ENH: simplify SplitRFE doc example to nore require errorfx + reorder …

…mappers to have mean(abs(normed)))

Closes PyMVPA#459
Closes PyMVPA#460

mvpa2/featsel/rfe.py

@@ -417,13 +417,11 @@ class SplitRFE(RFE):
     >>> rfe = SplitRFE(
     ...           LinearCSVMC(),
     ...           OddEvenPartitioner(),
-    ...           # take sensitivities per each split, L2 norm, mean, abs them
+    ...           # take sensitivities per each split, L2 norm, abs, mean them
     ...           fmeasure_postproc=ChainMapper([
     ...               FxMapper('features', l2_normed),
-    ...               FxMapper('samples', np.mean),
-    ...               FxMapper('samples', np.abs)]),
-    ...           # use the error stored in the confusion matrix of split classifier
-    ...           errorfx=ConfusionBasedError(rfesvm_split, confusion_state='stats'),
+    ...               FxMapper('samples', np.abs),
+    ...               FxMapper('samples', np.mean)]),
     ...           # select 50% of the best on each step
     ...           fselector=FractionTailSelector(
     ...               0.50,

mvpa2/tests/test_rfe.py

@@ -321,8 +321,8 @@ def test_rfe(self, clf):
             # of normed sensitivities across those splits
             (RFE(rfesvm_split.get_sensitivity_analyzer(
                     postproc=ChainMapper([ FxMapper('features', l2_normed),
-                                           FxMapper('samples', np.mean),
-                                           FxMapper('samples', np.abs)])),
+                                           FxMapper('samples', np.abs),
+                                           FxMapper('samples', np.mean)])),
                  ConfusionBasedError(rfesvm_split, confusion_state='stats'),
                  Repeater(2),             #  we will use the same full cv-training dataset
                  fselector=FractionTailSelector(