zz
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@ -18,11 +18,11 @@ import re
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import time
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import collections
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import math
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import json
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import random
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LANGUAGE= os.getenv("SUCKER_LANGUAGE","sk")
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DOMAIN = os.getenv("SUCKER_DOMAIN","sk")
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BATCHSIZE=os.getenv("SUCKER_BATCHSIZE",10)
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BATCHSIZE=int(os.getenv("SUCKER_BATCHSIZE","10"))
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CONNECTION=os.getenv("SUCKER_CONNECTION","mongodb://root:example@localhost:27017/")
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DBNAME=os.getenv("SUCKER_DBNAME","crawler")
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MINFILESIZE=300
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@ -102,11 +102,7 @@ def get_link_doc(link,status="frontlink"):
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return {"url":link,"host":host,"domain":domain,"status":status,"created_at":datetime.utcnow()}
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def fetch_pages(link_batch):
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htmls = []
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#print(link_batch)
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#print("zzzzzzzzzz")
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for link in link_batch:
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def fetch_page(link):
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print("fetching:::::")
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print(link)
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final_link = link
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@ -133,8 +129,7 @@ def fetch_pages(link_batch):
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# is there a meta-refresh on the page?
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if final_link is None: # malformed or malicious content
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html = None
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htmls.append((final_link,html))
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return htmls
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return final_link,html
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def fetch_robot(base_url):
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try:
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@ -227,7 +222,7 @@ def index_pages(db,hostname,extracted_pages):
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checkcol.insert_one({"_id":chs})
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except pymongo.errors.DuplicateKeyError as err:
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pass
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linkcol.update_one({"url":original_link},{"$set":{"status":state}})
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linkcol.update_one({"url":link},{"$set":{"status":state}})
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def extract_links(link_batch,responses,hostname,rules,default_status="frontlink"):
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@ -263,23 +258,28 @@ def index_links(db,extracted_links):
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for link,status in extracted_links:
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if not is_link_good(link):
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continue
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if status == "frontlink" or status == "backlink":
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doc = get_link_doc(link,status)
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try:
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linkcol.insert_one(doc)
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# dont overwrite
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except pymongo.errors.DuplicateKeyError as ex:
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pass
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else:
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print("updating " + link,status)
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linkcol.update_one({"url":link},{"$set":{"status":status,"updated_at":datetime.utcnow()}})
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def get_link_features(link):
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a, urlpath = courlan.get_host_and_path(link)
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features = re.split("[/?&]",urlpath)
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#features = re.split("[/?-_=]",urlpath)
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res = []
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for feature in features:
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for i,feature in enumerate(features):
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if len(feature) < 1:
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continue
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if feature.isdigit():
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feature = "<NUM>"
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res.append(feature)
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res.append(str(i)+ "-" + feature)
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if len(res) < 2:
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return None
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res = res[:-1]
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@ -295,11 +295,9 @@ class LinkClassifier:
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self.bad_count = 0
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self.alpha = 0.001
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def train(self,db,hostname):
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linkcol = db["links"]
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res = linkcol.find({"host":hostname,"status": {"$not":{"$in":["frontlink","backlink"]}}})
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def train(self,links):
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testset = []
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for i,item in enumerate(res):
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for i,item in enumerate(links):
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link = item["url"]
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state = item["status"]
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cl = 0
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@ -336,9 +334,13 @@ class LinkClassifier:
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print("MISS",l,cl,pcp)
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print("Accuracy:")
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print(len(testset))
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print(gg / len(testset))
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acc = gg / len(testset)
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print(acc)
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return acc
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def classify(self,link):
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if self.good_count + self.bad_count == 0:
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return random.uniform(-0.1,0.1)
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features = get_link_features(link)
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res = 0
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gp = math.log(self.good_count) - math.log(self.good_count + self.bad_count)
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@ -363,51 +365,15 @@ class LinkClassifier:
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return pa - pb
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def get_links(db,hostname,status,batch_size):
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linkcol = db["links"]
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# count downloaded links
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res = linkcol.aggregate([
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{ "$match": { "status": {"$not":{"$in":["frontlink","backlink"]}},"host":hostname } },
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{"$group":{"_id":None,
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"count":{"$count":{}},
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}
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},
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])
