Update update_transformers validation (#563)

* Add sdtype validation

* Add get_name method + rename error messag

* Fix lint

* Change error name

* Fix one liner

rdt/errors.py

@@ -7,3 +7,7 @@ class NotFittedError(Exception):
 
 class Error(Exception):
     """Error to raise when ``HyperTransformer`` produces a controlled error message."""
+
+
+class TransformerInputError(Exception):
+    """Error to raise when ``HyperTransformer`` receives incorrect input."""

rdt/hyper_transformer.py

@@ -9,7 +9,7 @@
 import pandas as pd
 import yaml
 
-from rdt.errors import Error, NotFittedError
+from rdt.errors import Error, NotFittedError, TransformerInputError
 from rdt.transformers import (
     BaseTransformer, get_class_by_transformer_name, get_default_transformer,
     get_transformer_instance, get_transformers_by_type)
@@ -414,21 +414,17 @@ def update_transformers(self, column_name_to_transformer):
         self._validate_update_columns(update_columns)
         self._validate_transformers(column_name_to_transformer)
 
-        incompatible_sdtypes = []
         for column_name, transformer in column_name_to_transformer.items():
             if transformer is not None:
                 current_sdtype = self.field_sdtypes.get(column_name)
                 if current_sdtype and current_sdtype not in transformer.get_supported_sdtypes():
-                    incompatible_sdtypes.append(column_name)
+                    raise TransformerInputError(
+                        f"Column '{column_name}' is a {current_sdtype} column, which is "
+                        f"incompatible with the '{transformer.get_name()}' transformer."
+                    )
 
             self.field_transformers[column_name] = transformer
 
-        if incompatible_sdtypes:
-            warnings.warn(
-                "Some transformers you've assigned are not compatible with the sdtypes. "
-                f"Use 'update_sdtypes' to update: {incompatible_sdtypes}"
-            )
-
         self._modified_config = True
 
     def remove_transformers(self, column_names):
@@ -575,7 +571,7 @@ def _get_transformer_tree_yaml(self):
         """
         modified_tree = deepcopy(self._transformers_tree)
         for field in modified_tree:
-            class_name = modified_tree[field]['transformer'].__class__.__name__
+            class_name = modified_tree[field]['transformer'].__class__.get_name()
             modified_tree[field]['transformer'] = class_name
 
         return yaml.safe_dump(dict(modified_tree))

rdt/performance/performance.py

@@ -82,7 +82,7 @@ def evaluate_transformer_performance(transformer, dataset_generator, verbose=Fal
         pandas.DataFrame:
             The performance test results.
     """
-    transformer_args = TRANSFORMER_ARGS.get(transformer.__name__, {})
+    transformer_args = TRANSFORMER_ARGS.get(transformer.get_name(), {})
     transformer_instance = transformer(**transformer_args)
 
     sizes = _get_dataset_sizes(dataset_generator.SDTYPE)
@@ -102,7 +102,7 @@ def evaluate_transformer_performance(transformer, dataset_generator, verbose=Fal
             performance['Number of fit rows'] = fit_size
             performance['Number of transform rows'] = transform_size
             performance['Dataset'] = dataset_generator.__name__
-            performance['Transformer'] = f'{transformer.__module__ }.{transformer.__name__}'
+            performance['Transformer'] = f'{transformer.__module__ }.{transformer.get_name()}'
 
         out.append(performance)
 

rdt/transformers/__init__.py

@@ -75,7 +75,7 @@ def get_transformer_name(transformer):
             The path of the transformer.
     """
     if inspect.isclass(transformer):
-        return transformer.__module__ + '.' + transformer.__name__
+        return transformer.__module__ + '.' + transformer.get_name()
 
     raise ValueError(f'The transformer {transformer} must be passed as a class.')
 
