def test_predict(self):
meta = Stacking(MeanEstimator(),
[('coxph', CoxPHSurvivalAnalysis()),
('svm', FastSurvivalSVM(random_state=0))],
probabilities=False)
meta.fit(self.x.values, self.y)
# result is different if randomForestSRC has not been compiled with OpenMP support
p = meta.predict(self.x.values)
actual_cindex = concordance_index_censored(self.y['fstat'], self.y['lenfol'], p)
expected_cindex = numpy.array([0.7848807, 58983, 16166, 0, 119])
assert_array_almost_equal(expected_cindex, actual_cindex)
def test_predict(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)
meta.fit(whas500.x, whas500.y)
# result is different if randomForestSRC has not been compiled with OpenMP support
p = meta.predict(whas500.x)
assert_cindex_almost_equal(whas500.y['fstat'], whas500.y['lenfol'], p,
(0.7848807, 58983, 16166, 0, 14))
def test_predict(self):
data = load_iris()
x = data["data"]
y = data["target"]
meta = Stacking(LogisticRegression(multi_class='multinomial', solver='lbfgs'),
[('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(x)
acc = accuracy_score(y, p)
self.assertGreaterEqual(acc, 0.98)
def test_predict():
data = load_iris()
x = data["data"]
y = data["target"]
meta = Stacking(LogisticRegression(multi_class='multinomial', solver='lbfgs'),
[('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(x)
acc = accuracy_score(y, p)
assert acc >= 0.98
