#!/usr/bin/env python # -*- coding: utf-8 -*- import argparse import glob import os from typing import List, Tuple import numpy as np import pandas as pd import matplotlib.pyplot as plt import matplotlib as mpl plt.rcParams['text.usetex'] = True plt.rcParams['font.family'] = 'sans-serif' plt.rcParams['font.sans-serif'] = ['Arial'] plt.rcParams['text.latex.preamble'] = r'\usepackage{amsmath} \usepackage{amssymb} \usepackage{amsfonts}' ENV_LIST: List[str] = [ "AutoEncodeEasy", "BattleShipEasy", "BreakoutEasy", "CartPoleEasy", "CountRecallEasy", "MineSweeperEasy", "NavigatorEasy", "NoisyCartPoleEasy", "SkittlesEasy", "TetrisEasy", ] MODEL_TYPES: List[str] = ["fart", "gru", "lru", "mingru"] def _find_csvs(env_name: str, memory_type: str, partial: bool, saliency_dir: str) -> List[str]: pattern = os.path.join( saliency_dir, f"saliency_results_{memory_type}_{env_name}_Partial={partial}_MODELSEED=*.csv", ) return sorted(glob.glob(pattern)) def _extract_pos_columns(df: pd.DataFrame) -> np.ndarray: pos_cols = [c for c in df.columns if c.startswith("pos_")] # sort by numeric suffix pos_cols.sort(key=lambda c: float(c.split("pos_")[-1])) return df[pos_cols].to_numpy(dtype=float) def _thirds_from_distribution_rows(pos_values: np.ndarray) -> np.ndarray: if pos_values.size == 0: return np.zeros((pos_values.shape[0], 3), dtype=float) num_cols = pos_values.shape[1] e1 = num_cols // 3 e2 = (num_cols * 2) // 3 thirds = np.stack( [ pos_values[:, :e1].sum(axis=1), pos_values[:, e1:e2].sum(axis=1), pos_values[:, e2:].sum(axis=1), ], axis=1, ) row_sums = thirds.sum(axis=1, keepdims=True) norm = np.zeros_like(thirds, dtype=float) mask = row_sums[:, 0] > 0 if np.any(mask): norm[mask] = thirds[mask] / row_sums[mask] return norm def _aggregate_thirds(env_name: str, memory_type: str, partial: bool, saliency_dir: str) -> np.ndarray: files = _find_csvs(env_name, memory_type, partial, saliency_dir) if not files: return np.zeros(3, dtype=float) thirds_all = [] for f in files: try: df = pd.read_csv(f) except Exception: continue if df.empty: continue pos_vals = _extract_pos_columns(df) thirds_rows = _thirds_from_distribution_rows(pos_vals) if thirds_rows.size == 0: continue thirds_all.append(thirds_rows) if not thirds_all: return np.zeros(3, dtype=float) thirds_concat = np.concatenate(thirds_all, axis=0) mean_thirds = thirds_concat.mean(axis=0) s = mean_thirds.sum() return mean_thirds / s if s > 0 else np.zeros(3, dtype=float) def plot_env(env_name: str, saliency_dir: str, output_dir: str, dpi: int = 300): mdp = np.vstack([ _aggregate_thirds(env_name, m, False, saliency_dir) for m in MODEL_TYPES ]) # [4,3] pomdp = np.vstack([ _aggregate_thirds(env_name, m, True, saliency_dir) for m in MODEL_TYPES ]) mdp_colors = ["#C6DBEF", "#6BAED6", "#2171B5"] pomdp_colors = ["#FDD0A2", "#FDAE6B", "#E6550D"] # Slightly shorter length than before fig = plt.figure(figsize=(10, 4)) gs = fig.add_gridspec(1, 2, width_ratios=[1, 1], wspace=0.18) ax_left = fig.add_subplot(gs[0, 0]) ax_right = fig.add_subplot(gs[0, 1]) x = np.arange(len(MODEL_TYPES)) width = 0.6 # MDP left = np.zeros(len(MODEL_TYPES)) for k in range(3): ax_left.bar(x, mdp[:, k], width, bottom=left, color=mdp_colors[k], edgecolor="white", linewidth=0.6) left += mdp[:, k] ax_left.set_xticks(x) ax_left.set_xticklabels([m.upper() for m in MODEL_TYPES], rotation=0, fontsize=12) ax_left.set_ylim(0, 1.05) ax_left.set_ylabel(r"$\mathbb{E}_{\pi, f}[\,\delta(Q_{\xi}(\mathbf{x},\tau))\,]$", fontsize=12) ax_left.set_title("MDP", fontsize=14) ax_left.spines["top"].set_visible(False) ax_left.spines["right"].set_visible(False) # POMDP left = np.zeros(len(MODEL_TYPES)) for k in range(3): ax_right.bar(x, pomdp[:, k], width, bottom=left, color=pomdp_colors[k], edgecolor="white", linewidth=0.6) left += pomdp[:, k] ax_right.set_xticks(x) ax_right.set_xticklabels([m.upper() for m in MODEL_TYPES], rotation=0, fontsize=12) ax_right.set_ylim(0, 1.05) ax_right.set_title("POMDP", fontsize=14) ax_right.tick_params(axis="y", left=False, labelleft=False) ax_right.spines["top"].set_visible(False) ax_right.spines["right"].set_visible(False) # Legends (thirds) thirds_labels = [r"$[0,\frac{1}{3})$", r"$[\frac{1}{3},\frac{2}{3})$", r"$[\frac{2}{3},1)$"] legend_handles_left = [plt.Rectangle((0, 0), 1, 1, color=c) for c in mdp_colors] legend_handles_right = [plt.Rectangle((0, 0), 1, 1, color=c) for c in pomdp_colors] anchor_x = 1.02 ax_left.legend( legend_handles_left, thirds_labels, title="MDP thirds", loc="center right", bbox_to_anchor=(anchor_x, 0.5), fontsize=10, frameon=True, fancybox=True, borderaxespad=0.0, labelspacing=0.4, handletextpad=0.6, ) ax_right.legend( legend_handles_right, thirds_labels, title="POMDP thirds", loc="center left", bbox_to_anchor=(-anchor_x + 0.0, 0.5), fontsize=10, frameon=True, fancybox=True, borderaxespad=0.0, labelspacing=0.4, handletextpad=0.6, ) fig.suptitle(env_name.replace("Easy", ""), fontsize=16) fig.tight_layout(rect=[0, 0, 1, 0.95]) os.makedirs(output_dir, exist_ok=True) out_png = os.path.join(output_dir, f"saliency_by_models_{env_name}.png") out_pdf = os.path.join(output_dir, f"saliency_by_models_{env_name}.pdf") fig.savefig(out_png, dpi=dpi) fig.savefig(out_pdf) plt.close(fig) def plot_model(model_type: str, saliency_dir: str, output_dir: str, dpi: int = 300): mdp_colors = ["#C6DBEF", "#6BAED6", "#2171B5"] pomdp_colors = ["#FDD0A2", "#FDAE6B", "#E6550D"] out_dir = os.path.join(output_dir, f"by_model_{model_type}") os.makedirs(out_dir, exist_ok=True) for env_name in ENV_LIST: mdp = _aggregate_thirds(env_name, model_type, False, saliency_dir) pomdp = _aggregate_thirds(env_name, model_type, True, saliency_dir) # Shorter figure length for per-model-per-env plots fig, ax = plt.subplots(figsize=(6.5, 4)) x = np.arange(2) width = 0.6 # MDP stacked left = 0.0 for k in range(3): ax.bar(x[0], mdp[k], width, bottom=left, color=mdp_colors[k], edgecolor="white", linewidth=0.6) left += mdp[k] # POMDP stacked left = 0.0 for k in range(3): ax.bar(x[1], pomdp[k], width, bottom=left, color=pomdp_colors[k], edgecolor="white", linewidth=0.6) left += pomdp[k] ax.set_xticks(x) ax.set_xticklabels(["MDP", "POMDP"], fontsize=12) ax.set_ylim(0, 1.05) ax.set_ylabel(r"$\mathbb{E}_{\pi, f}[\,\delta(Q_{\xi}(\mathbf{x},\tau))\,]$", fontsize=12) ax.set_title(f"{env_name.replace('Easy','')} — {model_type.upper()}", fontsize=14) # Remove top/right spines ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) thirds_labels = [r"$[0,\frac{1}{3})$", r"$[\frac{1}{3},\frac{2}{3})$", r"$[\frac{2}{3},1)$"] legend_handles = [ plt.Rectangle((0, 0), 1, 1, color=mdp_colors[0]), plt.Rectangle((0, 0), 1, 1, color=mdp_colors[1]), plt.Rectangle((0, 0), 1, 1, color=mdp_colors[2]), plt.Rectangle((0, 0), 1, 1, color=pomdp_colors[0]), plt.Rectangle((0, 0), 1, 1, color=pomdp_colors[1]), plt.Rectangle((0, 0), 1, 1, color=pomdp_colors[2]), ] legend_labels = [ "MDP " + thirds_labels[0], "MDP " + thirds_labels[1], "MDP " + thirds_labels[2], "POMDP " + thirds_labels[0], "POMDP " + thirds_labels[1], "POMDP " + thirds_labels[2], ] ax.legend(legend_handles, legend_labels, loc="upper center", bbox_to_anchor=(0.5, 1.15), ncol=3, fontsize=9, frameon=True, fancybox=True) fig.tight_layout(rect=[0, 0, 1, 0.92]) out_png = os.path.join(out_dir, f"{model_type}_{env_name}.png") out_pdf = os.path.join(out_dir, f"{model_type}_{env_name}.pdf") fig.savefig(out_png, dpi=dpi) fig.savefig(out_pdf) plt.close(fig) def main(): parser = argparse.ArgumentParser(description="Plot per-model figures: for each model, 10 env figures with MDP vs POMDP") parser.add_argument("--saliency_dir", type=str, default="your_saliency_csv_dir") parser.add_argument("--output_dir", type=str, default="your_output_dir") parser.add_argument("--dpi", type=int, default=300) parser.add_argument("--models", type=str, default=",".join(MODEL_TYPES), help="Comma-separated model types to include (fart,gru,lru,mingru)") args = parser.parse_args() selected_models = [m.strip() for m in args.models.split(",") if m.strip()] for m in selected_models: plot_model(m, args.saliency_dir, args.output_dir, dpi=args.dpi) print(f"Saved 10 figs for model: {m}") if __name__ == "__main__": main()