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#!/usr/bin/python3 | |
# -*- coding: utf-8 -* | |
import sys | |
import os | |
import math | |
import re | |
from App.bin import constants | |
from textblob import TextBlob as tb | |
class TechnologyFinder(object): | |
def __init__(self, corpus): | |
self.corpus = corpus | |
print("Extracting technologies") | |
def last_cleansing(self, tech): | |
tech = str(tech) | |
tech = re.sub(r'\s?\bcomprises\b', '', tech) | |
return tech | |
def get_technologies(self): | |
corpus = self.corpus | |
technologies = [] | |
def tf(word, blob): | |
return (float)(blob.noun_phrases.count(word)) / (float)(len(blob.noun_phrases)) | |
def n_containing(word, bloblist): | |
return sum(1 for blob in bloblist if word in blob.noun_phrases) | |
def idf(word, bloblist): | |
return math.log(len(bloblist) / (float)(1 + n_containing(word, bloblist))) | |
def tfidf(word, blob, bloblist): | |
return tf(word, blob) * idf(word, bloblist) | |
stopwords = open(constants.ASSETS+'stopwords', 'r').read().split('\r\n') | |
bloblist = [] | |
filenamelist = [] | |
for filepath,patent in corpus.items(): | |
filename = os.path.basename(os.path.normpath(filepath)) | |
#name, extension = filename.split('.') | |
filenamelist.append(filepath) | |
filteredtext = [t for t in patent if t.lower() not in stopwords] | |
filteredcontent = ''.join(filteredtext) | |
blob = tb(filteredcontent.lower()) | |
bloblist.append(blob) | |
for i, blob in enumerate(bloblist): | |
filename = [] | |
technologies.append(filename) | |
scores = {word: tfidf(word, blob, bloblist) for word in blob.noun_phrases} | |
sorted_words = sorted(scores.items(), key=lambda x: x[1], reverse=True) | |
for word, score in sorted_words[:6]: | |
word = self.last_cleansing(word) | |
print("techologies found") | |
filename.append(word) | |
technologies_list = dict(zip(filenamelist, technologies)) | |
return technologies_list | |