Merge branch 'main' of https://github.com/mhilya/sista_mif_ta

fastapi/app/retrain_worker.py

@@ -2,19 +2,17 @@
 """
 TRACER STUDY - RE-TRAINING WORKER FINAL (SUBPROCESS)
 Dipanggil oleh FastAPI (main3.py) sebagai background subprocess.
-
 Alur:
 1. Backup pkl lama → pkl.bak
 2. Merge corpus + manual_override (TANPA drop_duplicates)
-     Override diberi sample_weight lebih tinggi, bukan menghapus data historis.
 3. Dynamic train/test split dari data gabungan (selalu fresh)
 4. Evaluasi MODEL LAMA pada test set yang sama (fair comparison)
 5. K-Fold + train model final (candidate) dengan sample_weight
 6. Evaluasi candidate pada test set yang sama → delta terhadap old model
 7. Promote jika lebih baik; rollback jika tidak
 8. Setiap write status dilindungi FileLock (atomic)
+9. [v5] Semua event log disimpan ke file JSONL terpisah untuk audit BAB 4
 """
-
 import sys
 import json
 import shutil
@@ -39,11 +37,13 @@ from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
 warnings.filterwarnings("ignore")
 
 # ──────────────────────────────────────────────────────────────
-# KONFIGURASI
+# KONFIGURASI PATH
 # ──────────────────────────────────────────────────────────────
 BASE_DIR = Path(__file__).parent.parent
 ML_DIR   = BASE_DIR / "ml_assets"
 DATA_DIR = BASE_DIR.parent / "data" / "processed"
+LOG_DIR  = ML_DIR / "logs"   # [v5] Direktori log terpisah
+LOG_DIR.mkdir(parents=True, exist_ok=True)
 
 PIPELINE_PATH     = ML_DIR / "ml_pipeline_internal.pkl"
 PIPELINE_BAK_PATH = ML_DIR / "ml_pipeline_internal.pkl.bak"
@@ -54,46 +54,17 @@ LOCK_PATH         = ML_DIR / "retrain_status.lock"
 CORPUS_PATH       = DATA_DIR / "training_corpus.csv"
 TEST_PATH         = DATA_DIR / "test_set.csv"
 
+# [v5] Nama file log berbasis timestamp — satu file per sesi retraining
+SESSION_TS = datetime.now().strftime("%Y%m%d_%H%M%S")
+LOG_FILE_PATH = LOG_DIR / f"retrain_log_{SESSION_TS}.jsonl"
+
 TARGET_CLASSES = ["Programmer", "Data Analyst", "Wirausaha Informatika", "Non-IT"]
-MIN_IMPROVEMENT_THRESHOLD = 0.01   # Model baru harus lebih baik minimal +1% weighted F1
+MIN_IMPROVEMENT_THRESHOLD = 0.01
-MAX_REGRESSION_ALLOWED    = 0.02   # Toleransi degradasi maksimal 2% sebelum rollback keras
+MAX_REGRESSION_ALLOWED    = 0.02
 
