proximal gradient method @ group lasso fixed

[AnchorBlues]

In proximal gradient method, weights where those absolute values are smaller than the threshold must be 0.

[codecov-io]

Codecov Report

Merging #249 into master will decrease coverage by 0.04%. The diff coverage is 0%.

@@            Coverage Diff             @@
##           master     #249      +/-   ##
==========================================
- Coverage    56.8%   56.75%   -0.05%     
==========================================
  Files           7        7              
  Lines        1301     1302       +1     
  Branches      261      261              
==========================================
  Hits          739      739              
- Misses        492      495       +3     
+ Partials       70       68       -2

Impacted Files	Coverage Δ
pyglmnet/pyglmnet.py	73.33% <0%> (-0.17%)	:arrow_down:

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[codecov-io]

Codecov Report

Merging #249 into master will increase coverage by 1.64%. The diff coverage is 100%.

@@            Coverage Diff             @@
##           master     #249      +/-   ##
==========================================
+ Coverage    56.8%   58.44%   +1.64%     
==========================================
  Files           7        7              
  Lines        1301     1302       +1     
  Branches      261      261              
==========================================
+ Hits          739      761      +22     
+ Misses        492      472      -20     
+ Partials       70       69       -1

Impacted Files	Coverage Δ
pyglmnet/pyglmnet.py	78.22% <100%> (+4.72%)	:arrow_up:

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[jasmainak]

Thanks. Could you add a tiny test?

[jasmainak]

closes https://github.com/glm-tools/pyglmnet/issues/235

[AnchorBlues]

@jasmainak Sorry, I don't know what kind of test code I should write. I would be glad if you show me some example test code.

[jasmainak]

Think about how you discovered the bug. The test should basically reproduce that behavior in some sense. It should fail on master but pass on this branch.

You can add the test in this function. One easy check perhaps could be weather the estimated betas have the same non-zero indices as the simulated one.

[AnchorBlues]

@jasmainak Thank you for your advice. I added test code to test_group_lasso function. Please confirm.

[AnchorBlues]

@jasmainak I found that last assertion of test_group_lasso function,

assert (beta[group_norms <= thresh] == 0.0).all()

is not appropriate because there is a possibility that beta[group_norms <= thresh] will be nonzero if the values of result[idx_to_update] changes to the values less than thresh at the final iteration of optimization. So I removed the assertion from the test code.

[jasmainak]

@pavanramkumar please merge if you are happy. LGTM

[pavanramkumar]

Thanks @AnchorBlues and @jasmainak — great contribution!

[jasmainak]

@AnchorBlues looking forward to more contributions and bugfixes. Thanks!

[AnchorBlues]

@jasmainak @pavanramkumar Thank you for reviewing my fix and merging it. I am glad to contribute to your package. I'll continue to use this package for data science. Thanks!