Merge branch 'releases' into dfsg

* releases: (46 commits)
  REL: 0.14 release: update whats_new and version
  MAINT Update mailmap
  DOC: fix CSS bug
  DOC: update documentation for release
  MISC: deprection is in 2 releases
  MAINT: randn on float is deprecated
  TST: avoid nose running sklearn.test as a test
  MISC: fix wrong timing in example
  FIX integer types in Ward clustering
  TST: avoid a crash in Windows + Anaconda Py3.3
  FIX search and replace misstake
  DOC put the narrative documentation of roc_curve and roc_auc_score in one place
  ENH more explicit name for auc + consistency for scorer, fix scikit-learn#2096
  DOC: button layout tweak
  Website: bottom buttons
  DOC: bigger menu fonts
  DOC: layout tweaks
  Polishing on "Who's using scikit-learn"
  FIX typo in testimonials
  ENH testimonials img are now centered.
  ...

Author: yarikoptic

.mailmap

@@ -18,17 +18,25 @@ Brian Cheung <[email protected]> <cow@rusty.(none)>
 Brian Holt <[email protected]> <[email protected]>
 Christian Osendorfer <[email protected]>
 Clay Woolam <[email protected]>
+Denis Engemann <[email protected]>
+Denis Engemann <[email protected]> <[email protected]>
+Denis Engemann <[email protected]> <[email protected]>
+Denis Engemann <[email protected]> <dengemann <[email protected]>
 Diego Molla <[email protected]> <diego@diego-desktop.(none)>
 DraXus <[email protected]> draxus <[email protected]>
 Edouard DUCHESNAY <[email protected]> <duchesnay@is143433.(none)>
 Edouard DUCHESNAY <[email protected]> <[email protected]>
 Edouard DUCHESNAY <[email protected]> <edouard@is2206219.(none)>
 Emmanuelle Gouillart <[email protected]>
 Emmanuelle Gouillart <[email protected]> <emma@aleph.(none)>
-Fabian Pedregosa <[email protected]> <[email protected]>
+Fabian Pedregosa <[email protected]>
+Fabian Pedregosa <[email protected]> <[email protected]>
+Fabian Pedregosa <[email protected]> <[email protected]>
 Federico Vaggi <[email protected]>
-Gael Varoquaux <[email protected]>
-Gael Varoquaux <[email protected]> <[email protected]>
+Federico Vaggi <[email protected]> <[email protected]>
+Gael Varoquaux <[email protected]>
+Gael Varoquaux <[email protected]> <[email protected]>
+Gael Varoquaux <[email protected]> <[email protected]>
 Gilles Louppe <[email protected]> <[email protected]>
 Harikrishnan S <[email protected]>
 Hrishikesh Huilgolkar <[email protected]> <hrishikesh@QE-IND-WKS007.(none)>
@@ -37,6 +45,7 @@ Jake VanderPlas <[email protected]> <[email protected]>
 Jake VanderPlas <[email protected]> <[email protected]>
 Jake VanderPlas <[email protected]> <[email protected]>
 James Bergstra <[email protected]>
+Jaques Grobler <[email protected]> <[email protected]>
 Jan Schl�ter <[email protected]>
 Jean Kossaifi <[email protected]>
 Jean Kossaifi <[email protected]> <[email protected]>
@@ -52,6 +61,7 @@ Nelle Varoquaux <[email protected]>
 Nelle Varoquaux <[email protected]> <[email protected]> <nelle@[email protected]>
 Nicolas Pinto <[email protected]> <[email protected]>
 Noel Dawe <[email protected]> <[email protected]>
+Noel Dawe <[email protected]> <[email protected]>
 Olivier Grisel <[email protected]> <ogrisel@turingcarpet.(none)>
 Olivier Grisel <[email protected]> <[email protected]>
 Olivier Hervieu <[email protected]> <[email protected]>
@@ -78,3 +88,6 @@ Wei Li <[email protected]>
 Wei Li <[email protected]> <[email protected]>
 X006 <x006@x006-icsl.(none)> <x006@x006laptop.(none)>
 Xinfan Meng <[email protected]> <[email protected]>
+Yannick Schwartz <[email protected]> <[email protected]>
+Yannick Schwartz <[email protected]> <ys218403@is220245.(none)>
+

README.rst

@@ -35,8 +35,7 @@ Dependencies
 ============
 
 scikit-learn is tested to work under Python 2.6+ and Python 3.3+
-(using the same codebase thanks to an embedded copy of [six](
-http://pythonhosted.org/six/)).
+(using the same codebase thanks to an embedded copy of `six <http://pythonhosted.org/six/>`_).
 
