# Carl is free software; you can redistribute it and/or modify it
# under the terms of the Revised BSD License; see LICENSE file for
# more details.
from numpy.testing import assert_array_equal
from numpy.testing import assert_raises
from sklearn.datasets import make_moons
from sklearn.tree import DecisionTreeRegressor
from carl.learning import as_classifier
def test_as_classifier():
X, y = make_moons(n_samples=100, random_state=1)
y = 2 * y - 1 # use -1/+1 labels
clf = as_classifier(DecisionTreeRegressor())
clf.fit(X, y)
probas = clf.predict_proba(X)
predictions = clf.predict(X)
assert_array_equal(probas.shape, (len(X), 2))
assert_array_equal(predictions, y)
y[-1] = 2
clf = as_classifier(DecisionTreeRegressor())
assert_raises(ValueError, clf.fit, X, y)
