Implement the SuperVectorizer and dirty_cat's encoders to the search space #169
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This PR aims at implementing dirty_cat's encoders (currently SimilarityEncoder, GapEncoder and MinHashEncoder) to GAMA's search space via the use of the SuperVectorizer.
The point of adding the dirty_cat encoders is for GAMA to be able to handle dirty categorical features in tabular data.
Using the SuperVectorizer gives a simplified interface to the sklearn's ColumnTransformer, and allows to mix & match different encoding techniques.
For the content of this PR to run, the features implemented in dirty_cat 0.3 are required. However, at the time of writing these lines (August 2022), this version is not out yet.
TODO: