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| @@ -0,0 +1,108 @@ | ||
| from __future__ import division | ||
| from math import log | ||
| import re | ||
| from nltk.corpus import movie_reviews as mr | ||
| from nltk.corpus import stopwords | ||
| STOP_WORDS = set(stopwords.words('english')) | ||
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| SPLIT_AMOUNT = 0.6 # training amount from data | ||
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| def SplitData(): | ||
| type_dict={} | ||
| docs_count={} | ||
| train_test = [[],[]] | ||
| for category in mr.categories(): | ||
| type_dict[category]=mr.fileids(categories=category) | ||
| for cat in type_dict.keys(): | ||
| li = type_dict[cat] | ||
| size=int(len(li)*SPLIT_AMOUNT) | ||
| 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 = 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] | ||
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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() | ||
| testset = li[0][1] | ||
| trainset = li[0][0] | ||
| # li = Preprocessor.startup() | ||
| cat_num_docs = li[2] | ||
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| #3)Create a dictionary with a word as the key and a dictionary as the value | ||
| ## in the dictionary the category as key and number of documents in that category where it occurs as value | ||
| # 2d dict: word -> {pos ...}, {neg ...} | ||
| word_cat_num_doc_dict={} | ||
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| #4)Loop through the reuters dataset, to get the entire text from each file in the training set | ||
| ## Parse the string to get individual words - done by get_list_tokens_nltk() | ||
| for file_name in trainset: | ||
| list_words = tokenize(file_name) | ||
| cat = mr.categories(fileids = file_name)[0] | ||
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| for w in set(list_words): | ||
| word_cat_num_doc_dict[w]=word_cat_num_doc_dict.get(w,{}) | ||
| word_cat_num_doc_dict[w][cat]=word_cat_num_doc_dict[w].get(cat,0) | ||
| word_cat_num_doc_dict[w][cat]+=1 | ||
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| for w in word_cat_num_doc_dict: | ||
| for cat in cat_num_docs: | ||
| nct = word_cat_num_doc_dict[w].get(cat,0) | ||
| # convert #times a word appears into #times+1/#cat_reviews+2 | ||
| ratio = (nct+1)/(cat_num_docs[cat]+2) | ||
| word_cat_num_doc_dict[w][cat]=ratio | ||
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| print "The Classifier is trained and it took" | ||
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| li_results=[] | ||
| #5) Like in the training set,Loop through the test set, to get the individual words | ||
| for file_name in testset: | ||
| minimum_neg_log_prob=1000000000 | ||
| min_category='' | ||
| set_list_words = set(tokenize(file_name)) | ||
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| ##6) Get the probability for each category, | ||
| #using the cat_num_docs dictionary to wade through the categories | ||
| for cat in cat_num_docs: | ||
| neg_log_prob=-log(cat_num_docs[cat]/len(trainset)) | ||
| for w in word_cat_num_doc_dict: | ||
| if w in set_list_words: | ||
| neg_log_prob-=log(word_cat_num_doc_dict[w][cat]) | ||
| else: | ||
| neg_log_prob-=log(1-word_cat_num_doc_dict[w][cat]) | ||
| if minimum_neg_log_prob>neg_log_prob: | ||
| min_category=cat | ||
| minimum_neg_log_prob=neg_log_prob | ||
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| li_results.append((file_name,min_category,mr.categories(fileids = file_name)[0])) | ||
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| CalculateAccuracy(li_results) | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,58 @@ | ||
| import csv | ||
| import os | ||
| from sklearn.feature_extraction.text import CountVectorizer | ||
| from sklearn.feature_extraction.text import TfidfTransformer | ||
| from sklearn.naive_bayes import MultinomialNB | ||
| from sklearn import cross_validation | ||
| from sklearn.metrics import classification_report | ||
| import numpy as np | ||
| from sklearn.metrics import accuracy_score | ||
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| REVIEWS = os.path.join(os.path.dirname(os.path.abspath(__file__)),'allrevs.csv') | ||
| # review.csv contains two columns | ||
| # first column is the review content (quoted) | ||
| # second column is the assigned sentiment (positive or negative) | ||
| def load_file(): | ||
| with open(REVIEWS) as csv_file: | ||
| reader = csv.reader(csv_file,delimiter=",",quotechar='"') | ||
| reader.next() | ||
| data =[] | ||
| target = [] | ||
| for row in reader: | ||
| # skip missing data | ||
| if row[0] and row[1]: | ||
| data.append(row[0]) | ||
| target.append(row[1]) | ||
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| return data,target | ||
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| # preprocess creates the term frequency matrix for the review data set | ||
| def preprocess(): | ||
| data,target = load_file() | ||
| count_vectorizer = CountVectorizer(binary='true', stop_words='english') | ||
| data = count_vectorizer.fit_transform(data) | ||
| # tfidf_data = TfidfTransformer(norm='l2', use_idf=True, sublinear_tf=True, smooth_idf=True).fit_transform(data) | ||
| transformer = TfidfTransformer(norm='l2', use_idf=True, sublinear_tf=True, smooth_idf=True) | ||
| transformer.fit(data) | ||
| tfidf_data = transformer.transform(data) | ||
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| return tfidf_data | ||
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| def learn_model(data,target): | ||
| # preparing data for split validation. 60% training, 40% test | ||
| data_train,data_test,target_train,target_test = cross_validation.train_test_split(data,target,test_size=0.4,random_state=43) | ||
| classifier = MultinomialNB().fit(data_train,target_train) | ||
| predicted = classifier.predict(data_test) | ||
| evaluate_model(target_test,predicted) | ||
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| # | ||
| def evaluate_model(target_true,target_predicted): | ||
| # print classification_report(target_true,target_predicted) | ||
| print "The accuracy score is {:.2%}".format(accuracy_score(target_true,target_predicted)) | ||
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| data,target = load_file() | ||
| tf_idf = preprocess() | ||
| learn_model(tf_idf,target) | ||
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