wehub-resource-sync
61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
import logging
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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 sklearn.datasets import make_classification
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from sklearn.datasets import make_regression
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from mla.linear_models import LinearRegression, LogisticRegression
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from mla.metrics.metrics import mean_squared_error, accuracy
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# Change to DEBUG to see convergence
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logging.basicConfig(level=logging.ERROR)
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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=10000,
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n_features=100,
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n_informative=75,
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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=0.25, random_state=1111
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)
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model = LinearRegression(lr=0.01, max_iters=2000, penalty="l2", C=0.03)
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model.fit(X_train, y_train)
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predictions = model.predict(X_test)
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print("regression mse", mean_squared_error(y_test, predictions))
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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=1000,
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n_features=100,
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n_informative=75,
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random_state=1111,
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n_classes=2,
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class_sep=2.5,
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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 = LogisticRegression(lr=0.01, max_iters=500, penalty="l1", C=0.01)
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model.fit(X_train, y_train)
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predictions = model.predict(X_test)
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print("classification accuracy", accuracy(y_test, predictions))
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if __name__ == "__main__":
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regression()
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classification()
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