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15ff271 | 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 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | """Classical Machine Learning Baselines: MFCC + SVM and MFCC + Random Forest.
Reproduces:
- Baseline 1: 40-MFCC + PCA + Support Vector Machine (SVM)
- Baseline 2: 40-MFCC + Random Forest (RF)
Strictly trains on Train, validates on Val, evaluates on Test with zero leakage.
"""
import os
import joblib
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from src.utils.config import (
METADATA_DIR,
RANDOM_SEED,
EMOTION_CLASSES
)
from src.features.extract import build_and_cache_features
from src.evaluation.evaluator import ExperimentLogger
def train_baseline_svm(n_components: int = 40):
"""Baseline 1: MFCC + PCA + SVM."""
print("\n" + "=" * 70)
print("RUNNING BASELINE 1: MFCC + PCA + Support Vector Machine (SVM)")
print("=" * 70)
# 1. Load metadata and features
meta_df = pd.read_csv(METADATA_DIR / "metadata.csv")
feat_data = build_and_cache_features(meta_df)
X_all = feat_data["X"]
y_all = feat_data["emotions"]
splits = feat_data["splits"]
audio_ids = feat_data["audio_ids"]
train_idx = np.where(splits == "train")[0]
val_idx = np.where(splits == "val")[0]
test_idx = np.where(splits == "test")[0]
X_train, y_train = X_all[train_idx], y_all[train_idx]
X_val, y_val = X_all[val_idx], y_all[val_idx]
X_test, y_test = X_all[test_idx], y_all[test_idx]
# 2. Standard Scaling & PCA (fit strictly on train to prevent leakage)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_val_scaled = scaler.transform(X_val)
X_test_scaled = scaler.transform(X_test)
pca = PCA(n_components=min(n_components, X_train.shape[1]), random_state=RANDOM_SEED)
X_train_pca = pca.fit_transform(X_train_scaled)
X_val_pca = pca.transform(X_val_scaled)
X_test_pca = pca.transform(X_test_scaled)
# 3. Train SVM
svm = SVC(kernel="rbf", C=10.0, gamma="scale", probability=True, random_state=RANDOM_SEED)
svm.fit(X_train_pca, y_train)
# 4. Evaluate on Validation Set
val_preds = svm.predict(X_val_pca)
val_proba = svm.predict_proba(X_val_pca)
# 5. Evaluate on Held-Out Test Set
test_preds = svm.predict(X_test_pca)
test_proba = svm.predict_proba(X_test_pca)
# 6. Log Experiment
logger = ExperimentLogger(experiment_id="EXP001_Baseline_SVM", description="MFCC + PCA + SVM Baseline")
config = {
"experiment_id": "EXP001_Baseline_SVM",
"model_type": "Support Vector Machine (SVC)",
"kernel": "rbf",
"C": 10.0,
"features": "40 MFCC (Mean + Std = 80 dims)",
"pca_components": n_components,
"random_seed": RANDOM_SEED,
"split_version": "v1.0"
}
logger.log_config(config)
# Save model and preprocessors
joblib.dump({"scaler": scaler, "pca": pca, "model": svm}, logger.model_dir / "svm_pipeline.joblib")
# Evaluate test split
metrics = logger.evaluate_and_log(
y_true=y_test.tolist(),
y_pred=test_preds.tolist(),
y_proba=test_proba,
audio_ids=audio_ids[test_idx].tolist(),
split_name="test",
extra_metrics={
"val_accuracy": float(round(np.mean(val_preds == y_val), 4)),
"paper_benchmark_accuracy": 85.40,
"architecture": "MFCC + PCA + SVM (RBF)"
}
)
return metrics
def train_baseline_rf(n_estimators: int = 300):
"""Baseline 2: MFCC + Random Forest."""
print("\n" + "=" * 70)
print("RUNNING BASELINE 2: MFCC + Random Forest (RF)")
print("=" * 70)
# 1. Load metadata and features
meta_df = pd.read_csv(METADATA_DIR / "metadata.csv")
feat_data = build_and_cache_features(meta_df)
X_all = feat_data["X"]
y_all = feat_data["emotions"]
splits = feat_data["splits"]
audio_ids = feat_data["audio_ids"]
train_idx = np.where(splits == "train")[0]
val_idx = np.where(splits == "val")[0]
test_idx = np.where(splits == "test")[0]
X_train, y_train = X_all[train_idx], y_all[train_idx]
X_val, y_val = X_all[val_idx], y_all[val_idx]
X_test, y_test = X_all[test_idx], y_all[test_idx]
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_val_scaled = scaler.transform(X_val)
X_test_scaled = scaler.transform(X_test)
# 2. Train Random Forest
rf = RandomForestClassifier(n_estimators=n_estimators, max_depth=20, random_state=RANDOM_SEED, n_jobs=-1)
rf.fit(X_train_scaled, y_train)
# 3. Predict
val_preds = rf.predict(X_val_scaled)
test_preds = rf.predict(X_test_scaled)
test_proba = rf.predict_proba(X_test_scaled)
# 4. Log Experiment
logger = ExperimentLogger(experiment_id="EXP002_Baseline_RF", description="MFCC + Random Forest Baseline")
config = {
"experiment_id": "EXP002_Baseline_RF",
"model_type": "Random Forest Classifier",
"n_estimators": n_estimators,
"max_depth": 20,
"features": "40 MFCC (Mean + Std = 80 dims)",
"random_seed": RANDOM_SEED,
"split_version": "v1.0"
}
logger.log_config(config)
joblib.dump({"scaler": scaler, "model": rf}, logger.model_dir / "rf_pipeline.joblib")
metrics = logger.evaluate_and_log(
y_true=y_test.tolist(),
y_pred=test_preds.tolist(),
y_proba=test_proba,
audio_ids=audio_ids[test_idx].tolist(),
split_name="test",
extra_metrics={
"val_accuracy": float(round(np.mean(val_preds == y_val), 4)),
"paper_benchmark_accuracy": 78.00,
"architecture": "MFCC + Random Forest"
}
)
return metrics
if __name__ == "__main__":
train_baseline_svm()
train_baseline_rf()
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