mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-10-06 13:49:30 +00:00
83 lines
2.6 KiB
Python
83 lines
2.6 KiB
Python
# 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()
|