def test_fit_sample_weights(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))])
sample_weight = numpy.random.RandomState(0).uniform(size=x.shape[0])
meta.fit(x, y, tree__sample_weight=sample_weight, svm__sample_weight=sample_weight)
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_score(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)
c_index = meta.score(whas500.x, whas500.y)
assert round(abs(c_index - 0.7848807), 5) == 0
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(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_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_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():
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
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
def test_predict_log_proba(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))])
meta.fit(x, y)
p = meta.predict_log_proba(x)
scores = numpy.empty(3)
for i, c in enumerate(meta.meta_estimator.classes_):
scores[i] = roc_auc_score(numpy.asarray(y == c, dtype=int), p[:, i])
assert_array_almost_equal(numpy.array([1.0, 0.9986, 0.9986]), scores)
def test_base_estimator(estimator):
with pytest.raises(
TypeError,
match=
r"All base estimators should implement fit and predict/predict_proba (.+) doesn't"
):
Stacking(_PredictDummy, [('m1', estimator)])
def test_names_not_unique():
with pytest.raises(
ValueError,
match=r"Names provided are not unique: \('m1', 'm2', 'm1'\)"):
Stacking(_NoFitEstimator, [('m1', _PredictDummy),
('m2', _PredictDummy),
('m1', _PredictDummy)])
def test_predict_proba():
meta = Stacking(_PredictDummy(),
[('coxph', CoxPHSurvivalAnalysis()),
('svm', FastSurvivalSVM(random_state=0))],
probabilities=False)
with pytest.raises(AttributeError,
match="'_PredictDummy' object has no attribute 'predict_proba'"):
getattr(meta, "predict_proba")
def test_predict_proba(self):
meta = Stacking(_PredictDummy(),
[('coxph', CoxPHSurvivalAnalysis()),
('svm', FastSurvivalSVM(random_state=0))],
probabilities=False)
self.assertRaisesRegex(AttributeError,
"'_PredictDummy' object has no attribute 'predict_proba'",
getattr, meta, "predict_proba")
def test_set_params(self):
meta = Stacking(_PredictDummy(),
[('coxph', CoxPHSurvivalAnalysis()),
('svm', FastSurvivalSVM(random_state=0))],
probabilities=False)
meta.set_params(coxph__alpha=1.0, svm__alpha=0.4132)
self.assertEqual(1.0, meta.get_params()["coxph__alpha"])
self.assertEqual(0.4132, meta.get_params()["svm__alpha"])
def test_set_params(self):
meta = Stacking(LogisticRegression(), [('tree', DecisionTreeClassifier(max_depth=1, random_state=0)),
('svm', SVC(probability=True, random_state=0))],
probabilities=True)
self.assertEqual(2, len(meta))
meta.set_params(tree__min_samples_split=7, svm__C=0.05)
self.assertEqual(7, meta.get_params()["tree__min_samples_split"])
self.assertEqual(0.05, meta.get_params()["svm__C"])
self.assertIsInstance(meta.get_params()["meta_estimator"], LogisticRegression)
self.assertTrue(meta.get_params()["probabilities"])
meta.set_params(meta_estimator=DecisionTreeClassifier(), probabilities=False)
self.assertIsInstance(meta.get_params()["meta_estimator"], DecisionTreeClassifier)
self.assertFalse(meta.get_params()["probabilities"])
def test_set_params():
meta = Stacking(
LogisticRegression(),
[('tree', DecisionTreeClassifier(max_depth=1, random_state=0)),
('svm', SVC(probability=True, random_state=0))],
probabilities=True)
assert 2 == len(meta)
meta.set_params(tree__min_samples_split=7, svm__C=0.05)
assert 7 == meta.get_params()["tree__min_samples_split"]
assert 0.05 == meta.get_params()["svm__C"]
assert isinstance(meta.get_params()["meta_estimator"],
LogisticRegression)
assert meta.get_params()["probabilities"]
meta.set_params(meta_estimator=DecisionTreeClassifier(),
probabilities=False)
assert isinstance(meta.get_params()["meta_estimator"],
DecisionTreeClassifier)
assert not meta.get_params()["probabilities"]
p = meta.get_params(deep=False)
assert set(p.keys()) == {
"meta_estimator", "base_estimators", "probabilities"
}
def test_meta_no_fit():
with pytest.raises(
TypeError,
match=r"meta estimator should implement fit (.+) doesn't"):
Stacking(_NoFitEstimator, [('m1', _PredictDummy)])
