diff --git a/.gitignore b/.gitignore index d86e4ff..edc70ca 100644 --- a/.gitignore +++ b/.gitignore @@ -169,3 +169,7 @@ cython_debug/ # PyPI configuration file .pypirc + +# Project outputs +out/ +**/outputs/ diff --git a/LTC.jl/Project.toml b/LTC.jl/Project.toml deleted file mode 100644 index 3fd5d28..0000000 --- a/LTC.jl/Project.toml +++ /dev/null @@ -1,26 +0,0 @@ -name = "LTC" -uuid = "e80017db-848b-4f4e-8f0b-fdb87d8a817d" -authors = ["Krzysztof Rusek "] -version = "0.1.0" - -[deps] -DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" -DynamicExpressions = "a40a106e-89c9-4ca8-8020-a735e8728b6b" -MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7" -Pluto = "c3e4b0f8-55cb-11ea-2926-15256bba5781" -PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" -Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" -Revise = "295af30f-e4ad-537b-8983-00126c2a3abe" -StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" -SymbolicRegression = "8254be44-1295-4e6a-a16d-46603ac705cb" - -[compat] -DataStructures = "0.19.3" -DynamicExpressions = "1.10.3" -MLJ = "0.22.0" -Pluto = "0.20.21" -PythonCall = "0.9.30" -Random = "1.11.0" -Revise = "3.12.2" -StatsBase = "0.34.9" -SymbolicRegression = "1.12.0" diff --git a/LTC.jl/scripts/run.jl b/LTC.jl/scripts/run.jl deleted file mode 100644 index 06a5b4f..0000000 --- a/LTC.jl/scripts/run.jl +++ /dev/null @@ -1,81 +0,0 @@ -ENV["JULIA_CONDAPKG_BACKEND"] = "Null" -ENV["JULIA_PYTHONCALL_EXE"] = joinpath(@__DIR__, "..", "..", ".venv", "bin", "python") # optional - -import Pkg -Pkg.activate(joinpath(@__DIR__, "..")) # activate LTC project -using Revise - -using LTC -using PythonCall -using Base.Threads - - -function main() - (jax, jnp, P) = ltc_imports() - args = P.setup_args() - - n = args.n - n_drl = args.n_drl - n_epochs = args.n_epochs - n_steps = args.n_steps - window_size = args.window_size - seed = args.seed - traffic_type = args.traffic_type - - key = jax.random.key(seed) - num_actions = P._len(P.Actions) - - pyresults = P.Results(n, n_drl, seed) - - actions = jnp.zeros(n, dtype=jnp.int32) - - buffer_states = jnp.zeros(n, dtype=jnp.int32) - power_states = jnp.full(n, P.INITIAL_CAPACITY, dtype=jnp.int32) - channel_state = 0 - obs = P.make_obs(window_size, n) - rewards = jnp.zeros(n) - terminals = jnp.full(n, false, dtype=jnp.bool) - - obs = pyconvert(Observations, obs) - n = pyconvert(Int, args.n) - n_drl = pyconvert(Int, args.n_drl) - n_epochs = pyconvert(Int, args.n_epochs) - n_steps = pyconvert(Int, args.n_steps) - window_size = pyconvert(Int, args.window_size) - seed = pyconvert(Int, args.seed) - traffic_type = pyconvert(String, args.traffic_type) - num_actions = pyconvert(Int, num_actions) - - - env = Env(args, key) - - agents = [SRAgent(default_options(), 1000, num_actions=num_actions) for _ in 1:n_drl] - rewards = zeros(Float32, n, n_steps) - - for ep in 1:n_epochs - @info "Starting new epoch" epoch = ep - - for step in 1:n_steps - @info "Step" step = step - a = [take_action(ag, ob) for (ag, ob) in zip(agents, eachslice(obs, dims=1))] - transitions, output = step!(env, a) - pyresults.append(output) - - @threads for i in eachindex(agents) - train!(agents[i], transitions[i], batch_size=128, niterations=2) - @debug "Agent $(i) best equation: " eq = agents[i].equation - # @debug "Agent $(i) target equation: " t = typeof(agents[i].target_equation) - end - - rewards[:, step] = [t.r for t in transitions] - @debug "Total reward" total_reward = sum(transitions .|> t -> t.r) - - end - LTC.reset!(env) - end - pyresults.save('.') - - -end - -main() \ No newline at end of file diff --git a/LTC.jl/scripts/run_pluto.jl b/LTC.jl/scripts/run_pluto.jl deleted file mode 100644 index a5b81be..0000000 --- a/LTC.jl/scripts/run_pluto.jl +++ /dev/null @@ -1,5 +0,0 @@ -#!/usr/bin/env julia -using Pkg -Pkg.activate(joinpath(@__DIR__, "..")) -using Pluto -Pluto.run() diff --git a/LTC.jl/src/LTC.jl b/LTC.jl/src/LTC.jl deleted file mode 100644 index 78d9249..0000000 --- a/LTC.jl/src/LTC.jl +++ /dev/null @@ -1,113 +0,0 @@ -module LTC -# TODO: disable precompilation while developing, remove later -# __precompile__(false) -using SymbolicRegression -using PythonCall - -export ltc_imports, Env, step!, SARSD, Observations, Action, Actions -export SRAgent, train!, take_action, default_options - - -_pymods = nothing - -""" - ltc_imports() - -Returns a named tuple of necessary Python modules. -""" -function ltc_imports() - global _pymods - if _pymods !== nothing - return _pymods - end - _pymods = (jax=pyimport("jax"), jnp=pyimport("jax.numpy"), P=pyimport("ltc.symbolic.julia")) - return _pymods - -end - -""" - Env(args::Py, key::Union{Py,Nothing}=nothing) -A wrapper around the LTC environment. -""" -mutable struct Env - state::Py - pysim::Py - initial_state::Py - - function Env(args::Py, key::Union{Py,Nothing}=nothing) - (jax, _, P) = ltc_imports() - if key === nothing - key = jax.random.key(0) - end - key, k1 = jax.random.split(key, 2) - sim = P.Sim(args) - c = sim.init(k1) - new(c, sim, c) - - end -end - -function reset!(env::Env) - env.state = env.initial_state -end - -const Observation = AbstractMatrix{Float32} -const Observations = AbstractArray{Float32,3} -const Action = Int32 -const Actions = Array{Action,1} - - - -""" -A struct representing a single step transition in the environment. -""" -struct Transition - s::Observation - a::Action - r::Float32 - s2::Observation - done::Bool -end - - - - -""" - step!(env::Env, a::Actions) -> Tuple{AbstractVector{Transition}, Py} - -Advances the environment by one step using the provided actions. - -# Arguments -- `env::Env`: The environment to advance. This object will be mutated; its internal state will be updated to reflect the result of the step. -- `a::Actions`: A vector of actions to apply to the environment. - -# Returns -- `transitions::AbstractVector{Transition}`: A vector of `Transition` objects, each representing the result of applying an action to the environment. -- `o::Py`: A Python object containing additional information about the step (e.g., observations, rewards, terminals). - -# Side Effects -- Mutates `env.state` to the new state after the step. -""" -function step!