit_lapse_rate( pmodel=model_phat, @@ -223,10 +207,12 @@ def _compare_learning_curves( else: lapse_rate = None - msen = ( - np.square(model_phat - target_phat).mean(condition_dims) - - model_varhat_phat.mean(condition_dims) - - target_varhat_phat.mean(condition_dims) + msen = compare_msen( + model_phat=model_phat, + model_varhat_phat=model_varhat_phat, + target_phat=target_phat, + target_varhat_phat=target_varhat_phat, + condition_dims=condition_dims, ) return msen, lapse_rate @@ -235,7 +221,7 @@ def _fit_lapse_rate( pmodel: xr.DataArray, ptarget: xr.DataArray, condition_dims: Tuple[str, ...], - ) -> Union[np.ndarray, np.generic]: + ) -> xr.DataArray: nway = 2 numerator = -( 2 * pmodel / nway diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index eaaaaba..093ef77 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -3,10 +3,14 @@ from pathlib import Path from typing import Dict, List, Tuple -import numpy as np import xarray as xr from tqdm import tqdm +from hobj.benchmarks.common import ( + compare_msen, + simulate_sessions, + summarize_bootstrap_score, +) from hobj.benchmarks.generalization.estimator import GeneralizationStatistics from hobj.benchmarks.generalization.simulator import ( GeneralizationSessionResult, @@ -15,7 +19,6 @@ from hobj.data.behavior import load_oneshot_behavior from hobj.data.images import load_imageset_meta_oneshot from hobj.learning_models import BinaryLearningModel -from hobj.stats.ci import estimate_basic_bootstrap_CI from hobj.types import ImageId @@ -250,7 +253,7 @@ def __call__( disable=not show_pbar, ): results.extend( - self.simulate_model_behavior( + simulate_sessions( subtask=subtask, learner=learner, nsimulations=self.num_simulations_per_subtask, @@ -282,38 +285,18 @@ def __call__( condition_dims=("transformation",), ) - msen_sigma = np.std(msen_boot, ddof=1) - msen_CI95 = estimate_basic_bootstrap_CI( - alpha=0.05, + summary = summarize_bootstrap_score( point_estimate=msen_point, - bootstrapped_point_estimates=np.array(msen_boot), + bootstrapped_point_estimates=msen_boot, ) return self.GeneralizationBenchmarkResult( msen=float(msen_point), - msen_sigma=float(msen_sigma), - msen_CI95=msen_CI95, + msen_sigma=summary.sigma, + msen_CI95=summary.ci95, model_statistics=model_statistics, ) - @staticmethod - def simulate_model_behavior( - subtask: GeneralizationSubtask, - learner: BinaryLearningModel, - nsimulations: int, - ) -> List[GeneralizationSessionResult]: - results = [] - for _ in range(nsimulations): - learner.reset_state(seed=None) - results.append( - subtask.simulate_session( - learner=learner, - seed=None, - ) - ) - - return results - @staticmethod def _compare_generalization_patterns( model_phat: xr.DataArray, @@ -322,10 +305,12 @@ def _compare_generalization_patterns( target_varhat_phat: xr.DataArray, condition_dims: Tuple[str, ...], ) -> xr.DataArray: - msen = ( - np.square(model_phat - target_phat).mean(condition_dims) - - model_varhat_phat.mean(condition_dims) - - target_varhat_phat.mean(condition_dims) + msen = compare_msen( + model_phat=model_phat, + model_varhat_phat=model_varhat_phat, + target_phat=target_phat, + target_varhat_phat=target_varhat_phat, + condition_dims=condition_dims, ) return msen From 4cb2357d2bc74f0c5e8dec8b774cdcb45a46c183 Mon Sep 17 00:00:00 2001 From: himjl <mil@mit.edu> Date: Thu, 26 Mar 2026 21:15:28 -0400 Subject: [PATCH 144/156] readme update --- README.md | 62 ++- examples/score_example_models.ipynb | 87 ++-- .../binary_classification/estimator.py | 375 +++++++++--------- hobj/benchmarks/generalization/estimator.py | 260 ++++++------ hobj/benchmarks/mut_highvar_benchmark.py | 14 +- hobj/benchmarks/mut_oneshot_benchmark.py | 14 +- pyproject.toml | 7 +- tests/test_data_loader_cache.py | 216 ++++++++++ uv.lock | 8 +- 9 files changed, 630 insertions(+), 413 deletions(-) create mode 100644 tests/test_data_loader_cache.py diff --git a/README.md b/README.md index e1bfe64..d59e235 100644 --- a/README.md +++ b/README.md @@ -13,42 +13,56 @@ It also lets you download the raw data and images from the experiments in the pa </div> -### Example usage +### Installation -Benchmarking a learning model simply requires the user to define a `hobj.learning_models.BinaryLearningModel` subclass. -Then, an instance of that subclass may be passed in as an argument to a benchmark object: +The `hobj` package works for Python >=3.12. After cloning this repository on your machine, navigate to this directory in your shell and run: -```python -import hobj +``` pip install -e .``` + +### Quickstart: comparing a linear learner against human learning data + +The template script below shows you how you can run a benchmark on a linear learning model based on your image encoding model. -# Load model: -model = hobj.RandomGuesser() +All you need to do is have a way to process a `PIL.Image` into a vector of image features (as an `np.ndarray`). There are ~18,000 images that you'd need to compute image features for. -# Load benchmark: +```python + +# Import hobj +import hobj +import numpy as np + +# Compute your features for the images +my_image_features: dict[str, np.ndarray] = {} +for image_id in hobj.list_image_ids(): + image = hobj.load_image(image_id=image_id) # PIL.Image + my_image_features[image_id] = ... # Compute your features here! It should be a d-length vector. + +# Assemble the learning model: +model = hobj.create_linear_learner( + image_id_to_features=my_image_features, + update_rule_name='Square', # "Square", "Perceptron", "Hinge", "MAE", "Exponential", "CE", "REINFORCE", + alpha=1, # learning rate between [0, 1] +) + +# Load the benchmark: benchmark = hobj.MutatorHighVarBenchmark() # or hobj.MutatorOneshotBenchmark() -result = benchmark(model) -# Print the score and its CI: +# Score the model: +result = benchmark.score_model(model) + +# Print its score and its CI: print(result.msen, result.msen_CI95) + +# You can also check out more granular statistics of the model's behavior, like its learning curves: +# print(result.model_statistics) ``` -On first use, the packaged dataset is downloaded automatically into `./data`. +Note that on first use, the packaged dataset is downloaded automatically into `./data`. To use a different location, pass `cachedir=...` to a data loader or benchmark constructor, or prefetch manually with `hobj-download-data --cachedir /path/to/data`. For more details, check out `examples/`. -### Installation - -The `hobj` package works for Python >=3.11. After cloning this repository on your machine, navigate to this directory in your shell and run: - -``` pip install -e .``` - - -### Changes to codebase since publication -This codebase was refactored in 2026 to improve the accessibility, performance, and quality of the code. Along the way, minor changes to the statistical analysis of the original codebase were introduced (see [changelist](site/changelist.md)). - -To see the codebase at the time of publication, check out the repo with the `v1` tag [here](https://github.com/himjl/hobj/releases/tag/v1). ### Citation @@ -64,3 +78,7 @@ To see the codebase at the time of publication, check out the repo with the `v1` publisher={Public Library of Science San Francisco, CA USA} } ``` + + +### Changes to codebase since publication +This codebase was refactored in 2026 to improve the accessibility, performance, and quality of the code. Along the way, minor changes to the statistical analysis of the original codebase were introduced (see [changelist](site/changelist.md)). To see the codebase at the time of publication, check out the repo with the `v1` tag [here](https://github.com/himjl/hobj/releases/tag/v1). diff --git a/examples/score_example_models.ipynb b/examples/score_example_models.ipynb index 68326ef..a3f3b84 100644 --- a/examples/score_example_models.ipynb +++ b/examples/score_example_models.ipynb @@ -3,8 +3,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:43:13.750032Z", - "start_time": "2026-03-27T00:43:13.298543Z" + "end_time": "2026-03-27T00:59:18.817707Z", + "start_time": "2026-03-27T00:59:18.512737Z" } }, "cell_type": "code", @@ -21,8 +21,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:43:17.056353Z", - "start_time": "2026-03-27T00:43:13.750969Z" + "end_time": "2026-03-27T00:59:22.589878Z", + "start_time": "2026-03-27T00:59:18.823770Z" } }, "cell_type": "code", @@ -40,14 +40,14 @@ { "metadata": {}, "cell_type": "markdown", - "source": "# Benchmark a learning model", - "id": "220ecf6a69d69874" + "source": "## Load model", + "id": "82e3c177a4b43961" }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:43:17.103247Z", - "start_time": "2026-03-27T00:43:17.090474Z" + "end_time": "2026-03-27T00:59:22.636794Z", + "start_time": "2026-03-27T00:59:22.620737Z" } }, "cell_type": "code", @@ -65,8 +65,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:43:17.141612Z", - "start_time": "2026-03-27T00:43:17.104563Z" + "end_time": "2026-03-27T00:59:22.681025Z", + "start_time": "2026-03-27T00:59:22.638912Z" } }, "cell_type": "code", @@ -87,14 +87,14 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:43:29.726469Z", - "start_time": "2026-03-27T00:43:17.142838Z" + "end_time": "2026-03-27T00:59:35.267760Z", + "start_time": "2026-03-27T00:59:22.682833Z" } }, "cell_type": "code", "source": [ "# Benchmark on Experiment 1\n", - "result1 = benchmark1(learner=learner, show_pbar=True)\n", + "result1 = benchmark1.score_model(learner=learner, show_pbar=True)\n", "result1" ], "id": "a5710bce79c8e484", @@ -103,23 +103,23 @@ "name": "stderr", "output_type": "stream", "text": [ - "Subtask simulations:: 100%|██████████| 64/64 [00:10<00:00, 6.33it/s]\n" + "Subtask simulations:: 100%|██████████| 64/64 [00:10<00:00, 6.28it/s]\n" ] }, { "data": { "text/plain": [ - "MutatorHighVarBenchmark.LearningCurveBenchmarkResult(msen=0.00553912312537474, msen_sigma=0.0002812296484686281, msen_CI95=(0.004981950628377824, 0.006079281342646169), lapse_rate=<xarray.DataArray 'phat' ()> Size: 8B\n", - "array(0.07834207), model_statistics=<xarray.LearningCurveStatistics> Size: 103MB\n", + "MutatorHighVarBenchmark.LearningCurveBenchmarkResult(msen=0.005581336095785859, msen_sigma=0.00028932598735009263, msen_CI95=(0.005015524275339106, 0.0061321115686366525), lapse_rate=<xarray.DataArray 'phat' ()> Size: 8B\n", + "array(0.07933472), model_statistics=<xarray.Dataset> Size: 103MB\n", "Dimensions: (subtask: 64, trial: 100, boot_iter: 1000)\n", "Coordinates:\n", " * subtask (subtask) <U33 8kB 'MutatorObject000,MutatorObject095' ...\n", "Dimensions without coordinates: trial, boot_iter\n", "Data variables:\n", - " phat (subtask, trial) float64 51kB 0.484 0.668 ... 0.918 0.9\n", - " varhat_phat (subtask, trial) float64 51kB 0.0005005 ... 0.0001804\n", - " boot_phat (boot_iter, subtask, trial) float64 51MB 0.484 ... 0.908\n", - " boot_varhat_phat (boot_iter, subtask, trial) float64 51MB 0.0005005 ... ...)" + " phat (subtask, trial) float64 51kB 0.538 0.684 ... 0.904 0.916\n", + " varhat_phat (subtask, trial) float64 51kB 0.0004981 ... 0.0001542\n", + " boot_phat (boot_iter, subtask, trial) float64 51MB 0.512 ... 0.92\n", + " boot_varhat_phat (boot_iter, subtask, trial) float64 51MB 0.0005007 ... ...)" ] }, "execution_count": 5, @@ -132,8 +132,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:43:29.849327Z", - "start_time": "2026-03-27T00:43:29.742099Z" + "end_time": "2026-03-27T00:59:35.374159Z", + "start_time": "2026-03-27T00:59:35.276935Z" } }, "cell_type": "code", @@ -163,7 +163,7 @@ "text/plain": [ "<Figure size 640x480 with 1 Axes>" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -177,8 +177,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:43:29.913580Z", - "start_time": "2026-03-27T00:43:29.850095Z" + "end_time": "2026-03-27T00:59:35.452286Z", + "start_time": "2026-03-27T00:59:35.384724Z" } }, "cell_type": "code", @@ -206,7 +206,7 @@ "text/plain": [ "<Figure size 500x500 with 1 Axes>" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -220,13 +220,13 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:43:32.925132Z", - "start_time": "2026-03-27T00:43:29.914039Z" + "end_time": "2026-03-27T00:59:38.804077Z", + "start_time": "2026-03-27T00:59:35.459144Z" } }, "cell_type": "code", "source": [ - "result2 = benchmark2(learner = learner)\n", + "result2 = benchmark2.score_model(learner = learner)\n", "result2" ], "id": "545e5142766c2141", @@ -234,7 +234,7 @@ { "data": { "text/plain": [ - "MutatorOneshotBenchmark.GeneralizationBenchmarkResult(msen=0.019165030086165914, msen_sigma=0.0025134823168529168, msen_CI95=(0.014122805740139854, 0.02356936780519513), model_statistics=<xarray.GeneralizationStatistics> Size: 583kB\n", + "MutatorOneshotBenchmark.GeneralizationBenchmarkResult(msen=0.018127572924906018, msen_sigma=0.002420319066024304, msen_CI95=(0.013090980755670358, 0.022419578374065857), model_statistics=<xarray.Dataset> Size: 583kB\n", "Dimensions: (transformation: 36, boot_iter: 1000)\n", "Coordinates:\n", " * transformation (transformation) <U26 4kB 'backgrounds | 0.1' ... '...\n", @@ -242,10 +242,10 @@ " transformation_level (transformation) float64 288B 0.1 0.2154 ... 0.5 1.5\n", "Dimensions without coordinates: boot_iter\n", "Data variables:\n", - " phat (transformation) float64 288B 0.9723 0.977 ... 0.9501\n", - " varhat_phat (transformation) float64 288B 6.213e-06 ... 2.211e-05\n", - " boot_phat (boot_iter, transformation) float64 288kB 0.9699 .....\n", - " boot_varhat_phat (boot_iter, transformation) float64 288kB 6.707e-06...)" + " phat (transformation) float64 288B 0.9745 0.9752 ... 0.9432\n", + " varhat_phat (transformation) float64 288B 5.767e-06 ... 2.539e-05\n", + " boot_phat (boot_iter, transformation) float64 288kB 0.9774 .....