TCWNB2.py

######################
# This version is MNB only
######################

from __future__ import division
from math import log
import re
import csv
from nltk.corpus import movie_reviews as mr
from nltk.corpus import stopwords
import random
STOP_WORDS = set(stopwords.words('english'))
SPLIT_AMOUNT = 0.6          # training amount from data 

USE_IDF = 0
AMAZON = 0
REVIEW_POL={}
DEFINED_SIZE = 1
DEFINED_SIZES = {'pos': 600, 'neg': 600}
def SplitData():
    type_dict={}
    docs_count={}
    train_test = [[],[]]
    offset_sample = random.randint(-400,400)
    print "offset_sample", offset_sample
    if AMAZON:
        offset_sample = random.randint(-600,600)
        for category in ['pos', 'neg']:
            type_dict[category]=[]
        with open('amazon_revs.csv', 'rb') as csvfile:
            rev_read = csv.reader(csvfile)
            for row in rev_read:
                type_dict[row[1]].append(row[0])
                REVIEW_POL[row[0]] = row[1]
    else:
        for category in mr.categories():
            type_dict[category]=mr.fileids(categories=category)
    for cat in type_dict.keys():
        li = type_dict[cat]
        random.shuffle(li)
        size=int(len(li)*SPLIT_AMOUNT) + offset_sample
        if DEFINED_SIZE:
            size = DEFINED_SIZES[cat]
        print "Category: ", cat, "Size:", size
        offset_sample *= -1
        docs_count[cat]=size
        train_test[0].extend(li[:size])
        train_test[1].extend(li[size:])
    return [train_test,type_dict, docs_count]
    
def tokenize(file_name): 
    list_words = ()
    if AMAZON:
        list_words = re.split(r'\W+',file_name)
    else:
        list_words = re.split(r'\W+',mr.raw(fileids=file_name))
    
    return [w.lower() for w in list_words if w.isalpha() and len(w)>1 and w.lower() not in STOP_WORDS]


def CalculateAccuracy(li_results):
    a=0
    b=0
    c=0
    d=0
    cat = li_results[0][1]
    for t in li_results:
        if cat==t[1]:
            if cat==t[2]:
                a+=1
            else:
                b+=1
        else:
            if cat==t[2]:
                c+=1
            else:
                d+=1
    precision = a/(a+b)
    # recall = a/(a+c)
    # print "The following parameters are recorded for the category " , cat
    print "precision =", precision
    
# li = Preprocessor.get_testset_trainset(corpus)
li = SplitData()
# exit()
testset = li[0][1]
trainset = li[0][0]
# li = Preprocessor.startup()
cat_num_docs = li[2]
    


##4)Create a) a dictionary with a category as the key and dictionary of words-occurrences as values
          #b) a dictionary with a category as the key and the number of words in it as the value
# {pos-> {w1 = 17 times}, {w2 = 32 times}...} {neg-> ....}
cat_word_dict={}
# {pos-> 4000 words} {neg-> 7000 words}
cat_word_count_dict={}
#val = my_dict.get(key, mydefaultval)
complete_training_docs_tokens = []

##5)Loop through the training set, to get the entire text from  each file
##6) Parse the string to get individual words
for file_name in trainset:
    list_words = tokenize(file_name)
    complete_training_docs_tokens.append(list_words)
    

##7) Check if category exists in dictionary, if not, create an empty dictionary,
    #and put word count as zero
    #and then insert words into the category's dictionary in both cases and update the word count
    cat = ''
    if AMAZON:
        cat = REVIEW_POL[file_name]
    else:
        cat = mr.categories(fileids = file_name)[0]
    cat_word_dict[cat] = cat_word_dict.get(cat,{})
    cat_word_count_dict[cat] = cat_word_count_dict.get(cat,0)
    
# add number of words to total word count for cat
    cat_word_count_dict[cat]+=len(list_words)   
# start count for number of occurences for each word    
    for w in list_words:
        cat_word_dict[cat][w] = cat_word_dict[cat].get(w, 0)
        cat_word_dict[cat][w]+=1
        
        

##8) Get the vocabulary length
## number of words, total across categories
vocab_length=0     
num_docs_word_in = {}        
for dic in cat_word_dict.values():
     vocab_length+=len(dic)
     if USE_IDF:
         for uniq_word in dic.keys():
            num_docs_word_in[uniq_word] = num_docs_word_in.get(uniq_word, 1)
            num_docs_word_in[uniq_word] = sum(1 for sr in complete_training_docs_tokens if uniq_word in sr)

