Merge c6a61e3ba2 into e67da1d7a6
This commit is contained in:
Simon Alibert
GitHub
commit
c33442d2c1
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@ -0,0 +1,340 @@
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"""Compare two policies on based on metrics computed from an eval.
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Usage example:
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You just made changes to a policy and you want to assess its new performance against
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the reference policy (i.e. before your changes).
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```
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python lerobot/scripts/compare_policies.py \
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output/eval/ref_policy/eval_info.json \
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output/eval/new_policy/eval_info.json
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```
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This script can accept `eval_info.json` dicts with identical seeds between each eval episode of ref_policy and
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new_policy (paired-samples) or from evals performed with different seeds (independent samples).
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The script will first perform normality tests to determine if parametric tests can be used or not, then
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evaluate if policies metrics are significantly different using the appropriate tests.
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CAVEATS: by default, this script will compare seeds numbers to determine if samples can be considered paired.
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If changes have been made to this environment in-between the ref_policy eval and the new_policy eval, you
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should use the `--independent` flag to override this and not pair the samples even if they have identical
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seeds.
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"""
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import argparse
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import json
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import logging
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from pathlib import Path
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import matplotlib.pyplot as plt
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import numpy as np
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import scipy.stats as stats
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from scipy.stats import anderson, kstest, mannwhitneyu, normaltest, shapiro, ttest_ind, ttest_rel, wilcoxon
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from statsmodels.stats.contingency_tables import mcnemar
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from termcolor import colored
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from terminaltables import AsciiTable
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def init_logging() -> None:
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logging.basicConfig(
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level=logging.INFO,
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format="%(message)s",
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handlers=[logging.StreamHandler()],
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)
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logging.getLogger("matplotlib.font_manager").disabled = True
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def log_section(title: str) -> None:
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section_title = f"\n{'-'*21}\n {title.center(19)} \n{'-'*21}"
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logging.info(section_title)
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def log_test(msg: str, p_value: float):
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if p_value < 0.01:
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color, interpretation = "red", "H_0 Rejected"
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elif 0.01 <= p_value < 0.05:
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color, interpretation = "yellow", "Inconclusive"
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else:
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color, interpretation = "green", "H_0 Not Rejected"
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logging.info(
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f"{msg}, p-value = {colored(f'{p_value:.3f}', color)} -> {colored(f'{interpretation}', color, attrs=['bold'])}"
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)
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def get_eval_info_episodes(eval_info_path: Path) -> dict:
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with open(eval_info_path) as f:
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eval_info = json.load(f)
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return {
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"sum_rewards": np.array([ep_stat["sum_reward"] for ep_stat in eval_info["per_episode"]]),
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"max_rewards": np.array([ep_stat["max_reward"] for ep_stat in eval_info["per_episode"]]),
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"successes": np.array([ep_stat["success"] for ep_stat in eval_info["per_episode"]]),
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"seeds": [ep_stat["seed"] for ep_stat in eval_info["per_episode"]],
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"num_episodes": len(eval_info["per_episode"]),
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}
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def append_table_metric(table: list, metric: str, ref_sample: dict, new_sample: dict, mean_std: bool = False):
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if mean_std:
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ref_metric = f"{np.mean(ref_sample[metric]):.3f} ({np.std(ref_sample[metric]):.3f})"
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new_metric = f"{np.mean(new_sample[metric]):.3f} ({np.std(new_sample[metric]):.3f})"
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row_header = f"{metric} - mean (std)"
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else:
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ref_metric = ref_sample[metric]
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new_metric = new_sample[metric]
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row_header = metric
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row = [row_header, ref_metric, new_metric]
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table.append(row)
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return table
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def cohens_d(x, y):
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return (np.mean(x) - np.mean(y)) / np.sqrt((np.std(x, ddof=1) ** 2 + np.std(y, ddof=1) ** 2) / 2)
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def normality_tests(array: np.ndarray, name: str):
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ap_stat, ap_p = normaltest(array)
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sw_stat, sw_p = shapiro(array)
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ks_stat, ks_p = kstest(array, "norm", args=(np.mean(array), np.std(array)))
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ad_stat = anderson(array)
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log_test(f"{name} - D'Agostino and Pearson test: statistic = {ap_stat:.3f}", ap_p)
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log_test(f"{name} - Shapiro-Wilk test: statistic = {sw_stat:.3f}", sw_p)
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log_test(f"{name} - Kolmogorov-Smirnov test: statistic = {ks_stat:.3f}", ks_p)
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logging.info(f"{name} - Anderson-Darling test: statistic = {ad_stat.statistic:.3f}")
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for i in range(len(ad_stat.critical_values)):
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cv, sl = ad_stat.critical_values[i], ad_stat.significance_level[i]
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logging.info(f" Critical value at {sl}%: {cv:.3f}")
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return sw_p > 0.05 and ks_p > 0.05
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def perform_tests(ref_sample: dict, new_sample: dict, output_dir: Path, independent: bool = False):
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seeds_a, seeds_b = ref_sample["seeds"], new_sample["seeds"]
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if (seeds_a == seeds_b) and not independent:
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logging.info("\nSamples are paired (identical seeds).")
