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Unigram_bigram_models.py
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Unigram_bigram_models.py
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import os,re,time
import sys
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import metrics
from sklearn.pipeline import Pipeline
from nltk.stem import *
from nltk.stem.porter import *
from sklearn.svm import LinearSVC
import nltk.stem
from sklearn.naive_bayes import MultinomialNB
if __name__ == '__main__':
if len(sys.argv) != 3:
print ("Illegal use of Arguments: Best_configuration.py <Training_samples_location> <Testing_Samples_Location>")
exit(1)
train = sys.argv[1]
test = sys.argv[2]
''' Extracting the training samples '''
header_list = []
labels = []
i=0
for root, dirs, files in os.walk('C:/Users/sthatipally/Downloads/Training'):
for name in files:
fo = open(root +"/"+name, "r")
content = fo.read().replace('\n', ' ')
body = re.sub(r'^(.*) Lines: (\d)+ ', "", content)
header_list.append(unicode(body,errors='ignore'))
labels.append(i)
i=i+1
''' Extracting the testing samples '''
header_test = []
test_labels = []
i = 0
for root, dirs, files in os.walk('C:/Users/sthatipally/Downloads/Test'):
for name in files:
fo = open(root +"/"+name, "r")
content = fo.read().replace('\n', ' ')
body = re.sub(r'^(.*) Lines: (\d)+ ', "", content)
header_test.append(unicode(body,errors='ignore'))
test_labels.append(i)
i=i+1
print ("UNIGRAM BASELINE")
#### Naive bayes using pipeline #####
from sklearn.pipeline import Pipeline
start_time = time.time()
text_clf = Pipeline([('vect', CountVectorizer()),('tfidf', TfidfTransformer()),('clf', MultinomialNB()),])
text_clf = text_clf.fit(header_list, labels)
predicted = text_clf.predict(header_test)
print("Naive bayes")
print ("F1:",metrics.f1_score(test_labels, predicted, average='macro'))
print ("accuracy:", metrics.accuracy_score(test_labels, predicted))
print ("precision:",metrics.precision_score(test_labels, predicted, average='macro'))
print ("recall:",metrics.recall_score(test_labels, predicted, average='macro'))
print("Tine in seconds %s" %(time.time()-start_time))
#SVM###
from sklearn.linear_model import SGDClassifier
start_time = time.time()
text_clf = Pipeline([('vect', CountVectorizer()),('tfidf', TfidfTransformer()),('clf',
SGDClassifier(loss='hinge', penalty='l2',
)),])
text_clf = text_clf.fit(header_list, labels)
predicted = text_clf.predict(header_test)
print("SVM")
print ("F1:",metrics.f1_score(test_labels, predicted, average='macro'))
print ("accuracy:", metrics.accuracy_score(test_labels, predicted))
print ("precision:",metrics.precision_score(test_labels, predicted, average='macro'))
print ("recall:",metrics.recall_score(test_labels, predicted, average='macro'))
print("Tine in seconds %s" %(time.time()-start_time))
## logistic regression ##
from sklearn import linear_model
start_time = time.time()
logistic = linear_model.LogisticRegression()
text_clf = Pipeline([('vect', CountVectorizer(ngram_range=(1, 1))),('tfidf', TfidfTransformer()),('logistic', logistic)])
text_clf = text_clf.fit(header_list, labels)
predicted = text_clf.predict(header_test)
print ("Logistic")
print ("F1:",metrics.f1_score(test_labels, predicted, average='macro'))
print ("accuracy:", metrics.accuracy_score(test_labels, predicted))
print ("precision:",metrics.precision_score(test_labels, predicted, average='macro'))
print ("recall:",metrics.recall_score(test_labels, predicted, average='macro'))
print("Tine in seconds %s" %(time.time()-start_time))
