plot_ood_stem_heatmaps.py

#!/usr/bin/env python3
"""
OOD stem-based CLAP heatmaps — all 3 splits × 2 models.

Metric: CLAP cosine similarity between model output crop and isolated
        distractor stem crop (from evaluate_event_detection.py).

  High CLAP sim  →  distractor present in output  →  KEPT  (green)
  Low  CLAP sim  →  distractor removed             →  REMOVED (red)

Only no_input command type (unambiguous: no user command given).

Outputs (one figure per OOD split, two subplots per figure):
  heatmap_ood_stem_OOD_backgrounds.png
  heatmap_ood_stem_OOD_distractors.png
  heatmap_ood_stem_OOD_both.png
"""

import re
from pathlib import Path

import numpy as np
import pandas as pd
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import seaborn as sns

# ── Config ────────────────────────────────────────────────────────────────────
BASE_DIR = Path(__file__).parent

MODELS = {
    "combined_v1":          "experiments_final/combined_v1",
    "no_TSDL_old_mixtures": "experiments_final/no_TSDL_old_mixtures",
}

OOD_SPLITS = {
    "OOD_backgrounds": ("eval_outputs_OOD_backgrounds",
                        "Background scene (OOD)",
                        "Distractor class (in-distribution)"),
    "OOD_distractors": ("eval_outputs_OOD_distractors",
                        "Background scene (in-distribution)",
                        "Distractor class (OOD)"),
    "OOD_both":        ("eval_outputs_OOD_both",
                        "Background scene (OOD)",
                        "Distractor class (OOD)"),
}

CMD_TYPE = "no_input"
CSV_NAME = "event_detection_scores.csv"

# ── Distractor ordering + display names (matches reference figure) ─────────────
# Order: left-to-right as in the reference heatmap image
DISTRACTOR_CANONICAL_ORDER = [
    "computer_typing", "drill", "cricket", "sneeze", "cough",
    "jackhammer", "hammer", "engine", "applause", "birds_chirping",
    "fireworks", "helicopter", "dog", "car_horn", "footsteps",
    "alarm_clock", "slam", "baby_cry", "train_horn", "cat",
    "ringtone", "cellphone_buzz_vibrating_alert", "car_alarm",
    "glass_breaking", "door_knock", "siren", "boom",
    "doorbell", "fire_alarm", "gunshot",
]

DISPLAY_NAMES = {
    "computer_typing":               "Computer Typing",
    "drill":                         "Drill",
    "cricket":                       "Cricket",
    "sneeze":                        "Sneeze",
    "cough":                         "Cough",
    "jackhammer":                    "Jackhammer",
    "hammer":                        "Hammer",
    "engine":                        "Engine",
    "applause":                      "Applause",
    "birds_chirping":                "Birds Chirping",
    "fireworks":                     "Fireworks",
    "helicopter":                    "Helicopter",
    "dog":                           "Dog",
    "car_horn":                      "Car Horn",
    "footsteps":                     "Footsteps",
    "alarm_clock":                   "Alarm Clock",
    "slam":                          "Slam",
    "baby_cry":                      "Baby Cry",
    "train_horn":                    "Train Horn",
    "cat":                           "Cat",
    "ringtone":                      "Ringtone",
    "cellphone_buzz_vibrating_alert":"Smartphone Vibration",
    "car_alarm":                     "Car Alarm",
    "glass_breaking":                "Glass Breaking",
    "door_knock":                    "Door Knock",
    "siren":                         "Siren",
    "boom":                          "Boom",
    "doorbell":                      "Doorbell",
    "fire_alarm":                    "Fire Alarm",
    "gunshot":                       "Gunshot",
}

