def test_predict_proba(self): trainable = MLPClassifier() iris = sklearn.datasets.load_iris() trained = trainable.fit(iris.data, iris.target) # with self.assertWarns(DeprecationWarning): predicted = trainable.predict_proba(iris.data) predicted = trained.predict_proba(iris.data)
def test_mlp_classifier_6(self): from lale.lib.sklearn import MLPClassifier reg = MLPClassifier(momentum=0.8, solver='lbfgs') reg.fit(self.X_train, self.y_train)
def test_mlp_classifier_5(self): from lale.lib.sklearn import MLPClassifier reg = MLPClassifier(nesterovs_momentum=False, solver='lbfgs') reg.fit(self.X_train, self.y_train)
def test_mlp_classifier_4(self): from lale.lib.sklearn import MLPClassifier reg = MLPClassifier(early_stopping=True, solver='lbfgs') reg.fit(self.X_train, self.y_train)
def test_mlp_classifier_3(self): from lale.lib.sklearn import MLPClassifier reg = MLPClassifier(n_iter_no_change=100, solver='lbfgs') reg.fit(self.X_train, self.y_train)
def test_mlp_classifier_2b(self): reg = MLPClassifier(beta_2=0.8, solver="sgd") reg.fit(self.X_train, self.y_train)
def test_with_defaults(self): trainable = MLPClassifier() trained = trainable.fit(self.train_X, self.train_y) _ = trained.predict(self.test_X)
def test_mlp_classifier_10(self): from lale.lib.sklearn import MLPClassifier reg = MLPClassifier(learning_rate='invscaling', power_t = 0.4, solver='lbfgs') reg.fit(self.X_train, self.y_train)
def test_mlp_classifier_7(self): reg = MLPClassifier(shuffle=False, solver="lbfgs") reg.fit(self.X_train, self.y_train)
def test_mlp_classifier_6(self): reg = MLPClassifier(momentum=0.8, solver="lbfgs") reg.fit(self.X_train, self.y_train)
def test_mlp_classifier_5(self): reg = MLPClassifier(nesterovs_momentum=False, solver="lbfgs") reg.fit(self.X_train, self.y_train)
def test_mlp_classifier_4(self): reg = MLPClassifier(early_stopping=True, solver="lbfgs") reg.fit(self.X_train, self.y_train)
def test_mlp_classifier_3(self): reg = MLPClassifier(n_iter_no_change=100, solver="lbfgs") reg.fit(self.X_train, self.y_train)
def test_mlp_classifier_2e(self): reg = MLPClassifier(epsilon=0.8, solver="sgd") reg.fit(self.X_train, self.y_train)
def test_mlp_classifier_7(self): from lale.lib.sklearn import MLPClassifier reg = MLPClassifier(shuffle=False, solver='lbfgs') reg.fit(self.X_train, self.y_train)
def test_mlp_classifier_9(self): from lale.lib.sklearn import MLPClassifier reg = MLPClassifier(learning_rate_init=0.002, solver='lbfgs') reg.fit(self.X_train, self.y_train)
def test_mlp_classifier_9(self): reg = MLPClassifier(learning_rate_init=0.002, solver="lbfgs") reg.fit(self.X_train, self.y_train)
def test_mlp_classifier(self): from lale.lib.sklearn import MLPClassifier reg = MLPClassifier(early_stopping=False, validation_fraction=0.2) reg.fit(self.X_train, self.y_train)
def test_mlp_classifier_10(self): reg = MLPClassifier(learning_rate="invscaling", power_t=0.4, solver="lbfgs") reg.fit(self.X_train, self.y_train)
def test_mlp_classifier_2(self): from lale.lib.sklearn import MLPClassifier reg = MLPClassifier(epsilon=0.8, solver='sgd') reg.fit(self.X_train, self.y_train)
def test_mlp_classifier(self): reg = MLPClassifier(early_stopping=False, validation_fraction=0.2) reg.fit(self.X_train, self.y_train)