File size: 8,269 Bytes
9b19c29 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 |
"""The rliable-evaluation module provides a high-level interface to evaluate the results of an experiment with multiple runs
on different seeds using the rliable library. The API is experimental and subject to change!.
"""
import os
from dataclasses import asdict, dataclass, fields
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as sst
from rliable import library as rly
from rliable import plot_utils
from tianshou.highlevel.experiment import Experiment
from tianshou.utils import logging
from tianshou.utils.logger.base import DataScope
log = logging.getLogger(__name__)
@dataclass
class LoggedSummaryData:
mean: np.ndarray
std: np.ndarray
max: np.ndarray
min: np.ndarray
@dataclass
class LoggedCollectStats:
env_step: np.ndarray | None = None
n_collected_episodes: np.ndarray | None = None
n_collected_steps: np.ndarray | None = None
collect_time: np.ndarray | None = None
collect_speed: np.ndarray | None = None
returns_stat: LoggedSummaryData | None = None
lens_stat: LoggedSummaryData | None = None
@classmethod
def from_data_dict(cls, data: dict) -> "LoggedCollectStats":
"""Create a LoggedCollectStats object from a dictionary.
Converts SequenceSummaryStats from dict format to dataclass format and ignores fields that are not present.
"""
field_names = [f.name for f in fields(cls)]
for k, v in data.items():
if k not in field_names:
data.pop(k)
if isinstance(v, dict):
data[k] = LoggedSummaryData(**v)
return cls(**data)
@dataclass
class RLiableExperimentResult:
"""The result of an experiment that can be used with the rliable library."""
exp_dir: str
"""The base directory where each sub-directory contains the results of one experiment run."""
test_episode_returns_RE: np.ndarray
"""The test episodes for each run of the experiment where each row corresponds to one run."""
env_steps_E: np.ndarray
"""The number of environment steps at which the test episodes were evaluated."""
@classmethod
def load_from_disk(cls, exp_dir: str) -> "RLiableExperimentResult":
"""Load the experiment result from disk.
:param exp_dir: The directory from where the experiment results are restored.
"""
test_episode_returns = []
env_step_at_test = None
# TODO: env_step_at_test should not be defined in a loop and overwritten at each iteration
# just for retrieving them. We might need a cleaner directory structure.
for entry in os.scandir(exp_dir):
if entry.name.startswith(".") or not entry.is_dir():
continue
exp = Experiment.from_directory(entry.path)
logger = exp.logger_factory.create_logger(
entry.path,
entry.name,
None,
asdict(exp.config),
)
data = logger.restore_logged_data(entry.path)
if DataScope.TEST.value not in data or not data[DataScope.TEST.value]:
continue
restored_test_data = data[DataScope.TEST.value]
if not isinstance(restored_test_data, dict):
raise RuntimeError(
f"Expected entry with key {DataScope.TEST.value} data to be a dictionary, "
f"but got {restored_test_data=}.",
)
test_data = LoggedCollectStats.from_data_dict(restored_test_data)
if test_data.returns_stat is None:
continue
test_episode_returns.append(test_data.returns_stat.mean)
env_step_at_test = test_data.env_step
if not test_episode_returns or env_step_at_test is None:
raise ValueError(f"No experiment data found in {exp_dir}.")
return cls(
test_episode_returns_RE=np.array(test_episode_returns),
env_steps_E=np.array(env_step_at_test),
exp_dir=exp_dir,
)
def _get_rliable_data(
self,
algo_name: str | None = None,
score_thresholds: np.ndarray | None = None,
) -> tuple[dict, np.ndarray, np.ndarray]:
"""Return the data in the format expected by the rliable library.
:param algo_name: The name of the algorithm to be shown in the figure legend. If None, the name of the algorithm
is set to the experiment dir.
:param score_thresholds: The score thresholds for the performance profile. If None, the thresholds are inferred
from the minimum and maximum test episode returns.
:return: A tuple score_dict, env_steps, and score_thresholds.
"""
if score_thresholds is None:
score_thresholds = np.linspace(
np.min(self.test_episode_returns_RE),
np.max(self.test_episode_returns_RE),
101,
)
if algo_name is None:
algo_name = os.path.basename(self.exp_dir)
score_dict = {algo_name: self.test_episode_returns_RE}
return score_dict, self.env_steps_E, score_thresholds
def eval_results(
self,
algo_name: str | None = None,
score_thresholds: np.ndarray | None = None,
save_plots: bool = False,
show_plots: bool = True,
) -> tuple[plt.Figure, plt.Axes, plt.Figure, plt.Axes]:
"""Evaluate the results of an experiment and create a sample efficiency curve and a performance profile.
:param algo_name: The name of the algorithm to be shown in the figure legend. If None, the name of the algorithm
is set to the experiment dir.
:param score_thresholds: The score thresholds for the performance profile. If None, the thresholds are inferred
from the minimum and maximum test episode returns.
:param save_plots: If True, the figures are saved to the experiment directory.
:param show_plots: If True, the figures are shown.
:return: The created figures and axes.
"""
score_dict, env_steps, score_thresholds = self._get_rliable_data(
algo_name,
score_thresholds,
)
iqm = lambda scores: sst.trim_mean(scores, proportiontocut=0.25, axis=0)
iqm_scores, iqm_cis = rly.get_interval_estimates(score_dict, iqm)
# Plot IQM sample efficiency curve
fig_iqm, ax_iqm = plt.subplots(ncols=1, figsize=(7, 5), constrained_layout=True)
plot_utils.plot_sample_efficiency_curve(
env_steps,
iqm_scores,
iqm_cis,
algorithms=None,
xlabel="env step",
ylabel="IQM episode return",
ax=ax_iqm,
)
if show_plots:
plt.show(block=False)
if save_plots:
iqm_sample_efficiency_curve_path = os.path.abspath(
os.path.join(
self.exp_dir,
"iqm_sample_efficiency_curve.png",
),
)
log.info(f"Saving iqm sample efficiency curve to {iqm_sample_efficiency_curve_path}.")
fig_iqm.savefig(iqm_sample_efficiency_curve_path)
final_score_dict = {algo: returns[:, [-1]] for algo, returns in score_dict.items()}
score_distributions, score_distributions_cis = rly.create_performance_profile(
final_score_dict,
score_thresholds,
)
# Plot score distributions
fig_profile, ax_profile = plt.subplots(ncols=1, figsize=(7, 5), constrained_layout=True)
plot_utils.plot_performance_profiles(
score_distributions,
score_thresholds,
performance_profile_cis=score_distributions_cis,
xlabel=r"Episode return $(\tau)$",
ax=ax_profile,
)
if save_plots:
profile_curve_path = os.path.abspath(
os.path.join(self.exp_dir, "performance_profile.png"),
)
log.info(f"Saving performance profile curve to {profile_curve_path}.")
fig_profile.savefig(profile_curve_path)
if show_plots:
plt.show(block=False)
return fig_iqm, ax_iqm, fig_profile, ax_profile
|