Conditional sampling using GaussianCopula inefficient when categories are noised

[npatki]

Environment Details

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

• SDV version: 0.16.0.dev2
• Python version: 3.7
• Operating System: Colab Notebook

Error Description

The GaussianCopula is listed as the most efficient way to perform conditional sampling. Yet, there are some configurations of this model that are very inefficient compared to others.

Inefficient configurations: categorical_transformer='categorical_fuzzy'
Efficient configurations (up to 100x faster): categorical_transformer='categorical' or 'label-encoding' or 'one_hot_encoding'

Steps to reproduce

The following is inefficient:

from sdv.demo import load_tabular_demo
from sdv.tabular import GaussianCopula
from sdv.sampling import Condition

data = load_tabular_demo('student_placements')
data.head()

model = GaussianCopula() # no constraints
model.fit(data)

condition1 = Condition({'gender': 'F', 'high_spec': 'Science'}, num_rows=100)
condition2 = Condition({'gender': 'M', 'high_spec': 'Science'}, num_rows=100)

model.sample_conditions(conditions=[condition1, condition2])

Sampling conditions: 93%|█████████▎| 186/200 [02:05<00:09, 1.49it/s]
/usr/local/lib/python3.7/dist-packages/sdv/tabular/utils.py:211: UserWarning: Only able to sample 186 rows for the given conditions. To sample more rows, try increasing `max_tries_per_batch` (currently: 100). Note that increasing this value will also increase the sampling time.
 warnings.warn(user_msg)

Meanwhile, changing it to label_encoding is 100x faster:

model = GaussianCopula(categorical_transformer='label_encoding')
...
Sampling conditions: 100%|██████████| 200/200 [00:00<00:00, 327.30it/s]

[npatki]

Note that there are two related issues --

1. General performance degradation. Let's focus this issue on that.
2. Occasionally needing to reject rows even though this model is not supposed to need reject sampling. This is likely due to RDT #528