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fe66586 2607de5 fe66586 | 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 | import os
import joblib
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
from . import config, plots, explainability, features
def train_and_evaluate_split(X_train, y_train, X_test, y_test, classes):
"""
Accepts PRE-SPLIT and PRE-AUGMENTED data.
Trains models, generates plots, and saves artifacts.
"""
# 1. Define Models
techniques = {
"RF": RandomForestClassifier(n_estimators=100, class_weight='balanced'),
"SVM": SVC(probability=True, class_weight='balanced')
}
best_score = 0
best_model = None
best_scaler = None
best_name = ""
os.makedirs(config.MODEL_DIR, exist_ok=True)
# 2. Prepare Feature Names for XAI
# We fetch these once so we can use them for LIME (all models) and RF Importance
feature_names = features.get_feature_names()
# Safety fallback if feature count mismatches name list
if len(feature_names) != X_train.shape[1]:
print(f"Warning: Feature names count ({len(feature_names)}) != Data columns ({X_train.shape[1]})")
feature_names = [f"Feature_{i}" for i in range(X_train.shape[1])]
# 3. Scaling
# Important: Fit on Train, Transform Test
scaler = StandardScaler()
X_train_s = scaler.fit_transform(X_train)
X_test_s = scaler.transform(X_test)
# --- NEW: Save Training Sample for LIME in App ---
# We save a subset (e.g., 500 samples) to keep the app lightweight and fast.
# LIME needs this to understand the "background" distribution of features.
print("Saving training sample for App LIME initialization...")
# if X_train_s.shape[0] > 500:
# indices = np.random.choice(X_train_s.shape[0], 500, replace=False)
# X_sample = X_train_s[indices]
# else:
# keep all results for XAI
X_sample = X_train_s
np.save(os.path.join(config.MODEL_DIR, 'X_train_sample.npy'), X_sample)
# -------------------------------------------------
# 4. Training Loop
for name, model in techniques.items():
print(f"\n--- Training {name} ---")
model.fit(X_train_s, y_train)
preds = model.predict(X_test_s)
acc = accuracy_score(y_test, preds)
print(f"--> {name} Accuracy on Test Set: {acc:.4f}")
# --- PLOTTING METRICS ---
print(f"Generating ROC and Confusion Matrix for {name}...")
plots.plot_confusion_matrix(y_test, preds, classes, name)
plots.plot_multiclass_roc(model, X_test_s, y_test, classes, name)
plots.save_classification_report(y_test, preds, classes, name)
# --- EXPLAINABLE AI (Global: Feature Importance) ---
if name == "RF":
print("Generating Global Feature Importance Plot (RF)...")
explainability.plot_rf_feature_importance(model, feature_names)
# --- EXPLAINABLE AI (Local: LIME) ---
# This works for BOTH RF and SVM
print(f"Generating Local LIME Explanations for {name}...")
explainability.generate_lime_explanations(
model=model,
X_train=X_train_s, # LIME needs training distribution
X_test=X_test_s, # Instances to explain
y_test=y_test, # For labeling plots
feature_names=feature_names,
class_names=classes,
model_name=name
)
# Track Best
if acc > best_score:
best_score = acc
best_model = model
best_name = name
best_scaler = scaler
# Save Artifacts
print(f"\nSaving Best Model: {best_name}")
joblib.dump(best_model, os.path.join(config.MODEL_DIR, 'skin_cancer_model.pkl'))
joblib.dump(best_scaler, os.path.join(config.MODEL_DIR, 'scaler.pkl'))
joblib.dump(classes, os.path.join(config.MODEL_DIR, 'classes.pkl')) |