@@ -106,7 +106,7 @@ def get_class_by_transformer_name():
         BaseTransformer:
             BaseTransformer subclass class object.
     """
-    return {class_.__name__: class_ for class_ in BaseTransformer.get_subclasses()}
+    return {class_.get_name(): class_ for class_ in BaseTransformer.get_subclasses()}
 
 
 def get_transformer_class(transformer):

rdt/transformers/base.py

@@ -26,6 +26,16 @@ class BaseTransformer:
     column_prefix = None
     output_columns = None
 
+    @classmethod
+    def get_name(cls):
+        """Return transformer name.
+
+        Returns:
+            str:
+                Transformer name.
+        """
+        return cls.__name__
+
     @classmethod
     def get_subclasses(cls):
         """Recursively find subclasses of this Baseline.
@@ -208,7 +218,7 @@ def __repr__(self):
             str:
                 The name of the transformer followed by any non-default parameters.
         """
-        class_name = self.__class__.__name__
+        class_name = self.__class__.get_name()
         custom_args = []
         args = inspect.getfullargspec(self.__init__)
         keys = args.args[1:]

rdt/transformers/pii/anonymizer.py

@@ -161,7 +161,7 @@ def __repr__(self):
             str:
                 The name of the transformer followed by any non-default parameters.
         """
-        class_name = self.__class__.__name__
+        class_name = self.__class__.get_name()
         custom_args = []
         args = inspect.getfullargspec(self.__init__)
         keys = args.args[1:]

tests/code_style.py

@@ -76,7 +76,7 @@ def validate_transformer_addon(transformer):
                 module_py = True
             elif document.match('config.json'):
                 config_json_exist = True
-                _validate_config_json(document, transformer.__name__)
+                _validate_config_json(document, transformer.get_name())
 
         assert init_file_exist, 'Missing __init__.py file within the addon folder.'
         assert config_json_exist, 'Missing the config.json file within the addon folder.'
@@ -85,7 +85,7 @@ def validate_transformer_addon(transformer):
 
 def validate_transformer_importable_from_parent_module(transformer):
     """Validate wheter the transformer can be imported from the parent module."""
-    name = transformer.__name__
+    name = transformer.get_name()
     module = getattr(transformer, '__module__', '')
     module = module.rsplit('.', 1)[0]
     imported_transformer = getattr(importlib.import_module(module), name, None)
@@ -156,7 +156,7 @@ def validate_test_names(transformer):
     test_file = get_test_location(transformer)
     module = _load_module_from_path(test_file)
 
-    test_class = getattr(module, f'Test{transformer.__name__}', None)
+    test_class = getattr(module, f'Test{transformer.get_name()}', None)
     assert test_class is not None, 'The expected test class was not found.'
 
     test_functions = inspect.getmembers(test_class, predicate=inspect.isfunction)

tests/contributing.py

@@ -89,7 +89,7 @@ def validate_transformer_integration(transformer):
     if isinstance(transformer, str):
         transformer = get_transformer_class(transformer)
 
-    print(f'Validating Integration Tests for transformer {transformer.__name__}\n')
+    print(f'Validating Integration Tests for transformer {transformer.get_name()}\n')
 
     steps = []
     validation_error = None
@@ -384,14 +384,14 @@ def validate_transformer_quality(transformer):
     if isinstance(transformer, str):
         transformer = get_transformer_class(transformer)
 
-    print(f'Validating Quality Tests for transformer {transformer.__name__}\n')
+    print(f'Validating Quality Tests for transformer {transformer.get_name()}\n')
 
     input_sdtype = transformer.get_input_sdtype()
     test_cases = get_test_cases({input_sdtype})
     regression_scores = get_regression_scores(test_cases, get_transformers_by_type())
     results = get_results_table(regression_scores)
 
-    transformer_results = results[results['transformer_name'] == transformer.__name__]
+    transformer_results = results[results['transformer_name'] == transformer.get_name()]
     transformer_results = transformer_results.drop('transformer_name', axis=1)
     transformer_results['Acceptable'] = False
     passing_relative_scores = transformer_results['score_relative_to_average'] > TEST_THRESHOLD
@@ -430,7 +430,7 @@ def validate_transformer_performance(transformer):
     if isinstance(transformer, str):
         transformer = get_transformer_class(transformer)
 
