File size: 2,160 Bytes
75b9644 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | """Train and persist the credit risk model and evaluation artifacts."""
from __future__ import annotations
import sys
import pickle
from pathlib import Path
import pandas as pd
from sklearn.model_selection import train_test_split
PROJECT_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(PROJECT_ROOT / "src"))
from credit_risk.config import DATA_PROCESSED_DIR, DATA_RAW_PATH, MODEL_DIR, REPORTS_DIR
from credit_risk.features import build_training_frame
from credit_risk.modeling import evaluate_model, save_metrics, save_model, train_model
def main() -> None:
"""Main training flow used by local runs and CI validations."""
raw_df = pd.read_csv(DATA_RAW_PATH)
features, target = build_training_frame(raw_df)
# Stratification preserves class balance between train and test sets.
x_train, x_test, y_train, y_test = train_test_split(
features,
target,
test_size=0.3,
random_state=42,
stratify=target,
)
model = train_model(x_train=x_train, y_train=y_train, random_state=42)
metrics, y_hat = evaluate_model(model=model, x_test=x_test, y_test=y_test)
DATA_PROCESSED_DIR.mkdir(parents=True, exist_ok=True)
x_train.to_parquet(DATA_PROCESSED_DIR / "x_train.parquet", index=False)
x_test.to_parquet(DATA_PROCESSED_DIR / "x_test.parquet", index=False)
y_train.to_frame(name="target").to_parquet(DATA_PROCESSED_DIR / "y_train.parquet", index=False)
y_test.to_frame(name="target").to_parquet(DATA_PROCESSED_DIR / "y_test.parquet", index=False)
y_hat.to_frame(name="prediction").to_parquet(DATA_PROCESSED_DIR / "yhat.parquet", index=False)
save_model(model=model, model_path=MODEL_DIR / "model.joblib")
# Backward-compatible artifact used by the existing Gradio Space app.
with (MODEL_DIR / "model.pickle").open("wb") as file:
pickle.dump(model, file)
save_metrics(metrics=metrics, path=REPORTS_DIR / "metrics.json")
print("Training finished successfully.")
print(f"Saved model to: {MODEL_DIR / 'model.joblib'}")
print(f"Saved metrics to: {REPORTS_DIR / 'metrics.json'}")
if __name__ == "__main__":
main()
|