Change default categorical transformer to categorical_fuzzy in HMA1

[csala]

The HMA1 algorithm uses the GaussianCopula class, which, by default, uses the one_hot_encoding categorical transformer.

This may provoke high dimensionality issues when modeling tables with lots of columns with several depth levels, as reported in #209

To fix this, the default model_kwargs for the tabular model should set the categorical transformer to categorical_fuzzy, which does not create new columns.

[erenarkangil]

Hi Carles, thank you for the feedback.

I have a high dimensional data with over 2000 categoric variables, but still have memory problems. I wonder the limit of fuzzy transformer. Here is my code:

from sdv.relational import HMA1

fuzzy = {'categorical_transformer': 'categorical_fuzzy'}
model = HMA1(metadata1,model_kwargs=fuzzy)

Many thanks