 The required dependencies to build the software Numpy >= 1.3, SciPy >= 0.7
 and a working C/C++ compiler.

doc/conf.py

@@ -68,7 +68,7 @@
 # built documents.
 #
 # The short X.Y version.
-version = '0.14-git'
+version = '0.14'
 # The full version, including alpha/beta/rc tags.
 import sklearn
 release = sklearn.__version__

doc/datasets/covtype.rst

@@ -13,7 +13,7 @@ Each sample has 54 features, described on the
 Some of the features are boolean indicators,
 while others are discrete or continuous measurements.
 
-``sklearn.datasets.fetch_covtype`` will load the covertype dataset;
+:func:`sklearn.datasets.fetch_covtype` will load the covertype dataset;
 it returns a dictionary-like object
 with the feature matrix in the ``data`` member
 and the target values in ``target``.

doc/documentation.rst

@@ -2,7 +2,7 @@
 
   <div class="container-index">
 
-Documentation of scikit-learn 0.13
+Documentation of scikit-learn 0.14
 ==================================
 
 .. raw:: html
@@ -78,9 +78,10 @@ Documentation of scikit-learn 0.13
                 <div class="span4 box">
                     <h2>Other Versions</h2>
                     <ul>
-                        <li><a href="http://scikit-learn.org/0.13/user_guide.html">scikit-learn 0.13 (stable)</a></li>
-                        <li>scikit-learn 0.14 (development)</li>
+                        <li>scikit-learn 0.14 (stable)</li>
+                        <li><a href="http://scikit-learn.org/0.15/user_guide.html">scikit-learn 0.15 (development)</a></li>
 
+                        <li><a href="http://scikit-learn.org/0.13/user_guide.html">scikit-learn 0.13</a></li>
                         <li><a href="http://scikit-learn.org/0.12/user_guide.html">scikit-learn 0.12</a></li>
                         <li><a href="http://scikit-learn.org/0.11/user_guide.html">scikit-learn 0.11</a></li>
                         <li><a href="http://scikit-learn.org/0.10/user_guide.html">scikit-learn 0.10</a></li>

doc/index.rst

@@ -246,69 +246,92 @@
     <div class="container index-lower">
         <div class="row-fluid">
             <!-- News -->
-            <div class="span6">
+            <div class="span4">
                 <h4>News</h4>
                 <ul>
                 <li><em>On-going development:</em>
                 <a href="whats_new.html"><em>What's new</em> (changelog)</a>
                 </li>
-                <li><em>July 22th - 28th, 2013: internal sprint</em>
+                <li><em>August 2013.</em> scikit-learn 0.14 is available for download (<a href="whats_new.html">Changelog</a>).
+                </li>
+                <li><em>July 22-28th, 2013: international sprint.</em>
                 During this week-long sprint, we gathered most of the core
                 developers in Paris.
-                <!--
-                Here are some of the biggest changes in the upcoming version:
-                <ul>
-                <li>Python 3 support</li>
-                <li>Ensembles of Randomized Trees speed improvements</li>
-                <li>Restricted Boltzman Machines</li>
-                <li>Missing data imputation</li>
-                <li>Bi-clustering</li>
-                </ul>
-                -->
-
                 We want to thank our sponsors, our
                 hosts <a href="http://www.telecom-paristech.fr/">Télécom ParisTech</a>
                 and <a href="http://www.tinyclues.com/">tinyclues</a>, and
                 donations that helped fund this event.
 
