def test_transform_float(self):
y = [1.0, float("NaN"), 2.0, 3.0]
encoder = PMMLLabelEncoder(missing_value=-999)
encoder.fit(y)
self.assertEqual([0, 2, -999, 1],
encoder.transform([1.0, 3.0,
float("NaN"), 2.0]).tolist())
def test_fit_float(self):
y = [1.0, float("NaN"), 1.0, 2.0, float("NaN"), 3.0, 3.0, 2.0]
labels = [1.0, 2.0, 3.0]
encoder = PMMLLabelEncoder(missing_value=-999)
self.assertEqual(-999, encoder.missing_value)
encoder.fit(y)
self.assertEqual(labels, encoder.classes_.tolist())
def test_transform_float(self):
X = [1.0, float("NaN"), 2.0, 3.0]
encoder = PMMLLabelEncoder(missing_values=-999)
encoder.fit(X)
self.assertEqual([[0], [2], [-999], [1]],
encoder.transform([1.0, 3.0,
float("NaN"), 2.0]).tolist())
def test_transform_string(self): y = ["A", None, "B", "C"] encoder = PMMLLabelEncoder() encoder.fit(y) self.assertEqual([0, 2, None, 1], encoder.transform(["A", "C", None, "B"]).tolist()) self.assertEqual([None], encoder.transform(numpy.array([None])).tolist()) self.assertEqual([0, 1, 2], encoder.transform(Series(numpy.array(["A", "B", "C"]))).tolist())
def test_transform_string(self): X = ["A", None, "B", "C"] encoder = PMMLLabelEncoder() encoder.fit(X) self.assertEqual([[0], [2], [None], [1]], encoder.transform(["A", "C", None, "B"]).tolist()) self.assertEqual([[None]], encoder.transform(numpy.array([None])).tolist()) self.assertEqual([[0], [1], [2]], encoder.transform(Series(numpy.array(["A", "B", "C"]))).tolist())
def test_fit_string(self):
y = ["A", None, "A", "B", None, "C", "C", "B"]
labels = ["A", "B", "C"]
encoder = PMMLLabelEncoder()
self.assertFalse(hasattr(encoder, "classes_"))
encoder.fit(y)
self.assertEqual(labels, encoder.classes_.tolist())
encoder.fit(numpy.array(y))
self.assertEqual(labels, encoder.classes_.tolist())
encoder.fit(Series(numpy.array(y)))
self.assertEqual(labels, encoder.classes_.tolist())