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| """ | |
| Taxy ML — Inference | |
| =================== | |
| Kode sederhana untuk proses inferensi model: memuat model .keras + artefak | |
| preprocessing, lalu memprediksi risiko ketidakpatuhan pajak dari profil pengguna. | |
| Dipakai sebagai modul oleh serve.py, dan bisa dijalankan langsung untuk demo: | |
| python inference.py | |
| """ | |
| import os | |
| import numpy as np | |
| # keras/tensorflow di-import lazily agar startup ringan saat hanya dipakai modul | |
| from preprocessing import ( | |
| build_raw_features, | |
| load_preprocessing, | |
| CLASS_NAMES, | |
| ) | |
| HERE = os.path.dirname(os.path.abspath(__file__)) | |
| ARTIFACTS = os.path.join(HERE, "artifacts") | |
| MODEL_PATH = os.path.join(ARTIFACTS, "taxy_risk_model.keras") | |
| PREP_PATH = os.path.join(ARTIFACTS, "preprocessing.json") | |
| _model = None | |
| _prep = None | |
| def load(): | |
| """Muat model + preprocessing (sekali).""" | |
| global _model, _prep | |
| if _model is None: | |
| import keras | |
| # custom objects teregistrasi via @register_keras_serializable di import | |
| import custom_components # noqa: F401 (registrasi FeatureGatingLayer dll) | |
| _model = keras.models.load_model(MODEL_PATH) | |
| _prep = load_preprocessing(PREP_PATH) | |
| return _model, _prep | |
| def predict(record): | |
| """Prediksi risiko dari satu record profil pengguna (dict). | |
| Returns dict: risk_index, risk_category, confidence, probabilities. | |
| """ | |
| model, prep = load() | |
| x = build_raw_features(record).reshape(1, -1) | |
| x = prep["scaler"].transform(x) | |
| reg, cls = model.predict(x, verbose=0) | |
| risk_index = float(reg[0][0]) | |
| probs = cls[0] | |
| cls_idx = int(np.argmax(probs)) | |
| return { | |
| "risk_index": round(risk_index, 4), | |
| "risk_category": CLASS_NAMES[cls_idx], | |
| "risk_category_id": cls_idx, | |
| "confidence": round(float(probs[cls_idx]), 4), | |
| "probabilities": {CLASS_NAMES[i]: round(float(p), 4) for i, p in enumerate(probs)}, | |
| } | |
| def predict_batch(records): | |
| model, prep = load() | |
| X = np.stack([build_raw_features(r) for r in records]) | |
| X = prep["scaler"].transform(X) | |
| reg, cls = model.predict(X, verbose=0) | |
| out = [] | |
| for i in range(len(records)): | |
| probs = cls[i] | |
| cls_idx = int(np.argmax(probs)) | |
| out.append({ | |
| "risk_index": round(float(reg[i][0]), 4), | |
| "risk_category": CLASS_NAMES[cls_idx], | |
| "confidence": round(float(probs[cls_idx]), 4), | |
| }) | |
| return out | |
| if __name__ == "__main__": | |
| samples = [ | |
| {"usia": 28, "gaji_bulanan": 8_000_000, "skor_literasi_pajak": 80, | |
| "punya_npwp": 1, "aktif_taxy": 1, "status_ptkp": "TK/0", | |
| "pekerjaan": "Senior Staff"}, | |
| {"usia": 45, "gaji_bulanan": 26_000_000, "skor_literasi_pajak": 25, | |
| "punya_npwp": 0, "aktif_taxy": 0, "status_ptkp": "K/3", | |
| "pekerjaan": "Manager"}, | |
| {"usia": 22, "gaji_bulanan": 4_500_000, "skor_literasi_pajak": 55, | |
| "punya_npwp": 1, "aktif_taxy": 0, "status_ptkp": "TK/0", | |
| "pekerjaan": "Fresh Graduate"}, | |
| ] | |
| print("=== DEMO INFERENCE TAXY RISK PROFILER ===\n") | |
| for s in samples: | |
| r = predict(s) | |
| print(f"Profil : usia {s['usia']}, gaji {s['gaji_bulanan']:,}, " | |
| f"NPWP={s['punya_npwp']}, literasi={s['skor_literasi_pajak']}") | |
| print(f" -> Risiko: {r['risk_category']} " | |
| f"(index={r['risk_index']}, confidence={r['confidence']})") | |
| print(f" prob: {r['probabilities']}\n") | |