Python Classifier.classify方法代碼示例

# 需要導入模塊: from Classifier import Classifier [as 別名]
# 或者: from Classifier.Classifier import classify [as 別名]
def main():
    try:
        trainingData, tuningData, testData, priorSpam = buildDataSets()
        nbc = Classifier(priorSpam, COUNT_THRESHOLD, SMOOTHING_FACTOR, DEFAULT_PROBABILITY)
        # nbc = Classifier2(priorSpam, 0, .01, None)
        nbc.train(trainingData)

        nbc.classify(testData)
        report(testData)

    except Exception as e:
        print e
        return 5

# 需要導入模塊: from Classifier import Classifier [as 別名]
# 或者: from Classifier.Classifier import classify [as 別名]
class Parser(object):

    def __init__(self):
        self.classifier = Classifier()

    def parse(self, page, url, time):
        try:
            links = []
            print "Currently parsing: " + url
            soup = BeautifulSoup(page, 'html.parser')

            data = self.classifier.classify(soup, url)

            # get links only when the page is relevant
            if data is not None:
                links = self.getRelevantUris(soup, url)

            print 'No. of links retrieved: ' + str(len(links))
            return (links, data, time)

        except:
           print "Parser: cannot parse page"
           return ([], None, time)

    # takes in a html page
    def getRelevantUris(self, page, url):

        # extract domain from url
        parsed_uri = urlparse(url)
        domain = '{uri.scheme}://{uri.netloc}/'.format(uri=parsed_uri)

        listOfLinks = []
        for link in page.find_all('a'):
            listOfLinks.append(link.get('href'))

        # clean up the links
        listOfLinks = self.cleanLinks(listOfLinks, domain)

        # remove dups
        links = list(set(listOfLinks))
        return links

    def cleanLinks(self, listOfLinks, domain):
        newLinks = []
        for link in listOfLinks:

            if self.isErroneous(link):
                pass

            elif self.isRelativeLink(link):
                concatLink = self.concatRelativeLink(domain, link)
                newLinks.append(concatLink)

            else:
                newLinks.append(link) # absolute link

        return newLinks

    def isErroneous(self, link):
        if link is None or link.startswith('#') or link.startswith('.'):
            return True
        if 'mailto' in link or 'javascript' in link:
            return True
        else:
            return False

    def concatRelativeLink(self, domain, link):
        if link.startswith('/'):
            return (domain + link[1:]) # avoid double slashes //

        else:
            return (domain + link)

    def isRelativeLink(self, link):
        frontUrl = link.split('?',1)[0]
        if link.startswith('/'):
            return True
        if 'php' in frontUrl and '/' not in frontUrl: #php?param=1&param=2
            return True
        if len(link.split('.')) == 1: #games
            return True
        else:
            return False

# 需要導入模塊: from Classifier import Classifier [as 別名]
# 或者: from Classifier.Classifier import classify [as 別名]
                            dest="queries",
                            required=False)
        parser.add_argument("-c", dest="C", required=False)
        return parser


model = None

if __name__ == "__main__":
    parser = cmdline_parser()
    args = parser.parse_args()
    gta = list(SeqIO.parse(args.gta, "fasta"))
    viral = list(SeqIO.parse(args.viral, "fasta"))
    model = Classifier(gta, viral)
    queries = args.queries.split(',')

    for query in queries:
        query_seqs = list(SeqIO.parse(query, "fasta"))
        gene_num = int(query[query.find('orfg')+4])
        if not model:
            # dist_matrix = parse_dists.get_dist_matrix(gene_num)
            model = Classifier(gta, viral)
            model.get_training_set()
            # model.get_weights()
            SVs = model.learn_SVM_model(float(args.C))

        print model.classify(query_seqs)[1]

# 需要導入模塊: from Classifier import Classifier [as 別名]
# 或者: from Classifier.Classifier import classify [as 別名]
from Classifier import Classifier

hyp_tweets = [('I am so hungry I could eat a horse', 'hyperbole'),
              ('I have a million things to do', 'hyperbole'),
              ('I had to walk 15 miles to school in the snow, uphill', 'hyperbole'),
              ('She is as heavy as an elephant', 'hyperbole'),
              ('He is as fat as a whale', 'hyperbole'),
              ('Like a god', 'hyperbole'),
              ('They ran like greased lightning', 'hyperbole'),
              ('My grandmother is as old as the hills', 'hyperbole'),
              ('I am dying of shame', 'hyperbole'),
              ('I had a ton of homework', 'hyperbole'),
              ('If I can’t buy that new game I will die', 'hyperbole')]

nor_tweets = [('I do not like this car', 'normal'),
              ('I like this car', 'normal'),
              ('This view is horrible', 'normal'),
              ('I feel tired this morning', 'normal'),
              ('I am not looking forward to the concert', 'normal'),
              ('The door is black', 'normal'),
              ('I love you', 'normal'),
              ('He is my enemy', 'normal')]

tweets = hyp_tweets + nor_tweets

classifier = Classifier()

classifier.train(tweets)
for tweet in Fetcher.fetch("hyperbole", 10):
  print(classifier.classify(tweet))