import pandas as pd import numpy as np import re import joblib import json import warnings from pathlib import Path from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.linear_model import LogisticRegression from sklearn.model_selection import StratifiedKFold from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay from sklearn.pipeline import Pipeline from Sastrawi.Stemmer.StemmerFactory import StemmerFactory import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt warnings.filterwarnings('ignore') BASE_DIR = Path(__file__).parent TRAIN_FILE = BASE_DIR / "processed" / "training_corpus_3.csv" TEST_FILE = BASE_DIR / "processed" / "test_set_3.csv" ML_DIR = BASE_DIR.parent / "fastapi" / "ml_assets" ML_DIR.mkdir(parents=True, exist_ok=True) TARGET_CLASSES = ["Programmer", "Data Analyst", "Wirausaha Informatika", "Non-IT"] CONFIDENCE_THRESHOLD = 0.50 STOPWORDS = { "yang", "di", "ke", "dari", "dan", "atau", "dengan", "untuk", "pada", "dalam", "adalah", "ini", "itu", "tidak", "juga", "sudah", "akan", "bisa", "ada", "oleh", "karena", "secara", "serta", "sebagai", "bagi", "telah", "maka", "namun", "sehingga", "jika", "agar", "ketika", "saat", "sebelum", "sesudah", "hingga", "sampai", "antara", "sekitar", "hanya", "saja", "belum", "masih", "lagi", "pun", "justru", "walaupun", "meskipun", "bahkan", "cukup", "sangat", "paling", "lebih", "kurang", "lain", "macam", "cara", "hal", "tentang", "mengenai", "terhadap", "kepada", "menuju", "kecuali", "selain", "tanpa", "demi", "guna", "khususnya", "umumnya", "kebanyakan", "sebagian", "beberapa", "semua", "setiap", "tiap", "satu", "dua", "tiga", "empat", "lima", "enam", "tujuh", "delapan", "sembilan", "sepuluh", "ratus", "ribu", "juta" } stemmer = StemmerFactory().create_stemmer() def preprocess_text(text): if pd.isna(text) or not isinstance(text, str): return "" text = text.lower().strip() if text in {"nan", "none", "null", "-", "0", "", "tidak diisi"}: return "" text = text.replace('-', ' ').replace('/', ' ').replace('_', ' ').replace(',', ' ') text = re.sub(r'[^\w\s]', '', text) text = re.sub(r'\d+', '', text) words = [w for w in text.split() if w not in STOPWORDS and len(w) > 2] return " ".join([stemmer.stem(w) for w in words]) def main(): if not TRAIN_FILE.exists(): print("File training_corpus_3.csv belum ada"); return df_train = pd.read_csv(TRAIN_FILE, sep=";", dtype=str).dropna(subset=["job_text_raw", "label"]) df_test = pd.read_csv(TEST_FILE, sep=";", dtype=str).dropna(subset=["job_text_raw", "label"]) if TEST_FILE.exists() else None print("Sedang memproses teks (cleaning, hapus kata hubung, dan stemming)...") X_train = df_train["job_text_raw"].apply(preprocess_text) y_train = df_train["label"] mask = X_train.str.len() > 0 X_train, y_train = X_train[mask], y_train[mask] X_test, y_test = pd.Series(dtype=str), pd.Series(dtype=str) if df_test is not None: X_test = df_test["job_text_raw"].apply(preprocess_text) y_test = df_test["label"] mask_t = X_test.str.len() > 0 X_test, y_test = X_test[mask_t], y_test[mask_t] print(f"Jumlah data latih: {len(X_train)} baris | Data uji: {len(X_test)} baris\n") print("Melakukan pengujian K-Fold (5 putaran) pada data latih...") skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42) fold_metrics = [] for i, (tr_idx, te_idx) in enumerate(skf.split(X_train, y_train)): model = Pipeline([ ('tfidf', TfidfVectorizer(max_features=3000, ngram_range=(1,2), sublinear_tf=True, min_df=1)), ('clf', LogisticRegression(max_iter=1000, class_weight='balanced', solver='lbfgs')) ]) model.fit(X_train.iloc[tr_idx], y_train.iloc[tr_idx]) y_pred = model.predict(X_train.iloc[te_idx]) rep = classification_report(y_train.iloc[te_idx], y_pred, output_dict=True, zero_division=0) fold_metrics.append(rep) print(f" Akurasi putaran ke-{i+1}: {rep['accuracy']:.4f}") avg_acc = np.mean([f['accuracy'] for f in fold_metrics]) print(f"\n RATA-RATA PENGUJIAN K-FOLD:") print(f" Akurasi Keseluruhan : {avg_acc:.4f} ± {np.std([f['accuracy'] for f in fold_metrics]):.4f}") for cls in TARGET_CLASSES: p = np.mean([f.get(cls, {}).get('precision', 0) for f in fold_metrics]) r = np.mean([f.get(cls, {}).get('recall', 0) for f in fold_metrics]) f1 = np.mean([f.get(cls, {}).get('f1-score', 0) for f in fold_metrics]) print(f" {cls:25} | P: {p:.3f} | R: {r:.3f} | F1: {f1:.3f}") print("\n Membuat model final dari seluruh data latih yang tersedia...") final_model = Pipeline([ ('tfidf', TfidfVectorizer(max_features=3000, ngram_range=(1,2), sublinear_tf=True, min_df=1)), ('clf', LogisticRegression(max_iter=1000, class_weight='balanced', solver='lbfgs')) ]) final_model.fit(X_train, y_train) if len(X_test) > 0: y_pred = final_model.predict(X_test) print("\n HASIL PENGUJIAN PADA DATA TEST:") rep_test = classification_report(y_test, y_pred, output_dict=True, zero_division=0) print(classification_report(y_test, y_pred, zero_division=0)) for cls in TARGET_CLASSES: sup = rep_test[cls]['support'] if cls in rep_test else 0 if sup < 5: print(f"Catatan: Kelas '{cls}' cuma punya {sup} data uji, skor F1-nya mungkin kurang akurat.") cm = confusion_matrix(y_test, y_pred, labels=TARGET_CLASSES) disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=TARGET_CLASSES) disp.plot(cmap='Blues', values_format='d', xticks_rotation=45, colorbar=False) plt.title('Confusion Matrix - Hold-Out Test'); plt.tight_layout() plt.savefig(ML_DIR / 'confusion_matrix_test.png', dpi=300); plt.close() print("Grafik confusion matrix berhasil disimpan ke confusion_matrix_test.png") vec, clf = final_model.named_steps['tfidf'], final_model.named_steps['clf'] feats, coeffs = vec.get_feature_names_out(), clf.coef_ print("\n5 KATA PALING BERPENGARUH UNTUK TIAP KELAS:") for i, cls in enumerate(TARGET_CLASSES): top = [(feats[j], coeffs[i][j]) for j in coeffs[i].argsort()[-5:][::-1] if coeffs[i][j] > 0] print(f" [{cls}] " + ", ".join([f"{w}({c:.2f})" for w,c in top])) proba = final_model.predict_proba(X_test) max_p = np.max(proba, axis=1) plt.hist(max_p, bins=15, edgecolor='black', alpha=0.7, color='teal') plt.axvline(x=CONFIDENCE_THRESHOLD, color='red', linestyle='--', linewidth=2, label=f'Threshold {CONFIDENCE_THRESHOLD}') plt.xlabel('Confidence Score'); plt.ylabel('Count'); plt.title('Distribusi Confidence Score') plt.legend(); plt.grid(axis='y', alpha=0.3); plt.tight_layout() plt.savefig(ML_DIR / 'confidence_distribution.png', dpi=300); plt.close() below = np.sum(max_p < CONFIDENCE_THRESHOLD) print(f"\nPengecekan Keyakinan Model: Ada {below} dari {len(max_p)} prediksi ({below/len(max_p)*100:.1f}%) yang nilainya di bawah {CONFIDENCE_THRESHOLD}. Nanti bagian ini perlu dicek manual.") mis = np.where(y_test != y_pred)[0] if len(mis) > 0: print("\n DAFTAR PREDIKSI YANG MELESET:") for idx in mis[:min(6, len(mis))]: txt = df_test.iloc[idx]['job_text_raw'][:80] print(f" • Seharusnya: {y_test.iloc[idx]:20} | Ditebak: {y_pred[idx]:20} | Teks: '{txt}...'") else: print("\n Bagus") metrics_test = rep_test else: metrics_test = None model_path = ML_DIR / "ml_pipeline_internal.pkl" joblib.dump(final_model, model_path) metrics = {"methodology": "Internal MIF only", "k_fold": {"accuracy_mean": float(avg_acc), "accuracy_std": float(np.std([f['accuracy'] for f in fold_metrics])), "folds": fold_metrics}, "threshold_config": CONFIDENCE_THRESHOLD} if metrics_test: metrics["hold_out_test"] = metrics_test with open(ML_DIR / "metrics_internal_only.json", 'w') as f: json.dump(metrics, f, indent=2) print(f"\n Model berhasil disimpan di: {model_path}") print(f"Laporan metrik disimpan di: {ML_DIR / 'metrics_internal_only.json'}") if __name__ == "__main__": main()