XGB Regressor (TheAlgorithms#7107)

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* Update xgboostclassifier.py

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* Update xgboostregressor.py

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* Fixes: #{6551}

* Fixes : {TheAlgorithms#6551}

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Fixes: {TheAlgorithms#6551]

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* Update xgboostregressor.py

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* Update xgboostregressor.py

* Update xgboostregressor.py

* Fixes: { TheAlgorithms#6551}

* Update xgboostregressor.py

* Fixes: { TheAlgorithms#6551}

* Fixes: { TheAlgorithms#6551}

* Update and rename xgboostregressor.py to xgboost_regressor.py

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Author: 3 people

Diff for: machine_learning/xgboost_regressor.py

@@ -0,0 +1,64 @@
+# 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()