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https://github.com/TheAlgorithms/Python.git
synced 2026-09-28 13:33:11 +08:00
Remove xgboost demos and dependency (#15219)
Drop machine_learning/xgboost_classifier.py and machine_learning/xgboost_regressor.py. Both were thin "how-to-use" wrappers around sklearn's XGBClassifier/XGBRegressor rather than from-scratch implementations, and the classifier's only doctest was already disabled (# THIS TEST IS BROKEN!!), so it was never exercised in CI. xgboost is one of the heaviest compiled dependencies in the tree (large wheel, needs OpenMP/libgomp at runtime, no free-threaded wheel yet), and gradient boosting is already implemented from scratch in machine_learning/gradient_boosting_classifier.py and gradient_boosting_regressor.py, so no algorithm coverage is lost. Removes the xgboost dependency from pyproject.toml, its (and its xgboost-only transitive dep nvidia-nccl-cu13) entries from uv.lock, and the two DIRECTORY.md links. Refs #15081
This commit is contained in:
priya-sundaram-dev
@@ -696,8 +696,6 @@
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* [Support Vector Machines](machine_learning/support_vector_machines.py)
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* [T Stochastic Neighbour Embedding](machine_learning/t_stochastic_neighbour_embedding.py)
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* [Word Frequency Functions](machine_learning/word_frequency_functions.py)
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* [Xgboost Classifier](machine_learning/xgboost_classifier.py)
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* [Xgboost Regressor](machine_learning/xgboost_regressor.py)
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## [Maths](maths)
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* [Abs](maths/abs.py)
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@@ -1,79 +0,0 @@
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# XGBoost Classifier Example
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import numpy as np
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from matplotlib import pyplot as plt
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from sklearn.datasets import load_iris
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from sklearn.metrics import ConfusionMatrixDisplay
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from sklearn.model_selection import train_test_split
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from xgboost import XGBClassifier
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def data_handling(data: dict) -> tuple:
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# Split dataset into features and target
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# data is features
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"""
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>>> data_handling(({'data':'[5.1, 3.5, 1.4, 0.2]','target':([0])}))
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('[5.1, 3.5, 1.4, 0.2]', [0])
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>>> data_handling(
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... {'data': '[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', 'target': ([0, 0])}
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... )
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('[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', [0, 0])
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"""
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return (data["data"], data["target"])
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def xgboost(features: np.ndarray, target: np.ndarray) -> XGBClassifier:
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"""
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# THIS TEST IS BROKEN!! >>> xgboost(np.array([[5.1, 3.6, 1.4, 0.2]]), np.array([0]))
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XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,
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colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
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early_stopping_rounds=None, enable_categorical=False,
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eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
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importance_type=None, interaction_constraints='',
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learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
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max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
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missing=nan, monotone_constraints='()', n_estimators=100,
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n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,
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reg_alpha=0, reg_lambda=1, ...)
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"""
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classifier = XGBClassifier()
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classifier.fit(features, target)
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return classifier
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def main() -> None:
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"""
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Url for the algorithm:
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https://xgboost.readthedocs.io/en/stable/
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Iris type dataset is used to demonstrate algorithm.
