Revscoring: Implement fast scoring for fast cross validation

[codez266]

• Implements a new method score_many in sklearn.py to score a bunch of instances together to utilize numpy's underlying optimizations of matrix multiplication.
• _cross_score is made to call this new method to improve speeds of cross_validation. The original score method is preserved for use in ORES.
• New tests added for score and score_many as well as a test for probability classifier in test_sklearn to improve coverage.

[codecov]

Codecov Report

Merging #388 into master will decrease coverage by 0.01%.
The diff coverage is 85.29%.

@@            Coverage Diff             @@
##           master     #388      +/-   ##
==========================================
- Coverage   86.73%   86.71%   -0.02%     
==========================================
  Files         257      257              
  Lines        8231     8335     +104     
==========================================
+ Hits         7139     7228      +89     
- Misses       1092     1107      +15

Impacted Files	Coverage Δ
revscoring/scoring/models/tests/test_sklearn.py	100% <100%> (+2.85%)	⬆️
revscoring/scoring/models/model.py	87.27% <100%> (+0.23%)	⬆️
revscoring/scoring/models/sklearn.py	83.45% <72.22%> (-9.33%)	⬇️
revscoring/languages/tests/test_finnish.py	100% <0%> (ø)	⬆️
revscoring/languages/romanian.py	80% <0%> (+4%)	⬆️
revscoring/languages/finnish.py	89.47% <0%> (+4.85%)	⬆️
revscoring/languages/norwegian.py	84% <0%> (+5.05%)	⬆️
revscoring/languages/arabic.py	84% <0%> (+5.05%)	⬆️
revscoring/languages/hungarian.py	84% <0%> (+5.05%)	⬆️
... and 1 more

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[halfak]

How much faster is this? It's a lot of complication to add. I think we should quantify the benefit.

[codez266]

RF classifier parameters used for benchmarking:
num_samples - 10k
n_estimators - 400
max_depth - 4
features - word2vec( 300 dimensions )
binary classification with True/False

Time for current cv_train - 414.65737676620483
Time for new fast cv_train - 81.77100491523743

Performance gain is almost 5 times.