def test_with_observed_gridsearch(self): from lale.lib.sklearn import VotingClassifier from lale.lib.lale import GridSearchCV from lale.lib.lale import Observing from lale.lib.lale.observing import LoggingObserver from sklearn.metrics import accuracy_score, make_scorer clf = VotingClassifier(estimators=[('knn', KNeighborsClassifier()), ('rc', RidgeClassifier())], voting='hard') trained = clf.auto_configure(self.X_train, self.y_train, GridSearchCV, lale_num_samples=1, lale_num_grids=1, cv=2, scoring=make_scorer(accuracy_score), observer=LoggingObserver)
def test_with_hyperopt(self): from lale.lib.sklearn import VotingClassifier from lale.lib.lale import Hyperopt clf = VotingClassifier( estimators=[('knn', KNeighborsClassifier()), ('lr', LogisticRegression())]) trained = clf.auto_configure(self.X_train, self.y_train, Hyperopt, max_evals=1)
def test_with_gridsearch(self): from lale.lib.sklearn import VotingClassifier from lale.lib.lale import GridSearchCV from sklearn.metrics import accuracy_score, make_scorer clf = VotingClassifier( estimators=[('knn', KNeighborsClassifier()), ('lr', LogisticRegression())]) trained = clf.auto_configure(self.X_train, self.y_train, GridSearchCV, lale_num_samples=1, lale_num_grids=1, cv=2, scoring=make_scorer(accuracy_score))