diff --git a/resampling-texts/README.md b/resampling-texts/README.md index 69f6740..a8af826 100644 --- a/resampling-texts/README.md +++ b/resampling-texts/README.md @@ -32,7 +32,14 @@ Using only texts selected through LS sampling allows new models to be efficientl fig3

+### Prediction of Model's Performance (Figure4) +Using model coordinates from unique texts, we predict the average performance across six downstream tasks with ridge regression. +See [`code_for_prediction/`](./code_for_prediction/) for details. + +

+fig4 +

## 🦉 Misc. diff --git a/resampling-texts/code_for_prediction/.gitignore b/resampling-texts/code_for_prediction/.gitignore new file mode 100644 index 0000000..4e4683c --- /dev/null +++ b/resampling-texts/code_for_prediction/.gitignore @@ -0,0 +1,226 @@ +### Generated by gibo (https://github.com/simonwhitaker/gibo) +### https://raw.github.com/github/gitignore/d0b80a469983a7beece8fa1f5c48a8242318b531/Global/Vim.gitignore + +# Swap +[._]*.s[a-v][a-z] +!*.svg # comment out if you don't need vector files +[._]*.sw[a-p] +[._]s[a-rt-v][a-z] +[._]ss[a-gi-z] +[._]sw[a-p] + +# Session +Session.vim +Sessionx.vim + +# Temporary +.netrwhist +*~ +# Auto-generated tag files +tags +# Persistent undo +[._]*.un~ + + +### https://raw.github.com/github/gitignore/d0b80a469983a7beece8fa1f5c48a8242318b531/Python.gitignore + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + + +### https://raw.github.com/github/gitignore/d0b80a469983a7beece8fa1f5c48a8242318b531/C++.gitignore + +# Prerequisites +*.d + +# Compiled Object files +*.slo +*.lo +*.o +*.obj + +# Precompiled Headers +*.gch +*.pch + +# Compiled Dynamic libraries +*.so +*.dylib +*.dll + +# Fortran module files +*.mod +*.smod + +# Compiled Static libraries +*.lai +*.la +*.a +*.lib + +# Executables +*.exe +*.out +*.app + +# Added manually +.DS_Store +output/ \ No newline at end of file diff --git a/resampling-texts/code_for_prediction/.python-version b/resampling-texts/code_for_prediction/.python-version new file mode 100644 index 0000000..24ee5b1 --- /dev/null +++ b/resampling-texts/code_for_prediction/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/resampling-texts/code_for_prediction/README.md b/resampling-texts/code_for_prediction/README.md new file mode 100644 index 0000000..1fd2172 --- /dev/null +++ b/resampling-texts/code_for_prediction/README.md @@ -0,0 +1,59 @@ +## Model Performance Prediction (Section 4.3 and Appendix F) + +We use [`uv`](https://docs.astral.sh/uv/) for the experiment environment in this directory. See the official site for installation. + +### Setup + +With `uv` available, install the required packages: + +```bash +$ uv sync +``` + +The experiments use [`../data/modeldata_1018.pkl`](../data/modeldata_1018.pkl) and [`../data/uniq-idx-weight/`](../data/uniq-idx-weight/). See [`../README.md`](../README.md) for details of these data. + +### Data Preparation + +Prepare training and prediction splits for ridge regression with `GroupKFold`: + +```bash +$ uv run src/split_data.py +``` + +Five-fold splits with five seeds are saved to `output/split_data/groupkfold/`. + +### Train and Predict with Ridge Regression + +Train the ridge regression models and generate predictions (**This step takes about half a day !**) : + +```bash +$ uv run src/train_and_pred.py +``` + +Predictions for each method (Uniform, KL, LS) are saved to `output/train_and_pred/groupkfold/`. + +### Plot Figures + +Draw Figure 4 from the predictions: + +```bash +$ uv run src/figure4.py +``` + +Figure 4 is saved to `output/images/`. + +

+fig4 +

+ +Draw Figure 6 from the predictions: + +```bash +$ uv run src/figure6_and_table2.py +``` + +

+fig6 +

