Manually train an NLTK NaiveBayes Classifier

manual_nltk_bayes_classify.py

from nltk.probability import DictionaryProbDist, ELEProbDist, FreqDist
from nltk import NaiveBayesClassifier
from nltk import FreqDist, ConditionalFreqDist
from nltk import BigramAssocMeasures
from collections import defaultdict

train_samples = {
    'I hate you and you are a bad person': 'neg',
    'I love you and you are a good person': 'pos',
    'I fail at everything and I want to kill people' : 'neg',
    'I win at everything and I want to love people' : 'pos',
    'sad are things are heppening. fml' : 'neg',
    'good are things are heppening. gbu' : 'pos',
    'I am so poor' : 'neg',
    'I am so rich' : 'pos',
    'I hate you mommy ! You are my terrible person' : 'neg',
    'I love you mommy ! You are my amazing person' : 'pos',
    'I want to kill butterflies since they make me sad' : 'neg',
    'I want to chase butterflies since they make me happy' : 'pos',
    'I want to hurt bunnies' : 'neg',
    'I want to hug bunnies' : 'pos',
    'You make me frown' : 'neg',
    'You make me smile' : 'pos',
}

test_samples = [
  'You are a terrible person and everything you do is bad',
  'I love you all and you make me happy',
  'I frown whenever I see you in a poor state of mind',
  'Finally getting rich from my ideas. They make me smile.',
  'My mommy is poor',
  'I love butterflies. Yay for happy',
  'Everything is fail today and I hate stuff',
]


def gen_bow(text):
    words = text.split()
    bow = {}
    for word in words:
        bow[word.lower()] = True
    return bow


def get_label_freqdist(samples):
    label_freqdist = ConditionalFreqDist()
    for words, label  in train_samples.items():
        for word in words.split():
            label_freqdist[label].inc(word.lower())
    return label_freqdist


def get_feature_probdist(label_freqdist):
    feature_freqdist = defaultdict(FreqDist)
    feature_values = defaultdict(set)
    for label in label_freqdist._fdists:
        num_samples = len(train_samples) / 2
        for fname, count in label_freqdist._fdists[label].items():
            feature_freqdist[label, fname].inc(None, num_samples)
            feature_values[fname].add(None)
    feature_probdist = {}
    for ((label, fname), freqdist) in feature_freqdist.items():
        probdist = ELEProbDist(freqdist, bins=len(feature_values[fname]))
        feature_probdist[label,fname] = probdist
    return feature_probdist

def get_label_probdist(label_freqdist):
    label_fd = FreqDist()
    for label in ['pos','neg']:
        for word in label_freqdist._fdists[label].items():
            label_fd.inc(label)
    label_probdist = ELEProbDist(label_fd)
    return label_probdist

#fd._fdists['neg']

label_freqdist = get_label_freqdist(train_samples)

label_probdist = get_label_probdist(label_freqdist)

feature_probdist = get_feature_probdist(label_freqdist)

classifier = NaiveBayesClassifier(label_probdist, feature_probdist)

for sample in test_samples:
    print "%s | %s" % (sample, classifier.classify(gen_bow(sample)))

classifier.show_most_informative_features()