How to get the sorted probability and name from cross_val_predict in sklearn in python

Question

I am using the following code to get the probability of class 1.

from sklearn import datasets

iris = datasets.load_iris()
X = iris.data
y = iris.target

clf=RandomForestClassifier(n_estimators=10, random_state = 42, class_weight="balanced")

k_fold = StratifiedKFold(n_splits=10, shuffle=True, random_state=0)
proba = cross_val_predict(clf, X, y, cv=k_fold, method='predict_proba')

#print probability of class 1
print(proba[:,1])

My result looks as follows.

[0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.1 0.  0.  0.
 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.
 0.2 0.  0.  0.  0.  0.1 0.  0.  0.  0.  0.  0.  0.  0.  0.9 1.  0.7 1.
 1.  1.  1.  0.7 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.9 0.9 0.1 1.
 0.6 1.  1.  1.  0.9 0.  1.  1.  1.  1.  1.  0.4 0.9 0.9 1.  1.  1.  0.9
 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.9 0.
 0.1 0.  0.  0.  0.  0.  0.  0.  0.1 0.  0.  0.8 0.  0.1 0.  0.1 0.  0.1
 0.3 0.2 0.  0.6 0.  0.  0.  0.6 0.4 0.  0.  0.  0.8 0.  0.  0.  0.  0.
 0.  0.  0.  0.  0.  0. ]

However, this is only a list of probabilites and hard to interpret the results.

Suppose, I also have a list of names for each data point in the iris dataset` as follows (Iris dataset has 150 datapoints).

iris_names = ['iris_0', 'iris_1', 'iris_2', 'iris_3', 'iris_4', 'iris_5', 'iris_6', 'iris_7', 'iris_8', 'iris_9', 'iris_10', 'iris_11', 'iris_12', 'iris_13', 'iris_14', 'iris_15', 'iris_16', 'iris_17', 'iris_18', 'iris_19', 'iris_20', 'iris_21', 'iris_22', 'iris_23', 'iris_24', 'iris_25', 'iris_26', 'iris_27', 'iris_28', 'iris_29', 'iris_30', 'iris_31', 'iris_32', 'iris_33', 'iris_34', 'iris_35', 'iris_36', 'iris_37', 'iris_38', 'iris_39', 'iris_40', 'iris_41', 'iris_42', 'iris_43', 'iris_44', 'iris_45', 'iris_46', 'iris_47', 'iris_48', 'iris_49', 'iris_50', 'iris_51', 'iris_52', 'iris_53', 'iris_54', 'iris_55', 'iris_56', 'iris_57', 'iris_58', 'iris_59', 'iris_60', 'iris_61', 'iris_62', 'iris_63', 'iris_64', 'iris_65', 'iris_66', 'iris_67', 'iris_68', 'iris_69', 'iris_70', 'iris_71', 'iris_72', 'iris_73', 'iris_74', 'iris_75', 'iris_76', 'iris_77', 'iris_78', 'iris_79', 'iris_80', 'iris_81', 'iris_82', 'iris_83', 'iris_84', 'iris_85', 'iris_86', 'iris_87', 'iris_88', 'iris_89', 'iris_90', 'iris_91', 'iris_92', 'iris_93', 'iris_94', 'iris_95', 'iris_96', 'iris_97', 'iris_98', 'iris_99', 'iris_100', 'iris_101', 'iris_102', 'iris_103', 'iris_104', 'iris_105', 'iris_106', 'iris_107', 'iris_108', 'iris_109', 'iris_110', 'iris_111', 'iris_112', 'iris_113', 'iris_114', 'iris_115', 'iris_116', 'iris_117', 'iris_118', 'iris_119', 'iris_120', 'iris_121', 'iris_122', 'iris_123', 'iris_124', 'iris_125', 'iris_126', 'iris_127', 'iris_128', 'iris_129', 'iris_130', 'iris_131', 'iris_132', 'iris_133', 'iris_134', 'iris_135', 'iris_136', 'iris_137', 'iris_138', 'iris_139', 'iris_140', 'iris_141', 'iris_142', 'iris_143', 'iris_144', 'iris_145', 'iris_146', 'iris_147', 'iris_148', 'iris_149']

Now, I want to sort my cross_val_predict results for the class 1 and add it with the iris names .

So, my expected output is as follows.

sorted_probability_of_class_1 = [[iris_xxx, 1], [iris_xxx, 1], ........, [iris_xxx, 0.9], [iris_xxx, 0.8], ........, [iris_xxx, 0], [iris_xxx, 0]]

How can I do it? Does the probabilities in cross_val_predict are in the order of our original datapoints?

I am happy to provide more details if needed.

Answer 1

Merging both lists into one using zip() :

sorted_probability_of_class_1 = zip(proba[:, 1], iris_names)

You may need to convert proba to a list first using list(proba) . Here is a more readable example of the zip method:

>>> probabilities = [1, 2, 3, 0]
>>> labels = ['a', 'b', 'c', 'd']
>>> list(zip(labels, probabilities))
[('a', 1), ('b', 2), ('c', 3), ('d', 0)]

The zipped list can be sorted using sorted(iterable, key) and itemgetter :

>>> from operator import itemgetter
>>> merged_list = list(zip(labels, probabilities))
>>> merged_list
[('a', 1), ('b', 2), ('c', 3), ('d', 0)]
>>> sorted(merged_list, key=itemgetter(1))
[('d', 0), ('a', 1), ('b', 2), ('c', 3)]

itemgetter(1) accesses the second element of the tuple in the list of tuples. This may need to be adjusted depending on your working code.