def create_classifier(iterations=100):
"""
Return the classifier that did the best at classifying a subset of the data
after training for the given number of iterations
:param iterations: number of iterations to test on
:return: tuple: (classifier, accuracy of classifier)
"""
negids = reddit_politics.fileids("neg")
posids = reddit_politics.fileids("pos")
negfeats = [(word_feats(reddit_politics.words(fileids=[f]), FILTER_LIST), "neg") for f in negids]
posfeats = [(word_feats(reddit_politics.words(fileids=[f]), FILTER_LIST), "pos") for f in posids]
# track the most accurate classifier so far
best_classifier = None
highest_accuracy = 0
for iter_num in range(iterations):
# randomly shuffle the feature sets to get new subsets to test and train on
random.shuffle(negfeats)
random.shuffle(posfeats)
negcutoff = int(len(negfeats) * 3 / 4)
poscutoff = int(len(posfeats) * 3 / 4)
trainfeats = negfeats[:negcutoff] + posfeats[:poscutoff] # negfeats[:negcutoff] + posfeats[:poscutoff]
testfeats = negfeats[negcutoff:] + posfeats[poscutoff:] # negfeats[negcutoff:] + posfeats[poscutoff:]
if DEBUG:
print("Train on %d instances, test on %d instances.\n" % (len(trainfeats), len(testfeats)))
# train the classifier on the training features and determine its accuracy
classifier = NaiveBayesClassifier.train(trainfeats)
accuracy = nltk.classify.util.accuracy(classifier, testfeats)
if DEBUG:
print("\nAccuracy:", accuracy)
# if this classifier outperformed all before it, track it and its accuracy
if accuracy > highest_accuracy:
highest_accuracy = accuracy
best_classifier = classifier
utils.update_progress(iter_num / iterations, message="Testing Classifiers")
sys.stdout.write("\n\n")
return (classifier, highest_accuracy)
def word_feats(words, filter_list):
return dict([(word, True) for word in words if word not in filter_list and len(word) > 2])
# get all of the file IDs associated with the positive and negative entries in each corpus
reddit_negids = reddit_politics.fileids('neg')
reddit_posids = reddit_politics.fileids('pos')
movie_negids = movie_reviews.fileids('neg')
movie_posids = movie_reviews.fileids('pos')
# process the positive and negative features of each corpus
reddit_negfeats = [(word_feats(reddit_politics.words(fileids=[f]), FILTER_LIST), 'neg') for f in reddit_negids]
reddit_posfeats = [(word_feats(reddit_politics.words(fileids=[f]), FILTER_LIST), 'pos') for f in reddit_posids]
movie_negfeats = [(word_feats(movie_reviews.words(fileids=[f]), FILTER_LIST), 'neg') for f in movie_negids]
movie_posfeats = [(word_feats(movie_reviews.words(fileids=[f]), FILTER_LIST), 'pos') for f in movie_posids]
# list of tuples containing the accuracies of each training corpus
accuracies = []
for _ in range(ITERATIONS):
# shuffle the reddit features each iteration so that a new subset of them is being used for testing
random.shuffle(reddit_negfeats)
random.shuffle(reddit_posfeats)
# find the cutoff points in both the positive and negative feature sets to divide training and testing data
negcutoff = int(len(reddit_negfeats) * 3 / 4)
poscutoff = int(len(reddit_posfeats) * 3 / 4)