"""Evaluate the rule engine + LLM pipeline against the full DCASE 2025 dataset.

Loads cached features (1250 clips), reconstructs AudioFeatures, runs
rank_candidates, and measures diagnostic accuracy per machine type.
"""
import joblib
import collections
from audio_analyzer import AudioFeatures
from fault_rules import rank_candidates, RULES

FEATURE_NAMES = [
    "duration_s", "rms_db", "rms_variance", "zero_crossing_rate",
    "spectral_centroid_hz", "spectral_bandwidth_hz", "spectral_rolloff_hz",
    "dominant_frequency_hz", "harmonic_ratio", "onset_rate_per_sec",
    "has_regular_pattern", "pattern_interval_ms", "peak_db", "anomaly_score",
]

MACHINE_TO_APPLIANCE = {
    "fan": "Electric fan", "pump": "Dishwasher", "slider": "Power drill",
    "ToyCar": "Car engine", "ToyTrain": "Tumble dryer",
    "gearbox": "Electric motor (generic)", "bearing": "Electric motor (generic)",
    "valve": "Refrigerator/Freezer",
}


def load_dcase():
    cache = joblib.load("features_cache_r1.5_s42.joblib")
    X, y, machines = cache["X"], cache["y"], cache["machines"]
    clips = []
    for i in range(len(X)):
        feat_dict = {k: float(X[i][j]) for j, k in enumerate(FEATURE_NAMES)}
        feat_dict["has_regular_pattern"] = bool(round(feat_dict["has_regular_pattern"]))
        features = AudioFeatures(**feat_dict, signal_present=True)
        appliance = MACHINE_TO_APPLIANCE.get(machines[i], "Electric motor (generic)")
        clips.append({
            "machine": machines[i],
            "label": int(y[i]),  # 0=normal, 1=anomaly
            "appliance": appliance,
            "features": features,
        })
    return clips


def evaluate(clips):
    total = len(clips)
    fired_count = 0
    inconclusive_count = 0
    machine_stats = collections.defaultdict(lambda: {"total": 0, "fired": 0, "anomaly_fired": 0, "normal_inconclusive": 0})
    fault_names = collections.Counter()

    for clip in clips:
        cands = rank_candidates(clip["features"], clip["appliance"])
        top = cands[0]
        is_anomaly = clip["label"] == 1
        machine = clip["machine"]

        machine_stats[machine]["total"] += 1
        if top.name != "Inconclusive":
            fired_count += 1
            machine_stats[machine]["fired"] += 1
            if is_anomaly:
                machine_stats[machine]["anomaly_fired"] += 1
            fault_names[top.name] += 1
        else:
            inconclusive_count += 1
            if not is_anomaly:
                machine_stats[machine]["normal_inconclusive"] += 1

    print(f"Total clips: {total}")
    print(f"Rule engine fired: {fired_count} ({fired_count/total*100:.1f}%)")
    print(f"Inconclusive: {inconclusive_count} ({inconclusive_count/total*100:.1f}%)")
    print()

    print("=== Per-machine breakdown ===")
    print(f"{'Machine':<12} {'N':>4} {'Fired':>6} {'Hit%':>6} {'Anom Fired':>11} {'Normal Inconcl':>14}")
    for machine in sorted(machine_stats.keys()):
        s = machine_stats[machine]
        n_anom = sum(1 for c in clips if c["machine"] == machine and c["label"] == 1)
        n_norm = sum(1 for c in clips if c["machine"] == machine and c["label"] == 0)
        anom_fired = s["anomaly_fired"]
        print(f"{machine:<12} {s['total']:>4} {s['fired']:>6} {s['fired']/s['total']*100:>5.1f}% {anom_fired:>5}/{n_anom:<5} {s['normal_inconclusive']:>5}/{n_norm:<5}")

    print()
    print("=== Top fault names ===")
    for name, count in fault_names.most_common(15):
        print(f"  {name:<40} {count:>4}")

    # Check: for anomalous clips, what % get a specific fault (not Inconclusive)?
    anom_clips = [c for c in clips if c["label"] == 1]
    anom_fired = sum(1 for c in anom_clips if rank_candidates(c["features"], c["appliance"])[0].name != "Inconclusive")
    print(f"\nAnomalous clips: {len(anom_clips)}")
    print(f"  Rule engine fires: {anom_fired} ({anom_fired/len(anom_clips)*100:.1f}%)")
    print(f"  Missed (Inconclusive): {len(anom_clips) - anom_fired} ({(len(anom_clips)-anom_fired)/len(anom_clips)*100:.1f}%)")

    # Check: for normal clips, what % are correctly Inconclusive?
    norm_clips = [c for c in clips if c["label"] == 0]
    norm_inconcl = sum(1 for c in norm_clips if rank_candidates(c["features"], c["appliance"])[0].name == "Inconclusive")
    print(f"\nNormal clips: {len(norm_clips)}")
    print(f"  Correctly Inconclusive: {norm_inconcl} ({norm_inconcl/len(norm_clips)*100:.1f}%)")
    print(f"  False positive (fired): {len(norm_clips) - norm_inconcl} ({(len(norm_clips)-norm_inconcl)/len(norm_clips)*100:.1f}%)")

    # Show some false positive examples
    print("\n=== False positive examples (normal clips where rules fire) ===")
    shown = 0
    for clip in clips:
        if clip["label"] == 0 and shown < 5:
            cands = rank_candidates(clip["features"], clip["appliance"])
            if cands[0].name != "Inconclusive":
                f = clip["features"]
                print(f"  [{clip['machine']}] {cands[0].name} (w={cands[0].weight:.2f})")
                print(f"    centroid={f.spectral_centroid_hz:.0f}Hz  zcr={f.zero_crossing_rate:.3f}  "
                      f"onset={f.onset_rate_per_sec:.1f}/s  pattern={f.has_regular_pattern}  "
                      f"dom_freq={f.dominant_frequency_hz:.0f}Hz  rms_var={f.rms_variance:.4f}")
                shown += 1

    # Show some missed anomaly examples
    print("\n=== Missed anomalies (anomalous clips with Inconclusive) ===")
    shown = 0
    for clip in clips:
        if clip["label"] == 1 and shown < 5:
            cands = rank_candidates(clip["features"], clip["appliance"])
            if cands[0].name == "Inconclusive":
                f = clip["features"]
                print(f"  [{clip['machine']}] centroid={f.spectral_centroid_hz:.0f}Hz  "
                      f"zcr={f.zero_crossing_rate:.3f}  onset={f.onset_rate_per_sec:.1f}/s  "
                      f"pattern={f.has_regular_pattern}  dom_freq={f.dominant_frequency_hz:.0f}Hz  "
                      f"rms_var={f.rms_variance:.4f}  anomaly_score={f.anomaly_score:.3f}")
                shown += 1


if __name__ == "__main__":
    clips = load_dcase()
    evaluate(clips)