GreaterThan constraint raises TypeError when using datetime

[amontanez24]

Environment Details

Please indicate the following details about the environment in which you found the bug:

• SDV version: Any
• Python version: Any
• Operating System: Any

Error Description

When using the GreaterThan constraint with a datetime value as the high scalar, it raises the following error

    438         high = self._get_value(table_data, 'high')
    439 
--> 440         return self.operator(high, low).all(axis=1)
    441 
    442     def _transform(self, table_data):

TypeError: '>=' not supported between instances of 'Timestamp' and 'int'

I suspect this might have a similar fix to issue #590. I think it has to do with the way we are doing is_valid in the GreaterThan constraint.

Steps to reproduce

I used the student_placements dataset in this case.

transform_gt_constraint = GreaterThan(
    low='start_date',
    high='end_date',
    handling_strategy='reject_sampling')

upper_limit_constraint = GreaterThan(
    low='end_date',
    high=pd.to_datetime('now'),
    scalar='high',
    handling_strategy='reject_sampling')

# Add the constraints and fit the data
constraints = [transform_gt_constraint, upper_limit_constraint]
multi_constraint_gc = GaussianCopula(constraints=constraints)
multi_constraint_gc.fit(student_placements)

[npatki]

I am able to reproduce this error.

Note that if you specify transform instead of reject_sampling, there is a different error:

/usr/local/lib/python3.7/dist-packages/sdv/constraints/tabular.py in _transform(self, table_data)
    458         """
    459         table_data = table_data.copy()
--> 460         diff = self._get_value(table_data, 'high') - self._get_value(table_data, 'low')
    461 
    462         if self._is_datetime:

UFuncTypeError: ufunc 'subtract' cannot use operands with types dtype('O') and dtype('<M8[ns]')

Steps to Reproduce

Do a similar thing as above, except only input a single constraint using the transform strategy

upper_limit_constraint = GreaterThan(
    low='end_date',
    high=pd.to_datetime('now'),
    scalar='high',
    handling_strategy='transform')

# Add the constraints and fit the data
constraints = [upper_limit_constraint]
multi_constraint_gc = GaussianCopula(constraints=constraints)
multi_constraint_gc.fit(student_placements)

[katxiao]

One workaround to this issue is to use a numpy.datetime64 instead of pandas.Timestamp to specify the datetime.

We are working on a fix now!