+ +Figure 6 is saved to `output/images/`. This script also saves the results for Table 2 to `output/summary/`. diff --git a/resampling-texts/code_for_prediction/figures/fig4.png b/resampling-texts/code_for_prediction/figures/fig4.png new file mode 100644 index 0000000..360ffb7 Binary files /dev/null and b/resampling-texts/code_for_prediction/figures/fig4.png differ diff --git a/resampling-texts/code_for_prediction/figures/fig6.png b/resampling-texts/code_for_prediction/figures/fig6.png new file mode 100644 index 0000000..756f290 Binary files /dev/null and b/resampling-texts/code_for_prediction/figures/fig6.png differ diff --git a/resampling-texts/code_for_prediction/pyproject.toml b/resampling-texts/code_for_prediction/pyproject.toml new file mode 100644 index 0000000..38eb227 --- /dev/null +++ b/resampling-texts/code_for_prediction/pyproject.toml @@ -0,0 +1,14 @@ +[project] +name = "code-for-prediction" +version = "0.1.0" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "numpy", + "pandas", + "scikit-learn", + "fire", + "matplotlib", + "tqdm", + "jinja2>=3.1.6", +] diff --git a/resampling-texts/code_for_prediction/src/figure4.py b/resampling-texts/code_for_prediction/src/figure4.py new file mode 100644 index 0000000..26c6e1b --- /dev/null +++ b/resampling-texts/code_for_prediction/src/figure4.py @@ -0,0 +1,196 @@ +import logging +import pickle as pkl +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from scipy.stats import pearsonr, spearmanr +from tqdm import tqdm + +from utils import (KLS_WEIGHT_PATH, LSS_WEIGHT_PATH, TASK_NAMES, + UNIFORM_WEIGHT_PATH) + +logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, +) +logger = logging.getLogger(__name__) + + +def main(sample_seed=42): + split = "groupkfold" + input_dir = Path("output/split_data") / split + methods = ["uniform", "kls", "lss"] + method2results = dict() + for method in methods: + + if method == "lss": + WEIGHT_PATH = LSS_WEIGHT_PATH + elif method == "uniform": + WEIGHT_PATH = UNIFORM_WEIGHT_PATH + elif method == "kls": + WEIGHT_PATH = KLS_WEIGHT_PATH + + with open(WEIGHT_PATH, "rb") as f: + idx_weight = pkl.load(f) + + n_resamples_list = [10 * i for i in range(1, 10)] + n_resamples_list += [100 * i for i in range(1, 10)] + n_resamples_list += [1000 * i for i in range(1, 11)] + + pred_dir = Path("output/train_and_pred") / split / method + metric2task_name2results = { + "pearson": {task_name: [] for task_name in TASK_NAMES}, + "spearman": {task_name: [] for task_name in TASK_NAMES}, + } + for n_resamples in tqdm(n_resamples_list): + unique_index = idx_weight[f"{sample_seed}" + ][f"{n_resamples}"]["unique_index"] + d = len(unique_index) + task_name2scores = { + task_name: {"pearson": [], "spearman": [] + } for task_name in TASK_NAMES + } + for split_seed in range(5): + input_path = input_dir / f"split_seed{split_seed}.csv" + df = pd.read_csv(input_path) + + pred_path = ( + pred_dir / f"split_seed{split_seed}-" + f"sample_seed{sample_seed}-" + f"n_resamples{n_resamples}.csv" + ) + if not pred_path.exists(): + continue + + pred_df = pd.read_csv(pred_path) + + assert df["model_name"].equals(pred_df["model_name"]) + + for task_name in TASK_NAMES: + true = df[task_name].values + pred = pred_df[task_name].values + assert len(true) == len(pred) + + pearson = pearsonr(true, pred)[0] + task_name2scores[task_name]["pearson"].append(pearson) + spearman = spearmanr(true, pred)[0] + task_name2scores[task_name]["spearman"].append(spearman) + + for metric in ["pearson", "spearman"]: + for task_name in TASK_NAMES: + scores = task_name2scores[task_name][metric] + mean_score = np.mean(scores) + std_score = np.std(scores) + metric2task_name2results[metric][task_name].append( + { + "n_resamples": n_resamples, + "d": d, + "mean_score": mean_score, + "std_score": std_score, + } + ) + method2results[method] = metric2task_name2results + + for metric in ["pearson",]: + method2color = { + "uniform": "blue", + "lss": "red", + "kls": "green", + } + task_name2title = { + "ARC": "ARC", + "HellaSwag": "HellaSwag", + "MMLU": "MMLU", + "TruthfulQA": "TruthfulQA", + "Winogrande": "Winogrande", + "GSM8K": "GSM8K", + "6-taskmean": "6-TaskMean", + "mean_logp": "mean log-likelihood", + } + method2label = { + "uniform": "Uniform", + "lss": "LS", + "kls": "KL", + } + method2marker = { + "uniform": "o", + "lss": "s", + "kls": "^", + } + + title_fs = 36 + label_fs = 32 + label_yfs = 36 + tick_fs = 32 + tick_xfs = 28 + legend_fs = 28 + linewidth = 3 + markersize = 8 + + fig, ax = plt.subplots(figsize=(10, 6.5)) + task_name = "6-taskmean" + ax.set_title(task_name2title[task_name], fontsize=title_fs) + + for method in methods: + metric2task_name2results = method2results[method] + results = metric2task_name2results[metric][task_name] + ds = [result["d"] for result in results] + mean_score = [result["mean_score"] for result in results] + std_score = [result["std_score"] for result in results] + ax.plot( + ds, + mean_score, + label=method2label[method], + marker=method2marker[method], + markersize=markersize, + color=method2color[method], + linewidth=linewidth, + ) + ax.fill_between( + ds, + np.array(mean_score) - np.array(std_score), + np.array(mean_score) + np.array(std_score), + alpha=0.2, + color=method2color[method], + ) + + ax.set_xlabel("Unique Samples: $d$", fontsize=label_fs) + if metric == "pearson": + ax.set_ylabel("Pearson's $r$", fontsize=label_yfs) + elif metric == "spearman": + ax.set_ylabel("Spearman's $\\rho$", fontsize=label_fs) + ax.set_xscale("log") + ax.set_ylim(-0.1, 1.1) + ax.set_yticks( + [0, 0.25, 0.5, 0.75, 1], + ) + # grid + ax.grid(which="both", linestyle="--", linewidth=0.5) + # ax.tick_params(axis="both", which="major", labelsize=tick_fs) + # x axis + ax.tick_params(axis="x", which="major", labelsize=tick_xfs) + # y axis + ax.tick_params(axis="y", which="major", labelsize=tick_fs) + ax.legend(fontsize=legend_fs, loc="lower right") + + output_path = Path("output/images") /\ + f"fig4_{metric}.pdf" + output_path.parent.mkdir(parents=True, exist_ok=True) + fig.subplots_adjust( + left=0.17, + right=0.99, + bottom=0.16, + top=0.92, + wspace=0.35, + hspace=0.35 + ) + fig.savefig(output_path) + logger.info(f"Saved figure to {output_path}") + + +if __name__ == "__main__": + import fire + fire.Fire(main) diff --git a/resampling-texts/code_for_prediction/src/figure6_and_table2.py b/resampling-texts/code_for_prediction/src/figure6_and_table2.py new file mode 100644 index 0000000..f37be71 --- /dev/null +++ b/resampling-texts/code_for_prediction/src/figure6_and_table2.py @@ -0,0 +1,225 @@ +import logging +import pickle as pkl +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from scipy.stats import pearsonr, spearmanr +from tqdm import tqdm + +from utils import (KLS_WEIGHT_PATH, LSS_WEIGHT_PATH, TASK_NAMES, + UNIFORM_WEIGHT_PATH) + +logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, +) +logger = logging.getLogger(__name__) + + +def main(sample_seed=42): + split = "groupkfold" + input_dir = Path("output/split_data") / split + methods = ["uniform", "kls", "lss"] + method2results = dict() + for method in methods: + + if method == "lss": + WEIGHT_PATH = LSS_WEIGHT_PATH + elif method == "uniform": + WEIGHT_PATH = UNIFORM_WEIGHT_PATH + elif method == "kls": + WEIGHT_PATH = KLS_WEIGHT_PATH + + with open(WEIGHT_PATH, "rb") as f: + idx_weight = pkl.load(f) + + n_resamples_list = [10 * i for i in range(1, 10)] + n_resamples_list += [100 * i for i in range(1, 10)] + n_resamples_list += [1000 * i for i in range(1, 11)] + + pred_dir = Path("output/train_and_pred") / split / method + metric2task_name2results = { + "pearson": {task_name: [] for task_name in TASK_NAMES}, + "spearman": {task_name: [] for task_name in TASK_NAMES}, + } + for n_resamples in tqdm(n_resamples_list): + unique_index = idx_weight[f"{sample_seed}" + ][f"{n_resamples}"]["unique_index"] + d = len(unique_index) + task_name2scores = { + task_name: {"pearson": [], "spearman": [] + } for task_name in TASK_NAMES + } + for split_seed in range(5): + input_path = input_dir / f"split_seed{split_seed}.csv" + df = pd.read_csv(input_path) + + pred_path = ( + pred_dir / f"split_seed{split_seed}-" + f"sample_seed{sample_seed}-" + f"n_resamples{n_resamples}.csv" + ) + if not pred_path.exists(): + continue + + pred_df = pd.read_csv(pred_path) + + assert df["model_name"].equals(pred_df["model_name"]) + + for task_name in TASK_NAMES: + true = df[task_name].values + pred = pred_df[task_name].values + assert len(true) == len(pred) + + pearson = pearsonr(true, pred)[0] + task_name2scores[task_name]["pearson"].append(pearson) + spearman = spearmanr(true, pred)[0] + task_name2scores[task_name]["spearman"].append(spearman) + + for metric in ["pearson", "spearman"]: + for task_name in TASK_NAMES: + scores = task_name2scores[task_name][metric] + mean_score = np.mean(scores) + std_score = np.std(scores) + metric2task_name2results[metric][task_name].append( + { + "n_resamples": n_resamples, + "d": d, + "mean_score": mean_score, + "std_score": std_score, + } + ) + method2results[method] = metric2task_name2results + + for metric in ["pearson",]: + method2color = { + "uniform": "blue", + "lss": "red", + "kls": "green", + } + task_name2title = { + "ARC": "ARC", + "HellaSwag": "HellaSwag", + "MMLU": "MMLU", + "TruthfulQA": "TruthfulQA", + "Winogrande": "Winogrande", + "GSM8K": "GSM8K", + "6-taskmean": "6-TaskMean", + "mean_logp": "mean log-likelihood", + } + method2label = { + "uniform": "Uniform", + "lss": "LS", + "kls": "KL", + } + method2marker = { + "uniform": "o", + "lss": "s", + "kls": "^", + } + + fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(22, 10)) + title_fs = 24 + label_fs = 24 + tick_fs = 24 + legend_fs = 20 + linewidth = 3 + markersize = 8 + for idx, task_name in enumerate(TASK_NAMES): + ax = axes[idx % 2, (idx // 2) % 4] + ax.set_title(task_name2title[task_name], fontsize=title_fs) + + for method in methods: + metric2task_name2results = method2results[method] + results = metric2task_name2results[metric][task_name] + ds = [result["d"] for result in results] + mean_score = [result["mean_score"] for result in results] + std_score = [result["std_score"] for result in results] + ax.plot( + ds, + mean_score, + label=method2label[method], + marker=method2marker[method], + markersize=markersize, + color=method2color[method], + linewidth=linewidth, + ) + ax.fill_between( + ds, + np.array(mean_score) - np.array(std_score), + np.array(mean_score) + np.array(std_score), + alpha=0.2, + color=method2color[method], + ) + + ax.set_xlabel("Unique Samples: $d$", fontsize=label_fs) + if metric == "pearson": + ax.set_ylabel("Pearson's $r$", fontsize=label_fs) + elif metric == "spearman": + ax.set_ylabel("Spearman's $\\rho$", fontsize=label_fs) + ax.set_xscale("log") + ax.set_ylim(-0.1, 1.1) + ax.grid(which="both", linestyle="--", linewidth=0.5) + ax.tick_params(axis="both", which="major", labelsize=tick_fs) + ax.legend(fontsize=legend_fs, loc="lower right") + + output_path = Path("output/images") / f"fig6_{metric}.pdf" + output_path.parent.mkdir(parents=True, exist_ok=True) + fig.subplots_adjust( + left=0.06, + right=0.99, + bottom=0.09, + top=0.95, + wspace=0.35, + hspace=0.4 + ) + fig.savefig(output_path) + logger.info(f"Saved figure to {output_path}") + + # show n=10, 100, 1000, 10000 results in a table + for metric