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paired = True
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else:
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logging.info("\nSamples are considered independent (seeds are different).")
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paired = False
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table_data = [["Metric", "Ref.", "New"]]
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table_data = append_table_metric(table_data, "num_episodes", ref_sample, new_sample)
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table_data = append_table_metric(table_data, "successes", ref_sample, new_sample, mean_std=True)
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table_data = append_table_metric(table_data, "max_rewards", ref_sample, new_sample, mean_std=True)
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table_data = append_table_metric(table_data, "sum_rewards", ref_sample, new_sample, mean_std=True)
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table = AsciiTable(table_data)
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print(table.table)
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log_section("Effect Size")
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d_max_reward = cohens_d(ref_sample["max_rewards"], new_sample["max_rewards"])
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d_sum_reward = cohens_d(ref_sample["sum_rewards"], new_sample["sum_rewards"])
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logging.info(f"Cohen's d for Max Reward: {d_max_reward:.3f}")
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logging.info(f"Cohen's d for Sum Reward: {d_sum_reward:.3f}")
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if paired:
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paired_sample_tests(ref_sample, new_sample)
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else:
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independent_sample_tests(ref_sample, new_sample)
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output_dir.mkdir(exist_ok=True, parents=True)
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plot_boxplot(
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ref_sample["max_rewards"],
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new_sample["max_rewards"],
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["Ref Sample Max Reward", "New Sample Max Reward"],
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"Boxplot of Max Rewards",
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f"{output_dir}/boxplot_max_reward.png",
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)
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plot_boxplot(
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ref_sample["sum_rewards"],
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new_sample["sum_rewards"],
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["Ref Sample Sum Reward", "New Sample Sum Reward"],
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"Boxplot of Sum Rewards",
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f"{output_dir}/boxplot_sum_reward.png",
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)
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plot_histogram(
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ref_sample["max_rewards"],
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new_sample["max_rewards"],
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["Ref Sample Max Reward", "New Sample Max Reward"],
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"Histogram of Max Rewards",
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f"{output_dir}/histogram_max_reward.png",
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)
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plot_histogram(
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ref_sample["sum_rewards"],
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new_sample["sum_rewards"],
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["Ref Sample Sum Reward", "New Sample Sum Reward"],
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"Histogram of Sum Rewards",
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f"{output_dir}/histogram_sum_reward.png",
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)
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plot_qqplot(
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ref_sample["max_rewards"],
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"Q-Q Plot of Ref Sample Max Rewards",
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f"{output_dir}/qqplot_sample_a_max_reward.png",
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)
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plot_qqplot(
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new_sample["max_rewards"],
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"Q-Q Plot of New Sample Max Rewards",
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f"{output_dir}/qqplot_sample_b_max_reward.png",
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)
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plot_qqplot(
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ref_sample["sum_rewards"],
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"Q-Q Plot of Ref Sample Sum Rewards",
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f"{output_dir}/qqplot_sample_a_sum_reward.png",
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)
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plot_qqplot(
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new_sample["sum_rewards"],
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"Q-Q Plot of New Sample Sum Rewards",
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f"{output_dir}/qqplot_sample_b_sum_reward.png",
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)
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def paired_sample_tests(ref_sample: dict, new_sample: dict):
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log_section("Normality tests")
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max_reward_diff = ref_sample["max_rewards"] - new_sample["max_rewards"]
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sum_reward_diff = ref_sample["sum_rewards"] - new_sample["sum_rewards"]
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normal_max_reward_diff = normality_tests(max_reward_diff, "Max Reward Difference")
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normal_sum_reward_diff = normality_tests(sum_reward_diff, "Sum Reward Difference")
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log_section("Paired-sample tests")
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if normal_max_reward_diff:
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t_stat_max_reward, p_val_max_reward = ttest_rel(ref_sample["max_rewards"], new_sample["max_rewards"])
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log_test(f"Paired t-test for Max Reward: t-statistic = {t_stat_max_reward:.3f}", p_val_max_reward)
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else:
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w_stat_max_reward, p_wilcox_max_reward = wilcoxon(
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ref_sample["max_rewards"], new_sample["max_rewards"]
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)
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log_test(f"Wilcoxon test for Max Reward: statistic = {w_stat_max_reward:.3f}", p_wilcox_max_reward)
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if normal_sum_reward_diff:
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t_stat_sum_reward, p_val_sum_reward = ttest_rel(ref_sample["sum_rewards"], new_sample["sum_rewards"])
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log_test(f"Paired t-test for Sum Reward: t-statistic = {t_stat_sum_reward:.3f}", p_val_sum_reward)
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else:
