[ENH] Support for pairwise learning-to-rank models

[poonia92]

Current Situation

Train_model function does not support training for pairwise leaning-to-rank models as these models requires a grouping of observations passed to their model.fit function.

An example of model.fit function for pairwise learning can be found here

Enhancement

Improve fit function by adding another function argument for grouping of observations.

Use this grouping of data in Train_model function and also in create_shadow_feature function while shuffling the columns.

Also as mentioned in issue-57, grouping of observations need to be taken into account for train-test split of data.

Reasoning

This will add support for all the pairwise learning-to-rank models.

Implementation

• Add function argument for a list/np.array of groups in the BorutaShap.fit function.
• Use this array of groups in Train_model function while calling model.fit.
• Add capability to create_shadow_feature function to shuffle data inside these pre-defined groups.
• Add capability to split train-test data based on grouping of observations.

I'll be happy to share a PR in some time given the go-ahead