def test_fit1(self):
import warnings
warnings.filterwarnings(action="ignore")
from lale.lib.sklearn import MinMaxScaler, MLPClassifier
pipeline = Batching(
operator=MinMaxScaler() >> MLPClassifier(random_state=42),
batch_size=112)
trained = pipeline.fit(self.X_train, self.y_train)
predictions = trained.predict(self.X_test)
lale_accuracy = accuracy_score(self.y_test, predictions)
from sklearn.preprocessing import MinMaxScaler
from sklearn.neural_network import MLPClassifier
prep = MinMaxScaler()
trained_prep = prep.partial_fit(self.X_train, self.y_train)
X_transformed = trained_prep.transform(self.X_train)
clf = MLPClassifier(random_state=42)
import numpy as np
trained_clf = clf.partial_fit(X_transformed,
self.y_train,
classes=np.unique(self.y_train))
predictions = trained_clf.predict(trained_prep.transform(self.X_test))
sklearn_accuracy = accuracy_score(self.y_test, predictions)
self.assertEqual(lale_accuracy, sklearn_accuracy)
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_with_hyperopt(self):
planned = MLPClassifier(max_iter=20)
trained = planned.auto_configure(self.train_X,
self.train_y,
optimizer=Hyperopt,
cv=3,
max_evals=3)
_ = trained.predict(self.test_X)
def test_fit3(self):
from lale.lib.sklearn import MinMaxScaler, MLPClassifier, PCA
pipeline = PCA() >> Batching(
operator=MinMaxScaler() >> MLPClassifier(random_state=42),
batch_size=10)
trained = pipeline.fit(self.X_train, self.y_train)
predictions = trained.predict(self.X_test)
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_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_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_2b(self):
reg = MLPClassifier(beta_2=0.8, solver="sgd")
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_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_9(self):
reg = MLPClassifier(learning_rate_init=0.002, 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_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(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_with_defaults(self):
trainable = MLPClassifier()
trained = trainable.fit(self.train_X, self.train_y)
_ = trained.predict(self.test_X)
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_2e(self):
reg = MLPClassifier(epsilon=0.8, solver="sgd")
reg.fit(self.X_train, self.y_train)
def test_max_fun(self):
with self.assertRaisesRegex(jsonschema.ValidationError,
"argument 'max_fun' was unexpected"):
_ = MLPClassifier(max_fun=1000)
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_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(self):
reg = MLPClassifier(early_stopping=False, validation_fraction=0.2)
reg.fit(self.X_train, self.y_train)