Merge branch 'master' of git.kemt.fei.tuke.sk:dano/websucker-pip

mongo/mongocwarler.py

@@ -15,10 +15,13 @@ import logging as LOGGER
 import os
 import pprint
 import re
+import time
+import collections
+import math
 
 LANGUAGE= os.getenv("SUCKER_LANGUAGE","sk")
 DOMAIN = os.getenv("SUCKER_DOMAIN","sk")
-BATCHSIZE=os.getenv("SUCKER_BATCHSIZE",10)
+BATCHSIZE=os.getenv("SUCKER_BATCHSIZE",100)
 CONNECTION=os.getenv("SUCKER_CONNECTION","mongodb://root:example@localhost:27017/")
 DBNAME=os.getenv("SUCKER_DBNAME","crawler")
 MINFILESIZE=300
@@ -107,6 +110,7 @@ def fetch_pages(link_batch):
         print(link)
         final_link = link
         response = trafilatura.fetch_url(link,decode=False)
+        time.sleep(2)
         html = None
         if response is not None :
             good = True
@@ -256,6 +260,8 @@ def extract_links(link_batch,responses,hostname,rules,default_status="frontlink"
 def index_links(db,extracted_links):
     linkcol=db["links"]
     for link,status in extracted_links:
+        if not is_link_good(link):
+            continue
         doc = get_link_doc(link,status)
         try:
             linkcol.insert_one(doc)
@@ -264,63 +270,142 @@ def index_links(db,extracted_links):
 
 def get_link_features(link):
     a, urlpath = courlan.get_host_and_path(link)
-    features = urlpath.split("/?-_")
+    features = re.split("[/?&]",urlpath)
-    if len(features) < 2:
+    #features = re.split("[/?-_=]",urlpath)
+    res = []
+    for feature in features:
+        if len(feature) < 1:
+            continue
+        if feature.isdigit():
+            feature = "<NUM>"
+        res.append(feature)
+    if len(res) < 2:
         return None
-    # drop last part
+    res = res[:-1]
-    features = features[:-1]
+    print(res)
-    return features
+    return res
 
+class LinkClassifier:
+    def __init__(self):
 
-def link_classifier(db,hostname,batch_size):
+        self.goodcounter = collections.Counter()
-    res = linkcol.aggregate([
+        self.badcounter = collections.Counter()
-        { "$match": { "status": {"$not":{"$in":["frontlink","backlink"]}},"host":hostname } },
+        self.good_count = 0
-        { "$sample": { "size": 2000 } }
+        self.bad_count = 0
-    ])
+        self.alpha = 0.001
-    goodcounter = collections.Counter()
+
-    badcounter = collections.Counter()
+    def train(self,db,hostname):
-    for item in res:
+        linkcol = db["links"]
-        link = res["url"]
+        res = linkcol.find({"host":hostname,"status": {"$not":{"$in":["frontlink","backlink"]}}})
-        state = res["status"]
+        testset = []
+        for i,item in enumerate(res):
+            link = item["url"]
+            state = item["status"]
             cl = 0
             if state == "good":
                 cl = 1
+            print(cl,state,link)
+            if i % 10 == 1:
+                testset.append((link,cl))
+                continue
             features = get_link_features(link)
             if features is None:
                 continue
             lf = len(features)
-        for feature in features:
             if state == "good":
-                goodcounter[feature] += 1/lf
+                for feature in features:
+                    self.good_count += 1
+                    self.goodcounter[feature] += 1
             else:
-                badcounter[feature] += 1/lf
+                for feature in features:
-        tf = goodcounter.keys() + bacounter.keys()
+                    self.bad_count += 1
-        allcounter = collections.Counter()
+                    self.badcounter[feature] += 1
-        for key in tf:
+        self.bdictsize = len(self.badcounter)
-            gc = goodcounter[key]
+        self.gdictsize = len(self.goodcounter)
-            bc = badcounter[key]
+        # eval
-            p = gc / (gc + bc)
+        gg = 0
-            allcounter[key] = p
+        for l,cl in testset:
-        return allcounter
+            pcp = self.classify(l)
+            r = 0
+            if pcp > 0:
+                r = 1
+            if r == cl:
+                gg += 1
+            else:
+                print("MISS",l,cl,pcp)
+        print("Accuracy:")
+        print(len(testset))
+        print(gg / len(testset))
 
+    def classify(self,link):
+        features = get_link_features(link)
+        res = 0
+        gp = math.log(self.good_count) - math.log(self.good_count + self.bad_count)
+        bp = math.log(self.bad_count) - math.log(self.good_count + self.bad_count)
+        if features is None:
+            return math.exp(gp) - math.exp(bp)
+        gcc = math.log(self.gdictsize * self.alpha + self.good_count)
+        bcc = math.log(self.bdictsize * self.alpha + self.bad_count)
+        goodprob = 0
+        badprob = 0
+        for feature in features:
+            g = math.log((self.goodcounter[feature] + self.alpha)) - gcc 
+            goodprob += g
+            b = math.log(self.badcounter[feature] + self.alpha) - bcc
+            badprob += b
+            print(feature,g,b)
+        if (goodprob + gp)  > (badprob + bp):
+        #if goodprob > badprob:
+            res = 1
+        pa = math.exp(goodprob + gp)
+        pb = math.exp(badprob + bp)
+        return pa - pb
 
