zz
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@ -15,10 +15,13 @@ import logging as LOGGER
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import os
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import pprint
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import re
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import time
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import collections
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import math
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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=os.getenv("SUCKER_BATCHSIZE",100)
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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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@ -107,6 +110,7 @@ def fetch_pages(link_batch):
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print(link)
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final_link = link
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response = trafilatura.fetch_url(link,decode=False)
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time.sleep(2)
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html = None
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if response is not None :
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good = True
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@ -256,6 +260,8 @@ def extract_links(link_batch,responses,hostname,rules,default_status="frontlink"
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def index_links(db,extracted_links):
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linkcol=db["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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doc = get_link_doc(link,status)
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try:
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linkcol.insert_one(doc)
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@ -264,63 +270,139 @@ def index_links(db,extracted_links):
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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 = urlpath.split("/?-_")
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if len(features) < 2:
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return None
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# drop last part
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features = features[:-1]
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return features
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def link_classifier(db,hostname,batch_size):
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res = linkcol.aggregate([
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{ "$match": { "status": {"$not":{"$in":["frontlink","backlink"]}},"host":hostname } },
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{ "$sample": { "size": 2000 } }
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])
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goodcounter = collections.Counter()
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badcounter = collections.Counter()
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for item in res:
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link = res["url"]
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state = res["status"]
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cl = 0
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if state == "good":
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cl = 1
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features = get_link_features(link)
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if features is None:
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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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if len(feature) < 1:
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continue
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lf = len(features)
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for feature in features:
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if state == "good":
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goodcounter[feature] += 1/lf
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else:
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badcounter[feature] += 1/lf
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tf = goodcounter.keys() + bacounter.keys()
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allcounter = collections.Counter()
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for key in tf:
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gc = goodcounter[key]
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bc = badcounter[key]
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p = gc / (gc + bc)
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allcounter[key] = p
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return allcounter
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if feature.isdigit():
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feature = "<NUM>"
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res.append(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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print(res)
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return res
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class LinkClassifier:
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def __init__(self):
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self.goodcounter = collections.Counter()
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self.badcounter = collections.Counter()
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self.good_count = 0
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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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testset = []
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for i,item in enumerate(res):
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link = item["url"]
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state = item["status"]
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cl = 0
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if state == "good":
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cl = 1
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print(cl,state,link)
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if i % 10 == 1:
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testset.append((link,cl))
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continue
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features = get_link_features(link)
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if features is None:
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continue
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lf = len(features)
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if state == "good":
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for feature in features:
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self.good_count += 1
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self.goodcounter[feature] += 1
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else:
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for feature in features:
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self.bad_count += 1
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self.badcounter[feature] += 1
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self.bdictsize = len(self.badcounter)
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self.gdictsize = len(self.goodcounter)
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# eval
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gg = 0
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for l,cl in testset:
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pcp = self.classify(l)
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r = 0
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if pcp > 0:
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r = 1
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if r == cl:
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gg += 1
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else:
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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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def classify(self,link):
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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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bp = math.log(self.bad_count) - math.log(self.good_count + self.bad_count)
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if features is None:
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return math.exp(gp) - math.exp(bp)
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gcc = math.log(self.gdictsize * self.alpha + self.good_count)
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bcc = math.log(self.bdictsize * self.alpha + self.bad_count)
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goodprob = 0
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badprob = 0
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for feature in features:
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g = math.log((self.goodcounter[feature] + self.alpha)) - gcc
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goodprob += g
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b = math.log(self.badcounter[feature] + self.alpha) - bcc
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badprob += b
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print(feature,g,b)
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if (goodprob + gp) > (badprob + bp):
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#if goodprob > badprob:
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res = 1
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pa = math.exp(goodprob + gp)
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pb = math.exp(badprob + bp)
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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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#res = linkcol.find({"status":status,"host":hostname},{"url":1},limit=batch_size)
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# get random links
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# count downloaded 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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{ "$match": { "status": {"$not":{"$in":["frontlink","backlink"]}},"host":hostname } },
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{"$group":{"_id":None,
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"count":{"$count":{}},
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}
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},
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])
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links = set()
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for i,doc in enumerate(res):
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#print(">>>>>" + status)
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#print(doc);
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links.add(doc["url"])
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if i >= batch_size:
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break
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if list(res)[0]["count"] < 200:
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#res = linkcol.find({"status":status,"host":hostname},{"url":1},limit=batch_size)
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# get random links
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res = linkcol.aggregate([
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{ "$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": 2000 } }
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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
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return list(links)
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@ -427,6 +509,14 @@ def externaldomains(link):
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for d in domains:
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print(d)
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@cli.command()
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@click.argument("start_link")
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def classify(start_link):
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myclient = pymongo.MongoClient(CONNECTION)
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db=myclient[DBNAME]
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start_link,hostname = courlan.check_url(start_link)
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cl = LinkClassifier()
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cl.train(db,hostname)
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@cli.command()
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@click.argument("start_link")
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