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"""
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# Load Iris dataset
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iris = load_iris()
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features, targets = data_handling(iris)
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x_train, x_test, y_train, y_test = train_test_split(
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features, targets, test_size=0.25
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)
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names = iris["target_names"]
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# Create an XGBoost Classifier from the training data
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xgboost_classifier = xgboost(x_train, y_train)
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# Display the confusion matrix of the classifier with both training and test sets
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ConfusionMatrixDisplay.from_estimator(
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xgboost_classifier,
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x_test,
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y_test,
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display_labels=names,
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cmap="Blues",
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normalize="true",
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)
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plt.title("Normalized Confusion Matrix - IRIS Dataset")
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plt.show()
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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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@@ -1,66 +0,0 @@
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# 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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@@ -27,7 +27,6 @@ dependencies = [
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"statsmodels>=0.14.4",
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"sympy>=1.13.3",
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"typing-extensions>=4.12.2",
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"xgboost>=2.1.3",
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]
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[dependency-groups]
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@@ -902,15 +902,6 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/b4/07/458c344f0f0c178f4481dad5cca790626ffe4c34eabf9467069d06ee4999/numpy-2.5.2-cp315-cp315t-win_arm64.whl", hash = "sha256:5f8e00be2ec6f45f4e8a41a527f68d44a7d96fee92a650e4d8b1326f77f61e6e", size = 10748103, upload-time = "2026-08-09T13:48:24.21Z" },
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]
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[[package]]
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name = "nvidia-nccl-cu13"
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version = "2.31.2"
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source = { registry = "https://pypi.org/simple" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/52/a0/530efd7db8857c0436868bb7df9764f09fde2bd4d1f0bae546eec9fc40d0/nvidia_nccl_cu13-2.31.2-py3-none-manylinux_2_18_aarch64.whl", hash = "sha256:b5563f8e2534f363d93ace022670ba016d3717e190ac4eba564d05fbbe8495b1", size = 252479893, upload-time = "2026-08-11T23:22:01.53Z" },
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{ url = "https://files.pythonhosted.org/packages/14/fb/94933e00bb3dcfdf66ea3456739c6a51d322353f7cc64fa1f5f660e695ac/nvidia_nccl_cu13-2.31.2-py3-none-manylinux_2_18_x86_64.whl", hash = "sha256:0bcaf0308854cb55fcc35af72e2c83143f3b71e65a4e865e2c586b1cdcdb5ae0", size = 252442223, upload-time = "2026-08-11T23:22:40.341Z" },
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]
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[[package]]
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name = "oauthlib"
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version = "3.3.1"
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@@ -1527,7 +1518,6 @@ dependencies = [
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{ name = "sympy" },
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{ name = "tweepy" },
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{ name = "typing-extensions" },
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{ name = "xgboost" },
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]
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[package.dev-dependencies]
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@@ -1567,7 +1557,6 @@ requires-dist = [
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{ name = "sympy", specifier = ">=1.13.3" },
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{ name = "tweepy", specifier = ">=4.14" },
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{ name = "typing-extensions", specifier = ">=4.12.2" },
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{ name = "xgboost", specifier = ">=2.1.3" },
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]
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[package.metadata.requires-dev]
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@@ -1666,21 +1655,3 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/00/39/3daf9f47be208606586de4568ba6713db53ebc8fd7a575aea1fe57983b69/wrapt-2.3.0-py3-none-any.whl", hash = "sha256:d8c7ed08477429752b8c44991f40ad7838b18332a160698740a6bfbc10d998a2", size = 61866, upload-time = "2026-07-28T06:06:12.9Z" },
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]
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[[package]]
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name = "xgboost"
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version = "3.4.1"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "numpy" },
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{ name = "nvidia-nccl-cu13", marker = "sys_platform == 'linux'" },
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{ name = "scipy" },
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]
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sdist = { url = "https://files.pythonhosted.org/packages/38/a9/295320f741c5be4be996c73ee65a2a11852028c50daa7229adb0d61c330b/xgboost-3.4.1.tar.gz", hash = "sha256:6968a4c71efdfa859df0dfcad0d99211c95c28c4ffd6aecff46efff77d18026a", size = 1231819, upload-time = "2026-08-15T08:39:21.197Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/57/ea/0bdcd374241a86f1986e87e272516f0a70d841c3aa86aa9ca167fb651573/xgboost-3.4.1-py3-none-macosx_10_15_x86_64.whl", hash = "sha256:1ea15f15f661825b6a67d87674fb9604a1abb38dd0d4c5cf0486fc85f5203e83", size = 2541584, upload-time = "2026-08-15T08:38:48.484Z" },
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]
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