# Scene display names (title-case with spaces)
SCENE_DISPLAY_NAMES = {
    "airplane":      "Airplane",
    "bus_station":   "Bus Station",
    "gym":           "Gym",
    "harbour":       "Harbour",
    "library":       "Library",
    "museum":        "Museum",
    "office":        "Office",
    "park":          "Park",
    "shopping_mall": "Shopping Mall",
    "train_station": "Train Station",
    "airport":       "Airport",
    "beach":         "Beach",
    "bus":           "Bus",
    "cafe":          "Cafe",
    "coffee_shop":   "Coffee Shop",
    "city":          "City",
    "forest":        "Forest",
    "home":          "Home",
    "kitchen":       "Kitchen",
    "restaurant":    "Restaurant",
    "supermarket":   "Supermarket",
    "train":         "Train",
}


def canonical_dist_order(present: list) -> list:
    """Return distractors in canonical order; extras appended at end."""
    ordered = [d for d in DISTRACTOR_CANONICAL_ORDER if d in present]
    extras  = [d for d in present if d not in DISTRACTOR_CANONICAL_ORDER]
    return ordered + extras


# ── Helpers ───────────────────────────────────────────────────────────────────
def load(path: Path) -> pd.DataFrame:
    df = pd.read_csv(path)
    df = df[df["error"].isna() | (df["error"] == "")]
    df = df[df["command_type"] == CMD_TYPE].copy()
    df["clap_sim"] = pd.to_numeric(df["clap_sim"], errors="coerce")
    df["scene"]    = df["mixture_id"].str.extract(r"^(.+?)_\d+dist_")
    return df.dropna(subset=["clap_sim", "scene"])


def build_pivot(df: pd.DataFrame, scene_order, dist_order) -> pd.DataFrame:
    return (
        df.groupby(["scene", "distractor_name"])["clap_sim"]
          .mean()
          .unstack("distractor_name")
          .reindex(index=scene_order, columns=dist_order)
    )


def annot_array(pivot: pd.DataFrame) -> np.ndarray:
    """Single-line value annotation — colour already conveys K/R."""
    arr = np.empty(pivot.shape, dtype=object)
    for i in range(pivot.shape[0]):
        for j in range(pivot.shape[1]):
            v = pivot.iloc[i, j]
            arr[i, j] = "" if np.isnan(v) else f"{v:.2f}"
    return arr


def dist_xlabel(name, mean_val, threshold):
    tag = "K" if mean_val >= threshold else "R"
    disp = DISPLAY_NAMES.get(name, name)
    return f"{disp}\n({tag} {mean_val:.2f})"


def scene_ylabel(name, mean_val, threshold):
    tag = "K" if mean_val >= threshold else "R"
    disp = SCENE_DISPLAY_NAMES.get(name, name.replace("_", " ").title())
    return f"{disp}  ({tag} {mean_val:.2f})"


# ── Main ──────────────────────────────────────────────────────────────────────
def plot_split(split_key: str, dist_order_override=None):
    split_dir, ylabel, xlabel = OOD_SPLITS[split_key]

    # Load both models
    dfs = {}
    for model_name, model_dir in MODELS.items():
        path = BASE_DIR / model_dir / split_dir / CSV_NAME
        if not path.exists():
            print(f"  [WARN] missing: {path}")
            continue
        df = load(path)
        print(f"  {model_name}/{split_key}: {len(df)} rows, "
              f"{df['scene'].nunique()} scenes, "
              f"{df['distractor_name'].nunique()} distractors")
        dfs[model_name] = df

    if not dfs:
        print(f"  [SKIP] {split_key} — no data")
        return

    # Shared ordering + threshold (pooled across both models)
    all_df    = pd.concat(dfs.values(), ignore_index=True)
    threshold = all_df["clap_sim"].median()
    print(f"  Threshold (pooled median): {threshold:.3f}")

    # Fixed canonical distractor order; scenes sorted by mean CLAP sim
    present_dists = all_df["distractor_name"].unique().tolist()
    if dist_order_override is not None:
        # Use shared override; keep only those actually present in this split
        dist_order = [d for d in dist_order_override if d in present_dists]
        # Append any extras not in the canonical override (safety net)
        extras = [d for d in present_dists if d not in dist_order_override]
        dist_order = dist_order + canonical_dist_order(extras)
    else:
        dist_order = canonical_dist_order(present_dists)
    scene_order   = (
        all_df.groupby("scene")["clap_sim"]
              .mean().sort_values(ascending=False).index.tolist()
    )

    n_dist   = len(dist_order)
    n_scene  = len(scene_order)

    ood_tag = {
        "OOD_backgrounds": "OOD: background scenes unseen, distractors known",
        "OOD_distractors": "OOD: distractor classes unseen, backgrounds known",
        "OOD_both":        "OOD: both background scenes and distractor classes unseen",
    }[split_key]

