Added a unit test to ensure that there are no spurious repeating values in the thresholds returned by roc_curve because of machine precision, and a quick stab at a fix.
Slightly expanded the docstring for the test_roc_nonrepeating_thresholds test. Added named slice variables to same, for readability.
Improved the treatment of the machine precision issue for calculating ROC thresholds. Added a comment explaining why the treatment is needed.
Minor refactoring of the repeated-roc-thresholds test, for clarity.
Added a backport of numpy.isclose from numpy v1.8.1. This function was not available until numpy v1.7
Changed metrics.py to use the new backward-compatible version of np.isclose
PEP8 fix
Merge pull request #3268 from jblackburne/nonrepeating_roc_thresholds

[MRG+2] Nonrepeating ROC thresholds
MAINT bump joblib to 0.8.2
Merge pull request #3328 from ogrisel/joblib-0.8.2

MAINT bump joblib to 0.8.2
DOC SVMs take string class labels as well as integers

http://stackoverflow.com/a/24512272/166749
MAINT remove residual sparsefuncs*.so when compiling

The sparsefuncs -> sparsefuncs_fast rename in bbb8f1d has caused a lot of errors being reported due to dirty working directories. This attempts to avoid that.
tree: add min_weight_fraction_leaf
forest: add min_weight_fraction_leaf
min_weight_fraction_leaf and min_samples_leaf: test both best-first and depth-first tree building
gbrt: min_weight_fraction_leaf value test
tree: use min_weight_leaf internally while exposing min_weight_fraction_leaf
utils.testing: add assert_greater_equal and assert_less_equal
forest: test min_samples_leaf and min_weight_fraction_leaf
min_weight_fraction: comments and pep8