Speed up feature selection

[alex-hse-repository]

Before submitting (must do checklist)

• Did you read the contribution guide?
• Did you update the docs? We use Numpy format for all the methods and classes.
• Did you write any new necessary tests?
• Did you update the CHANGELOG?

Proposed Changes

1. Explicitly set ml_task="regression" in calculate_relevance_table, in case of integer target it is set with incorrect value and significantly slows down get_statistics_relevance_table
2. Change redundancy computation method in mrmr, now it computes redundancy only inside segment, which change the time complexity term from n_segments^2 to n_segments. Add fast_redundancy flag for for backward compatibility
3. For further speed up It is suggested to use numba as we do the same operation on many pairs of columns

Closing issues

closes #886

[codecov-commenter]

Codecov Report

Merging #1294 (e7d8a75) into master (99782c8) will decrease coverage by 0.24%.
The diff coverage is 100.00%.

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@@            Coverage Diff             @@
##           master    #1294      +/-   ##
==========================================
- Coverage   88.46%   88.22%   -0.24%     
==========================================
  Files         193      193              
  Lines       11750    11758       +8     
==========================================
- Hits        10395    10374      -21     
- Misses       1355     1384      +29     

Impacted Files	Coverage Δ
etna/analysis/feature_relevance/relevance_table.py	100.00% <100.00%> (ø)
etna/analysis/feature_selection/mrmr_selection.py	100.00% <100.00%> (ø)
...transforms/feature_selection/feature_importance.py	98.88% <100.00%> (+0.01%)	⬆️

... and 2 files with indirect coverage changes

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[github-actions]

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