-    print(f'Validating Performance for transformer {transformer.__name__}\n')
+    print(f'Validating Performance for transformer {transformer.get_name()}\n')
 
     sdtype = transformer.get_input_sdtype()
     transformers = get_transformers_by_type().get(sdtype, [])
@@ -445,8 +445,8 @@ def validate_transformer_performance(transformer):
             results = pd.DataFrame({
                 'Value': performance.to_numpy(),
                 'Valid': valid,
-                'transformer': current_transformer.__name__,
-                'dataset': dataset_generator.__name__,
+                'transformer': current_transformer.get_name(),
+                'dataset': dataset_generator.get_name(),
             })
             results['Evaluation Metric'] = performance.index
             total_results = total_results.append(results)
@@ -456,10 +456,10 @@ def validate_transformer_performance(transformer):
     else:
         print('ERROR: One or more Performance Tests were NOT successful.')
 
-    other_results = total_results[total_results.transformer != transformer.__name__]
+    other_results = total_results[total_results.transformer != transformer.get_name()]
     average = other_results.groupby('Evaluation Metric')['Value'].mean()
 
-    total_results = total_results[total_results.transformer == transformer.__name__]
+    total_results = total_results[total_results.transformer == transformer.get_name()]
     final_results = total_results.groupby('Evaluation Metric').agg({
         'Value': 'mean',
         'Valid': 'any'

tests/integration/test_transformers.py

@@ -254,8 +254,15 @@ def _test_transformer_with_hypertransformer(transformer_class, input_data, steps
             TEST_COL: transformer_class
         }
 
-    hypertransformer.detect_initial_config(input_data)
-    hypertransformer.update_transformers(field_transformers)
+    sdtypes = {}
+    for field, transformer in field_transformers.items():
+        sdtypes[field] = transformer.get_supported_sdtypes()[0]
+
+    config = {
+        'sdtypes': sdtypes,
+        'transformers': field_transformers
+    }
+    hypertransformer.set_config(config)
     hypertransformer.fit(input_data)
 
     transformed = hypertransformer.transform(input_data)

tests/quality/test_quality.py

@@ -101,7 +101,7 @@ def get_transformer_regression_scores(data, sdtype, dataset_name, transformers,
             transformed_features = transformed_features[~nans]
             score = get_regression_score(transformed_features, target)
             row = pd.Series({
-                'transformer_name': transformer.__name__,
+                'transformer_name': transformer.get_name(),
                 'dataset_name': dataset_name,
                 'column': column,
                 'score': score

tests/unit/test_hyper_transformer.py

@@ -10,7 +10,7 @@
 import pytest
 
 from rdt import HyperTransformer
-from rdt.errors import Error, NotFittedError
+from rdt.errors import Error, NotFittedError, TransformerInputError
 from rdt.transformers import (
     AnonymizedFaker, BinaryEncoder, FloatFormatter, FrequencyEncoder, GaussianNormalizer,
     LabelEncoder, OneHotEncoder, RegexGenerator, UnixTimestampEncoder)
@@ -2484,16 +2484,15 @@ def test_update_transformers_missmatch_sdtypes(self, mock_warnings):
             'my_column': transformer
         }
 
-        # Run
-        instance.update_transformers(column_name_to_transformer)
-
-        # Assert
-        expected_call = (
-            "Some transformers you've assigned are not compatible with the sdtypes. "
-            f"Use 'update_sdtypes' to update: {'my_column'}"
+        # Run and Assert
+        err_msg = re.escape(
+            "Column 'my_column' is a categorical column, which is incompatible "
+            "with the 'BinaryEncoder' transformer."
         )
+        with pytest.raises(TransformerInputError, match=err_msg):
+            instance.update_transformers(column_name_to_transformer)
 
-        assert mock_warnings.called_once_with(expected_call)
+        assert mock_warnings.called_once_with(err_msg)
         instance._validate_transformers.assert_called_once_with(column_name_to_transformer)
 
     def test_update_transformers_transformer_is_none(self):