-# ── KONFIGURASI BOBOT OVERRIDE ──────────────────────────────────────────────
+OVERRIDE_INFLUENCE_TARGET = 0.30
-#
+OVERRIDE_MIN_WEIGHT       = 2.0
-# Masalah yang diselesaikan:
-#   Pada skala besar (N_corpus >> N_override), bobot statis kehilangan daya
-#   akibat dilusi. Formula proporsi murni w = t*N / (M*(1-t)) menyelesaikan
-#   dilusi, tapi menghasilkan w=42.85 pada skenario 10k/100 — yang berisiko
-#   overfitting ekstrem pada noise override.
-#
-# Solusi: Logarithmic damping — tanpa tembok statis.
-#   w_raw = formula proporsi  (jaminan 30% jika tidak di-damp)
-#   w     = MIN + ln(1 + max(0, w_raw - MIN))
-#
-#   Fase linear (w_raw rendah): w ≈ w_raw  → proporsi terpenuhi
-#   Fase log (w_raw tinggi):    w tumbuh tapi melambat → damp alami
-#
-# Implikasi jujur:
-#   Target 30% TIDAK dipertahankan di skala ekstrem. Ini trade-off yang
-#   disengaja: degradasi gradual lebih aman daripada overfitting ke 10
-#   baris override berbobot 42x. Tanpa tembok statis, redaman terjadi
-#   secara natural mengikuti kurva logaritmik, bukan menabrak batas arbitrer.
-#
-# Perilaku nyata (MIN=2.0, TARGET=0.30):
-#   w_raw  │  w_log  │ influence aktual
-#   ──────────────────────────────────
-#    2.00  │  2.000  │ formula <= MIN, pakai floor
-#    4.29  │  3.178  │ (1k corpus, 100 override) → ~24%
-#    8.57  │  3.999  │ (100 korpus, 5 override)  → ~17%
-#   42.86  │  5.723  │ (10k corpus, 100 override)→  ~5.4%
-#  428.60  │  8.063  │ (10k corpus, 10 override) →  ~0.8%
-#
-# Jika angka influence aktual dianggap terlalu kecil → naikkan TARGET.
-# Jika model terlalu sensitif ke override → naikkan MIN agar floor lebih tinggi.
-OVERRIDE_INFLUENCE_TARGET = 0.30  # Titik acuan proporsi (valid di skala normal)
-OVERRIDE_MIN_WEIGHT       = 2.0   # Lantai: override selalu minimal 2× korpus
 
-# [FIX v4] STOPWORDS lengkap — versi retrain_worker2 hanya punya 10 kata (bug terpotong)
 STOPWORDS = {
     "yang", "di", "ke", "dari", "dan", "atau", "dengan", "untuk", "pada", "dalam",
     "adalah", "ini", "itu", "tidak", "juga", "sudah", "akan", "bisa", "ada", "oleh",
@@ -109,49 +80,73 @@ STOPWORDS = {
 
 stemmer = StemmerFactory().create_stemmer()
 
+# ──────────────────────────────────────────────────────────────
+# [v5] CUSTOM JSON FORMATTER — setiap log entry jadi JSON valid
+# ──────────────────────────────────────────────────────────────
+class JsonFormatter(logging.Formatter):
+    """Format log record sebagai JSON satu baris (JSONL)."""
+    def format(self, record: logging.LogRecord) -> str:
+        log_entry = {
+            "timestamp": datetime.fromtimestamp(record.created).isoformat(),
+            "level": record.levelname,
+            "logger": record.name,
+            "message": record.getMessage(),
+        }
+        # [v5] Jika ada extra data (stage, metrics, dll), gabungkan ke root
+        if hasattr(record, "stage"):
+            log_entry["stage"] = record.stage
+        if hasattr(record, "event_type"):
+            log_entry["event_type"] = record.event_type
+        if hasattr(record, "metadata") and isinstance(record.metadata, dict):
+            log_entry["metadata"] = record.metadata
+        return json.dumps(log_entry, ensure_ascii=False)
+
+# ──────────────────────────────────────────────────────────────
+# [v5] DUAL LOGGER SETUP — console + file JSONL
+# ──────────────────────────────────────────────────────────────
+logger = logging.getLogger("retrain_worker")
+logger.setLevel(logging.INFO)
+logger.propagate = False  # mencegah duplikasi ke root logger
+
+# Handler 1: Console (untuk dev/debugging)
+console_handler = logging.StreamHandler(sys.stdout)
+console_handler.setFormatter(logging.Formatter(
+    "%(asctime)s | %(levelname)s | %(message)s"
+))
+logger.addHandler(console_handler)
+
+# Handler 2: File JSONL (untuk audit BAB 4 & debugging production)
+file_handler = logging.FileHandler(LOG_FILE_PATH, mode="w", encoding="utf-8")
+file_handler.setFormatter(JsonFormatter())
+logger.addHandler(file_handler)
+
+logger.info(f"[init] Log file tersimpan di: {LOG_FILE_PATH}")
+
+# ──────────────────────────────────────────────────────────────
+# [v5] STRUCTURED LOG HELPER — untuk event penting dengan metadata
+# ──────────────────────────────────────────────────────────────
+def log_event(event_type: str, message: str, stage: str = "info", metadata: dict = None):
+    """
+    Tulis event terstruktur ke log file.
+    Berbeda dengan logger.info biasa, fungsi ini menyertakan metadata
+    yang bisa diparse programmatically (misal: old_f1, new_f1, delta).
+    """
+    extra = {"event_type": event_type, "stage": stage}
+    if metadata:
+        extra["metadata"] = metadata
+    logger.info(message, extra=extra)
+
 # ──────────────────────────────────────────────────────────────
 # DYNAMIC WEIGHT CALCULATOR
 # ──────────────────────────────────────────────────────────────
 def compute_override_weight(n_corpus: int, n_override: int) -> float:
-    """
-    Hitung bobot override dengan logarithmic damping.
-
-    TIDAK ada OVERRIDE_MAX_WEIGHT — tembok statis menghancurkan jaminan
-    pengaruh tepat saat skala besar membutuhkannya. Sebagai gantinya,
-    fungsi ln(1+x) menyediakan redaman alami:
-
-        w_raw = (t * n_corpus) / (n_override * (1 - t))   # proporsi murni
-        w     = MIN + ln(1 + max(0, w_raw - MIN))          # log damping
-
-    Jaminan matematis:
-        - w selalu >= OVERRIDE_MIN_WEIGHT (floor tetap ada)
-        - w tidak pernah meledak ke infinity karena ln tumbuh O(log n)
-        - Tidak ada tembok statis yang membuat influence kolaps tiba-tiba
-
-    Catatan interaksi:
-        LogisticRegression dipanggil dengan class_weight='balanced' DAN
-        sample_weight. Keduanya dikalikan oleh sklearn secara internal.
-        Artinya sampel override dari kelas minoritas mendapat boost ganda.
-        Pantau per-class F1 di metrics JSON untuk mendeteksi efek ini.
-    """
     if n_override == 0:
         return 1.0
     t = OVERRIDE_INFLUENCE_TARGET
     w_raw = (t * n_corpus) / (n_override * (1.0 - t))
-    # ln damping: linear di zona rendah, melambat secara alami di skala besar
     w_log = OVERRIDE_MIN_WEIGHT + math.log1p(max(0.0, w_raw - OVERRIDE_MIN_WEIGHT))
     return round(w_log, 4)
 