from sklearn.ensemble import RandomForestClassifier
start_time = time.time()
Randomforest = RandomForestClassifier(n_estimators=100)
text_clf = Pipeline([('vect', CountVectorizer()),('tfidf', TfidfTransformer()),('Randomforest', Randomforest)])
text_clf = text_clf.fit(header_list, labels)
predicted = text_clf.predict(header_test)
print ("Random Forest")
print ("F1:",metrics.f1_score(test_labels, predicted, average='macro'))
print ("accuracy:", metrics.accuracy_score(test_labels, predicted))
print ("precision:",metrics.precision_score(test_labels, predicted, average='macro'))
print ("recall:",metrics.recall_score(test_labels, predicted, average='macro'))
print("Tine in seconds %s" %(time.time()-start_time))
print("####### BIGRAM BASELINE #######")
start_time = time.time()
text_clf = Pipeline([('vect', CountVectorizer(ngram_range=(2, 2),
token_pattern=r'\b\w+\b', min_df=1)),('tfidf', TfidfTransformer()),('clf', MultinomialNB()),])
text_clf = text_clf.fit(header_list, labels)
print ("Bigram Model-- Naive Bayes")
print ("F1:",metrics.f1_score(test_labels, predicted, average='macro'))
print ("accuracy:", metrics.accuracy_score(test_labels, predicted))
print ("precision:",metrics.precision_score(test_labels, predicted, average='macro'))
print ("recall:",metrics.recall_score(test_labels, predicted, average='macro'))
print("Tine in seconds %s" %(time.time()-start_time))
from sklearn.linear_model import SGDClassifier
start_time = time.time()
text_clf = Pipeline([('vect', CountVectorizer(ngram_range=(2, 2))),('tfidf', TfidfTransformer()),('clf',
SGDClassifier(loss='hinge', penalty='l2',
)),])
text_clf = text_clf.fit(header_list, labels)
predicted = text_clf.predict(header_test)
print ("SVM")
print ("F1:",metrics.f1_score(test_labels, predicted, average='macro'))
print ("accuracy:", metrics.accuracy_score(test_labels, predicted))
print ("precision:",metrics.precision_score(test_labels, predicted, average='macro'))
print ("recall:",metrics.recall_score(test_labels, predicted, average='macro'))
print("Tine in seconds %s" %(time.time()-start_time))
from sklearn import linear_model
start_time = time.time()
logistic = linear_model.LogisticRegression()
text_clf = Pipeline([('vect', CountVectorizer(ngram_range=(2, 2),
token_pattern=r'\b\w+\b', min_df=1)),('tfidf', TfidfTransformer()),('logistic', logistic)])
text_clf = text_clf.fit(header_list, labels)
predicted = text_clf.predict(header_test)
print ("logistic")
print ("F1:",metrics.f1_score(test_labels, predicted, average='macro'))
print ("accuracy:", metrics.accuracy_score(test_labels, predicted))
print ("precision:",metrics.precision_score(test_labels, predicted, average='macro'))
print ("recall:",metrics.recall_score(test_labels, predicted, average='macro'))
print("Tine in seconds %s" %(time.time()-start_time))
from sklearn.ensemble import RandomForestClassifier
start_time = time.time()
Randomforest = RandomForestClassifier(n_estimators=100)
text_clf = Pipeline([('vect',CountVectorizer(ngram_range=(2, 2),
token_pattern=r'\b\w+\b', min_df=1) ),('tfidf', TfidfTransformer()),('Randomforest', Randomforest)])
text_clf = text_clf.fit(header_list, labels)
predicted = text_clf.predict(header_test)
print ("Random Forest")
print ("F1:",metrics.f1_score(test_labels, predicted, average='macro'))
print ("accuracy:", metrics.accuracy_score(test_labels, predicted))
print ("precision:",metrics.precision_score(test_labels, predicted, average='macro'))
print ("recall:",metrics.recall_score(test_labels, predicted, average='macro'))
print("Tine in seconds %s" %(time.time()-start_time))