def test_api_multiple_trees(self):
mrbbagging = MRBBagging(5,
tree.DecisionTreeClassifier(random_state=0),
random_state=0)
mrbbagging.fit(X_train, y_train)
y_pred = mrbbagging.predict(X_test)
assert all(y_pred == y_test)
def test_api_with_feature_selection(self):
mrbbagging = MRBBagging(1,
tree.DecisionTreeClassifier(random_state=0),
feature_selection=True,
random_state=0)
mrbbagging.fit(X_train, y_train)
y_pred = mrbbagging.predict(X_test)
assert all(y_pred == y_test)
def test__group_data(self):
mrbbagging = MRBBagging(1, tree.DecisionTreeClassifier())
x = [[1, 1, 1], [2, 2, 2], [3, 3, 3]]
y = ["A", "B", "C"]
classes, grouped_data = mrbbagging._group_data(x, y)
self.assertEqual(classes, {'A', 'B', 'C'})
self.assertEqual(
grouped_data, {
'C': [[[3, 3, 3], 'C']],
'A': [[[1, 1, 1], 'A']],
'B': [[[2, 2, 2], 'B']]
})
def test__group_data_with_none(self):
mrbbagging = MRBBagging(1, tree.DecisionTreeClassifier())
x = [[1, 1, 1], [2, 2, 2], [3, 3, 3]]
y = ["A", None, "C"]
with self.assertRaises(AssertionError):
mrbbagging._group_data(x, y)
def test_with_invalid_k(self):
with self.assertRaises(AssertionError):
MRBBagging(0, tree.DecisionTreeClassifier())
def test_fit_with_invalid_classifier(self):
with self.assertRaises(AssertionError):
MRBBagging(1, None)
def test_fit_with_invalid_labels(self):
mrbbagging = MRBBagging(1, MagicMock())
x = [[1, 1, 1], [2, 2, 2], [3, 3, 3]]
y = ["A", "B"]
with self.assertRaises(AssertionError):
mrbbagging.fit(x, y)