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construct_models.py
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construct_models.py
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# System
import datetime
import traceback
import ast
import os
# import datetime
from collections import Counter
import math
# ML
import numpy as np
import pandas as pd
import pickle
# WEB
import urllib.request
from urllib.request import urlopen
from bs4 import BeautifulSoup
# NLTK
import nltk
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import RegexpTokenizer
# Selenium
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as ec
from selenium.webdriver.common.by import By
def remove_stopwords(tokens):
for i, token in enumerate(tokens):
tokens[i] = ''.join([ch for ch in token if ch not in char_blacklist])
tokens_sw = [w.lower() for w in tokens if w not in stopwords]
tokens_lemmatize = [wnl.lemmatize(token) for token in tokens_sw]
return tokens_lemmatize
def get_en_words(tokens_lemmatize):
english_tokens = []
for word in tokens_lemmatize:
english_tokens.append(word) if word in english_vocab else ''
english_confidence = round(len(english_tokens) / len(tokens_lemmatize) * 100, 2) if len(english_tokens) > 0 else 0
return english_tokens, english_confidence
def translate_words():
foreign_words = list(set(tokens_lemmatize) - set(en_tokens))
translated_words = []
if len(foreign_words):
chunk_size = math.ceil(len(foreign_words) / 5000)
chunks = np.array_split(foreign_words, chunk_size)
for chunk in chunks:
foreign_text = " ".join(chunk)
input_box = driver.find_element_by_id('source')
driver.execute_script(f"document.getElementById('source').value = '{foreign_text}';")
# input_box.send_keys(foreign_text)
WebDriverWait(driver, 20).until(ec.visibility_of_element_located((By.CSS_SELECTOR, "span.tlid-translation.translation")))
output_box = driver.find_element_by_css_selector("span.tlid-translation.translation").text
translated_words.extend(output_box.split(' '))
input_box.clear()
return translated_words
start = datetime.datetime.now()
print(start)
date = "2019-05-05"
input_path = f'Datasets/Feature_dataset_{date}.csv'
output_path = f'Datasets/Translated_tokens_{date}.csv'
words_filename = f"Frequency_models/word_frequency_{date}.picle"
if os.path.isfile(input_path) and not os.path.isfile(output_path) and not os.path.isfile(words_filename):
nltk.download('stopwords')
nltk.download('words')
options = webdriver.ChromeOptions()
options.add_argument('headless')
# driver = webdriver.Chrome('/home/domantas/Documents/selenium_drivers/chromedriver')
driver = webdriver.Chrome('/home/domantas/Documents/selenium_drivers/chromedriver', chrome_options=options)
driver.get("https://translate.google.com/")
english_vocab = set(w.lower() for w in nltk.corpus.words.words('en'))
english_tolerance = 50
english_confidence = []
char_blacklist = list(chr(i) for i in range(32, 127) if (i <= 47 or i >= 58)\
and (i <= 64 or i >= 91) and (i <= 96 or i >= 123))
stopwords = nltk.corpus.stopwords.words('english')
stopwords.extend(char_blacklist)
top = 2500
hdr = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.3',
'Accept-Encoding': 'none',
'Accept-Language': 'en-US,en;q=0.8',
'Connection': 'keep-alive'}
df = pd.read_csv(input_folder)
df = df[~df['tokens'].isnull()]
df['tokens'] = df['tokens'].map(lambda x: ast.literal_eval(x))
df['tokens_en'] = ''
df['en_confidence'] = ''
counter = 0
for row_id, row in df.iterrows():
counter +=1
try:
wnl = WordNetLemmatizer()
tokens_lemmatize = remove_stopwords(row['tokens'])
en_tokens, en_confidence = get_en_words(tokens_lemmatize)
translated_words = translate_words()
en_tokens_tr, en_confidence_tr = get_en_words(translated_words)
en_tokens.extend(remove_stopwords(en_tokens_tr))
df.at[row_id, 'tokens_en'] = en_tokens if len(en_tokens) else ''
df.at[row_id, 'en_confidence'] = round(len(en_tokens) / len(tokens_lemmatize) * 100, 2) if len(en_tokens) > 0 else 0
print(f"{counter}/{df.shape[0]} || {row['url']}")
except Exception:
print(f"{counter}/{df.shape[0]} || FAILED. {row['url']}")
# print(traceback.print_exc())
continue
driver.close()
stop = datetime.datetime.now()
exec_time = stop - start
print(exec_time)
df = df[df['tokens_en'] != '']
df.to_csv(output_path, index=False)
words_frequency = {}
for category in set(df['main_category'].values):
print(category)
all_words = []
for row in df[df['main_category'] == category]['tokens_en']:
for word in row:
all_words.append(word)
most_common = nltk.FreqDist(w for w in all_words).most_common(top)
words_frequency[category] = most_common
# Extract only words
for category in set(df['main_category'].values):
words_frequency[category] = [word for word, number in words_frequency[category]]
# Save words_frequency model
import pickle
import os
pickle_out = open(words_filename,"wb")
pickle.dump(words_frequency, pickle_out)
pickle_out.close()