Log columns in numerical_distributions which have been renamed

sdv/single_table/copulagan.py

@@ -1,11 +1,15 @@
 """Combination of GaussianCopula transformation and GANs."""
+import logging
 from copy import deepcopy
 
 import rdt
 
 from sdv.single_table.copulas import GaussianCopulaSynthesizer
 from sdv.single_table.ctgan import CTGANSynthesizer
-from sdv.single_table.utils import validate_numerical_distributions
+from sdv.single_table.utils import (
+    log_numerical_distributions_error, validate_numerical_distributions)
+
+LOGGER = logging.getLogger(__name__)
 
 
 class CopulaGANSynthesizer(CTGANSynthesizer):
@@ -145,7 +149,7 @@ def __init__(self, metadata, enforce_min_max_values=True, enforce_rounding=True,
         )
         self._numerical_distributions = {
             field: GaussianCopulaSynthesizer.get_distribution_class(distribution)
-            for field, distribution in (numerical_distributions or {}).items()
+            for field, distribution in self.numerical_distributions.items()
         }
 
     def _create_gaussian_normalizer_config(self, processed_data):
@@ -179,8 +183,10 @@ def _fit(self, processed_data):
             processed_data (pandas.DataFrame):
                 Data to be learned.
         """
-        gaussian_normalizer_config = self._create_gaussian_normalizer_config(processed_data)
+        log_numerical_distributions_error(
+            self.numerical_distributions, processed_data.columns, LOGGER)
 
+        gaussian_normalizer_config = self._create_gaussian_normalizer_config(processed_data)
         self._gaussian_normalizer_hyper_transformer = rdt.HyperTransformer()
         self._gaussian_normalizer_hyper_transformer.set_config(gaussian_normalizer_config)
         processed_data = self._gaussian_normalizer_hyper_transformer.fit_transform(processed_data)

sdv/single_table/copulas.py

@@ -1,4 +1,5 @@
 """Wrappers around copulas models."""
+import logging
 import warnings
 from copy import deepcopy
 
@@ -12,7 +13,11 @@
 
 from sdv.errors import NonParametricError
 from sdv.single_table.base import BaseSingleTableSynthesizer
-from sdv.single_table.utils import flatten_dict, unflatten_dict, validate_numerical_distributions
+from sdv.single_table.utils import (
+    flatten_dict, log_numerical_distributions_error, unflatten_dict,
+    validate_numerical_distributions)
+
+LOGGER = logging.getLogger(__name__)
 
 
 class GaussianCopulaSynthesizer(BaseSingleTableSynthesizer):
@@ -97,7 +102,7 @@ def __init__(self, metadata, enforce_min_max_values=True, enforce_rounding=True,
         self._default_distribution = self.get_distribution_class(self.default_distribution)
         self._numerical_distributions = {
             field: self.get_distribution_class(distribution)
-            for field, distribution in (numerical_distributions or {}).items()
+            for field, distribution in self.numerical_distributions.items()
         }
         self._num_rows = None
 
@@ -108,9 +113,11 @@ def _fit(self, processed_data):
             processed_data (pandas.DataFrame):
                 Data to be learned.
         """
+        log_numerical_distributions_error(
+            self.numerical_distributions, processed_data.columns, LOGGER)
         self._num_rows = len(processed_data)
-        numerical_distributions = deepcopy(self._numerical_distributions)
 
+        numerical_distributions = deepcopy(self._numerical_distributions)
         for column in processed_data.columns:
             if column not in numerical_distributions:
                 numerical_distributions[column] = self._numerical_distributions.get(

sdv/single_table/utils.py

@@ -308,3 +308,14 @@ def validate_numerical_distributions(numerical_distributions, metadata_columns):
                 f'{invalid_columns}. The column names you provide must be present '
                 'in the metadata.'
             )
+
+
+def log_numerical_distributions_error(numerical_distributions, processed_data_columns, logger):
+    """Log error when numerical distributions columns don't exist anymore."""
+    unseen_columns = numerical_distributions.keys() - set(processed_data_columns)
+    for column in unseen_columns:
+        logger.info(
+            f"Requested distribution '{numerical_distributions[column]}' "
+            f"cannot be applied to column '{column}' because it no longer "
+            'exists after preprocessing.'
+        )

tests/unit/single_table/test_copulagan.py

@@ -235,6 +235,31 @@ def test__create_gaussian_normalizer_config(self, mock_rdt):
         assert config == expected_config
         assert mock_rdt.transformers.GaussianNormalizer.call_args_list == expected_calls
 
+    @patch('sdv.single_table.copulagan.LOGGER')
+    @patch('sdv.single_table.copulagan.CTGANSynthesizer._fit')
+    @patch('sdv.single_table.copulagan.rdt')
+    def test__fit_logging(self, mock_rdt, mock_ctgansynthesizer__fit, mock_logger):
+        """Test a message is logged.
+
+        A message should be logged if the columns passed in ``numerical_distributions``
+        were renamed/dropped during preprocessing.
+        """
+        # Setup
+        metadata = SingleTableMetadata()
+        metadata.add_column('col', sdtype='numerical')
+        numerical_distributions = {'col': 'gamma'}
+        instance = CopulaGANSynthesizer(metadata, numerical_distributions=numerical_distributions)
+        processed_data = pd.DataFrame()
+
+        # Run
+        instance._fit(processed_data)
+
+        # Assert
+        mock_logger.info.assert_called_once_with(
+            "Requested distribution 'gamma' cannot be applied to column 'col' "
+            'because it no longer exists after preprocessing.'
+        )
+
     @patch('sdv.single_table.copulagan.CTGANSynthesizer._fit')
     @patch('sdv.single_table.copulagan.rdt')
     def test__fit(self, mock_rdt, mock_ctgansynthesizer__fit):
@@ -248,7 +273,7 @@ def test__fit(self, mock_rdt, mock_ctgansynthesizer__fit):
         metadata = SingleTableMetadata()
         instance = CopulaGANSynthesizer(metadata)
         instance._create_gaussian_normalizer_config = Mock()
-        processed_data = Mock()
+        processed_data = pd.DataFrame()
 
         # Run
         instance._fit(processed_data)

tests/unit/single_table/test_copulas.py

@@ -131,6 +131,30 @@ def test_get_parameters(self):
             'default_distribution': 'beta'
         }
 
+    @patch('sdv.single_table.copulas.LOGGER')
+    def test__fit_logging(self, mock_logger):
+        """Test a message is logged.
+
+        A message should be logged if the columns passed in ``numerical_distributions``
+        were renamed/dropped during preprocessing.
+        """
+        # Setup
+        metadata = SingleTableMetadata()
+        metadata.add_column('col', sdtype='numerical')
+        numerical_distributions = {'col': 'gamma'}
+        instance = GaussianCopulaSynthesizer(
+            metadata, numerical_distributions=numerical_distributions)
+        processed_data = pd.DataFrame({'updated_col': [1, 2, 3]})
+
+        # Run
+        instance._fit(processed_data)
+
+        # Assert
+        mock_logger.info.assert_called_once_with(
+            "Requested distribution 'gamma' cannot be applied to column 'col' "
+            'because it no longer exists after preprocessing.'
+        )
+
     @patch('sdv.single_table.copulas.warnings')
     @patch('sdv.single_table.copulas.multivariate')
     def test__fit(self, mock_multivariate, mock_warnings):