def checkPage(link):
checkPage = page.Page(link)
print 'Getting distribution for page over categories...'
allCatsLinks = unpack('allCatsLinks.p')
occurMatrix = unpack('occurMatrix.p')
totals = unpack('totals.p')
keyDict = unpack('keyDict.p')
useLinks = unpack('useLinks.p')
distribution = classify.naiveBayes(checkPage,allCatsLinks,occurMatrix,totals,keyDict,useLinks)
for result in distribution:
print link,'is a subpage of', result[0], 'with probability', round(result[1]*100,5),'%'
return True
def main():
already_made = False
while True:
made = raw_input("Have you already initialized a prior probability?(Y/N) ")
if made == "Y":
already_made = True
break
elif made == "N":
already_made = False
break
if not already_made:
while True:
catCount = raw_input("How many categories will you be using? ")
try:
count = int(catCount)
break
except:
print "not a valid integer"
while True:
inpDepth = raw_input("How deep do you want to traverse (increases exponentially)? ")
try:
depth = int(inpDepth)
break
except:
print "not a valid integer"
while True:
bagOfWords = raw_input("Use link approach or bag of words?(enter 1/0 respectively): ")
try:
BOWval = int(bagOfWords)
useLinks = not not BOWval
break
except:
print "not a valid integer"
allCategoriesLinks = []
print 'Please give the input in the form \"/wiki/Category:Example_category\".'
print 'If a category is not valid input or not recognized, it will be dropped by the classifier.'
inc = 0
while inc < count:
catLink = raw_input("enter your category: ")
if categories.isCategory(catLink):
allCategoriesLinks.append(catLink)
inc += 1
else:
print "invalid category format"
print 'Creating prior probilities for naive bayes clssification...'
allCatsLinks,occurMatrix,totals,keyDict,useLinks = classify.createClassifier(allCategoriesLinks,useLinks,depth)
serialize(allCatsLinks,'allCatsLinks.p')
serialize(occurMatrix,'occurMatrix.p')
serialize(totals,'totals.p')
serialize(keyDict,'keyDict.p')
serialize(useLinks,'useLinks.p')
print 'Prior probabilities are now stored in serialized files.'
while True:
checkPageLink = raw_input("In similar format, give URL suffix of page you would like to classify: ")
if page.isPage(checkPageLink):
break
else:
print "There was an error connecting to the given page."
checkPage = page.Page(checkPageLink)
print 'Getting distribution for page over categories...'
allCatsLinks = unpack('allCatsLinks.p')
occurMatrix = unpack('occurMatrix.p')
totals = unpack('totals.p')
keyDict = unpack('keyDict.p')
useLinks = unpack('useLinks.p')
distribution = classify.naiveBayes(checkPage,allCatsLinks,occurMatrix,totals,keyDict,useLinks)
for result in distribution:
print checkPageLink,'is a subpage of', result[0], 'with probability', round(result[1]*100,5),'%'
return None