Python LinearSVC Examples

Example #1

File: test_json_pretty_viz.py Project: vickyvishal/lale

    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))

Example #2

File: test_core_pipeline.py Project: IBM/lale

 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)