\n", + " boot_varhat_phat (boot_iter, transformation) float64 288kB 5.235e-06...)" ] }, "execution_count": 8, @@ -258,8 +258,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:43:32.971254Z", - "start_time": "2026-03-27T00:43:32.933381Z" + "end_time": "2026-03-27T00:59:38.855329Z", + "start_time": "2026-03-27T00:59:38.811594Z" } }, "cell_type": "code", @@ -285,7 +285,7 @@ "text/plain": [ "<Figure size 500x500 with 1 Axes>" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -295,19 +295,6 @@ } ], "execution_count": 9 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2026-03-27T00:43:32.986019Z", - "start_time": "2026-03-27T00:43:32.972769Z" - } - }, - "cell_type": "code", - "source": "", - "id": "87b4757fc69e04ad", - "outputs": [], - "execution_count": 9 } ], "metadata": { diff --git a/hobj/benchmarks/binary_classification/estimator.py b/hobj/benchmarks/binary_classification/estimator.py index 5419532..13c0267 100644 --- a/hobj/benchmarks/binary_classification/estimator.py +++ b/hobj/benchmarks/binary_classification/estimator.py @@ -10,207 +10,202 @@ from hobj.stats import binomial as binomial_funcs -# %% -class LearningCurveStatistics(xr.Dataset): - __slots__ = () - - def __init__( - self, - subtask_name_to_results: Dict[str, List[BinaryClassificationSubtaskResult]], - nbootstrap_samples: int, - bootstrap_by_worker: bool, # versus by session - ): - # Get canonical order of subtask names - subtask_names = sorted(subtask_name_to_results.keys()) - subtask_name_to_i = {name: i for i, name in enumerate(subtask_names)} - nsubtasks = len(subtask_names) - - # Ensure all subtasks have the same number of trials - ntrials_observed = set() - all_workers = set() - nsessions = 0 - for subtask, results in subtask_name_to_results.items(): - nsessions += len(results) - for r in results: - ntrials_observed.add(len(r.perf_seq)) - all_workers.add(r.worker_id) - - if not len(ntrials_observed) == 1: - raise ValueError( - f"Expected all subtasks to have the same number of trials, but got {ntrials_observed}" - ) - ntrials = ntrials_observed.pop() - all_workers = sorted(all_workers) - worker_to_i = {worker: i for i, worker in enumerate(all_workers)} - - # Reshape data into a [session, trial] matrix and associated [session] meta vectors - i_subtasks = [] - i_workers = [] - i_session = 0 - perf_mat = np.zeros((nsessions, ntrials), dtype=np.bool) - for subtask, results in subtask_name_to_results.items(): - i_subtask = subtask_name_to_i[subtask] - for result in results: - i_worker = worker_to_i[result.worker_id] - perf_mat[i_session] = result.perf_seq - i_session += 1 - i_subtasks.append(i_subtask) - i_workers.append(i_worker) - - # Collate data into [subtask, trial] matrices - k_mat = np.zeros(shape=(nsubtasks, ntrials), dtype=int) - n_mat = np.zeros(shape=nsubtasks, dtype=int) - - for i_subtask, perf in zip(i_subtasks, perf_mat): - k_mat[i_subtask] += perf - n_mat[i_subtask] += 1 - - # Get point estimate of the [subtask, trial] of learning curve first and second moments - phat, varhat_phat = self.get_point_estimates(k=k_mat, n=n_mat[:, None]) - - # Perform bootstrap resampling of data - if bootstrap_by_worker: - bootstrap_resamples = self._get_bootstrap_resamples_by_worker( - perf_mat=perf_mat, - i_subtasks=i_subtasks, - i_workers=i_workers, - nbootstrap_samples=nbootstrap_samples, - ) - else: - bootstrap_resamples = self._get_bootstrap_resamples_by_session( - perf_mat=perf_mat, - i_subtasks=i_subtasks, - nbootstrap_samples=nbootstrap_samples, - ) - - # Calculate bootstrapped point estimates - boot_phat, boot_varhat_phat = self.get_point_estimates( - k=bootstrap_resamples.boot_k, # [boot_iter, subtask, trial] - n=bootstrap_resamples.boot_n[..., None], # [boot_iter, subtask] +@dataclass(frozen=True) +class _BootstrapSamples: + """Bootstrap resamples for binary-classification benchmark statistics.""" + + boot_k: np.ndarray + boot_n: np.ndarray + + +def build_learning_curve_statistics( + subtask_name_to_results: Dict[str, List[BinaryClassificationSubtaskResult]], + nbootstrap_samples: int, + bootstrap_by_worker: bool, +) -> xr.Dataset: + """Build learning-curve summary statistics as a plain xarray dataset. + + Args: + subtask_name_to_results: Mapping from subtask name to session results. + nbootstrap_samples: Number of bootstrap replicates to generate. + bootstrap_by_worker: Whether to resample workers instead of sessions. + + Returns: + A dataset containing point estimates and bootstrap replicates. + """ + + subtask_names = sorted(subtask_name_to_results.keys()) + subtask_name_to_i = {name: i for i, name in enumerate(subtask_names)} + nsubtasks = len(subtask_names) + + ntrials_observed = set() + all_workers = set() + nsessions = 0 + for results in subtask_name_to_results.values(): + nsessions += len(results) + for result in results: + ntrials_observed.add(len(result.perf_seq)) + all_workers.add(result.worker_id) + + if len(ntrials_observed) != 1: + raise ValueError( + f"Expected all subtasks to have the same number of trials, but got {ntrials_observed}" ) - super().__init__( - data_vars=dict( - phat=(["subtask", "trial"], phat), - varhat_phat=(["subtask", "trial"], varhat_phat), - boot_phat=(["boot_iter", "subtask", "trial"], boot_phat), - boot_varhat_phat=(["boot_iter", "subtask", "trial"], boot_varhat_phat), - ), - coords=dict( - subtask=subtask_names, - ), + ntrials = ntrials_observed.pop() + all_workers = sorted(all_workers) + worker_to_i = {worker: i for i, worker in enumerate(all_workers)} + + i_subtasks: List[int] = [] + i_workers: List[int] = [] + i_session = 0 + perf_mat = np.zeros((nsessions, ntrials), dtype=bool) + for subtask_name, results in subtask_name_to_results.items(): + i_subtask = subtask_name_to_i[subtask_name] + for result in results: + i_worker = worker_to_i[result.worker_id] + perf_mat[i_session] = result.perf_seq + i_session += 1 + i_subtasks.append(i_subtask) + i_workers.append(i_worker) + + k_mat = np.zeros(shape=(nsubtasks, ntrials), dtype=int) + n_mat = np.zeros(shape=nsubtasks, dtype=int) + for i_subtask, perf in zip(i_subtasks, perf_mat): + k_mat[i_subtask] += perf + n_mat[i_subtask] += 1 + + phat, varhat_phat = _get_point_estimates(k=k_mat, n=n_mat[:, None]) + + if bootstrap_by_worker: + bootstrap_resamples = _get_bootstrap_resamples_by_worker( + perf_mat=perf_mat, + i_subtasks=i_subtasks, + i_workers=i_workers, + nbootstrap_samples=nbootstrap_samples, ) - - @staticmethod - def get_point_estimates( - k: np.ndarray, - n: np.ndarray, - ) -> Tuple[np.ndarray, np.ndarray]: - phat = k / n - varhat_phat = binomial_funcs.estimate_variance_of_binomial_proportion( - kvec=k, nvec=n + else: + bootstrap_resamples = _get_bootstrap_resamples_by_session( + perf_mat=perf_mat, + i_subtasks=i_subtasks, + nbootstrap_samples=nbootstrap_samples, ) - return phat, varhat_phat - - @dataclass - class BootstrapSamples: - boot_k: np.ndarray # [boot_iter, subtask, trial] - boot_n: np.ndarray # [boot_iter, subtask] - - @staticmethod - def _get_bootstrap_resamples_by_worker( - perf_mat: np.ndarray, # [session, trial] - i_subtasks: List[int], # [session] - i_workers: List[int], # [session] - nbootstrap_samples: int, - ) -> BootstrapSamples: - if not perf_mat.dtype == np.bool: - raise ValueError( - f"Expected perf_mat to have dtype bool, but got {perf_mat.dtype}" - ) - ntrials = perf_mat.shape[1] - nworkers = max(i_workers) + 1 - nsubtasks = max(i_subtasks) + 1 - - if not len(set(i_subtasks)) == nsubtasks: - raise ValueError( - f"Expected i_subtasks to be a contiguous range, but got {set(i_subtasks)}" - ) + boot_phat, boot_varhat_phat = _get_point_estimates( + k=bootstrap_resamples.boot_k, + n=bootstrap_resamples.boot_n[..., None], + ) + + return xr.Dataset( + data_vars=dict( + phat=(["subtask", "trial"], phat), + varhat_phat=(["subtask", "trial"], varhat_phat), + boot_phat=(["boot_iter", "subtask", "trial"], boot_phat), + boot_varhat_phat=(["boot_iter", "subtask", "trial"], boot_varhat_phat), + ), + coords=dict(subtask=subtask_names), + ) + + +def _get_point_estimates( + k: np.ndarray, + n: np.ndarray, +) -> Tuple[np.ndarray, np.ndarray]: + """Estimate binomial means and variances from count matrices.""" + + phat = k / n + varhat_phat = binomial_funcs.estimate_variance_of_binomial_proportion( + kvec=k, nvec=n + ) + return phat, varhat_phat + + +def _get_bootstrap_resamples_by_worker( + perf_mat: np.ndarray, + i_subtasks: List[int], + i_workers: List[int], + nbootstrap_samples: int, +) -> _BootstrapSamples: + """Bootstrap binary-classification sessions by worker.""" + + if perf_mat.dtype != np.dtype(bool): + raise ValueError( + f"Expected perf_mat to have dtype bool, but got {perf_mat.dtype}" + ) - if not len(set(i_workers)) == nworkers: - raise ValueError( - f"Expected i_workers to be a contiguous range, but got {set(i_workers)}" - ) + ntrials = perf_mat.shape[1] + nworkers = max(i_workers) + 1 + nsubtasks = max(i_subtasks) + 1 - # Reshape data - perf_table = np.zeros(shape=(nworkers, nsubtasks, ntrials), dtype=np.bool) - was_recorded_table = np.zeros(shape=(nworkers, nsubtasks), dtype=np.bool) - - for i_subtask, i_worker, perf in zip(i_subtasks, i_workers, perf_mat): - perf_table[i_worker, i_subtask] = perf - was_recorded_table[i_worker, i_subtask] = True - - # Perform bootstrapping - gen = np.random.default_rng() - boot_k = np.zeros(shape=(nbootstrap_samples, nsubtasks, ntrials), dtype=int) - boot_n = np.zeros(shape=(nbootstrap_samples, nsubtasks), dtype=int) - for i_bootstrap in range(nbootstrap_samples): - # Bootstrap resample workers - i_workers_boot = gen.integers(low=0, high=nworkers, size=nworkers) - boot_k[i_bootstrap] = perf_table[i_workers_boot].sum(axis=0) - boot_n[i_bootstrap] = was_recorded_table[i_workers_boot].sum(axis=0) - - return LearningCurveStatistics.BootstrapSamples( - boot_k=boot_k, - boot_n=boot_n, + if len(set(i_subtasks)) != nsubtasks: + raise ValueError( + f"Expected i_subtasks to be a contiguous range, but got {set(i_subtasks)}" ) - @staticmethod - def _get_bootstrap_resamples_by_session( - perf_mat: np.ndarray, # [session, trial] - i_subtasks: List[int], # [session] - nbootstrap_samples: int, - ) -> BootstrapSamples: - # Bootstrap by session, within each subtask + if len(set(i_workers)) != nworkers: + raise ValueError( + f"Expected i_workers to be a contiguous range, but got {set(i_workers)}" + ) - ntrials = perf_mat.shape[1] - nsubtasks = max(i_subtasks) + 1 + perf_table = np.zeros(shape=(nworkers, nsubtasks, ntrials), dtype=bool) + was_recorded_table = np.zeros(shape=(nworkers, nsubtasks), dtype=bool) + for i_subtask, i_worker, perf in zip(i_subtasks, i_workers, perf_mat): + perf_table[i_worker, i_subtask] = perf + was_recorded_table[i_worker, i_subtask] = True + + gen = np.random.default_rng() + boot_k = np.zeros(shape=(nbootstrap_samples, nsubtasks, ntrials), dtype=int) + boot_n = np.zeros(shape=(nbootstrap_samples, nsubtasks), dtype=int) + for i_bootstrap in range(nbootstrap_samples): + i_workers_boot = gen.integers(low=0, high=nworkers, size=nworkers) + boot_k[i_bootstrap] = perf_table[i_workers_boot].sum(axis=0) + boot_n[i_bootstrap] = was_recorded_table[i_workers_boot].sum(axis=0) + + return _BootstrapSamples( + boot_k=boot_k, + boot_n=boot_n, + ) + + +def _get_bootstrap_resamples_by_session( + perf_mat: np.ndarray, + i_subtasks: List[int], + nbootstrap_samples: int, +) -> _BootstrapSamples: + """Bootstrap binary-classification sessions within each subtask.""" + + ntrials = perf_mat.shape[1] + nsubtasks = max(i_subtasks) + 1 + + if len(set(i_subtasks)) != nsubtasks: + raise ValueError( + f"Expected i_subtasks to be a contiguous range, but got {set(i_subtasks)}" + ) - if not len(set(i_subtasks)) == nsubtasks: - raise ValueError( - f"Expected i_subtasks to be a contiguous range, but got {set(i_subtasks)}" - ) + i_subtask_to_perf_data: Dict[int, np.ndarray] = {} + for i_subtask, perf in zip(i_subtasks, perf_mat): + if i_subtask not in i_subtask_to_perf_data: + i_subtask_to_perf_data[i_subtask] = np.empty((0, ntrials), dtype=bool) + i_subtask_to_perf_data[i_subtask] = np.vstack( + [i_subtask_to_perf_data[i_subtask], perf] + ) - # Reshape data to {i_subtask: [session, trial]} - i_subtask_to_perf_data = {} - for i_subtask, perf in zip(i_subtasks, perf_mat): - if i_subtask not in i_subtask_to_perf_data: - i_subtask_to_perf_data[i_subtask] = [] - i_subtask_to_perf_data[i_subtask].append(perf) - - # Cast to np arrays - for i_subtask in i_subtask_to_perf_data: - i_subtask_to_perf_data[i_subtask] = np.array( - i_subtask_to_perf_data[i_subtask], dtype=np.bool + gen = np.random.default_rng() + boot_k = np.zeros(shape=(nbootstrap_samples, nsubtasks, ntrials), dtype=int) + boot_n = np.zeros(shape=(nbootstrap_samples, nsubtasks), dtype=int) + for i_bootstrap in range(nbootstrap_samples): + for i_subtask in range(nsubtasks): + perf_data = i_subtask_to_perf_data[i_subtask] + nsessions_for_subtask = len(perf_data) + i_sessions = gen.integers( + low=0, + high=nsessions_for_subtask, + size=nsessions_for_subtask, ) + boot_k[i_bootstrap, i_subtask] = perf_data[i_sessions].sum(axis=0) + boot_n[i_bootstrap, i_subtask] = len(i_sessions) - # Perform bootstrapping - gen = np.random.default_rng() - boot_k = np.zeros(shape=(nbootstrap_samples, nsubtasks, ntrials), dtype=int) - boot_n = np.zeros(shape=(nbootstrap_samples, nsubtasks), dtype=int) - for i_bootstrap in range(nbootstrap_samples): - for i_subtask in range(nsubtasks): - nsessions_for_subtask = len(i_subtask_to_perf_data[i_subtask]) - i_sessions = gen.integers( - low=0, high=nsessions_for_subtask, size=nsessions_for_subtask - ) - boot_k[i_bootstrap, i_subtask] = i_subtask_to_perf_data[i_subtask][ - i_sessions - ].sum(axis=0) - boot_n[i_bootstrap, i_subtask] = len(i_sessions) - - return LearningCurveStatistics.BootstrapSamples( - boot_k=boot_k, - boot_n=boot_n, - ) + return _BootstrapSamples( + boot_k=boot_k, + boot_n=boot_n, + ) diff --git a/hobj/benchmarks/generalization/estimator.py b/hobj/benchmarks/generalization/estimator.py index e3e57b2..775842c 100644 --- a/hobj/benchmarks/generalization/estimator.py +++ b/hobj/benchmarks/generalization/estimator.py @@ -1,5 +1,5 @@ import warnings -from typing import List +from typing import List, Tuple import numpy as np import xarray as xr @@ -8,139 +8,139 @@ from hobj.stats import binomial as binomial_funcs -class GeneralizationStatistics(xr.Dataset): - __slots__ = () - - def __init__( - self, - results: List[GeneralizationSessionResult], - perform_lapse_rate_correction: bool, - n_bootstrap_iterations: int, - bootstrap_by_worker: bool, # versus by session - ): - # Get all transformations - all_transformations = set() - for result in results: - all_transformations.update(result.transformation_to_kn.keys()) - - all_transformations = sorted(all_transformations) - transformation_to_i = { - transformation: i for i, transformation in enumerate(all_transformations) +def build_generalization_statistics( + results: List[GeneralizationSessionResult], + perform_lapse_rate_correction: bool, + n_bootstrap_iterations: int, + bootstrap_by_worker: bool, +) -> xr.Dataset: + """Build one-shot generalization statistics as a plain xarray dataset. + + Args: + results: Session-level generalization results. + perform_lapse_rate_correction: Whether to correct point estimates using + catch-trial lapse rates. + n_bootstrap_iterations: Number of bootstrap replicates to generate. + bootstrap_by_worker: Whether to resample workers instead of sessions. + + Returns: + A dataset containing point estimates and bootstrap replicates. + """ + + all_transformations = sorted( + { + transformation + for result in results + for transformation in result.transformation_to_kn.keys() } + ) + transformation_to_i = { + transformation: i for i, transformation in enumerate(all_transformations) + } + + all_workers = sorted({result.worker_id for result in results}) + nworkers = len(all_workers) + worker_to_i = {worker: i for i, worker in enumerate(all_workers)} + + nsessions = len(results) + ntransformations = len(all_transformations) + + if bootstrap_by_worker: + if nworkers == 1: + raise ValueError( + f"Only one worker {all_workers}, cannot perform valid bootstrap by worker." + ) + if nworkers < 20: + warnings.warn( + ( + f"Only {nworkers} unique workers, which is less than 20. " + "Bootstrapping by worker may not be reliable." + ), + stacklevel=2, + ) - # Get all workers - all_workers = sorted({result.worker_id for result in results}) - nworkers = len(all_workers) - worker_to_i = {worker: i for i, worker in enumerate(all_workers)} - - # Package data into [session, transformation] kn matrices - nsessions = len(results) - ntransformations = len(all_transformations) - + kmat = np.zeros(shape=(nworkers, ntransformations)) + nmat = np.zeros(shape=(nworkers, ntransformations)) + kcatch = np.zeros(shape=nworkers) + ncatch = np.zeros(shape=nworkers) + else: + kmat = np.zeros(shape=(nsessions, ntransformations)) + nmat = np.zeros(shape=(nsessions, ntransformations)) + kcatch = np.zeros(shape=nsessions) + ncatch = np.zeros(shape=nsessions) + + for i_session, result in enumerate(results): + i_row = worker_to_i[result.worker_id] if bootstrap_by_worker else i_session + kcatch[i_row] = result.kcatch + ncatch[i_row] = result.ncatch + + for transformation, (k, n) in result.transformation_to_kn.items(): + i_transformation = transformation_to_i[transformation] + kmat[i_row, i_transformation] += k + nmat[i_row, i_transformation] += n + + phat, varhat_phat = _get_point_estimates( + k=kmat.sum(0), + n=nmat.sum(0), + kcatch=kcatch.sum(), + ncatch=ncatch.sum(), + perform_lapse_rate_correction=perform_lapse_rate_correction, + ) + + gen = np.random.default_rng() + boot_phat = np.zeros(shape=(n_bootstrap_iterations, ntransformations)) + boot_varhat_phat = np.zeros(shape=(n_bootstrap_iterations, ntransformations)) + for i_boot_iter in range(n_bootstrap_iterations): if bootstrap_by_worker: - if nworkers == 1: - raise ValueError( - f"Only one worker {all_workers}, cannot perform valid bootstrap by worker." - ) - elif nworkers < 20: - warnings.warn( - f"Only {nworkers} unique workers, which is less than 20. Bootstrapping by worker may not be reliable." - ) - - kmat = np.zeros(shape=(nworkers, ntransformations)) - nmat = np.zeros(shape=(nworkers, ntransformations)) - kcatch = np.zeros(shape=nworkers) - ncatch = np.zeros(shape=nworkers) + i_boot = gen.integers(low=0, high=nworkers, size=nworkers) else: - # Will be bootstrapping by session - kmat = np.zeros(shape=(nsessions, ntransformations)) - nmat = np.zeros(shape=(nsessions, ntransformations)) - kcatch = np.zeros(shape=nsessions) - ncatch = np.zeros(shape=nsessions) - - for i_session, result in enumerate(results): - i = worker_to_i[result.worker_id] if bootstrap_by_worker else i_session - kcatch[i] = result.kcatch - ncatch[i] = result.ncatch - - for transformation, (k, n) in result.transformation_to_kn.items(): - i_transformation = transformation_to_i[transformation] - kmat[i, i_transformation] += k - nmat[i, i_transformation] += n - - # Calculate statistics - phat, varhat_phat = self._get_point_estimates( - k=kmat.sum(0), - n=nmat.sum(0), - kcatch=kcatch.sum(), - ncatch=ncatch.sum(), + i_boot = gen.integers(low=0, high=nsessions, size=nsessions) + + kboot = kmat[i_boot].sum(0) + nboot = nmat[i_boot].sum(0) + kcatch_boot = kcatch[i_boot].sum() + ncatch_boot = ncatch[i_boot].sum() + + phat_cur, varhat_phat_cur = _get_point_estimates( + k=kboot, + n=nboot, + kcatch=kcatch_boot, + ncatch=ncatch_boot, perform_lapse_rate_correction=perform_lapse_rate_correction, ) - # Compute bootstrap replicates by resampling sessions or workers - gen = np.random.default_rng() - boot_phat = np.zeros(shape=(n_bootstrap_iterations, ntransformations)) - boot_varhat_phat = np.zeros(shape=(n_bootstrap_iterations, ntransformations)) - - for i_boot_iter in range(n_bootstrap_iterations): - if bootstrap_by_worker: - # Resample by worker - i_boot = gen.integers(low=0, high=nworkers, size=nworkers) - else: - # Resample by session - i_boot = gen.integers(low=0, high=nsessions, size=nsessions) - kboot = kmat[i_boot].sum(0) - nboot = nmat[i_boot].sum(0) - kcatch_boot = kcatch[i_boot].sum() - ncatch_boot = ncatch[i_boot].sum() - - # Compute bootstrapped point estimates - phat_cur, varhat_phat_cur = self._get_point_estimates( - k=kboot, - n=nboot, - kcatch=kcatch_boot, - ncatch=ncatch_boot, - perform_lapse_rate_correction=perform_lapse_rate_correction, - ) - - # Save - boot_phat[i_boot_iter] = phat_cur - boot_varhat_phat[i_boot_iter] = varhat_phat_cur - - super().__init__( - data_vars=dict( - phat=(["transformation"], phat), - varhat_phat=(["transformation"], varhat_phat), - boot_phat=(["boot_iter", "transformation"], boot_phat), - boot_varhat_phat=(["boot_iter", "transformation"], boot_varhat_phat), - ), - coords=dict(transformation=all_transformations), - ) - - @staticmethod - def _get_point_estimates( - k: np.ndarray, # [transformation] - n: np.ndarray, # [transformation] - kcatch: np.ndarray, # () - ncatch: np.ndarray, # () - perform_lapse_rate_correction: bool, - ): - phat = k / n - varhat_phat = binomial_funcs.estimate_variance_of_binomial_proportion( - kvec=k, nvec=n - ) - - if perform_lapse_rate_correction: - # Return an estimate of the performance that would be expected if the system had a lapse rate of 0. - # The lapse rate is estimated using the catch trials. - hat_lapse_rate = 2 - 2 * (kcatch / ncatch) - - # Estimate the performance that would be expected if the system had a lapse rate of 0, using the plug-in estimator: - phat = (phat - hat_lapse_rate / 2) / (1 - hat_lapse_rate) - - # Estimate the variance of the corrected performance by treating the lapse rate as a constant: - varhat_phat = varhat_phat / (1 - hat_lapse_rate) ** 2 - - phat = np.clip(phat, 0, 1) - - return phat, varhat_phat + boot_phat[i_boot_iter] = phat_cur + boot_varhat_phat[i_boot_iter] = varhat_phat_cur + + return xr.Dataset( + data_vars=dict( + phat=(["transformation"], phat), + varhat_phat=(["transformation"], varhat_phat), + boot_phat=(["boot_iter", "transformation"], boot_phat), + boot_varhat_phat=(["boot_iter", "transformation"], boot_varhat_phat), + ), + coords=dict(transformation=all_transformations), + ) + + +def _get_point_estimates( + k: np.ndarray, + n: np.ndarray, + kcatch: np.ndarray, + ncatch: np.ndarray, + perform_lapse_rate_correction: bool, +) -> Tuple[np.ndarray, np.ndarray]: + """Estimate generalization means and variances from binomial counts.""" + + phat = k / n + varhat_phat = binomial_funcs.estimate_variance_of_binomial_proportion( + kvec=k, nvec=n + ) + + if perform_lapse_rate_correction: + hat_lapse_rate = 2 - 2 * (kcatch / ncatch) + phat = (phat - hat_lapse_rate / 2) / (1 - hat_lapse_rate) + varhat_phat = varhat_phat / (1 - hat_lapse_rate) ** 2 + + phat = np.clip(phat, 0, 1) + return phat, varhat_phat diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py index e291517..ca213ce 100644 --- a/hobj/benchmarks/mut_highvar_benchmark.py +++ b/hobj/benchmarks/mut_highvar_benchmark.py @@ -11,7 +11,9 @@ simulate_sessions, summarize_bootstrap_score, ) -from hobj.benchmarks.binary_classification.estimator import LearningCurveStatistics +from hobj.benchmarks.binary_classification.estimator import ( + build_learning_curve_statistics, +) from hobj.benchmarks.binary_classification.simulation import ( BinaryClassificationSubtask, BinaryClassificationSubtaskResult, @@ -110,7 +112,7 @@ def __init__(self, cachedir: Path | None = None): ) self._target_data[name][worker_id] = [bool(v) for v in result.perf_seq] - self._target_statistics = LearningCurveStatistics( + self._target_statistics = build_learning_curve_statistics( subtask_name_to_results=self.subtask_name_to_results, nbootstrap_samples=self.num_bootstrap_samples, bootstrap_by_worker=self.bootstrap_by_worker, @@ -121,7 +123,7 @@ def target_data(self) -> Dict[str, Dict[str, List[bool]]]: return self._target_data @property - def target_statistics(self) -> LearningCurveStatistics: + def target_statistics(self) -> xr.Dataset: return self._target_statistics @dataclass @@ -130,9 +132,9 @@ class LearningCurveBenchmarkResult: msen_sigma: float msen_CI95: Tuple[float, float] lapse_rate: xr.DataArray | None - model_statistics: LearningCurveStatistics + model_statistics: xr.Dataset - def __call__( + def score_model( self, learner: BinaryLearningModel, show_pbar: bool = False ) -> "MutatorHighVarBenchmark.LearningCurveBenchmarkResult": subtask_name_to_model_results: Dict[ @@ -149,7 +151,7 @@ def __call__( nsimulations=self.num_simulations_per_subtask, ) - model_statistics = LearningCurveStatistics( + model_statistics = build_learning_curve_statistics( subtask_name_to_results=subtask_name_to_model_results, nbootstrap_samples=self.num_bootstrap_samples, bootstrap_by_worker=False, diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py index 093ef77..0240a56 100644 --- a/hobj/benchmarks/mut_oneshot_benchmark.py +++ b/hobj/benchmarks/mut_oneshot_benchmark.py @@ -11,7 +11,9 @@ simulate_sessions, summarize_bootstrap_score, ) -from hobj.benchmarks.generalization.estimator import GeneralizationStatistics +from hobj.benchmarks.generalization.estimator import ( + build_generalization_statistics, +) from hobj.benchmarks.generalization.simulator import ( GeneralizationSessionResult, GeneralizationSubtask, @@ -209,7 +211,7 @@ def __init__(self, cachedir: Path | None = None): ) ) - self._target_statistics = GeneralizationStatistics( + self._target_statistics = build_generalization_statistics( results=self.results, perform_lapse_rate_correction=True, n_bootstrap_iterations=self.num_bootstrap_samples, @@ -217,7 +219,7 @@ def __init__(self, cachedir: Path | None = None): ) @property - def target_statistics(self) -> GeneralizationStatistics: + def target_statistics(self) -> xr.Dataset: gen_statistics = self._target_statistics return gen_statistics.assign_coords( transformation_type=( @@ -241,9 +243,9 @@ class GeneralizationBenchmarkResult: msen: float msen_sigma: float msen_CI95: Tuple[float, float] - model_statistics: GeneralizationStatistics + model_statistics: xr.Dataset - def __call__( + def score_model( self, learner: BinaryLearningModel, show_pbar: bool = False ) -> "MutatorOneshotBenchmark.GeneralizationBenchmarkResult": results: List[GeneralizationSessionResult] = [] @@ -260,7 +262,7 @@ def __call__( ) ) - model_statistics = GeneralizationStatistics( + model_statistics = build_generalization_statistics( results=results, perform_lapse_rate_correction=False, n_bootstrap_iterations=self.num_bootstrap_samples, diff --git a/pyproject.toml b/pyproject.toml index 6098d68..4569a12 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,18 +23,15 @@ dependencies = [ "pydantic>=2.10", "xarray>=2025.1", "scipy>=1.17.1", - "ipykernel>=7.2.0", - "scipy-stubs~=1.17.1", ] -[project.scripts] -hobj-download-data = "hobj.data_loaders.download:main" - [dependency-groups] dev = [ "enczoo==0.1.3.dev1", "netcdf4>=1.7.4", "pytest>=8.3", + "ipykernel>=7.2.0", + "scipy-stubs~=1.17.1", ] [project.urls] diff --git a/tests/test_data_loader_cache.py b/tests/test_data_loader_cache.py new file mode 100644 index 0000000..b610678 --- /dev/null +++ b/tests/test_data_loader_cache.py @@ -0,0 +1,216 @@ +import tarfile +from pathlib import Path + +import pandas as pd +from PIL import Image + +from hobj.data import behavior as behavior_loaders +from hobj.data import download as download_module +from hobj.data.images import get_image_path, load_image + + +def _write_minimal_packaged_dataset(data_root: Path) -> None: + data_root.mkdir(parents=True, exist_ok=True) + + pd.DataFrame( + [ + { + "image_id": "img-highvar", + "category": "cat-a", + "sha256": "unused", + "relpath": "images/highvar.png", + } + ] + ).to_csv(data_root / "meta-MutatorHighVarImageset.csv", index=False) + + pd.DataFrame( + [ + { + "image_id": "img-oneshot", + "category": "cat-b", + "transformation": "original", + "transformation_level": 