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w_stat_sum_reward, p_wilcox_sum_reward = wilcoxon(
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ref_sample["sum_rewards"], new_sample["sum_rewards"]
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)
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log_test(f"Wilcoxon test for Sum Reward: statistic = {w_stat_sum_reward:.3f}", p_wilcox_sum_reward)
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table = np.array(
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[
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[
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np.sum((ref_sample["successes"] == 1) & (new_sample["successes"] == 1)),
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np.sum((ref_sample["successes"] == 1) & (new_sample["successes"] == 0)),
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],
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[
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np.sum((ref_sample["successes"] == 0) & (new_sample["successes"] == 1)),
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np.sum((ref_sample["successes"] == 0) & (new_sample["successes"] == 0)),
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],
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]
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)
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mcnemar_result = mcnemar(table, exact=True)
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log_test(f"McNemar's test for Success: statistic = {mcnemar_result.statistic:.3f}", mcnemar_result.pvalue)
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def independent_sample_tests(ref_sample: dict, new_sample: dict):
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log_section("Normality tests")
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normal_max_rewards_a = normality_tests(ref_sample["max_rewards"], "Max Rewards Ref Sample")
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normal_max_rewards_b = normality_tests(new_sample["max_rewards"], "Max Rewards New Sample")
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normal_sum_rewards_a = normality_tests(ref_sample["sum_rewards"], "Sum Rewards Ref Sample")
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normal_sum_rewards_b = normality_tests(new_sample["sum_rewards"], "Sum Rewards New Sample")
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log_section("Independent samples tests")
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table = [["Test", "max_rewards", "sum_rewards"]]
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if normal_max_rewards_a and normal_max_rewards_b:
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table = append_independent_test(
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table, ref_sample, new_sample, ttest_ind, "Two-Sample t-test", kwargs={"equal_var": False}
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)
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t_stat_max_reward, p_val_max_reward = ttest_ind(
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ref_sample["max_rewards"], new_sample["max_rewards"], equal_var=False
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)
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log_test(f"Two-Sample t-test for Max Reward: t-statistic = {t_stat_max_reward:.3f}", p_val_max_reward)
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else:
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table = append_independent_test(table, ref_sample, new_sample, mannwhitneyu, "Mann-Whitney U")
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u_stat_max_reward, p_u_max_reward = mannwhitneyu(ref_sample["max_rewards"], new_sample["max_rewards"])
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log_test(f"Mann-Whitney U test for Max Reward: U-statistic = {u_stat_max_reward:.3f}", p_u_max_reward)
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if normal_sum_rewards_a and normal_sum_rewards_b:
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t_stat_sum_reward, p_val_sum_reward = ttest_ind(
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ref_sample["sum_rewards"], new_sample["sum_rewards"], equal_var=False
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)
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log_test(f"Two-Sample t-test for Sum Reward: t-statistic = {t_stat_sum_reward:.3f}", p_val_sum_reward)
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else:
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u_stat_sum_reward, p_u_sum_reward = mannwhitneyu(ref_sample["sum_rewards"], new_sample["sum_rewards"])
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log_test(f"Mann-Whitney U test for Sum Reward: U-statistic = {u_stat_sum_reward:.3f}", p_u_sum_reward)
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table = AsciiTable(table)
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print(table.table)
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def append_independent_test(
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table: list,
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ref_sample: dict,
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new_sample: dict,
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test: callable,
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test_name: str,
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kwargs: dict | None = None,
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) -> list:
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kwargs = {} if kwargs is None else kwargs
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row = [f"{test_name}: p-value ≥ alpha"]
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for metric in table[0][1:]:
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_, p_val = test(ref_sample[metric], new_sample[metric], **kwargs)
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alpha = 0.05
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status = "✅" if p_val >= alpha else "❌"
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row.append(f"{status} {p_val:.3f} ≥ {alpha}")
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table.append(row)
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return table
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||||
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def plot_boxplot(data_a: np.ndarray, data_b: np.ndarray, labels: list[str], title: str, filename: str):
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plt.boxplot([data_a, data_b], labels=labels)
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plt.title(title)
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plt.savefig(filename)
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plt.close()
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||||
def plot_histogram(data_a: np.ndarray, data_b: np.ndarray, labels: list[str], title: str, filename: str):
|
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plt.hist(data_a, bins=30, alpha=0.7, label=labels[0])
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plt.hist(data_b, bins=30, alpha=0.7, label=labels[1])
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plt.title(title)
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||||
plt.legend()
|
||||
plt.savefig(filename)
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||||
plt.close()
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||||
|
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|
||||
def plot_qqplot(data: np.ndarray, title: str, filename: str):
|
||||
stats.probplot(data, dist="norm", plot=plt)
|
||||
plt.title(title)
|
||||
plt.savefig(filename)
|
||||
plt.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(
|
||||
description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
|
||||
)
|
||||
parser.add_argument("ref_sample_path", type=Path, help="Path to the reference sample JSON file.")
