def test_predictions_shapes(self):
"""
Test the resulting shapes of probabilities for SGD
"""
# one where probabilities computed from data
# hinge loss do not enable predict_proba
lrn = SGDClassificationLearner()
mod = lrn(self.iris)
self.assertTupleEqual((50, 3), mod(self.iris[:50], mod.Probs).shape)
self.assertTupleEqual((50, ), mod(self.iris[:50], mod.Value).shape)
# in this case probabilities are retrieved by skl_learner.predict_proba
lrn = SGDClassificationLearner(loss='modified_huber')
mod = lrn(self.iris)
self.assertTupleEqual((50, 3), mod(self.iris[:50], mod.Probs).shape)
self.assertTupleEqual((50, ), mod(self.iris[:50], mod.Value).shape)
def test_kernel_explainer_sgd(self):
learner = SGDClassificationLearner()
model = learner(self.titanic)
np.random.shuffle(self.titanic.X)
shap_values, _, sample_mask, _ = compute_shap_values(
model, self.titanic[:200], self.titanic[:200])
def test_result_shape(self):
"""
This test function will be extended for all models in on of the
following pull requests.
"""
iris = Table('iris')
learner = SGDClassificationLearner()
# model trained on only one value (but three in the domain)
model = learner(iris)
res = model(iris[0:50])
self.assertTupleEqual((50, ), res.shape)
# probabilities must still be for three classes
res = model(iris[0:50], model.Probs)
self.assertTupleEqual((50, 3), res.shape)
def test_coefficients(self):
lrn = SGDClassificationLearner()
mod = lrn(self.iris)
self.assertEqual(len(mod.coefficients[0]), len(mod.domain.attributes))
def test_SGDClassification(self):
sgd = SGDClassificationLearner()
cv = CrossValidation(k=3)
res = cv(self.iris, [sgd])
self.assertGreater(AUC(res)[0], 0.8)