Add documentation for and invert default

sdv/constraints/base.py

@@ -4,6 +4,7 @@
 import importlib
 import inspect
 import logging
+import warnings
 
 import pandas as pd
 from copulas.multivariate.gaussian import GaussianMultivariate
@@ -111,7 +112,7 @@ class Constraint(metaclass=ConstraintMeta):
     def _identity(self, table_data):
         return table_data
 
-    def __init__(self, handling_strategy, fit_columns_model=True):
+    def __init__(self, handling_strategy, fit_columns_model=False):
         self.fit_columns_model = fit_columns_model
         if handling_strategy == 'transform':
             self.filter_valid = self._identity
@@ -226,6 +227,14 @@ def _validate_constraint_columns(self, table_data):
         """
         missing_columns = [col for col in self.constraint_columns if col not in table_data.columns]
         if missing_columns:
+            if not self._columns_model:
+                warning_message = (
+                    'The following constraint columns are missing from the table data '
+                    f'- `{missing_columns}`. When `fit_columns_model` is `False` and one or more '
+                    'constraint columns are missing, reject sampling can become slow.'
+                )
+                warnings.warn(warning_message, UserWarning)
+
             all_columns_missing = len(missing_columns) == len(self.constraint_columns)
             if self._columns_model is None or all_columns_missing:
                 raise MissingConstraintColumnError()

sdv/constraints/tabular.py

@@ -78,14 +78,18 @@ class UniqueCombinations(Constraint):
         handling_strategy (str):
             How this Constraint should be handled, which can be ``transform``,
             ``reject_sampling`` or ``all``. Defaults to ``transform``.
+        fit_columns_model (bool):
+            If False, reject sampling will be used to handle conditional sampling.
+            Otherwise, a model will be trained and used to sample other columns
+            based on the conditioned column. Defaults to False.
     """
 
     _separator = None
     _joint_column = None
     _combinations_to_uuids = None
     _uuids_to_combinations = None
 
-    def __init__(self, columns, handling_strategy='transform', fit_columns_model=True):
+    def __init__(self, columns, handling_strategy='transform', fit_columns_model=False):
         if len(columns) < 2:
             raise ValueError('UniqueCombinations requires at least two constraint columns.')
 
@@ -212,6 +216,10 @@ class GreaterThan(Constraint):
         handling_strategy (str):
             How this Constraint should be handled, which can be ``transform``
             or ``reject_sampling``. Defaults to ``transform``.
+        fit_columns_model (bool):
+            If False, reject sampling will be used to handle conditional sampling.
+            Otherwise, a model will be trained and used to sample other columns
+            based on the conditioned column. Defaults to False.
         drop (str):
             Which column to drop during transformation. Can be ``'high'``,
             ``'low'`` or ``None``.
@@ -230,7 +238,7 @@ class GreaterThan(Constraint):
     _column_to_reconstruct = None
 
     def __init__(self, low, high, strict=False, handling_strategy='transform',
-                 fit_columns_model=True, drop=None, high_is_scalar=None,
+                 fit_columns_model=False, drop=None, high_is_scalar=None,
                  low_is_scalar=None):
         self._low = low
         self._high = high
@@ -437,13 +445,17 @@ class Positive(GreaterThan):
         handling_strategy (str):
             How this Constraint should be handled, which can be ``transform``
             or ``reject_sampling``. Defaults to ``transform``.
+        fit_columns_model (bool):
+            If False, reject sampling will be used to handle conditional sampling.
+            Otherwise, a model will be trained and used to sample other columns
+            based on the conditioned column. Defaults to False.
         drop (str):
             Which column to drop during transformation. Can be ``'high'``
             or ``None``.
     """
 
     def __init__(self, high, strict=False, handling_strategy='transform',
-                 fit_columns_model=True, drop=None):
+                 fit_columns_model=False, drop=None):
         super().__init__(handling_strategy=handling_strategy,
                          fit_columns_model=fit_columns_model,
                          high=high, low=0, high_is_scalar=False,
@@ -468,13 +480,17 @@ class Negative(GreaterThan):
         handling_strategy (str):
             How this Constraint should be handled, which can be ``transform``
             or ``reject_sampling``. Defaults to ``transform``.
+        fit_columns_model (bool):
+            If False, reject sampling will be used to handle conditional sampling.
+            Otherwise, a model will be trained and used to sample other columns
+            based on the conditioned column. Defaults to False.
         drop (str):
             Which column to drop during transformation. Can be ``'low'``
             or ``None``.
     """
 
     def __init__(self, low, strict=False, handling_strategy='transform',
-                 fit_columns_model=True, drop=None):
+                 fit_columns_model=False, drop=None):
         super().__init__(handling_strategy=handling_strategy,
                          fit_columns_model=fit_columns_model,
                          high=0, low=low, high_is_scalar=True,
@@ -586,6 +602,10 @@ class Between(Constraint):
         handling_strategy (str):
             How this Constraint should be handled, which can be ``transform``
             or ``reject_sampling``. Defaults to ``transform``.
+        fit_columns_model (bool):
+            If False, reject sampling will be used to handle conditional sampling.
+            Otherwise, a model will be trained and used to sample other columns
+            based on the conditioned column. Defaults to False.
         high_is_scalar(bool or None):
             Whether or not the value for high is a scalar or a column name.
             If ``None``, this will be determined during the ``fit`` method
@@ -599,7 +619,7 @@ class Between(Constraint):
     _transformed_column = None
 
     def __init__(self, column, low, high, strict=False, handling_strategy='transform',
-                 fit_columns_model=True, high_is_scalar=None, low_is_scalar=None):
+                 fit_columns_model=False, high_is_scalar=None, low_is_scalar=None):
         self.constraint_column = column
         self.constraint_columns = (column,)
         self._low = low