def rate(review):
review = SVM.asciify(review)
inputData = []
# 1st element = bayes with unigram
bayesClassifier.loadData("U")
inputData.append(bayesClassifier.percentPositive(review))
# bayes with adjective
bayesClassifier.loadData("A")
inputData.append(bayesClassifier.percentPositive(review))
# bayes with POS
bayesClassifier.loadData("P")
bayesClassifier.partOfSpeech = True
inputData.append(bayesClassifier.percentPositive(review))
# SVM with unigram
review = review.split()
SVM.loadModule("U")
SVM.loadWords("U")
X = SVM.intersection(SVM.wordList, review)
# SVM with adjective
inputData.append(SVM.movieReviewer.predict(X)[0])
SVM.loadModule("A")
SVM.loadWords("A")
X = SVM.intersection(SVM.wordList, review)
inputData.append(SVM.movieReviewer.predict(X)[0])
return Tree.predict(inputData)[0]
#!/usr/bin/python
import SVM
from sklearn.externals import joblib
import os
from sys import argv
os.system("echo -n 'loading keywords...\t\t'")
SVM.loadModule(argv[1])
SVM.loadWords(argv[1])
os.system("echo -n '[done]\n'")
os.system("echo -n 'testing files:\n[00%'")
files = os.listdir('./test/pos')[:3000]
total = float(len(files)) * 2
done = 0
progress = 0
correct = 0
for i in files:
f = SVM.asciify(open('./test/pos/' + i, 'r').read()).split()
result = SVM.movieReviewer.predict(SVM.intersection(SVM.wordList, f))[0]
if result > 0:
correct += 1
done += 1
if (done / total * 100 >= progress + 5):
progress += 5
#os.system("echo -n '\b\b\b=%2d%%'" % progress)
files = os.listdir('./test/neg')[:3000]
