def test_fit(self):
meta = Stacking(MeanEstimator(),
[('coxph', CoxPHSurvivalAnalysis()),
('svm', FastSurvivalSVM(random_state=0))],
probabilities=False)
self.assertEqual(2, len(meta))
meta.fit(self.x.values, self.y)
p = meta._predict_estimators(self.x.values)
self.assertTupleEqual((self.x.shape[0], 2), p.shape)
def test_fit(make_whas500):
whas500 = make_whas500(with_mean=False, with_std=False, to_numeric=True)
meta = Stacking(MeanEstimator(),
[('coxph', CoxPHSurvivalAnalysis()),
('svm', FastSurvivalSVM(random_state=0))],
probabilities=False)
assert 2 == len(meta)
meta.fit(whas500.x, whas500.y)
p = meta._predict_estimators(whas500.x)
assert (whas500.x.shape[0], 2) == p.shape
def test_fit(self):
data = load_iris()
x = data["data"]
y = data["target"]
meta = Stacking(LogisticRegression(), [('tree', DecisionTreeClassifier(max_depth=1, random_state=0)),
('svm', SVC(probability=True, random_state=0))])
self.assertEqual(2, len(meta))
meta.fit(x, y)
p = meta._predict_estimators(x)
self.assertTupleEqual((x.shape[0], 3 * 2), p.shape)
self.assertTupleEqual((3, 3 * 2), meta.meta_estimator.coef_.shape)
def test_fit():
data = load_iris()
x = data["data"]
y = data["target"]
meta = Stacking(LogisticRegression(solver='liblinear', multi_class='ovr'),
[('tree', DecisionTreeClassifier(max_depth=1, random_state=0)),
('svm', SVC(probability=True, gamma='auto', random_state=0))])
assert 2 == len(meta)
meta.fit(x, y)
p = meta._predict_estimators(x)
assert (x.shape[0], 3 * 2) == p.shape
assert (3, 3 * 2) == meta.meta_estimator.coef_.shape