Combining Algorithms with NLTK





Now that we know how to use a bunch of algorithmic classifiers, like a child in the candy isle, told they can only pick one, we may find it difficult to choose just one classifier. The good news is, you don't have to! Combining classifier algorithms is is a common technique, done by creating a sort of voting system, where each algorithm gets one vote, and the classification that has the votes votes is the chosen one.

To do this, we want our new classifier to act like a typical NLTK classifier, with all of the methods. Simple enough, using object oriented programming, we can just be sure to inherit from the NLTK classifier class. To do this, we'll import it:

from nltk.classify import ClassifierI
from statistics import mode

We also import mode, as it will be our method for choosing the most popular vote.

Now, let's build our classifier class:

class VoteClassifier(ClassifierI):
    def __init__(self, *classifiers):
        self._classifiers = classifiers

We're calling our class the VoteClassifier, and we're inheriting from NLTK's ClassifierI. Next, we're assigning the list of classifiers that are passed to our class to self._classifiers.

Next, we want to go ahead and create our own classify method. We want to call it classify, so that we can invoke .classify later on, like a traditional NLTK classifier would allow.

    def classify(self, features):
        votes = []
        for c in self._classifiers:
            v = c.classify(features)
            votes.append(v)
        return mode(votes)

Easy enough, all we're doing here is iterating through our list of classifier objects. Then, for each one, we ask it to classify based on the features. The classification is being treated as a vote. After we are done iterating, we then return the mode(votes), which is just returning the most popular vote.

This is all we really need, but I think it would be useful to have another parameter, confidence. Since we have algorithms voting, we can also tally the votes for and against the winning vote, and call this "confidence." For example, 3/5 votes for positive is weaker than 5/5 votes. As such, we can literally return the ratio of votes as a sort of confidence indicator. Here's our confidence method:

    def confidence(self, features):
        votes = []
        for c in self._classifiers:
            v = c.classify(features)
            votes.append(v)

        choice_votes = votes.count(mode(votes))
        conf = choice_votes / len(votes)
        return conf

Now, let's put everything together:

import nltk
import random
from nltk.corpus import movie_reviews
from nltk.classify.scikitlearn import SklearnClassifier
import pickle

from sklearn.naive_bayes import MultinomialNB, BernoulliNB
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.svm import SVC, LinearSVC, NuSVC

from nltk.classify import ClassifierI
from statistics import mode


class VoteClassifier(ClassifierI):
    def __init__(self, *classifiers):
        self._classifiers = classifiers

    def classify(self, features):
        votes = []
        for c in self._classifiers:
            v = c.classify(features)
            votes.append(v)
        return mode(votes)

    def confidence(self, features):
        votes = []
        for c in self._classifiers:
            v = c.classify(features)
            votes.append(v)

        choice_votes = votes.count(mode(votes))
        conf = choice_votes / len(votes)
        return conf

documents = [(list(movie_reviews.words(fileid)), category)
             for category in movie_reviews.categories()
             for fileid in movie_reviews.fileids(category)]

random.shuffle(documents)

all_words = []

for w in movie_reviews.words():
    all_words.append(w.lower())

all_words = nltk.FreqDist(all_words)

word_features = list(all_words.keys())[:3000]

def find_features(document):
    words = set(document)
    features = {}
    for w in word_features:
        features[w] = (w in words)

    return features

#print((find_features(movie_reviews.words('neg/cv000_29416.txt'))))

featuresets = [(find_features(rev), category) for (rev, category) in documents]
        
training_set = featuresets[:1900]
testing_set =  featuresets[1900:]

#classifier = nltk.NaiveBayesClassifier.train(training_set)

classifier_f = open("naivebayes.pickle","rb")
classifier = pickle.load(classifier_f)
classifier_f.close()




print("Original Naive Bayes Algo accuracy percent:", (nltk.classify.accuracy(classifier, testing_set))*100)
classifier.show_most_informative_features(15)

MNB_classifier = SklearnClassifier(MultinomialNB())
MNB_classifier.train(training_set)
print("MNB_classifier accuracy percent:", (nltk.classify.accuracy(MNB_classifier, testing_set))*100)

BernoulliNB_classifier = SklearnClassifier(BernoulliNB())
BernoulliNB_classifier.train(training_set)
print("BernoulliNB_classifier accuracy percent:", (nltk.classify.accuracy(BernoulliNB_classifier, testing_set))*100)

LogisticRegression_classifier = SklearnClassifier(LogisticRegression())
LogisticRegression_classifier.train(training_set)
print("LogisticRegression_classifier accuracy percent:", (nltk.classify.accuracy(LogisticRegression_classifier, testing_set))*100)

SGDClassifier_classifier = SklearnClassifier(SGDClassifier())
SGDClassifier_classifier.train(training_set)
print("SGDClassifier_classifier accuracy percent:", (nltk.classify.accuracy(SGDClassifier_classifier, testing_set))*100)

