XGBoost Classifier

machine_learning/xgboostclassifier.py

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+# XGBoost Classifier Example
+import numpy as np
+from matplotlib import pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.metrics import plot_confusion_matrix
+from sklearn.model_selection import train_test_split
+from xgboost import XGBClassifier
+
+
+def data_handling(data: dict) -> tuple:
+ # Split dataset into features and target
+ # data is features
+ """
+ >>> data_handling(({'data':'[5.1, 3.5, 1.4, 0.2]','target':([0])}))
+ ('[5.1, 3.5, 1.4, 0.2]', [0])
+ >>> data_handling(
+ ... {'data': '[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', 'target': ([0, 0])}
+ ... )
+ ('[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', [0, 0])
+ """
+ return (data["data"], data["target"])
+
+
+def xgboost(features: np.ndarray, target: np.ndarray) -> XGBClassifier:
+ """
+ >>> xgboost(np.array([[5.1, 3.6, 1.4, 0.2]]), np.array([0]))
+ XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,
+ colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
+ early_stopping_rounds=None, enable_categorical=False,
+ eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
+ importance_type=None, interaction_constraints='',
+ learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
+ max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
+ missing=nan, monotone_constraints='()', n_estimators=100,
+ n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,
+ reg_alpha=0, reg_lambda=1, ...)
+ """
+ classifier = XGBClassifier()
+ classifier.fit(features, target)
+ return classifier
+
+
+def main() -> None:
+
+ """
+ >>> main()
+
+ Url for the algorithm:
+ https://xgboost.readthedocs.io/en/stable/
+ Iris type dataset is used to demonstrate algorithm.
+ """
+
+ # Load Iris dataset
+ iris = load_iris()
+ features, targets = data_handling(iris)
+ x_train, x_test, y_train, y_test = train_test_split(
+ features, targets, test_size=0.25
+ )
+
+ names = iris["target_names"]
+
+ # Create an XGBoost Classifier from the training data
+ xgboost_classifier = xgboost(x_train, y_train)
+
+ # Display the confusion matrix of the classifier with both training and test sets
+ plot_confusion_matrix(
+ xgboost_classifier,
+ x_test,
+ y_test,
+ display_labels=names,
+ cmap="Blues",
+ normalize="true",
+ )
+ plt.title("Normalized Confusion Matrix - IRIS Dataset")
+ plt.show()
+
+
+if __name__ == "__main__":
+ import doctest
+
+ doctest.testmod(verbose=True)
+ main()

machine_learning/xgboostclassifier.py

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+# XGBoost Classifier Example
+import numpy as np
+from matplotlib import pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.metrics import plot_confusion_matrix
+from sklearn.model_selection import train_test_split
+from xgboost import XGBClassifier
+
+
+def data_handling(data: dict) -> tuple:
+ # Split dataset into features and target
+ # data is features
+ """
+ >>> data_handling(({'data':'[5.1, 3.5, 1.4, 0.2]','target':([0])}))
+ ('[5.1, 3.5, 1.4, 0.2]', [0])
+ >>> data_handling(
+ ... {'data': '[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', 'target': ([0, 0])}
+ ... )
+ ('[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', [0, 0])
+ """
+ return (data["data"], data["target"])
+
+
+def xgboost(features: np.ndarray, target: np.ndarray) -> XGBClassifier:
+ """
+ >>> xgboost(np.array([[5.1, 3.6, 1.4, 0.2]]), np.array([0]))
+ XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,
+ colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
+ early_stopping_rounds=None, enable_categorical=False,
+ eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
+ importance_type=None, interaction_constraints='',
+ learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
+ max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
+ missing=nan, monotone_constraints='()', n_estimators=100,
+ n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,
+ reg_alpha=0, reg_lambda=1, ...)
+ """
+ classifier = XGBClassifier()
+ classifier.fit(features, target)
+ return classifier
+
+
+def main() -> None:
+
+ """
+ >>> main()
+
+ Url for the algorithm:
+ https://xgboost.readthedocs.io/en/stable/
+ Iris type dataset is used to demonstrate algorithm.
+ """
+
+ # Load Iris dataset
+ iris = load_iris()
+ features, targets = data_handling(iris)
+ x_train, x_test, y_train, y_test = train_test_split(
+ features, targets, test_size=0.25
+ )
+
+ names = iris["target_names"]
+
+ # Create an XGBoost Classifier from the training data
+ xgboost_classifier = xgboost(x_train, y_train)
+
+ # Display the confusion matrix of the classifier with both training and test sets
+ plot_confusion_matrix(
+ xgboost_classifier,
+ x_test,
+ y_test,
+ display_labels=names,
+ cmap="Blues",
+ normalize="true",
+ )
+ plt.title("Normalized Confusion Matrix - IRIS Dataset")
+ plt.show()
+
+
+if __name__ == "__main__":
+ import doctest
+
+ doctest.testmod(verbose=True)
+ main()