    # ── One figure per model ──────────────────────────────────────────────────
    for model_name, df in dfs.items():

        # Layout
        cell_w     = 0.62
        cell_h     = 0.28
        margin_w   = 3.5
        hm_title   = 0.8
        bp_h       = 2.8
        xlbl_h     = 2.5
        suptitle_h = 0.9

        fw = n_dist * cell_w + margin_w
        heatmap_h  = n_scene * cell_h + hm_title
        fh = suptitle_h + heatmap_h + bp_h + xlbl_h

        fig, axes = plt.subplots(
            2, 1,
            figsize=(fw, fh),
            gridspec_kw={"height_ratios": [heatmap_h, bp_h + xlbl_h],
                         "hspace": 0.02},
            squeeze=False,
        )
        ax_hm  = axes[0][0]
        ax_box = axes[1][0]

        fig.suptitle(
            f"{split_key}  |  {model_name}  |  no_input  |  stem-based CLAP similarity\n"
            f"{ood_tag}\n"
            f"green = KEPT (distractor present), red = REMOVED  "
            f"|  K/R boundary = pooled median ({threshold:.3f})",
            fontsize=11, fontweight="bold", y=1.01,
        )

        # ── Heatmap ───────────────────────────────────────────────────────────
        pivot  = build_pivot(df, scene_order, dist_order)
        annots = annot_array(pivot)

        sns.heatmap(
            pivot, ax=ax_hm, mask=pivot.isna(),
            annot=annots, fmt="",
            annot_kws={"size": 7, "weight": "bold"},
            vmin=0.20, vmax=0.70, center=threshold,
            cmap="RdYlGn", linewidths=0.4, linecolor="#cccccc",
            cbar_kws={
                "label": "CLAP sim  ←  REMOVED  |  KEPT  →",
                "shrink": 0.7,
                "ticks": [0.20, 0.30, threshold, 0.50, 0.60, 0.70],
            },
        )

        scene_means = df.groupby("scene")["clap_sim"].mean()
        ax_hm.set_xticklabels([])
        ax_hm.set_xlabel("")
        ax_hm.set_yticklabels(
            [scene_ylabel(s, scene_means.get(s, np.nan), threshold)
             for s in scene_order],
            fontsize=9, rotation=0,
        )
        kept_pct = (df["clap_sim"] >= threshold).mean() * 100
        ax_hm.set_title(f"KEPT {kept_pct:.0f}% overall",
                        fontsize=10, fontweight="bold", pad=6)
        ax_hm.set_ylabel(ylabel, fontsize=9)

        # ── Bar chart: mean kept rate (%) per distractor ──────────────────────
        # Use only distractors present in this model — same column order as heatmap
        model_dists      = df["distractor_name"].unique()
        model_dist_order = [d for d in dist_order if d in model_dists]

        bp_df = df[["distractor_name", "clap_sim"]].copy()
        bp_df["kept"] = (bp_df["clap_sim"] >= threshold).astype(float)
        mean_kept = bp_df.groupby("distractor_name")["kept"].mean() * 100  # percentage

        # Map each distractor to its column centre in dist_order
        col_idx      = {d: i for i, d in enumerate(dist_order)}
        bar_positions = np.array([col_idx[d] + 0.5 for d in model_dist_order])
        bar_values    = [mean_kept.get(d, np.nan) for d in model_dist_order]
        bar_colors    = [
            "#2ca02c" if (not np.isnan(v) and v >= 50) else "#d62728"
            for v in bar_values
        ]

        ax_box.bar(
            bar_positions, bar_values,
            width=0.6, color=bar_colors, alpha=0.85, zorder=3,
            edgecolor="white", linewidth=0.4,
        )

        # Annotate each bar with its value
        for pos, val, color in zip(bar_positions, bar_values, bar_colors):
            if not np.isnan(val):
                ax_box.text(
                    pos, val + 1.5, f"{val:.0f}%",
                    ha="center", va="bottom", fontsize=5.5,
                    fontweight="bold", color="black",
                )

        ax_box.axhline(50, color="gray", linestyle="--",
                       linewidth=1.0, alpha=0.8, label="50% boundary (K/R)")