|
||||
parser.add_argument("new_sample_path", type=Path, help="Path to the new sample JSON file.")
|
||||
parser.add_argument(
|
||||
"--independent",
|
||||
action="store_true",
|
||||
help="Ignore seeds and consider samples to be independent (unpaired).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output_dir",
|
||||
type=Path,
|
||||
default=Path("outputs/compare/"),
|
||||
help="Directory to save the output results. Defaults to outputs/compare/",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
init_logging()
|
||||
|
||||
ref_sample = get_eval_info_episodes(args.ref_sample_path)
|
||||
new_sample = get_eval_info_episodes(args.new_sample_path)
|
||||
perform_tests(ref_sample, new_sample, args.output_dir, args.independent)
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "absl-py"
|
||||
|
|
@ -442,6 +442,69 @@ files = [
|
|||
{file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "contourpy"
|
||||
version = "1.2.1"
|
||||
description = "Python library for calculating contours of 2D quadrilateral grids"
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "contourpy-1.2.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bd7c23df857d488f418439686d3b10ae2fbf9bc256cd045b37a8c16575ea1040"},
|
||||
{file = "contourpy-1.2.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5b9eb0ca724a241683c9685a484da9d35c872fd42756574a7cfbf58af26677fd"},
|
||||
{file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4c75507d0a55378240f781599c30e7776674dbaf883a46d1c90f37e563453480"},
|
||||
{file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:11959f0ce4a6f7b76ec578576a0b61a28bdc0696194b6347ba3f1c53827178b9"},
|
||||
{file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:eb3315a8a236ee19b6df481fc5f997436e8ade24a9f03dfdc6bd490fea20c6da"},
|
||||
{file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:39f3ecaf76cd98e802f094e0d4fbc6dc9c45a8d0c4d185f0f6c2234e14e5f75b"},
|
||||
{file = "contourpy-1.2.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:94b34f32646ca0414237168d68a9157cb3889f06b096612afdd296003fdd32fd"},
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||||
]
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||||
|
||||
[package.dependencies]
|
||||
numpy = ">=1.20"
|
||||
|
||||
[package.extras]
|
||||
bokeh = ["bokeh", "selenium"]
|
||||
docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
|
||||
mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.8.0)", "types-Pillow"]
|
||||
test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
|
||||
test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
|
||||
|
||||
[[package]]
|
||||
name = "coverage"
|
||||
version = "7.5.1"
|
||||
|
|
@ -509,6 +572,21 @@ tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.1
|
|||
[package.extras]
|
||||
toml = ["tomli"]
|
||||
|
||||
[[package]]
|
||||
name = "cycler"
|
||||
version = "0.12.1"
|
||||
description = "Composable style cycles"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
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||||
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||||
[package.extras]
|
||||
docs = ["ipython", "matplotlib", "numpydoc", "sphinx"]
|
||||
tests = ["pytest", "pytest-cov", "pytest-xdist"]
|
||||
|
||||
[[package]]
|
||||
name = "datasets"
|
||||
version = "2.19.1"
|
||||
|
|
@ -830,6 +908,71 @@ docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1
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|||
testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
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typing = ["typing-extensions (>=4.8)"]
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|
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[[package]]
|
||||
name = "fonttools"
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version = "4.51.0"
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description = "Tools to manipulate font files"
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optional = true
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
|
||||
[[package]]
|
||||
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||||
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||||
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|