(env::Env, a::Actions)::Tuple{AbstractVector{Transition}, Py} - (_, jnp, _) = ltc_imports() - c, o = env.pysim.step(env.state, jnp.asarray(a)) - s = pyconvert(Observations, env.state.obs) - s2 = pyconvert(Observations, o.observations) - r = pyconvert(Vector{Float32}, o.rewards) - done = pyconvert(Vector{Bool}, o.terminals) - ret = [Transition( - s[i, :, :], - a[i], - r[i], - s2[i, :, :], - done[i], - ) for i in eachindex(a)] - - env.state = c - return ret,o - -end - -include("agent.jl") - -end # module LTC diff --git a/LTC.jl/src/agent.jl b/LTC.jl/src/agent.jl deleted file mode 100644 index 45693cb..0000000 --- a/LTC.jl/src/agent.jl +++ /dev/null @@ -1,161 +0,0 @@ -import SymbolicRegression: Options, equation_search, HallOfFame -using Random, DataStructures, StatsBase -using SymbolicRegression.MLJInterfaceModule: choose_best -using SymbolicRegression.HallOfFameModule: format_hall_of_fame -using DynamicExpressions.ExpressionModule: Expression - -"""Noise function for SR""" -r(x) = randn(eltype(x), size(x)...) - -function default_options() - Options( - binary_operators=[+, *, /, -, ^], - unary_operators=[exp, r], - populations=20, - constraints=[(^) => (-1, 1), (r) => 1, (sin) => 3, (cos) => 3, (exp) => 3], - save_to_file=false, - verbosity=0, - ) -end - -struct SymQ - state::Union{Nothing,AbstractVector} - hof::Union{Nothing,HallOfFame} -end - -function best_equation(model::SymQ, options::Options) - trees, scores, losses, complexities = format_hall_of_fame(model.hof, options) - best_idx = choose_best(trees=trees, losses=losses, scores=scores, complexities=complexities, options=options) - return trees[best_idx] -end - -function predict(model::SymQ, X::AbstractMatrix{Float32}, options::Options)::Vector{Float32} - - best_eq = best_equation(model, options) - return best_eq(X) -end - -abstract type AbstractFirstTarget end - -struct ZeroFirstTarget <: AbstractFirstTarget end -struct RandomFirstTarget <: AbstractFirstTarget end -struct RewardsFirstTarget <: AbstractFirstTarget end - -make(_::ZeroFirstTarget, x::AbstractVector) = zeros(eltype(x), size(x)...) -make(_::RandomFirstTarget, x::AbstractVector) = r(x) -make(_::RewardsFirstTarget, x::AbstractVector) = x - - -mutable struct SRAgent - model::Union{Nothing,SymQ} - equation::Union{Nothing,Expression} - target_equation::Union{Nothing,Expression} - - options::Options - replay_buffer::CircularBuffer{Transition} - step::Int64 - epsilon::Float32 - gamma::Float32 - num_actions::Int - - first_target::AbstractFirstTarget - - function SRAgent(options::Options, replay_buffer_capacity::Int; num_actions::Int=3, epsilon::Float32=1.0f0, gamma::Float32=0.99f0) - new(nothing, nothing, nothing, options, CircularBuffer{Transition}(replay_buffer_capacity), 0, epsilon, gamma, num_actions, ZeroFirstTarget()) - end -end - -function train!(agent::SRAgent, transition::Transition; batch_size::Int, niterations::Int=20) - push!(agent.replay_buffer, transition) - - function common!(agent::SRAgent) - agent.epsilon = max(0.1f0, agent.epsilon * 0.995f0) - agent.step += 1 - end - - if length(agent.replay_buffer) < batch_size - return - end - - batch = rand(agent.replay_buffer, batch_size) - X = hcat([make_X(sarsd.s, sarsd.a) for sarsd in batch]...) - y = make_target(agent, batch) - - if agent.model === nothing - state, hof = equation_search(X, y; options=agent.options, niterations=1, return_state=true, parallelism=:serial) - else - state, hof = equation_search(X, y; options=agent.options, niterations=niterations, return_state=true, saved_state=(agent.model.state, agent.model.hof), parallelism=:serial) - end - agent.model = SymQ(state, hof) - agent.equation = best_equation(agent.model, agent.options) - - - common!(agent) - - - if agent.step % 10 == 0 || agent.target_equation === nothing - agent.target_equation = deepcopy(agent.equation) - end -end - - -function make_X(obs::Observation, action::Action, n_actions=3)::AbstractMatrix{Float32} - hot1_act = zeros(eltype(obs), n_actions) - hot1_act[action+1] = 1 # assuming actions are 0-indexed - X = [obs[:]; hot1_act] - return reshape(X, :, 1) - -end - -function take_action(agent::SRAgent, observations::Observation)::Action - if agent.model === nothing || agent.model.hof === nothing || length(agent.model.hof.members) == 0 - return rand(0:agent.num_actions-1) - end - - q = [agent.equation(make_X(observations, Int32(a)))[1] for a in 0:agent.num_actions-1] - - mq = maximum(q) - weights = [x == mq ? 1.0f0 : 0.0f0 for x in q] - - action = sample(1:agent.num_actions, Weights(weights)) - 1 - - # Explore - if rand() < agent.epsilon - action = rand(0:agent.num_actions-1) - end - return action - -end - -function make_target(agent::SRAgent, batch::AbstractVector{Transition})::Vector{Float32} - n = length(batch) - targets = Vector{Float32}(undef, n) - if agent.target_equation === nothing - - for i in 1:n - targets[i] = batch[i].r - end - return make(agent.first_target, targets) - end - - best_eq = agent.target_equation - - for i in 1:n - sarsd = batch[i] - if sarsd.done - targets[i] = sarsd.r - else - max_q_next = -Inf32 - for a in 0:agent.num_actions-1 - q_next = best_eq(make_X(sarsd.s2, Int32(a)))[1] # vector output - if q_next > max_q_next - max_q_next = q_next - end - end - - targets[i] = sarsd.r + agent.gamma * max_q_next - end - end - return targets - -end \ No newline at end of file diff --git a/LTC.jl/test/runtests.jl b/LTC.jl/test/runtests.jl deleted file mode 100644 index 702d542..0000000 --- a/LTC.jl/test/runtests.jl +++ /dev/null @@ -1,7 +0,0 @@ -ENV["JULIA_CONDAPKG_BACKEND"] = "Null" -ENV["JULIA_PYTHONCALL_EXE"] = joinpath(@__DIR__, "..", "..", ".venv", "bin", "python") # optional - -using PythonCall - -P = pyimport("ltc.symbolic.julia") -P.main() \ No newline at end of file diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..4333e45 --- /dev/null +++ b/Makefile @@ -0,0 +1,48 @@ +PKL_FILES := $(wildcard data/nosvi/*.pkl.lz4) +BASENAMES := $(notdir $(PKL_FILES:.pkl.lz4=)) +CSV_FILES := $(addprefix out/, $(addsuffix .csv, $(BASENAMES))) +FOREST_FILES := $(addprefix out/, $(addsuffix .forest.pkl, $(BASENAMES))) +SR_STAMPS := $(addprefix out/, $(addsuffix .sr.done, $(BASENAMES))) +SR_SPLIT_STAMP := $(addprefix out/, $(addsuffix .split.done, $(BASENAMES))) + +.PHONY: all distill sr report split report-split clean + +all: distill sr report split report-split + +distill: $(FOREST_FILES) + +sr: $(SR_STAMPS) + +split: $(SR_SPLIT_STAMP) + +report: out/report.html + +report-split: out/report_split.html + +.PRECIOUS: $(CSV_FILES) + +out: + mkdir -p out + +out/%.csv: explain/nosvi/%.pkl.lz4 | out + python -m ltc.symbolic.history2csv --file "$<" --output "$@" + +out/%.forest.pkl: out/%.csv + python -m ltc.symbolic.tree --file "$<" --output "$@" + +out/%.sr.done: out/%.csv ltc/symbolic/sr.py + python -m ltc.symbolic.sr --file "$<" --output "out/$*" --pysr_output_dir out/output + touch "$@" + +out/report.html: $(SR_STAMPS) $(FOREST_FILES) + marimo export html ltc/symbolic/report.py -o "$@" -f + +out/%.split.done: out/%.csv ltc/symbolic/sr_split.py + python -m ltc.symbolic.sr_split --file "$<" --output "out/$*" --pysr_output_dir out/output_split + touch "$@" + +out/report_split.html: $(SR_SPLIT_STAMP) + marimo export html ltc/symbolic/report_split.py -o "$@" -f + +clean: + rm -rf out diff --git a/ltc/__init__.py b/ltc/__init__.py index e69de29..3dc1f76 100644 --- a/ltc/__init__.py +++ b/ltc/__init__.py @@ -0,0 +1 @@ +__version__ = "0.1.0" diff --git a/ltc/symbolic/history2csv.py b/ltc/symbolic/history2csv.py new file mode 100644 index 0000000..427e452 --- /dev/null +++ b/ltc/symbolic/history2csv.py @@ -0,0 +1,93 @@ +import argparse +import functools as ft +import itertools as it + +import cloudpickle +import jax +import jax.numpy as jnp +import lz4.frame +import numpy as np +import pandas as pd + +from ltc.agents import QNetwork +from ltc.utils.history import resolve_history_file, unpack_history + +FEATURE_NAMES = ["buffer", "channel", "ret_c", "no_tx", "action"] + +_model = QNetwork(num_actions=2, num_layers=1, dim=64, num_heads=4) + + +def build_column_names(window_size: int) -> list[str]: + return [f"{f}_{t}" for t, f in it.product(range(window_size), FEATURE_NAMES)] + + +@jax.jit +def predict_one(params, state, obs, key): + keys = map(ft.partial(jax.random.fold_in, key), it.count()) + rngs = {"params": next(keys), "dropout": next(keys), "rlib": next(keys)} + q_vals, _ = _model.apply( + {"params": params, **state}, obs, training=False, rngs=rngs, mutable=["loss"] + ) + return q_vals + + +@jax.jit +def compute_qvals(params, state, observations, key): + return jax.vmap(predict_one, in_axes=(0, 0, 1, None))(params, state, observations, key) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Generate Q-network observations and Q-values from history file" + ) + parser.add_argument("--file", type=str, help="Path to history .pkl.lz4 file") + parser.add_argument( + "--n", type=int, default=5, help="Number of agents used for filename matching" + ) + parser.add_argument( + "--n_drl", + type=int, + default=5, + help="Number of DRL agents used for filename matching", + ) + parser.add_argument( + "--seed", type=int, default=42, help="Seed used for filename matching" + ) + parser.add_argument("--output", type=str, default=None, help="Output .csv path") + + args = parser.parse_args() + + history_file = resolve_history_file( + n=args.n, n_drl=args.n_drl, seed=args.seed, file_path=args.file + ) + + print(f"Loading: {history_file}") + with lz4.frame.open(history_file, "rb") as f: + payload = cloudpickle.load(f) + + drl_state, history, _ = unpack_history(payload) + params, state = drl_state.params, drl_state.net_state + n_agents = jax.tree.leaves(params)[0].shape[0] + + # Last epoch: shape [n_steps, n_agents, window_size, n_features] + observations = history.observations[-1, ...] + n_steps, _, window_size, _ = observations.shape + + key = jax.random.key(42) + qvals = compute_qvals( + params, state, observations, key + ) # [n_agents, n_steps, n_actions] + + # Build flat feature matrix: [n_agents * n_steps, window_size * n_features] + # transpose [n_steps, n_agents, w, f] -> [n_agents, n_steps, w, f] then flatten last two dims + XX = np.asarray(observations).transpose(1, 0, 2, 3).reshape(n_agents * n_steps, -1) + actions = np.asarray(qvals.argmax(axis=-1)).flatten() # [n_agents * n_steps] + agent_ids = np.repeat(np.arange(n_agents), n_steps) + + df = pd.DataFrame(XX, columns=build_column_names(window_size)) + df["agent"] = agent_ids + df["action"] = actions + + out_path = args.output if args.output else str(history_file).replace(".pkl.lz4", ".csv") + df.to_csv(out_path, index=False) + print(f"Saved: {out_path} ({len(df)} rows)") diff --git a/ltc/symbolic/report.py b/ltc/symbolic/report.py new file mode 100644 index 0000000..48a7703 --- /dev/null +++ b/ltc/symbolic/report.py @@ -0,0 +1,429 @@ +import marimo + +__generated_with = "0.23.3" +app = marimo.App(width="full") + + +@app.cell +def _(): + import marimo as mo + + return (mo,) + + +@app.cell +def _(): + import os + import pickle + import glob + + import joblib + import numpy as np + import pandas as pd + import matplotlib.pyplot as plt + import seaborn as sns + import sympy + + os.environ.setdefault("PYTHON_JULIAPKG_EXE", "/opt/homebrew/bin/julia") + + from ltc.symbolic.util import SimplexCode + + return SimplexCode, glob, joblib, np, os, pd, pickle, plt, sns + + +@app.cell +def _(glob, mo, os): + _csv_files = sorted(glob.glob("out/*.csv")) + _basenames = [os.path.splitext(os.path.basename(f))[0] for f in _csv_files] + experiment = mo.ui.dropdown( + options=_basenames, + value=_basenames[1] if _basenames else None, + label="Experiment", + ) + experiment + return (experiment,) + + +@app.cell +def _(SimplexCode, experiment, joblib, os, pd, pickle): + base = experiment.value + df = pd.read_csv(f"out/{base}.csv") + forest = joblib.load(f"out/{base}.forest.pkl") + agents = sorted(df["agent"].unique().tolist()) + sr_models = {} + for _ag in agents: + _path = f"out/{base}_agent_{_ag}.sr.pkl" + if os.path.exists(_path): + with open(_path, "rb") as _fh: + sr_models[_ag] = pickle.load(_fh) + simplex_code = SimplexCode(T=df["action"].nunique()) + return agents, base, df, forest, simplex_code, sr_models + + +@app.cell +def _(np, simplex_code): + def predict_labels(model, X): + """Predict integer action labels by decoding the SR simplex-code output.""" + _eqs = model.equations_ + if isinstance(_eqs, list): + _idx = [_e["score"].idxmax() for _e in _eqs] + else: + _idx = _eqs["score"].idxmax() + _codes = np.asarray(model.predict(X.values, _idx)) + _codes = _codes.reshape(len(X), simplex_code.T - 1) + return np.asarray(simplex_code.decode(_codes)) + + return (predict_labels,) + + +@app.cell +def _(mo): + mo.md(""" + ## Feature Importance + """) + return + + +@app.cell +def _(df, forest, plt): + _feat_cols = [c for c in df.columns if c not in {"agent", "action"}] + _imp = dict(zip(_feat_cols, forest.feature_importances_)) + _imp_series = sorted(_imp.items(), key=lambda x: x[1], reverse=True)[:15] + _names, _vals = zip(*_imp_series) + + fig_fi, ax_fi = plt.subplots(figsize=(12, 4)) + ax_fi.bar(_names, _vals) + ax_fi.set_xticklabels(_names, rotation=45, ha="right") + ax_fi.set_ylabel("Importance") + ax_fi.set_title("Random Forest Feature Importance (top 15)") + plt.tight_layout() + fig_fi + return + + +@app.cell +def _(mo): + mo.md(""" + ## Symbolic Equations + """) + return + + +@app.cell +def _(agents, mo): + agent_sel = mo.ui.slider( + min(agents), max(agents), value=agents[0], label="Agent" + ) + agent_sel + return (agent_sel,) + + +@app.cell +def _(agent_sel, mo, sr_models): + _ag = agent_sel.value + _model = sr_models[_ag] + _eqs = _model.equations_[["equation", "loss", "complexity","score"]].copy() + + # from PySR + # threshold = 1.5 * _model.equations_["loss"].min() + # filtered_equations = _model.equations_.query(f"loss <= {threshold}") + # _best_idx = filtered_equations["score"].idxmax() + + _best_idx = _model.equations_["score"].idxmax() + + + + _styled = _eqs.style.apply( + lambda row: [ + "background-color: #90EE90; font-weight: bold" if row.name == _best_idx else "" + for _ in row + ], + axis=1, + ).format({"loss": "{:.6f}"}) + + mo.as_html(_styled) + return + + +@app.cell +def _(mo): + mo.md(""" + ## Cross-Agent Accuracy (corner plot) + """) + return + + +@app.cell +def _(agents, base, df, np, plt, predict_labels, sns, sr_models): + _feat_cols = [c for c in df.columns if c not in {"agent", "action"}] + + n = len(agents) + _acc = np.zeros((n, n)) + for _i, _ag_i in enumerate(agents): + for _j, _ag_j in enumerate(agents): + _df_j = df[df["agent"] == _ag_j] + _X_j = _df_j[_feat_cols].astype(np.float32) + _y_j = _df_j["action"].values + _y_pred = predict_labels(sr_models[_ag_i], _X_j) + _acc[_i, _j] = (_y_pred == _y_j).mean() + + fig_cp, ax_cp = plt.subplots(figsize=(9, 7)) + sns.heatmap( + _acc, + annot=True, + fmt=".2f", + ax=ax_cp, + xticklabels=agents, + yticklabels=agents, + cmap="RdYlGn", + vmin=0, + vmax=1, + ) + ax_cp.set_xlabel("Test agent j (data source)") + ax_cp.set_ylabel("Train agent i (model source)") + ax_cp.set_title(f"Accuracy: equation of agent i evaluated on data of agent j \n{base}") + plt.tight_layout() + fig_cp + return + + +@app.cell +def _(mo): + mo.md(""" + ## Statystyki klasyfikacji vs. baseline (najczęstsza klasa) + """) + return + + +@app.cell +def _(agents, df, np, predict_labels, sr_models): + from sklearn.metrics import balanced_accuracy_score, f1_score + + _feat_cols = [c for c in df.columns if c not in {"agent", "action"}] + + _n = len(agents) + _acc2 = np.zeros((_n, _n)) + baseline_acc = np.zeros(_n) + lift = np.zeros((_n, _n)) + bal_acc = np.zeros((_n, _n)) + f1 = np.zeros((_n, _n)) + + for _j, _ag_j in enumerate(agents): + _df_j = df[df["agent"] == _ag_j] + _X_j = _df_j[_feat_cols].astype(np.float32) + _y_j = _df_j["action"].values + _counts = np.bincount(_y_j.astype(int)) + baseline_acc[_j] = _counts.max() / len(_y_j) + + for _i, _ag_i in enumerate(agents): + _y_pred = predict_labels(sr_models[_ag_i], _X_j) + _acc2[_i, _j] = (_y_pred == _y_j).mean() + lift[_i, _j] = _acc2[_i, _j] - baseline_acc[_j] + bal_acc[_i, _j] = balanced_accuracy_score(_y_j, _y_pred) + f1[_i, _j] = f1_score(_y_j, _y_pred, zero_division=0) + return bal_acc, baseline_acc, f1, lift + + +@app.cell +def _(agents, baseline_acc, plt, sns): + _n = len(agents) + fig_bl, ax_bl = plt.subplots(figsize=(9, 2)) + sns.heatmap( + baseline_acc.reshape(1, _n), + annot=True, + fmt=".2f", + ax=ax_bl, + xticklabels=agents, + yticklabels=["baseline"], + cmap="RdYlGn", + vmin=0, + vmax=1, + ) + ax_bl.set_xlabel("Agent (źródło danych)") + ax_bl.set_title("Baseline: accuracy najczęstszej klasy (per agent)") + plt.tight_layout() + fig_bl + return + + +@app.cell +def _(agents, base, lift, plt, sns): + _max_abs = max(float(abs(lift).max()), 0.01) + fig_lift, ax_lift = plt.subplots(figsize=(9, 7)) + sns.heatmap( + lift, + annot=True, + fmt=".2f", + ax=ax_lift, + xticklabels=agents, + yticklabels=agents, + cmap="RdBu", + center=0, + vmin=-_max_abs, + vmax=_max_abs, + ) + ax_lift.set_xlabel("Test agent j (źródło danych)") + ax_lift.set_ylabel("Train agent i (źródło modelu)") + ax_lift.set_title(f"Lift nad baseline — SR (accuracy − baseline)\n{base}") + plt.tight_layout() + fig_lift + return + + +@app.cell +def _(agents, base, baseline_acc, df, forest, np, plt, sns): + _rf_feat_cols = [c for c in df.columns if c != "action"] + _n = len(agents) + _rf_lift = np.zeros(_n) + for _j, _ag_j in enumerate(agents): + _df_j = df[df["agent"] == _ag_j] + _X_j = _df_j[_rf_feat_cols] + _y_j = _df_j["action"].values + _y_pred_rf = forest.predict(_X_j) + _rf_lift[_j] = (_y_pred_rf == _y_j).mean() - baseline_acc[_j] + + _max_abs_rf = max(float(abs(_rf_lift).max()), 0.01) + fig_rf_lift, ax_rf_lift = plt.subplots(figsize=(9, 2)) + sns.heatmap( + _rf_lift.reshape(1, _n), + annot=True, + fmt=".2f", + ax=ax_rf_lift, + xticklabels=agents, + yticklabels=["RF"], + cmap="RdBu", + center=0, + vmin=-_max_abs_rf, + vmax=_max_abs_rf, + ) + ax_rf_lift.set_xlabel("Agent j (źródło danych)") + ax_rf_lift.set_title(f"Lift nad baseline — Random Forest (accuracy − baseline)\n{base}") + plt.tight_layout() + fig_rf_lift + return + + +@app.cell +def _(agents, bal_acc, base, plt, sns): + fig_bal, ax_bal = plt.subplots(figsize=(9, 7)) + sns.heatmap( + bal_acc, + annot=True, + fmt=".2f", + ax=ax_bal, + xticklabels=agents, + yticklabels=agents, + cmap="RdYlGn", + vmin=0, + vmax=1, + ) + ax_bal.set_xlabel("Test agent j (źródło danych)") + ax_bal.set_ylabel("Train agent i (źródło modelu)") + ax_bal.set_title(f"Balanced Accuracy\n{base}") + plt.tight_layout() + fig_bal + return + + +@app.cell +def _(agents, base, f1, plt, sns): + fig_f1, ax_f1 = plt.subplots(figsize=(9, 7)) + sns.heatmap( + f1, + annot=True, + fmt=".2f", + ax=ax_f1, + xticklabels=agents, + yticklabels=agents, + cmap="RdYlGn", + vmin=0, + vmax=1, + ) + ax_f1.set_xlabel("Test agent j (źródło danych)") + ax_f1.set_ylabel("Train agent i (źródło modelu)") + ax_f1.set_title(f"F1 Score (klasa mniejszościowa)\n{base}") + plt.tight_layout() + fig_f1 + return + + +@app.cell +def _(mo): + mo.md(""" + ## Macierze pomyłek — siatka (model i → dane j) + """) + return + + +@app.cell +def _(agents, base, df, np, plt, predict_labels, sns, sr_models): + from sklearn.metrics import confusion_matrix as _confusion_matrix + + _feat_cols = [c for c in df.columns if c not in {"agent", "action"}] + + _n = len(agents) + _cell = 2.6 + fig_grid, axes_grid = plt.subplots( + _n, _n, figsize=(_n * _cell, _n * _cell), squeeze=False + ) + + for _i, _ag_i in enumerate(agents): + for _j, _ag_j in enumerate(agents): + _ax = axes_grid[_i, _j] + _df_j = df[df["agent"] == _ag_j] + _X_j = _df_j[_feat_cols].astype(np.float32) + _y_j = _df_j["action"].values + _y_pred = predict_labels(sr_models[_ag_i], _X_j) + _cm = _confusion_matrix(_y_j, _y_pred, normalize="true") + sns.heatmap( + _cm, + annot=True, + fmt=".2f", + ax=_ax, + cmap="Blues", + cbar=False, + xticklabels=["0", "1"], + yticklabels=["0", "1"], + vmin=0, + vmax=1, + linewidths=0.4, + linecolor="gray", + ) + _ax.tick_params(labelsize=6) + _is_diag = _i == _j + _ax.set_title( + f"i={_ag_i} → j={_ag_j}", + fontsize=7, + fontweight="bold" if _is_diag else "normal", + color="#1a7a1a" if _is_diag else "black", + ) + if _i < _n - 1: + _ax.set_xticklabels([]) + if _j > 0: + _ax.set_yticklabels([]) + if _i == _n - 1: + _ax.set_xlabel("pred", fontsize=6) + if _j == 0: + _ax.set_ylabel(f"true\n(i={_ag_i})", fontsize=6) + + for _j, _ag_j in enumerate(agents): + axes_grid[0, _j].set_title( + f"j={_ag_j}\ni={agents[0]}→j={_ag_j}" if _n == 1 else f"j={_ag_j}", + fontsize=8, + fontweight="bold", + color="steelblue", + ) + + fig_grid.suptitle( + f"Macierze pomyłek (znormalizowane wierszowo — recall per klasa)\n" + f"wiersze=model agenta i, kolumny=dane agenta j | {base}", + fontsize=10, + fontweight="bold", + ) + plt.tight_layout() + fig_grid + return + + +if __name__ == "__main__": + app.run() diff --git a/ltc/symbolic/report_split.py b/ltc/symbolic/report_split.py new file mode 100644 index 0000000..0d49984 --- /dev/null +++ b/ltc/symbolic/report_split.py @@ -0,0 +1,424 @@ +import marimo + +__generated_with = "0.23.3" +app = marimo.App(width="full") + + +@app.cell +def _(): + import marimo as mo + + return (mo,) + + +@app.cell +def _(): + import os + import json + import glob + import pickle + + import joblib + import numpy as np + import pandas as pd + import matplotlib.pyplot as plt + import seaborn as sns + + os.environ.setdefault("PYTHON_JULIAPKG_EXE", "/opt/homebrew/bin/julia") + + from ltc.symbolic.util import SimplexCode + + return SimplexCode, glob, joblib, json, np, os, pd, pickle, plt, sns + + +@app.cell +def _(glob, mo, os): + _csv_files = sorted(glob.glob("out/*.csv")) + _basenames = [os.path.splitext(os.path.basename(f))[0] for f in _csv_files] + experiment = mo.ui.dropdown( + options=_basenames, + value=_basenames[0] if _basenames else None, + label="Experiment", + ) + experiment + return (experiment,) + + +@app.cell +def _(SimplexCode, experiment, joblib, json, pd, pickle): + base = experiment.value + df = pd.read_csv(f"out/{base}.csv") + + with open(f"out/{base}.split.json") as _fh: + _split = json.load(_fh) + train_agents = _split["train_agents"] + test_agents = _split["test_agents"] + + with open(f"out/{base}.split_sr.pkl", "rb") as _fh: + sr_model = pickle.load(_fh) + forest = joblib.load(f"out/{base}.split_forest.pkl") + + simplex_code = SimplexCode(T=df["action"].nunique()) + feat_cols = [c for c in df.columns if c not in {"agent", "action"}] + return ( + base, + df, + feat_cols, + forest, + simplex_code, + sr_model, + test_agents, + train_agents, + ) + + +@app.cell +def _(sr_model): + sr_model + return + + +@app.cell +def _(mo, sr_model): + + _best_idx = sr_model.equations_["score"].idxmax() + + _eqs = sr_model.equations_[["equation", "loss", "complexity","score"]].copy() + + _styled = _eqs.style.apply( + lambda row: [ + "background-color: #90EE90; font-weight: bold" if row.name == _best_idx else "" + for _ in row + ], + axis=1, + ).format({"loss": "{:.6f}"}) + + mo.as_html(_styled) + return + + +@app.cell +def _(mo, test_agents, train_agents): + mo.md(f""" + ## Split + + A single symbolic-regression model and a random-forest baseline are + trained on the **train half** of the agents and evaluated on the + **held-out half**. + + - **Train agents:** {train_agents} + - **Held-out test agents:** {test_agents} + """) + return + + +@app.cell +def _(np, simplex_code): + def predict_labels_sr(model, X): + """Predict integer action labels by decoding the SR simplex-code output.""" + _eqs = model.equations_ + if isinstance(_eqs, list): + _idx = [_e["score"].idxmax() for _e in _eqs] + else: + _idx = _eqs["score"].idxmax() + _codes = np.asarray(model.predict(X.values, _idx)) + _codes = _codes.reshape(len(X), simplex_code.T - 1) + return np.asarray(simplex_code.decode(_codes)) + + return (predict_labels_sr,) + + +@app.cell +def _(df, feat_cols, forest, np, pd, predict_labels_sr, sr_model, test_agents): + _rows = [] + for _ag in sorted(df["agent"].unique()): + _df_ag = df[df["agent"] == _ag] + _X = _df_ag[feat_cols].astype(np.float32) + _y = _df_ag["action"].values + _sr_pred = predict_labels_sr(sr_model, _X) + _rf_pred = forest.predict(_X) + _rows.append( + { + "agent": _ag, + "split": "held-out" if _ag in test_agents else "train", + "n": len(_y), + "sr_acc": (_sr_pred == _y).mean(), + "rf_acc": (_rf_pred == _y).mean(), + } + ) + acc_df = pd.DataFrame(_rows) + return (acc_df,) + + +@app.cell +def _(mo): + mo.md(""" + ## Accuracy by agent (SR vs Random Forest) + """) + return + + +@app.cell +def _(acc_df, base, np, plt): + _held = acc_df[acc_df["split"] == "held-out"] + _x = np.arange(len(_held)) + _w = 0.38 + + fig_acc, ax_acc = plt.subplots(figsize=(10, 4)) + ax_acc.bar(_x - _w / 2, _held["sr_acc"], _w, label="Symbolic regression") + ax_acc.bar(_x + _w / 2, _held["rf_acc"], _w, label="Random forest") + ax_acc.set_xticks(_x) + ax_acc.set_xticklabels(_held["agent"]) + ax_acc.set_xlabel("Held-out agent (data source)") + ax_acc.set_ylabel("Accuracy") + ax_acc.set_ylim(0, 1) + ax_acc.set_title(f"Held-out accuracy grouped by agent\n{base}") + ax_acc.legend() + ax_acc.grid(axis="y", alpha=0.3) + plt.tight_layout() + fig_acc + return + + +@app.cell +def _(acc_df): + acc_df.style.format({"sr_acc": "{:.4f}", "rf_acc": "{:.4f}"}) + return + + +@app.cell +def _(mo): + mo.md(""" + ## Overall accuracy + """) + return + + +@app.cell +def _(acc_df, mo, np): + def _overall(_sub): + _w = _sub["n"].to_numpy() + _sr = np.average(_sub["sr_acc"], weights=_w) + _rf = np.average(_sub["rf_acc"], weights=_w) + return _sr, _rf + + _held = acc_df[acc_df["split"] == "held-out"] + _train = acc_df[acc_df["split"] == "train"] + + _h_sr, _h_rf = _overall(_held) + _t_sr, _t_rf = _overall(_train) + _a_sr, _a_rf = _overall(acc_df) + + mo.md( + f""" + Sample-weighted accuracy aggregated over agent groups: + + | Group | Symbolic regression | Random forest | + |---|---|---| + | Held-out agents | {_h_sr:.4f} | {_h_rf:.4f} | + | Train agents (in-sample) | {_t_sr:.4f} | {_t_rf:.4f} | + | **All agents** | **{_a_sr:.4f}** | **{_a_rf:.4f}** | + """ + ) + return + + +@app.cell +def _(mo): + mo.md(""" + ## Head-to-head: SR vs Random Forest (all agents) + """) + return + + +@app.cell +def _(acc_df, base, np, plt): + _x = np.arange(len(acc_df)) + _w = 0.38 + _colors_split = ["#1a7a1a" if _s == "held-out" else "#999999" for _s in acc_df["split"]] + + fig_h2h, ax_h2h = plt.subplots(figsize=(12, 4)) + _b1 = ax_h2h.bar(_x - _w / 2, acc_df["sr_acc"], _w, label="Symbolic regression") + _b2 = ax_h2h.bar(_x + _w / 2, acc_df["rf_acc"], _w, label="Random forest") + ax_h2h.set_xticks(_x) + ax_h2h.set_xticklabels( + [f"{_a}\n({_s})" for _a, _s in zip(acc_df["agent"], acc_df["split"])] + ) + ax_h2h.set_xlabel("Agent") + ax_h2h.set_ylabel("Accuracy") + ax_h2h.set_ylim(0, 1) + ax_h2h.set_title(f"SR vs RF accuracy per agent\n{base}") + ax_h2h.legend() + ax_h2h.grid(axis="y", alpha=0.3) + plt.tight_layout() + fig_h2h + return + + +@app.cell +def _(acc_df, base, np, plt, sns): + _delta = (acc_df["sr_acc"] - acc_df["rf_acc"]).to_numpy().reshape(1, -1) + _max_abs = max(float(np.abs(_delta).max()), 0.01) + + fig_delta, ax_delta = plt.subplots(figsize=(12, 1.8)) + sns.heatmap( + _delta, + annot=True, + fmt=".3f", + ax=ax_delta, + xticklabels=[ + f"{_a} ({_s})" for _a, _s in zip(acc_df["agent"], acc_df["split"]) + ], + yticklabels=["SR − RF"], + cmap="RdBu", + center=0, + vmin=-_max_abs, + vmax=_max_abs, + ) + ax_delta.set_title(f"Accuracy advantage of SR over RF (positive = SR wins)\n{base}") + plt.tight_layout() + fig_delta + return + + +@app.cell +def _(mo): + mo.md(""" + ## Confusion matrices — held-out agents + + Row-normalised (recall per class). Top row: symbolic regression, + bottom row: random forest. + """) + return + + +@app.cell +def _( + base, + df, + feat_cols, + forest, + np, + plt, + predict_labels_sr, + sns, + sr_model, + test_agents, +): + from sklearn.metrics import confusion_matrix + + _n = len(test_agents) + _cell = 2.8 + fig_cm, axes_cm = plt.subplots( + 2, _n, figsize=(_n * _cell, 2 * _cell), squeeze=False + ) + + for _col, _ag in enumerate(test_agents): + _df_ag = df[df["agent"] == _ag] + _X = _df_ag[feat_cols].astype(np.float32) + _y = _df_ag["action"].values + + _sr_pred = predict_labels_sr(sr_model, _X) + _rf_pred = forest.predict(_X) + + for _row, (_name, _pred) in enumerate( + [("SR", _sr_pred), ("RF", _rf_pred)] + ): + _ax = axes_cm[_row, _col] + _cm = confusion_matrix(_y, _pred, labels=[0, 1], normalize="true") + sns.heatmap( + _cm, + annot=True, + fmt=".2f", + ax=_ax, + cmap="Blues", + cbar=False, + xticklabels=["0", "1"], + yticklabels=["0", "1"], + vmin=0, + vmax=1, + linewidths=0.4, + linecolor="gray", + ) + _ax.set_title(f"{_name} agent {_ag}", fontsize=8, fontweight="bold") + _ax.tick_params(labelsize=7) + if _col == 