def test_concat_with_hyperopt(self):
from lale.lib.lale import Hyperopt
pca = PCA(n_components=3)
nys = Nystroem(n_components=10)
concat = ConcatFeatures()
lr = LogisticRegression(random_state=42, C=0.1)
trainable = (pca & nys) >> concat >> lr
clf = Hyperopt(estimator=trainable, max_evals=2)
from sklearn.datasets import load_iris
iris_data = load_iris()
clf.fit(iris_data.data, iris_data.target)
clf.predict(iris_data.data)
def test_concat_with_hyperopt2(self):
from lale.lib.lale import Hyperopt
from lale.operators import make_pipeline, make_union
pca = PCA(n_components=3)
nys = Nystroem(n_components=10)
lr = LogisticRegression(random_state=42, C=0.1)
trainable = make_pipeline(make_union(pca, nys), lr)
clf = Hyperopt(estimator=trainable, max_evals=2)
from sklearn.datasets import load_iris
iris_data = load_iris()
clf.fit(iris_data.data, iris_data.target)
clf.predict(iris_data.data)
def test_using_scoring(self):
lr = LogisticRegression()
clf = Hyperopt(estimator=lr, scoring="accuracy", cv=5, max_evals=1)
trained = clf.fit(self.X_train, self.y_train)
predictions = trained.predict(self.X_test)
predictions_1 = clf.predict(self.X_test)
assert np.array_equal(predictions_1, predictions)
def test_using_scoring(self):
from sklearn.metrics import hinge_loss, make_scorer, f1_score, accuracy_score
lr = LogisticRegression()
clf = Hyperopt(estimator=lr, scoring='accuracy', cv=5, max_evals=1)
trained = clf.fit(self.X_train, self.y_train)
predictions = trained.predict(self.X_test)
predictions_1 = clf.predict(self.X_test)
assert np.array_equal(predictions_1, predictions)
def test_custom_scoring(self):
from sklearn.metrics import f1_score, make_scorer
lr = LogisticRegression()
clf = Hyperopt(estimator=lr, scoring=make_scorer(f1_score, average='macro'), cv = 5, max_evals=1)
trained = clf.fit(self.X_train, self.y_train)
predictions = trained.predict(self.X_test)
predictions_1 = clf.predict(self.X_test)
assert np.array_equal(predictions_1, predictions)
def test_other_algorithms(self):
for alg in ["rand", "tpe", "atpe", "anneal"]:
hyperopt = Hyperopt(
estimator=LogisticRegression, algo=alg, cv=3, max_evals=3
)
trained = hyperopt.fit(self.X_train, self.y_train)
predictions = trained.predict(self.X_test)
predictions_1 = hyperopt.predict(self.X_test)
self.assertTrue(np.array_equal(predictions_1, predictions), alg)
def test_custom_scorer(self):
from sklearn.metrics import f1_score, make_scorer
pipeline = PCA() >> LogisticRegression()
def custom_scorer(estimator, X, y, factor=0.1):
#This is a custom scorer for demonstrating the use of kwargs
#Just applies some factor to the accuracy
from sklearn.metrics import accuracy_score
predictions = estimator.predict(X)
self.assertEqual(factor, 0.5)
return factor*accuracy_score(y, predictions)
clf = Hyperopt(estimator=pipeline, scoring=custom_scorer, cv = 5, max_evals=1, args_to_scorer={'factor':0.5})
trained = clf.fit(self.X_train, self.y_train)
predictions = trained.predict(self.X_test)
predictions_1 = clf.predict(self.X_test)
assert np.array_equal(predictions_1, predictions)
