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8c5a642 0dac2bf 8c5a642 0dac2bf 8c5a642 0dac2bf 8c5a642 0dac2bf 8c5a642 0dac2bf 8c5a642 | 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 | #!/usr/bin/env python
"""Visualize A1 fit outputs for target-mask evaluation."""
from __future__ import annotations
import argparse
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from a1_pipeline.io_utils import ensure_directory
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="Visualize A1 core ROI fit results")
parser.add_argument(
"--fit-output-dir",
type=str,
default="/home/mohith/ds005345/outputs/a1_bootstrap/fit_results/Qwen_Qwen3-0.6B",
help="Directory containing run_a1_fit.py outputs",
)
parser.add_argument(
"--output-dir",
type=str,
default=None,
help="Plot output directory (default: <fit-output-dir>/plots)",
)
parser.add_argument(
"--metric",
type=str,
default="mean_corr",
choices=[
"mean_corr",
"mean_r2",
"mean_2v2_accuracy",
"2v2",
"2v2_accuracy",
"two_v_two_accuracy",
],
help="Metric used for plots",
)
return parser
def _check_required_files(fit_output_dir: Path) -> tuple[Path, Path]:
layer_summary_path = fit_output_dir / "core_roi_layer_summary.csv"
best_summary_path = fit_output_dir / "core_roi_best_layer_summary.csv"
missing = [path for path in [layer_summary_path, best_summary_path] if not path.exists()]
if missing:
raise FileNotFoundError("Missing fit summary files: " + ", ".join(str(path) for path in missing))
return layer_summary_path, best_summary_path
def _plot_protocol_heatmap(
layer_summary_df: pd.DataFrame,
protocol: str,
metric: str,
output_path: Path,
) -> None:
protocol_df = layer_summary_df[layer_summary_df["protocol"] == protocol].copy()
if protocol_df.empty:
return
pivot_df = protocol_df.pivot(index="roi_name", columns="layer_idx", values=metric)
pivot_df = pivot_df.sort_index()
plt.figure(figsize=(max(8, 0.4 * len(pivot_df.columns)), 4.8))
sns.heatmap(pivot_df, cmap="viridis", annot=False)
plt.title(f"{protocol} {metric} by Target Mask and Layer")
plt.xlabel("Layer")
plt.ylabel("Target Mask")
plt.tight_layout()
plt.savefig(output_path, dpi=180)
plt.close()
def _plot_best_layer_bar(
best_df: pd.DataFrame,
metric: str,
output_path: Path,
) -> None:
if best_df.empty:
return
chart_df = best_df.copy()
chart_df["label"] = chart_df["roi_name"] + "\nL" + chart_df["layer_idx"].astype(int).astype(str)
plt.figure(figsize=(11, 5))
sns.barplot(data=chart_df, x="label", y=metric, hue="protocol")
plt.title(f"Best Layer per Target Mask ({metric})")
plt.xlabel("ROI and selected layer")
plt.ylabel(metric)
plt.xticks(rotation=0)
plt.tight_layout()
plt.savefig(output_path, dpi=180)
plt.close()
def _plot_protocol_layer_curve(
layer_summary_df: pd.DataFrame,
metric: str,
output_path: Path,
) -> None:
if layer_summary_df.empty:
return
curve_df = (
layer_summary_df.groupby(["protocol", "layer_idx"], as_index=False)[metric]
.mean()
.sort_values(["protocol", "layer_idx"])
)
plt.figure(figsize=(10, 4.8))
sns.lineplot(data=curve_df, x="layer_idx", y=metric, hue="protocol", marker="o")
plt.title(f"Average Target-Mask {metric} by Layer")
plt.xlabel("Layer")
plt.ylabel(metric)
plt.tight_layout()
plt.savefig(output_path, dpi=180)
plt.close()
def main() -> None:
parser = _build_parser()
args = parser.parse_args()
fit_output_dir = Path(args.fit_output_dir).resolve()
layer_summary_path, best_summary_path = _check_required_files(fit_output_dir=fit_output_dir)
plot_output_dir = Path(args.output_dir).resolve() if args.output_dir else fit_output_dir / "plots"
ensure_directory(plot_output_dir)
layer_summary_df = pd.read_csv(layer_summary_path)
best_summary_df = pd.read_csv(best_summary_path)
metric = _resolve_metric_column(str(args.metric))
if metric not in layer_summary_df.columns:
raise ValueError(
f"Metric column '{metric}' not found in {layer_summary_path}. "
f"Available columns: {sorted(layer_summary_df.columns.tolist())}"
)
if metric not in best_summary_df.columns:
raise ValueError(
f"Metric column '{metric}' not found in {best_summary_path}. "
f"Available columns: {sorted(best_summary_df.columns.tolist())}"
)
for protocol in sorted(set(layer_summary_df["protocol"].tolist())):
heatmap_path = plot_output_dir / f"{protocol}_{metric}_heatmap.png"
_plot_protocol_heatmap(
layer_summary_df=layer_summary_df,
protocol=protocol,
metric=metric,
output_path=heatmap_path,
)
_plot_best_layer_bar(
best_df=best_summary_df,
metric=metric,
output_path=plot_output_dir / f"best_layer_{metric}_bar.png",
)
_plot_protocol_layer_curve(
layer_summary_df=layer_summary_df,
metric=metric,
output_path=plot_output_dir / f"protocol_layer_curve_{metric}.png",
)
print("=" * 72)
print("A1 visualization complete")
print(f"Fit output directory: {fit_output_dir}")
print(f"Plot output directory: {plot_output_dir}")
print(f"Metric: {metric}")
print("=" * 72)
if __name__ == "__main__":
main()
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