def test_fit_args(self):
from sklearn.datasets import load_iris
from lale.lib.lale import TopKVotingClassifier
from lale.lib.sklearn import Nystroem
from sklearn.metrics import accuracy_score
ensemble = TopKVotingClassifier(estimator=(PCA() | Nystroem()) >> (LogisticRegression()|KNeighborsClassifier()), k=2)
trained = ensemble.fit(self.X_train, self.y_train)
trained.predict(self.X_test)
def test_fit_smaller_trials(self):
from sklearn.datasets import load_iris
from lale.lib.lale import TopKVotingClassifier
from lale.lib.sklearn import Nystroem
from sklearn.metrics import accuracy_score
ensemble = TopKVotingClassifier(estimator=(PCA() | Nystroem()) >> (LogisticRegression()|KNeighborsClassifier()), args_to_optimizer={'max_evals':3}, k=20)
trained = ensemble.fit(self.X_train, self.y_train)
final_ensemble = trained._impl._best_estimator
self.assertLessEqual(len(final_ensemble._impl._wrapped_model.estimators), 3)
def test_fit_args(self):
from lale.lib.lale import TopKVotingClassifier
from lale.lib.sklearn import Nystroem
ensemble = TopKVotingClassifier(
estimator=(PCA() | Nystroem())
>> (LogisticRegression() | KNeighborsClassifier()),
k=2,
)
trained = ensemble.fit(self.X_train, self.y_train)
trained.predict(self.X_test)
def test_fit_smaller_trials(self):
from lale.lib.lale import TopKVotingClassifier
from lale.lib.sklearn import Nystroem
ensemble = TopKVotingClassifier(
estimator=(PCA() | Nystroem())
>> (LogisticRegression() | KNeighborsClassifier()),
args_to_optimizer={"max_evals": 3},
k=20,
)
trained = ensemble.fit(self.X_train, self.y_train)
final_ensemble = trained._impl._best_estimator
self.assertLessEqual(len(final_ensemble._impl_instance().estimators), 3)