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functions
i did a thing - but i did it at 2 am so this might be bad
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###################### | ||
# This version is CWMNB only | ||
###################### | ||
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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 | ||
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COMPLEMENT = 0 | ||
WEIGHTED = 0 | ||
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] | ||
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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)) | ||
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return [w.lower() for w in list_words if w.isalpha() and len(w)>1 and w.lower() not in STOP_WORDS] | ||
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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 | ||
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# 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] | ||
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##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 = [] | ||
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##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) | ||
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##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) | ||
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# 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 | ||
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##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) | ||
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####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) | ||
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##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' | ||
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neg_log_prob = log(cat_num_docs[cat]/length_train) | ||
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# neg_log_prob = cat_num_docs[cat]/length_train | ||
opp_word_dict = cat_word_dict[inv_cat] | ||
opp_count_cat = cat_word_count_dict[inv_cat] | ||
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word_dict = cat_word_dict[cat] | ||
count_cat = cat_word_count_dict[cat] | ||
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my_word_count = {} | ||
for aw in list_words: | ||
my_word_count[aw] = my_word_count.get(aw, 0) | ||
my_word_count[aw]+=1 | ||
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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) | ||
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if COMPLEMENT: | ||
count_word_train=opp_word_dict.get(kw,0) | ||
ratio = (count_word_train+1)/(opp_count_cat+vocab_length) | ||
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# weight norm | ||
weight_normalizing_ratio += abs(log(ratio)) | ||
## TF | ||
# my_word_count[kw] = log(my_word_count[kw]+1) | ||
## length norm | ||
# length_norm += (my_word_count[kw]**(2)) | ||
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# length_norm = length_norm**(0.5) | ||
# print "WNR: ", weight_normalizing_ratio | ||
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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 | ||
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if COMPLEMENT: | ||
count_word_train=opp_word_dict.get(w,0) | ||
ratio = (count_word_train+1)/(opp_count_cat+vocab_length) | ||
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word_freq = my_word_count[w] | ||
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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 | ||
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ratio = log(ratio) # weight factor log(theta_c) = weight_c,w | ||
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if WEIGHTED: | ||
ratio = ratio/weight_normalizing_ratio # weight normalization | ||
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if COMPLEMENT: | ||
neg_log_prob -= word_freq*ratio | ||
else: | ||
neg_log_prob += word_freq*ratio # class probability | ||
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# 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 | ||
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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 | ||
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###--------------------DEBUG STATEMENTS---------------------- | ||
#for t in li_results: | ||
# if t[1]!=t[2]: | ||
# print t | ||
###--------------------DEBUG STATEMENTS---------------------- | ||
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###--------------------DEBUG STATEMENTS---------------------- | ||
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#12) Evaluating the classifier | ||
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CalculateAccuracy(li_results) |
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