0.0, + "base_image_id": "img-oneshot", + "sha256": "unused", + "relpath": "images/oneshot.png", + } + ] + ).to_csv(data_root / "meta-MutatorOneShotImageset.csv", index=False) + + pd.DataFrame( + [ + { + "image_id": "img-warmup", + "category": "cat-c", + "sha256": "unused", + "relpath": "images/warmup.png", + } + ] + ).to_csv(data_root / "meta-MutatorWarmupImageset.csv", index=False) + + pd.DataFrame( + [ + { + "image_id": "img-catch", + "sha256": "unused", + "relpath": "images/catch.png", + } + ] + ).to_csv(data_root / "meta-CatchImageset.csv", index=False) + + behavior_root = data_root / "behavior" + behavior_root.mkdir(parents=True, exist_ok=True) + + pd.DataFrame( + [ + { + "trial": 0, + "assignment_id": "assignment-1", + "worker_id": "worker-1", + "subtask": "cat-a,cat-d", + "image_id": "img-highvar", + "trial_type": "train", + "stimulus_duration_msec": 100, + "reaction_time_msec": 200, + "timed_out": False, + "perf": True, + "timestamp_start": "2025-01-01T00:00:00", + }, + { + "trial": 1, + "assignment_id": "assignment-1", + "worker_id": "worker-1", + "subtask": "cat-a,cat-d", + "image_id": "img-highvar", + "trial_type": "probe", + "stimulus_duration_msec": 100, + "reaction_time_msec": 200, + "timed_out": False, + "perf": False, + "timestamp_start": "2025-01-01T00:00:01", + }, + { + "trial": 2, + "assignment_id": "assignment-1", + "worker_id": "worker-1", + "subtask": "cat-a,cat-d", + "image_id": "img-highvar", + "trial_type": "train", + "stimulus_duration_msec": 100, + "reaction_time_msec": 200, + "timed_out": False, + "perf": True, + "timestamp_start": "2025-01-01T00:00:02", + }, + ] + ).to_csv(behavior_root / "human-behavior-highvar-subtasks.csv", index=False) + + pd.DataFrame( + [ + { + "trial": 0, + "assignment_id": "assignment-2", + "slot": 0, + "worker_id": "worker-2", + "subtask": "cat-b,cat-e", + "image_id": "img-oneshot", + "trial_type": "support", + "stimulus_duration_msec": 100, + "reaction_time_msec": 200, + "timed_out": False, + "perf": True, + "timestamp_start": "2025-01-01T00:00:03", + } + ] + ).to_csv(behavior_root / "human-behavior-oneshot-subtasks.csv", index=False) + + +def test_download_data_extracts_into_custom_cachedir(tmp_path: Path) -> None: + staging_root = tmp_path / "staging" / "data" + _write_minimal_packaged_dataset(staging_root) + + archive_path = tmp_path / "fixture.tar.gz" + with tarfile.open(archive_path, mode="w:gz") as archive: + archive.add(staging_root, arcname="data") + + custom_cache = tmp_path / "custom-cache" + resolved_cache = download_module.download_data( + url="https://example.com/fixture.tar.gz", + cachedir=custom_cache, + ) + + assert resolved_cache == custom_cache.resolve() + assert (custom_cache / "meta-MutatorHighVarImageset.csv").exists() + assert (custom_cache / "behavior" / "human-behavior-highvar-subtasks.csv").exists() + assert not (tmp_path / "data").exists() + + +def test_resolve_data_root_downloads_missing_data_once( + monkeypatch, + tmp_path: Path, +) -> None: + custom_cache = tmp_path / "resolved-cache" + calls: list[Path] = [] + + def fake_download_data( + *, + url: str = download_module.DATA_ARCHIVE_URL, + repo_root: Path | None = None, + cachedir: Path | None = None, + force_download: bool = False, + ) -> Path: + assert url == download_module.DATA_ARCHIVE_URL + assert repo_root is None + assert force_download is False + assert cachedir is not None + calls.append(cachedir) + _write_minimal_packaged_dataset(cachedir) + return cachedir.resolve() + + monkeypatch.setattr(download_module, "download_data", fake_download_data) + + resolved_cache = download_module.resolve_data_root(cachedir=custom_cache) + assert resolved_cache == custom_cache.resolve() + assert calls == [custom_cache.resolve()] + + resolved_cache = download_module.resolve_data_root(cachedir=custom_cache) + assert resolved_cache == custom_cache.resolve() + assert calls == [custom_cache.resolve()] + + +def test_load_highvar_behavior_uses_custom_cachedir(tmp_path: Path) -> None: + custom_cache = tmp_path / "behavior-cache" + _write_minimal_packaged_dataset(custom_cache) + + df = behavior_loaders.load_highvar_behavior(cachedir=custom_cache) + + assert df["trial"].tolist() == [0, 1] + assert set(df["trial_type"]) == {"train"} + assert df["image_id"].tolist() == ["img-highvar", "img-highvar"] + + +def test_load_image_uses_custom_cachedir(tmp_path: Path) -> None: + custom_cache = tmp_path / "image-cache" + _write_minimal_packaged_dataset(custom_cache) + + image_path = custom_cache / "images" / "highvar.png" + image_path.parent.mkdir(parents=True, exist_ok=True) + Image.new("RGB", (2, 3), color=(255, 0, 0)).save(image_path) + + image = load_image("img-highvar", cachedir=custom_cache) + + assert image.size == (2, 3) + image.close() + + +def test_get_image_path_uses_custom_cachedir(tmp_path: Path) -> None: + custom_cache = tmp_path / "image-path-cache" + _write_minimal_packaged_dataset(custom_cache) + + image_path = custom_cache / "images" / "highvar.png" + image_path.parent.mkdir(parents=True, exist_ok=True) + Image.new("RGB", (2, 3), color=(255, 0, 0)).save(image_path) + + resolved_path = get_image_path("img-highvar", cachedir=custom_cache) + + assert resolved_path == image_path diff --git a/uv.lock b/uv.lock index 417bd06..84c8fc8 100644 --- a/uv.lock +++ b/uv.lock @@ -697,14 +697,12 @@ name = "hobj" version = "2.0.0" source = { editable = "." } dependencies = [ - { name = "ipykernel" }, { name = "matplotlib" }, { name = "numpy" }, { name = "pandas" }, { name = "pydantic" }, { name = "requests" }, { name = "scipy" }, - { name = "scipy-stubs" }, { name = "tqdm" }, { name = "xarray" }, ] @@ -712,20 +710,20 @@ dependencies = [ [package.dev-dependencies] dev = [ { name = "enczoo" }, + { name = "ipykernel" }, { name = "netcdf4" }, { name = "pytest" }, + { name = "scipy-stubs" }, ] [package.metadata] requires-dist = [ - { name = "ipykernel", specifier = ">=7.2.0" }, { name = "matplotlib", specifier = ">=3.9" }, { name = "numpy", specifier = ">=2.2" }, { name = "pandas", specifier = ">=2.2" }, { name = "pydantic", specifier = ">=2.10" }, { name = "requests", specifier = ">=2.32" }, { name = "scipy", specifier = ">=1.17.1" }, - { name = "scipy-stubs", specifier = "~=1.17.1" }, { name = "tqdm", specifier = ">=4.67" }, { name = "xarray", specifier = ">=2025.1" }, ] @@ -733,8 +731,10 @@ requires-dist = [ [package.metadata.requires-dev] dev = [ { name = "enczoo", specifier = "==0.1.3.dev1" }, + { name = "ipykernel", specifier = ">=7.2.0" }, { name = "netcdf4", specifier = ">=1.7.4" }, { name = "pytest", specifier = ">=8.3" }, + { name = "scipy-stubs", specifier = "~=1.17.1" }, ] [[package]] From 4932df4cd1e31cbb5b73b30428d747980f722d12 Mon Sep 17 00:00:00 2001 From: himjl <mil@mit.edu> Date: Thu, 26 Mar 2026 21:17:22 -0400 Subject: [PATCH 145/156] refine --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index d59e235..826f706 100644 --- a/README.md +++ b/README.md @@ -26,8 +26,6 @@ The template script below shows you how you can run a benchmark on a linear lear All you need to do is have a way to process a `PIL.Image` into a vector of image features (as an `np.ndarray`). There are ~18,000 images that you'd need to compute image features for. ```python - -# Import hobj import hobj import numpy as np @@ -35,7 +33,9 @@ import numpy as np my_image_features: dict[str, np.ndarray] = {} for image_id in hobj.list_image_ids(): image = hobj.load_image(image_id=image_id) # PIL.Image - my_image_features[image_id] = ... # Compute your features here! It should be a d-length vector. + + # Compute your features here: + my_image_features[image_id] = ... # replace right hand side with your image-computable model # Assemble the learning model: model = hobj.create_linear_learner( From 4bc03e278ed6fd1e9cec0d13e9ca87c9e114cb5c Mon Sep 17 00:00:00 2001 From: himjl <mil@mit.edu> Date: Thu, 26 Mar 2026 22:13:01 -0400 Subject: [PATCH 146/156] improve examples --- README.md | 20 +- examples/documentation.ipynb | 690 --------- examples/loading_raw_data_and_metadata.ipynb | 1321 +++++++++++++++++ ...example_models.ipynb => score_model.ipynb} | 0 4 files changed, 1333 insertions(+), 698 deletions(-) delete mode 100644 examples/documentation.ipynb create mode 100644 examples/loading_raw_data_and_metadata.ipynb rename examples/{score_example_models.ipynb => score_model.ipynb} (100%) diff --git a/README.md b/README.md index 826f706..bf3bf09 100644 --- a/README.md +++ b/README.md @@ -4,22 +4,22 @@ from hobj.learning_models import random_guesser [![CI](https://github.com/himjl/hobj/actions/workflows/ci.yml/badge.svg)](https://github.com/himjl/hobj/actions/workflows/ci.yml) -This repository contains benchmarks for comparing models of object learning against measurements of human behavior, from Lee and DiCarlo 2023 (["How well do rudimentary plasticity rules predict adult visual object learning?"](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011713)). - -It also lets you download the raw data and images from the experiments in the paper. +This repository contains benchmarks for comparing models of object learning against measurements of human behavior, from Lee and DiCarlo 2023 (["How well do rudimentary plasticity rules predict adult visual object learning?"](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011713)). It also lets you download the raw data and images from the experiments in the paper. <div style="text-align: center;"> <img src="site/readme_images/human_learning_curves.svg" alt="Alt text" > </div> -### Installation +## Quickstart + +### Install The `hobj` package works for Python >=3.12. After cloning this repository on your machine, navigate to this directory in your shell and run: ``` pip install -e .``` -### Quickstart: comparing a linear learner against human learning data +### Using `hobj` to comparing a linear learner against human learning data The template script below shows you how you can run a benchmark on a linear learning model based on your image encoding model. @@ -61,10 +61,14 @@ Note that on first use, the packaged dataset is downloaded automatically into `. To use a different location, pass `cachedir=...` to a data loader or benchmark constructor, or prefetch manually with `hobj-download-data --cachedir /path/to/data`. -For more details, check out `examples/`. +For more details (e.g., how to load the raw behavioral data or images), check out the Jupyter notebooks in `examples/`. + +### Need help or have questions? + +Please don't hesitate to send me an email ([mil@mit.edu](mailto:name@example.com)), or open an issue on this repo! -### Citation +## Citation ``` @article{lee2023well, @@ -80,5 +84,5 @@ For more details, check out `examples/`. ``` -### Changes to codebase since publication +## Changes to codebase since publication This codebase was refactored in 2026 to improve the accessibility, performance, and quality of the code. Along the way, minor changes to the statistical analysis of the original codebase were introduced (see [changelist](site/changelist.md)). To see the codebase at the time of publication, check out the repo with the `v1` tag [here](https://github.com/himjl/hobj/releases/tag/v1). diff --git a/examples/documentation.ipynb b/examples/documentation.ipynb deleted file mode 100644 index 91912b4..0000000 --- a/examples/documentation.ipynb +++ /dev/null @@ -1,690 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "id": "initial_id", - "metadata": { - "collapsed": true, - "ExecuteTime": { - "end_time": "2026-03-26T22:12:09.366306Z", - "start_time": "2026-03-26T22:12:08.985875Z" - } - }, - "source": "import hobj", - "outputs": [], - "execution_count": 1 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2026-03-26T22:14:09.088796Z", - "start_time": "2026-03-26T22:14:08.689507Z" - } - }, - "cell_type": "code", - "source": [ - "df_highvar_behavior = hobj.load_highvar_behavior()\n", - "df_highvar_behavior\n" - ], - "id": "b3d7df3ce4bda73c", - "outputs": [ - { - "data": { - "text/plain": [ - " trial assignment_id \\\n", - 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", 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" - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 17 - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "", - "id": "c3889be965d793dc" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "", - "id": "f58c06464094275c" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "", - "id": "e3910bb009633ba8" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "", - "id": "eac169bed3ab7ca2" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/loading_raw_data_and_metadata.ipynb b/examples/loading_raw_data_and_metadata.ipynb new file mode 100644 index 0000000..81a37b5 --- /dev/null +++ b/examples/loading_raw_data_and_metadata.ipynb @@ -0,0 +1,1321 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2026-03-27T02:01:44.301642Z", + "start_time": "2026-03-27T02:01:43.898649Z" + } + }, + "source": "import hobj", + "outputs": [], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Loading image meta\n", + "\n", + "The cells below show you how to load the image metadata for the two experiments. This is not necessary for scoring a model, but you might have another reason for doing this." + ], + "id": "3c595fba1f12f519" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-27T02:06:16.259921Z", + "start_time": "2026-03-27T02:06:16.120069Z" + } + }, + "cell_type": "code", + "source": [ + "df_meta_highvar = hobj.load_imageset_meta_highvar()\n", + "df_meta_highvar" + ], + "id": "15d6b734f6956ddc", + "outputs": [ + { + "data": { + "text/plain": [ + " image_id category \\\n", + "0 MutatorObject000-Image00.png MutatorObject000 \n", + "1 MutatorObject000-Image01.png MutatorObject000 \n", + "2 MutatorObject000-Image02.png MutatorObject000 \n", + "3 MutatorObject000-Image03.png MutatorObject000 \n", + "4 MutatorObject000-Image04.png MutatorObject000 \n", + "... ... ... \n", + "12795 MutatorObject127-Image95.png MutatorObject127 \n", + "12796 MutatorObject127-Image96.png MutatorObject127 \n", + "12797 MutatorObject127-Image97.png MutatorObject127 \n", + "12798 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<td>0.215443</td>\n", + " <td>MutatorB2000_46</td>\n", + " <td>MutatorB2000_46_rx0.00000_ry0.00000_rz0.00000_...