removed 321 col tests

Author: PaulWestenthanner

tests/test_encoders.py

@@ -48,7 +48,7 @@ def test_np(self):
     def test_classification(self):
         for encoder_name in encoders.__all__:
             with self.subTest(encoder_name=encoder_name):
-                cols = ['unique_str', 'underscore', 'extra', 'none', 'invariant', 321, 'categorical', 'na_categorical', 'categorical_int']
+                cols = ['unique_str', 'underscore', 'extra', 'none', 'invariant', 'categorical', 'na_categorical', 'categorical_int']
 
                 enc = getattr(encoders, encoder_name)(cols=cols)
                 enc.fit(X, np_y)
@@ -249,7 +249,7 @@ def test_inverse_transform(self):
         # we do not allow None in these data (but "none" column without any missing value is ok)
         X = th.create_dataset(n_rows=100, has_missing=False)
         X_t = th.create_dataset(n_rows=50, has_missing=False)
-        cols = ['underscore', 'none', 321, 'categorical', 'categorical_int']
+        cols = ['underscore', 'none', 'categorical', 'categorical_int']
 
         for encoder_name in ['BaseNEncoder', 'BinaryEncoder', 'OneHotEncoder', 'OrdinalEncoder']:
             with self.subTest(encoder_name=encoder_name):
@@ -403,7 +403,7 @@ def test_unique_column_is_not_predictive(self):
     def test_cols(self):
         # Test cols argument with different data types, which are array-like or scalars
         cols_list = ['extra', 'invariant']
-        cols_types = [cols_list, pd.Series(cols_list), np.array(cols_list), 'extra', 321, set(cols_list),
+        cols_types = [cols_list, pd.Series(cols_list), np.array(cols_list), 'extra', set(cols_list),
                       ('extra', 'invariant'), pd.Categorical(cols_list, categories=cols_list)]
 
         for encoder_name in encoders.__all__:
@@ -713,7 +713,6 @@ def test_columns(self):
         self.assertTrue(result['float'].min() < 1, 'should still be a number and untouched')
         self.assertTrue(result['float_edge'].min() < 1, 'should still be a number and untouched')
         self.assertTrue(result['unique_int'].min() < 1, 'should still be a number and untouched')
-        self.assertTrue(result[321].min() < 1, 'should still be a number')
 
     def test_ignored_columns_are_untouched(self):
         # Make sure None values in ignored columns are preserved.

tests/test_one_hot.py

@@ -47,7 +47,7 @@ def test_one_hot(self):
         # test inverse_transform
         X_i = th.create_dataset(n_rows=100, has_missing=False)
         X_i_t = th.create_dataset(n_rows=50, has_missing=False)
-        cols = ['underscore', 'none', 'extra', 321, 'categorical']
+        cols = ['underscore', 'none', 'extra', 'categorical']
 
         enc = encoders.OneHotEncoder(verbose=1, use_cat_names=True, cols=cols)
         enc.fit(X_i)

tests/test_ordinal.py

@@ -242,7 +242,7 @@ def test_inverse_transform_HaveHandleMissingValueAndHandleUnknownReturnNan_Expec
 
     def test_inverse_with_mapping(self):
         df = X.copy(deep=True)
-        categoricals = ['unique_int', 'unique_str', 'invariant', 'underscore', 'none', 'extra', 321]
+        categoricals = ['unique_int', 'unique_str', 'invariant', 'underscore', 'none', 'extra']
         mapping = [{'col': c, 'mapping': pd.Series(data=range(len(df[c].unique())), index=df[c].unique()), 'data_type': X[c].dtype} for c in categoricals]
         enc = encoders.OrdinalEncoder(cols=categoricals, handle_unknown='ignore', mapping=mapping, return_df=True)
         df[categoricals] = enc.fit_transform(df[categoricals])

tests/test_woe.py

@@ -17,7 +17,7 @@
 
 class TestWeightOfEvidenceEncoder(TestCase):
     def test_woe(self):
-        cols = ['unique_str', 'underscore', 'extra', 'none', 'invariant', 321, 'categorical', 'na_categorical', 'categorical_int']
+        cols = ['unique_str', 'underscore', 'extra', 'none', 'invariant', 'categorical', 'na_categorical', 'categorical_int']
 
         # balanced label with balanced features
         X_balanced = pd.DataFrame(data=['1', '1', '1', '2', '2', '2'], columns=['col1'])