featureSelector = MIFeatureSelector()
validate.regularBayes(bayes, featureSelector, DISPLAY_ACCURACY)
with open("cleanData/output.txt") as methodOutput:
for line in methodOutput.read().split():
finalOutput.write(method + "," + line + "\n")
method = "MultinomialBayes"
print method
shutil.rmtree("cleanData/", True)
bayes = Bayes('multinomial')
bayes.initalPreprocess(DR, DT, L, TEST, "custom", 1, False)
validate = CrossValidate("cleanData")
featureSelector = FeatureSelector() # Multinomial does much worse with MIFeatureSelector()
validate.regularBayes(bayes, featureSelector, DISPLAY_ACCURACY)
with open("cleanData/output.txt") as methodOutput:
for line in methodOutput.read().split():
finalOutput.write(method + "," + line + "\n")
method = "Perceptron"
print method
shutil.rmtree("cleanData/", True)
classifier = Perceptron()
classifier.initalPreprocess(DR, DT, L, TEST, "custom", 1, False)
validate = CrossValidate("cleanData")
featureSelector = MIFeatureSelector()
accuracy = validate.regularPerceptron(classifier, featureSelector, DISPLAY_ACCURACY)
with open("cleanData/output.txt") as methodOutput:
for line in methodOutput.read().split():
finalOutput.write(method + "," + line + "\n")
shutil.rmtree("cleanData/", True)
self.docWords = {}
self.knownClasses = {}
self.classDocs = { docClass : set() for docClass in self.docClasses }
allWords = set()
for docClass in self.docClasses:
for docPath in trainData[docClass]:
self.knownClasses[docPath] = docClass
self.classDocs[docClass].add(docPath)
with open(docPath) as docFile:
self.docWords[docPath] = set(docFile.read().split())
allWords.update(self.docWords[docPath])
info = Counter()
for word in allWords:
info[word] = self.mutualInformation(word)
top = info.most_common()[0:numFeatures]
return [ word for (word, mutualInfo) in top ]
if __name__ == "__main__":
classifier = Perceptron()
print "Preprocessing"
classifier.initalPreprocess("web", 1, True)
# classifier.initalPreprocess("web",1)
# classifier.initalPreprocess(None,3)
# classifier.initalPreprocess("web",3)
validate = CrossValidate("cleanData")
featureSelector = MIFeatureSelector()
accuracy = validate.crossFoldOnPerceptron(classifier, featureSelector)
print "Perceptron Accuracy: ",accuracy
