Merge pull request #415 from PaulWestenthanner/fix/issue_414_412

Fix/issue 414 412

Author: PaulWestenthanner

category_encoders/backward_difference.py

@@ -71,7 +71,7 @@ class BackwardDifferenceEncoder(BaseContrastEncoder):
     https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/
 
     .. [2] Gregory Carey (2003). Coding Categorical Variables, from
-    http://psych.colorado.edu/~carey/Courses/PSYC5741/handouts/Coding%20Categorical%20Variables%202006-03-03.pdf
+    http://ibgwww.colorado.edu/~carey/p5741ndir/Coding_Categorical_Variables.pdf
 
     """
 

category_encoders/base_contrast_encoder.py

@@ -41,7 +41,7 @@ class BaseContrastEncoder(util.BaseEncoder, util.UnsupervisedTransformerMixin):
     https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/
 
     .. [2] Gregory Carey (2003). Coding Categorical Variables, from
-    http://psych.colorado.edu/~carey/Courses/PSYC5741/handouts/Coding%20Categorical%20Variables%202006-03-03.pdf
+    http://ibgwww.colorado.edu/~carey/p5741ndir/Coding_Categorical_Variables.pdf
 
     """
     prefit_ordinal = True

category_encoders/helmert.py

@@ -72,8 +72,8 @@ class HelmertEncoder(BaseContrastEncoder):
     https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/
 
     .. [2] Gregory Carey (2003). Coding Categorical Variables, from
-    http://psych.colorado.edu/~carey/Courses/PSYC5741/handouts/Coding%20Categorical%20Variables%202006-03-03.pdf
-
+    http://ibgwww.colorado.edu/~carey/p5741ndir/Coding_Categorical_Variables.pdf
+    
     """
     def get_contrast_matrix(self, values_to_encode: np.array) -> ContrastMatrix:
         return Helmert().code_without_intercept(values_to_encode)

category_encoders/one_hot.py

@@ -86,8 +86,8 @@ class OneHotEncoder(util.BaseEncoder, util.UnsupervisedTransformerMixin):
     https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/
 
     .. [2] Gregory Carey (2003). Coding Categorical Variables, from
-    http://psych.colorado.edu/~carey/Courses/PSYC5741/handouts/Coding%20Categorical%20Variables%202006-03-03.pdf
-
+    http://ibgwww.colorado.edu/~carey/p5741ndir/Coding_Categorical_Variables.pdf
+    
     """
     prefit_ordinal = True
     encoding_relation = util.EncodingRelation.ONE_TO_N_UNIQUE

category_encoders/ordinal.py

@@ -77,8 +77,7 @@ class OrdinalEncoder(util.BaseEncoder, util.UnsupervisedTransformerMixin):
     https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/
 
     .. [2] Gregory Carey (2003). Coding Categorical Variables, from
-    http://psych.colorado.edu/~carey/Courses/PSYC5741/handouts/Coding%20Categorical%20Variables%202006-03-03.pdf
-
+    http://ibgwww.colorado.edu/~carey/p5741ndir/Coding_Categorical_Variables.pdf
     """
     prefit_ordinal = False
     encoding_relation = util.EncodingRelation.ONE_TO_ONE
@@ -225,7 +224,7 @@ def ordinal_encoding(X_in, mapping=None, cols=None, handle_unknown='value', hand
                 if pd.isna(categories).any():
                     categories = [c for c in categories if not pd.isna(c)] + [nan_identity]
                 else:
-                    categories = categories.tolist()
+                    categories = list(categories)
                 if util.is_category(X[col].dtype):
                     # Avoid using pandas category dtype meta-data if possible, see #235, #238.
                     if X[col].dtype.ordered:

category_encoders/polynomial.py

@@ -71,8 +71,8 @@ class PolynomialEncoder(BaseContrastEncoder):
     https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/
 
     .. [2] Gregory Carey (2003). Coding Categorical Variables, from
-    http://psych.colorado.edu/~carey/Courses/PSYC5741/handouts/Coding%20Categorical%20Variables%202006-03-03.pdf
-
+    http://ibgwww.colorado.edu/~carey/p5741ndir/Coding_Categorical_Variables.pdf
+    
     """
     def get_contrast_matrix(self, values_to_encode: np.array) -> ContrastMatrix:
         return Poly().code_without_intercept(values_to_encode)

category_encoders/sum_coding.py

@@ -71,7 +71,7 @@ class SumEncoder(BaseContrastEncoder):
     https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/
 
     .. [2] Gregory Carey (2003). Coding Categorical Variables, from
-    http://psych.colorado.edu/~carey/Courses/PSYC5741/handouts/Coding%20Categorical%20Variables%202006-03-03.pdf
+    http://ibgwww.colorado.edu/~carey/p5741ndir/Coding_Categorical_Variables.pdf
 
     """
 

tests/test_ordinal.py

@@ -177,6 +177,33 @@ def test_HaveNaNInTrain_ExpectCodedAsOne(self):
 
         self.assertEqual(expected, result)
 
+    def test_Timestamp(self):
+        df = pd.DataFrame(
+            {
+                "timestamps": {
+                    0: pd.Timestamp("1997-09-03 00:00:00"),
+                    1: pd.Timestamp("1997-09-03 00:00:00"),
+                    2: pd.Timestamp("2000-09-03 00:00:00"),
+                    3: pd.Timestamp("1997-09-03 00:00:00"),
+                    4: pd.Timestamp("1999-09-04 00:00:00"),
+                    5: pd.Timestamp("2001-09-03 00:00:00"),
+                },
+            }
+        )
+        enc = encoders.OrdinalEncoder(cols=["timestamps"])
+        encoded_df = enc.fit_transform(df)
+        expected_index = [pd.Timestamp("1997-09-03 00:00:00"),
+                          pd.Timestamp("2000-09-03 00:00:00"),
+                          pd.Timestamp("1999-09-04 00:00:00"),
+                          pd.Timestamp("2001-09-03 00:00:00"),
+                          pd.NaT
+                          ]
+        expected_mapping = pd.Series([1, 2, 3, 4, -2], index=expected_index)
+        expected_values = [1, 1, 2, 1, 3, 4]
+
+        pd.testing.assert_series_equal(expected_mapping, enc.mapping[0]["mapping"])
+        self.assertListEqual(expected_values, encoded_df["timestamps"].tolist())
+
     def test_NoGaps(self):
         train = pd.DataFrame({"city": ["New York", np.nan, "Rio", None, "Rosenheim"]})
         expected_mapping_value = pd.Series([1, 2, 3, 4], index=["New York", "Rio", "Rosenheim", np.nan])