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))