-                <li><em>February 2013.</em> scikit-learn 0.13.1 is available for download (<a href="whats_new.html">Changelog</a>).
-                </li>
                 </ul>
             </div>
 
             <!-- Community -->
-            <div class="span6">
+            <div class="span4">
                 <h4>Community</h4>
                 <ul>
-                <li><em>Questions?</em> See <a href="http://stackoverflow.com/questions/tagged/scikit-learn">stackoverflow</a> # scikit-learn for usage questions</li>
-                <li><em>Mailing list:</em> [email protected]</li>
+                <li><em>Questions?</em> See <a href="http://stackoverflow.com/questions/tagged/scikit-learn">stackoverflow</a> # scikit-learn</li>
+                <li><em>Mailing list:</em> <a href="https://lists.sourceforge.net/lists/listinfo/scikit-learn-general">scikit-learn-general@lists.sourceforge.net</a></li>
                 <li><em>IRC:</em> #scikit-learn @ <a href="http://webchat.freenode.net/">freenode</a></li>
-                <li><em>Help us:</em>
-                  <button class="btn btn-warning btn-big" onclick="document.getElementById('paypal-form').submit(); return false;"><b>Donate!</b></button> (<a href="about.html#funding">read more</a>)</li>
-                    <form target="_top" id="paypal-form" method="post" action="https://www.paypal.com/cgi-bin/webscr">
+                </ul>
+
+                <form target="_top" id="paypal-form" method="post" action="https://www.paypal.com/cgi-bin/webscr">
                     <input type="hidden" value="_s-xclick" name="cmd">
                     <input type="hidden" value="74EYUMF3FTSW8" name="hosted_button_id">
-                    </form>
-                </ul>
+                </form>
+
+                <a class="btn btn-warning btn-big" onclick="document.getElementById('paypal-form').submit(); return false;">Help us, <strong>donate!</strong></a>
+                <a class="btn btn-warning btn-big cite-us" href="./about.html#citing-scikit-learn"><strong>Cite us!</strong></a>
+
+                <small style="display: block; margin-top: 10px"><a href="about.html#funding">Read more about donations</a></small>
             </div>
 
             <!-- who using -->
-            <!--
             <div class="span4">
-                <h4>Who is using scikit-learn?</h4>
+                <h4>Who uses scikit-learn?</h4>
 
-                </h4>
-                <div id="myCarousel" class="carousel slide">
+                <div id="testimonials_carousel" class="carousel slide">
                     <div class="carousel-inner">
-                        <div class="active item"><img src="_images/inria.jpg" class="thumbnail" /><br /> <em>-- Great stuff!</em></div>
-                        <div class="item"><img src="_static/img/google.png" class="thumbnail" /><br /> <em>-- So good!</em></div>
+                        <div class="active item">
+                          <img src="_images/inria.jpg" class="thumbnail" />
+                          <p>
+                          <em>"We use scikit-learn to support leading-edge basic research [...]"</em>
+                          </p>
+                        </div>
+                        <div class="item">
+                          <img src="_images/evernote.png" class="thumbnail" />
+                          <p>
+                          <em>"For these tasks, we relied on the excellent scikit-learn package for Python."</em>
+                          </p>
+                        </div>
+                        <div class="item">
+                          <img src="_images/telecomparistech.jpg"
+                               class="thumbnail" />
+                          <p>
+                          <em>"The great benefit of scikit-learn is its fast learning curve [...]"</em>
+                          </p>
+                        </div>
+                        <div class="item">
+                          <img src="_images/aweber.png" class="thumbnail" />
+                          <p>
+                          <em>"It allows us to do AWesome stuff we would not otherwise accomplish"</em>
+                          </p>
+                        </div>
                     </div>
-                    <div style="margin-top: 5px"><a href="#">More testimonials</a></div>
                 </div>
-                <script>$('#myCarousel').carousel()</script>
+                <p align="right">
+                <small class="example-link">
+                <a href="testimonials/testimonials.html">More testimonials</a>
+                </small>
+                </p>
             </div>
-            -->
 
         </div>
     </div>
     </div>
+
+
+    <script>
+      $('#testimonials_carousel').carousel()
+    </script>
+
+

doc/modules/classes.rst

@@ -715,7 +715,6 @@ details.
 
