File size: 2,286 Bytes
6ca0d2e | 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 | #!/usr/bin/env python3
"""
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,
)
# Feature layout:
# [0:13] MFCC mean
# [13:26] MFCC std
# [26:36] low-band mel mean
# [36:46] low-band mel std
# [46] harmonicity mean
# [47] harmonicity std
# [48] trimmed duration
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() |