-# ──────────────────────────────────────────────────────────────
-# LOGGING
-# ──────────────────────────────────────────────────────────────
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s | %(levelname)s | %(message)s",
-    handlers=[logging.StreamHandler(sys.stdout)]
-)
-logger = logging.getLogger("retrain_worker")
-
 # ──────────────────────────────────────────────────────────────
 # STATUS WRITER — atomic dengan FileLock (v4)
 # ──────────────────────────────────────────────────────────────
@@ -161,13 +156,13 @@ def write_status(stage: str, message: str, extra: dict = None):
         payload = {"stage": stage, "message": message, "timestamp": datetime.now().isoformat()}
         if extra: payload.update(extra)
         STATUS_PATH.write_text(json.dumps(payload, indent=2, ensure_ascii=False), encoding="utf-8")
-    logger.info(f"[{stage}] {message}")
+    # [v5] Tulis juga ke log file dengan stage tracking
+    logger.info(f"[{stage}] {message}", extra={"stage": stage})
 
 # ──────────────────────────────────────────────────────────────
-# PREPROCESSING — dua fungsi terpisah (v4)
+# PREPROCESSING
 # ──────────────────────────────────────────────────────────────
 def preprocess_stemmed(text: str) -> str:
-    """Untuk data korpus yang SUDAH di-stem oleh prepare_corpus — tidak re-stem."""
     if pd.isna(text) or not isinstance(text, str): return ""
     text = text.lower().strip()
     if text in {"nan", "none", "null", "-", "0", "", "tidak diisi"}: return ""
@@ -178,7 +173,6 @@ def preprocess_stemmed(text: str) -> str:
     return " ".join(words)
 
 def preprocess_raw(text: str) -> str:
-    """Untuk data manual_override yang BELUM di-stem — jalankan Sastrawi."""
     if pd.isna(text) or not isinstance(text, str): return ""
     text = text.lower().strip()
     if text in {"nan", "none", "null", "-", "0", "", "tidak diisi"}: return ""
@@ -210,18 +204,7 @@ def evaluate_model(model, X_test: pd.Series, y_test: pd.Series) -> dict:
         }
     }
 
