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)