MeanSegmentEncoderTransform

[alex-hse-repository]

IMPORTANT: Please do not create a Pull Request without creating an issue first.

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?

Type of Change

• Examples / docs / tutorials / contributors update
• Bug fix (non-breaking change which fixes an issue)
• Improvement (non-breaking change which improves an existing feature)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)

Proposed Changes

Add MeanSegmentEncoderTransform

Related Issue

Closing issues

closes #233

[codecov-commenter]

Codecov Report

Merging #265 (e68dc3e) into master (426003b) will increase coverage by 0.53%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master     #265      +/-   ##
==========================================
+ Coverage   86.66%   87.19%   +0.53%     
==========================================
  Files          92       92              
  Lines        4461     4522      +61     
==========================================
+ Hits         3866     3943      +77     
+ Misses        595      579      -16     

Impacted Files	Coverage Δ
etna/transforms/__init__.py	100.00% <100.00%> (ø)
etna/transforms/segment_encoder.py	100.00% <100.00%> (ø)
etna/transforms/feature_importance.py	100.00% <0.00%> (ø)
etna/libs/tsfresh/relevance.py	60.95% <0.00%> (+3.80%)	⬆️
etna/libs/tsfresh/significance_tests.py	31.88% <0.00%> (+13.04%)	⬆️
etna/libs/tsfresh/distribution.py	83.33% <0.00%> (+50.00%)	⬆️

---

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 426003b...e68dc3e. Read the comment docs.

[Mr-Geekman]

Try to use R2 here, it is more intuitive in sense of good/bad result. May be noise should be smaller.