-# ──────────────────────────────────────────────────────────────
-# BASELINE F1 — re-evaluasi model lama pada test set yang sama
-# ──────────────────────────────────────────────────────────────
 def get_baseline_f1_on_testset(X_test: pd.Series, y_test: pd.Series) -> float:
-    """
-    Evaluasi model lama (backup) pada X_test/y_test yang SAMA dengan yang
-    akan digunakan mengevaluasi candidate. Ini satu-satunya cara yang adil
-    (apples-to-apples) karena distribusi test set berubah setiap retrain.
-
-    Membandingkan F1 baru dengan angka JSON lama (dihitung di distribusi berbeda)
-    adalah perbandingan apel vs jeruk — tidak valid untuk keputusan promote/rollback.
-    """
     if not PIPELINE_BAK_PATH.exists():
         logger.warning("Backup .bak tidak ditemukan — baseline F1 diasumsikan 0.0")
         return 0.0
@@ -244,6 +227,15 @@ def do_rollback(reason: str, old_f1: float, new_f1: float):
         logger.info("Rollback berhasil: pkl lama dipulihkan dari .bak")
     else:
         logger.warning("File .bak tidak ditemukan — pkl aktif dibiarkan.")
+
+    # [v5] Log event rollback dengan metadata lengkap
+    log_event(
+        event_type="rollback",
+        message=f"Model lama dipertahankan. {reason}",
+        stage="rolled_back",
+        metadata={"reason": reason, "old_f1": old_f1, "new_f1": new_f1}
+    )
+
     write_status(
         stage="rolled_back",
         message=f"Model lama dipertahankan. {reason}",
@@ -254,6 +246,13 @@ def do_rollback(reason: str, old_f1: float, new_f1: float):
 # MAIN
 # ──────────────────────────────────────────────────────────────
 def main(extra_csv_path: str = None):
+    # [v5] Log awal sesi — penting untuk audit trail
+    log_event(
+        event_type="session_start",
+        message=f"Retraining session dimulai. Log file: {LOG_FILE_PATH.name}",
+        stage="started",
+        metadata={"extra_csv_provided": extra_csv_path is not None}
+    )
     write_status("started", "Worker dimulai")
 
     # ── STEP 1: BACKUP ──────────────────────────────────────
@@ -264,8 +263,6 @@ def main(extra_csv_path: str = None):
     shutil.copy2(PIPELINE_PATH, PIPELINE_BAK_PATH)
     logger.info(f"Backup tersimpan: {PIPELINE_BAK_PATH}")
 
-    # baseline_f1 akan dihitung setelah split dinamis terbentuk
-    # (evaluasi model lama pada test set yang sama dengan candidate)
     baseline_f1 = 0.0
 
     try:
@@ -277,7 +274,7 @@ def main(extra_csv_path: str = None):
 
         df_corpus = pd.read_csv(CORPUS_PATH, sep=";", dtype=str).dropna(subset=["job_text_raw", "label"])
         df_corpus["features"] = df_corpus["job_text_raw"].apply(preprocess_stemmed)
-        df_corpus["_weight"] = 1.0   # Bobot normal untuk data historis
+        df_corpus["_weight"] = 1.0
         logger.info(f"Corpus asli: {len(df_corpus)} baris")
 
         df_extra = pd.DataFrame()
@@ -288,9 +285,6 @@ def main(extra_csv_path: str = None):
             override_w = compute_override_weight(len(df_corpus), len(df_extra))
             df_extra["_weight"] = override_w
 
-        # Pre-compute total bobot sekali — dipakai di logging & metrics JSON.
-        # .sum() lebih benar daripada len() * .iloc[0] karena tidak mengasumsikan
-        # homogenitas bobot di seluruh baris (future-proof jika bobot per-baris ditambahkan).
         total_corpus_weight   = df_corpus["_weight"].sum()
         total_override_weight = df_extra["_weight"].sum() if len(df_extra) > 0 else 0.0
         actual_influence_pct  = (
@@ -308,9 +302,7 @@ def main(extra_csv_path: str = None):
         else:
             logger.info("Tidak ada data tambahan — menggunakan corpus asli saja")
 
