DOC : fix move of isotonic in doc + examples

Author: agramfort

doc/modules/classes.rst

@@ -434,6 +434,30 @@ From text
    hmm.MultinomialHMM
    hmm.GMMHMM
 
+.. _isotonic_ref:
+
+:mod:`sklearn.isotonic`: Isotonic regression
+============================================
+
+.. automodule:: sklearn.isotonic
+   :no-members:
+   :no-inherited-members:
+
+**User guide:** See the :ref:`isotonic` section for further details.
+
+.. currentmodule:: sklearn
+
+.. autosummary::
+   :toctree: generated/
+   :template: class.rst
+
+   isotonic.IsotonicRegression
+
+.. autosummary::
+   :toctree: generated
+   :template: function.rst
+
+   isotonic.isotonic_regression
 
 .. _kernel_approximation_ref:
 
@@ -515,7 +539,6 @@ From text
    linear_model.BayesianRidge
    linear_model.ElasticNet
    linear_model.ElasticNetCV
-   linear_model.IsotonicRegression
    linear_model.Lars
    linear_model.LarsCV
    linear_model.Lasso
@@ -544,7 +567,6 @@ From text
    :toctree: generated/
    :template: function.rst
 
-   linear_model.isotonic_regression
    linear_model.lars_path
    linear_model.lasso_path
    linear_model.lasso_stability_path

doc/modules/isotonic.rst

@@ -0,0 +1,23 @@
+.. _isotonic:
+
+===================
+Isotonic regression
+===================
+
+.. currentmodule:: sklearn.isotonic
+
+The :class:`IsotonicRegression` fits a non-decreasing function to the data.
+It solves the following problem:
+
+  minimize :math:`\sum_i w_i (y_i - \hat{y}_i)^2`
+
+  subject to :math:`\hat{y}_{min} = \hat{y}_1 \le \hat{y}_2 ... \le \hat{y}_n = \hat{y}_{max}`
+
+where each :math:`w_i` is strictly positive and each :math:`y_i` is an
+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
+   :align: center

doc/modules/linear_model.rst

@@ -657,25 +657,6 @@ zero) model.
    thus be used to perform feature selection, as detailed in
    :ref:`l1_feature_selection`.
 
-Isotonic regression
-====================
-
-The :class:`IsotonicRegression` fits a non-decreasing function to the data.
-It solves the following problem:
-
-  minimize :math:`\sum_i w_i (y_i - \hat{y}_i)^2`
-
-  subject to :math:`\hat{y}_{min} = \hat{y}_1 \le \hat{y}_2 ... \le \hat{y}_n = \hat{y}_{max}`
-
-where each :math:`w_i` is strictly positive and each :math:`y_i` is an
-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
-   :align: center
-
 Stochastic Gradient Descent - SGD
 =================================
 

doc/supervised_learning.rst

@@ -20,3 +20,4 @@ Supervised learning
     modules/feature_selection.rst
     modules/label_propagation.rst
     modules/lda_qda.rst
+    modules/isotonic.rst

examples/linear_model/plot_isotonic_regression.py renamed to examples/plot_isotonic_regression.py

@@ -20,7 +20,8 @@
 import pylab as pl
 from matplotlib.collections import LineCollection
 
-from sklearn.linear_model import IsotonicRegression, LinearRegression
+from sklearn.linear_model import LinearRegression
+from sklearn.isotonic import IsotonicRegression
 from sklearn.utils import check_random_state
 
 n = 100