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67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
# XGBoost Regressor Example
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import numpy as np
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from sklearn.datasets import fetch_california_housing
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from sklearn.metrics import mean_absolute_error, mean_squared_error
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from sklearn.model_selection import train_test_split
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from xgboost import XGBRegressor
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def data_handling(data: dict) -> tuple:
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# Split dataset into features and target. Data is features.
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"""
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>>> data_handling((
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... {'data':'[ 8.3252 41. 6.9841269 1.02380952 322. 2.55555556 37.88 -122.23 ]'
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... ,'target':([4.526])}))
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('[ 8.3252 41. 6.9841269 1.02380952 322. 2.55555556 37.88 -122.23 ]', [4.526])
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"""
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return (data["data"], data["target"])
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def xgboost(
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features: np.ndarray, target: np.ndarray, test_features: np.ndarray
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) -> np.ndarray:
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"""
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>>> xgboost(np.array([[ 2.3571 , 52. , 6.00813008, 1.06775068,
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... 907. , 2.45799458, 40.58 , -124.26]]),np.array([1.114]),
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... np.array([[1.97840000e+00, 3.70000000e+01, 4.98858447e+00, 1.03881279e+00,
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... 1.14300000e+03, 2.60958904e+00, 3.67800000e+01, -1.19780000e+02]]))
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array([[1.1139996]], dtype=float32)
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"""
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xgb = XGBRegressor(
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verbosity=0, random_state=42, tree_method="exact", base_score=0.5
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)
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xgb.fit(features, target)
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# Predict target for test data
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predictions = xgb.predict(test_features)
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predictions = predictions.reshape(len(predictions), 1)
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return predictions
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def main() -> None:
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"""
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The URL for this algorithm
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https://xgboost.readthedocs.io/en/stable/
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California house price dataset is used to demonstrate the algorithm.
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Expected error values:
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Mean Absolute Error: 0.30957163379906033
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Mean Square Error: 0.22611560196662744
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"""
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# Load California house price dataset
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california = fetch_california_housing()
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data, target = data_handling(california)
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x_train, x_test, y_train, y_test = train_test_split(
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data, target, test_size=0.25, random_state=1
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)
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predictions = xgboost(x_train, y_train, x_test)
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# Error printing
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print(f"Mean Absolute Error: {mean_absolute_error(y_test, predictions)}")
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print(f"Mean Square Error: {mean_squared_error(y_test, predictions)}")
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if __name__ == "__main__":
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import doctest
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doctest.testmod(verbose=True)
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main()
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