-        # Concat tanpa deduplication — semua frekuensi historis dipertahankan
         df_all = pd.concat([df_corpus, df_extra], ignore_index=True) if len(df_extra) > 0 else df_corpus.copy()
-
         mask = df_all["features"].str.len() > 0
         df_all = df_all[mask]
         logger.info(f"Total setelah merge (tanpa deduplicate): {len(df_all)} baris")
@@ -322,19 +314,27 @@ def main(extra_csv_path: str = None):
         y_all = df_all["label"]
         w_all = df_all["_weight"]
 
-        # ── STEP 3: DYNAMIC SPLIT — selalu dari data gabungan terkini ────
+        # [v5] Log event data merge dengan metadata
-        # Test set statis (test_set.csv) tidak digunakan karena:
+        log_event(
-        # (a) tidak mencerminkan pola baru dari data override
+            event_type="data_merged",
-        # (b) baseline_f1 dari JSON dihitung pada distribusi berbeda →
+            message=f"Data training siap: {len(df_all)} baris",
-        #     perbandingan apel vs jeruk, tidak valid untuk keputusan promote.
+            stage="loading_data",
-        # Solusi: split dinamis, lalu evaluasi model LAMA pada test set YANG SAMA.
+            metadata={
+                "corpus_rows": len(df_corpus),
+                "override_rows": len(df_extra),
+                "override_weight": override_w if len(df_extra) > 0 else 1.0,
+                "influence_pct": round(actual_influence_pct, 2),
+                "total_merged": len(df_all),
+            }
+        )
+
+        # ── STEP 3: DYNAMIC SPLIT ────────────────────────────
         X_train, X_test, y_train, y_test, w_train, _ = train_test_split(
             X_all, y_all, w_all, test_size=0.3, random_state=42, stratify=y_all
         )
         logger.info(f"Dynamic split: {len(X_train)} train | {len(X_test)} test")
 
-        # ── STEP 4: BASELINE — evaluasi model LAMA pada test set yang sama ──
+        # ── STEP 4: BASELINE ─────────────────────────────────
-        # Ini satu-satunya cara perbandingan yang jujur (apples-to-apples).
         write_status("evaluating", "Mengevaluasi model lama pada test set baru...")
         baseline_f1 = get_baseline_f1_on_testset(X_test, y_test)
 
@@ -342,12 +342,12 @@ def main(extra_csv_path: str = None):
         write_status("training", f"K-Fold validation & training... ({len(X_train)} sampel)")
         skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
         fold_metrics = []
+        fold_accuracies = []  # [v5] untuk log ringkas
         for i, (tr_idx, te_idx) in enumerate(skf.split(X_train, y_train)):
             m = 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'))
             ])
-            # sample_weight diteruskan ke step 'clf' via Pipeline naming convention
             m.fit(
                 X_train.iloc[tr_idx], y_train.iloc[tr_idx],
                 clf__sample_weight=w_train.iloc[tr_idx].values
@@ -355,8 +355,21 @@ def main(extra_csv_path: str = None):
             y_pred = m.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)
+            fold_accuracies.append(round(rep['accuracy'], 4))
             write_status("training", f"K-Fold selesai: fold {i+1}/5 | acc={rep['accuracy']:.4f}")
 
+        # [v5] Log ringkas hasil K-Fold
+        log_event(
+            event_type="kfold_completed",
+            message=f"K-Fold 5 lipatan selesai. Akurasi per fold: {fold_accuracies}",
+            stage="training",
+            metadata={
+                "fold_accuracies": fold_accuracies,
+                "mean_accuracy": round(float(np.mean(fold_accuracies)), 4),
+                "std_accuracy": round(float(np.std(fold_accuracies)), 4),
+            }
+        )
+
         # ── STEP 6: TRAINING MODEL FINAL ─────────────────────
         write_status("training", "Training model final pada seluruh data training...")
         candidate_model = Pipeline([
@@ -392,11 +405,29 @@ def main(extra_csv_path: str = None):
                 f"minimum dibutuhkan: +{MIN_IMPROVEMENT_THRESHOLD*100:.1f}%)"
             )
 
