diff --git a/doc/modules/feature_selection.rst b/doc/modules/feature_selection.rst
new file mode 100644
index 0000000000000000000000000000000000000000..8b47f61b18c37ce556482ea4c98bdb98994bdb70
--- /dev/null
+++ b/doc/modules/feature_selection.rst
@@ -0,0 +1,57 @@
+
+=======================
+Feature selection
+=======================
+
+
+
+Univariate feature selection
+=============================
+
+Univariate feature selection works by selecting features based on a
+univariate test statistic. Although it can seen as a preprocessing step
+to an estimator, `scikit.learn` exposes an object to wrap as existing
+estimator with feature selection and expose a new estimator:
+
+.. autofunction:: scikits.learn.feature_select.univ_selection.UnivSelection
+
+
+
+Feature scoring functions
+--------------------------
+
+.. warning::
+
+ A common case of non-functionning for feature selection is to use a
+ regression scoring function with a classification problem.
+
+For classification
+.......................
+
+.. autofunction:: scikits.learn.feature_select.univ_selection.f_classif
+
+For regression
+.................
+
+.. autofunction:: scikits.learn.feature_select.univ_selection.f_regression
+
+Feature selection functions
+----------------------------
+
+.. autofunction:: scikits.learn.feature_select.univ_selection.select_k_best
+
+.. autofunction:: scikits.learn.feature_select.univ_selection.select_percentile
+
+.. autofunction:: scikits.learn.feature_select.univ_selection.select_fpr
+
+.. autofunction:: scikits.learn.feature_select.univ_selection.select_fdr
+
+.. autofunction:: scikits.learn.feature_select.univ_selection.select_fwe
+
+
+Examples
+----------
+
+.. literalinclude:: ../../examples/feature_select.py
+
+
diff --git a/doc/modules/glm.rst b/doc/modules/glm.rst
index c32171ca1958468b0f7c44efa3b5a4da456335c4..ba1e3d14776beb1d6d51af691b192a5266e76153 100644
--- a/doc/modules/glm.rst
+++ b/doc/modules/glm.rst
@@ -25,4 +25,6 @@ Examples
.. literalinclude:: ../../examples/lasso_enet_coordinate_descent.py
-TODO: include image
+.. image:: lasso_enet_coordinate_descent.png
+ :align: center
+
diff --git a/doc/modules/lasso_enet_coordinate_descent.png b/doc/modules/lasso_enet_coordinate_descent.png
new file mode 100644
index 0000000000000000000000000000000000000000..23dae332a76ba443a5451297ce7e0ce0e82f53e7
Binary files /dev/null and b/doc/modules/lasso_enet_coordinate_descent.png differ
diff --git a/examples/lasso_enet_coordinate_descent.py b/examples/lasso_enet_coordinate_descent.py
index ca5814f8b24ded05c366130c0a86afeb84235374..f2ca536217977b5dfad8e7c8e9d09d109df64d9d 100644
--- a/examples/lasso_enet_coordinate_descent.py
+++ b/examples/lasso_enet_coordinate_descent.py
@@ -1,3 +1,8 @@
+"""
+Lasso and elastic net (L1 and L2 penalisation) implemented using a
+coordinate descent.
+"""
+
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD Style.
@@ -6,69 +11,63 @@
from itertools import cycle
import numpy as np
import pylab as pl
+
from scikits.learn.glm.cd import Lasso, ElasticNet, lasso_path, enet_path
-def demo_glm():
- """
- Sample usage of GLM to predict with a linear model.
- It will plot the paths for the Lasso and the Elastic-Net problem
-
- """
- n_samples, n_features, maxit = 5, 10, 30
- np.random.seed(0)
- y = np.random.randn(n_samples)
- X = np.random.randn(n_samples, n_features)
-
- """Lasso model
- """
- alpha = 1.0
-
- lasso = Lasso(alpha=alpha)
- lasso.fit(X, y, maxit=maxit)
-
- print "Duality gap Lasso (should be small): %f" % lasso.gap
-
- import pylab as pl
- pl.close('all')
- pl.plot(lasso.E)
- pl.xlabel('Iteration')
- pl.ylabel('Cost function')
- pl.title('Lasso')
- pl.show()
-
- """Elastic-Net model
- """
- alpha = 1.0
- beta = 1.0
-
- enet = ElasticNet(alpha=alpha, beta=beta)
- enet.fit(X, y, maxit=maxit)
-
- print "Duality gap (should be small): %f" % enet.gap
-
- pl.figure()
- pl.plot(enet.E)
- pl.xlabel('Iteration')
- pl.ylabel('Cost function')
- pl.title('Elastic-Net')
- pl.show()
-
- """Test path functions
- """
-
- alphas_lasso, weights_lasso = lasso_path(X, y, factor=0.97, n_alphas = 100)
- alphas_enet, weights_enet = enet_path(X, y, factor=0.97, n_alphas = 100, beta=0.1)
-
- pl.figure()
- color_iter = cycle(['b', 'g', 'r', 'c', 'm', 'y', 'k'])
- for color, weight_lasso, weight_enet in zip(color_iter, weights_lasso.T, weights_enet.T):
- pl.plot(-np.log(alphas_lasso), weight_lasso, color)
- pl.plot(-np.log(alphas_enet), weight_enet, color+'x')
- pl.xlabel('-log(lambda)')
- pl.ylabel('weights')
- pl.title('Lasso and Elastic-Net Paths')
- pl.legend(['Lasso','Elastic-Net'])
- pl.show()
-
-if __name__ == '__main__':
- demo_glm()
+n_samples, n_features, maxit = 5, 10, 30
+
+np.random.seed(0)
+y = np.random.randn(n_samples)
+X = np.random.randn(n_samples, n_features)
+
+################################################################################
+# Fit models
+################################################################################
+
+# Lasso
+lasso = Lasso(alpha=1)
+lasso.fit(X, y, maxit=maxit)
+
+print "Duality gap Lasso (should be small): %f" % lasso.gap
+
+
+# Elastic-Net
+enet = ElasticNet(alpha=1, beta=1)
+enet.fit(X, y, maxit=maxit)
+
+print "Duality gap (should be small): %f" % enet.gap
+
+# Display results
+pl.figure(-1, figsize=(8, 4))
+pl.clf()
+pl.subplots_adjust(wspace=.4, right=.95)
+pl.subplot(1, 2, 1)
+pl.plot(lasso.E, label='Lasso')
+pl.plot(enet.E, label='Elastic Net')
+pl.xlabel('Iteration')
+pl.ylabel('Cost function')
+pl.legend()
+pl.title('Convergence')
+
+################################################################################
+# Demo path functions
+################################################################################
+
+alphas_lasso, weights_lasso = lasso_path(X, y, factor=0.97, n_alphas = 100)
+alphas_enet, weights_enet = enet_path(X, y, factor=0.97, n_alphas = 100,
+ beta=0.1)
+
+# Display results
+pl.subplot(1, 2, 2)
+color_iter = cycle(['b', 'g', 'r', 'c', 'm', 'y', 'k'])
+for color, weight_lasso, weight_enet in zip(color_iter,
+ weights_lasso.T, weights_enet.T):
+ pl.plot(-np.log(alphas_lasso), weight_lasso, color)
+ pl.plot(-np.log(alphas_enet), weight_enet, color+'x')
+
+pl.xlabel('-log(lambda)')
+pl.ylabel('weights')
+pl.title('Lasso and Elastic-Net Paths')
+pl.legend(['Lasso','Elastic-Net'])
+pl.show()
+