machine_learning/xgboostclassifier.py

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+# XGBoost Classifier Example
+import numpy as np
+from matplotlib import pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.metrics import plot_confusion_matrix
+from sklearn.model_selection import train_test_split
+from xgboost import XGBClassifier
+
+
+def data_handling(data: dict) -> tuple:
+ # Split dataset into features and target
+ # data is features
+ """
+ >>> data_handling(({'data':'[5.1, 3.5, 1.4, 0.2]','target':([0])}))
+ ('[5.1, 3.5, 1.4, 0.2]', [0])
+ >>> data_handling(
+ ... {'data': '[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', 'target': ([0, 0])}
+ ... )
+ ('[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', [0, 0])
+ """
+ return (data["data"], data["target"])
+
+
+def xgboost(features: np.ndarray, target: np.ndarray) -> XGBClassifier:
+ """
+ >>> xgboost(np.array([[5.1, 3.6, 1.4, 0.2]]), np.array([0]))
+ XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,
+ colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
+ early_stopping_rounds=None, enable_categorical=False,
+ eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
+ importance_type=None, interaction_constraints='',
+ learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
+ max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
+ missing=nan, monotone_constraints='()', n_estimators=100,
+ n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,
+ reg_alpha=0, reg_lambda=1, ...)
+ """
+ classifier = XGBClassifier()
+ classifier.fit(features, target)
+ return classifier
+
+
+def main() -> None:
+
+ """
+ >>> main()
+
+ Url for the algorithm:
+ https://xgboost.readthedocs.io/en/stable/
+ Iris type dataset is used to demonstrate algorithm.
+ """
+
+ # Load Iris dataset
+ iris = load_iris()
+ features, targets = data_handling(iris)
+ x_train, x_test, y_train, y_test = train_test_split(
+ features, targets, test_size=0.25
+ )
+
+ names = iris["target_names"]
+
+ # Create an XGBoost Classifier from the training data
+ xgboost_classifier = xgboost(x_train, y_train)
+
+ # Display the confusion matrix of the classifier with both training and test sets
+ plot_confusion_matrix(
+ xgboost_classifier,
+ x_test,
+ y_test,
+ display_labels=names,
+ cmap="Blues",
+ normalize="true",
+ )
+ plt.title("Normalized Confusion Matrix - IRIS Dataset")
+ plt.show()
+
+
+if __name__ == "__main__":
+ import doctest
+
+ doctest.testmod(verbose=True)
+ main()