@ -1481,6 +1624,119 @@ MarkupSafe = ">=2.0"
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|||
[package.extras]
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||||
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|
||||
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|
||||
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||||
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||||
{file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:6512cb89e334e4700febbffaaa52761b65b4f5a3cf33f960213d5656cea36a77"},
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||||
{file = "kiwisolver-1.4.5-cp39-cp39-win32.whl", hash = "sha256:9db8ea4c388fdb0f780fe91346fd438657ea602d58348753d9fb265ce1bca67f"},
|
||||
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||||
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|
||||
{file = "kiwisolver-1.4.5-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:ba55dce0a9b8ff59495ddd050a0225d58bd0983d09f87cfe2b6aec4f2c1234e4"},
|
||||
{file = "kiwisolver-1.4.5-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:fd32ea360bcbb92d28933fc05ed09bffcb1704ba3fc7942e81db0fd4f81a7892"},
|
||||
{file = "kiwisolver-1.4.5-pp38-pypy38_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:5e7139af55d1688f8b960ee9ad5adafc4ac17c1c473fe07133ac092310d76544"},
|
||||
{file = "kiwisolver-1.4.5-pp38-pypy38_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:dced8146011d2bc2e883f9bd68618b8247387f4bbec46d7392b3c3b032640126"},
|
||||
{file = "kiwisolver-1.4.5-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c9bf3325c47b11b2e51bca0824ea217c7cd84491d8ac4eefd1e409705ef092bd"},
|
||||
{file = "kiwisolver-1.4.5-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:5794cf59533bc3f1b1c821f7206a3617999db9fbefc345360aafe2e067514929"},
|
||||
{file = "kiwisolver-1.4.5-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:e368f200bbc2e4f905b8e71eb38b3c04333bddaa6a2464a6355487b02bb7fb09"},
|
||||
{file = "kiwisolver-1.4.5-pp39-pypy39_pp73-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e5d706eba36b4c4d5bc6c6377bb6568098765e990cfc21ee16d13963fab7b3e7"},
|
||||
{file = "kiwisolver-1.4.5-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:85267bd1aa8880a9c88a8cb71e18d3d64d2751a790e6ca6c27b8ccc724bcd5ad"},
|
||||
{file = "kiwisolver-1.4.5-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:210ef2c3a1f03272649aff1ef992df2e724748918c4bc2d5a90352849eb40bea"},
|
||||
{file = "kiwisolver-1.4.5-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:11d011a7574eb3b82bcc9c1a1d35c1d7075677fdd15de527d91b46bd35e935ee"},
|
||||
{file = "kiwisolver-1.4.5.tar.gz", hash = "sha256:e57e563a57fb22a142da34f38acc2fc1a5c864bc29ca1517a88abc963e60d6ec"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "labmaze"
|
||||
version = "1.0.6"
|
||||
|
|
@ -1673,6 +1929,7 @@ files = [
|
|||
{file = "lxml-5.2.1-cp37-cp37m-musllinux_1_2_x86_64.whl", hash = "sha256:9e2addd2d1866fe112bc6f80117bcc6bc25191c5ed1bfbcf9f1386a884252ae8"},
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||||
{file = "lxml-5.2.1-cp37-cp37m-win32.whl", hash = "sha256:f51969bac61441fd31f028d7b3b45962f3ecebf691a510495e5d2cd8c8092dbd"},
|
||||
{file = "lxml-5.2.1-cp37-cp37m-win_amd64.whl", hash = "sha256:b0b58fbfa1bf7367dde8a557994e3b1637294be6cf2169810375caf8571a085c"},
|
||||
{file = "lxml-5.2.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:3e183c6e3298a2ed5af9d7a356ea823bccaab4ec2349dc9ed83999fd289d14d5"},
|
||||
{file = "lxml-5.2.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:804f74efe22b6a227306dd890eecc4f8c59ff25ca35f1f14e7482bbce96ef10b"},
|
||||
{file = "lxml-5.2.1-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:08802f0c56ed150cc6885ae0788a321b73505d2263ee56dad84d200cab11c07a"},
|
||||
{file = "lxml-5.2.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0f8c09ed18ecb4ebf23e02b8e7a22a05d6411911e6fabef3a36e4f371f4f2585"},
|
||||
|
|
@ -1813,6 +2070,54 @@ files = [
|
|||
{file = "MarkupSafe-2.1.5.tar.gz", hash = "sha256:d283d37a890ba4c1ae73ffadf8046435c76e7bc2247bbb63c00bd1a709c6544b"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "matplotlib"
|
||||
version = "3.8.4"
|
||||
description = "Python plotting package"
