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82 lines
2.6 KiB
Python
82 lines
2.6 KiB
Python
# 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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>>> main()
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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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