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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")
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