+        # [v5] Log event evaluasi dengan metadata lengkap — ini yang akan dikutip di BAB 4
+        log_event(
+            event_type="evaluation_completed",
+            message=f"Evaluasi selesai. Delta F1: {delta:+.4f} ({'promote' if should_promote else 'rollback'})",
+            stage="evaluating",
+            metadata={
+                "baseline_f1": baseline_f1,
+                "candidate_f1": new_f1,
+                "delta": round(delta, 4),
+                "delta_pct": round(delta * 100, 2),
+                "threshold_min": MIN_IMPROVEMENT_THRESHOLD,
+                "decision": "promote" if should_promote else "rollback",
+                "reason": reason,
+                "new_accuracy": new_metrics["accuracy"],
+                "per_class_f1": {cls: v["f1"] for cls, v in new_metrics["per_class"].items()},
+            }
+        )
+
         if not should_promote:
             do_rollback(reason, baseline_f1, new_f1)
             return
 
-        # ── STEP 7: PROMOTE ──────────────────────────────────
+        # ── STEP 8: PROMOTE ──────────────────────────────────
         write_status("promoting", "Model baru lebih baik — mempromote model baru...")
         shutil.move(str(CANDIDATE_PATH), str(PIPELINE_PATH))
         logger.info(f"Model baru dipromote: {PIPELINE_PATH}")
@@ -420,7 +451,6 @@ def main(extra_csv_path: str = None):
                 y_test, candidate_model.predict(X_test),
                 output_dict=True, zero_division=0
             ),
-            # Perbandingan fair: kedua model dievaluasi pada test set yang SAMA
             "comparison": {
                 "note": "Both models evaluated on identical dynamic test set",
                 "old_weighted_f1": baseline_f1,
@@ -430,6 +460,20 @@ def main(extra_csv_path: str = None):
         }
         METRICS_PATH.write_text(json.dumps(new_metrics_full, indent=2, ensure_ascii=False), encoding="utf-8")
 
+        # [v5] Log event promote final
+        log_event(
+            event_type="model_promoted",
+            message=f"Model baru berhasil dipromote. {reason}",
+            stage="promoted",
+            metadata={
+                "old_f1": baseline_f1,
+                "new_f1": new_f1,
+                "delta": round(delta, 4),
+                "log_file": LOG_FILE_PATH.name,
+                "metrics_file": METRICS_PATH.name,
+            }
+        )
+
         write_status(
             stage="promoted",
             message=f"Model baru berhasil dipromote. {reason}",
@@ -447,12 +491,20 @@ def main(extra_csv_path: str = None):
 
     except Exception as e:
         logger.error(f"ERROR saat training: {type(e).__name__}: {e}", exc_info=True)
+
+        # [v5] Log event error dengan traceback info
+        log_event(
+            event_type="training_error",
+            message=f"Training gagal: {type(e).__name__}: {str(e)[:200]}",
+            stage="failed",
+            metadata={"error_type": type(e).__name__, "error_message": str(e)[:500]}
+        )
+
         do_rollback(
             reason=f"Training gagal karena error: {type(e).__name__}: {str(e)[:200]}",
             old_f1=baseline_f1,
             new_f1=0.0
         )
-        # Override stage ke 'failed' agar UI tahu ini bukan rollback biasa
         lock = FileLock(LOCK_PATH, timeout=10)
         with lock:
             status = json.loads(STATUS_PATH.read_text(encoding="utf-8"))

fastapi/get_model_data.py

@@ -0,0 +1,11 @@
+import joblib
+pipeline = joblib.load('ml_assets/ml_pipeline_internal.pkl')
+vec = pipeline.named_steps['tfidf']
+clf = pipeline.named_steps['clf']
+
+# Ambil 5 fitur dengan bobot tertinggi untuk kelas Programmer
+feats = vec.get_feature_names_out()
+prog_idx = list(clf.classes_).index('Programmer')
+top5 = sorted(zip(feats, clf.coef_[prog_idx]), key=lambda x: -x[1])[:10]
+for w, c in top5:
+    print(f"{w}: {c:.4f}")

