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| """Simple scikit-learn genre classification baselines on FreeTZAN. |
| |
| For every feature set written by scripts/extract_features.py and every classifier below: |
| features are standardized (fit on train), each hyperparameter setting is trained on train, |
| the setting with the best valid accuracy is kept (the first one listed on ties), and its |
| test accuracy is reported. Nothing is trained on valid or test. Everything is deterministic. |
| |
| Output: resources/baselines.csv (one row per feature set x classifier) and a Markdown table |
| of test accuracies on stdout. |
| |
| Usage: uv run scripts/run_baselines.py [--features-dir ~/.cache/freetzan/features] |
| """ |
| import argparse |
| from pathlib import Path |
|
|
| import numpy as np |
| import pandas as pd |
| from sklearn.base import clone |
| from sklearn.ensemble import RandomForestClassifier |
| from sklearn.linear_model import LogisticRegression |
| from sklearn.model_selection import ParameterGrid |
| from sklearn.neighbors import KNeighborsClassifier |
| from sklearn.pipeline import make_pipeline |
| from sklearn.preprocessing import StandardScaler |
| from sklearn.svm import SVC |
|
|
| REPO = Path(__file__).resolve().parent.parent |
| FEATURES = { |
| 'mfcc': 'MFCC mean/std', |
| 'fma': 'FMA librosa features', |
| 'clap': 'LAION-CLAP (music)', |
| 'mert': 'MERT-v1-95M', |
| } |
| CLASSIFIERS = { |
| 'kNN': (KNeighborsClassifier(), {'n_neighbors': [1, 5, 10, 20]}), |
| 'LogReg': (LogisticRegression(max_iter=10000), {'C': [1e-3, 1e-2, 1e-1, 1, 10]}), |
| 'SVM (RBF)': (SVC(), {'C': [0.1, 1, 10, 100]}), |
| 'Random forest': (RandomForestClassifier(n_estimators=500, random_state=0, n_jobs=-1), {}), |
| } |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) |
| ap.add_argument('--features-dir', default=str(Path.home() / '.cache' / 'freetzan' / 'features')) |
| ap.add_argument('--out', default=str(REPO / 'resources' / 'baselines.csv')) |
| args = ap.parse_args() |
|
|
| md = pd.read_csv(REPO / 'metadata.csv') |
| y = md.genre.to_numpy() |
| idx = {s: (md.split == s).to_numpy() for s in ('train', 'valid', 'test')} |
| rows = [] |
| for feat, feat_name in FEATURES.items(): |
| f = Path(args.features_dir) / f'{feat}.npz' |
| if not f.exists(): |
| print(f'{feat}: {f} not found, skipping') |
| continue |
| z = np.load(f) |
| assert (z['track_id'] == md.track_id.to_numpy()).all(), f'{f} rows do not match metadata.csv' |
| X = z['X'] |
| for clf_name, (clf, grid) in CLASSIFIERS.items(): |
| best = None |
| for params in ParameterGrid(grid): |
| model = make_pipeline(StandardScaler(), clone(clf).set_params(**params)) |
| model.fit(X[idx['train']], y[idx['train']]) |
| valid_acc = model.score(X[idx['valid']], y[idx['valid']]) |
| if best is None or valid_acc > best[0]: |
| best = (valid_acc, params, model) |
| valid_acc, params, model = best |
| test_acc = model.score(X[idx['test']], y[idx['test']]) |
| rows.append(dict(features=feat, dim=X.shape[1], classifier=clf_name, params=str(params), |
| train_acc=round(model.score(X[idx['train']], y[idx['train']]), 3), |
| valid_acc=round(valid_acc, 3), test_acc=round(test_acc, 3))) |
| print(f'{feat:5s} {clf_name:14s} {str(params):22s} valid {valid_acc:.2f} test {test_acc:.2f}', flush=True) |
|
|
| res = pd.DataFrame(rows) |
| res.to_csv(args.out, index=False) |
| table = res.pivot(index='features', columns='classifier', values='test_acc') |
| table = table.loc[[f for f in FEATURES if f in table.index], list(CLASSIFIERS)] |
| dims = res.groupby('features').dim.first() |
| print(f'\nwrote {args.out}\n') |
| print('| Features | Dim | ' + ' | '.join(table.columns) + ' |') |
| print('|---|---:|' + '---:|' * len(table.columns)) |
| for feat, r in table.iterrows(): |
| print(f'| {FEATURES[feat]} | {dims[feat]} | ' + ' | '.join(f'{100 * v:.0f}%' for v in r) + ' |') |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