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links = set()
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out = list(res)
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if len(out) == 0:
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return list()
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if out[0]["count"] < 200:
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#res = linkcol.find({"status":status,"host":hostname},{"url":1},limit=batch_size)
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# get random links
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res = linkcol.aggregate([
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{ "$match": { "status": status,"host":hostname } },
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{ "$sample": { "size": batch_size } }
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])
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for i,doc in enumerate(res):
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#print(">>>>>" + status)
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#print(doc);
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links.add(doc["url"])
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if i >= batch_size:
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break
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else:
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cl = LinkClassifier()
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cl.train(db,hostname)
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res = linkcol.aggregate([
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{ "$match": { "status": status,"host":hostname } },
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{ "$sample": { "size": batch_size * 100 } }
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])
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outlinks = []
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for i,doc in enumerate(res):
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#print(">>>>>" + status)
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#print(doc);
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link = doc["url"]
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outlinks.append((doc["url"],cl.classify(link)))
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outlinks = sorted(outlinks, key=lambda x: x[1],reverse=True)
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links = [l[0] for l in outlinks[0:batch_size]]
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# todo remove very bad links from database
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return list(links)
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res = linkcol.find({"host":hostname,"status":status},limit=batch_size)
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links = []
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for item in res:
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links.append(item["url"])
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print("Got {} {}".format(len(links),status))
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return links
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def fetch_sitemap_links(start_link):
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@ -415,42 +381,48 @@ def fetch_sitemap_links(start_link):
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navigation_links = trafilatura.sitemaps.sitemap_search(start_link,target_lang=LANGUAGE)
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for link in navigation_links:
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out.append((link,"frontlink"))
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print("Fetched {} sitemap links".format(len(out)))
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return out
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def process_links(db,hostname,status,links=[],rules=None,batch_size=BATCHSIZE):
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#print(links)
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responses = fetch_pages(links)
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#print(responses)
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extracted_pages = extract_pages(links,responses)
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#print(extracted_pages)
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extracted_links = extract_links(links,responses,hostname,rules,status)
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#print(extracted_links)
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index_links(db,extracted_links)
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index_pages(db,hostname,extracted_pages)
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def fetch_front_links(start_link,rules):
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start_link,hostname = courlan.check_url(start_link)
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response = fetch_page(start_link)
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extracted_links = extract_links([start_link],[response],hostname,rules,"frontlink")
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print("Fetched {} frontlinks".format(len(extracted_links)))
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return extracted_links
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def link_summary(db,hostname):
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linkcol = db["links"]
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#res = linkcol.distinct("hostname",{"hostname":hostname})
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# count links
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res = linkcol.aggregate([
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{"$match":{"host":hostname}},
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{"$group":{"_id":"$status","count":{"$sum":1}}},
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{"$group":{"_id":"$status",
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"count":{"$count":{}},
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}
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},
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])
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badcount = 0
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goodcount = 0
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out = ["good","frontlink","backlink"]
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info = {}
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crawled_count = 0
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for item in res:
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if item["_id"] not in out:
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badcount += item["count"]
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if item["_id"] == "good":
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goodcount = item["count"]
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info[item["_id"]] = item["count"]
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good_prob = goodcount / (goodcount + badcount)
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count = item["count"]
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st = item["_id"]
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print(st,count)
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if st == "good":
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goodcount += count
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if st != "frontlink" and st != "backlink":
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crawled_count += count
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info[st] = count
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baclink_cout = 0
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if "backlink" in info:
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backlink_count = info["backlink"]
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good_prob= 0
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if crawled_count > 0:
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good_prob = goodcount / crawled_count
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info["good_prob"] = good_prob
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info["bad_documents"] = badcount
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print(">>>Domain Content")
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contentcol = db["content"]
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res = contentcol.aggregate([
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@ -465,35 +437,53 @@ def link_summary(db,hostname):
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for item in res:
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text_size = item["text_size_sum"]
