Simplify and fix InformationWeight Transform with column groups

[ryandewolfe33]

The initial PR #150 that added column groups to the information weight transform added a few more branching paths and had an error (silent, answers were wrong) when both column groups and targets were passed. This PR fixes that error and simplifies the code. I also consolidated the info weight tests into a single test with more parameters.

I have also changed the interface to remove the single_column_group_weight parameter to simplify and since I am not sure why it would be used. I hope this is okay because there was no release since the last PR

[Copilot]

Pull request overview

This PR fixes incorrect behavior in InformationWeight when combining supervision (target) with column_groups, and simplifies the implementation by consolidating baseline computation and removing the single_column_group_weight interface path.

Changes:

• Add group-aware baseline marginal computation used consistently for both supervised and unsupervised information weighting.
• Update supervised weighting to support column-group-aware baselines in InformationWeightTransformer.fit.
• Consolidate/parameterize information-weight transformer tests and remove coverage for single_column_group_weight.

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.

File	Description
vectorizers/transformers/info_weight.py	Refactors baseline probability computation, removes single_column_group_weight, and fixes grouped+supervised weighting flow.
vectorizers/tests/test_transformers.py	Consolidates IW transformer tests into a single parametrized test covering more fit argument combinations.

Comments suppressed due to low confidence (1)

vectorizers/transformers/info_weight.py:90

• supervised_column_kl can return NaN when baseline_probabilities contains zeros (common when a class has zero mass within a column group). The expression observed * np.log(observed / baseline_probabilities) produces 0 * log(0/0) => NaN, which then propagates into weights.

    observed += prior_strength * baseline_probabilities
    observed /= observed.sum()

    return np.sum(observed * np.log(observed / baseline_probabilities))

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