Improved support for Pandas extension data types

sklearn2pmml/decoration/__init__.py

@@ -136,8 +136,8 @@ def _to_missing(self, X, where):
 	def _missing_value_mask(self, X):
 		if self.missing_values is not None:
 			def is_missing(X, missing_value):
-				# float("NaN") != float("NaN")
-				if isinstance(missing_value, float) and numpy.isnan(missing_value):
+				# Values like float("NaN"), Numpy.NaN and Pandas.NA fail the '==' operator
+				if pandas.isnull(missing_value):
 					return pandas.isnull(X)
 				return X == missing_value
 
@@ -380,10 +380,14 @@ def fit(self, X, y = None):
 			return self
 		X = to_numpy(X)
 		if self.with_data:
-			if issubclass(self.dtype_.type, numbers.Integral):
-				info = numpy.iinfo(self.dtype_)
+			dtype = self.dtype_
+			# Unbox Pandas' extension data type to Numpy data type
+			if hasattr(dtype, "numpy_dtype"):
+				dtype = dtype.numpy_dtype
+			if issubclass(dtype.type, numbers.Integral):
+				info = numpy.iinfo(dtype)
 			else:
-				info = numpy.finfo(self.dtype_)
+				info = numpy.finfo(dtype)
 			missing_mask = self._missing_value_mask(X)
 			nonmissing_mask = ~missing_mask
 			if self.data_min is None:

sklearn2pmml/decoration/tests/__init__.py

@@ -1,5 +1,5 @@
 from datetime import datetime
-from pandas import Categorical, CategoricalDtype, DataFrame, Series
+from pandas import BooleanDtype, Categorical, CategoricalDtype, DataFrame, Int64Dtype, Series
 from sklearn.base import clone
 from sklearn.impute import SimpleImputer
 from sklearn.pipeline import Pipeline
@@ -80,6 +80,27 @@ def test_init(self):
 
 class CategoricalDomainTest(TestCase):
 
+	def test_fit_boolean_missing(self):
+		domain = clone(CategoricalDomain())
+		X = Series([0, pandas.NA, 0, 1], dtype = BooleanDtype())
+		self.assertIsInstance(X.dtype, BooleanDtype)
+		self.assertEqual([False, True, False, False], domain._missing_value_mask(X).tolist())
+		Xt = domain.fit_transform(X)
+		self.assertIsInstance(Xt, Series)
+		self.assertIsInstance(Xt.dtype, BooleanDtype)
+		self.assertFalse(hasattr(domain, "n_features_in_"))
+		self.assertEqual([False, True], domain.data_values_.tolist())
+		self.assertEqual([False, pandas.NA, False, True], Xt.tolist())
+		domain = clone(CategoricalDomain())
+		X = to_numpy(X)
+		self.assertEqual([False, True, False, False], domain._missing_value_mask(X).tolist())
+		Xt = domain.fit_transform(X)
+		self.assertIsInstance(Xt, numpy.ndarray)
+		self.assertEqual(numpy.dtype("O"), Xt.dtype)
+		self.assertFalse(hasattr(domain, "n_features_in_"))
+		self.assertEqual([False, True], domain.data_values_.tolist())
+		self.assertEqual([False, None, False, True], Xt.tolist())
+
 	def test_fit_int(self):
 		domain = clone(CategoricalDomain(with_data = False, with_statistics = False))
 		self.assertTrue(domain._empty_fit())
@@ -162,10 +183,12 @@ def test_fit_int_categorical(self):
 
 	def test_fit_int64(self):
 		domain = clone(CategoricalDomain())
-		X = Series([-1, None, 1, 2, -1]).astype("Int64")
+		X = Series([-1, pandas.NA, 1, 2, -1], dtype = "Int64")
+		self.assertIsInstance(X.dtype, Int64Dtype)
 		self.assertEqual([False, True, False, False, False], domain._missing_value_mask(X).tolist())
 		Xt = domain.fit_transform(X)
 		self.assertIsInstance(Xt, Series)
+		self.assertIsInstance(Xt.dtype, Int64Dtype)
 		self.assertFalse(hasattr(domain, "n_features_in_"))
 		self.assertEqual([-1, 1, 2], domain.data_values_.tolist())
 		self.assertEqual([-1, pandas.NA, 1, 2, -1], Xt.tolist())
@@ -401,6 +424,18 @@ def test_fit_int(self):
 		self.assertEqual(3, domain.data_max_)
 		self.assertEqual({"totalFreq" : 5, "missingFreq" : 2, "invalidFreq" : 0}, domain.counts_)
 
+	def test_fit_int64(self):
+		domain = clone(ContinuousDomain(with_statistics = True))
+		X = Series([-2, -1, 0, -1, 3], dtype = "Int64")
+		self.assertIsInstance(X.dtype, Int64Dtype)
+		Xt = domain.fit_transform(X)
+		self.assertIsInstance(Xt, Series)
+		self.assertIsInstance(Xt.dtype, Int64Dtype)
+		self.assertFalse(hasattr(domain, "n_features_in_"))
+		self.assertEqual(-2, domain.data_min_)
+		self.assertEqual(3, domain.data_max_)
+		self.assertEqual({"totalFreq" : 5, "missingFreq" : 0, "invalidFreq" : 0}, domain.counts_)
+
 	def test_mapper(self):
 		domain = ContinuousDomain(with_statistics = True)
 		X = DataFrame([[2.0, 2], [1.0, 0.5], [2, float("NaN")], [float("NaN"), 2], [2.0, float("NaN")], [3.0, 3.5]], columns = ["x1", "x2"])