class TestFitPredict(unittest.TestCase):
def setUp(self):
self.roc_floor = 0.9
self.accuracy_floor = 0.9
random_state = 42
X, y = load_breast_cancer(return_X_y=True)
self.X_train, self.X_test, self.y_train, self.y_test = \
train_test_split(X, y, test_size=0.4, random_state=random_state)
classifiers = [
DecisionTreeClassifier(random_state=random_state),
LogisticRegression(random_state=random_state),
KNeighborsClassifier(),
RandomForestClassifier(random_state=random_state),
GradientBoostingClassifier(random_state=random_state)
]
self.clf = SimpleClassifierAggregator(classifiers, method='average')
def test_fit_predict(self):
y_train_predicted = self.clf.fit_predict(self.X_train, self.y_train)
assert_equal(len(y_train_predicted), self.X_train.shape[0])
# check performance
assert_greater(accuracy_score(self.y_train, y_train_predicted),
self.accuracy_floor)
class TestFitPredict(unittest.TestCase):
def setUp(self):
self.roc_floor = 0.9
self.accuracy_floor = 0.9
random_state = 42
X, y = load_breast_cancer(return_X_y=True)
self.X_train, self.X_test, self.y_train, self.y_test = \
train_test_split(X, y, test_size=0.4, random_state=random_state)
classifiers = [
DecisionTreeClassifier(random_state=random_state),
LogisticRegression(random_state=random_state),
KNeighborsClassifier(),
RandomForestClassifier(random_state=random_state),
GradientBoostingClassifier(random_state=random_state)
]
self.clf = SimpleClassifierAggregator(classifiers, method='average')
def test_fit_predict(self):
with assert_raises(NotImplementedError):
y_train_predicted = self.clf.fit_predict(self.X_train,
self.y_train)