204 lines
6.4 KiB
Python
204 lines
6.4 KiB
Python
#We have to import the files
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#Split the indexer into 4 parts
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#Alphanumeric sequences into the dataset
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#Stemming
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#Text in bold, headings and other titles should be treated as more important
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#Posting structure > tf-idf score. Name/id the token was found in . So hashmap.
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#We need shelves to hold the data.
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#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
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#Data input
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import json
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import os
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import shelve
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from bs4 import BeautifulSoup
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from time import perf_counter
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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 sklearn.feature_extraction.text import TfidfVectorizer
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import pandas as pd
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import numpy as np
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import re
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#Logging postings
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from posting import Posting
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class Indexer():
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def __init__(self,restart,trimming):
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#Config stuffs
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self.path = "data/DEV/"
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self.restart = restart
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self.trimming = trimming
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self.stemmer = PorterStemmer()
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#Shelves for index
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#https://www3.nd.edu/~busiforc/handouts/cryptography/letterfrequencies.html
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#https://www.irishtimes.com/news/science/how-many-numbers-begin-with-a-1-more-than-30-per-cent-1.4162466
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#According to this will be how we split things
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#Save #1 = ABCD + (1) ~ 18.3% of words
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#Save #2 = EFGHIJK + (2-3)~ 27.1% of words
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#Save #3 = LMNOPQ + (4-7) ~ 25.4% of words
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#Save #4 = RSTUVWXYZ + (8-9)~ 29.2% of words
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#Save #5 = Special characters
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if os.path.exists("save_1.shelve") and restart:
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os.remove("save_1.shelve")
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if os.path.exists("save_2.shelve") and restart:
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os.remove("save_2.shelve")
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if os.path.exists("save_3.shelve") and restart:
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os.remove("save_3.shelve")
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if os.path.exists("save_4.shelve") and restart:
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os.remove("save_4.shelve")
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if os.path.exists("save_5.shelve") and restart:
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os.remove("save_5.shelve")
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self.save_1 = shelve.open("save_1.shelve")
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self.save_2 = shelve.open("save_2.shelve")
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self.save_3 = shelve.open("save_3.shelve")
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self.save_4 = shelve.open("save_4.shelve")
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self.save_5 = shelve.open("save_5.shelve")
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def save_index(self,word,posting):
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cur_save = self.get_save_file(word)
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shelve_list = list()
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try:
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shelve_list = cur_save[word]
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shelve_list.append(posting)
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tic = perf_counter()
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shelve_list.sort(key=lambda x: x.tf_idf, reverse = True)
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toc = perf_counter()
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if toc - tic > 1 :
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print("Took " + str(toc - tic) + "seconds to sort shelve list !")
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cur_save.sync()
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except:
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shelve_list.append(posting)
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cur_save[word] = shelve_list
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cur_save.sync()
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def get_save_file(self,word):
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#return the correct save depending on the starting letter of word
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word_lower = word.lower()
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if re.match(r"^[a-d0-1].*",word_lower):
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return self.save_1
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elif re.match(r"^[e-k2-3].*",word_lower):
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return self.save_2
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elif re.match(r"^[l-q4-7].*",word_lower):
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return self.save_3
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elif re.match(r"^[r-z8-9].*",word_lower):
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return self.save_4
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else:
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print(word)
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print("You have somehow went beyond the magic")
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return self.save_5
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# retuns a dict of words/n-grams with their assosiated tf-idf score *can also return just a single score or a pandas dataframe
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# https://stackoverflow.com/questions/34449127/sklearn-tfidf-transformer-how-to-get-tf-idf-values-of-given-words-in-documen
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def get_tf_idf(self,words,word):
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#tf_idf
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#words = whole text
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#word the word we finding the score for
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#return the score
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try:
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tfidf = TfidfVectorizer(ngram_range=(1,3)) # ngram_range is range of n-values for different n-grams to be extracted (1,3) gets unigrams, bigrams, trigrams
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tfidf_matrix = tfidf.fit_transform(words) # fit trains the model, transform creates matrix
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df = pd.DataFrame(tfidf_matrix.toarray(), columns = tfidf.get_feature_names_out()) # store value of matrix to associated word/n-gram
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#return(df.iloc[0][''.join(word)]) #used for finding single word in dataset
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data = df.to_dict() # transform dataframe to dict *could be expensive the larger the data gets, tested on ~1000 word doc and took 0.002 secs to run
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return data # returns the dict of words/n-grams with tf-idf
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#print(df) # debugging
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except:
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print("Error in tf_idf!")
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return
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def get_data(self):
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for directory in os.listdir(self.path):
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for file in os.listdir(self.path + "/" + directory + "/"):
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#Actual files here
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#JSON["url"] = url of crawled page, ignore fragments
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#JSON["content"] = actual HTML
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#JSON["encoding"] = ENCODING
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ticker = perf_counter()
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tic = perf_counter()
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file_load = open(self.path + "/" + directory + "/"+file)
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data = json.load(file_load)
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soup = BeautifulSoup(data["content"],from_encoding=data["encoding"])
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words = word_tokenize(soup.get_text())
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toc = perf_counter()
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if toc - tic > 1 :
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print("Took " + str(toc - tic) + "seconds to tokenize text !")
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tokenized_words = list()
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stemmed_words = list()
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tic = perf_counter()
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for word in words:
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if word != "" and re.fullmatch('[A-Za-z0-9]+',word):
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#So all the tokenized words are here,
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tokenized_words.append(word)
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toc = perf_counter()
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if toc - tic > 1 :
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print("Took " + str(toc - tic) + "seconds to isalnum text !")
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#YOUR CODE HERE
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tic = perf_counter()
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for word in tokenized_words:
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stemmed_words.append(self.stemmer.stem(word))
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#stemming,
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#tf_idf
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#get_tf_idf(stemmed_words,word)
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#post = Posting()
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toc = perf_counter()
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if toc - tic > 1 :
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print("Took " + str(toc - tic) + "seconds to stemmed text !")
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for word in stemmed_words:
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#posting = Posting(data["url"],self.get_tf_idf(list(' '.join(stemmed_words)),word))
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tic = perf_counter()
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posting = Posting(data["url"],self.tf_idf_raw(stemmed_words,word))
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toc = perf_counter()
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if toc - tic > 1 :
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print("Took " + str(toc - tic) + "seconds to tf_idf text !")
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tic = perf_counter()
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self.save_index(word,posting)
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toc = perf_counter()
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if toc - tic > 1 :
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print("Took " + str(toc - tic) + "seconds to save text !")
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tocker = perf_counter()
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print("Finished " + data['url'] + " in \t " + str(tocker-ticker))
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def tf_idf_raw(self,words,word):
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tf_times = words.count(word)
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tf = tf_times/len(words)
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return tf
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def main():
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indexer = Indexer(True,0)
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indexer.get_data()
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if __name__ == "__main__":
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main() |