I am getting this error in spider pls help me out
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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
from sklearn.svm import LinearSVC
from nltk.classify import ClassifierI
from statistics import mode
from nltk.tokenize import word_tokenize
##from sklearn.preprocessing import LabelEncoder
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
short_pos = open("positive.txt","r").read()
short_neg = open("negative.txt","r").read()
# move this up here
all_words = []
documents = []
# j is adject, r is adverb, and v is verb
#allowed_word_types = ["J","R","V"]
allowed_word_types = ["J"]
for p in short_pos.split('\n'):
documents.append( (p, "pos") )
words = word_tokenize(p)
pos = nltk.pos_tag(words)
for w in pos:
if w[1][0] in allowed_word_types:
all_words.append(w[0].lower())
for p in short_neg.split('\n'):
documents.append( (p, "neg") )
words = word_tokenize(p)
pos = nltk.pos_tag(words)
for w in pos:
if w[1][0] in allowed_word_types:
all_words.append(w[0].lower())
save_documents = open("pickled_algos/documents.pickle","wb")
pickle.dump(documents, save_documents)
save_documents.close()
all_words = nltk.FreqDist(all_words)
word_features = list(all_words.keys())[:5000]
save_word_features = open("pickled_algos/word_features5k.pickle","wb")
pickle.dump(word_features, save_word_features)
save_word_features.close()
def find_features(document):
words = word_tokenize(document)
features = {}
for w in word_features:
features[w] = (w in words)
return features
featuresets = [(find_features(rev), category) for (rev, category) in documents]
random.shuffle(featuresets)
print(len(featuresets))
testing_set = featuresets[10000:]
training_set = featuresets[:10000]
save_word_features = open("pickled_algos/featuresets.pickle","wb")
pickle.dump(word_features, save_word_features)
save_word_features.close()
classifier = nltk.NaiveBayesClassifier.train(training_set)
print("Original Naive Bayes Algo accuracy percent:", (nltk.classify.accuracy(classifier, testing_set))*100)
classifier.show_most_informative_features(15)
###############
save_classifier = open("pickled_algos/originalnaivebayes5k.pickle","wb")
pickle.dump(classifier, save_classifier)
save_classifier.close()
MNB_classifier = SklearnClassifier(MultinomialNB())
MNB_classifier.train(training_set)
print("MNB_classifier accuracy percent:", (nltk.classify.accuracy(MNB_classifier, testing_set))*100)
save_classifier = open("pickled_algos/MNB_classifier5k.pickle","wb")
pickle.dump(MNB_classifier, save_classifier)
save_classifier.close()
BernoulliNB_classifier = SklearnClassifier(BernoulliNB())
BernoulliNB_classifier.train(training_set)
print("BernoulliNB_classifier accuracy percent:", (nltk.classify.accuracy(BernoulliNB_classifier, testing_set))*100)
save_classifier = open("pickled_algos/BernoulliNB_classifier5k.pickle","wb")
pickle.dump(BernoulliNB_classifier, save_classifier)
save_classifier.close()
LogisticRegression_classifier = SklearnClassifier(LogisticRegression())
LogisticRegression_classifier.train(training_set)
print("LogisticRegression_classifier accuracy percent:", (nltk.classify.accuracy(LogisticRegression_classifier, testing_set))*100)
save_classifier = open("pickled_algos/LogisticRegression_classifier5k.pickle","wb")
pickle.dump(LogisticRegression_classifier, save_classifier)
save_classifier.close()
LinearSVC_classifier = SklearnClassifier(LinearSVC())
LinearSVC_classifier.train(training_set)
print("LinearSVC_classifier accuracy percent:", (nltk.classify.accuracy(LinearSVC_classifier, testing_set))*100)
save_classifier = open("pickled_algos/LinearSVC_classifier5k.pickle","wb")
pickle.dump(LinearSVC_classifier, save_classifier)
save_classifier.close()
##NuSVC_classifier = SklearnClassifier(NuSVC())
##NuSVC_classifier.train(training_set)
##print("NuSVC_classifier accuracy percent:", (nltk.classify.accuracy(NuSVC_classifier, testing_set))*100)
##SGDC_classifier = SklearnClassifier(SGDClassifier())
##SGDC_classifier.train(training_set)
##print("SGDClassifier accuracy percent:",nltk.classify.accuracy(SGDC_classifier, testing_set)*100)
##save_classifier = open("pickled_algos/SGDC_classifier5k.pickle","wb")
##pickle.dump(SGDC_classifier, save_classifier)
##save_classifier.close()
