124 lines
4.6 KiB
Python
124 lines
4.6 KiB
Python
# 2 pelatihan model SVM, KNN dan ensemble
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import joblib
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import os
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from sklearn.svm import SVC
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.ensemble import VotingClassifier
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from sklearn.model_selection import GridSearchCV, cross_val_score
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print("🏋️ [TAHAP 2] Training: DATA ASLI (Mode TURBO Aktif 🚀)...")
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# --- PENYESUAIAN FOLDER ---
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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FOLDER_OUTPUT = os.path.join(BASE_DIR, 'output')
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FOLDER_MODELS = os.path.join(BASE_DIR, 'models')
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# 0. CEK KEAMANAN: Pastikan folder dan file Tahap 1 sudah ada
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file_xtrain = f'{FOLDER_OUTPUT}/X_train.pkl'
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if not os.path.exists(file_xtrain):
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raise FileNotFoundError(f"❌ File '{file_xtrain}' tidak ditemukan! Pastikan kamu sudah menjalankan '1_preprocessing.py' terlebih dahulu.")
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# Jaga-jaga kalau folder models belum ada, otomatis dibikin
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os.makedirs(FOLDER_MODELS, exist_ok=True)
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# 1. AMBIL DATA DARI TAHAP 1
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print(" - Memuat data latih...")
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X_train = joblib.load(f'{FOLDER_OUTPUT}/X_train.pkl')
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y_train = joblib.load(f'{FOLDER_OUTPUT}/y_train.pkl')
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print(f" - Jumlah Data Latih Asli: {len(y_train)} baris")
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print(f" - Komposisi Label: {y_train.value_counts().to_dict()}")
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# ---------------------------------------------------------
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# 2. LATIH SVM (MODEL UTAMA)
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# ---------------------------------------------------------
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print("\n🚀 Melatih SVM (Mencari Settingan Terbaik)...")
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print(" (Menggunakan seluruh inti CPU laptop...)")
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param_svm = {
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'C': [0.01, 0.1, 1, 10, 100],
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'kernel': ['linear'],
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}
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svm_grid = GridSearchCV(SVC(probability=True, random_state=42), param_svm, cv=5, scoring='f1_macro', verbose=1, n_jobs=-1)
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svm_grid.fit(X_train, y_train)
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best_svm = svm_grid.best_estimator_
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joblib.dump(best_svm, f'{FOLDER_MODELS}/model_svm.pkl')
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# ⭐ TAMBAHAN: Print akurasi validasi & parameter terbaik SVM
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svm_acc_val = svm_grid.best_score_ * 100
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print(f" ✅ SVM Selesai (Akurasi Validasi: {svm_acc_val:.2f}%)")
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print(f" 📌 Parameter Terbaik SVM: {svm_grid.best_params_}")
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# ---------------------------------------------------------
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# 3. LATIH KNN (METRIC COSINE)
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# ---------------------------------------------------------
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print("\n🚀 Melatih KNN (Wajib Cosine)...")
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param_knn = {
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'n_neighbors': [3, 5, 7, 9, 11, 13, 15],
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'metric': ['cosine'],
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'weights': ['uniform', 'distance']
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}
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knn_grid = GridSearchCV(KNeighborsClassifier(algorithm='brute'), param_knn, cv=5, scoring='f1_macro', verbose=1, n_jobs=-1)
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knn_grid.fit(X_train, y_train)
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best_knn = knn_grid.best_estimator_
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joblib.dump(best_knn, f'{FOLDER_MODELS}/model_knn.pkl')
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# ⭐ TAMBAHAN: Print akurasi validasi & parameter terbaik KNN
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knn_acc_val = knn_grid.best_score_ * 100
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print(f" ✅ KNN Selesai (Best K: {knn_grid.best_params_['n_neighbors']})")
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print(f" 📌 Parameter Terbaik KNN: {knn_grid.best_params_}")
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print(f" 📌 Akurasi Validasi KNN: {knn_acc_val:.2f}%")
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# ---------------------------------------------------------
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# 4. LATIH ENSEMBLE (SVM + KNN)
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# ---------------------------------------------------------
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print("\n🚀 Melatih ENSEMBLE (Voting SVM + KNN)...")
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# Gabungkan dua model terbaik
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ensemble_model = VotingClassifier(
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estimators=[
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('svm', best_svm),
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('knn', best_knn)
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],
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voting='soft',
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weights=[2, 1] # SVM kita beri bobot suara lebih tinggi
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)
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ensemble_model.fit(X_train, y_train)
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joblib.dump(ensemble_model, f'{FOLDER_MODELS}/model_ensemble.pkl')
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print(" ✅ Ensemble Selesai.")
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# ⭐ TAMBAHAN: Hitung akurasi validasi Ensemble pakai cross_val_score
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print(" 📌 Menghitung Akurasi Validasi Ensemble (5-fold CV)...")
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print(" (Sabar ya, ini agak lama karena ensemble = SVM + KNN x 5 fold)")
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ensemble_scores = cross_val_score(ensemble_model, X_train, y_train, cv=5, scoring='f1_macro', n_jobs=-1)
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ensemble_acc_val = ensemble_scores.mean() * 100
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print(f" 📌 Akurasi Validasi Ensemble: {ensemble_acc_val:.2f}%")
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# ---------------------------------------------------------
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# 5. RINGKASAN AKHIR
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# ---------------------------------------------------------
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print("\n" + "="*60)
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print("📊 RINGKASAN HASIL TRAINING")
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print("="*60)
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print(f"\n🔹 SVM")
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print(f" Parameter : {svm_grid.best_params_}")
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print(f" Akurasi Validasi : {svm_acc_val:.2f}%")
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print(f"\n🔹 KNN")
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print(f" Parameter : {knn_grid.best_params_}")
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print(f" Akurasi Validasi : {knn_acc_val:.2f}%")
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print(f"\n🔹 Ensemble (SVM + KNN)")
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print(f" Voting : soft, weights=[2, 1]")
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print(f" Akurasi Validasi : {ensemble_acc_val:.2f}%")
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print("\n" + "="*60)
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print("🎉 TRAINING DATA MURNI SELESAI!")
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print("👉 Silakan jalankan '3_evaluasi.py' untuk melihat hasil akhirnya.")
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print("="*60) |