MRG fix bincount mess I made in kmeans. #1234
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| """Replacing np.bincount in numpy < 1.6 to provide minlength.""" | ||
| out = np.zeros(minlength, np.int) | ||
| result = np.bincount(X, weights) | ||
| out[:len(result)] = result |
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what if len(result) > minlength? You'll get a exception, won't you?
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yes, yes I will....
seems like I haven't been up to speed yesterday...
|
This is good. I am merging it in. |
|
Thanks @GaelVaroquaux :) |
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…ision_score (#9980) <!-- Thanks for contributing a pull request! Please ensure you have taken a look at the contribution guidelines: https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md#pull-request-checklist --> #### Reference Issues/PRs <!-- Example: Fixes #1234. See also #3456. Please use keywords (e.g., Fixes) to create link to the issues or pull requests you resolved, so that they will automatically be closed when your pull request is merged. See https://github.com/blog/1506-closing-issues-via-pull-requests --> part of #9829 #### What does this implement/fix? Explain your changes. (1)add pos_label parameter to average_precision_score (Although we finally decide not to introduce pos_label in roc_auc_score, I think we might need pos_label here. Because there are no relationship between the results if we reverse the true labels, also, precision/recall all support pos_label) (2)fix a bug where average_precision_score will sometimes return nan when sample_weight contains 0 ```python y_true = np.array([0, 0, 0, 1, 1, 1]) y_score = np.array([0.1, 0.4, 0.85, 0.35, 0.8, 0.9]) average_precision_score(y_true, y_score, sample_weight=[1, 1, 0, 1, 1, 0]) # output:nan ``` I do it here because of (3) (3)move average_precision scores out of METRIC_UNDEFINED_BINARY (this should contain the regression test for (1) and (2)) Some comments: (1)For the underlying method(precision_recall_curve), the default value of pos_label is None, but I choose to set the default value of pos_label to 1 because this is what P/R/F is doing. What's more, the meaning of pos_label=None is not clear even in scikit-learn itself (see #10010) (2)I slightly modified the common test. Currently, the part I modified is only designed for brier_score_loss(I'm doing the same thing in #9562) . I think it is right because as a common test, it seems not good to force metrics to accept str y_true without pos_label. #### Any other comments? cc @jnothman Could you please take some time to review or at least judge whether this is the right way to go? Thanks a lot :) <!-- Please be aware that we are a loose team of volunteers so patience is necessary; assistance handling other issues is very welcome. We value all user contributions, no matter how minor they are. If we are slow to review, either the pull request needs some benchmarking, tinkering, convincing, etc. or more likely the reviewers are simply busy. In either case, we ask for your understanding during the review process. For more information, see our FAQ on this topic: http://scikit-learn.org/dev/faq.html#why-is-my-pull-request-not-getting-any-attention. Thanks for contributing! -->
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Fix bug in mst_linkage_core function initialization - Corrected initialization of new_node to handle edge cases. - Added validation to ensure the algorithm continues properly when no new node is found. Closes scikit-learn#1234
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This should clean up the stuff I pushed earlier.
cc @ogrisel @GaelVaroquaux Could you have a brief look? What I pushed earlier is buggy but I didn't dare push again after so many failed fixes.