89 lines
2.5 KiB
Python
89 lines
2.5 KiB
Python
import numpy as np
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from numpy.testing import assert_array_equal
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import nose
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from nose.tools import assert_raises, raises
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from sklearn.linear_model import logistic
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from sklearn import datasets
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X = [[-1, 0], [0, 1], [1, 1]]
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Y1 = [0, 1, 1]
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Y2 = [2, 1, 0]
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iris = datasets.load_iris()
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def test_predict_2_classes():
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"""Simple sanity check on a 2 classes dataset
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Make sure it predicts the correct result on simple datasets.
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"""
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clf = logistic.LogisticRegression().fit(X, Y1)
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assert_array_equal(clf.predict(X), Y1)
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assert_array_equal(clf.predict_proba(X).argmax(axis=1), Y1)
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clf = logistic.LogisticRegression(C=100).fit(X, Y1)
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assert_array_equal(clf.predict(X), Y1)
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assert_array_equal(clf.predict_proba(X).argmax(axis=1), Y1)
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clf = logistic.LogisticRegression(fit_intercept=False).fit(X, Y1)
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assert_array_equal(clf.predict(X), Y1)
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assert_array_equal(clf.predict_proba(X).argmax(axis=1), Y1)
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def test_error():
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"""Test for appropriate exception on errors"""
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assert_raises(ValueError, logistic.LogisticRegression(C=-1).fit, X, Y1)
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def test_predict_3_classes():
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clf = logistic.LogisticRegression(C=10).fit(X, Y2)
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assert_array_equal(clf.predict(X), Y2)
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assert_array_equal(clf.predict_proba(X).argmax(axis=1), Y2)
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def test_predict_iris():
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"""Test logisic regression with the iris dataset"""
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clf = logistic.LogisticRegression().fit(iris.data, iris.target)
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pred = clf.predict(iris.data)
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assert np.mean(pred == iris.target) > .95
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pred = clf.predict_proba(iris.data).argmax(axis=1)
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assert np.mean(pred == iris.target) > .95
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def test_inconsistent_input():
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"""Test that an exception is raised on inconsistent input to predict"""
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X_ = np.random.random((5, 10))
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y_ = np.ones(X_.shape[0])
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assert_raises(ValueError,
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logistic.LogisticRegression().fit(X_, y_).predict,
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np.random.random((3, 12)))
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@raises(ValueError)
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def test_nan():
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"""Test proper NaN handling.
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Regression test for Issue #252: fit used to go into an infinite loop.
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"""
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Xnan = np.array(X)
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Xnan[0, 1] = np.nan
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logistic.LogisticRegression().fit(X, Y1)
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def test_transform():
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clf = logistic.LogisticRegression(penalty="l1")
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clf.fit(iris.data, iris.target)
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X_new = clf.transform(iris.data)
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clf = logistic.LogisticRegression()
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clf.fit(X_new, iris.target)
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pred = clf.predict(X_new)
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assert np.mean(pred == iris.target) >= 0.75
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if __name__ == '__main__':
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nose.runmodule()
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