add additional validation on type equivalence for pandastypeselector …

…in fit and transform (#220)

sklego/preprocessing.py

@@ -84,7 +84,7 @@ def fit(self, X, y=None):
         """
         self._check_X_for_type(X)
         self.type_columns_ = list(X.select_dtypes(include=self.include, exclude=self.exclude))
-
+        self.X_dtypes_ = X.dtypes
         if len(self.type_columns_) == 0:
             raise ValueError(f'Provided type(s) results in empty dateframe')
 
@@ -96,10 +96,14 @@ def transform(self, X):
 
         :param X: pandas dataframe to select dtypes for
         """
-        check_is_fitted(self, 'type_columns_')
+        check_is_fitted(self, ['type_columns_', 'X_dtypes_'])
+        if (self.X_dtypes_ != X.dtypes).any():
+            raise ValueError(f'Column dtypes were not equal during fit and transform. Fit types: \n'
+                             f'{self.X_dtypes_}\n'
+                             f'transform: \n'
+                             f'{X.dtypes}')
 
         self._check_X_for_type(X)
-
         transformed_df = X.select_dtypes(include=self.include, exclude=self.exclude)
 
         if set(list(transformed_df)) != set(self.type_columns_):

tests/test_preprocessing/test_pandastypeselector.py

@@ -46,6 +46,21 @@ def test_get_params_np(include, exclude):
     }
 
 
+def test_value_error_differrent_dtyes():
+    fit_df = pd.DataFrame({
+        'a': [1, 2, 3],
+        'b': [4, 5, 6]
+    })
+    transform_df = pd.DataFrame({
+        'a': [4, 5, 6],
+        'b': ['4', '5', '6']
+    })
+    transformer = PandasTypeSelector(exclude=['category']).fit(fit_df)
+
+    with pytest.raises(ValueError):
+        transformer.transform(transform_df)
+
+
 def test_value_error_empty(random_xy_dataset_regr):
     transformer = PandasTypeSelector(exclude=['number'])
     X, y = random_xy_dataset_regr