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"""Compare A1 fit results across multiple model slugs.
Reads ``core_roi_layer_summary.csv`` and ``core_roi_best_layer_summary.csv``
from each ``--fit-dir`` and renders side-by-side plots tagged by model slug.
Outputs go to a dedicated comparison directory so the per-model visualize
step (run_a1_visualize.py) is never overwritten.
"""
from __future__ import annotations
import argparse
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt # noqa: E402
import pandas as pd # noqa: E402
import seaborn as sns # noqa: E402
METRIC_ALIAS_MAP: dict[str, str] = {
"2v2": "mean_2v2_accuracy",
"2v2_accuracy": "mean_2v2_accuracy",
"two_v_two_accuracy": "mean_2v2_accuracy",
}
def _resolve_metric_column(metric: str) -> str:
token = str(metric).strip().lower()
return METRIC_ALIAS_MAP.get(token, str(metric))
def _build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(description="Compare A1 fits across models")
parser.add_argument(
"--fit-dir",
action="append",
required=True,
help=(
"Path to a fit_results/<model-slug> directory. Repeat for each model. "
"Optionally prefix with a label, e.g. 'qwen3=outputs/.../fit_results/Qwen_Qwen3-0.6B'."
),
)
parser.add_argument(
"--output-dir",
type=str,
required=True,
help="Where to write comparison plots and merged CSVs",
)
parser.add_argument(
"--metric",
type=str,
default="mean_corr",
choices=[
"mean_corr",
"mean_r2",
"mean_2v2_accuracy",
"2v2",
"2v2_accuracy",
"two_v_two_accuracy",
],
)
parser.add_argument(
"--title-suffix",
type=str,
default="",
help="Optional title suffix to add to comparison plots",
)
return parser
def _split_label(entry: str) -> tuple[str, Path]:
if "=" in entry:
label, path = entry.split("=", 1)
return label.strip() or Path(path).name, Path(path).expanduser().resolve()
p = Path(entry).expanduser().resolve()
return p.name, p
def _load_fit_summaries(
entries: list[str],
) -> tuple[pd.DataFrame, pd.DataFrame]:
layer_frames: list[pd.DataFrame] = []
best_frames: list[pd.DataFrame] = []
for entry in entries:
label, fit_dir = _split_label(entry)
layer_path = fit_dir / "core_roi_layer_summary.csv"
best_path = fit_dir / "core_roi_best_layer_summary.csv"
missing = [p for p in (layer_path, best_path) if not p.exists()]
if missing:
raise FileNotFoundError(
"Missing fit summaries for "
f"{label} ({fit_dir}): {[str(p) for p in missing]}"
)
layer_df = pd.read_csv(layer_path)
layer_df["model_label"] = label
layer_df["fit_dir"] = str(fit_dir)
layer_frames.append(layer_df)
best_df = pd.read_csv(best_path)
best_df["model_label"] = label
best_df["fit_dir"] = str(fit_dir)
best_frames.append(best_df)
return (
pd.concat(layer_frames, ignore_index=True),
pd.concat(best_frames, ignore_index=True),
)
def _plot_layer_curve(
layer_df: pd.DataFrame,
metric: str,
output_path: Path,
title_suffix: str,
) -> None:
if layer_df.empty:
return
curve_df = (
layer_df.groupby(["model_label", "protocol", "layer_idx"], as_index=False)[metric]
.mean()
.sort_values(["model_label", "protocol", "layer_idx"])
)
protocols = sorted(curve_df["protocol"].unique())
fig, axes = plt.subplots(
1, len(protocols), figsize=(6 * len(protocols), 4.5), sharey=True, squeeze=False
)
for ax, protocol in zip(axes[0], protocols):
sub = curve_df[curve_df["protocol"] == protocol]
