def setUpClass(cls):
from sklearn.datasets import load_iris
irisArr = load_iris()
cls._irisArr = {"X": irisArr.data, "y": irisArr.target}
from lale.datasets import sklearn_to_pandas
(train_X, train_y), (test_X, test_y) = sklearn_to_pandas.load_iris_df()
cls._irisDf = {"X": train_X, "y": train_y}
(train_X, train_y), (test_X, test_y) = sklearn_to_pandas.digits_df()
cls._digits = {"X": train_X, "y": train_y}
(train_X,
train_y), (test_X,
test_y) = sklearn_to_pandas.california_housing_df()
cls._housing = {"X": train_X, "y": train_y}
from lale.datasets import openml
(train_X, train_y), (test_X, test_y) = openml.fetch("credit-g",
"classification",
preprocess=False)
cls._creditG = {"X": train_X, "y": train_y}
from lale.datasets import load_movie_review
train_X, train_y = load_movie_review()
cls._movies = {"X": train_X, "y": train_y}
from lale.datasets.uci.uci_datasets import fetch_drugscom
train_X, train_y, test_X, test_y = fetch_drugscom()
cls._drugRev = {"X": train_X, "y": train_y}
def test_preprocessing_union(self):
from lale.datasets import openml
(train_X, train_y), (test_X, test_y) = openml.fetch(
'credit-g', 'classification', preprocess=False)
from lale.lib.lale import Project
from lale.lib.sklearn import Normalizer, OneHotEncoder
from lale.lib.lale import ConcatFeatures as Concat
from lale.lib.sklearn import RandomForestClassifier as Forest
prep_num = Project(columns={'type': 'number'}) >> Normalizer
prep_cat = Project(columns={'not': {'type': 'number'}}) >> OneHotEncoder(sparse=False)
planned = (prep_num & prep_cat) >> Concat >> Forest
from lale.lib.lale import Hyperopt
hyperopt_classifier = Hyperopt(estimator=planned, max_evals=1)
best_found = hyperopt_classifier.fit(train_X, train_y)