class_docstr: Leave-One-Label_Out cross-validation iterator:
  Provides train/test indexes to split data in train test sets
docstr: Leave-One-Label_Out cross validation:
  Provides train/test indexes to split data in train test sets
  
  Parameters
  ----------
  labels : list
          List of labels
  
  Examples
  --------
  >>> from scikits.learn import cross_val
  >>> X = [[1, 2], [3, 4], [5, 6], [7, 8]]
  >>> y = [1, 2, 1, 2]
  >>> labels = [1, 1, 2, 2]
  >>> lol = cross_val.LeaveOneLabelOut(labels)
  >>> for train_index, test_index in lol:
  ...    print "TRAIN:", train_index, "TEST:", test_index
  ...    X_train, X_test, y_train, y_test = cross_val.split(train_index,             test_index, X, y)
  ...    print X_train, X_test, y_train, y_test
  TRAIN: [False False  True  True] TEST: [ True  True False False]
  [[5 6]
  [7 8]] [[1 2]
  [3 4]] [1 2] [1 2]
  TRAIN: [ True  True False False] TEST: [False False  True  True]
  [[1 2]
  [3 4]] [[5 6]
  [7 8]] [1 2] [1 2]
docstr_tok: ["Leave-One-Label_Out","cross","validation",":","Provides","train\/test","indexes","to","split","data","in","train","test","sets","Parameters","--","--","--","--","--","labels",":","list","List","of","labels","Examples","--","--","--","--",">",">",">","from","scikits.learn","import","cross_val",">",">",">","X","=","[","[","1",",","2","]",",","[","3",",","4","]",",","[","5",",","6","]",",","[","7",",","8","]","]",">",">",">","y","=","[","1",",","2",",","1",",","2","]",">",">",">","labels","=","[","1",",","1",",","2",",","2","]",">",">",">","lol","=","cross_val.LeaveOneLabelOut","(","labels",")",">",">",">","for","train_index",",","test_index","in","lol",":","...","print","``","TRAIN",":","''",",","train_index",",","``","TEST",":","''",",","test_index","...","X_train",",","X_test",",","y_train",",","y_test","=","cross_val.split","(","train_index",",","test_index",",","X",",","y",")","...","print","X_train",",","X_test",",","y_train",",","y_test","TRAIN",":","[","False","False","True","True","]","TEST",":","[","True","True","False","False","]","[","[","5","6","]","[","7","8","]","]","[","[","1","2","]","[","3","4","]","]","[","1","2","]","[","1","2","]","TRAIN",":","[","True","True","False","False","]","TEST",":","[","False","False","True","True","]","[","[","1","2","]","[","3","4","]","]","[","[","5","6","]","[","7","8","]","]","[","1","2","]","[","1","2","]"]
code: def __init__(self, labels):
      """
          Leave-One-Label_Out cross validation:
          Provides train/test indexes to split data in train test sets
  
          Parameters
          ----------
          labels : list
                  List of labels
  
          Examples
          --------
          >>> from scikits.learn import cross_val
          >>> X = [[1, 2], [3, 4], [5, 6], [7, 8]]
          >>> y = [1, 2, 1, 2]
          >>> labels = [1, 1, 2, 2]
          >>> lol = cross_val.LeaveOneLabelOut(labels)
          >>> for train_index, test_index in lol:
          ...    print "TRAIN:", train_index, "TEST:", test_index
          ...    X_train, X_test, y_train, y_test = cross_val.split(train_index, \
              test_index, X, y)
          ...    print X_train, X_test, y_train, y_test
          TRAIN: [False False  True  True] TEST: [ True  True False False]
          [[5 6]
          [7 8]] [[1 2]
          [3 4]] [1 2] [1 2]
          TRAIN: [ True  True False False] TEST: [False False  True  True]
          [[1 2]
          [3 4]] [[5 6]
          [7 8]] [1 2] [1 2]
          """
      self.labels = labels
  
imports: from statsmodels.compat.python import lrange
  import numpy
  from itertools import combinations
label_desc: Use image node_id 1 to create a new LeaveOneLabelOut object  with example: obj = LeaveOneLabelOut(labels)