fastapi/ml_assets/jobs/1403faae-4f33-4d87-b6b6-8d17f7db5302.json

@@ -0,0 +1 @@
+{"status": "error", "message": "Processing failed: 'charmap' codec can't encode character '\\U0001f602' in position 885944: character maps to <undefined>", "processed_rows": 0, "total_rows": 0, "source_type": "unknown"}

fastapi/ml_assets/jobs/aa28f0ea-6d30-4f9f-81ee-c5502a45cf0a.json

@@ -0,0 +1 @@
+{"status": "error", "message": "Processing failed: 'charmap' codec can't encode character '\\U0001f602' in position 885944: character maps to <undefined>", "processed_rows": 0, "total_rows": 0, "source_type": "unknown"}

fastapi/ml_assets/logs/retrain_log_20260605_190842.jsonl

@@ -0,0 +1,29 @@
+{"timestamp": "2026-06-05T19:08:42.267450", "level": "INFO", "logger": "retrain_worker", "message": "[init] Log file tersimpan di: D:\\msaif\\Project\\Laravel\\sista_mif_ta\\fastapi\\ml_assets\\logs\\retrain_log_20260605_190842.jsonl"}
+{"timestamp": "2026-06-05T19:08:42.267450", "level": "INFO", "logger": "retrain_worker", "message": "Retraining session dimulai. Log file: retrain_log_20260605_190842.jsonl", "stage": "started", "event_type": "session_start", "metadata": {"extra_csv_provided": true}}
+{"timestamp": "2026-06-05T19:08:42.268452", "level": "INFO", "logger": "retrain_worker", "message": "[started] Worker dimulai", "stage": "started"}
+{"timestamp": "2026-06-05T19:08:42.269454", "level": "INFO", "logger": "retrain_worker", "message": "[backup] Membuat backup model lama...", "stage": "backup"}
+{"timestamp": "2026-06-05T19:08:42.271473", "level": "INFO", "logger": "retrain_worker", "message": "Backup tersimpan: D:\\msaif\\Project\\Laravel\\sista_mif_ta\\fastapi\\ml_assets\\ml_pipeline_internal.pkl.bak"}
+{"timestamp": "2026-06-05T19:08:42.271473", "level": "INFO", "logger": "retrain_worker", "message": "[loading_data] Memuat dan menggabungkan data training...", "stage": "loading_data"}
+{"timestamp": "2026-06-05T19:08:42.275472", "level": "INFO", "logger": "retrain_worker", "message": "Corpus asli: 250 baris"}
+{"timestamp": "2026-06-05T19:08:42.445530", "level": "INFO", "logger": "retrain_worker", "message": "Data manual_override: 1 baris | weight=6.6648x (log-damped) | influence aktual=2.6% (target nominal 30%)"}
+{"timestamp": "2026-06-05T19:08:42.465047", "level": "INFO", "logger": "retrain_worker", "message": "Total setelah merge (tanpa deduplicate): 251 baris"}
+{"timestamp": "2026-06-05T19:08:42.465047", "level": "INFO", "logger": "retrain_worker", "message": "Data training siap: 251 baris", "stage": "loading_data", "event_type": "data_merged", "metadata": {"corpus_rows": 250, "override_rows": 1, "override_weight": 6.6648, "influence_pct": 2.6, "total_merged": 251}}
+{"timestamp": "2026-06-05T19:08:42.479066", "level": "INFO", "logger": "retrain_worker", "message": "Dynamic split: 175 train | 76 test"}
+{"timestamp": "2026-06-05T19:08:42.480074", "level": "INFO", "logger": "retrain_worker", "message": "[evaluating] Mengevaluasi model lama pada test set baru...", "stage": "evaluating"}
+{"timestamp": "2026-06-05T19:08:42.492120", "level": "INFO", "logger": "retrain_worker", "message": "Baseline F1 (old model, same test set): 0.9059"}
+{"timestamp": "2026-06-05T19:08:42.494119", "level": "INFO", "logger": "retrain_worker", "message": "[training] K-Fold validation & training... (175 sampel)", "stage": "training"}
