def test_gp_search():
# Test that the best estimator contains the right value for foo_param
clf = MockDiscreteClassifier()
gp_search = GPSearchCV(clf, {'foo_param': ['int', [1, 3]]})
# make sure it selects the smallest parameter in case of ties
old_stdout = sys.stdout
sys.stdout = StringIO()
gp_search.fit(X, y)
sys.stdout = old_stdout
assert_equal(gp_search.best_estimator_.foo_param, 2)
clf = MockContinuousClassifier()
gp_search = GPSearchCV(clf, {'foo_param': ['float', [-3, 3]]}, verbose=3)
# make sure it selects the smallest parameter in case of ties
old_stdout = sys.stdout
sys.stdout = StringIO()
gp_search.fit(X, y)
sys.stdout = old_stdout
assert_almost_equal(gp_search.best_estimator_.foo_param, 0, decimal=1)
# Smoke test the score etc:
gp_search.score(X, y)
gp_search.predict_proba(X)
gp_search.decision_function(X)
gp_search.transform(X)
# Test exception handling on scoring
gp_search.scoring = 'sklearn'
assert_raises(ValueError, gp_search.fit, X, y)
def test_gp_search():
clf = MockClassifier()
gp_search = GPSearchCV(clf, {'foo_param': ['int', [1, 3]]}, verbose=3)
# make sure it selects the smallest parameter in case of ties
old_stdout = sys.stdout
sys.stdout = StringIO()
gp_search.fit(X, y)
sys.stdout = old_stdout
assert_equal(gp_search.best_estimator_.foo_param, 2)
for i, foo_i in enumerate([1, 2, 3]):
assert_true(gp_search.scores_[i][0]
== {'foo_param': foo_i})
# Smoke test the score etc:
gp_search.score(X, y)
gp_search.predict_proba(X)
gp_search.decision_function(X)
gp_search.transform(X)
# Test exception handling on scoring
gp_search.scoring = 'sklearn'
assert_raises(ValueError, gp_search.fit, X, y)
