class TestAverage(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')
self.clf.fit(self.X_train, self.y_train)
def test_parameters(self):
assert_true(hasattr(self.clf, 'base_estimators') and
self.clf.base_estimators is not None)
def test_train_scores(self):
y_train_predicted = self.clf.predict(self.X_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)
def test_prediction_scores(self):
y_test_predicted = self.clf.predict(self.X_test)
assert_equal(len(y_test_predicted), self.X_test.shape[0])
# check performance
assert_greater(accuracy_score(self.y_test, y_test_predicted),
self.accuracy_floor)
# test utility function
evaluate_print('averaging', self.y_test, y_test_predicted)
def test_prediction_proba(self):
y_test_predicted = self.clf.predict_proba(self.X_test)
assert_greater_equal(y_test_predicted.min(), 0)
assert_less_equal(y_test_predicted.max(), 1)
# check performance
assert_greater(roc_auc_score(self.y_test, y_test_predicted[:, 1]),
self.roc_floor)
# check shape of integrity
n_classes = len(np.unique(self.y_train))
assert_equal(y_test_predicted.shape, (self.X_test.shape[0], n_classes))
# check probability sum is 1
y_test_predicted_sum = np.sum(y_test_predicted, axis=1)
assert_allclose(np.ones([self.X_test.shape[0], ]),
y_test_predicted_sum)
def tearDown(self):
pass
class TestWeightedAverage(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)
clf_weights = np.array([0.1, 0.4, 0.1, 0.2, 0.2])
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',
weights=clf_weights)
self.clf.fit(self.X_train, self.y_train)
def test_weights(self):
assert_equal(np.sum(self.clf.weights), self.clf.n_base_estimators_)
def test_parameters(self):
assert (hasattr(self.clf, 'base_estimators')
and self.clf.base_estimators is not None)
# print clf details
print(self.clf)
# set parameters
self.clf.set_params()
def test_train_scores(self):
y_train_predicted = self.clf.predict(self.X_train)
assert_equal(len(y_train_predicted), self.X_train.shape[0])
# check performance
assert (accuracy_score(self.y_train, y_train_predicted) >=
self.accuracy_floor)
def test_prediction_scores(self):
y_test_predicted = self.clf.predict(self.X_test)
assert_equal(len(y_test_predicted), self.X_test.shape[0])
# check performance
assert (accuracy_score(self.y_test, y_test_predicted) >=
self.accuracy_floor)
def test_prediction_proba(self):
y_test_predicted = self.clf.predict_proba(self.X_test)
assert (y_test_predicted.min() >= 0)
assert (y_test_predicted.max() <= 1)
# check performance
assert (roc_auc_score(self.y_test, y_test_predicted[:, 1]) >=
self.roc_floor)
# check shape of integrity
n_classes = len(np.unique(self.y_train))
assert_equal(y_test_predicted.shape, (self.X_test.shape[0], n_classes))
# check probability sum is 1
y_test_predicted_sum = np.sum(y_test_predicted, axis=1)
assert_allclose(np.ones([
self.X_test.shape[0],
]), y_test_predicted_sum)
def tearDown(self):
pass