in ["pearson",]: + n_resamples_list = [10, 100, 1000, 10000] + rows = [] + for n_resamples in n_resamples_list: + # 100 -> $10^2$ + for method in methods: + row = {'$n$': "$10^" + str(int(np.log10(n_resamples))) + "$"} + for task_name in TASK_NAMES: + method2task_name2results = \ + method2results[method][metric][task_name] + results = [ + result for result in method2task_name2results + if result["n_resamples"] == n_resamples + ] + if len(results) == 0: + continue + result = results[0] + if "d" not in row: + row["$d$"] = result["d"] + row["Method"] = method2label[method] + row[task_name2title[task_name]] = \ + f"{result['mean_score']:.3f} "\ + r"$\pm$" + f" {result['std_score']:.3f}" + rows.append(row) + df = pd.DataFrame(rows) + output_path = Path("output/summary") /\ + f"table2_{metric}.csv" + logger.info(f"Saving summary to {output_path}") + output_path.parent.mkdir(parents=True, exist_ok=True) + df.to_csv(output_path, index=False) + df.to_latex( + output_path.with_suffix(".tex"), + index=False, + escape=False, + float_format="%.3f", + column_format=r"@{\hspace{1.2em}}r" * 2 + r"@{\hspace{1.2em}}l@{\hspace{1.2em}}" + r"r@{\hspace{1.2em}}" * (len(df.columns)-3) # noqa + ) + logger.info(f"Saved LaTeX table to {output_path.with_suffix('.tex')}") + + +if __name__ == "__main__": + import fire + fire.Fire(main) diff --git a/resampling-texts/code_for_prediction/src/split_data.py b/resampling-texts/code_for_prediction/src/split_data.py new file mode 100644 index 0000000..064335c --- /dev/null +++ b/resampling-texts/code_for_prediction/src/split_data.py @@ -0,0 +1,94 @@ +import logging +import pickle as pkl +from pathlib import Path + +import numpy as np +import pandas as pd +from sklearn.model_selection import GroupKFold, KFold + +from utils import ADDITIONAL_DEEPSEEK_MODELS, DATA_PATH, TASK_NAMES + +logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, +) +logger = logging.getLogger(__name__) + + +def main(split): + logger.info(f"split: {split}") + + with open(DATA_PATH, "rb") as f: + modeldata = pkl.load(f) + + rows = [] + for model_dict in modeldata: + model_name = model_dict["model_name"] + + isvalid = True + for task_name in TASK_NAMES: + if task_name == "mean_logp": + key_name = task_name + else: + key_name = f"score_v1_{task_name}" + value = model_dict[key_name] + if value is None or np.isnan(value): + isvalid = False + if model_name not in ADDITIONAL_DEEPSEEK_MODELS: + logger.info(f"{key_name} is invalid in {model_name}") + break + if isvalid: + row = { + "model_name": model_name, + "model_type": model_dict["model_type"], + } + for task_name in TASK_NAMES: + if task_name == "mean_logp": + key_name = task_name + else: + key_name = f"score_v1_{task_name}" + value = model_dict[key_name] + row[task_name] = value + rows.append(row) + logger.info(f"number of valid models: {len(rows)}") + + n_splits = 5 + split_seeds = [0, 1, 2, 3, 4] + logger.info(f"n_splits: {n_splits}") + output_dir = Path("output/split_data") / split + output_dir.mkdir(parents=True, exist_ok=True) + for split_seed in split_seeds: + logger.info(f"split_seed: {split_seed}") + + df = pd.DataFrame(rows) + df["fold"] = -1 + + if split == "groupkfold": + kf = GroupKFold(n_splits=n_splits, shuffle=True, + random_state=split_seed) + for fold, (_, val_index) in enumerate( + kf.split(df, groups=df["model_type"]) + ): + df.loc[val_index, "fold"] = fold + else: + kf = KFold(n_splits=n_splits, shuffle=True, + random_state=split_seed) + for fold, (_, val_index) in enumerate(kf.split(df)): + df.loc[val_index, "fold"] = fold + + # distribution of each fold + logger.info("distribution of each fold") + for fold in range(n_splits): + fold_df = df[df["fold"] == fold] + logger.info(f"fold {fold}: {len(fold_df)}") + + # save