Cleaned up code

jpmml · Dec 5, 2023 · af6d6c0 · af6d6c0
1 parent d2f7a28
commit af6d6c0
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Showing 4 changed files with 6 additions and 6 deletions.
diff --git a/sklearn2pmml/cross_reference/tests/__init__.py b/sklearn2pmml/cross_reference/tests/__init__.py
@@ -155,7 +155,7 @@ def test_make_recaller_union(self):
 			"int": [-1, 1]
 		}
 		recaller_union = make_recaller_union(memory, ["int"])
-		X = numpy.full((2, 1), 0, dtype = int)
+		X = numpy.full((2, 1), fill_value = 0)
 		Xt = recaller_union.fit_transform(X)
 		self.assertEqual((2, 2), Xt.shape)
 		self.assertEqual([-1, 1], Xt[:, 0].tolist())

diff --git a/sklearn2pmml/decoration/__init__.py b/sklearn2pmml/decoration/__init__.py
@@ -105,7 +105,7 @@ def is_missing(X, missing_value):
 					return pandas.isnull(X)
 				return X == missing_value
 			if type(self.missing_values) is list:
-				mask = numpy.full(X.shape, False, dtype = bool)
+				mask = numpy.full(X.shape, fill_value = False)
 				for missing_value in self.missing_values:
 					mask = numpy.logical_or(mask, is_missing(X, missing_value))
 				return mask

diff --git a/sklearn2pmml/ensemble/tests/__init__.py b/sklearn2pmml/ensemble/tests/__init__.py
@@ -163,17 +163,17 @@ def test_mask_params(self):
 			"second" : numpy.asarray([[False, "A"], [False, "B"], [True, "C"], [False, "D"], [True, "E"]], dtype = object),
 			"any" : "any"
 		}
-		mask = numpy.full((5, ), True)
+		mask = numpy.full((5, ), fill_value = True)
 		masked_params = _mask_params(params, mask)
 		self.assertTrue(params["first"].tolist(), masked_params["first"].tolist())
 		self.assertTrue(params["second"].tolist(), masked_params["second"].tolist())
 		self.assertTrue(params["any"], masked_params["any"])
-		mask = numpy.asarray([False, True, False, False, True], dtype = bool)
+		mask = numpy.asarray([False, True, False, False, True])
 		masked_params = _mask_params(params, mask)
 		self.assertEqual([[1], [0]], masked_params["first"].tolist())
 		self.assertEqual([[False, "B"], [True, "E"]], masked_params["second"].tolist())
 		self.assertEqual(params["any"], masked_params["any"])
-		mask = numpy.full((5, ), False)
+		mask = numpy.full((5, ), fill_value = False)
 		masked_params = _mask_params(params, mask)
 		self.assertEqual([], masked_params["first"].tolist())
 		self.assertEqual([], masked_params["second"].tolist())

diff --git a/sklearn2pmml/feature_selection/__init__.py b/sklearn2pmml/feature_selection/__init__.py
@@ -19,7 +19,7 @@ def _get_support_mask(self):
 
 	def fit(self, X, y = None):
 		rows, cols = X.shape
-		mask = numpy.full((cols), True, dtype = bool)
+		mask = numpy.full((cols), fill_value = True)
 		if isinstance(X, DataFrame):
 			X = X.values
 		for left in range(cols):