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61b4af1 | 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 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | # scripts/train_model.py
import pandas as pd
from pathlib import Path
from lightgbm import LGBMRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error
from math import sqrt
import joblib
TRAIN_DATA_PATH = Path("data/processed/training_data.parquet")
MODEL_PATH = Path("models/price_lgbm_model.pkl")
def main():
print(f"Loading training data from {TRAIN_DATA_PATH} ...")
df = pd.read_parquet(TRAIN_DATA_PATH)
# feature columns
lag_cols = [f"lag_{l}" for l in [1, 2, 3, 7]]
time_cols = ["dayofweek", "month"]
cat_cols = ["Mandi", "Commodity"]
feature_cols = lag_cols + time_cols + cat_cols
# Train/validation split by time (no leakage)
max_date = df["date"].max()
cutoff = max_date - pd.Timedelta(days=60) # last 60 days for validation
train_mask = df["date"] <= cutoff
valid_mask = df["date"] > cutoff
train_df = df.loc[train_mask].copy()
valid_df = df.loc[valid_mask].copy()
print(f"Train size: {train_df.shape}, Valid size: {valid_df.shape}")
X_train = train_df[feature_cols].copy()
y_train = train_df["target_price"]
X_valid = valid_df[feature_cols].copy()
y_valid = valid_df["target_price"]
# LightGBM can handle category dtype directly
for col in cat_cols:
X_train.loc[:, col] = X_train[col].astype("category")
X_valid.loc[:, col] = X_valid[col].astype("category")
print("Training LightGBM regressor...")
model = LGBMRegressor(
n_estimators=500,
learning_rate=0.05,
max_depth=-1,
subsample=0.8,
colsample_bytree=0.8,
random_state=42,
n_jobs=-1,
)
model.fit(
X_train,
y_train,
eval_set=[(X_valid, y_valid)],
eval_metric="l2",
)
# Evaluate
y_pred = model.predict(X_valid)
mae = mean_absolute_error(y_valid, y_pred)
rmse = sqrt(mean_squared_error(y_valid, y_pred))
print(f"Validation MAE: {mae:.2f}")
print(f"Validation RMSE: {rmse:.2f}")
MODEL_PATH.parent.mkdir(parents=True, exist_ok=True)
joblib.dump(model, MODEL_PATH)
print(f"Saved trained model to {MODEL_PATH}")
if __name__ == "__main__":
main()
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