def processdata(urllists, word_count_threshold, depth):
content = []
nums = []
nums.append(0)
for url in urllists:
crawler = webCrawler(url, depth)
crawler.crawl()
nums.append(len(crawler.data))
content.extend(crawler.data)
instance = features(word_count_threshold)
word_counts, wordtoix = instance.extractwords(content)
N = len(word_counts)
for i in range(1, len(nums)):
nums[i] = nums[i-1] + nums[i]
cid = 0
output = np.zeros((nums[len(nums)-1], N+1))
for url in urllists:
crawler = webCrawler(url, depth)
crawler.crawl()
currlen = len(crawler.data)
feats = instance.bagofwords(crawler.data, word_counts, wordtoix)
print feats.shape
b = np.zeros((currlen,N+1))
print b[:, :-1].shape
b[:,0:N] = feats
b[:,N] = cid +1
output[nums[cid]:nums[cid+1],:] = b
cid = cid + 1
np.savetxt('test.out', output, delimiter=',') # X is an array
def getdata(urllists, depth):
content = []
nums = []
nums.append(0)
for url in urllists:
#if url != "https://en.wikipedia.org/wiki/1990_RTHK_Top_10_Gold_Songs_Awards":
# continue
crawler = webCrawler(url, depth)
crawler.crawl()
nums.append(len(crawler.data))
content.extend(crawler.data)
return content
urllists =[]
urllists.append( "https://en.wikipedia.org/wiki/Sandra_Bullock");
urllists.append( "https://en.wikipedia.org/wiki/Far_East_scarlet-like_fever");
filepath = os.path.dirname(os.path.realpath(__file__))
dictname = 'dictionary.txt'
dict2idx = 'dict2idx.txt'
with open(os.path.join(filepath,dictname), 'r') as fread:
word_counts = json.load(fread)
with open(os.path.join(filepath,dict2idx), 'r') as fread:
wordtoix = json.load(fread)
#np.random.shuffle
clf = joblib.load('model.pkl')
for url in urllists:
crawler = webCrawler(url, 1)
crawler.crawl()
instance = features(word_count_threshold)
feats = instance.bagofwords(crawler.data, word_counts, wordtoix)
X = feats
#print X.shape
print fsum(X)
transformer = TfidfTransformer()
tfidf = transformer.fit_transform(X)
X = tfidf.toarray()
print fsum(X)
yhat = clf.predict(X)
print yhat
print "finish page testing"
