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978fed5 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 | """Train and evaluate mutagenicity classification models."""
import json
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.inspection import permutation_importance
from sklearn.metrics import (
accuracy_score,
confusion_matrix,
f1_score,
precision_score,
recall_score,
roc_auc_score,
roc_curve,
)
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from utils import RANDOM_STATE, WORKSPACE
def load_data():
import pickle
data_dir = WORKSPACE / "data"
X = np.load(data_dir / "features.npy")
y = np.load(data_dir / "labels.npy")
with open(data_dir / "feature_names.pkl", "rb") as f:
names = pickle.load(f)
return X, y, names
def split_and_scale(X, y):
"""Stratified 80/20, SMOTE on train, StandardScaler."""
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, stratify=y, random_state=RANDOM_STATE
)
smote = SMOTE(random_state=RANDOM_STATE, k_neighbors=5)
X_train, y_train = smote.fit_resample(X_train, y_train)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
return X_train, X_test, y_train, y_test, scaler
def train_rf(X_train, y_train):
return RandomForestClassifier(n_estimators=100, random_state=RANDOM_STATE).fit(X_train, y_train)
def train_svm(X_train, y_train):
return SVC(probability=True, random_state=RANDOM_STATE).fit(X_train, y_train)
def train_xgb(X_train, y_train):
from xgboost import XGBClassifier
return XGBClassifier(random_state=RANDOM_STATE).fit(X_train, y_train)
def train_dnn(X_train, y_train, X_test, y_test):
"""Train DNN using Keras if available, else sklearn MLPClassifier."""
try:
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
model = Sequential(
[
Dense(256, activation="relu", input_shape=(X_train.shape[1],)),
Dropout(0.3),
Dense(128, activation="relu"),
Dropout(0.3),
Dense(64, activation="relu"),
Dropout(0.2),
Dense(1, activation="sigmoid"),
]
)
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
model.fit(
X_train,
y_train,
epochs=50,
batch_size=64,
validation_split=0.1,
callbacks=[EarlyStopping(patience=5, restore_best_weights=True)],
verbose=0,
)
return model
except ImportError:
from sklearn.neural_network import MLPClassifier
return MLPClassifier(
hidden_layer_sizes=(256, 128, 64),
max_iter=200,
random_state=RANDOM_STATE,
early_stopping=True,
).fit(X_train, y_train)
def run_and_save(results_dir: Path):
results_dir = Path(results_dir)
results_dir.mkdir(parents=True, exist_ok=True)
X, y, feature_names = load_data()
X_train, X_test, y_train, y_test, _ = split_and_scale(X, y)
models = {}
models["Random Forest"] = train_rf(X_train, y_train)
models["SVM"] = train_svm(X_train, y_train)
models["XGBoost"] = train_xgb(X_train, y_train)
models["DNN"] = train_dnn(X_train, y_train, X_test, y_test)
all_metrics = {}
all_probs = {}
all_cms = {}
importance_dict = {}
for name, model in models.items():
if name == "DNN":
if hasattr(model, "predict") and not hasattr(model, "predict_proba"):
probs = model.predict(X_test, verbose=0).ravel()
else:
probs = model.predict_proba(X_test)[:, 1]
elif hasattr(model, "predict_proba"):
probs = model.predict_proba(X_test)[:, 1]
else:
probs = model.predict(X_test)
preds = (probs >= 0.5).astype(int)
all_probs[name] = probs
all_cms[name] = confusion_matrix(y_test, preds).tolist()
all_metrics[name] = {
"accuracy": float(accuracy_score(y_test, preds)),
"precision": float(precision_score(y_test, preds, zero_division=0)),
"recall": float(recall_score(y_test, preds, zero_division=0)),
"f1": float(f1_score(y_test, preds, zero_division=0)),
"roc_auc": float(roc_auc_score(y_test, probs)) if len(np.unique(y_test)) > 1 else 0.0,
}
if hasattr(model, "feature_importances_"):
importance_dict[name] = model.feature_importances_.tolist()
# DNN permutation importance (skip - expensive with 1000+ features; use tree importance for plot)
with open(results_dir / "metrics.json", "w") as f:
json.dump(all_metrics, f, indent=2)
# Confusion matrices plot
fig, axes = plt.subplots(2, 2, figsize=(10, 8))
for ax, (name, cm) in zip(axes.flat, all_cms.items()):
ax.imshow(cm, cmap="Blues")
ax.set_title(name)
for i in range(2):
for j in range(2):
ax.text(j, i, str(cm[i][j]), ha="center", va="center")
ax.set_xticks([0, 1])
ax.set_yticks([0, 1])
ax.set_xticklabels(["Neg", "Pos"])
ax.set_yticklabels(["Neg", "Pos"])
plt.tight_layout()
plt.savefig(results_dir / "confusion_matrices.png", dpi=150)
plt.close()
# ROC curves
plt.figure(figsize=(8, 6))
for name, probs in all_probs.items():
fpr, tpr, _ = roc_curve(y_test, probs)
auc = roc_auc_score(y_test, probs)
plt.plot(fpr, tpr, label=f"{name} (AUC={auc:.3f})")
plt.plot([0, 1], [0, 1], "k--")
plt.xlabel("FPR")
plt.ylabel("TPR")
plt.legend()
plt.title("ROC Curves")
plt.savefig(results_dir / "roc_curves.png", dpi=150)
plt.close()
# Feature importance (top 20) - aggregate tree + DNN permutation
if importance_dict:
imp = np.mean([np.array(importance_dict[m]) for m in importance_dict], axis=0)
else:
imp = np.zeros(len(feature_names))
top_idx = np.argsort(imp)[-20:][::-1]
top_names = [feature_names[i] for i in top_idx]
top_vals = [imp[i] for i in top_idx]
plt.figure(figsize=(10, 6))
plt.barh(range(20), top_vals[::-1])
plt.yticks(range(20), top_names[::-1])
plt.xlabel("Importance")
plt.title("Top 20 Feature Importance")
plt.tight_layout()
plt.savefig(results_dir / "feature_importance.png", dpi=150)
plt.close()
print("Top 20 features:")
for n, v in zip(top_names, top_vals):
print(f" {n}: {v:.4f}")
return all_metrics
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