0: + _ax.set_ylabel("true", fontsize=7) + _ax.set_xlabel("pred", fontsize=7) + + fig_cm.suptitle( + f"Confusion matrices on held-out agents | {base}", + fontsize=10, + fontweight="bold", + ) + plt.tight_layout() + fig_cm + return (confusion_matrix,) + + +@app.cell +def _(mo): + mo.md(""" + ## Aggregate confusion matrix — all held-out data + """) + return + + +@app.cell +def _( + base, + confusion_matrix, + df, + feat_cols, + forest, + np, + plt, + predict_labels_sr, + sns, + sr_model, + test_agents, +): + _df_held = df[df["agent"].isin(test_agents)] + _X = _df_held[feat_cols].astype(np.float32) + _y = _df_held["action"].values + + _sr_pred = predict_labels_sr(sr_model, _X) + _rf_pred = forest.predict(_X) + + fig_cm_agg, axes_cm_agg = plt.subplots(1, 2, figsize=(5, 3.2)) + for _ax, _name, _pred in [ + (axes_cm_agg[0], "Symbolic regression", _sr_pred), + (axes_cm_agg[1], "Random forest", _rf_pred), + ]: + _cm = confusion_matrix(_y, _pred, labels=[0, 1], normalize="true") + sns.heatmap( + _cm, + annot=True, + fmt=".3f", + ax=_ax, + cmap="Blues", + cbar=False, + xticklabels=["0", "1"], + yticklabels=["0", "1"], + vmin=0, + vmax=1, + linewidths=0.4, + linecolor="gray", + ) + _acc = (_pred == _y).mean() + _ax.set_title(f"{_name}\nacc={_acc:.4f}", fontsize=9, fontweight="bold") + _ax.set_xlabel("pred", fontsize=8) + _ax.set_ylabel("true", fontsize=8) + + fig_cm_agg.suptitle( + f"Aggregate over all held-out agents | {base}", + fontsize=10, + fontweight="bold", + ) + plt.tight_layout() + fig_cm_agg + return + + +if __name__ == "__main__": + app.run() diff --git a/ltc/symbolic/sr.py b/ltc/symbolic/sr.py new file mode 100644 index 0000000..2be5b41 --- /dev/null +++ b/ltc/symbolic/sr.py @@ -0,0 +1,94 @@ +import argparse +import os +import pickle + +from ltc.symbolic.util import SimplexCode + +os.environ.setdefault("PYTHON_JULIAPKG_EXE", "/opt/homebrew/bin/julia") + +import numpy as np +import pandas as pd +from pysr import PySRRegressor + + +def fit_sr( + df_ag: pd.DataFrame, + label_codes: SimplexCode | None = None, + n_iterations: int = 100, + n_populations: int = 10, + output_directory: str | None = None, +) -> PySRRegressor: + exclude_cols = {"agent", "action"} + feat_cols = [c for c in df_ag.columns if c not in exclude_cols] + X = df_ag[feat_cols].astype(np.float32) + # y = (2.0 * df_ag["action"].astype(np.float32) - 1.0).to_numpy() + yi = df_ag["action"].astype(int).to_numpy() + simplex_code = label_codes or SimplexCode(T=2) # Assuming binary actions; adjust T if more actions + y = simplex_code.encode(yi) + + # Balanced sample weights so the model can't win by predicting the + # most frequent class. + classes, counts = np.unique(yi, return_counts=True) + class_weight = {c: len(yi) / (len(classes) * n) for c, n in zip(classes, counts)} + weights = np.array([class_weight[c] for c in yi], dtype=np.float32) + + model = PySRRegressor( + niterations=n_iterations, + populations=n_populations, + binary_operators=["+", "*", "/", "-", "^"], + unary_operators=["exp"], + constraints={"^": (-1, 1), "exp": 3}, + # elementwise_loss="LogitMarginLoss()", + temp_equation_file=output_directory is None, + turbo=True, + output_directory=output_directory, + ) + model.fit(X, y, weights=weights) + return model + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Fit per-agent symbolic regression models on a history CSV" + ) + parser.add_argument("--file", type=str, required=True, help="Path to .csv file") + parser.add_argument( + "--output", + type=str, + default=None, + help="Output path prefix; per-agent files are saved as _agent_.sr.pkl", + ) + parser.add_argument( + "--agent", type=int, default=None, help="Single agent to fit (default: all)" + ) + parser.add_argument("--n_iterations", type=int, default=100, help="PySR iterations") + parser.add_argument( + "--n_populations", type=int, default=10, help="PySR populations" + ) + parser.add_argument( + "--pysr_output_dir", + type=str, + default=None, + help="Directory for PySR equation files (default: system temp)", + ) + args = parser.parse_args() + + df = pd.read_csv(args.file) + out_prefix = args.output if args.output else args.file.replace(".csv", "") + + agents = [args.agent] if args.agent is not None else sorted(df["agent"].unique()) + label_codes = SimplexCode(T=df["action"].nunique()) + for ag in agents: + print(f"Fitting agent {ag}...") + model = fit_sr( + df[df["agent"] == ag], + label_codes=label_codes, + n_iterations=args.n_iterations, + n_populations=args.n_populations, + output_directory=args.pysr_output_dir, + ) + out_path = f"{out_prefix}_agent_{ag}.sr.pkl" + with open(out_path, "wb") as f: + pickle.dump(model, f) + print(f"Saved: {out_path}") + print(model) diff --git a/ltc/symbolic/sr_split.py b/ltc/symbolic/sr_split.py new file mode 100644 index 0000000..2b6939c --- /dev/null +++ b/ltc/symbolic/sr_split.py @@ -0,0 +1,105 @@ +import argparse +import json +import os +import pickle + +import joblib + +from ltc.symbolic.sr import fit_sr +from ltc.symbolic.util import SimplexCode + +os.environ.setdefault("PYTHON_JULIAPKG_EXE", "/opt/homebrew/bin/julia") + +import numpy as np +import pandas as pd +from sklearn.ensemble import RandomForestClassifier + + +def split_agents(agents: list[int]) -> tuple[list[int], list[int]]: + """Split the sorted agent ids into a train half and a held-out test half.""" + agents = sorted(agents) + half = len(agents) // 2 + return agents[:half], agents[half:] + + +def fit_forest_split( + df_train: pd.DataFrame, n_estimators: int = 1500 +) -> RandomForestClassifier: + """Random forest on the same feature set as SR (agent id excluded).""" + feat_cols = [c for c in df_train.columns if c not in {"agent", "action"}] + X = df_train[feat_cols] + y = df_train["action"] + forest = RandomForestClassifier( + n_estimators=n_estimators, oob_score=True, n_jobs=-1 + ) + forest.fit(X, y) + return