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qinhanmin2014 and amueller [MRG+2] ENH&BUG Add pos_label parameter and fix a bug in average_prec…
…ision_score (#9980)

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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 :) 

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README.rst

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scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the AUTHORS.rst file for a complete list of contributors.

It is currently maintained by a team of volunteers.

Website: http://scikit-learn.org

Installation

Dependencies

scikit-learn requires:

  • Python (>= 2.7 or >= 3.4)
  • NumPy (>= 1.8.2)
  • SciPy (>= 0.13.3)

For running the examples Matplotlib >= 1.3.1 is required. A few examples require scikit-image >= 0.9.3 and a few examples require pandas >= 0.13.1.

scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra Subprograms library. scikit-learn comes with a reference implementation, but the system CBLAS will be detected by the build system and used if present. CBLAS exists in many implementations; see Linear algebra libraries for known issues.

User installation

If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip

pip install -U scikit-learn

or conda:

conda install scikit-learn

The documentation includes more detailed installation instructions.

Changelog

See the changelog for a history of notable changes to scikit-learn.

Development

We welcome new contributors of all experience levels. The scikit-learn community goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation, tests, and more. We've included some basic information in this README.

Important links

Source code

You can check the latest sources with the command:

git clone https://github.com/scikit-learn/scikit-learn.git

Setting up a development environment

Quick tutorial on how to go about setting up your environment to contribute to scikit-learn: https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md

Testing

After installation, you can launch the test suite from outside the source directory (you will need to have the pytest package installed):

pytest sklearn

See the web page http://scikit-learn.org/dev/developers/advanced_installation.html#testing for more information.

Random number generation can be controlled during testing by setting the SKLEARN_SEED environment variable.

Submitting a Pull Request

Before opening a Pull Request, have a look at the full Contributing page to make sure your code complies with our guidelines: http://scikit-learn.org/stable/developers/index.html

Project History

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the AUTHORS.rst file for a complete list of contributors.

The project is currently maintained by a team of volunteers.

Note: scikit-learn was previously referred to as scikits.learn.

Help and Support

Documentation

Communication

Citation

If you use scikit-learn in a scientific publication, we would appreciate citations: http://scikit-learn.org/stable/about.html#citing-scikit-learn