</td>\n", + " <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>MutatorOneshotObject00-backgrounds-0.215443-1.png</td>\n", + " <td>MutatorOneshotObject00</td>\n", + " <td>f99d0949a742bf9240a87226735bc122e21a49c4c464e1...</td>\n", + " <td>MutatorOneshotObject00-original.png</td>\n", + " <td>backgrounds</td>\n", + " <td>0.215443</td>\n", + " <td>MutatorB2000_46</td>\n", + " <td>MutatorB2000_46_rx0.00000_ry0.00000_rz0.00000_...</td>\n", + " <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>MutatorOneshotObject00-backgrounds-0.464159-0.png</td>\n", + " <td>MutatorOneshotObject00</td>\n", + " <td>f290df90e4d32f42a3008d2696afa5e8c2e7b5391215c0...</td>\n", + " <td>MutatorOneshotObject00-original.png</td>\n", + " <td>backgrounds</td>\n", + " <td>0.464159</td>\n", + " <td>MutatorB2000_46</td>\n", + " <td>MutatorB2000_46_rx0.00000_ry0.00000_rz0.00000_...</td>\n", + " <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3899</th>\n", + " <td>MutatorOneshotObject63-outplanerotation-90.0-3...</td>\n", + " <td>MutatorOneshotObject63</td>\n", + " <td>1cbb3ed038c52b1b64aa3ee8c4b6948d53d8339e872e12...</td>\n", + " <td>MutatorOneshotObject63-original.png</td>\n", + " <td>outplanerotation</td>\n", + " <td>90.000000</td>\n", + " <td>MutatorB2000_4792</td>\n", + " <td>MutatorB2000_4792_rx90.00000_ry0.00000_rz0.000...</td>\n", + " <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3900</th>\n", + " <td>MutatorOneshotObject63-scale-0.125-0.png</td>\n", + " <td>MutatorOneshotObject63</td>\n", + " <td>c928c3e293faf146fea3452d583021edc01928f6d91dff...</td>\n", + " <td>MutatorOneshotObject63-original.png</td>\n", + " <td>scale</td>\n", + " <td>0.125000</td>\n", + " <td>MutatorB2000_4792</td>\n", + " <td>MutatorB2000_4792_rx0.00000_ry0.00000_rz0.0000...</td>\n", + " <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3901</th>\n", + " <td>MutatorOneshotObject63-scale-0.25-0.png</td>\n", + " <td>MutatorOneshotObject63</td>\n", + " <td>929a7df3077bf2af726320c6fef57f04f2c43541c04830...</td>\n", + " <td>MutatorOneshotObject63-original.png</td>\n", + " <td>scale</td>\n", + " <td>0.250000</td>\n", + " <td>MutatorB2000_4792</td>\n", + " <td>MutatorB2000_4792_rx0.00000_ry0.00000_rz0.0000...</td>\n", + " <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3902</th>\n", + " <td>MutatorOneshotObject63-scale-0.5-0.png</td>\n", + " <td>MutatorOneshotObject63</td>\n", + " <td>e4fcf112d188822385ad433a1f390b5fd8095883bbf418...</td>\n", + " <td>MutatorOneshotObject63-original.png</td>\n", + " <td>scale</td>\n", + " <td>0.500000</td>\n", + " <td>MutatorB2000_4792</td>\n", + " <td>MutatorB2000_4792_rx0.00000_ry0.00000_rz0.0000...</td>\n", + " <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3903</th>\n", + " <td>MutatorOneshotObject63-scale-1.5-0.png</td>\n", + " <td>MutatorOneshotObject63</td>\n", + " <td>68b34b82ba92d25a8f09185a888a0605b01ca09bf26362...</td>\n", + " <td>MutatorOneshotObject63-original.png</td>\n", + " <td>scale</td>\n", + " <td>1.500000</td>\n", + " <td>MutatorB2000_4792</td>\n", + " <td>MutatorB2000_4792_rx0.00000_ry0.00000_rz0.0000...</td>\n", + " <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>3904 rows × 9 columns</p>\n", + "</div>" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 10 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-27T02:07:47.382485Z", + "start_time": "2026-03-27T02:07:47.352896Z" + } + }, + "cell_type": "code", + "source": [ + "df_meta_catch = hobj.load_imageset_meta_catch() # These are used for catch trials in Experiment 1.\n", + "df_meta_warmup = hobj.load_imageset_meta_warmup() # These were used to 'warm up' the human participants, and also to normalize image features prior to benchmarking.\n" + ], + "id": "353a0c85e5abca6a", + "outputs": [], + "execution_count": 12 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Loading images\n", + "\n", + "`hobj` offers the `load_image` function for loading images by `image_id` as `PIL.Images`, and the `get_image_path` function as an alternative. See below!" + ], + "id": "41f2abd21b6a50f6" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-27T02:08:37.557729Z", + "start_time": "2026-03-27T02:08:37.536141Z" + } + }, + "cell_type": "code", + "source": [ + "image_id = df_meta_highvar['image_id'].iloc[3]\n", + "image = hobj.load_image(image_id = image_id)\n", + "image" + ], + "id": "511d8b0d0f7fdd52", + "outputs": [ + { + "data": { + "text/plain": [ + "<PIL.PngImagePlugin.PngImageFile image mode=L size=256x256>" + ], + "image/png": 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", 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" + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 16 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-27T02:09:12.544394Z", + "start_time": "2026-03-27T02:09:12.521145Z" + } + }, + "cell_type": "code", + "source": "hobj.get_image_path(image_id=image_id)", + "id": "aa94e3b9654c93d5", + "outputs": [ + { + "data": { + "text/plain": [ + "PosixPath('/Users/mjl/PycharmProjects/hobj/data/images/MutatorHighVarImageset/MutatorObject000/MutatorObject000-Image03.png')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 18 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Loading raw human learning behavior\n", + "\n", + "It's recommended you use the `hobj.MutatorHighVarBenchmark().target_statistics` and `hobj.MutatorOneshotBenchmark().target_statistics` if you just want to see learning curves (and associated uncertainty estimates and bootstrap resamples), but if you'd like to see the raw behavioral data, see below!" + ], + "id": "911fd7cf3ac548f8" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-27T02:04:06.781468Z", + "start_time": "2026-03-27T02:04:06.398253Z" + } + }, + "cell_type": "code", + "source": "hobj.load_highvar_behavior()", + "id": "443a8b78aff3e604", + "outputs": [ + { + "data": { + "text/plain": [ + " trial assignment_id \\\n", + "156000 0 3018Q3ZVOJ00HY15URPTWYSDSTCARE \n", + "156001 1 3018Q3ZVOJ00HY15URPTWYSDSTCARE \n", + "156002 2 3018Q3ZVOJ00HY15URPTWYSDSTCARE \n", + "156003 3 3018Q3ZVOJ00HY15URPTWYSDSTCARE \n", + "156004 4 3018Q3ZVOJ00HY15URPTWYSDSTCARE \n", + "... ... ... \n", + "317194 95 3ZY8KE4ISKDKXLP3S7RPNVWLITLVQY \n", + "317195 96 3ZY8KE4ISKDKXLP3S7RPNVWLITLVQY \n", + "317196 97 3ZY8KE4ISKDKXLP3S7RPNVWLITLVQY \n", + "317197 98 3ZY8KE4ISKDKXLP3S7RPNVWLITLVQY \n", + "317198 99 3ZY8KE4ISKDKXLP3S7RPNVWLITLVQY \n", + "\n", + " worker_id \\\n", + "156000 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<td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50935</th>\n", + " <td>15</td>\n", + " <td>3ZWFC4W1UVHVDPID57BLAZ34X3VRFA</td>\n", + " <td>4</td>\n", + " <td>e43cc225dbf11ef57616e082e51622e8f1e7d9c9b27b2c...</td>\n", + " <td>MutatorOneshotObject09,MutatorOneshotObject58</td>\n", + " <td>MutatorOneshotObject58-outplanerotation-135.0-...</td>\n", + " <td>generalization</td>\n", + " <td>200</td>\n", + " <td>653.0</td>\n", + " <td>False</td>\n", + " <td>False</td>\n", + " <td>1623769458</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50936</th>\n", + " <td>16</td>\n", + " <td>3ZWFC4W1UVHVDPID57BLAZ34X3VRFA</td>\n", + " <td>4</td>\n", + " <td>e43cc225dbf11ef57616e082e51622e8f1e7d9c9b27b2c...</td>\n", + " <td>MutatorOneshotObject09,MutatorOneshotObject58</td>\n", + " <td>MutatorOneshotObject09-contrast-0.8-0.png</td>\n", + " <td>generalization</td>\n", + " <td>200</td>\n", + " <td>296.0</td>\n", + " <td>False</td>\n", + " <td>False</td>\n", + " <td>1623769461</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50937</th>\n", + " <td>17</td>\n", + " <td>3ZWFC4W1UVHVDPID57BLAZ34X3VRFA</td>\n", + " <td>4</td>\n", + " <td>e43cc225dbf11ef57616e082e51622e8f1e7d9c9b27b2c...</td>\n", + " <td>MutatorOneshotObject09,MutatorOneshotObject58</td>\n", + " <td>MutatorOneshotObject09-noise-0.375-0.png</td>\n", + " <td>generalization</td>\n", + " <td>200</td>\n", + " <td>601.0</td>\n", + " <td>False</td>\n", + " <td>True</td>\n", + " <td>1623769463</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50938</th>\n", + " <td>18</td>\n", + " <td>3ZWFC4W1UVHVDPID57BLAZ34X3VRFA</td>\n", + " <td>4</td>\n", + " <td>e43cc225dbf11ef57616e082e51622e8f1e7d9c9b27b2c...</td>\n", + " <td>MutatorOneshotObject09,MutatorOneshotObject58</td>\n", + " <td>MutatorOneshotObject58-blur-0.0625-0.png</td>\n", + " <td>generalization</td>\n", + " <td>200</td>\n", + " <td>1535.0</td>\n", + " <td>False</td>\n", + " <td>False</td>\n", + " <td>1623769464</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50939</th>\n", + " <td>19</td>\n", + " <td>3ZWFC4W1UVHVDPID57BLAZ34X3VRFA</td>\n", + " <td>4</td>\n", + " <td>e43cc225dbf11ef57616e082e51622e8f1e7d9c9b27b2c...</td>\n", + " <td>MutatorOneshotObject09,MutatorOneshotObject58</td>\n", + " <td>MutatorOneshotObject09-original.png</td>\n", + " <td>catch</td>\n", + " <td>200</td>\n", + " <td>293.0</td>\n", + " <td>False</td>\n", + " <td>True</td>\n", + " <td>1623769468</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>50940 rows × 12 columns</p>\n", + "</div>" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 6 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/score_example_models.ipynb b/examples/score_model.ipynb similarity index 100% rename from examples/score_example_models.ipynb rename to examples/score_model.ipynb From 56c3ff589d23e7f6425b1f775b5a45c6da04f781 Mon Sep 17 00:00:00 2001 From: himjl <mil@mit.edu> Date: Thu, 26 Mar 2026 22:14:40 -0400 Subject: [PATCH 147/156] todo stub --- README.md | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index bf3bf09..ff335d7 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,3 @@ -from hobj.learning_models import random_guesser - # `hobj`: human object learning benchmarks [![CI](https://github.com/himjl/hobj/actions/workflows/ci.yml/badge.svg)](https://github.com/himjl/hobj/actions/workflows/ci.yml) @@ -17,7 +15,7 @@ This repository contains benchmarks for comparing models of object learning agai The `hobj` package works for Python >=3.12. After cloning this repository on your machine, navigate to this directory in your shell and run: -``` pip install -e .``` +# todo ### Using `hobj` to comparing a linear learner against human learning data @@ -65,11 +63,11 @@ For more details (e.g., how to load the raw behavioral data or images), check ou ### Need help or have questions? -Please don't hesitate to send me an email ([mil@mit.edu](mailto:name@example.com)), or open an issue on this repo! - +Please don't hesitate to email me ([mil@mit.edu](mailto:name@example.com)), or open an issue on this repo! ## Citation + ``` @article{lee2023well, title={How well do rudimentary plasticity rules predict adult visual object learning?}, From 31f117ffbfa1901156fe88ecc5152897e70eb2e9 Mon Sep 17 00:00:00 2001 From: himjl <mil@mit.edu> Date: Thu, 26 Mar 2026 22:54:19 -0400 Subject: [PATCH 148/156] osf repo linked in readme --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index ff335d7..c2279cc 100644 --- a/README.md +++ b/README.md @@ -8,6 +8,7 @@ This repository contains benchmarks for comparing models of object learning agai <img src="site/readme_images/human_learning_curves.svg" alt="Alt text" > </div> +If you just want to download the raw data and images without using the `hobj` library, check out the [OSF repository](https://osf.io/pj6wm/files/osfstorage) for this project. ## Quickstart From 0582ea9ebac2cfd95ca333f4775b2537856fe8d2 Mon Sep 17 00:00:00 2001 From: himjl <mil@mit.edu> Date: Thu, 26 Mar 2026 23:29:03 -0400 Subject: [PATCH 149/156] download --- README.md | 24 +- examples/loading_raw_data_and_metadata.ipynb | 58 ++--- hobj/data/download.py | 63 +++++- tests/test_data_loader_cache.py | 58 +++-- tests/test_statistics_builders.py | 102 +++++++++ tests/test_statistics_dataset_usage.py | 220 +++++++++++++++++++ 6 files changed, 466 insertions(+), 59 deletions(-) create mode 100644 tests/test_statistics_builders.py create mode 100644 tests/test_statistics_dataset_usage.py diff --git a/README.md b/README.md index c2279cc..0e899c3 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,8 @@ If you just want to download the raw data and images without using the `hobj` li The `hobj` package works for Python >=3.12. After cloning this repository on your machine, navigate to this directory in your shell and run: -# todo +Note that on first use, the packaged dataset is downloaded automatically into `./data` from the OSF repository. In total, `hobj` takes up around ~600 MB of space on your computer. + ### Using `hobj` to comparing a linear learner against human learning data @@ -56,19 +57,22 @@ print(result.msen, result.msen_CI95) # print(result.model_statistics) ``` -Note that on first use, the packaged dataset is downloaded automatically into `./data`. -To use a different location, pass `cachedir=...` to a data loader or benchmark -constructor, or prefetch manually with `hobj-download-data --cachedir /path/to/data`. -For more details (e.g., how to load the raw behavioral data or images), check out the Jupyter notebooks in `examples/`. +For more details (e.g., how to load the raw behavioral data or images in Python), check out the Jupyter notebooks in `examples/`. -### Need help or have questions? -Please don't hesitate to email me ([mil@mit.edu](mailto:name@example.com)), or open an issue on this repo! -## Citation +## Contact +If you have any questions, need help, or experience a bug, please don't hesitate to email me ([mil@mit.edu](mailto:name@example.com)), or open an issue on this repo! + + + +## Changes to codebase since publication +This codebase was overhauled in 2026 to improve its accessibility, performance, and quality. Along the way, minor changes to the statistical analysis procedure were introduced, along with changes to the names of the original filenames (see [changelist](site/changelist.md)). To see the codebase at the time of publication, check out the repo with the `v1` tag [here](https://github.com/himjl/hobj/releases/tag/v1). +## Citation + ``` @article{lee2023well, title={How well do rudimentary plasticity rules predict adult visual object learning?