How to customize Stanford NER in python?

I learned how to customize Stanford NER (Named Entity Recognizer) in Java from here:

http://nlp.stanford.edu/software/crf-faq.shtml#a

But I am developing my project with Python and here I need to train my classier with some custom entities.

I searched a lot for a solution but could not find any. Any idea? If it is not possible, is there any other way to train my classifier with custom entities, i.e, with nltk or others in python?

EDIT: Code addition This is what I did to set up and test Stanford NER which worked nicely:

from nltk.tag.stanford import StanfordNERTagger
path_to_model = "C:\..\stanford-ner-2016-10-31\classifiers\english.all.3class.distsim.crf.ser"
path_to_jar = "C:\..\stanford-ner-2016-10-31\stanford-ner.jar"
nertagger=StanfordNERTagger(path_to_model, path_to_jar)
query="Show  me the best eye doctor in Munich"
print(nertagger.tag(query.split()))

This code worked successfully. Then, I downloaded the sample austen.prop file and both jane-austen-emma-ch1.tsv and jane-austen-emma-ch2.tsv file and put it in a custom folder in NerTragger library folder. I modified the jane-austen-emma-ch1.tsv file with my custom entity tags. The code of austen.prop file has link to jane-austen-emma-ch1.tsv file. Now, I modified the above code as follow but it is not working:

from nltk.tag.stanford import StanfordNERTagger
path_to_model = "C:\..\stanford-ner-2016-10-31\custom/austen.prop"
path_to_jar = "C:\..\stanford-ner-2016-10-31\stanford-ner.jar"
nertagger=StanfordNERTagger(path_to_model, path_to_jar)
query="Show  me the best eye doctor in Munich"
print(nertagger.tag(query.split()))

But this code is producing the following error:

Exception in thread "main" edu.stanford.nlp.io.RuntimeIOException: java.io.StreamCorruptedException: invalid stream header: 236C6F63
    raise OSError('Java command failed : ' + str(cmd))
    at edu.stanford.nlp.ie.AbstractSequenceClassifier.loadClassifierNoExceptions(AbstractSequenceClassifier.java:1507)
    at edu.stanford.nlp.ie.crf.CRFClassifier.main(CRFClassifier.java:3017)
Caused by: java.io.StreamCorruptedException: invalid stream header: 236C6F63
OSError: Java command failed : ['C:\\Program Files\\Java\\jdk1.8.0_111\\bin\\java.exe', '-mx1000m', '-cp', 'C:/Users/HP/Desktop/Downloads1/Compressed/stanford-ner-2016-10-31/stanford-ner-2016-10-31\\stanford-ner-3.7.0-javadoc.jar;C:/Users/HP/Desktop/Downloads1/Compressed/stanford-ner-2016-10-31/stanford-ner-2016-10-31\\stanford-ner-3.7.0-sources.jar;C:/Users/HP/Desktop/Downloads1/Compressed/stanford-ner-2016-10-31/stanford-ner-2016-10-31\\stanford-ner-3.7.0.jar;C:/Users/HP/Desktop/Downloads1/Compressed/stanford-ner-2016-10-31/stanford-ner-2016-10-31\\stanford-ner.jar;C:/Users/HP/Desktop/Downloads1/Compressed/stanford-ner-2016-10-31/stanford-ner-2016-10-31\\lib\\joda-time.jar;C:/Users/HP/Desktop/Downloads1/Compressed/stanford-ner-2016-10-31/stanford-ner-2016-10-31\\lib\\jollyday-0.4.9.jar;C:/Users/HP/Desktop/Downloads1/Compressed/stanford-ner-2016-10-31/stanford-ner-2016-10-31\\lib\\stanford-ner-resources.jar', 'edu.stanford.nlp.ie.crf.CRFClassifier', '-loadClassifier', 'C:/Users/HP/Desktop/Downloads1/Compressed/stanford-ner-2016-10-31/stanford-ner-2016-10-31/custom/austen.prop', '-textFile', 'C:\\Users\\HP\\AppData\\Local\\Temp\\tmppk8_741f', '-outputFormat', 'slashTags', '-tokenizerFactory', 'edu.stanford.nlp.process.WhitespaceTokenizer', '-tokenizerOptions', '"tokenizeNLs=false"', '-encoding', 'utf8']
    at java.io.ObjectInputStream.readStreamHeader(ObjectInputStream.java:808)
    at java.io.ObjectInputStream.<init>(ObjectInputStream.java:301)
    at edu.stanford.nlp.ie.AbstractSequenceClassifier.loadClassifier(AbstractSequenceClassifier.java:1462)
    at edu.stanford.nlp.ie.AbstractSequenceClassifier.loadClassifier(AbstractSequenceClassifier.java:1494)
    at edu.stanford.nlp.ie.AbstractSequenceClassifier.loadClassifierNoExceptions(AbstractSequenceClassifier.java:1505)
    ... 1 more

Solution

The Stanford NER classifier is a java program. The NLTK's module is only an interface to the java executable. So you train a model exactly as you did before (or as you saw done in the link you provide).

In your code, you are confusing the training of a model with its use to chunk new text. The .prop file contains instructions for training a new model; it is not itself a model. This is what I recommend:

Forget about python/nltk for the moment, and train a new model from the Windows command line (CMD prompt or whatever): Follow the how-to you mention in your question, to generate a serialized model (.ser file) named ner-model.ser.gz or whatever you decide to call it from your .prop file.
In your python code, set the path_to_model variable to point to the .ser file you generated in step 1.

If you really want to control the training process from python, you could use the subprocess module to issue the appropriate command line commands. But it sounds like you don't really need this; just try to understand what these steps do so that you can carry them out properly.