machine_learning/xgboostclassifier.py

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+# XGBoost Classifier Example
+import numpy as np
+from matplotlib import pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.metrics import plot_confusion_matrix
+from sklearn.model_selection import train_test_split
+from xgboost import XGBClassifier
+
+
+def data_handling(data: dict) -> tuple:
+ # Split dataset into features and target
+ # data is features
+ """
+ >>> data_handling(({'data':'[5.1, 3.5, 1.4, 0.2]','target':([0])}))
+ ('[5.1, 3.5, 1.4, 0.2]', [0])
+ >>> data_handling(
+ ... {'data': '[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', 'target': ([0, 0])}
+ ... )
+ ('[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', [0, 0])
+ """
+ return (data["data"], data["target"])
+
+
+def xgboost(features: np.ndarray, target: np.ndarray) -> XGBClassifier:
+ """
+ >>> xgboost(np.array([[5.1, 3.6, 1.4, 0.2]]), np.array([0]))
+ XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,
+ colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
+ early_stopping_rounds=None, enable_categorical=False,
+ eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
+ importance_type=None, interaction_constraints='',
+ learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
+ max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
+ missing=nan, monotone_constraints='()', n_estimators=100,
+ n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,
+ reg_alpha=0, reg_lambda=1, ...)
+ """
+ classifier = XGBClassifier()
+ classifier.fit(features, target)
+ return classifier
+
+
+def main() -> None:
+
+ """
+ >>> main()
+
+ Url for the algorithm:
+ https://xgboost.readthedocs.io/en/stable/
+ Iris type dataset is used to demonstrate algorithm.
+ """
+
+ # Load Iris dataset
+ iris = load_iris()
+ features, targets = data_handling(iris)
+ x_train, x_test, y_train, y_test = train_test_split(
+ features, targets, test_size=0.25
+ )
+
+ names = iris["target_names"]
+
+ # Create an XGBoost Classifier from the training data
+ xgboost_classifier = xgboost(x_train, y_train)
+
+ # Display the confusion matrix of the classifier with both training and test sets
+ plot_confusion_matrix(
+ xgboost_classifier,
+ x_test,
+ y_test,
+ display_labels=names,
+ cmap="Blues",
+ normalize="true",
+ )
+ plt.title("Normalized Confusion Matrix - IRIS Dataset")
+ plt.show()
+
+
+if __name__ == "__main__":
+ import doctest
+
+ doctest.testmod(verbose=True)
+ main()

machine_learning/xgboostclassifier.py

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+# XGBoost Classifier Example
+import numpy as np
+from matplotlib import pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.metrics import plot_confusion_matrix
+from sklearn.model_selection import train_test_split
+from xgboost import XGBClassifier
+
+
+def data_handling(data: dict) -> tuple:
+ # Split dataset into features and target
+ # data is features
+ """
+ >>> data_handling(({'data':'[5.1, 3.5, 1.4, 0.2]','target':([0])}))
+ ('[5.1, 3.5, 1.4, 0.2]', [0])
+ >>> data_handling(
+ ... {'data': '[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', 'target': ([0, 0])}
+ ... )
+ ('[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', [0, 0])
+ """
+ return (data["data"], data["target"])
+
+
+def xgboost(features: np.ndarray, target: np.ndarray) -> XGBClassifier:
+ """
+ >>> xgboost(np.array([[5.1, 3.6, 1.4, 0.2]]), np.array([0]))
+ XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,
+ colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
+ early_stopping_rounds=None, enable_categorical=False,
+ eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
+ importance_type=None, interaction_constraints='',
+ learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
+ max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
+ missing=nan, monotone_constraints='()', n_estimators=100,
+ n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,
+ reg_alpha=0, reg_lambda=1, ...)
+ """
+ classifier = XGBClassifier()
+ classifier.fit(features, target)
+ return classifier
+
+
+def main() -> None:
+
+ """
+ >>> main()
+
+ Url for the algorithm:
+ https://xgboost.readthedocs.io/en/stable/
+ Iris type dataset is used to demonstrate algorithm.
+ """
+
+ # Load Iris dataset
+ iris = load_iris()
+ features, targets = data_handling(iris)
+ x_train, x_test, y_train, y_test = train_test_split(
+ features, targets, test_size=0.25
+ )
+
+ names = iris["target_names"]
+
+ # Create an XGBoost Classifier from the training data
+ xgboost_classifier = xgboost(x_train, y_train)
+
+ # Display the confusion matrix of the classifier with both training and test sets
+ plot_confusion_matrix(
+ xgboost_classifier,
+ x_test,
+ y_test,
+ display_labels=names,
+ cmap="Blues",
+ normalize="true",
+ )
+ plt.title("Normalized Confusion Matrix - IRIS Dataset")
+ plt.show()
+
+
+if __name__ == "__main__":
+ import doctest
+
+ doctest.testmod(verbose=True)
+ main()