}, @@ -81,7 +85,3 @@ Please don't hesitate to email me ([mil@mit.edu](mailto:name@example.com)), or o publisher={Public Library of Science San Francisco, CA USA} } ``` - - -## Changes to codebase since publication -This codebase was refactored in 2026 to improve the accessibility, performance, and quality of the code. Along the way, minor changes to the statistical analysis of the original codebase were introduced (see [changelist](site/changelist.md)). To see the codebase at the time of publication, check out the repo with the `v1` tag [here](https://github.com/himjl/hobj/releases/tag/v1). diff --git a/examples/loading_raw_data_and_metadata.ipynb b/examples/loading_raw_data_and_metadata.ipynb index 81a37b5..a144c4a 100644 --- a/examples/loading_raw_data_and_metadata.ipynb +++ b/examples/loading_raw_data_and_metadata.ipynb @@ -6,8 +6,8 @@ "metadata": { "collapsed": true, "ExecuteTime": { - "end_time": "2026-03-27T02:01:44.301642Z", - "start_time": "2026-03-27T02:01:43.898649Z" + "end_time": "2026-03-27T03:25:10.359485Z", + "start_time": "2026-03-27T03:25:09.984598Z" } }, "source": "import hobj", @@ -27,8 +27,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T02:06:16.259921Z", - "start_time": "2026-03-27T02:06:16.120069Z" + "end_time": "2026-03-27T03:25:10.486574Z", + "start_time": "2026-03-27T03:25:10.360160Z" } }, "cell_type": "code", @@ -326,18 +326,18 @@ "</div>" ] }, - "execution_count": 8, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 8 + "execution_count": 2 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T02:06:48.946791Z", - "start_time": "2026-03-27T02:06:48.861054Z" + "end_time": "2026-03-27T03:25:10.596806Z", + "start_time": "2026-03-27T03:25:10.507768Z" } }, "cell_type": "code", @@ -612,18 +612,18 @@ "</div>" ] }, - "execution_count": 10, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 10 + "execution_count": 3 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T02:07:47.382485Z", - "start_time": "2026-03-27T02:07:47.352896Z" + "end_time": "2026-03-27T03:25:10.975037Z", + "start_time": "2026-03-27T03:25:10.964839Z" } }, "cell_type": "code", @@ -633,7 +633,7 @@ ], "id": "353a0c85e5abca6a", "outputs": [], - "execution_count": 12 + "execution_count": 4 }, { "metadata": {}, @@ -648,8 +648,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T02:08:37.557729Z", - "start_time": "2026-03-27T02:08:37.536141Z" + "end_time": "2026-03-27T03:25:11.263172Z", + "start_time": "2026-03-27T03:25:10.995997Z" } }, "cell_type": "code", @@ -668,18 +668,18 @@ "image/png": 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", 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" }, - "execution_count": 16, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 16 + "execution_count": 5 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T02:09:12.544394Z", - "start_time": "2026-03-27T02:09:12.521145Z" + "end_time": "2026-03-27T03:25:11.289879Z", + "start_time": "2026-03-27T03:25:11.264772Z" } }, "cell_type": "code", @@ -692,12 +692,12 @@ "PosixPath('/Users/mjl/PycharmProjects/hobj/data/images/MutatorHighVarImageset/MutatorObject000/MutatorObject000-Image03.png')" ] }, - "execution_count": 18, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 18 + "execution_count": 6 }, { "metadata": {}, @@ -712,8 +712,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T02:04:06.781468Z", - "start_time": "2026-03-27T02:04:06.398253Z" + "end_time": "2026-03-27T03:25:11.682894Z", + "start_time": "2026-03-27T03:25:11.291361Z" } }, "cell_type": "code", @@ -983,18 +983,18 @@ "</div>" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 5 + "execution_count": 7 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T02:04:29.333688Z", - "start_time": "2026-03-27T02:04:29.257067Z" + "end_time": "2026-03-27T03:25:11.784589Z", + "start_time": "2026-03-27T03:25:11.702409Z" } }, "cell_type": "code", @@ -1289,12 +1289,12 @@ "</div>" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 6 + "execution_count": 8 } ], "metadata": { diff --git a/hobj/data/download.py b/hobj/data/download.py index 78aa9cb..f58205a 100644 --- a/hobj/data/download.py +++ b/hobj/data/download.py @@ -5,6 +5,7 @@ import argparse import shutil import tarfile +import zipfile from pathlib import Path from tempfile import TemporaryDirectory from urllib.parse import urlparse @@ -13,9 +14,10 @@ from tqdm import tqdm +# Public OSF node that hosts the packaged HOBJ dataset files. +OSF_NODE_ID = "pj6wm" DATA_ARCHIVE_URL = ( - "https://hlbdatasets.s3.us-east-1.amazonaws.com/" - "lee-dicarlo-2023-learning-data.tar.gz" + f"https://files.osf.io/v1/resources/{OSF_NODE_ID}/providers/osfstorage/?zip=" ) EXPECTED_DATA_RELATIVE_PATHS = ( Path("meta-MutatorHighVarImageset.csv"), @@ -52,7 +54,16 @@ def _get_missing_expected_paths(data_root: Path) -> list[Path]: def _derive_archive_path(repo_root: Path, url: str) -> Path: """Return the on-disk path for the downloaded archive.""" - archive_name = Path(urlparse(url).path).name + parsed_url = urlparse(url) + path_segments = [segment for segment in parsed_url.path.split("/") if segment] + if ( + parsed_url.query == "zip=" + and len(path_segments) >= 4 + and path_segments[:3] == ["v1", "resources", path_segments[2]] + ): + archive_name = f"{path_segments[2]}.zip" + else: + archive_name = Path(parsed_url.path).name if not archive_name: raise ValueError(f"Could not derive archive filename from URL: {url}") return repo_root / archive_name @@ -95,6 +106,25 @@ def _safe_extract_tarball(archive_path: Path, destination: Path) -> None: tar.extractall(destination, filter="data") +def _safe_extract_zipfile(archive_path: Path, destination: Path) -> None: + """Extract a zipfile while preventing path traversal. + + Args: + archive_path: Zipfile to extract. + destination: Destination directory for extracted contents. + + Raises: + ValueError: If a zip entry would escape the extraction directory. + """ + destination = destination.resolve() + + with zipfile.ZipFile(archive_path) as archive: + for member in archive.infolist(): + member_path = (destination / member.filename).resolve() + member_path.relative_to(destination) + archive.extractall(destination) + + def _find_extracted_data_root(extraction_root: Path) -> Path: """Return the packaged data directory from an extracted archive tree. @@ -120,6 +150,26 @@ def _find_extracted_data_root(extraction_root: Path) -> Path: ) +def _extract_nested_images_archive(data_root: Path) -> None: + """Extract ``images.tar.gz`` into ``data/images`` when present. + + Args: + data_root: Extracted packaged data directory. + """ + images_archive_path = data_root / "images.tar.gz" + images_root = data_root / "images" + if images_root.exists(): + if images_archive_path.exists(): + images_archive_path.unlink() + return + + if not images_archive_path.exists(): + return + + _safe_extract_tarball(archive_path=images_archive_path, destination=data_root) + images_archive_path.unlink() + + def resolve_data_root( *, cachedir: Path | None = None, @@ -141,6 +191,7 @@ def resolve_data_root( ).resolve() if _get_missing_expected_paths(data_root): return download_data(cachedir=data_root, url=DATA_ARCHIVE_URL) + _extract_nested_images_archive(data_root) return data_root @@ -171,7 +222,8 @@ def download_data( Raises: requests.HTTPError: If the archive download fails. - tarfile.TarError: If the archive cannot be unpacked. + tarfile.TarError: If a tar archive cannot be unpacked. + zipfile.BadZipFile: If the zip archive cannot be unpacked. ValueError: If the archive path is invalid or extraction would escape the extraction directory. FileNotFoundError: If extraction completes but the expected dataset @@ -192,8 +244,9 @@ def download_data( with TemporaryDirectory(dir=archive_root) as extraction_root_str: extraction_root = Path(extraction_root_str) - _safe_extract_tarball(archive_path=archive_path, destination=extraction_root) + _safe_extract_zipfile(archive_path=archive_path, destination=extraction_root) extracted_data_root = _find_extracted_data_root(extraction_root) + _extract_nested_images_archive(extracted_data_root) shutil.copytree(extracted_data_root, data_root, dirs_exist_ok=True) missing_paths = _get_missing_expected_paths(data_root) diff --git a/tests/test_data_loader_cache.py b/tests/test_data_loader_cache.py index b610678..1228772 100644 --- a/tests/test_data_loader_cache.py +++ b/tests/test_data_loader_cache.py @@ -1,4 +1,5 @@ import tarfile +import zipfile from pathlib import Path import pandas as pd @@ -125,23 +126,46 @@ def _write_minimal_packaged_dataset(data_root: Path) -> None: ).to_csv(behavior_root / "human-behavior-oneshot-subtasks.csv", index=False) +def _write_packaged_images_archive(data_root: Path) -> None: + """Write the nested ``images.tar.gz`` archive expected by OSF downloads. + + Args: + data_root: Packaged data directory containing metadata and behavior CSVs. + """ + staging_root = data_root.parent / "images-staging" + images_root = staging_root / "images" + images_root.mkdir(parents=True, exist_ok=True) + + Image.new("RGB", (2, 3), color=(255, 0, 0)).save(images_root / "highvar.png") + Image.new("RGB", (2, 3), color=(0, 255, 0)).save(images_root / "oneshot.png") + Image.new("RGB", (2, 3), color=(0, 0, 255)).save(images_root / "warmup.png") + Image.new("RGB", (2, 3), color=(255, 255, 0)).save(images_root / "catch.png") + + with tarfile.open(data_root / "images.tar.gz", mode="w:gz") as archive: + archive.add(images_root, arcname="images") + + def test_download_data_extracts_into_custom_cachedir(tmp_path: Path) -> None: staging_root = tmp_path / "staging" / "data" _write_minimal_packaged_dataset(staging_root) + _write_packaged_images_archive(staging_root) - archive_path = tmp_path / "fixture.tar.gz" - with tarfile.open(archive_path, mode="w:gz") as archive: - archive.add(staging_root, arcname="data") + archive_path = tmp_path / "download" + with zipfile.ZipFile(archive_path, mode="w") as archive: + for path in sorted(staging_root.rglob("*")): + archive.write(path, arcname=Path("data") / path.relative_to(staging_root)) custom_cache = tmp_path / "custom-cache" resolved_cache = download_module.download_data( - url="https://example.com/fixture.tar.gz", + url="https://example.com/download?zip=", cachedir=custom_cache, ) assert resolved_cache == custom_cache.resolve() assert (custom_cache / "meta-MutatorHighVarImageset.csv").exists() assert (custom_cache / "behavior" / "human-behavior-highvar-subtasks.csv").exists() + assert (custom_cache / "images" / "highvar.png").exists() + assert not (custom_cache / "images.tar.gz").exists() assert not (tmp_path / "data").exists() @@ -192,10 +216,7 @@ def test_load_highvar_behavior_uses_custom_cachedir(tmp_path: Path) -> None: def test_load_image_uses_custom_cachedir(tmp_path: Path) -> None: custom_cache = tmp_path / "image-cache" _write_minimal_packaged_dataset(custom_cache) - - image_path = custom_cache / "images" / "highvar.png" - image_path.parent.mkdir(parents=True, exist_ok=True) - Image.new("RGB", (2, 3), color=(255, 0, 0)).save(image_path) + _write_packaged_images_archive(custom_cache) image = load_image("img-highvar", cachedir=custom_cache) @@ -206,11 +227,22 @@ def test_load_image_uses_custom_cachedir(tmp_path: Path) -> None: def test_get_image_path_uses_custom_cachedir(tmp_path: Path) -> None: custom_cache = tmp_path / "image-path-cache" _write_minimal_packaged_dataset(custom_cache) - - image_path = custom_cache / "images" / "highvar.png" - image_path.parent.mkdir(parents=True, exist_ok=True) - Image.new("RGB", (2, 3), color=(255, 0, 0)).save(image_path) + _write_packaged_images_archive(custom_cache) resolved_path = get_image_path("img-highvar", cachedir=custom_cache) - assert resolved_path == image_path + assert resolved_path == custom_cache / "images" / "highvar.png" + + +def test_resolve_data_root_extracts_nested_images_archive_without_redownload( + tmp_path: Path, +) -> None: + custom_cache = tmp_path / "resolved-cache" + _write_minimal_packaged_dataset(custom_cache) + _write_packaged_images_archive(custom_cache) + + resolved_cache = download_module.resolve_data_root(cachedir=custom_cache) + + assert resolved_cache == custom_cache.resolve() + assert (custom_cache / "images" / "highvar.png").exists() + assert not (custom_cache / "images.tar.gz").exists() diff --git a/tests/test_statistics_builders.py b/tests/test_statistics_builders.py new file mode 100644 index 0000000..b5ac802 --- /dev/null +++ b/tests/test_statistics_builders.py @@ -0,0 +1,102 @@ +import numpy as np +import xarray as xr + +from hobj.benchmarks.binary_classification.estimator import ( + build_learning_curve_statistics, +) +from hobj.benchmarks.binary_classification.simulation import ( + BinaryClassificationSubtaskResult, +) +from hobj.benchmarks.generalization.estimator import build_generalization_statistics +from hobj.benchmarks.generalization.simulator import GeneralizationSessionResult + + +def test_build_learning_curve_statistics_returns_expected_dataset_shape(): + stats = build_learning_curve_statistics( + subtask_name_to_results={ + "task-b": [ + BinaryClassificationSubtaskResult( + perf_seq=np.array([True, False, True]), + worker_id="worker-1", + ), + BinaryClassificationSubtaskResult( + perf_seq=np.array([False, False, True]), + worker_id="worker-2", + ), + ], + "task-a": [ + BinaryClassificationSubtaskResult( + perf_seq=np.array([True, True, False]), + worker_id="worker-3", + ), + BinaryClassificationSubtaskResult( + perf_seq=np.array([True, False, False]), + worker_id="worker-4", + ), + ], + }, + nbootstrap_samples=4, + bootstrap_by_worker=False, + ) + + assert isinstance(stats, xr.Dataset) + assert set(stats.data_vars) == { + "phat", + "varhat_phat", + "boot_phat", + "boot_varhat_phat", + } + assert dict(stats.sizes) == {"subtask": 2, "trial": 3, "boot_iter": 4} + assert list(stats.subtask.values) == ["task-a", "task-b"] + np.testing.assert_allclose( + stats.phat.values, + np.array( + [ + [1.0, 0.5, 0.0], + [0.5, 0.0, 1.0], + ] + ), + ) + assert stats.boot_phat.shape == (4, 2, 3) + assert stats.boot_varhat_phat.shape == (4, 2, 3) + + +def test_build_generalization_statistics_returns_expected_dataset_shape(): + stats = build_generalization_statistics( + results=[ + GeneralizationSessionResult( + transformation_to_kn={ + "blur | 0.1": [1, 2], + "noise | 0.2": [0, 1], + }, + kcatch=1, + ncatch=2, + worker_id="worker-1", + ), + GeneralizationSessionResult( + transformation_to_kn={ + "blur | 0.1": [1, 1], + "noise | 0.2": [1, 2], + }, + kcatch=2, + ncatch=2, + worker_id="worker-2", + ), + ], + perform_lapse_rate_correction=False, + n_bootstrap_iterations=5, + bootstrap_by_worker=False, + ) + + assert isinstance(stats, xr.Dataset) + assert set(stats.data_vars) == { + "phat", + "varhat_phat", + "boot_phat", + "boot_varhat_phat", + } + assert dict(stats.sizes) == {"transformation": 2, "boot_iter": 5} + assert list(stats.transformation.values) == ["blur | 0.1", "noise | 0.2"] + np.testing.assert_allclose(stats.phat.values, np.array([2 / 3, 1 / 3])) + assert stats.boot_phat.shape == (5, 2) + assert stats.boot_varhat_phat.shape == (5, 2) diff --git a/tests/test_statistics_dataset_usage.py b/tests/test_statistics_dataset_usage.py new file mode 100644 index 0000000..e532a75 --- /dev/null +++ b/tests/test_statistics_dataset_usage.py @@ -0,0 +1,220 @@ +import pandas as pd +import xarray as xr + +import hobj.benchmarks.mut_highvar_benchmark as highvar_module +import hobj.benchmarks.mut_oneshot_benchmark as oneshot_module +from hobj.learning_models.random_guesser import RandomGuesser + + +def _make_highvar_images_df() -> pd.DataFrame: + rows = [] + for category in ["CatA", "CatB", "CatC", "CatD"]: + for index in range(50): + rows.append( + { + "image_id": f"{category}_{index:03d}", + "category": category, + } + ) + return pd.DataFrame(rows) + + +def _make_highvar_behavior_df() -> pd.DataFrame: + rows = [] + for assignment_id, worker_id, class_a, class_b in [ + ("assignment-1", "worker-1", "CatA", "CatB"), + ("assignment-2", "worker-2", "CatC", "CatD"), + ]: + for trial in range(100): + category = class_a if trial % 2 == 0 else class_b + rows.append( + { + "assignment_id": assignment_id, + "worker_id": worker_id, + "trial": trial, + "image_id": f"{category}_{trial // 2:03d}", + "perf": trial % 3 == 0, + } + ) + return pd.DataFrame(rows) + + +def _make_oneshot_images_df() -> pd.DataFrame: + rows = [] + for category in ["CatA", "CatB"]: + rows.append( + { + "image_id": f"{category}_original", + "category": category, + "transformation": "original", + "transformation_level": 0.0, + } + ) + for index in range(30): + rows.append( + { + "image_id": f"{category}_blur_{index:02d}", + "category": category, + "transformation": "blur", + "transformation_level": 0.1, + } + ) + for index in range(30): + rows.append( + { + "image_id": f"{category}_noise_{index:02d}", + "category": category, + "transformation": "noise", + "transformation_level": 0.2, + } + ) + return pd.DataFrame(rows) + + +def _make_oneshot_behavior_df() -> pd.DataFrame: + transformed_a = [ + "CatA_blur_00", + "CatB_noise_00", + "CatA_noise_01", + "CatB_blur_01", + "CatA_blur_02", + "CatB_noise_02", + "CatA_noise_03", + "CatB_blur_03", + ] + transformed_b = [ + "CatB_blur_04", + "CatA_noise_04", + "CatB_noise_05", + "CatA_blur_05", + "CatB_blur_06", + "CatA_noise_06", + "CatB_noise_07", + "CatA_blur_07", + ] + rows = [] + for assignment_id, slot, worker_id, transformed_images in [ + ("assignment-1", 0, "worker-1", transformed_a), + ("assignment-2", 0, "worker-2", transformed_b), + ]: + transformed_iter = iter(transformed_images) + for trial in range(20): + if trial in {9, 14, 19} or trial < 9: + image_id = "CatA_original" if trial % 2 == 0 else "CatB_original" + else: + image_id = next(transformed_iter) + + rows.append( + { + "assignment_id": assignment_id, + "slot": slot, + "worker_id": worker_id, + "trial": trial, + "image_id": image_id, + "perf": trial % 2 == 0, + "subtask": "CatA,CatB", + } + ) + return pd.DataFrame(rows) + + +def test_highvar_target_and_model_statistics_behave_like_datasets(monkeypatch): + monkeypatch.setattr( + highvar_module, + "load_imageset_meta_highvar", + lambda cachedir=None: _make_highvar_images_df(), + ) + monkeypatch.setattr( + highvar_module, + "load_highvar_behavior", + lambda remove_probe_trials=True, cachedir=None: _make_highvar_behavior_df(), + ) + monkeypatch.setattr( + highvar_module.MutatorHighVarBenchmark, + "num_bootstrap_samples", + 4, + ) + monkeypatch.setattr( + highvar_module.MutatorHighVarBenchmark, + "num_simulations_per_subtask", + 3, + ) + + benchmark = highvar_module.MutatorHighVarBenchmark() + target_stats = benchmark.target_statistics + + assert isinstance(target_stats, xr.Dataset) + assert target_stats.phat.mean("subtask").sizes == {"trial": 100} + assert target_stats.boot_phat.mean("subtask").sizes == { + "boot_iter": 4, + "trial": 100, + } + assert target_stats.sel(subtask=benchmark.subtask_names[0]).phat.sizes == { + "trial": 100 + } + + result = benchmark.score_model(RandomGuesser(seed=0)) + assert isinstance(result.model_statistics, xr.Dataset) + assert result.model_statistics.boot_phat.sizes == { + "boot_iter": 4, + "subtask": 2, + "trial": 100, + } + + +def test_oneshot_target_and_model_statistics_behave_like_datasets(monkeypatch): + monkeypatch.setattr( + oneshot_module, + "load_imageset_meta_oneshot", + lambda cachedir=None: _make_oneshot_images_df(), + ) + monkeypatch.setattr( + oneshot_module, + "load_oneshot_behavior", + lambda cachedir=None: _make_oneshot_behavior_df(), + ) + monkeypatch.setattr( + oneshot_module.MutatorOneshotBenchmark, + "subtask_names", + ["CatA,CatB"], + ) + monkeypatch.setattr( + oneshot_module.MutatorOneshotBenchmark, + "transformation_ids", + ["blur | 0.1", "noise | 0.2"], + ) + monkeypatch.setattr( + oneshot_module.MutatorOneshotBenchmark, + "num_bootstrap_samples", + 4, + ) + monkeypatch.setattr( + oneshot_module.MutatorOneshotBenchmark, + "num_simulations_per_subtask", + 3, + ) + monkeypatch.setattr( + oneshot_module.MutatorOneshotBenchmark, + "bootstrap_target_by_worker", + False, + ) + + benchmark = oneshot_module.MutatorOneshotBenchmark() + target_stats = benchmark.target_statistics + + assert isinstance(target_stats, xr.Dataset) + assert [name for name, _ in target_stats.groupby("transformation_type")] == [ + "blur", + "noise", + ] + assert target_stats.sel(transformation="blur | 0.1").phat.sizes == {} + assert target_stats.boot_phat.sizes == { + "boot_iter": 4, + "transformation": 2, + } + + result = benchmark.score_model(RandomGuesser(seed=0)) + assert isinstance(result.model_statistics, xr.Dataset) + assert result.model_statistics.sel( + transformation=target_stats.transformation + ).phat.sizes == {"transformation": 2} From 1e7bddcc8782c7e0bdbd4e1bdd64d2916051b002 Mon Sep 17 00:00:00 2001 From: himjl <mil@mit.edu> Date: Thu, 26 Mar 2026 23:39:32 -0400 Subject: [PATCH 150/156] platformdirs --- README.md | 7 +++++++ hobj/data/download.py | 20 +++++++++++--------- pyproject.toml | 4 ++++ tests/test_data_loader_cache.py | 12 ++++++++++++ uv.lock | 2 ++ 5 files changed, 36 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 0e899c3..0662f85 100644 --- a/README.md +++ b/README.md @@ -57,6 +57,13 @@ print(result.msen, result.msen_CI95) # print(result.model_statistics) ``` +On first use, the packaged dataset is downloaded automatically into a +user-scoped data directory managed by `platformdirs` rather than into the +repository root. On macOS this is typically +`~/Library/Application Support/hobj/data`. +To use a different location, pass `cachedir=...` to a data loader or benchmark +constructor, or prefetch manually with +`hobj-download-data --cachedir /path/to/data`. For more details (e.g., how to load the raw behavioral data or images in Python), check out the Jupyter notebooks in `examples/`. diff --git a/hobj/data/download.py b/hobj/data/download.py index f58205a..e1eaad2 100644 --- a/hobj/data/download.py +++ b/hobj/data/download.py @@ -11,6 +11,7 @@ from urllib.parse import urlparse import requests +from platformdirs import user_data_dir from tqdm import tqdm @@ -30,17 +31,18 @@ _DOWNLOAD_CHUNK_SIZE_BYTES = 1024 * 1024 -def _get_repo_root() -> Path: - """Return the repository root for this package.""" - return Path(__file__).resolve().parents[2] +def _get_default_data_root(repo_root: Path | None = None) -> Path: + """Return the default packaged data directory. + Args: + repo_root: Unused legacy parameter kept for compatibility with the + public function signature. -def _get_default_data_root(repo_root: Path | None = None) -> Path: - """Return the default packaged data directory.""" - resolved_repo_root = ( - repo_root if repo_root is not None else _get_repo_root() - ).resolve() - return resolved_repo_root / "data" + Returns: + A user-scoped persistent data directory outside the installed package. + """ + del repo_root + return Path(user_data_dir("hobj", appauthor=False)).resolve() / "data" def _get_missing_expected_paths(data_root: Path) -> list[Path]: diff --git a/pyproject.toml b/pyproject.toml index 4569a12..d75a317 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,6 +18,7 @@ dependencies = [ "numpy>=2.2", "pandas>=2.2", "matplotlib>=3.9", + "platformdirs>=4.3", "requests>=2.32", "tqdm>=4.67", "pydantic>=2.10", @@ -25,6 +26,9 @@ dependencies = [ "scipy>=1.17.1", ] +[project.scripts] +hobj-download-data = "hobj.data.download:main" + [dependency-groups] dev = [ "enczoo==0.1.3.dev1", diff --git a/tests/test_data_loader_cache.py b/tests/test_data_loader_cache.py index 1228772..d275d50 100644 --- a/tests/test_data_loader_cache.py +++ b/tests/test_data_loader_cache.py @@ -246,3 +246,15 @@ def test_resolve_data_root_extracts_nested_images_archive_without_redownload( assert resolved_cache == custom_cache.resolve() assert (custom_cache / "images" / "highvar.png").exists() assert not (custom_cache / "images.tar.gz").exists() + + +def test_get_default_data_root_uses_user_scoped_directory(monkeypatch) -> None: + monkeypatch.setattr( + download_module, + "user_data_dir", + lambda *args, **kwargs: "/tmp/hobj-platformdirs-root", + ) + + resolved_path = download_module._get_default_data_root() + + assert resolved_path == Path("/tmp/hobj-platformdirs-root/data").resolve() diff --git a/uv.lock b/uv.lock index 84c8fc8..4a3d1e8 100644 --- a/uv.lock +++ b/uv.lock @@ -700,6 +700,7 @@ dependencies = [ { name = "matplotlib" }, { name = "numpy" }, { name = "pandas" }, + { name = "platformdirs" }, { name = "pydantic" }, { name = "requests" }, { name = "scipy" }, @@ -721,6 +722,7 @@ requires-dist = [ { name = "matplotlib", specifier = ">=3.9" }, { name = "numpy", specifier = ">=2.2" }, { name = "pandas", specifier = ">=2.2" }, + { name = "platformdirs", specifier = ">=4.3" }, { name = "pydantic", specifier = ">=2.10" }, { name = "requests", specifier = ">=2.32" }, { name = "scipy", specifier = ">=1.17.1" }, From 6d38f83f65ac6e4c8383f8077fdb3106f1aa7c8a Mon Sep 17 00:00:00 2001 From: himjl <mil@mit.edu> Date: Thu, 26 Mar 2026 23:52:29 -0400 Subject: [PATCH 151/156] download to home --- README.md | 15 ++++++--------- hobj/data/download.py | 12 +++++++++--- pyproject.toml | 1 - tests/test_data_loader_cache.py | 16 +++++++++------- uv.lock | 2 -- 5 files changed, 24 insertions(+), 22 deletions(-) diff --git a/README.md b/README.md index 0662f85..9825640 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,10 @@ If you just want to download the raw data and images without using the `hobj` li The `hobj` package works for Python >=3.12. After cloning this repository on your machine, navigate to this directory in your shell and run: -Note that on first use, the packaged dataset is downloaded automatically into `./data` from the OSF repository. In total, `hobj` takes up around ~600 MB of space on your computer. +On first use, the packaged dataset is downloaded automatically from the OSF +repository into a versioned cache directory under `~/.hobj_cache`. The default +path is `~/.hobj_cache/pj6wm-v1/data`. In total, `hobj` takes up around ~1 GB +of space on your computer. ### Using `hobj` to comparing a linear learner against human learning data @@ -57,17 +60,11 @@ print(result.msen, result.msen_CI95) # print(result.model_statistics) ``` -On first use, the packaged dataset is downloaded automatically into a -user-scoped data directory managed by `platformdirs` rather than into the -repository root. On macOS this is typically -`~/Library/Application Support/hobj/data`. -To use a different location, pass `cachedir=...` to a data loader or benchmark -constructor, or prefetch manually with -`hobj-download-data --cachedir /path/to/data`. For more details (e.g., how to load the raw behavioral data or images in Python), check out the Jupyter notebooks in `examples/`. - +To use a different location, pass `cachedir=...