src/05_xgboost_lowmem.py

import pyarrow.parquet as pq
import numpy as np
import xgboost as xgb
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error, r2_score

def load_parquet_as_numpy(path):
    # Read Parquet file into PyArrow table
    table = pq.read_table(path)

    # Convert to pandas DataFrame
    df = table.to_pandas()

    # Drop only the label column to get features
    X = df.drop(columns=["value"]).values.astype(np.float32)

    # Extract target column
    y = df["value"].values.astype(np.float32)

    return xgb.DMatrix(X, label=y), y

def load_parquet_as_dmatrix(path):
    # Load selected columns only
    cols = [f"feature_{i}" for i in range(2048)] + ["value"]
    table = pq.read_table(path, columns=cols)

    # Convert columns to NumPy arrays directly using Arrow
    X = np.column_stack([table[col].to_numpy(zero_copy_only=False) for col in table.column_names if col != "value"]).astype(np.float32)
    y = table["value"].to_numpy(zero_copy_only=False).astype(np.float32)

    return xgb.DMatrix(X, label=y), y

def main():
    print("Loading training data...")
    dtrain, y_train = load_parquet_as_dmatrix("intermediate_data/d2/data_train_features.parquet")

    print("Loading validation data...")
    dval, y_val = load_parquet_as_dmatrix("intermediate_data/d2/data_val_features.parquet")

    print("Loading test data...")
    dtest, y_test = load_parquet_as_dmatrix("intermediate_data/d2/data_test_features.parquet")

    print("Training model with histogram-based tree method...")
    params = {
        "objective": "reg:squarederror",
        "tree_method": "hist",
        "max_depth": 8,
        "eta": 0.1,
        "nthread": 10,
        "verbosity": 1
    }

    evals_result = {}
    model = xgb.train(
        params,
        dtrain,
        num_boost_round=300,
        evals=[(dtrain, "train"), (dval, "eval")],
        early_stopping_rounds=20,
        evals_result=evals_result,
        verbose_eval=10
    )

    # Evaluate on test set
    y_pred = model.predict(dtest)
    rmse = mean_squared_error(y_test, y_pred, squared=False)
    r2 = r2_score(y_test, y_pred)

    print(f"Test RMSE: {rmse:.4f}")
    print(f"Test R^2: {r2:.4f}")
    # Plot learning curve
    os.makedirs("results", exist_ok=True)
    plt.figure()
    plt.plot(evals_result["train"]["rmse"], label="Train RMSE")
    plt.plot(evals_result["eval"]["rmse"], label="Validation RMSE")
    plt.xlabel("Boosting Round")
    plt.ylabel("RMSE")
    plt.title("XGBoost RMSE over Epochs")
    plt.legend()
    plt.savefig("results/d2/xgboost_d2_learning_curve.png", dpi=300)
    print("Saved learning curve to results/xgboost_d2_learning_curve.png")

if __name__ == "__main__":
    main()