+{"timestamp": "2026-06-05T19:08:42.534414", "level": "INFO", "logger": "retrain_worker", "message": "[training] K-Fold selesai: fold 1/5 | acc=0.5143", "stage": "training"}
+{"timestamp": "2026-06-05T19:08:42.542922", "level": "INFO", "logger": "retrain_worker", "message": "[training] K-Fold selesai: fold 2/5 | acc=0.6857", "stage": "training"}
+{"timestamp": "2026-06-05T19:08:42.552262", "level": "INFO", "logger": "retrain_worker", "message": "[training] K-Fold selesai: fold 3/5 | acc=0.5429", "stage": "training"}
+{"timestamp": "2026-06-05T19:08:42.561273", "level": "INFO", "logger": "retrain_worker", "message": "[training] K-Fold selesai: fold 4/5 | acc=0.5714", "stage": "training"}
+{"timestamp": "2026-06-05T19:08:42.569782", "level": "INFO", "logger": "retrain_worker", "message": "[training] K-Fold selesai: fold 5/5 | acc=0.7143", "stage": "training"}
+{"timestamp": "2026-06-05T19:08:42.569782", "level": "INFO", "logger": "retrain_worker", "message": "K-Fold 5 lipatan selesai. Akurasi per fold: [0.5143, 0.6857, 0.5429, 0.5714, 0.7143]", "stage": "training", "event_type": "kfold_completed", "metadata": {"fold_accuracies": [0.5143, 0.6857, 0.5429, 0.5714, 0.7143], "mean_accuracy": 0.6057, "std_accuracy": 0.0796}}
+{"timestamp": "2026-06-05T19:08:42.570788", "level": "INFO", "logger": "retrain_worker", "message": "[training] Training model final pada seluruh data training...", "stage": "training"}
+{"timestamp": "2026-06-05T19:08:42.579235", "level": "INFO", "logger": "retrain_worker", "message": "Candidate model tersimpan: D:\\msaif\\Project\\Laravel\\sista_mif_ta\\fastapi\\ml_assets\\ml_pipeline_candidate.pkl"}
+{"timestamp": "2026-06-05T19:08:42.589948", "level": "INFO", "logger": "retrain_worker", "message": "[evaluating] Mengevaluasi model baru vs model lama (test set sama)...", "stage": "evaluating"}
+{"timestamp": "2026-06-05T19:08:42.593983", "level": "INFO", "logger": "retrain_worker", "message": "Baseline F1 (old model, same test) : 0.9059"}
+{"timestamp": "2026-06-05T19:08:42.593983", "level": "INFO", "logger": "retrain_worker", "message": "Candidate F1 (new model, same test): 0.7011  (delta: -0.2048)"}
+{"timestamp": "2026-06-05T19:08:42.593983", "level": "INFO", "logger": "retrain_worker", "message": "Evaluasi selesai. Delta F1: -0.2048 (rollback)", "stage": "evaluating", "event_type": "evaluation_completed", "metadata": {"baseline_f1": 0.9059, "candidate_f1": 0.7011, "delta": -0.2048, "delta_pct": -20.48, "threshold_min": 0.01, "decision": "rollback", "reason": "Model baru lebih buruk secara signifikan (-20.48% weighted F1)", "new_accuracy": 0.6711, "per_class_f1": {"Programmer": 0.8, "Data Analyst": 0.19, "Wirausaha Informatika": 0.474, "Non-IT": 0.897}}}
+{"timestamp": "2026-06-05T19:08:42.593983", "level": "INFO", "logger": "retrain_worker", "message": "Rollback berhasil: pkl lama dipulihkan dari .bak"}
+{"timestamp": "2026-06-05T19:08:42.593983", "level": "INFO", "logger": "retrain_worker", "message": "Model lama dipertahankan. Model baru lebih buruk secara signifikan (-20.48% weighted F1)", "stage": "rolled_back", "event_type": "rollback", "metadata": {"reason": "Model baru lebih buruk secara signifikan (-20.48% weighted F1)", "old_f1": 0.9059, "new_f1": 0.7011}}
+{"timestamp": "2026-06-05T19:08:42.594976", "level": "INFO", "logger": "retrain_worker", "message": "[rolled_back] Model lama dipertahankan. Model baru lebih buruk secara signifikan (-20.48% weighted F1)", "stage": "rolled_back"}