        # Both axes share [0, n_dist] so column i is at [i, i+1]
        ax_hm.set_xlim(0, n_dist)
        ax_box.set_xlim(0, n_dist)

        ax_box.set_ylim(0, 115)
        ax_box.set_yticks([0, 25, 50, 75, 100])
        ax_box.set_yticklabels(["0%", "25%", "50%", "75%", "100%"], fontsize=8)
        # Tick at each heatmap column centre
        ax_box.set_xticks(np.arange(n_dist) + 0.5)
        ax_box.set_xticklabels(
            [DISPLAY_NAMES.get(d, d) for d in dist_order],
            fontsize=7.5, rotation=45, ha="right",
        )
        ax_box.set_xlabel(xlabel, fontsize=10)
        ax_box.set_ylabel("Mean Kept Rate (%)", fontsize=9)
        ax_box.set_title("Mean kept rate per distractor across scenes  "
                         "(green ≥ 50%, red < 50%)",
                         fontsize=10, fontweight="bold", pad=6)
        ax_box.yaxis.grid(True, linestyle="--", linewidth=0.5, alpha=0.5, zorder=0)
        ax_box.set_axisbelow(True)
        ax_box.legend(fontsize=8, loc="upper left", framealpha=0.85)

        # ── Force exact horizontal alignment: boxplot width = heatmap width ───
        # tight_layout makes the colorbar steal space from ax_hm; ax_box ends up
        # wider. After layout is resolved, snap ax_box to ax_hm's x-extent.
        plt.tight_layout()
        fig.canvas.draw()
        pos_hm  = ax_hm.get_position()
        pos_box = ax_box.get_position()
        ax_box.set_position([pos_hm.x0, pos_box.y0,
                              pos_hm.width, pos_box.height])

        out = BASE_DIR / f"heatmap_ood_stem_{split_key}_{model_name}.png"
        fig.savefig(out, dpi=150, bbox_inches="tight")
        plt.close(fig)
        print(f"  Saved: {out}")


def print_summary():
    print(f"\n{'═'*72}")
    print("  OOD no_input summary — % KEPT (CLAP sim ≥ pooled median)")
    print(f"{'═'*72}")
    print(f"  {'split':<20} {'combined_v1':>20} {'no_TSDL_old_mixtures':>22}")
    print("  " + "─" * 65)

    for split_key, (split_dir, _, _) in OOD_SPLITS.items():
        dfs = {}
        for model_name, model_dir in MODELS.items():
            path = BASE_DIR / model_dir / split_dir / CSV_NAME
            if path.exists():
                dfs[model_name] = load(path)

        if not dfs:
            continue

        threshold = pd.concat(dfs.values())["clap_sim"].median()
        row = f"  {split_key:<20}"
        for model_name in MODELS:
            if model_name in dfs:
                pct = (dfs[model_name]["clap_sim"] >= threshold).mean() * 100
                row += f"  {pct:>19.1f}%"
            else:
                row += f"  {'N/A':>20}"
        print(row)


if __name__ == "__main__":
    # Pre-compute a shared distractor ordering for OOD_distractors + OOD_both
    # so that the same distractor always appears in the same column in both figures.
    SHARED_SPLITS = {"OOD_distractors", "OOD_both"}
    shared_all_dists = set()
    for sk in SHARED_SPLITS:
        split_dir = OOD_SPLITS[sk][0]
        for model_dir in MODELS.values():
            path = BASE_DIR / model_dir / split_dir / CSV_NAME
            if path.exists():
                df_tmp = load(path)
                shared_all_dists.update(df_tmp["distractor_name"].unique())
    shared_dist_order = canonical_dist_order(list(shared_all_dists))
    print(f"\nShared distractor order (OOD_distractors + OOD_both): "
          f"{len(shared_dist_order)} classes")

    for split_key in OOD_SPLITS:
        print(f"\n{'─'*60}")
        print(f"  Plotting {split_key} ...")
        override = shared_dist_order if split_key in SHARED_SPLITS else None
        plot_split(split_key, dist_order_override=override)

    print_summary()