betterTrainingData.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Feb  2 15:53:02 2019

@author: jeetu
"""


import io
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

from nltk.tokenize import word_tokenize


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 = io.open("positive.txt", "r", encoding='latin-1').read()
short_neg = io.open("negative.txt", "r", encoding='latin-1').read()
documents = []

for r in short_pos.split('\n'):
    documents.append((r, "pos"))

for r in short_neg.split('\n'):
    documents.append((r, "neg"))

all_words = []

short_pos_words = word_tokenize(short_pos)
short_neg_words = word_tokenize(short_neg)

for w in short_pos_words:
    all_words.append(w.lower())

for w in short_neg_words:
    all_words.append(w.lower())

all_words = nltk.FreqDist(all_words)

word_features = list(all_words.keys())[:5000]


def find_features(document):
    words = word_tokenize(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]

random.shuffle(featuresets)

# positive data example:
training_set = featuresets[:10000]
testing_set = featuresets[10000:]

##
### negative data example:
##training_set = featuresets[100:]
##testing_set =  featuresets[:100]


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)

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(
    NuSVC_classifier,
    LinearSVC_classifier,
    MNB_classifier,
    BernoulliNB_classifier,
    LogisticRegression_classifier)

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





#.............................................




#import io
##io.open(filename, encoding='latin-1')
#
#import nltk
#import random
##from nltk.corpus import movie_reviews
#from nltk.classify.scikitlearn import SklearnClassifier
#import pickle
#from sklearn.naive_bayes import MultinomialNB, GaussianNB, BernoulliNB
#from sklearn.linear_model import LogisticRegression, SGDClassifier
#from sklearn.svm import SVC, LinearSVC,NuSVC
#from nltk.classify import ClassifierI
#from statistics import mode
#from nltk.tokenize import word_tokenize
#
#
#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 = io.open("positive.txt","r", encoding='latin-1').read()
#short_neg = io.open("negative.txt","r", encoding='latin-1').read()
##os.path.join(root, f)
#
#documents=[]
#
#for r in short_pos.split('/n'):
#    documents.append((r,"pos"))
#    
#for r in short_neg.split('/n'):
#    documents.append((r,"neg"))
#
#
#all_words=[]
#short_pos_words=word_tokenize(short_pos)
#short_neg_words=word_tokenize(short_neg)
#
#for w in short_pos_words:
#    all_words.append(w.lower())
#    
#for w in short_neg_words:
#    all_words.append(w.lower())
#
##
##
##documents = [(list(movie_reviews.words(fileid)), category)
##                for category in movie_reviews.categories()
##                for fileid in movie_reviews.fileids(category)]
##
###this is similar to this method
##'''
##
##documents=[]
##
##for category in movie_reviews.categories():
##    for fileid in movie_reviews.fileids(category):
##        documents.append(list(movie_reviews.word(fileid)),category)
##        
## '''       
##random.shuffle(documents)
###print (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())[:5000]
#
#def find_features(document):
#    words = word_tokenize(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] 
#
#random.shuffle(featuresets)
#
##positive data set example
#training_set =featuresets[:10000]
#testing_set = featuresets[10000:]
#
##negative data set example
##training_set =featuresets[100:]
##testing_set = featuresets[:100]
#
#
#classifier=nltk.NaiveBayesClassifier.train(training_set)
##classifier_file=open("naivebayes.pickle","rb")
##classifier=pickle.load(classifier_file)
##classifier_file.close()
#
#print ("Original Naive Bayes Algo accuracy in percentage:- ",(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 Algo accuracy in percentage:- ",(nltk.classify.accuracy(MNB_Classifier,testing_set))*100)
#
#MNB_classifier = SklearnClassifier(MultinomialNB())
#MNB_classifier.train(training_set)
#print("MNB_classifier accuracy percent:", (nltk.classify.accuracy(MNB_classifier, testing_set)) * 100)
#
##BernoulliNB
#BNB_Classifier=SklearnClassifier(BernoulliNB())
#BNB_Classifier.train(training_set)
#print ("BernoulliNB Algo accuracy in percentage:- ",(nltk.classify.accuracy(BNB_Classifier,testing_set))*100)
#
##LogisticRegression, SGDClassifier, SVC, LinearSVC, NuSVC
#
#LogisticRegression_Classifier=SklearnClassifier(LogisticRegression())
#LogisticRegression_Classifier.train(training_set)
#print ("LogisticRegression_Classifier Algo accuracy in percentage:- ",(nltk.classify.accuracy(LogisticRegression_Classifier,testing_set))*100)
#
#
#SGDClassifier=SklearnClassifier(SGDClassifier())
#SGDClassifier.train(training_set)
#print ("SGDClassifier Algo accuracy in percentage:- ",(nltk.classify.accuracy(SGDClassifier,testing_set))*100)
#
#
##SVC_Classifier=SklearnClassifier(SVC())
##SVC_Classifier.train(testing_set)
##print ("SVC_Classifier Algo accuracy in percentage:- ",(nltk.classify.accuracy(SVC_Classifier,testing_set))*100)
#
#
#LinearSVC_Classifier=SklearnClassifier(LinearSVC())
#LinearSVC_Classifier.train(training_set)
#print ("LinearSVC_Classifier Algo accuracy in percentage:- ",(nltk.classify.accuracy(LinearSVC_Classifier,testing_set))*100)
#
#
#NuSVC_Classifier=SklearnClassifier(NuSVC())
#NuSVC_Classifier.train(training_set)
#print ("NuSVC_Classifier Algo accuracy in percentage:- ",(nltk.classify.accuracy(NuSVC_Classifier,testing_set))*100)
#
#########   VOTE
#voted_classifier = VoteClassifier(
#        MNB_Classifier,
#        BNB_Classifier,
#        LogisticRegression_Classifier,
#        LinearSVC_Classifier,
#        NuSVC_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)
##
#


#..................