the split data + df = df.sort_values(by=["model_name"]) + output_path = output_dir / f"split_seed{split_seed}.csv" + df.to_csv(output_path, index=False) + logger.info(f"saved to {output_path}") + + +if __name__ == "__main__": + main(split="groupkfold") diff --git a/resampling-texts/code_for_prediction/src/train_and_pred.py b/resampling-texts/code_for_prediction/src/train_and_pred.py new file mode 100644 index 0000000..14b7a11 --- /dev/null +++ b/resampling-texts/code_for_prediction/src/train_and_pred.py @@ -0,0 +1,176 @@ +import logging +import pickle as pkl +from pathlib import Path + +import numpy as np +import pandas as pd +from scipy.stats import pearsonr, spearmanr +from sklearn import linear_model + +from utils import (DATA_PATH, KLS_WEIGHT_PATH, LSS_WEIGHT_PATH, TASK_NAMES, + UNIFORM_WEIGHT_PATH) + +logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, +) +logger = logging.getLogger(__name__) + + +def run(pred_dir, split, split_seed, n_resamples, + sample_seed, unique_index, weights): + with open(DATA_PATH, "rb") as f: + modeldata = pkl.load(f) + + log_likelihood_vectors = np.array( + [x['raw_log-likelihood-10k'] for x in modeldata]) + lower_p2 = np.quantile(log_likelihood_vectors, q=0.02) + model_name2llv = { + model_dict["model_name"]: np.maximum( + model_dict["raw_log-likelihood-10k"], lower_p2 + ) + for model_dict in modeldata + } + + task_alphas = [10**i for i in range(1, 10)] + meanlogp_alphas = [10**i for i in range(-4, 5)] + + input_dir = Path("output/split_data") / split + + def get_tildeQ(L, unique_index, weights): + L_d = L[:, unique_index] + term_for_mean_L_d = weights**2 + numerator_bar_L_d = np.sum( + L_d * term_for_mean_L_d[np.newaxis, :], axis=1 + ) # K-dim vector + denominator_bar_L_d = np.sum(term_for_mean_L_d) # scalar + bar_L_d = numerator_bar_L_d / denominator_bar_L_d + Q_d_centered_cols = L_d - bar_L_d[:, np.newaxis] + tilde_Q_d = Q_d_centered_cols * weights[np.newaxis, :] + tilde_Q_d = tilde_Q_d - np.mean(tilde_Q_d, axis=0) + return tilde_Q_d + + input_path = input_dir / f"split_seed{split_seed}.csv" + df = pd.read_csv(input_path) + + model_names = df["model_name"].values + L = [] + for model_name in model_names: + L.append(model_name2llv[model_name]) + L = np.array(L) + Q = get_tildeQ(L, unique_index, weights) + logger.info(f"Q shape: {Q.shape}") + + pred_df = None + columns = ["model_name", "fold"] + TASK_NAMES + for fold in range(5): + train_ids = df["fold"] != fold + test_ids = df["fold"] == fold + + logger.info(f"fold: {fold} " + f"train: {train_ids.sum()} " + f"test: {test_ids.sum()}") + + test_model_names = df["model_name"][test_ids].values + fold_pred_df = pd.DataFrame(columns=columns) + fold_pred_df["model_name"] = test_model_names + fold_pred_df["fold"] = fold + + train_Q = Q[train_ids] + test_Q = Q[test_ids] + + for task_name in TASK_NAMES: + train_f = df[task_name][train_ids].values + test_f = df[task_name][test_ids].values + + if task_name == "mean_logp": + params = {"alphas": meanlogp_alphas, "cv": 5} + else: + params = {"alphas": task_alphas, "cv": 5} + + reg = linear_model.RidgeCV(**params) + reg.fit(train_Q, train_f) + + # predict + f_pred = reg.predict(test_Q) + if task_name != "mean_logp": + f_pred = np.clip(f_pred, 0, 100) + fold_pred_df[task_name] = f_pred + + sr = spearmanr(test_f, f_pred)[0] + pr = pearsonr(test_f, f_pred)[0] + + logger.info(f"{task_name}: spearman: {sr:.3f} pearson: {pr:.3f}") + + if pred_df is None: + pred_df = fold_pred_df.copy() + else: + pred_df = pd.concat([pred_df, fold_pred_df], axis=0) + + # save prediction + pred_df = pred_df.sort_values(by=["model_name"]).reset_index(drop=True) + assert df["model_name"].equals(pred_df["model_name"]) + pred_path = ( + pred_dir / f"split_seed{split_seed}-" + f"sample_seed{sample_seed}-" + f"n_resamples{n_resamples}.csv" + ) + pred_df.to_csv(pred_path, index=False) + + +def main(method, sample_seed, split_seed): + assert method in ["lss", "uniform", "kls"] + + if method == "lss": + WEIGHT_PATH = LSS_WEIGHT_PATH + elif method == "uniform": + WEIGHT_PATH = UNIFORM_WEIGHT_PATH + elif method == "kls": + WEIGHT_PATH = KLS_WEIGHT_PATH + + with open(WEIGHT_PATH, "rb") as f: + idx_weight = pkl.load(f) + + split = "groupkfold" + n_resamples_list = [10 * i for i in range(1, 10)] + n_resamples_list += [100 * i for i in range(1, 10)] + n_resamples_list += [1000 * i for i in range(1, 11)] + + for n_resamples in n_resamples_list: + logger.info( + f"split_seed: {split_seed} " + f"sample_seed: {sample_seed} " + f"n_resamples: {n_resamples}" + ) + + pred_dir = Path("output/train_and_pred") / split / method + pred_dir.mkdir(parents=True, exist_ok=True) + pred_path = ( + pred_dir / f"split_seed{split_seed}-" + f"sample_seed{sample_seed}-" + f"n_resamples{n_resamples}.csv" + ) + if pred_path.exists(): + logger.info(f"pred_path exists: {pred_path}") + continue + + unique_index = idx_weight[ + f"{sample_seed}"][f"{n_resamples}"]["unique_index"] + weights = idx_weight[f"{sample_seed}"][f"{n_resamples}"]["weights"] + + run( + pred_dir=pred_dir, + split=split, + split_seed=split_seed, + n_resamples=n_resamples, + sample_seed=sample_seed, + unique_index=unique_index, + weights=weights, + ) + + +if __name__ == "__main__": + for method in ["lss", "uniform", "kls"]: + for split_seed in range(5): + main(method=method, sample_seed=42, split_seed=split_seed) diff --git a/resampling-texts/code_for_prediction/src/utils.py b/resampling-texts/code_for_prediction/src/utils.py new file mode 100644 index 0000000..cc63e91 --- /dev/null +++ b/resampling-texts/code_for_prediction/src/utils.py @@ -0,0 +1,36 @@ +DATA_PATH = "../data/modeldata_1018.pkl" +LSS_WEIGHT_PATH = "../data/uniq-idx-weight/lss-uniq-idx-weight.pkl" +UNIFORM_WEIGHT_PATH = "../data/uniq-idx-weight/uniform-uniq-idx-weight.pkl" +KLS_WEIGHT_PATH = "../data/uniq-idx-weight/kls-uniq-idx-weight.pkl" + +TASK_NAMES = [ + "ARC", + "HellaSwag", + "MMLU", + "TruthfulQA", + "Winogrande", + "GSM8K", + "6-taskmean", + "mean_logp", +] + +ADDITIONAL_DEEPSEEK_MODELS = [ + "deepseek-ai/DeepSeek-Coder-V2-Lite-Base", + "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", + "deepseek-ai/deepseek-coder-1.3b-base", + "deepseek-ai/deepseek-coder-7b-base-v1.5", + "deepseek-ai/deepseek-llm-7b-base", + "deepseek-ai/deepseek-moe-16b-base", + "deepseek-ai/deepseek-moe-16b-chat", + "deepseek-ai/DeepSeek-Prover-V1", + "deepseek-ai/DeepSeek-Prover-V1.5-Base", + "deepseek-ai/DeepSeek-Prover-V1.5-RL", + "deepseek-ai/DeepSeek-Prover-V1.5-SFT", + "deepseek-ai/DeepSeek-R1-Distill-Llama-8B", + "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", + "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", + "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", + "deepseek-ai/DeepSeek-V2-Lite", + "deepseek-ai/DeepSeek-V2-Lite-Chat", + "deepseek-ai/ESFT-vanilla-lite", +] diff --git a/resampling-texts/code_for_prediction/uv.lock b/resampling-texts/code_for_prediction/uv.lock new file mode 100644 index 0000000..ae834b2 --- /dev/null +++ b/resampling-texts/code_for_prediction/uv.lock @@ -0,0 +1,642 @@ +version = 1 +revision = 3 +requires-python = ">=3.13" + +[[package]] +name = "code-for-prediction" +version = "0.1.0" +source = { virtual = "." } +dependencies = [ + { name = "fire" }, + { name = "jinja2" }, + { name = "matplotlib" }, + { name = "numpy" }, + { name = "pandas" }, + { name = "scikit-learn" }, + { name = "tqdm" }, +] + +[package.metadata] +requires-dist = [ + { name = "fire" 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