wehub-resource-sync
71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
import logging
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from sklearn.datasets import make_classification
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from sklearn.datasets import make_regression
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from sklearn.metrics import roc_auc_score
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try:
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from sklearn.model_selection import train_test_split
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except ImportError:
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from sklearn.cross_validation import train_test_split
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from mla.ensemble.gbm import GradientBoostingClassifier, GradientBoostingRegressor
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from mla.metrics.metrics import mean_squared_error
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logging.basicConfig(level=logging.DEBUG)
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def classification():
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# Generate a random binary classification problem.
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X, y = make_classification(
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n_samples=350,
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n_features=15,
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n_informative=10,
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random_state=1111,
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n_classes=2,
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class_sep=1.0,
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n_redundant=0,
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)
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.15, random_state=1111
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)
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model = GradientBoostingClassifier(
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n_estimators=50, max_depth=4, max_features=8, learning_rate=0.1
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)
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model.fit(X_train, y_train)
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predictions = model.predict(X_test)
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print(predictions)
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print(predictions.min())
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print(predictions.max())
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print("classification, roc auc score: %s" % roc_auc_score(y_test, predictions))
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def regression():
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# Generate a random regression problem
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X, y = make_regression(
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n_samples=500,
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n_features=5,
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n_informative=5,
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n_targets=1,
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noise=0.05,
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random_state=1111,
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bias=0.5,
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)
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.1, random_state=1111
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)
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model = GradientBoostingRegressor(n_estimators=25, max_depth=5, max_features=3)
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model.fit(X_train, y_train)
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predictions = model.predict(X_test)
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print(
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"regression, mse: %s"
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% mean_squared_error(y_test.flatten(), predictions.flatten())
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)
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if __name__ == "__main__":
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classification()
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# regression()
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