I know this question is old but it pops up every time I google nltk's NgramModel class. NgramModel's prob implementation is a little unintuitive. The asker is confused. As far as I can tell, the answers aren't great. Since I don't use NgramModel often, this means I get confused. No more.
The source code lives here: https://github.com/nltk/nltk/blob/master/nltk/model/ngram.py. Here is the definition of NgramModel's prob method:
def prob(self, word, context):
"""
Evaluate the probability of this word in this context using Katz Backoff.
:param word: the word to get the probability of
:type word: str
:param context: the context the word is in
:type context: list(str)
"""
context = tuple(context)
if (context + (word,) in self._ngrams) or (self._n == 1):
return self[context].prob(word)
else:
return self._alpha(context) * self._backoff.prob(word, context[1:])
(note: 'self[context].prob(word) is equivalent to 'self._model[context].prob(word)')
Okay. Now at least we know what to look for. What does context need to be? Let's look at an excerpt from the constructor:
for sent in train:
for ngram in ingrams(chain(self._lpad, sent, self._rpad), n):
self._ngrams.add(ngram)
context = tuple(ngram[:-1])
token = ngram[-1]
cfd[context].inc(token)
if not estimator_args and not estimator_kwargs:
self._model = ConditionalProbDist(cfd, estimator, len(cfd))
else:
self._model = ConditionalProbDist(cfd, estimator, *estimator_args, **estimator_kwargs)
Alright. The constructor creates a conditional probability distribution (self._model) out of a conditional frequency distribution whose "context" is tuples of unigrams. This tells us 'context' should not be a string or a list with a single multi-word string. 'context' MUST be something iterable containing unigrams. In fact, the requirement is a little more strict. These tuples or lists must be of size n-1. Think of it this way. You told it to be a trigram model. You better give it the appropriate context for trigrams.
Let's see this in action with a simpler example:
>>> import nltk
>>> obs = 'the rain in spain falls mainly in the plains'.split()
>>> lm = nltk.NgramModel(2, obs, estimator=nltk.MLEProbDist)
>>> lm.prob('rain', 'the') #wrong
0.0
>>> lm.prob('rain', ['the']) #right
0.5
>>> lm.prob('spain', 'rain in') #wrong
0.0
>>> lm.prob('spain', ['rain in']) #wrong
'''long exception'''
>>> lm.prob('spain', ['rain', 'in']) #right
1.0
(As a side note, actually trying to do anything with MLE as your estimator in NgramModel is a bad idea. Things will fall apart. I guarantee it.)
As for the original question, I suppose my best guess at what OP wants is this:
print lm.prob("word", "generates a".split())
print lm.prob("b", "generates a".split())
...but there are so many misunderstandings going on here that I can't possible tell what he was actually trying to do.
