158 lines
4.8 KiB
Python
158 lines
4.8 KiB
Python
#We have to import the files
|
|
#Split the indexer into 4 parts
|
|
#Alphanumeric sequences into the dataset
|
|
#Stemming
|
|
#Text in bold, headings and other titles should be treated as more important
|
|
|
|
#Posting structure > tf-idf score. Name/id the token was found in . So hashmap.
|
|
#We need shelves to hold the data.
|
|
|
|
#Posting ---> Source of file, tf-idf score. #for now we will only use these two, as we get more complex posting will be change accordingly
|
|
|
|
#Data input
|
|
import json
|
|
import os
|
|
import shelve
|
|
from bs4 import BeautifulSoup
|
|
|
|
|
|
|
|
#Data process
|
|
from nltk.tokenize import word_tokenize
|
|
from nltk.stem import PorterStemmer
|
|
from sklearn.feature_extraction.text import TfidfVectorizer
|
|
import pandas as pd
|
|
import numpy as np
|
|
|
|
import re
|
|
|
|
|
|
class Indexer():
|
|
def __init__(self,restart,trimming):
|
|
#Config stuffs
|
|
self.path = "data/DEV/"
|
|
self.restart = restart
|
|
self.trimming = trimming
|
|
self.stemmer = PorterStemmer()
|
|
self.vectorizer = TfidfVectorizer(lowercase=True,ngram_range = (1,3))
|
|
|
|
#Shelves for index
|
|
#https://www3.nd.edu/~busiforc/handouts/cryptography/letterfrequencies.html
|
|
#https://www.irishtimes.com/news/science/how-many-numbers-begin-with-a-1-more-than-30-per-cent-1.4162466
|
|
#According to this will be how we split things
|
|
#Save #1 = ABCD + (1) ~ 18.3% of words
|
|
#Save #2 = EFGHIJK + (2-3)~ 27.1% of words
|
|
#Save #3 = LMNOPQ + (4-7) ~ 25.4% of words
|
|
#Save #4 = RSTUVWXYZ + (8-9)~ 29.2% of words
|
|
#Save #5 = Numbers ???
|
|
if os.path.exists("save_1.shelve") and restart:
|
|
os.remove("save_1.shelve")
|
|
if os.path.exists("save_2.shelve") and restart:
|
|
os.remove("save_2.shelve")
|
|
if os.path.exists("save_3.shelve") and restart:
|
|
os.remove("save_3.shelve")
|
|
if os.path.exists("save_4.shelve") and restart:
|
|
os.remove("save_4.shelve")
|
|
|
|
|
|
self.save_1 = shelve.open("save_1.shelve")
|
|
self.save_2 = shelve.open("save_2.shelve")
|
|
self.save_3 = shelve.open("save_3.shelve")
|
|
self.save_4 = shelve.open("save_4.shelve")
|
|
|
|
|
|
def save_index(self,word,posting):
|
|
wordhash = hash(word) ##Honestly do not know why hashing is even needed, might cause more problems
|
|
cur_save = get_save(word)
|
|
shelve_list = list()
|
|
|
|
if wordhash not in cur_save:
|
|
shelve_list.append(posting)
|
|
cur_save[wordhash] = shelve_list
|
|
cur_save.sync()
|
|
else:
|
|
shelve_list = cur_save[wordhash]
|
|
shelve_list.append(posting)
|
|
shelve_list.sort(key=lambda x: x.tf_idf, reverse = True)
|
|
cur_save.sync()
|
|
|
|
def get_save_file(self,word):
|
|
#return the correct save depending on the starting letter of word
|
|
word_lower = word.lower()
|
|
|
|
if re.match(r"^[a-d1-1].*",word_lower):
|
|
return self.save_1
|
|
elif re.match(r"^[e-k2-3].*",word_lower):
|
|
return self.save_2
|
|
elif re.match(r"^[l-q4-7].*",word_lower):
|
|
return self.save_3
|
|
elif re.match(r"^[r-z8-9].*",word_lower):
|
|
return self.save_4
|
|
else:
|
|
print("You have somehow went beyond the magic")
|
|
return None
|
|
# I have a test file (mytest.py) with pandas but couldn't figure out how to grab just a single cell.
|
|
# so I came up with this, if anyone knows how to get a single cell and can explain it to
|
|
# me I would love to know, as I think that method might be quicker, maybe, idk it like
|
|
# 4am
|
|
# https://stackoverflow.com/questions/34449127/sklearn-tfidf-transformer-how-to-get-tf-idf-values-of-given-words-in-documen
|
|
def get_tf_idf(self,words,word):
|
|
#tf_idf
|
|
#words = whole text
|
|
#word the word we finding the score for
|
|
#return the score
|
|
try:
|
|
tfidf = TfidfVectorizer()
|
|
tfidf_matrix = tfidf.fit_transform(words)
|
|
df = pd.DataFrame(tfidf_matrix.toarray(), columns = tfidf.get_feature_names_out())
|
|
return(df.iloc[0][''.join(word)])
|
|
#print(df)
|
|
except KeyError:
|
|
return -1
|
|
|
|
|
|
def get_data(self):
|
|
for directory in os.listdir(self.path):
|
|
for file in os.listdir(self.path + "/" + directory + "/"):
|
|
#Actual files here
|
|
#JSON["url"] = url of crawled page, ignore fragments
|
|
#JSON["content"] = actual HTML
|
|
#JSON["encoding"] = ENCODING
|
|
file_load = open(self.path + "/" + directory + "/"+file)
|
|
data = json.load(file_load)
|
|
soup = BeautifulSoup(data["content"],from_encoding=data["encoding"])
|
|
words = word_tokenize(soup.get_text())
|
|
tokenized_words = list()
|
|
stemmed_words = list()
|
|
for word in words:
|
|
if word != "" and word.isalnum():
|
|
#So all the tokenized words are here,
|
|
tokenized_words.append(word)
|
|
#YOUR CODE HERE
|
|
print(tokenized_words)
|
|
|
|
for word in tokenized_words:
|
|
stemmed_words.append(self.stemmer.stem(word))
|
|
|
|
print(X)
|
|
#stemming,
|
|
#tf_idf
|
|
#get_tf_idf(stemmed_words,word)
|
|
#post = Posting()
|
|
|
|
print(stemmed_words)
|
|
#
|
|
exit(1)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def main():
|
|
indexer = Indexer(True,0)
|
|
indexer.get_data()
|
|
|
|
if __name__ == "__main__":
|
|
main() |