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_fit2(self):
import warnings
warnings.filterwarnings(action="ignore")
from lale.lib.sklearn import MinMaxScaler, MLPClassifier
pipeline = Batching(operator=MinMaxScaler() >> MinMaxScaler(),
batch_size=112)
trained = pipeline.fit(self.X_train, self.y_train)
lale_transforms = trained.transform(self.X_test)
from sklearn.preprocessing import MinMaxScaler
prep = MinMaxScaler()
trained_prep = prep.partial_fit(self.X_train, self.y_train)
X_transformed = trained_prep.transform(self.X_train)
clf = MinMaxScaler()
import numpy as np
trained_clf = clf.partial_fit(X_transformed, self.y_train)
sklearn_transforms = trained_clf.transform(
trained_prep.transform(self.X_test))
for i in range(5):
for j in range(2):
self.assertAlmostEqual(lale_transforms[i, j],
sklearn_transforms[i, j])
def test_no_partial_fit(self):
pipeline = Batching(operator=NoOp() >> LogisticRegression())
with self.assertRaises(AttributeError):
trained = pipeline.fit(self.X_train, self.y_train)
