| |
| """ |
| Which features carry the word identity? |
| |
| Train the classifier, then ablate individual feature families |
| and report the drop in test accuracy. |
| |
| Run: python examples/feature_importance.py |
| """ |
| import numpy as np |
| from whisper_decoder import ( |
| MLP, train_mlp, generate_corpus, FEATURE_DIM, |
| ) |
|
|
| |
| |
| |
| |
| |
| |
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| |
|
|
| GROUPS = { |
| 'MFCC mean': list(range(0, 13)), |
| 'MFCC std': list(range(13, 26)), |
| 'low-band mean': list(range(26, 36)), |
| 'low-band std': list(range(36, 46)), |
| 'harmonicity': [46, 47], |
| 'duration': [48], |
| } |
|
|
|
|
| def train_and_eval(X_tr, y_tr, X_te, y_te, cols): |
| mu = X_tr[:, cols].mean(axis=0) |
| sigma = X_tr[:, cols].std(axis=0) + 1e-9 |
| Xtr = (X_tr[:, cols] - mu) / sigma |
| Xte = (X_te[:, cols] - mu) / sigma |
| model = MLP(len(cols), 64, 32, 10, seed=0) |
| train_mlp(model, Xtr, y_tr, epochs=150, batch=64, |
| lr=3e-3, seed=0) |
| return float((model.predict(Xte) == y_te).mean()) |
|
|
|
|
| def main(): |
| print("generating corpus...") |
| train = generate_corpus('whisper', 150, 40, seed=0, |
| reverb_train_frac=0.25) |
| test = generate_corpus('whisper', 10, 40, seed=99) |
|
|
| all_cols = list(range(FEATURE_DIM)) |
| base = train_and_eval(train.X_train, train.y_train, |
| test.X_test, test.y_test, all_cols) |
| print(f"\nall {FEATURE_DIM} features: accuracy " |
| f"{base*100:.1f}%\n") |
|
|
| print(f" {'ablation':<22} {'accuracy':>10} {'drop':>8}") |
| print(" " + "-" * 44) |
| for name, cols in GROUPS.items(): |
| remaining = [c for c in all_cols if c not in cols] |
| acc = train_and_eval(train.X_train, train.y_train, |
| test.X_test, test.y_test, remaining) |
| drop = base - acc |
| print(f" {'without ' + name:<22} " |
| f"{acc*100:>9.1f}% {drop*100:>+7.1f}%") |
|
|
| print() |
| print(" A large drop means the removed family was " |
| "load-bearing.") |
| print(" A near-zero drop means the classifier did not use it.") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
