plot_gt_score_histograms.py

#!/usr/bin/env python3
"""
Plot score distributions (REMOVED vs PRESENT) for each model.

Two figures — one per model — each with 3 subplots (SI-SNR, NXCorr, CLAP sim).
REMOVED and PRESENT distributions are overlaid with a vertical Youden T* line.
"""

from pathlib import Path

import numpy as np
import pandas as pd
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score, roc_curve

BASE_DIR = Path(__file__).parent

MODELS = {
    "combined_v1":          BASE_DIR / "experiments_final/combined_v1/eval_outputs_test_3k/event_detection_scores_gt.csv",
    "no_TSDL_old_mixtures": BASE_DIR / "experiments_final/no_TSDL_old_mixtures/eval_outputs_test_3k/event_detection_scores_gt.csv",
}

METRICS = [
    ("si_snr_db", "SI-SNR (dB)",      (-40, 20),  1.0),
    ("nxcorr",    "NXCorr",           (0,   1.0),  0.02),
    ("clap_sim",  "CLAP similarity",  (0,   1.0),  0.02),
]

COLORS = {"REMOVED": "#E05C5C", "PRESENT": "#4C9BE8"}
ALPHA  = 0.55


def load(path):
    df = pd.read_csv(path)
    df = df[df["error"].isna() | (df["error"] == "")]
    for col, *_ in METRICS:
        df[col] = pd.to_numeric(df[col], errors="coerce")
    df["gt_binary"] = df["gt_label"].map({"PRESENT": 1, "REMOVED": 0})
    return df


def youden_t(df, col):
    valid = df[col].notna() & df["gt_binary"].notna()
    fpr, tpr, thresholds = roc_curve(
        df.loc[valid, "gt_binary"], df.loc[valid, col]
    )
    j = tpr + (1 - fpr) - 1
    return float(thresholds[np.argmax(j)])


def dprime(present, removed):
    sigma_pooled = np.sqrt((present.std()**2 + removed.std()**2) / 2)
    return (present.mean() - removed.mean()) / sigma_pooled


def pooled_youden_t(dfs, col):
    all_s = pd.concat([df[col].dropna() for df in dfs.values()])
    all_l = pd.concat([
        df.loc[df[col].notna(), "gt_binary"].dropna()
        for df in dfs.values()
    ])
    valid = all_s.notna() & all_l.notna()
    fpr, tpr, thresholds = roc_curve(all_l[valid].values, all_s[valid].values)
    j = tpr + (1 - fpr) - 1
    return float(thresholds[np.argmax(j)])


def plot_model(df, model_name, pooled_thresholds, out_path):
    fig, axes = plt.subplots(1, 3, figsize=(15, 5.0))
    fig.suptitle(
        f"Score distributions: REMOVED vs PRESENT  |  Model: {model_name}",
        fontsize=13, fontweight="bold", y=1.01,
    )

    for ax, (col, xlabel, xlim, bw) in zip(axes, METRICS):
        present = df.loc[df["gt_label"] == "PRESENT", col].dropna()
        removed = df.loc[df["gt_label"] == "REMOVED",  col].dropna()

        bins = np.arange(xlim[0], xlim[1] + bw, bw)
        for vals, label in [(removed, "REMOVED"), (present, "PRESENT")]:
            ax.hist(vals, bins=bins, color=COLORS[label], alpha=ALPHA,
                    label=f"{label} (n={len(vals)})", density=True, edgecolor="none")

        # Youden T* line
        t = pooled_thresholds[col]
        ax.axvline(t, color="black", linewidth=1.5, linestyle="--",
                   label=f"Youden T*={t:.3f}")

        # AUC and d' annotation
        valid = df[col].notna() & df["gt_binary"].notna()
        auc = roc_auc_score(df.loc[valid, "gt_binary"], df.loc[valid, col])
        dp  = dprime(present.values, removed.values)
        ax.text(0.97, 0.97, f"AUC = {auc:.4f}\nd′  = {dp:.4f}",
                transform=ax.transAxes, fontsize=9,
                verticalalignment="top", horizontalalignment="right",
                bbox=dict(boxstyle="round,pad=0.3", facecolor="white",
                          edgecolor="grey", alpha=0.8))

        ax.set_xlabel(xlabel, fontsize=11)
        ax.set_ylabel("Density", fontsize=10)
        ax.set_xlim(xlim)
        ax.legend(fontsize=8.5, framealpha=0.7)
        ax.set_title(xlabel, fontsize=11)
        ax.spines[["top", "right"]].set_visible(False)

    plt.tight_layout()
    fig.savefig(out_path, dpi=150, bbox_inches="tight")
    plt.close(fig)
    print(f"Saved: {out_path}")


def main():
    dfs = {name: load(path) for name, path in MODELS.items() if path.exists()}
    if not dfs:
        print("No CSVs found.")
        return

    # Compute pooled Youden thresholds (same for both plots)
    pooled_t = {col: pooled_youden_t(dfs, col) for col, *_ in METRICS}
    print("Pooled Youden T*:")
    for col, label, *_ in METRICS:
        print(f"  {label:<20}: {pooled_t[col]:.4f}")

    for model_name, df in dfs.items():
        out = BASE_DIR / f"gt_score_hist_{model_name}.png"
        plot_model(df, model_name, pooled_t, out)


if __name__ == "__main__":
    main()