[WIP] sklearn-like LogisticRegression

[mathurinm]

#85

[mathurinm]

@agramfort I don't support multinomial Logreg and check_estimator tries to fit with y = [0, 1, 2] so that doesn't look too good

[agramfort]

I just looked at estimator tags we have now in sklearn and you cannot limit to binary classification. one option is to wrap the estimator using OneVsRestClassifier metaestimator

…

[mathurinm]

That did the trick @agramfort: tests are convinving. only remains to write some doc + example.
I'll address intercept fitting and LogRegCV in other PRs

[codecov-io]

Codecov Report

Merging #99 into master will increase coverage by 2.26%.
The diff coverage is 96.49%.

@@            Coverage Diff             @@
##           master      #99      +/-   ##
==========================================
+ Coverage   68.95%   71.21%   +2.26%     
==========================================
  Files          10       10              
  Lines         612      667      +55     
  Branches       91       93       +2     
==========================================
+ Hits          422      475      +53     
  Misses        166      166              
- Partials       24       26       +2

Impacted Files	Coverage Δ
celer/homotopy.py	77.94% <ø> (ø)	⬆️
celer/tests/test_lasso.py	100% <100%> (ø)	⬆️
celer/dropin_sklearn.py	97.5% <94.59%> (-2.5%)	⬇️
celer/datasets/libsvm.py	27.41% <0%> (ø)	⬆️

---

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[agramfort]

great !

…

[codecov-io]

Codecov Report

Merging #99 into master will increase coverage by 2.26%.
The diff coverage is 96.49%.

@@            Coverage Diff             @@
##           master      #99      +/-   ##
==========================================
+ Coverage   68.95%   71.21%   +2.26%     
==========================================
  Files          10       10              
  Lines         612      667      +55     
  Branches       91       93       +2     
==========================================
+ Hits          422      475      +53     
  Misses        166      166              
- Partials       24       26       +2

Impacted Files	Coverage Δ
celer/homotopy.py	77.94% <ø> (ø)	⬆️
celer/tests/test_lasso.py	100% <100%> (ø)	⬆️
celer/dropin_sklearn.py	97.5% <94.59%> (-2.5%)	⬇️
celer/datasets/libsvm.py	27.41% <0%> (ø)	⬆️

---

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 ed85ac6...cde6301. Read the comment docs.