        
        
####Congratulations! the Classifier is trained, now it is time to run the Multinomial Naive Bayes Classifier on the test dataset
length_train = len(trainset)
li_results=[]
#9) Like in the training set,Loop through the test set, to get the entire text from  each file
##10) Similar step, parse the string to get individual words
for file_name in testset:
    # print "File: ", file_name
    # minimum_neg_log_prob=1000000000
    minimum_neg_log_prob = -1000000000      # NEW
    min_category=''
    list_words = tokenize(file_name)


    
##11) Get the probability for each category,
    #can use any of the created dictionaries to wade through the categories
    for cat in  cat_word_count_dict:
        # print cat , cat_num_docs[cat]/len(trainset)
        # print "________________________________________________________________"
        # print "________________________________________________________________"
        # print "\n\n" , cat, cat, cat, cat, cat, cat, cat, cat, cat, cat, "\n\n"
        # neg_log_prob=-log(cat_num_docs[cat]/length_train)
        inv_cat = 'pos'
        if cat == 'pos':
            inv_cat = 'neg'
        
        
        neg_log_prob=log(cat_num_docs[cat]/length_train)
        
        # neg_log_prob = cat_num_docs[cat]/length_train
        # word_dict = cat_word_dict[inv_cat]
        # count_cat = cat_word_count_dict[inv_cat]
        
        word_dict = cat_word_dict[cat]
        count_cat = cat_word_count_dict[cat]
                
        my_word_count = {}
        for aw in list_words:
            my_word_count[aw] = my_word_count.get(aw, 0)
            my_word_count[aw]+=1
        
        length_norm = 0
        weight_normalizing_ratio = 0
        for kw in my_word_count.keys():
            count_word_train=word_dict.get(kw,0)
            ratio = (count_word_train+1)/(count_cat+vocab_length)
            ## weight norm
            # weight_normalizing_ratio+=log(ratio)
            ## TF
            # my_word_count[kw] = log(my_word_count[kw]+1)
            ## length norm
            # length_norm += (my_word_count[kw]**(2))
            
        # length_norm = length_norm**(0.5)   
        # print "WNR: ", weight_normalizing_ratio
        
        for w in my_word_count.keys():
            count_word_train=word_dict.get(w,0)
            ratio = (count_word_train+1)/(count_cat+vocab_length)   #Nw,c+1/Nc+|V| = theta_c
            # neg_log_prob-=log(ratio)
            word_freq = my_word_count[w]
            if USE_IDF:
                word_freq = word_freq*log(length_train/num_docs_word_in.get(w,1))     #IDF
            # word_freq = word_freq/length_norm       # length normalization
            
            # neg_log_prob += word_freq*log(ratio)    #switch to 
            ratio = log(ratio)      # weight factor     log(theta_c) = weight_c,w
            # ratio = ratio/weight_normalizing_ratio      # weight normalization
            neg_log_prob += word_freq*ratio    # class probability
            
            
            # neg_log_prob *= ratio
            # print w, "Ratio found:",ratio, "new_neg_log:", neg_log_prob
            # break
        # print "NLP: ", neg_log_prob
        # print "\n\n", cat, minimum_neg_log_prob , '<' , neg_log_prob                 
        # if minimum_neg_log_prob>neg_log_prob:
        if minimum_neg_log_prob<neg_log_prob:
            min_category=cat
            minimum_neg_log_prob=neg_log_prob
    # print "Min cat: ", min_category
    # correct_cat = 'pos'
    # if file_name in all_review_cats['neg']:
        # correct_cat = 'neg'
    if AMAZON:
        li_results.append((file_name,min_category,REVIEW_POL[file_name]))
    else:
        li_results.append((file_name,min_category,mr.categories(fileids = file_name)[0]))
    # break

###--------------------DEBUG STATEMENTS----------------------
#for t in li_results:
 #   if t[1]!=t[2]:
  #      print t
###--------------------DEBUG STATEMENTS----------------------
    
###--------------------DEBUG STATEMENTS----------------------

#12) Evaluating the classifier

CalculateAccuracy(li_results)