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "matplotlib-3.8.4-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:abc9d838f93583650c35eca41cfcec65b2e7cb50fd486da6f0c49b5e1ed23014"},
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||||
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{file = "matplotlib-3.8.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ce1edd9f5383b504dbc26eeea404ed0a00656c526638129028b758fd43fc5f10"},
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{file = "matplotlib-3.8.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ecd79298550cba13a43c340581a3ec9c707bd895a6a061a78fa2524660482fc0"},
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||||
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||||
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||||
{file = "matplotlib-3.8.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:232ce322bfd020a434caaffbd9a95333f7c2491e59cfc014041d95e38ab90d1c"},
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||||
{file = "matplotlib-3.8.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6addbd5b488aedb7f9bc19f91cd87ea476206f45d7116fcfe3d31416702a82fa"},
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{file = "matplotlib-3.8.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc4ccdc64e3039fc303defd119658148f2349239871db72cd74e2eeaa9b80b71"},
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{file = "matplotlib-3.8.4-cp311-cp311-win_amd64.whl", hash = "sha256:8080d5081a86e690d7688ffa542532e87f224c38a6ed71f8fbed34dd1d9fedae"},
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{file = "matplotlib-3.8.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:50bac6e4d77e4262c4340d7a985c30912054745ec99756ce213bfbc3cb3808eb"},
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{file = "matplotlib-3.8.4-cp312-cp312-win_amd64.whl", hash = "sha256:7a6769f58ce51791b4cb8b4d7642489df347697cd3e23d88266aaaee93b41d9a"},
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||||
{file = "matplotlib-3.8.4-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:843cbde2f0946dadd8c5c11c6d91847abd18ec76859dc319362a0964493f0ba6"},
|
||||
{file = "matplotlib-3.8.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:1c13f041a7178f9780fb61cc3a2b10423d5e125480e4be51beaf62b172413b67"},
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||||
{file = "matplotlib-3.8.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fb44f53af0a62dc80bba4443d9b27f2fde6acfdac281d95bc872dc148a6509cc"},
|
||||
{file = "matplotlib-3.8.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:606e3b90897554c989b1e38a258c626d46c873523de432b1462f295db13de6f9"},
|
||||
{file = "matplotlib-3.8.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:9bb0189011785ea794ee827b68777db3ca3f93f3e339ea4d920315a0e5a78d54"},
|
||||
{file = "matplotlib-3.8.4-cp39-cp39-win_amd64.whl", hash = "sha256:6209e5c9aaccc056e63b547a8152661324404dd92340a6e479b3a7f24b42a5d0"},
|
||||
{file = "matplotlib-3.8.4-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:c7064120a59ce6f64103c9cefba8ffe6fba87f2c61d67c401186423c9a20fd35"},
|
||||
{file = "matplotlib-3.8.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a0e47eda4eb2614300fc7bb4657fced3e83d6334d03da2173b09e447418d499f"},
|
||||
{file = "matplotlib-3.8.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:493e9f6aa5819156b58fce42b296ea31969f2aab71c5b680b4ea7a3cb5c07d94"},
|
||||
{file = "matplotlib-3.8.4.tar.gz", hash = "sha256:8aac397d5e9ec158960e31c381c5ffc52ddd52bd9a47717e2a694038167dffea"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
contourpy = ">=1.0.1"
|
||||
cycler = ">=0.10"
|
||||
fonttools = ">=4.22.0"
|
||||
kiwisolver = ">=1.3.1"
|
||||
numpy = ">=1.21"
|
||||
packaging = ">=20.0"
|
||||
pillow = ">=8"
|
||||
pyparsing = ">=2.3.1"
|
||||
python-dateutil = ">=2.7"
|
||||
|
||||
[[package]]
|
||||
name = "mkl"
|
||||
version = "2021.4.0"
|
||||
|
|
@ -2451,6 +2756,24 @@ sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-d
|
|||
test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
|
||||
xml = ["lxml (>=4.9.2)"]
|
||||
|
||||
[[package]]
|
||||
name = "patsy"
|
||||
version = "0.5.6"
|
||||
description = "A Python package for describing statistical models and for building design matrices."
