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0161e74 | 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 | #!/usr/bin/env python3
"""Per-metric bar charts: PS vs BL across all perturbations, with % difference."""
from __future__ import annotations
import sys
from pathlib import Path
_THIS_DIR = Path(__file__).resolve().parent
if str(_THIS_DIR.parent) not in sys.path:
sys.path.insert(0, str(_THIS_DIR.parent))
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from prompt_selection import config as cfg
CSV_PATH = cfg.EVAL_DIR / "all_comparison.csv"
OUTPUT_DIR = cfg.EVAL_DIR / "per_metric_charts"
# Metrics to visualize
SELECTED_METRICS = [
"pearson_delta",
"de_direction_match",
"de_sig_genes_recall",
"roc_auc",
"mse",
"mae",
]
DISPLAY_NAMES = {
"pearson_delta": "Pearson Delta",
"de_direction_match": "DE Direction Match",
"de_sig_genes_recall": "DE Sig Genes Recall",
"roc_auc": "ROC AUC",
"mse": "MSE",
"mae": "MAE",
}
LOWER_IS_BETTER = {"mse", "mae"}
def plot_one_metric(df_metric: pd.DataFrame, metric_name: str, output_dir: Path):
"""Generate a grouped bar chart for one metric across all perturbations."""
display_name = DISPLAY_NAMES.get(metric_name, metric_name)
lower_better = metric_name in LOWER_IS_BETTER
# Sort by PS value (descending for quality, ascending for error)
df_metric = df_metric.sort_values("prompt_selection", ascending=lower_better).reset_index(drop=True)
# Shorten long perturbation names for display
short_names = {
"O-Demethylated Adapalene": "O-Demeth. Adapalene",
"Porcn Inhibitor III": "Porcn Inhib. III",
"Dimethyl Sulfoxide": "DMSO",
}
df_metric["display_pert"] = df_metric["perturbation"].map(short_names).fillna(df_metric["perturbation"])
n = len(df_metric)
y = np.arange(n)
bar_h = 0.35
fig, ax = plt.subplots(figsize=(12, max(6, n * 0.55)))
bars_ps = ax.barh(y - bar_h / 2, df_metric["prompt_selection"], bar_h,
label="Prompt Selection", color="#4C72B0", edgecolor="white", linewidth=0.5)
bars_bl = ax.barh(y + bar_h / 2, df_metric["random_baseline"], bar_h,
label="Random Baseline", color="#DD8452", edgecolor="white", linewidth=0.5)
ax.set_yticks(y)
ax.set_yticklabels(df_metric["display_pert"], fontsize=11)
ax.invert_yaxis()
ax.set_xlabel(display_name, fontsize=12)
ax.legend(loc="lower right", fontsize=10, framealpha=0.9)
ax.grid(axis="x", alpha=0.3, linestyle="--")
ax.set_axisbelow(True)
if lower_better:
subtitle = "(lower is better)"
else:
subtitle = "(higher is better)"
ax.set_title(f"{display_name} — Prompt Selection vs Random Baseline\n{subtitle}",
fontsize=14, fontweight="bold", pad=12)
# Annotate percentage difference
for idx, row in df_metric.iterrows():
ps_val = row["prompt_selection"]
bl_val = row["random_baseline"]
max_val = max(abs(ps_val), abs(bl_val))
if abs(bl_val) > 1e-12:
pct = (ps_val - bl_val) / abs(bl_val) * 100
else:
pct = 0.0
if abs(pct) < 0.01:
label = "0%"
color = "gray"
else:
sign = "+" if pct > 0 else ""
label = f"{sign}{pct:.1f}%"
if lower_better:
color = "#388E3C" if pct < 0 else "#D32F2F" # green if lower (better)
else:
color = "#388E3C" if pct > 0 else "#D32F2F" # green if higher (better)
# Position label to the right of the longer bar
text_x = max(ps_val, bl_val)
if text_x < 0:
text_x = min(ps_val, bl_val)
ax.text(text_x * 1.02, idx, label,
va="center", ha="right", fontsize=10, fontweight="bold", color=color)
else:
ax.text(text_x * 1.02 + max_val * 0.01, idx, label,
va="center", ha="left", fontsize=10, fontweight="bold", color=color)
# Add margin for labels
x_vals = pd.concat([df_metric["prompt_selection"], df_metric["random_baseline"]])
x_min, x_max = x_vals.min(), x_vals.max()
margin = (x_max - x_min) * 0.2 if x_max > x_min else abs(x_max) * 0.3
if x_min < 0:
ax.set_xlim(left=x_min - margin * 0.5)
ax.set_xlim(right=x_max + margin)
plt.tight_layout()
out_path = output_dir / f"{metric_name}.png"
fig.savefig(out_path, dpi=150, bbox_inches="tight", facecolor="white")
plt.close(fig)
print(f"Saved: {out_path}")
def main():
df = pd.read_csv(CSV_PATH)
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
for metric in SELECTED_METRICS:
df_metric = df[df["metric"] == metric].copy()
df_metric = df_metric.dropna(subset=["prompt_selection", "random_baseline"])
if df_metric.empty:
print(f"No data for {metric}, skipping.")
continue
plot_one_metric(df_metric, metric, OUTPUT_DIR)
print(f"\nAll charts saved to: {OUTPUT_DIR}")
if __name__ == "__main__":
main()
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