machine_learning/xgboostclassifier.py

@@ -0,0 +1,82 @@
+# XGBoost Classifier Example
+import numpy as np
+from matplotlib import pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.metrics import plot_confusion_matrix
+from sklearn.model_selection import train_test_split
+from xgboost import XGBClassifier
+
+
+def data_handling(data: dict) -> tuple:
+ # Split dataset into features and target
+ # data is features
+ """
+ >>> data_handling(({'data':'[5.1, 3.5, 1.4, 0.2]','target':([0])}))
+ ('[5.1, 3.5, 1.4, 0.2]', [0])
+ >>> data_handling(
+ ... {'data': '[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', 'target': ([0, 0])}
+ ... )
+ ('[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', [0, 0])
+ """
+ return (data["data"], data["target"])
+
+
+def xgboost(features: np.ndarray, target: np.ndarray) -> XGBClassifier:
+ """
+ >>> xgboost(np.array([[5.1, 3.6, 1.4, 0.2]]), np.array([0]))
+ XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,
+ colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
+ early_stopping_rounds=None, enable_categorical=False,
+ eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
+ importance_type=None, interaction_constraints='',
+ learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
+ max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
+ missing=nan, monotone_constraints='()', n_estimators=100,
+ n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,
+ reg_alpha=0, reg_lambda=1, ...)
+ """
+ classifier = XGBClassifier()
+ classifier.fit(features, target)
+ return classifier
+
+
+def main() -> None:
+
+ """
+ >>> main()
+
+ Url for the algorithm:
+ https://xgboost.readthedocs.io/en/stable/
+ Iris type dataset is used to demonstrate algorithm.
+ """
+
+ # Load Iris dataset
+ iris = load_iris()
+ features, targets = data_handling(iris)
+ x_train, x_test, y_train, y_test = train_test_split(
+ features, targets, test_size=0.25
+ )
+
+ names = iris["target_names"]
+
+ # Create an XGBoost Classifier from the training data
+ xgboost_classifier = xgboost(x_train, y_train)
+
+ # Display the confusion matrix of the classifier with both training and test sets
+ plot_confusion_matrix(
+ xgboost_classifier,
+ x_test,
+ y_test,
+ display_labels=names,
+ cmap="Blues",
+ normalize="true",
+ )
+ plt.title("Normalized Confusion Matrix - IRIS Dataset")
+ plt.show()
+
+
+if __name__ == "__main__":
+ import doctest
+
+ doctest.testmod(verbose=True)
+ main()