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "patsy-0.5.6-py2.py3-none-any.whl", hash = "sha256:19056886fd8fa71863fa32f0eb090267f21fb74be00f19f5c70b2e9d76c883c6"},
|
||||
{file = "patsy-0.5.6.tar.gz", hash = "sha256:95c6d47a7222535f84bff7f63d7303f2e297747a598db89cf5c67f0c0c7d2cdb"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = ">=1.4"
|
||||
six = "*"
|
||||
|
||||
[package.extras]
|
||||
test = ["pytest", "pytest-cov", "scipy"]
|
||||
|
||||
[[package]]
|
||||
name = "pettingzoo"
|
||||
version = "1.24.3"
|
||||
|
|
@ -3045,7 +3368,6 @@ files = [
|
|||
{file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
|
||||
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|
||||
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
|
||||
|
|
@ -3677,6 +3999,51 @@ files = [
|
|||
{file = "soupsieve-2.5.tar.gz", hash = "sha256:5663d5a7b3bfaeee0bc4372e7fc48f9cff4940b3eec54a6451cc5299f1097690"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "statsmodels"
|
||||
version = "0.14.2"
|
||||
description = "Statistical computations and models for Python"
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "statsmodels-0.14.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:df5d6f95c46f0341da6c79ee7617e025bf2b371e190a8e60af1ae9cabbdb7a97"},
|
||||
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|
||||
{file = "statsmodels-0.14.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5827a12e3ede2b98a784476d61d6bec43011fedb64aa815f2098e0573bece257"},
|
||||
{file = "statsmodels-0.14.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:10f2b7611a61adb7d596a6d239abdf1a4d5492b931b00d5ed23d32844d40e48e"},
|
||||
{file = "statsmodels-0.14.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:c254c66142f1167b4c7d031cf8db55294cc62ff3280e090fc45bd10a7f5fd029"},
|
||||
{file = "statsmodels-0.14.2-cp310-cp310-win_amd64.whl", hash = "sha256:0e46e9d59293c1af4cc1f4e5248f17e7e7bc596bfce44d327c789ac27f09111b"},
|
||||
{file = "statsmodels-0.14.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:50fcb633987779e795142f51ba49fb27648d46e8a1382b32ebe8e503aaabaa9e"},
|
||||
{file = "statsmodels-0.14.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:876794068abfaeed41df71b7887000031ecf44fbfa6b50d53ccb12ebb4ab747a"},
|
||||
{file = "statsmodels-0.14.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7a91f6c4943de13e3ce2e20ee3b5d26d02bd42300616a421becd53756f5deb37"},
|
||||
{file = "statsmodels-0.14.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4864a1c4615c5ea5f2e3b078a75bdedc90dd9da210a37e0738e064b419eccee2"},
|
||||
{file = "statsmodels-0.14.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:afbd92410e0df06f3d8c4e7c0e2e71f63f4969531f280fb66059e2ecdb6e0415"},
|
||||
{file = "statsmodels-0.14.2-cp311-cp311-win_amd64.whl", hash = "sha256:8e004cfad0e46ce73fe3f3812010c746f0d4cfd48e307b45c14e9e360f3d2510"},
|
||||
{file = "statsmodels-0.14.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:eb0ba1ad3627705f5ae20af6b2982f500546d43892543b36c7bca3e2f87105e7"},
|
||||
{file = "statsmodels-0.14.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:90fd2f0110b73fc3fa5a2f21c3ca99b0e22285cccf38e56b5b8fd8ce28791b0f"},
|
||||
{file = "statsmodels-0.14.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ac780ad9ff552773798829a0b9c46820b0faa10e6454891f5e49a845123758ab"},
|
||||
{file = "statsmodels-0.14.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:55d1742778400ae67acb04b50a2c7f5804182f8a874bd09ca397d69ed159a751"},
|
||||
{file = "statsmodels-0.14.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:f870d14a587ea58a3b596aa994c2ed889cc051f9e450e887d2c83656fc6a64bf"},
|
||||
{file = "statsmodels-0.14.2-cp312-cp312-win_amd64.whl", hash = "sha256:f450fcbae214aae66bd9d2b9af48e0f8ba1cb0e8596c6ebb34e6e3f0fec6542c"},