 
 
 def get_links(db,hostname,status,batch_size):
     linkcol = db["links"]
+    # count downloaded links
+    res = linkcol.aggregate([
+        { "$match": { "status": {"$not":{"$in":["frontlink","backlink"]}},"host":hostname } },
+        {"$group":{"_id":None,
+                   "count":{"$count":{}},
+                   }
+         },
+    ])
+    links = set()
+    out = list(res)
+    if len(out) == 0:
+        return list()
+    if out[0]["count"] < 200:
     #res  = linkcol.find({"status":status,"host":hostname},{"url":1},limit=batch_size)
         # get random links
         res = linkcol.aggregate([
             { "$match": { "status": status,"host":hostname } },
             { "$sample": { "size": batch_size } }
         ])
-    links = set()
         for i,doc in enumerate(res):
             #print(">>>>>" + status)
             #print(doc);
             links.add(doc["url"])
             if i >= batch_size:
                 break
+    else:
+        cl = LinkClassifier()
+        cl.train(db,hostname)
+        res = linkcol.aggregate([
+            { "$match": { "status": status,"host":hostname } },
+            { "$sample": { "size": batch_size * 100 } }
+        ])
+        outlinks = []
+        for i,doc in enumerate(res):
+            #print(">>>>>" + status)
+            #print(doc);
+            link = doc["url"]
+            outlinks.append((doc["url"],cl.classify(link)))
+        outlinks = sorted(outlinks, key=lambda x: x[1],reverse=True)
+        links = [l[0] for l in outlinks[0:batch_size]]
+        # todo remove very bad links
     return list(links)
 
 
@@ -352,8 +437,19 @@ def link_summary(db,hostname):
         {"$match":{"host":hostname}},
         {"$group":{"_id":"$status","count":{"$sum":1}}},
     ])
+    badcount = 0
+    goodcount = 0
+    out = ["good","frontlink","backlink"]
+    info = {}
     for item in res:
-        print(item)
+        if item["_id"] not in out:
+            badcount += item["count"]
+        if item["_id"] == "good":
+            goodcount = item["count"]
+        info[item["_id"]] = item["count"]
+    good_prob = goodcount / (goodcount + badcount)
+    info["good_prob"] = good_prob
+    info["bad_documents"] = badcount
     print(">>>Domain Content")
     contentcol = db["content"]
     res = contentcol.aggregate([
@@ -364,8 +460,17 @@ def link_summary(db,hostname):
                    }
          },
     ])
+    text_size = 0
     for item in res:
-        print(item)
+        text_size = item["text_size_sum"]
+    good_document_characters = text_size / goodcount
+    fetch_average_characters = text_size / (goodcount +  badcount)
+    info["total_good_characters"] = text_size
+    info["average_good_characters"] = good_document_characters
+    info["average_fetch_characters"] = fetch_average_characters
+    domaincol = db["domain"]
+    print(json.dumps(info))
+    domaincol.update_one({"host":domain},{"$set":info},usert=True)
 
 def domain_summary(db,hostname):
     linkcol = db["links"]
@@ -395,6 +500,8 @@ def createdb():
     contentcol.create_index("host")
     htmlcol = db["html"]
     htmlcol.create_index("url",unique=True)
+    domaincol = db["domains"]
+    domaincol.create_index("host",unique=True)
 
 @cli.command()
 @click.argument("link")
@@ -427,6 +534,14 @@ def externaldomains(link):
     for d in domains:
         print(d)
 
+@cli.command()
+@click.argument("start_link")
+def classify(start_link):
+    myclient = pymongo.MongoClient(CONNECTION)
+    db=myclient[DBNAME]
+    start_link,hostname = courlan.check_url(start_link)
+    cl = LinkClassifier()
+    cl.train(db,hostname)
 
 @cli.command()
 @click.argument("start_link")
@@ -443,15 +558,19 @@ def visit(start_link):
         print("Fetching sitemap links")
         sitemap_links = fetch_sitemap_links(start_link)
         index_links(db,sitemap_links)
-    links.append(start_link)
+        links = get_links(db,hostname,"frontlink",batch_size)
+    links.insert(0,start_link)
+    if len(links) < batch_size:
+        back_links = get_links(db,hostname,"backlink",batch_size - len(links))
+        links += back_links
 
-    print("Processing frontlinks")
+    print("Processing links")
     rules = fetch_robot(hostname)
-    process_links(db,hostname,"frontlink",links,rules)
+    responses = fetch_pages(links)
-    print("Getting  backlinks")
+    extracted_pages = extract_pages(links,responses)
-    back_links = get_links(db,hostname,"backlink",batch_size)
+    extracted_links = extract_links(links,responses,hostname,rules,"backlink")
-    print("Processing backlinks")
+    index_links(db,extracted_links)
-    process_links(db,hostname,"backlink",back_links,rules=rules)
+    index_pages(db,hostname,extracted_pages)
     link_summary(db,hostname)
 
 if __name__ == "__main__":