##SVC_classifier = SklearnClassifier(SVC())
##SVC_classifier.train(training_set)
##print("SVC_classifier accuracy percent:", (nltk.classify.accuracy(SVC_classifier, testing_set))*100)

LinearSVC_classifier = SklearnClassifier(LinearSVC())
LinearSVC_classifier.train(training_set)
print("LinearSVC_classifier accuracy percent:", (nltk.classify.accuracy(LinearSVC_classifier, testing_set))*100)

NuSVC_classifier = SklearnClassifier(NuSVC())
NuSVC_classifier.train(training_set)
print("NuSVC_classifier accuracy percent:", (nltk.classify.accuracy(NuSVC_classifier, testing_set))*100)


voted_classifier = VoteClassifier(classifier,
                                  NuSVC_classifier,
                                  LinearSVC_classifier,
                                  SGDClassifier_classifier,
                                  MNB_classifier,
                                  BernoulliNB_classifier,
                                  LogisticRegression_classifier)

print("voted_classifier accuracy percent:", (nltk.classify.accuracy(voted_classifier, testing_set))*100)

print("Classification:", voted_classifier.classify(testing_set[0][0]), "Confidence %:",voted_classifier.confidence(testing_set[0][0])*100)
print("Classification:", voted_classifier.classify(testing_set[1][0]), "Confidence %:",voted_classifier.confidence(testing_set[1][0])*100)
print("Classification:", voted_classifier.classify(testing_set[2][0]), "Confidence %:",voted_classifier.confidence(testing_set[2][0])*100)
print("Classification:", voted_classifier.classify(testing_set[3][0]), "Confidence %:",voted_classifier.confidence(testing_set[3][0])*100)
print("Classification:", voted_classifier.classify(testing_set[4][0]), "Confidence %:",voted_classifier.confidence(testing_set[4][0])*100)
print("Classification:", voted_classifier.classify(testing_set[5][0]), "Confidence %:",voted_classifier.confidence(testing_set[5][0])*100)

So at the end here, we're running a few classification examples against text. All of our output:

Original Naive Bayes Algo accuracy percent: 66.0
Most Informative Features
                thematic = True              pos : neg    =      9.1 : 1.0
                secondly = True              pos : neg    =      8.5 : 1.0
                narrates = True              pos : neg    =      7.8 : 1.0
                 layered = True              pos : neg    =      7.1 : 1.0
                 rounded = True              pos : neg    =      7.1 : 1.0
                 supreme = True              pos : neg    =      7.1 : 1.0
                  crappy = True              neg : pos    =      6.9 : 1.0
               uplifting = True              pos : neg    =      6.2 : 1.0
                     ugh = True              neg : pos    =      5.3 : 1.0
                 gaining = True              pos : neg    =      5.1 : 1.0
                   mamet = True              pos : neg    =      5.1 : 1.0
                   wanda = True              neg : pos    =      4.9 : 1.0
                   onset = True              neg : pos    =      4.9 : 1.0
               fantastic = True              pos : neg    =      4.5 : 1.0
                   milos = True              pos : neg    =      4.4 : 1.0
MNB_classifier accuracy percent: 67.0
BernoulliNB_classifier accuracy percent: 67.0
LogisticRegression_classifier accuracy percent: 68.0
SGDClassifier_classifier accuracy percent: 57.99999999999999
LinearSVC_classifier accuracy percent: 67.0
NuSVC_classifier accuracy percent: 65.0
voted_classifier accuracy percent: 65.0
Classification: neg Confidence %: 100.0
Classification: pos Confidence %: 57.14285714285714
Classification: neg Confidence %: 57.14285714285714
Classification: neg Confidence %: 57.14285714285714
Classification: pos Confidence %: 57.14285714285714
Classification: pos Confidence %: 85.71428571428571

The next tutorial:






  • Tokenizing Words and Sentences with NLTK
  • Stop words with NLTK
  • Stemming words with NLTK
  • Part of Speech Tagging with NLTK
  • Chunking with NLTK
  • Chinking with NLTK
  • Named Entity Recognition with NLTK
  • Lemmatizing with NLTK
  • The corpora with NLTK
  • Wordnet with NLTK
  • Text Classification with NLTK
  • Converting words to Features with NLTK
  • Naive Bayes Classifier with NLTK
  • Saving Classifiers with NLTK
  • Scikit-Learn Sklearn with NLTK
  • Combining Algorithms with NLTK
  • Investigating bias with NLTK
  • Improving Training Data for sentiment analysis with NLTK
  • Creating a module for Sentiment Analysis with NLTK
  • Twitter Sentiment Analysis with NLTK
  • Graphing Live Twitter Sentiment Analysis with NLTK with NLTK
  • Named Entity Recognition with Stanford NER Tagger
  • Testing NLTK and Stanford NER Taggers for Accuracy
  • Testing NLTK and Stanford NER Taggers for Speed
  • Using BIO Tags to Create Readable Named Entity Lists