Move string formatting to utils

sdv/constraints/base.py

@@ -14,7 +14,7 @@
 from sdv.constraints.errors import (
     AggregateConstraintsError, ConstraintMetadataError, MissingConstraintColumnError)
 from sdv.errors import ConstraintsNotMetError
-from sdv.utils import groupby_list
+from sdv.utils import format_invalid_values_string, groupby_list
 
 LOGGER = logging.getLogger(__name__)
 
@@ -201,12 +201,11 @@ def _validate_data_meets_constraint(self, table_data):
             if not is_valid_data.all():
                 constraint_data = table_data[list(self.constraint_columns)]
                 invalid_rows = constraint_data[~is_valid_data]
+                invalid_rows_str = format_invalid_values_string(invalid_rows)
                 err_msg = (
                     f"Data is not valid for the '{self.__class__.__name__}' constraint:\n"
-                    f'{invalid_rows[:5]}'
+                    f'{invalid_rows_str}'
                 )
-                if len(invalid_rows) > 5:
-                    err_msg += f'\n+{len(invalid_rows) - 5} more'
 
                 raise ConstraintsNotMetError(err_msg)
 

sdv/metadata/single_table.py

@@ -9,7 +9,6 @@
 
 import pandas as pd
 
-from sdv.constraints.errors import AggregateConstraintsError
 from sdv.errors import InvalidDataError
 from sdv.metadata.anonymization import SDTYPE_ANONYMIZERS, is_faker_function
 from sdv.metadata.errors import InvalidMetadataError
@@ -18,8 +17,8 @@
 from sdv.metadata.visualization import (
     create_columns_node, create_summarized_columns_node, visualize_graph)
 from sdv.utils import (
-    cast_to_iterable, is_boolean_type, is_datetime_type, is_numerical_type, load_data_from_csv,
-    validate_datetime_format)
+    cast_to_iterable, format_invalid_values_string, is_boolean_type, is_datetime_type,
+    is_numerical_type, load_data_from_csv, validate_datetime_format)
 
 LOGGER = logging.getLogger(__name__)
 
@@ -542,22 +541,14 @@ def _validate_keys_dont_have_missing_values(self, data):
 
         return errors
 
-    @staticmethod
-    def _format_invalid_values_string(invalid_values):
-        invalid_values = sorted(invalid_values, key=lambda x: str(x))
-        if len(invalid_values) > 3:
-            return invalid_values[:3] + [f'+ {len(invalid_values) - 3} more']
-
-        return invalid_values
-
     def _validate_key_values_are_unique(self, data):
         errors = []
         keys = self._get_primary_and_alternate_keys()
         for key in sorted(keys):
             repeated_values = set(data[key][data[key].duplicated()])
             if repeated_values:
-                repeated_values = self._format_invalid_values_string(repeated_values)
-                errors.append(f"Key column '{key}' contains repeating values: {repeated_values}")
+                repeated_values = format_invalid_values_string(repeated_values)
+                errors.append(f"Key column '{key}' contains repeating values: " + repeated_values)
 
         return errors
 
@@ -597,21 +588,11 @@ def _validate_column_data(self, column):
                 )
 
         if invalid_values:
-            invalid_values = self._format_invalid_values_string(invalid_values)
+            invalid_values = format_invalid_values_string(invalid_values)
             return [f"Invalid values found for {sdtype} column '{column.name}': {invalid_values}."]
 
         return []
 
-    def _validate_constraints(self, data):
-        """Validate that the data satisfies the constraints."""
-        errors = []
-        try:
-            self._data_processor._fit_constraints(data)
-        except AggregateConstraintsError as e:
-            errors.append(e)
-
-        return errors
-
     def validate_data(self, data):
         """Validate the data matches the metadata.
 

sdv/utils.py

@@ -193,3 +193,27 @@ def create_unique_name(name, list_names):
         result += '_'
 
     return result
+
+
+def format_invalid_values_string(invalid_values):
+    """Convert ``invalid_values`` into a string of invalid values.
+
+    Args:
+        invalid_values (pd.DataFrame, set):
+            Object of values to be converted into string.
+
+    Returns:
+        str:
+            A stringified version of the object.
+    """
+    if isinstance(invalid_values, pd.DataFrame):
+        if len(invalid_values) > 5:
+            return f'{invalid_values.head(5)}\n+{len(invalid_values) - 5} more'
+
+    if isinstance(invalid_values, set):
+        invalid_values = sorted(invalid_values, key=lambda x: str(x))
+        if len(invalid_values) > 3:
+            extra_missing_values = [f'+ {len(invalid_values) - 3} more']
+            return f'{invalid_values[:3] + extra_missing_values}'
+
+    return f'{invalid_values}'