machine_learning/xgboostclassifier.py

@@ -0,0 +1,82 @@
+# XGBoost Classifier Example
+import numpy as np
+from matplotlib import pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.metrics import plot_confusion_matrix
+from sklearn.model_selection import train_test_split
+from xgboost import XGBClassifier
+
+
+def data_handling(data: dict) -> tuple:
+ # Split dataset into features and target
+ # data is features
+ """
+ >>> data_handling(({'data':'[5.1, 3.5, 1.4, 0.2]','target':([0])}))
+ ('[5.1, 3.5, 1.4, 0.2]', [0])
+ >>> data_handling(
+ ... {'data': '[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', 'target': ([0, 0])}
+ ... )
+ ('[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', [0, 0])
+ """
+ return (data["data"], data["target"])
+
+
+def xgboost(features: np.ndarray, target: np.ndarray) -> XGBClassifier:
+ """
+ >>> xgboost(np.array([[5.1, 3.6, 1.4, 0.2]]), np.array([0]))
+ XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,
+ colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
+ early_stopping_rounds=None, enable_categorical=False,
+ eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
+ importance_type=None, interaction_constraints='',
+ learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
+ max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
+ missing=nan, monotone_constraints='()', n_estimators=100,
+ n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,
+ reg_alpha=0, reg_lambda=1, ...)
+ """
+ classifier = XGBClassifier()
+ classifier.fit(features, target)
+ return classifier
+
+
+def main() -> None:
+
+ """
+ >>> main()
+
+ Url for the algorithm:
+ https://xgboost.readthedocs.io/en/stable/
+ Iris type dataset is used to demonstrate algorithm.
+ """
+
+ # Load Iris dataset
+ iris = load_iris()
+ features, targets = data_handling(iris)
+ x_train, x_test, y_train, y_test = train_test_split(
+ features, targets, test_size=0.25
+ )
+
+ names = iris["target_names"]
+
+ # Create an XGBoost Classifier from the training data
+ xgboost_classifier = xgboost(x_train, y_train)
+
+ # Display the confusion matrix of the classifier with both training and test sets
+ plot_confusion_matrix(
+ xgboost_classifier,
+ x_test,
+ y_test,
+ display_labels=names,
+ cmap="Blues",
+ normalize="true",
+ )
+ plt.title("Normalized Confusion Matrix - IRIS Dataset")
+ plt.show()
+
+
+if __name__ == "__main__":
+ import doctest
+
+ doctest.testmod(verbose=True)
+ main()

machine_learning/xgboostclassifier.py

@@ -0,0 +1,82 @@
+# XGBoost Classifier Example
+import numpy as np
+from matplotlib import pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.metrics import plot_confusion_matrix
+from sklearn.model_selection import train_test_split
+from xgboost import XGBClassifier
+
+
+def data_handling(data: dict) -> tuple:
+ # Split dataset into features and target
+ # data is features
+ """
+ >>> data_handling(({'data':'[5.1, 3.5, 1.4, 0.2]','target':([0])}))
+ ('[5.1, 3.5, 1.4, 0.2]', [0])
+ >>> data_handling(
+ ... {'data': '[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', 'target': ([0, 0])}
+ ... )
+ ('[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', [0, 0])
+ """
+ return (data["data"], data["target"])
+
+
+def xgboost(features: np.ndarray, target: np.ndarray) -> XGBClassifier:
+ """
+ >>> xgboost(np.array([[5.1, 3.6, 1.4, 0.2]]), np.array([0]))
+ XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,
+ colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
+ early_stopping_rounds=None, enable_categorical=False,
+ eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
+ importance_type=None, interaction_constraints='',
+ learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
+ max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
+ missing=nan, monotone_constraints='()', n_estimators=100,
+ n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,
+ reg_alpha=0, reg_lambda=1, ...)
+ """
+ classifier = XGBClassifier()
+ classifier.fit(features, target)
+ return classifier
+
+
+def main() -> None:
+
+ """
+ >>> main()
+
+ Url for the algorithm:
+ https://xgboost.readthedocs.io/en/stable/
+ Iris type dataset is used to demonstrate algorithm.
+ """
+
+ # Load Iris dataset
+ iris = load_iris()
+ features, targets = data_handling(iris)
+ x_train, x_test, y_train, y_test = train_test_split(
+ features, targets, test_size=0.25
+ )
+
+ names = iris["target_names"]
+
+ # Create an XGBoost Classifier from the training data
+ xgboost_classifier = xgboost(x_train, y_train)
+
+ # Display the confusion matrix of the classifier with both training and test sets
+ plot_confusion_matrix(
+ xgboost_classifier,
+ x_test,
+ y_test,
+ display_labels=names,
+ cmap="Blues",
+ normalize="true",
+ )
+ plt.title("Normalized Confusion Matrix - IRIS Dataset")
+ plt.show()
+
+
+if __name__ == "__main__":
+ import doctest
+
+ doctest.testmod(verbose=True)
+ main()