sns.lineplot(data=sub, x="layer_idx", y=metric, hue="model_label", marker="o", ax=ax)
ax.set_title(f"Protocol {protocol}")
ax.set_xlabel("Layer")
ax.set_ylabel(metric)
suffix = f" — {title_suffix}" if title_suffix else ""
fig.suptitle(f"Avg target-mask {metric} by layer across models{suffix}")
fig.tight_layout()
fig.savefig(output_path, dpi=180)
plt.close(fig)
def _plot_best_layer_bar(
best_df: pd.DataFrame,
metric: str,
output_path: Path,
title_suffix: str,
) -> None:
if best_df.empty:
return
chart_df = best_df.copy()
chart_df["roi_label"] = chart_df["roi_name"].astype(str)
protocols = sorted(chart_df["protocol"].unique())
fig, axes = plt.subplots(
len(protocols), 1, figsize=(12, 4.5 * len(protocols)), squeeze=False
)
for ax, protocol in zip(axes[:, 0], protocols):
sub = chart_df[chart_df["protocol"] == protocol]
sns.barplot(data=sub, x="roi_label", y=metric, hue="model_label", ax=ax)
ax.set_title(f"Best-layer {metric} per ROI — protocol {protocol}")
ax.set_xlabel("ROI")
ax.set_ylabel(metric)
ax.tick_params(axis="x", rotation=20)
suffix = f" — {title_suffix}" if title_suffix else ""
fig.suptitle(f"Best layer {metric} per target mask across models{suffix}")
fig.tight_layout()
fig.savefig(output_path, dpi=180)
plt.close(fig)
def _plot_overall_summary(
best_df: pd.DataFrame,
metric: str,
output_path: Path,
title_suffix: str,
) -> None:
if best_df.empty:
return
summary_df = (
best_df.groupby(["model_label", "protocol"], as_index=False)[metric]
.mean()
.sort_values(["protocol", "model_label"])
)
fig, ax = plt.subplots(figsize=(8, 4.8))
sns.barplot(data=summary_df, x="protocol", y=metric, hue="model_label", ax=ax)
ax.set_title(
f"Mean best-layer {metric} per protocol{(' — ' + title_suffix) if title_suffix else ''}"
)
ax.set_ylabel(metric)
ax.set_xlabel("Protocol")
fig.tight_layout()
fig.savefig(output_path, dpi=180)
plt.close(fig)
def main() -> None:
args = _build_parser().parse_args()
output_dir = Path(args.output_dir).expanduser().resolve()
output_dir.mkdir(parents=True, exist_ok=True)
layer_df, best_df = _load_fit_summaries(entries=list(args.fit_dir))
metric = _resolve_metric_column(args.metric)
if metric not in layer_df.columns:
raise ValueError(
f"Metric column '{metric}' missing from layer summary. "
f"Available: {sorted(layer_df.columns.tolist())}"
)
if metric not in best_df.columns:
raise ValueError(
f"Metric column '{metric}' missing from best summary. "
f"Available: {sorted(best_df.columns.tolist())}"
)
layer_df.to_csv(output_dir / "merged_core_roi_layer_summary.csv", index=False)
best_df.to_csv(output_dir / "merged_core_roi_best_layer_summary.csv", index=False)
_plot_layer_curve(
layer_df=layer_df,
metric=metric,
output_path=output_dir / f"compare_layer_curve_{metric}.png",
title_suffix=args.title_suffix,
)
_plot_best_layer_bar(
best_df=best_df,
metric=metric,
output_path=output_dir / f"compare_best_layer_bar_{metric}.png",
title_suffix=args.title_suffix,
)
_plot_overall_summary(
best_df=best_df,
metric=metric,
output_path=output_dir / f"compare_overall_{metric}.png",
title_suffix=args.title_suffix,
)
print("=" * 72)
print("A1 model comparison complete")
print(f"Models compared : {sorted(set(layer_df['model_label']))}")
print(f"Metric : {metric}")
print(f"Output directory: {output_dir}")
print("=" * 72)
if __name__ == "__main__":
main()
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