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# 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, tree_method="exact", base_score=0.5

)

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:

"""

The URL for this algorithm

https://xgboost.readthedocs.io/en/stable/

California house price dataset is used to demonstrate the algorithm.

Expected error values:

Mean Absolute Error: 0.30957163379906033

Mean Square Error: 0.22611560196662744

"""

# 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()

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