def test_multimodal(self):
from lale.lib.lale import ConcatFeatures as Cat
from lale.lib.lale import Project
from lale.lib.sklearn import LinearSVC
from lale.lib.sklearn import Normalizer as Norm
from lale.lib.sklearn import OneHotEncoder as OneHot
project_0 = Project(columns={"type": "number"})
project_1 = Project(columns={"type": "string"})
linear_svc = LinearSVC(C=29617.4, dual=False, tol=0.005266)
pipeline = (
((project_0 >> Norm()) & (project_1 >> OneHot())) >> Cat >> linear_svc
)
expected = """from lale.lib.lale import Project
from sklearn.preprocessing import Normalizer as Norm
from sklearn.preprocessing import OneHotEncoder as OneHot
from lale.lib.lale import ConcatFeatures as Cat
from sklearn.svm import LinearSVC
import lale
lale.wrap_imported_operators()
project_0 = Project(columns={"type": "number"})
project_1 = Project(columns={"type": "string"})
linear_svc = LinearSVC(C=29617.4, dual=False, tol=0.005266)
pipeline = (
((project_0 >> Norm()) & (project_1 >> OneHot())) >> Cat >> linear_svc
)"""
self._roundtrip(expected, lale.pretty_print.to_string(pipeline))
def test_multiple_estimators_predict_predict_proba(self):
pipeline = (StandardScaler() >>
(LogisticRegression() & PCA()) >> ConcatFeatures() >>
(NoOp() & LinearSVC()) >> ConcatFeatures() >>
KNeighborsClassifier())
pipeline.fit(self.X_train, self.y_train)
_ = pipeline.predict_proba(self.X_test)
_ = pipeline.predict(self.X_test)