Write a classification script named “classifier.py’ that trains on reviews_train.txt and achieves the maximum possible accuracy on reviews_test.txt.
You can use any type of classifier that you want.
DT Notepad
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A simple script that demonstrates how we classify textual data with sklearn.
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from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
#read the reviews and their polarities from a given file
def loadData(fname):
reviews=[]
labels=[]
f=open(fname)
for line in f:
review,rating=line.strip().split(“t’)
reviews.append(review.lower())
labels.append(int(rating))
f.close()
return reviews,labels
rev_train,labels_train=loadData(“reviews_train.txt’)
rev_test,labels_test=loadData(“reviews_test.txt’)
#Build a counter based on the training dataset
counter = CountVectorizer()
counter.fit(rev_train)
#count the number of times each term appears in a document and transform each doc into a count vector
counts_train = counter.transform(rev_train)#transform the training data
counts_test = counter.transform(rev_test)#transform the testing data
#train classifier
clf = DecisionTreeClassifier()
#train all classifier on the same datasets
clf.fit(counts_train,labels_train)
#use hard voting to predict (majority voting)
pred=clf.predict(counts_test)
#print accuracy
print (accuracy_score(pred,labels_test))
