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_gridsearch(self):
from sklearn.metrics import accuracy_score, make_scorer
from lale.lib.lale import GridSearchCV
from lale.lib.sklearn import VotingClassifier
clf = VotingClassifier(
estimators=[("knn", KNeighborsClassifier()), ("rc", RidgeClassifier())],
voting="hard",
)
_ = 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),
)
def test_ridge_classifier_1(self):
from lale.lib.sklearn import RidgeClassifier
reg = RidgeClassifier(solver='svd', max_iter=10)
reg.fit(self.X_train, self.y_train)
def test_ridge_classifier(self):
from lale.lib.sklearn import RidgeClassifier
reg = RidgeClassifier(fit_intercept=False, normalize=True)
reg.fit(self.X_train, self.y_train)
def test_ridge_classifier_1(self):
reg = RidgeClassifier(solver="svd", max_iter=10)
reg.fit(self.X_train, self.y_train)
def test_ridge_classifier(self):
reg = RidgeClassifier(fit_intercept=False, normalize=True)
reg.fit(self.X_train, self.y_train)
