Gsk 2559 add tabular classification pipeline

[cy-moi]

Common Problems:

1. no dataset (blocking)
2. no config (not blocking)
   • could be solved by using dataset column names and sample predictions
3. ML console trained models -> unable to process
4. Pytorch -> Almost no usable models (classification 1, regressions 9 but all the same one bcwarner/audit-icu-gpt2-25_3M)
5. Most models are scikit-learn with joblib/skops workflows, Keras models are almost all from Keras-io (valid ones less than 10 in total)

Version problems (deserialization):
Scikit-learn (skops)
Example: python cli.py --loader huggingface --model scikit-learn/Fish-Weight --dataset scikit-learn/Fish

Scikit-learn (joblib) -> XgBoost
Example: Almost all titanic models
python cli.py --loader huggingface --model vabadeh213/autotrain-titanic-744222727 --dataset phihung/titanic

Keras -> AdamW
Example: python cli.py --loader huggingface --model keras-io/tab_transformer --dataset scikit-learn/adult-census-income

[linear]

GSK-2559 Add customized tabular classification pipeline

[Inokinoki]

Common Problems:

1. no dataset (blocking)

2. no config (not blocking)

   • could be solved by using dataset column names and sample predictions

3. ML console trained models -> unable to process

4. Pytorch -> Almost no usable models (classification 1, regressions 9 but all the same one bcwarner/audit-icu-gpt2-25_3M)

Version problems (deserialization): Scikit-learn (skops) Example: python cli.py --loader huggingface --model scikit-learn/Fish-Weight --dataset scikit-learn/Fish

Scikit-learn (joblib) -> XgBoost Example: Almost all titanic models `python cli.py --loader huggingface --model vabadeh213/autotrain-titanic-744222727 --dataset phihung/titanic` Keras -> AdamW Example: `python cli.py --loader huggingface --model keras-io/tab_transformer --dataset scikit-learn/adult-census-income`

The last two are apparently deps version issues.

I have also met the first one, no clue.