    metrics.accuracy_score
    metrics.auc
-   metrics.auc_score
    metrics.average_precision_score
    metrics.classification_report
    metrics.confusion_matrix
@@ -730,6 +729,7 @@ details.
    metrics.precision_recall_fscore_support
    metrics.precision_score
    metrics.recall_score
+   metrics.roc_auc_score
    metrics.roc_curve
    metrics.zero_one_loss
 

doc/modules/model_evaluation.rst

@@ -56,7 +56,7 @@ Scoring                    Function
 'f1'                       :func:`sklearn.metrics.f1_score`
 'precision'                :func:`sklearn.metrics.precision_score`
 'recall'                   :func:`sklearn.metrics.recall_score`
-'roc_auc'                  :func:`sklearn.metrics.auc_score`
+'roc_auc'                  :func:`sklearn.metrics.roc_auc_score`
 
 **Clustering**
 'adjusted_rand_score'      :func:`sklearn.metrics.adjusted_rand_score`
@@ -182,11 +182,11 @@ Some of these are restricted to the binary classification case:
 .. autosummary::
    :template: function.rst
 
-   auc_score
    average_precision_score
    hinge_loss
    matthews_corrcoef
    precision_recall_curve
+   roc_auc_score
    roc_curve
 
 
@@ -268,27 +268,6 @@ and with a list of labels format:
     for an example of accuracy score usage using permutations of
     the dataset.
 
-Area under the curve (AUC)
-...........................
-
-The :func:`auc_score` function computes the 'area under the curve' (AUC) which
-is the area under the receiver operating characteristic (ROC) curve.
-
-This function requires  the true binary value and the target scores, which can
-either be probability estimates of the positive class, confidence values, or
-binary decisions.
-
-  >>> import numpy as np
-  >>> from sklearn.metrics import auc_score
-  >>> y_true = np.array([0, 0, 1, 1])
-  >>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
-  >>> auc_score(y_true, y_scores)
-  0.75
-
-For more information see the
-`Wikipedia article on AUC
-<http://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_curve>`_
-and the :ref:`roc_metrics` section.
 
 .. _average_precision_metrics:
 
@@ -713,7 +692,7 @@ with a svm classifier::
 
 
 Log loss
---------
+........
 The log loss, also called logistic regression loss or cross-entropy loss,
 is a loss function defined on probability estimates.
 It is commonly used in (multinomial) logistic regression and neural networks,
@@ -795,7 +774,7 @@ function:
 .. _roc_metrics:
 
 Receiver operating characteristic (ROC)
-........................................
+.......................................
 
 The function :func:`roc_curve` computes the `receiver operating characteristic
 curve, or ROC curve (quoting
@@ -809,16 +788,36 @@ Wikipedia) <http://en.wikipedia.org/wiki/Receiver_operating_characteristic>`_:
   positive rate), at various threshold settings. TPR is also known as
   sensitivity, and FPR is one minus the specificity or true negative rate."
 
+This function requires the true binary
+value and the target scores, which can either be probability estimates of the
+positive class, confidence values, or binary decisions.
 Here a small example of how to use the :func:`roc_curve` function::
 
     >>> import numpy as np
-    >>> from sklearn import metrics
+    >>> from sklearn.metrics import roc_curve
     >>> y = np.array([1, 1, 2, 2])
     >>> scores = np.array([0.1, 0.4, 0.35, 0.8])
-    >>> fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=2)
+    >>> fpr, tpr, thresholds = roc_curve(y, scores, pos_label=2)
     >>> fpr
     array([ 0. ,  0.5,  0.5,  1. ])
+    >>> tpr
+    array([ 0.5,  0.5,  1. ,  1. ])
+    >>> thresholds
+    array([ 0.8 ,  0.4 ,  0.35,  0.1 ])
+
+The :func:`roc_auc_score` function computes the area under the receiver
+operating characteristic (ROC) curve, which is also denoted by
+AUC or AUROC.  By computing the
+area under the roc curve, the curve information is summarized in one number.
+For more information see the `Wikipedia article on AUC
+<http://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_curve>`_.
 
+  >>> import numpy as np
+  >>> from sklearn.metrics import roc_auc_score
+  >>> y_true = np.array([0, 0, 1, 1])
+  >>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
+  >>> roc_auc_score(y_true, y_scores)
+  0.75
 
 The following figure shows an example of such ROC curve.