Doc: better tests for logistic.

Reverse order of classes after sugestion Methieu Blondel, this way we
also check that predict_proba does the right sorting for each class.

Also removed obsolete tests.

Author: Fabian Pedregosa

scikits/learn/glm/sparse/tests/test_logistic.py

@@ -10,7 +10,7 @@
 
 X = [[-1, 0], [0, 1], [1, 1]]
 Y1 = [0, 1, 1]
-Y2 = [0, 1, 2]
+Y2 = [2, 1, 0]
 iris = datasets.load_iris()
 
 def test_predict_2_classes():

scikits/learn/glm/tests/test_logistic.py

@@ -10,7 +10,7 @@
 
 X = [[-1, 0], [0, 1], [1, 1]]
 Y1 = [0, 1, 1]
-Y2 = [0, 1, 2]
+Y2 = [2, 1, 0]
 iris = datasets.load_iris()
 
 def test_predict_2_classes():
@@ -54,19 +54,6 @@ def test_predict_iris():
     pred = clf.predict_proba(iris.data).argmax(axis=1)
     assert np.mean(pred == iris.target) > .95
 
-def test_predict_proba():
-    """
-    I think this test is wrong. Is there a way to know the right results ?
-    """
-    raise nose.SkipTest("XFailed test")
-    clf = logistic.LogisticRegression().fit(X, Y2)
-    assert_array_almost_equal(clf.predict_proba([[1, 1]]),
-                              [[ 0.21490268,  0.32639437,  0.45870294]])
-
-    clf = logistic.LogisticRegression(penalty='l1').fit(X, Y2)
-    assert_array_almost_equal(clf.predict_proba([[2, 2]]),
-                              [[ 0.33333333,  0.33333333,  0.33333333]])
-
 
 if __name__ == '__main__':
     import nose