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good_document_characters = 0
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fetch_average_characters = 0
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if goodcount > 0:
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good_document_characters = text_size / goodcount
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fetch_average_characters = text_size / (goodcount + badcount)
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fetch_average_characters = text_size / crawled_count
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info["total_good_characters"] = text_size
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info["average_good_characters"] = good_document_characters
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info["average_fetch_characters"] = fetch_average_characters
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domaincol = db["domain"]
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if goodcount + badcount > 100:
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cl = LinkClassifier()
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cl.train(db,hostname)
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res = linkcol.aggregate([
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{ "$match": { "status": "backlink","host":hostname } },
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{ "$sample": { "size": BATCHSIZE * 100 } }
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])
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predicted_good = 0
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predicted_bad = 0
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for item in res:
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cll = cl.classify(item["url"])
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if cll > 0:
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predicted_good += 1
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else:
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predicted_bad += 1
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predicted_good_prob = 0
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if predicted_good + predicted_bad > 0:
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predicted_good_prob = predicted_good / (predicted_good + predicted_bad)
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info["predicted_good_prob"] = predicted_good_prob
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print(info)
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domaincol.update_one({"host":hostname},{"$set":info},upsert=True)
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def sample_links(db,hostname,status,batch_size):
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print("Getting backlinks")
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linkcol = db["links"]
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res = linkcol.find({"host":hostname,"status": {"$not":{"$in":["frontlink","backlink"]}}})
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cl = LinkClassifier()
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crawled_links = list(res)
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crawled_count = len(crawled_links)
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min_train_size = 200
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prediction_accuracy = 0
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if crawled_count > min_train_size:
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# train on crawled links
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prediction_accuracy = cl.train(crawled_links)
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sample_set_size = 10000
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res = linkcol.find({"host":hostname,"status": status},limit = sample_set_size)
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sample_links = []
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predicted_good = 0
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for item in res:
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for item in res:
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cll = cl.classify(item["url"])
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sample_links.append((item["url"],cll))
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if cll > 0:
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predicted_good += 1
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# TODO frontlinks are not unique!
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sample_links.sort(key=lambda x: x[1],reverse=True)
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predicted_good_prob = predicted_good / len(sample_links)
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domaincol = db["domain"]
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info = {
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"predicted_good_prob":predicted_good_prob,
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"prediction_accuracy": prediction_accuracy,
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"crawled_count": crawled_count,
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}
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print(info)
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domaincol.update_one({"host":hostname},{"$set":info})
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links = [l[0] for l in sample_links[0:batch_size]]
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return links
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def domain_summary(db,hostname):
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linkcol = db["links"]
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#res = linkcol.distinct("hostname",{"hostname":hostname})
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@ -572,23 +562,25 @@ def visit(start_link):
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db=myclient[DBNAME]
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start_link,hostname = courlan.check_url(start_link)
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batch_size = BATCHSIZE
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print("Getting frontlinks")
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links = get_links(db,hostname,"frontlink",batch_size)
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print(f"Got {len(links)} frontlinks")
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if len(links) < batch_size:
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print("Fetching sitemap links")
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rules = fetch_robot(hostname)
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# renew front links
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sitemap_links = fetch_sitemap_links(start_link)
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index_links(db,sitemap_links)
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links = get_links(db,hostname,"frontlink",batch_size)
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front_links = fetch_front_links(start_link,rules)
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index_links(db,front_links)
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# start crawling
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# frontlinks first
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links = sample_links(db,hostname,"frontlink",batch_size)
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links.insert(0,start_link)
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# then backlinks
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if len(links) < batch_size:
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back_links = get_links(db,hostname,"backlink",batch_size - len(links))
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back_links = sample_links(db,hostname,"backlink",batch_size - len(links))
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links += back_links
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# index results
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print("Processing links")
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rules = fetch_robot(hostname)
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responses = fetch_pages(links)
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responses = []
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for link in links:
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responses.append(fetch_page(link))
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extracted_pages = extract_pages(links,responses)
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extracted_links = extract_links(links,responses,hostname,rules,"backlink")
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index_links(db,extracted_links)
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