` to a data loader or benchmark +constructor, or prefetch manually with `hobj-download-data --cachedir /path/to/data`. ## Contact If you have any questions, need help, or experience a bug, please don't hesitate to email me ([mil@mit.edu](mailto:name@example.com)), or open an issue on this repo! diff --git a/hobj/data/download.py b/hobj/data/download.py index e1eaad2..62a797e 100644 --- a/hobj/data/download.py +++ b/hobj/data/download.py @@ -11,12 +11,13 @@ from urllib.parse import urlparse import requests -from platformdirs import user_data_dir from tqdm import tqdm # Public OSF node that hosts the packaged HOBJ dataset files. OSF_NODE_ID = "pj6wm" +# Bump this to invalidate the local cache when the packaged OSF dataset changes. +DATASET_CACHE_VERSION = "v2" DATA_ARCHIVE_URL = ( f"https://files.osf.io/v1/resources/{OSF_NODE_ID}/providers/osfstorage/?zip=" ) @@ -39,10 +40,15 @@ def _get_default_data_root(repo_root: Path | None = None) -> Path: public function signature. Returns: - A user-scoped persistent data directory outside the installed package. + A persistent per-user cache directory outside the installed package. """ del repo_root - return Path(user_data_dir("hobj", appauthor=False)).resolve() / "data" + return ( + Path.home().resolve() + / ".hobj_cache" + / f"{OSF_NODE_ID}-{DATASET_CACHE_VERSION}" + / "data" + ) def _get_missing_expected_paths(data_root: Path) -> list[Path]: diff --git a/pyproject.toml b/pyproject.toml index d75a317..14eef81 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,7 +18,6 @@ dependencies = [ "numpy>=2.2", "pandas>=2.2", "matplotlib>=3.9", - "platformdirs>=4.3", "requests>=2.32", "tqdm>=4.67", "pydantic>=2.10", diff --git a/tests/test_data_loader_cache.py b/tests/test_data_loader_cache.py index d275d50..4ef9035 100644 --- a/tests/test_data_loader_cache.py +++ b/tests/test_data_loader_cache.py @@ -248,13 +248,15 @@ def test_resolve_data_root_extracts_nested_images_archive_without_redownload( assert not (custom_cache / "images.tar.gz").exists() -def test_get_default_data_root_uses_user_scoped_directory(monkeypatch) -> None: - monkeypatch.setattr( - download_module, - "user_data_dir", - lambda *args, **kwargs: "/tmp/hobj-platformdirs-root", - ) +def test_get_default_data_root_uses_versioned_home_cache(monkeypatch) -> None: + monkeypatch.setattr(download_module.Path, "home", lambda: Path("/tmp/fake-home")) resolved_path = download_module._get_default_data_root() - assert resolved_path == Path("/tmp/hobj-platformdirs-root/data").resolve() + assert ( + resolved_path + == Path( + f"/tmp/fake-home/.hobj_cache/{download_module.OSF_NODE_ID}-" + f"{download_module.DATASET_CACHE_VERSION}/data" + ).resolve() + ) diff --git a/uv.lock b/uv.lock index 4a3d1e8..84c8fc8 100644 --- a/uv.lock +++ b/uv.lock @@ -700,7 +700,6 @@ dependencies = [ { name = "matplotlib" }, { name = "numpy" }, { name = "pandas" }, - { name = "platformdirs" }, { name = "pydantic" }, { name = "requests" }, { name = "scipy" }, @@ -722,7 +721,6 @@ requires-dist = [ { name = "matplotlib", specifier = ">=3.9" }, { name = "numpy", specifier = ">=2.2" }, { name = "pandas", specifier = ">=2.2" }, - { name = "platformdirs", specifier = ">=4.3" }, { name = "pydantic", specifier = ">=2.10" }, { name = "requests", specifier = ">=2.32" }, { name = "scipy", specifier = ">=1.17.1" }, From 7d3990561160e0c6ee9d6967a7bb59095986d02b Mon Sep 17 00:00:00 2001 From: himjl <mil@mit.edu> Date: Thu, 26 Mar 2026 23:53:46 -0400 Subject: [PATCH 152/156] regen --- examples/score_model.ipynb | 66 +++++++++++++++++++------------------- 1 file changed, 33 insertions(+), 33 deletions(-) diff --git a/examples/score_model.ipynb b/examples/score_model.ipynb index a3f3b84..deb69bf 100644 --- a/examples/score_model.ipynb +++ b/examples/score_model.ipynb @@ -3,8 +3,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:59:18.817707Z", - "start_time": "2026-03-27T00:59:18.512737Z" + "end_time": "2026-03-27T03:53:14.780221Z", + "start_time": "2026-03-27T03:53:14.450467Z" } }, "cell_type": "code", @@ -21,8 +21,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:59:22.589878Z", - "start_time": "2026-03-27T00:59:18.823770Z" + "end_time": "2026-03-27T03:53:18.055705Z", + "start_time": "2026-03-27T03:53:14.783113Z" } }, "cell_type": "code", @@ -46,8 +46,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:59:22.636794Z", - "start_time": "2026-03-27T00:59:22.620737Z" + "end_time": "2026-03-27T03:53:18.102448Z", + "start_time": "2026-03-27T03:53:18.084732Z" } }, "cell_type": "code", @@ -65,8 +65,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:59:22.681025Z", - "start_time": "2026-03-27T00:59:22.638912Z" + "end_time": "2026-03-27T03:53:18.142566Z", + "start_time": "2026-03-27T03:53:18.104374Z" } }, "cell_type": "code", @@ -87,8 +87,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:59:35.267760Z", - "start_time": "2026-03-27T00:59:22.682833Z" + "end_time": "2026-03-27T03:53:30.344682Z", + "start_time": "2026-03-27T03:53:18.143795Z" } }, "cell_type": "code", @@ -103,23 +103,23 @@ "name": "stderr", "output_type": "stream", "text": [ - "Subtask simulations:: 100%|██████████| 64/64 [00:10<00:00, 6.28it/s]\n" + "Subtask simulations:: 100%|██████████| 64/64 [00:09<00:00, 6.49it/s]\n" ] }, { "data": { "text/plain": [ - "MutatorHighVarBenchmark.LearningCurveBenchmarkResult(msen=0.005581336095785859, msen_sigma=0.00028932598735009263, msen_CI95=(0.005015524275339106, 0.0061321115686366525), lapse_rate=<xarray.DataArray 'phat' ()> Size: 8B\n", - "array(0.07933472), model_statistics=<xarray.Dataset> Size: 103MB\n", + "MutatorHighVarBenchmark.LearningCurveBenchmarkResult(msen=0.0055892710374308065, msen_sigma=0.0002920322833183841, msen_CI95=(0.005035330830028786, 0.006137235513358789), lapse_rate=<xarray.DataArray 'phat' ()> Size: 8B\n", + "array(0.07971531), model_statistics=<xarray.Dataset> Size: 103MB\n", "Dimensions: (subtask: 64, trial: 100, boot_iter: 1000)\n", "Coordinates:\n", " * subtask (subtask) <U33 8kB 'MutatorObject000,MutatorObject095' ...\n", "Dimensions without coordinates: trial, boot_iter\n", "Data variables:\n", - " phat (subtask, trial) float64 51kB 0.538 0.684 ... 0.904 0.916\n", - " varhat_phat (subtask, trial) float64 51kB 0.0004981 ... 0.0001542\n", - " boot_phat (boot_iter, subtask, trial) float64 51MB 0.512 ... 0.92\n", - " boot_varhat_phat (boot_iter, subtask, trial) float64 51MB 0.0005007 ... ...)" + " phat (subtask, trial) float64 51kB 0.496 0.684 ... 0.928 0.918\n", + " varhat_phat (subtask, trial) float64 51kB 0.000501 ... 0.0001509\n", + " boot_phat (boot_iter, subtask, trial) float64 51MB 0.47 ... 0.918\n", + " boot_varhat_phat (boot_iter, subtask, trial) float64 51MB 0.0004992 ... ...)" ] }, "execution_count": 5, @@ -132,8 +132,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:59:35.374159Z", - "start_time": "2026-03-27T00:59:35.276935Z" + "end_time": "2026-03-27T03:53:30.445355Z", + "start_time": "2026-03-27T03:53:30.353255Z" } }, "cell_type": "code", @@ -163,7 +163,7 @@ "text/plain": [ "<Figure size 640x480 with 1 Axes>" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -177,8 +177,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:59:35.452286Z", - "start_time": "2026-03-27T00:59:35.384724Z" + "end_time": "2026-03-27T03:53:30.519863Z", + "start_time": "2026-03-27T03:53:30.453561Z" } }, "cell_type": "code", @@ -206,7 +206,7 @@ "text/plain": [ "<Figure size 500x500 with 1 Axes>" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -220,8 +220,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:59:38.804077Z", - "start_time": "2026-03-27T00:59:35.459144Z" + "end_time": "2026-03-27T03:53:33.098646Z", + "start_time": "2026-03-27T03:53:30.525642Z" } }, "cell_type": "code", @@ -234,7 +234,7 @@ { "data": { "text/plain": [ - "MutatorOneshotBenchmark.GeneralizationBenchmarkResult(msen=0.018127572924906018, msen_sigma=0.002420319066024304, msen_CI95=(0.013090980755670358, 0.022419578374065857), model_statistics=<xarray.Dataset> Size: 583kB\n", + "MutatorOneshotBenchmark.GeneralizationBenchmarkResult(msen=0.018940506085930897, msen_sigma=0.002367913012416273, msen_CI95=(0.013982943571075011, 0.02329907055826775), model_statistics=<xarray.Dataset> Size: 583kB\n", "Dimensions: (transformation: 36, boot_iter: 1000)\n", "Coordinates:\n", " * transformation (transformation) <U26 4kB 'backgrounds | 0.1' ... '...\n", @@ -242,10 +242,10 @@ " transformation_level (transformation) float64 288B 0.1 0.2154 ... 0.5 1.5\n", "Dimensions without coordinates: boot_iter\n", "Data variables:\n", - " phat (transformation) float64 288B 0.9745 0.9752 ... 0.9432\n", - " varhat_phat (transformation) float64 288B 5.767e-06 ... 2.539e-05\n", - " boot_phat (boot_iter, transformation) float64 288kB 0.9774 .....\n", - " boot_varhat_phat (boot_iter, transformation) float64 288kB 5.235e-06...)" + " phat (transformation) float64 288B 0.9751 0.9745 ... 0.9563\n", + " varhat_phat (transformation) float64 288B 5.639e-06 ... 1.944e-05\n", + " boot_phat (boot_iter, transformation) float64 288kB 0.9747 .....\n", + " boot_varhat_phat (boot_iter, transformation) float64 288kB 5.855e-06...)" ] }, "execution_count": 8, @@ -258,8 +258,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:59:38.855329Z", - "start_time": "2026-03-27T00:59:38.811594Z" + "end_time": "2026-03-27T03:53:33.146922Z", + "start_time": "2026-03-27T03:53:33.106147Z" } }, "cell_type": "code", @@ -285,7 +285,7 @@ "text/plain": [ "<Figure size 500x500 with 1 Axes>" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", From 5b184aafef650406e1a7e3c7c7558259e64b7f94 Mon Sep 17 00:00:00 2001 From: himjl <mil@mit.edu> Date: Fri, 27 Mar 2026 00:04:59 -0400 Subject: [PATCH 153/156] readme note --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9825640..a479c7e 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ The `hobj` package works for Python >=3.12. After cloning this repository on you On first use, the packaged dataset is downloaded automatically from the OSF repository into a versioned cache directory under `~/.hobj_cache`. The default -path is `~/.hobj_cache/pj6wm-v1/data`. In total, `hobj` takes up around ~1 GB +path is `~/.hobj_cache/pj6wm-v2/data`. In total, `hobj` takes up around ~1 GB of space on your computer. From 87efc56fcb6d8ae796343d143da16f8b60e49ea8 Mon Sep 17 00:00:00 2001 From: himjl <mil@mit.edu> Date: Fri, 27 Mar 2026 00:15:02 -0400 Subject: [PATCH 154/156] published to pypi --- Makefile | 17 +++++++++++++++++ README.md | 20 ++++++++++++++------ pyproject.toml | 2 +- 3 files changed, 32 insertions(+), 7 deletions(-) diff --git a/Makefile b/Makefile index bf3fd22..ef16cf7 100644 --- a/Makefile +++ b/Makefile @@ -9,3 +9,20 @@ check: test: uv run pytest tests + + +publish: + @version="$$(uv version --short)"; \ + case "$$version" in \ + *.dev*) ;; \ + *) \ + printf "Version %s does not include .dev. Continue? [y/N] " "$$version"; \ + read -r answer; \ + case "$$answer" in \ + y|Y) ;; \ + *) echo "Aborted."; exit 1 ;; \ + esac ;; \ + esac; \ + rm -rf dist && \ + uv build && \ + uv publish diff --git a/README.md b/README.md index a479c7e..85c27db 100644 --- a/README.md +++ b/README.md @@ -8,18 +8,26 @@ This repository contains benchmarks for comparing models of object learning agai <img src="site/readme_images/human_learning_curves.svg" alt="Alt text" > </div> -If you just want to download the raw data and images without using the `hobj` library, check out the [OSF repository](https://osf.io/pj6wm/files/osfstorage) for this project. +If you just want to download the raw data and images without using the `hobj` library, you can do so at the [OSF repository](https://osf.io/pj6wm/files/osfstorage) for this project. ## Quickstart ### Install -The `hobj` package works for Python >=3.12. After cloning this repository on your machine, navigate to this directory in your shell and run: +The `hobj` package works for Python >=3.12. -On first use, the packaged dataset is downloaded automatically from the OSF -repository into a versioned cache directory under `~/.hobj_cache`. The default -path is `~/.hobj_cache/pj6wm-v2/data`. In total, `hobj` takes up around ~1 GB -of space on your computer. +Install it from [PyPI](https://pypi.org/project/hobj/): + +```bash +pip install hobj +``` + +If you prefer to use [uv](https://docs.astral.sh/uv/getting-started/installation/), +you can install it with: + +```bash +uv pip install hobj +``` ### Using `hobj` to comparing a linear learner against human learning data diff --git a/pyproject.toml b/pyproject.toml index 14eef81..93b9a1b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "uv_build" [project] name = "hobj" -version = "2.0.0" +version = "2.0.0.dev1" description = "Human object learning benchmarks" authors = [ { name = "Michael J. Lee", email = "mil@mit.edu" } From d28ca55593616c96eadbd7063521c517c0eafd89 Mon Sep 17 00:00:00 2001 From: himjl <mil@mit.edu> Date: Fri, 27 Mar 2026 00:16:10 -0400 Subject: [PATCH 155/156] v2 version number --- pyproject.toml | 2 +- uv.lock | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 93b9a1b..14eef81 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "uv_build" [project] name = "hobj" -version = "2.0.0.dev1" +version = "2.0.0" description = "Human object learning benchmarks" authors = [ { name = "Michael J. Lee", email = "mil@mit.edu" } diff --git a/uv.lock b/uv.lock index 84c8fc8..8eaedae 100644 --- a/uv.lock +++ b/uv.lock @@ -694,7 +694,7 @@ wheels = [ [[package]] name = "hobj" -version = "2.0.0" +version = "2.0.0.dev1" source = { editable = "." } dependencies = [ { name = "matplotlib" }, From df3070c377f9785758576fd41c193f5aec440988 Mon Sep 17 00:00:00 2001 From: himjl <mil@mit.edu> Date: Fri, 27 Mar 2026 00:17:01 -0400 Subject: [PATCH 156/156] update uv lock --- uv.lock | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/uv.lock b/uv.lock index 8eaedae..84c8fc8 100644 --- a/uv.lock +++ b/uv.lock @@ -694,7 +694,7 @@ wheels = [ [[package]] name = "hobj" -version = "2.0.0.dev1" +version = "2.0.0" source = { editable = "." } dependencies = [ { name = "matplotlib" },

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