def test_import_from_sklearn_pipeline2(self):
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_regression
from sklearn import svm
from sklearn.pipeline import Pipeline
anova_filter = SelectKBest(f_regression, k=3)
clf = svm.SVC(kernel='linear')
sklearn_pipeline = Pipeline([('anova', anova_filter), ('svc', clf)])
sklearn_pipeline.fit(self.X_train, self.y_train)
lale_pipeline = import_from_sklearn_pipeline(sklearn_pipeline)
lale_pipeline.predict(self.X_test)
def test_import_from_sklearn_pipeline2(self):
from sklearn.feature_selection import SelectKBest, f_regression
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC as SklearnSVC
anova_filter = SelectKBest(f_regression, k=3)
clf = SklearnSVC(kernel="linear")
sklearn_pipeline = Pipeline([("anova", anova_filter), ("svc", clf)])
sklearn_pipeline.fit(self.X_train, self.y_train)
lale_pipeline = import_from_sklearn_pipeline(sklearn_pipeline)
lale_pipeline.predict(self.X_test)
def test_pipeline_create(self):
from lale.lib.sklearn import PCA, LogisticRegression
from lale.operators import Pipeline
pipeline = Pipeline(([('pca1', PCA()), ('lr1', LogisticRegression())]))
trained = pipeline.fit(self.X_train, self.y_train)
predictions = trained.predict(self.X_test)
from sklearn.metrics import accuracy_score
accuracy_score(self.y_test, predictions)
def test_pipeline_clone(self):
from sklearn.base import clone
from lale.lib.sklearn import PCA, LogisticRegression
from lale.operators import Pipeline
pipeline = Pipeline(([('pca1', PCA()), ('lr1', LogisticRegression())]))
trained = pipeline.fit(self.X_train, self.y_train)
predictions = trained.predict(self.X_test)
from sklearn.metrics import accuracy_score
orig_acc = accuracy_score(self.y_test, predictions)
cloned_pipeline = clone(pipeline)
trained = cloned_pipeline.fit(self.X_train, self.y_train)
predictions = trained.predict(self.X_test)
from sklearn.metrics import accuracy_score
cloned_acc = accuracy_score(self.y_test, predictions)
self.assertEqual(orig_acc, cloned_acc)
