def __init__(self, dataset, date, entry,prefix, eps, cluster_rank,crawl_size, rank_algo="bfs"):
self.dataset = dataset
self.date = date
self.eps = eps
self.cluster_rank = cluster_rank
self.rank_algo = rank_algo
self.crawl_size = crawl_size
self.rules = self.get_rules()
self.entry, self.prefix = entry,prefix
self.history_set = set()
self.path_prefix = "../../Crawler/{}_samples/{}/".format(date,dataset.replace("new_",""))
self.folder_path = ["../../Crawler/{}_samples/{}/".format(date,dataset.replace("new_",""))]
self.sitemap = pageCluster(dataset,date,self.folder_path,0)
self.full_folder = "../../Crawler/full_data/" + dataset
self.trans = {}
self.queue = {}
self.crawled_cluster_count = defaultdict(int)
self.trans_dict = read_trans_dict(dataset,date)
#self.cluster_dict = get_cluster_dict(dataset,date)
#self.cluster_num = int(self.sitemap.DBSCAN(eps_val=self.eps))
self.cluster_num = int(self.sitemap.DBSCAN())
self.build_gold_cluster_dict()
self.cluster_xpath_trans = self.get_xpath_transition()
self.trans_prob_mat = self.calculate_trans_prob_mat()
self.max_score = 500
self.target_cluster = self.get_sample_cluster()
def get_xpath_transition(self):
sampled_urls = self.gold_dict.keys()
counts_dict = defaultdict(int) # (cluster_id, xpath) -> int
xpath_counts_dict = defaultdict(lambda : defaultdict(float)) # (cluster_id, xpath) - > dict[cluster_id] -> int
trans_dict = read_trans_dict(self.dataset,self.date) # [page][xpath] = [url list] ->[cluster][xpath] = {probability list}
print "sample_url", sampled_urls
for page_path, trans in trans_dict.iteritems():
page_url = page_path.replace(".html","").replace("_","/")
if page_url not in self.gold_dict:
#print "?" + page_url, " is missing"
continue
else:
cluster_id = self.cluster_dict[page_url]
for xpath,url_list in trans.iteritems():
length = len(url_list)
count = 0
for path in url_list:
url = path.replace(".html","").replace("_","/")
if url in sampled_urls:
count += 1
#print "for xpath: {0} --- {1} out of {2} have been crawled and have cluster id".format(xpath,count, length)
if count == 0:
continue
else:
#if cluster_id == 1:
# print page_path, xpath, url_list, "xpath_url_list in train"
key = (cluster_id,xpath)
#if key == (1,"/html/body/div/div/div/div/div/div/div/div/div/div/ul/li/div/div/div/div/div/div/div/ul/li/div/div/div/h3/a[yt-uix-sessionlink yt-uix-tile-link spf-link yt-ui-ellipsis yt-ui-ellipsis-2]"):
# print page_path,url_list, "why 9 not 7???"
counts_dict[key] += 1
ratio = float(length)/float(count)
for path in url_list:
url = path.replace(".html","").replace("_","/")
if url in sampled_urls:
destination_id = self.cluster_dict[url]
#print url, destination_id
xpath_counts_dict[key][destination_id] += 1 * ratio
#if cluster_id == 1:
# print ""
# average
for key,count in counts_dict.iteritems():
for destination_id in xpath_counts_dict[key]:
xpath_counts_dict[key][destination_id] /= count
print "Micro average entropy is " + str(self.entropy(xpath_counts_dict))
''' output
for key in xpath_counts_dict:
if key[0] == 1:
print key, xpath_counts_dict[key]
'''
print "=========== end of training ============"
self.xpath_counts_dict = xpath_counts_dict
return xpath_counts_dict
def __init__(self,
dataset,
date,
entry,
prefix,
eps,
cluster_rank,
crawl_size,
rank_algo="bfs"):
self.dataset = dataset
self.date = date
self.eps = eps
self.cluster_rank = cluster_rank
self.rank_algo = rank_algo
self.crawl_size = crawl_size
self.rules = self.get_rules()
self.entry, self.prefix = entry, prefix
self.history_set = set()
self.path_prefix = "../../Crawler/{}_samples/{}/".format(
date, dataset.replace("new_", ""))
self.folder_path = [
"../../Crawler/{}_samples/{}/".format(date,
dataset.replace("new_", ""))
]
self.sitemap = pageCluster(dataset, date, self.folder_path, 0)
self.full_folder = "../../Crawler/full_data/" + dataset
self.trans = {}
self.queue = {}
self.crawled_cluster_count = defaultdict(int)
self.trans_dict = read_trans_dict(dataset, date)
#self.cluster_dict = get_cluster_dict(dataset,date)
#self.cluster_num = int(self.sitemap.DBSCAN(eps_val=self.eps))
self.cluster_num = int(self.sitemap.DBSCAN())
self.build_gold_cluster_dict()
self.cluster_xpath_trans = self.get_xpath_transition()
self.trans_prob_mat = self.calculate_trans_prob_mat()
self.max_score = 500
self.target_cluster = self.get_sample_cluster()
def get_xpath_transition(self):
sampled_urls = self.gold_dict.keys()
counts_dict = defaultdict(int) # (cluster_id, xpath) -> int
xpath_counts_dict = defaultdict(lambda: defaultdict(
float)) # (cluster_id, xpath) - > dict[cluster_id] -> int
trans_dict = read_trans_dict(
self.dataset, self.date
) # [page][xpath] = [url list] ->[cluster][xpath] = {probability list}
print "sample_url", sampled_urls
for page_path, trans in trans_dict.iteritems():
page_url = page_path.replace(".html", "").replace("_", "/")
if page_url not in self.gold_dict:
#print "?" + page_url, " is missing"
continue
else:
cluster_id = self.cluster_dict[page_url]
for xpath, url_list in trans.iteritems():
length = len(url_list)
count = 0
for path in url_list:
url = path.replace(".html", "").replace("_", "/")
if url in sampled_urls:
count += 1
#print "for xpath: {0} --- {1} out of {2} have been crawled and have cluster id".format(xpath,count, length)
if count == 0:
continue
else:
#if cluster_id == 1:
# print page_path, xpath, url_list, "xpath_url_list in train"
key = (cluster_id, xpath)
#if key == (1,"/html/body/div/div/div/div/div/div/div/div/div/div/ul/li/div/div/div/div/div/div/div/ul/li/div/div/div/h3/a[yt-uix-sessionlink yt-uix-tile-link spf-link yt-ui-ellipsis yt-ui-ellipsis-2]"):
# print page_path,url_list, "why 9 not 7???"
counts_dict[key] += 1
ratio = float(length) / float(count)
for path in url_list:
url = path.replace(".html", "").replace("_", "/")
if url in sampled_urls:
destination_id = self.cluster_dict[url]
#print url, destination_id
xpath_counts_dict[key][destination_id] += 1 * ratio
#if cluster_id == 1:
# print ""
# average
for key, count in counts_dict.iteritems():
for destination_id in xpath_counts_dict[key]:
xpath_counts_dict[key][destination_id] /= count
print "Micro average entropy is " + str(
self.entropy(xpath_counts_dict))
''' output
for key in xpath_counts_dict:
if key[0] == 1:
print key, xpath_counts_dict[key]
'''
print "=========== end of training ============"
self.xpath_counts_dict = xpath_counts_dict
return xpath_counts_dict
