DOC: Better plotting in RFE example

examples/rfe_with_cross_validation.py → examples/plot_rfe_with_cross_validation.py

@@ -7,6 +7,7 @@ Recursive feature elimination with automatic tuning of the
 number of features selected with cross-validation
 """
 print __doc__
+import numpy as np
 
 from scikits.learn.svm import SVC
 from scikits.learn.cross_val import StratifiedKFold
@@ -30,6 +31,14 @@ print 'Optimal number of features : %d' % rfecv.support_.sum()
 
 import pylab as pl
 pl.figure()
-pl.plot(rfecv.cv_scores_)
+pl.semilogx(rfecv.n_features_, rfecv.cv_scores_)
+pl.xlabel('Number of features selected')
+pl.ylabel('Cross validation score (nb of misclassifications)')
+# 15 ticks regularly-space in log
+x_ticks = np.unique(np.logspace(np.log10(2), 
+                                np.log10(rfecv.n_features_.max()),
+                                15,
+                    ).astype(np.int))
+pl.xticks(x_ticks, x_ticks)
 pl.show()
 

scikits/learn/feature_selection/rfe.py

@@ -181,6 +181,7 @@ class RFECV(RFE):
         clf = self.estimator
         n_models = np.max(self.ranking_)
         self.cv_scores_ = np.zeros(n_models)
+        self.n_features_ = np.bincount(self.ranking_)[::-1].cumsum()[-2::-1] 
 
         for train, test in cv:
             ranking_ = rfe.fit(X[train], y[train]).ranking_