Merge pull request scikit-learn#24 from glouppe/adaboost

Fix some nitpicks

COPYING

@@ -1,6 +1,6 @@
 New BSD License
 
-Copyright (c) 2007 - 2012 The scikit-learn developers.
+Copyright (c) 2007–2013 The scikit-learn developers.
 All rights reserved.
 
 

benchmarks/bench_covertype.py

@@ -192,7 +192,7 @@ def benchmark(clf):
     t0 = time()
     pred = clf.predict(X_test)
     test_time = time() - t0
-    err = metrics.zero_one_loss(y_test, pred) / float(pred.shape[0])
+    err = metrics.zero_one_loss(y_test, pred, normalize=True)
     return err, train_time, test_time
 
 ######################################################################

doc/conf.py

@@ -33,16 +33,10 @@
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
 extensions = ['gen_rst',
               'sphinx.ext.autodoc', 'sphinx.ext.autosummary',
-              'sphinx.ext.pngmath',
+              'sphinx.ext.pngmath', 'numpy_ext.numpydoc'
               ]
-try:
-    import numpy_ext.numpydoc
-    extensions.append('numpy_ext.numpydoc')
-    # With older versions of sphinx, this causes a crash
-    autosummary_generate = True
-except:
-    # Older version of sphinx
-    extensions.append('numpy_ext_old.numpydoc')
+
+autosummary_generate = True
 
 autodoc_default_flags = ['members', 'inherited-members']
 
@@ -66,7 +60,7 @@
 
 # General information about the project.
 project = u'scikit-learn'
-copyright = u'2010–2011, scikit-learn developers (BSD License)'
+copyright = u'2010–2013, scikit-learn developers (BSD License)'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
@@ -126,8 +120,8 @@
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
 # documentation.
-html_theme_options = {'oldversion':False, 'collapsiblesidebar': True,
-                      'google_analytics':True}
+html_theme_options = {'oldversion': False, 'collapsiblesidebar': True,
+                      'google_analytics': True}
 
 # Add any paths that contain custom themes here, relative to this directory.
 html_theme_path = ['themes']

doc/modules/isotonic.rst

@@ -18,6 +18,6 @@ arbitrary real number. It yields the vector which is composed of non-decreasing
 elements the closest in terms of mean squared error. In practice this list
 of elements forms a function that is piecewise linear.
 
-.. figure:: ../auto_examples/linear_model/images/plot_isotonic_regression_1.png
-   :target: ../auto_examples/linear_model/images/plot_isotonic_regression.html
+.. figure:: ../auto_examples/images/plot_isotonic_regression_1.png
+   :target: ../auto_examples/images/plot_isotonic_regression.html
    :align: center

doc/modules/linear_model.rst

@@ -268,7 +268,17 @@ They also tend to break when the problem is badly conditioned
 Elastic Net
 ===========
 :class:`ElasticNet` is a linear model trained with L1 and L2 prior as
-regularizer.
+regularizer. This combination allows for learning a sparse model where
+few of the weights are non-zero like :class:`Lasso`, while still maintaining the
+the regularization properties of :class:`Ridge`. We control this tradeoff
+using the `l1_ratio` parameter.
+
+Elastic-net is useful when there are multiple features which are
+correlated with one another. Lasso is likely to pick one of these
+at random, while elastic-net is likely to pick both.
+
+A practical advantage of trading-off between Lasso and Ridge is it allows
+Elastic-Net to inherit some of Ridge's stability under rotation.
 
 The objective function to minimize is in this case
 

doc/modules/model_evaluation.rst

@@ -50,6 +50,7 @@ Others also work in the multiclass case:
   fbeta_score
   precision_recall_fscore_support
   precision_score
+  recall_score
   zero_one_loss
 
 Some metrics might require probability estimates of the positive class,