def test_transform():
clf = logistic.LogisticRegression(penalty="l1")
clf.fit(iris.data, iris.target)
X_new = clf.transform(iris.data)
clf = logistic.LogisticRegression()
clf.fit(X_new, iris.target)
pred = clf.predict(X_new)
assert np.mean(pred == iris.target) >= 0.75
def test_inconsistent_input():
"""Test that an exception is raised when input to predict is inconsistent"""
X_ = np.random.random((5, 10))
y_ = np.ones(X_.shape[0])
assert_raises(ValueError,
logistic.LogisticRegression().fit(X_, y_).predict,
np.random.random((3,12)))
def test_predict_2_classes():
"""Simple sanity check on a 2 classes dataset
Make sure it predicts the correct result on simple datasets.
"""
clf = logistic.LogisticRegression().fit(X, Y1)
assert_array_equal(clf.predict(X), Y1)
assert_array_equal(clf.predict_proba(X).argmax(axis=1), Y1)
clf = logistic.LogisticRegression(C=100).fit(X, Y1)
assert_array_equal(clf.predict(X), Y1)
assert_array_equal(clf.predict_proba(X).argmax(axis=1), Y1)
clf = logistic.LogisticRegression(fit_intercept=False).fit(X, Y1)
assert_array_equal(clf.predict(X), Y1)
assert_array_equal(clf.predict_proba(X).argmax(axis=1), Y1)
def test_predict_iris():
"""Test logisic regression with the iris dataset"""
clf = logistic.LogisticRegression().fit(iris.data, iris.target)
pred = clf.predict(iris.data)
assert np.mean(pred == iris.target) > .95
pred = clf.predict_proba(iris.data).argmax(axis=1)
assert np.mean(pred == iris.target) > .95
def test_predict_3_classes():
clf = logistic.LogisticRegression(C=10).fit(X, Y2)
assert_array_equal(clf.predict(X), Y2)
assert_array_equal(clf.predict_proba(X).argmax(axis=1), Y2)
def test_error():
"""Test for appropriate exception on errors"""
assert_raises (ValueError, logistic.LogisticRegression(C=-1).fit, X, Y1)
