How to find the corresponding class in clf.predict_proba()

I have a number of classes and corresponding feature vectors, and when I run predict_proba() I will get this:

classes = ['one','two','three','one','three']

feature = [[0,1,1,0],[0,1,0,1],[1,1,0,0],[0,0,0,0],[0,1,1,1]]

from sklearn.naive_bayes import BernoulliNB

clf = BernoulliNB()
clf.fit(feature,classes)
clf.predict_proba([0,1,1,0])
>> array([[ 0.48247836,  0.40709111,  0.11043053]])

I would like to get what probability that corresponds to what class. On this page it says that they are ordered by arithmetical order, i'm not 100% sure of what that means: http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC.predict_proba

Does it mean that I have go trough my training examples assign the corresponding index to the first encounter of a class, or is there a command like

clf.getClasses() = ['one','two','three']?

Just use the .classes_ attribute of the classifier to recover the mapping. In your example that gives:

>>> clf.classes_
array(['one', 'three', 'two'], 
      dtype='|S5')

And thanks for putting a minimalistic reproduction script in your question, it makes answering really easy by just copy and pasting in a IPython shell :)

import pandas as pd
test = [[0,1,1,0],[1,1,1,0]]
pd.DataFrame(clf.predict_proba(test), columns=clf.classes_)

Out[2]:
         one	   three	     two
0	0.542815	0.361876	0.095309
1	0.306431	0.612863	0.080706

As a rule, any attribute in a learner that ends with _ is a learned one. In your case you're looking for clf.classes_.

Generally in Python, you can use the dir function to find out which attributes an object has.