forest + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Fit a single SR model (and a RF baseline) on half of the " + "agents, leaving the other half as a held-out evaluation set." + ) + parser.add_argument("--file", type=str, required=True, help="Path to .csv file") + parser.add_argument( + "--output", + type=str, + default=None, + help="Output path prefix; artifacts saved as .split_*", + ) + parser.add_argument("--n_iterations", type=int, default=100, help="PySR iterations") + parser.add_argument( + "--n_populations", type=int, default=10, help="PySR populations" + ) + parser.add_argument( + "--n_estimators", type=int, default=1500, help="RF trees" + ) + parser.add_argument( + "--pysr_output_dir", + type=str, + default=None, + help="Directory for PySR equation files (default: system temp)", + ) + args = parser.parse_args() + + df = pd.read_csv(args.file) + out_prefix = args.output if args.output else args.file.replace(".csv", "") + + agents = sorted(df["agent"].unique().tolist()) + train_agents, test_agents = split_agents(agents) + print(f"Train agents: {train_agents}") + print(f"Held-out test agents: {test_agents}") + + df_train = df[df["agent"].isin(train_agents)] + label_codes = SimplexCode(T=df["action"].nunique()) + + print("Fitting symbolic regression on the train half...") + sr_model = fit_sr( + df_train, + label_codes=label_codes, + n_iterations=args.n_iterations, + n_populations=args.n_populations, + output_directory=args.pysr_output_dir, + ) + + print("Fitting random forest baseline on the train half...") + forest = fit_forest_split(df_train, n_estimators=args.n_estimators) + print(f"RF OOB score: {forest.oob_score_:.4f}") + + sr_path = f"{out_prefix}.split_sr.pkl" + forest_path = f"{out_prefix}.split_forest.pkl" + split_path = f"{out_prefix}.split.json" + + with open(sr_path, "wb") as f: + pickle.dump(sr_model, f) + joblib.dump(forest, forest_path) + with open(split_path, "w") as f: + json.dump( + {"train_agents": train_agents, "test_agents": test_agents}, f, indent=2 + ) + + print(f"Saved: {sr_path}") + print(f"Saved: {forest_path}") + print(f"Saved: {split_path}") + print(sr_model) diff --git a/ltc/symbolic/tree.py b/ltc/symbolic/tree.py new file mode 100644 index 0000000..d9ee979 --- /dev/null +++ b/ltc/symbolic/tree.py @@ -0,0 +1,32 @@ +import argparse + +import joblib +import pandas as pd +from sklearn.ensemble import RandomForestClassifier + + +def fit_forest(df: pd.DataFrame, n_estimators: int = 1500) -> RandomForestClassifier: + X = df.drop(columns=["action"]) + y = df["action"] + forest = RandomForestClassifier(n_estimators=n_estimators, oob_score=True, n_jobs=-1) + forest.fit(X, y) + return forest + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Fit a random forest on a history CSV") + parser.add_argument("--file", type=str, required=True, help="Path to .csv file") + parser.add_argument( + "--n_estimators", type=int, default=1500, help="Number of trees" + ) + parser.add_argument("--output", type=str, default=None, help="Output .forest.pkl path") + args = parser.parse_args() + + df = pd.read_csv(args.file) + forest = fit_forest(df, n_estimators=args.n_estimators) + + print(f"OOB score: {forest.oob_score_:.4f}") + + out_path = args.output if args.output else args.file.replace(".csv", ".forest.pkl") + joblib.dump(forest, out_path) + print(f"Saved: {out_path}") diff --git a/ltc/symbolic/util.py b/ltc/symbolic/util.py new file mode 100644 index 0000000..8a32fe0 --- /dev/null +++ b/ltc/symbolic/util.py @@ -0,0 +1,79 @@ +from dataclasses import dataclass, field + +import jax +import jax.numpy as jnp + +from ltc.sim.constants import Actions + + +def simplex_code(T: int) -> jax.Array: + """ + Simplex code matrix for T classes, shape (T-1, T). + + Each column is a unit-norm codeword with pairwise inner product -1/(T-1). + + Algorithm 1 from: Multiclass Learning with Simplex Coding, + Crammer & Singer (2002), Supplementary Material. + + C[2] = [[1, -1]] + C[i+1] = | 1 u^T | u = (-1/i, ..., -1/i) in R^i + | 0...0 sqrt(1-1/i²)·C[i] | + """ + assert T >= 2, "T must be >= 2" + C = jnp.array([[1.0, -1.0]]) # C[2], shape (1, 2) + + for i in range(2, T): + scale = jnp.sqrt(1.0 - 1.0 / i**2) + u = jnp.full((1, i), -1.0 / i) + v = jnp.zeros((i - 1, 1)) + top = jnp.concatenate([jnp.ones((1, 1)), u], axis=1) + bottom = jnp.concatenate([v, scale * C], axis=1) + C = jnp.concatenate([top, bottom], axis=0) + + return C + + +@jax.tree_util.register_dataclass +@dataclass +class SimplexCode: + T: int = field(metadata=dict(static=True), default=len(Actions)) + codes: jax.Array | None = None + + def __post_init__(self): + if self.codes is None: + self.codes = simplex_code(self.T) + else: + assert self.codes.shape == (self.T - 1, self.T), ( + "Codes must have shape (T-1, T)" + ) + self.codes = self.codes + + @jax.jit + def encode(self, labels: jax.Array) -> jax.Array: + """ + Encode integer labels (shape (N,)) to simplex codes (shape (N, T-1)). + """ + return jnp.take(self.codes, labels, axis=1).T + + @jax.jit + def decode(self, codes: jax.Array) -> jax.Array: + """ + Decode simplex codes (shape (N, T-1)) to integer labels (shape (N,)). + """ + # Compute inner products with codewords, shape (N, T) + inner_products = codes @ self.codes + return jnp.argmax(inner_products, axis=1) + + +if __name__ == "__main__": + T = 5 + sc = SimplexCode(T=T) + for i in range(T): + print(jnp.take(sc.codes, i, axis=1)) + + labels = jnp.array([0, 1, 2, 3, 4]) + codes = sc.encode(labels) + print("Codes:\n", codes) + decoded_labels = sc.decode(codes) + print("Decoded labels:\n", decoded_labels) + assert jnp.array_equal(labels, decoded_labels), "Decoded labels do not match original" diff --git a/pyproject.toml b/pyproject.toml index 93dfc63..232d81a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "ltc" 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