Python/machine_learning/xgboost_regressor.py
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XGB Regressor ()
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Christian Clauss <cclauss@me.com>
2022-10-23 13:47:19 +02:00

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Python

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