Anyone knows what I have missed? I got this from Travis:

FAIL: sklearn.tests.test_common.test_root_import_all_completeness
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/travis/build/scikit-learn/scikit-learn/testvenv/local/lib/python2.7/site-packages/nose/case.py", line 197, in runTest
    self.test(*self.arg)
  File "/home/travis/build/scikit-learn/scikit-learn/sklearn/tests/test_common.py", line 144, in test_root_import_all_completeness
    assert_in(modname, sklearn.__all__)
AssertionError: 'metric_learning' not found in ['calibration', 'cluster', 'covariance', 'cross_decomposition', 'cross_validation', 'datasets', 'decomposition', 'dummy', 'ensemble', 'externals', 'feature_extraction', 'feature_selection', 'gaussian_process', 'grid_search', 'isotonic', 'kernel_approximation', 'kernel_ridge', 'lda', 'learning_curve', 'linear_model', 'manifold', 'metrics', 'mixture', 'multiclass', 'naive_bayes', 'neighbors', 'neural_network', 'pipeline', 'preprocessing', 'qda', 'random_projection', 'semi_supervised', 'svm', 'tree', 'discriminant_analysis', 'clone']

It looks like I need to add this metric_learning into somewhere? Any help would be appreciated.

It looks like I need to add this metric_learning into somewhere?

Modify __all__ in sklearn/__init__.py.

Huh. Wait, this is being worked on in #4789, and was the subject of GSoC funding... If you can see benefits of your implementation over that one, please comment there. Otherwise I think you should expect that implementation to take precedence.

@Barmaley-exe @jnothman Still failing after the changes. Any ideas?

@terrytangyuan you can take a look at Travis' output to find failed tests. You can also see the failing test's code to understand what's going on.

@Barmaley-exe I got this

  File "/home/travis/build/scikit-learn/scikit-learn/sklearn/metric_learning/NCA.py", line 219, in fit
    res = opt.minimize(fun=self.objective,
AttributeError: 'module' object has no attribute 'minimize'

which is not expected since it's imported from here import scipy.optimize as opt and it passed on my local pc.

BTW, do you suggest that I keep working on this or help you finish yours (I'd love to help)? Yours seems to have a problem of not improving the classification accuracy? The main goal is to get metric learning on this package since it's extremely useful.

@terrytangyuan I'm not sure why this exception arises. One possibility is that sklearn supports SciPy >= 0.9, and minimize was added in 0.11

My personal opinion is that my PR is much more developed, so we'd be better off finishing that. Main problem is that it's not well-tested, and it'd benefit from more documentation and examples.

Okay. Let's finish that instead of making a new one then. Can you update your existing code? How should I make changes on your PR? I assume there's a better way than fork your forked repo.

In my opinion, the point of metric learning is that it can leverage supervised information in the form of pairwise similarity or dissimilarity labels. If you are going to generate those labels from just (X, y), one is probably better off just using a classifier. So before we add NCA to scikit-learn, I really want to see empirical evidence that it is worth it both in terms of accuracy and training time.