|
||||
{file = "statsmodels-0.14.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:201c3d00929c4a67cda1fe05b098c8dcf1b1eeefa88e80a8f963a844801ed59f"},
|
||||
{file = "statsmodels-0.14.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9edefa4ce08e40bc1d67d2f79bc686ee5e238e801312b5a029ee7786448c389a"},
|
||||
{file = "statsmodels-0.14.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:29c78a7601fdae1aa32104c5ebff2e0b72c26f33e870e2f94ab1bcfd927ece9b"},
|
||||
{file = "statsmodels-0.14.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f36494df7c03d63168fccee5038a62f469469ed6a4dd6eaeb9338abedcd0d5f5"},
|
||||
{file = "statsmodels-0.14.2-cp39-cp39-win_amd64.whl", hash = "sha256:8875823bdd41806dc853333cc4e1b7ef9481bad2380a999e66ea42382cf2178d"},
|
||||
{file = "statsmodels-0.14.2.tar.gz", hash = "sha256:890550147ad3a81cda24f0ba1a5c4021adc1601080bd00e191ae7cd6feecd6ad"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = ">=1.22.3"
|
||||
packaging = ">=21.3"
|
||||
pandas = ">=1.4,<2.1.0 || >2.1.0"
|
||||
patsy = ">=0.5.6"
|
||||
scipy = ">=1.8,<1.9.2 || >1.9.2"
|
||||
|
||||
[package.extras]
|
||||
build = ["cython (>=0.29.33)"]
|
||||
develop = ["colorama", "cython (>=0.29.33)", "cython (>=3.0.10,<4)", "flake8", "isort", "joblib", "matplotlib (>=3)", "pytest (>=7.3.0,<8)", "pytest-cov", "pytest-randomly", "pytest-xdist", "pywinpty", "setuptools-scm[toml] (>=8.0,<9.0)"]
|
||||
docs = ["ipykernel", "jupyter-client", "matplotlib", "nbconvert", "nbformat", "numpydoc", "pandas-datareader", "sphinx"]
|
||||
|
||||
[[package]]
|
||||
name = "sympy"
|
||||
version = "1.12"
|
||||
|
|
@ -3718,6 +4085,17 @@ files = [
|
|||
[package.extras]
|
||||
tests = ["pytest", "pytest-cov"]
|
||||
|
||||
[[package]]
|
||||
name = "terminaltables"
|
||||
version = "3.1.10"
|
||||
description = "Generate simple tables in terminals from a nested list of strings."
|
||||
optional = true
|
||||
python-versions = ">=2.6"
|
||||
files = [
|
||||
{file = "terminaltables-3.1.10-py2.py3-none-any.whl", hash = "sha256:e4fdc4179c9e4aab5f674d80f09d76fa436b96fdc698a8505e0a36bf0804a874"},
|
||||
{file = "terminaltables-3.1.10.tar.gz", hash = "sha256:ba6eca5cb5ba02bba4c9f4f985af80c54ec3dccf94cfcd190154386255e47543"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tifffile"
|
||||
version = "2024.5.10"
|
||||
|
|
@ -4239,6 +4617,7 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
|
|||
|
||||
[extras]
|
||||
aloha = ["gym-aloha"]
|
||||
compare = ["matplotlib", "statsmodels", "terminaltables"]
|
||||
dev = ["debugpy", "pre-commit"]
|
||||
pusht = ["gym-pusht"]
|
||||
test = ["pytest", "pytest-cov"]
|
||||
|
|
@ -4248,4 +4627,4 @@ xarm = ["gym-xarm"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.10,<3.13"
|
||||
content-hash = "e4834d67df32c8c617c259b0e59bb33ddaccde08fe940d771e74046cbffe3399"
|
||||
content-hash = "f85a71ac284fe1090a0b3188a6cfddeb346c8712c6d899f24c19b59962d557d4"
|
||||
|
|
|
|||
|
|
@ -58,15 +58,19 @@ imagecodecs = { version = ">=2024.1.1", optional = true }
|
|||
pyav = ">=12.0.5"
|
||||
moviepy = ">=1.0.3"
|
||||
rerun-sdk = ">=0.15.1"
|
||||
statsmodels = {version = ">=0.14.2", optional = true}
|
||||
matplotlib = {version = ">=3.8.4", optional = true}
|
||||
terminaltables = {version = ">=3.1.10", optional = true}
|
||||
|
||||
|
||||
[tool.poetry.extras]
|
||||
pusht = ["gym-pusht"]
|
||||
xarm = ["gym-xarm"]
|
||||
aloha = ["gym-aloha"]
|
||||
umi = ["imagecodecs"]
|
||||
compare = ["statsmodels", "matplotlib", "terminaltables"]
|
||||
dev = ["pre-commit", "debugpy"]
|
||||
test = ["pytest", "pytest-cov"]
|
||||
umi = ["imagecodecs"]
|
||||
|
||||
[tool.ruff]
|
||||
line-length = 110
|
||||
|
|
|
|||
Loading…
Reference in New Issue