138 lines
4.1 KiB
Python
138 lines
4.1 KiB
Python
from threading import Thread
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import json
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import os
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from bs4 import BeautifulSoup
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import re
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#Data process
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from nltk.tokenize import word_tokenize
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from nltk.stem import PorterStemmer
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from posting import Posting
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import math
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import sys
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class Node():
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index_value = ''
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postings = list()
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class Index():
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length = 0
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index = list()
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class Worker(Thread):
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def __init__(self,worker_id,indexer):
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self.indexer = indexer
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self.stemmer = PorterStemmer()
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self.worker_id = worker_id
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self.num_partial = 0
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self.index = dict()
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super().__init__(daemon=True)
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def dump(self):
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part_index = Index()
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part_index.length = 0
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part_index.index = list()
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cur_partial_index_str = "temp/" + str(self.worker_id) + "_" + str(self.num_partial) + '.partial'
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cur_partial_index_index_str = "temp/" + str(self.worker_id) + "_" + str(self.num_partial) + '.index'
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cur_partial_index = open(cur_partial_index_str,'w')
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cur_partial_index_index = open(cur_partial_index_index_str,'w')
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for key in self.index:
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node = Node()
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node.index_value = key
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node.postings = self.index[key]
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jsonStr = json.dumps(node, default=lambda o: o.__dict__,sort_keys=False)
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part_index.index.append((node.index_value,cur_partial_index.tell()))
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cur_partial_index.write(jsonStr + '\n')
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part_index.length = part_index.length + 1
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part_index.index.sort(key=lambda y:y[0])
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jsonStr =json.dumps(part_index, default=lambda o: o.__dict__,sort_keys=False)
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cur_partial_index_index.write(jsonStr)
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self.indexer.add_partial_index(str(self.worker_id) + "_" + str(self.num_partial))
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self.num_partial = self.num_partial + 1
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self.index.clear()
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def run(self):
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while True:
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target = self.indexer.get_next_file()
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if not target:
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self.dump()
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print("Worker " + str(self.worker_id) + " died")
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break
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file_load = open(target)
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data = json.load(file_load)
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soup = BeautifulSoup(data["content"],features="lxml")
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doc_id = target[target.rfind('/')+1:-5]
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url = data['url']
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print("Worker " + str(self.worker_id) + " working on " + url)
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important = {'b' : [], 'h1' : [], 'h2' : [], 'h3' : [], 'title' : []}
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for key_words in important.keys():
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for i in soup.findAll(key_words):
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for word in word_tokenize(i.text):
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important[key_words].append(self.stemmer.stem(word))
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# Gets a cleaner version text comparative to soup.get_text()
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clean_text = ' '.join(soup.stripped_strings)
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# Looks for large white space, tabbed space, and other forms of spacing and removes it
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# Regex expression matches for space characters excluding a single space or words
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clean_text = re.sub(r'\s[^ \w]', '', clean_text)
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# Tokenizes text and joins it back into an entire string. Make sure it is an entire string is essential for get_tf_idf to work as intended
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clean_text = " ".join([i for i in clean_text.split() if i != "" and re.fullmatch('[A-Za-z0-9]+', i)])
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# Stems tokenized text
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clean_text = " ".join([self.stemmer.stem(i) for i in clean_text.split()])
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# Put clean_text as an element in a list because get_tf_idf workers properly with single element lists
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tokens = word_tokenize(clean_text)
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#counter(count,positionals)
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counter = dict()
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#We calculating tf_raw, and positionals here
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for i in range(len(tokens)):
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word = tokens[i]
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if word in counter:
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counter[word][0] = counter[word][0] + 1
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counter[word][1].append(i)
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else:
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counter[word] = [1,list()]
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counter[word][1].append(i)
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doc_length = len(tokens)
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total = 0
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for index in counter:
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tf = counter[index][0]/doc_length
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log_tf = 1 + math.log(tf)
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total = total + log_tf * log_tf
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if index in self.index:
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postings = self.index[index]
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postings.append(Posting(doc_id,url,tf,0,counter[index][1]))
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else:
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self.index[index] = list()
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self.index[index].append(Posting(doc_id,url,tf,0,counter[index][1]))
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self.index[index].sort(key=lambda y:y.doc_id)
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self.indexer.weight[doc_id] = math.sqrt(total)
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#10 Megabytes index (in Ram approx)
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if sys.getsizeof(self.index) > 1000000:
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self.dump()
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