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#2648 - changed av_reward_per_episode to total_reward_per_episode in primaite_benchmark.py and report.py

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Chris McCarthy
2024-06-26 13:26:18 +01:00
parent e7f979b78e
commit 7f0a0c562f
2 changed files with 10 additions and 10 deletions
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18
benchmark/report.py
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@@ -35,19 +35,19 @@ def _build_benchmark_results_dict(start_datetime: datetime, metadata_dict: Dict,
"av_s_per_step": sum(d["s_per_step"] for d in metadata_dict.values()) / num_sessions,
"av_s_per_100_steps_10_nodes": sum(d["s_per_100_steps_10_nodes"] for d in metadata_dict.values())
/ num_sessions,
"combined_av_reward_per_episode": {},
"session_av_reward_per_episode": {k: v["av_reward_per_episode"] for k, v in metadata_dict.items()},
"combined_total_reward_per_episode": {},
"session_total_reward_per_episode": {k: v["total_reward_per_episode"] for k, v in metadata_dict.items()},
"config": config,
}
# find the average of each episode across all sessions
episodes = metadata_dict[1]["av_reward_per_episode"].keys()
episodes = metadata_dict[1]["total_reward_per_episode"].keys()
for episode in episodes:
combined_av_reward = (
sum(metadata_dict[k]["av_reward_per_episode"][episode] for k in metadata_dict.keys()) / num_sessions
sum(metadata_dict[k]["total_reward_per_episode"][episode] for k in metadata_dict.keys()) / num_sessions
)
averaged_data["combined_av_reward_per_episode"][episode] = combined_av_reward
averaged_data["combined_total_reward_per_episode"][episode] = combined_av_reward
return averaged_data
@@ -83,7 +83,7 @@ def _plot_benchmark_metadata(
fig = go.Figure(layout=layout)
fig.update_layout(template=PLOT_CONFIG["template"])
for session, av_reward_dict in benchmark_metadata_dict["session_av_reward_per_episode"].items():
for session, av_reward_dict in benchmark_metadata_dict["session_total_reward_per_episode"].items():
df = _get_df_from_episode_av_reward_dict(av_reward_dict)
fig.add_trace(
go.Scatter(
@@ -96,7 +96,7 @@ def _plot_benchmark_metadata(
)
)
df = _get_df_from_episode_av_reward_dict(benchmark_metadata_dict["combined_av_reward_per_episode"])
df = _get_df_from_episode_av_reward_dict(benchmark_metadata_dict["combined_total_reward_per_episode"])
fig.add_trace(
go.Scatter(
x=df["episode"], y=df["av_reward"], mode="lines", name="Combined Session Av", line={"color": "#FF0000"}
@@ -132,7 +132,7 @@ def _plot_all_benchmarks_combined_session_av(results_directory: Path) -> Figure:
Does this by iterating over the ``benchmark/results`` directory and
extracting the benchmark metadata json for each version that has been
benchmarked. The combined_av_reward_per_episode is extracted from each,
benchmarked. The combined_total_reward_per_episode is extracted from each,
converted into a polars dataframe, and plotted as a scatter line in plotly.
"""
major_v = primaite.__version__.split(".")[0]
@@ -158,7 +158,7 @@ def _plot_all_benchmarks_combined_session_av(results_directory: Path) -> Figure:
metadata_file = dir / f"{dir.name}_benchmark_metadata.json"
with open(metadata_file, "r") as file:
metadata_dict = json.load(file)
df = _get_df_from_episode_av_reward_dict(metadata_dict["combined_av_reward_per_episode"])
df = _get_df_from_episode_av_reward_dict(metadata_dict["combined_total_reward_per_episode"])
fig.add_trace(go.Scatter(x=df["episode"], y=df["rolling_av_reward"], mode="lines", name=dir.name))