Constraints should work with timezone-aware datetime columns

[npatki]

I'm filing this issue as a result of #1570

Problem Description

Currently, I am able to use a variety of constraints with datetime columns: SclarInequality, Inequality, ChainedInequality etc. These constraints should work for datetime columns even when there are missing values.

However, if my datetime columns are timezone aware, then the constraint causes the synthesizer to crash with a cryptic error that seems to have nothing to do with the issue.

InvalidDataError: The provided data does not match the metadata:

ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

Expected behavior

I expect that all constraints should be able to work with timezone-aware datetime columns. See the example code below.

from sdv.datasets.demo import download_demo
from sdv.metadata import SingleTableMetadata
from sdv.single_table import GaussianCopulaSynthesizer

import pandas as pd

data, metadata = download_demo(
    modality='single_table',
    dataset_name='fake_hotel_guests'
)

# update our datetime columns to be timezone aware
data['checkin_date'] = pd.to_datetime(data['checkin_date']).dt.tz_localize('UTC')
data['checkout_date'] = pd.to_datetime(data['checkout_date']).dt.tz_localize('UTC')

# metadata does not need a datetime_format since the columns are
# already represented as datetimes
metadata = SingleTableMetadata.load_from_dict({'METADATA_SPEC_VERSION': 'SINGLE_TABLE_V1',
 'columns': {'guest_email': {'sdtype': 'email', 'pii': True},
  'has_rewards': {'sdtype': 'boolean'},
  'room_type': {'sdtype': 'categorical'},
  'amenities_fee': {'sdtype': 'numerical', 'computer_representation': 'Float'},
  'checkin_date': {'sdtype': 'datetime' },
  'checkout_date': {'sdtype': 'datetime' },
  'room_rate': {'sdtype': 'numerical', 'computer_representation': 'Float'},
  'billing_address': {'sdtype': 'address', 'pii': True},
  'credit_card_number': {'sdtype': 'credit_card_number', 'pii': True}},
 'primary_key': 'guest_email'})

metadata.validate()
metadata.validate_data(data)

my_constraint = {
    'constraint_class': 'Inequality',
    'constraint_parameters': {
        'low_column_name': 'checkin_date',
        'high_column_name': 'checkout_date',
        'strict_boundaries': True
    }
}

synth = GaussianCopulaSynthesizer(metadata)
synth.add_constraints(constraints=[my_constraint])
synth.fit(data)

Output:

---------------------------------------------------------------------------
InvalidDataError                          Traceback (most recent call last)
<ipython-input-22-01f2dfadf5a6> in <cell line: 14>()
     12 synth = GaussianCopulaSynthesizer(metadata)
     13 synth.add_constraints(constraints=[my_constraint])
---> 14 synth.fit(data)

2 frames
/usr/local/lib/python3.10/dist-packages/sdv/single_table/base.py in fit(self, data)
    375         self._data_processor.reset_sampling()
    376         self._random_state_set = False
--> 377         processed_data = self._preprocess(data)
    378         self.fit_processed_data(processed_data)
    379 

/usr/local/lib/python3.10/dist-packages/sdv/single_table/base.py in _preprocess(self, data)
    319 
    320     def _preprocess(self, data):
--> 321         self.validate(data)
    322         self._data_processor.fit(data)
    323         return self._data_processor.transform(data)

/usr/local/lib/python3.10/dist-packages/sdv/single_table/base.py in validate(self, data)
    145 
    146         if errors:
--> 147             raise InvalidDataError(errors)
    148 
    149     def _validate_transformers(self, column_name_to_transformer):

InvalidDataError: The provided data does not match the metadata:

ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

Additional context

If it is not feasible to allow timezone-aware columns through a constraint, then the data validation should fail upfront (even when no constraints are added) as well as giving the user a clearer message as to why.