CTGANSynthesizer is assigning LabelEncoder to boolean columns (instead of None)

[npatki]

Environment Details

• SDV version: 1.2.1

Error Description

If I am using the CTGANsynthesizer, then there is no need to apply any transformers to discrete data. Currently, the data preprocessor assigns None (no transformer) to categorical columns but it assigns LabelEncoder(add_noise=True) to boolean columns. This makes it inefficient to model boolean columns, as the CTGANSynthesizer then one hot encodes all those values (see #1450 as to why)

Workaround

Just update the sdtype from boolean to categorical and then the preprocessing will work as intended.

Steps to reproduce

import pandas as pd
from sdv.metadata import SingleTableMetadata
from sdv.single_table import CTGANSynthesizer

data = pd.DataFrame(data={
    'age': [56, 61, 36, 52, 42],
    'biomarker': [0.863, 0.885, 0.285, 0.293, 0.192],
    'therapy': [True, False, True, False, True],
    'alcohol': ['medium', 'medium', 'low', 'high', 'low'],
})

metadata = SingleTableMetadata.load_from_dict({
    'columns': {
        'age': { 'sdtype': 'numerical' },
        'biomarker': { 'sdtype': 'numerical' },
        'therapy': { 'sdtype': 'boolean' },
        'alcohol': { 'sdtype': 'categorical' }
    }
})

synth = CTGANSynthesizer(metadata)
synth.auto_assign_transformers(data)
synth.get_transformers()

Output:

{'age': FloatFormatter(learn_rounding_scheme=True, enforce_min_max_values=True),
 'biomarker': FloatFormatter(learn_rounding_scheme=True, enforce_min_max_values=True),
 'therapy': LabelEncoder(add_noise=True),
 'alcohol': None}