Error when run custom model using benchmark_single_table

[T0217]

Environment Details

• SDGym version: 0.8.0
• Python version: 3.11.5
• Operating System: Windows 11

Error Description

When running the same code as #321 , the following error was encountered.

Steps to reproduce

import os
import shutil
import sdgym
from sdgym import create_single_table_synthesizer
from sdgym.synthesizers import (UniformSynthesizer,
                                GaussianCopulaSynthesizer,
                                TVAESynthesizer)
import warnings
warnings.filterwarnings('ignore')

synthesizers = [
    UniformSynthesizer,
    GaussianCopulaSynthesizer,
    TVAESynthesizer
]


# YData
# CTGAN
def ctgan_get_trained_synthesizer(data, metadata):
    from ydata_synthetic.synthesizers.regular import RegularSynthesizer
    from ydata_synthetic.synthesizers import ModelParameters, TrainParameters

    ctgan_args = ModelParameters(batch_size=500, lr=2e-4, betas=(0.5, 0.9))
    train_args = TrainParameters(epochs=2)

    synthesizer = RegularSynthesizer(modelname='ctgan', model_parameters=ctgan_args)

    num_cols = [col for col, sdtype in metadata['columns'].items() if sdtype['sdtype'] in ['numerical', 'datetime']]
    cat_cols = [col for col, sdtype in metadata['columns'].items() if sdtype['sdtype'] == 'categorical']

    synthesizer.fit(data=data,
                    train_arguments=train_args,
                    num_cols=num_cols,
                    cat_cols=cat_cols)

    return synthesizer


def sample_from_synthesizer(synthesizer, n_rows):
    synthetic_data = synthesizer.sample(n_rows)
    return synthetic_data


YData_CTGANSynthesizer = create_single_table_synthesizer(
    get_trained_synthesizer_fn=ctgan_get_trained_synthesizer,
    sample_from_synthesizer_fn=sample_from_synthesizer,
    display_name='YData-CTGAN'
)


custom_synthesizers = [YData_CTGANSynthesizer]

# Detect the existence of the folder
detailed_results_folder = r"C:\Users\18840\Desktop\result"

if os.path.isdir(detailed_results_folder) and\
   os.path.exists(detailed_results_folder):
    print('The folder where the intermediate files are stored already exists and is processed for deletion.')
    shutil.rmtree(detailed_results_folder, ignore_errors=True)
    print('-' * 50)

results = sdgym.benchmark_single_table(
    synthesizers=synthesizers,
    custom_synthesizers=custom_synthesizers,
    show_progress=True,
    multi_processing_config={
     'package_name': 'multiprocessing',
     'num_workers': 8
    },
    sdv_datasets=['adult'],
    detailed_results_folder=detailed_results_folder
)