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ETL.py
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ETL.py
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import bs4
from bs4 import BeautifulSoup
import requests
import pandas as pd
import datetime
import numpy as np
import nltk
nltk.download('punkt') ##this downloads the default word tokenizer
nltk.download('stopwords') ##this downloads all stopwords
nltk.download('popular') ##this downloads many different popular libraries
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
#__________________________________MET__________________________________________
#__________________________________MET__________________________________________
#__________________________________MET__________________________________________
#__________________________________MET_______________________________________end
#MET - requests object will not allow us to pull info from thier site
# _link = 'https://www.metmuseum.org/art/collection/search#!?showOnly=highlights&offset='
# link_ = '&pageSize=0&perPage=80&sortBy=relevance&sortOrder=asc&searchField=All'
# List_of_MET_Hilight_Links = []
#
# for increment in range(0, 200):
# List_of_MET_Hilight_Links.append(_link + str(increment*80) + link_)
#
# response_page_MET = []
#
# for link in List_of_MET_Hilight_Links:
# response = requests.get(link)
# try:
# response.raise_for_status()
# response_page_MET.append(response)
# except:
# break
#
# a = response_page_MET[15].text
#_________________________________NEU___________________________________________
#_________________________________NEU___________________________________________
#_________________________________NEU___________________________________________
#_________________________________NEU________________________________________end
#NEU https://www.neuegalerie.org/artist-list/all
#
# response_page_NEU = 'https://www.neuegalerie.org/artist-list/all'
# BeautifulSoup_page_NEU = BeautifulSoup(requests.get(response_page_NEU).text, "lxml")
# BeautifulSoup_page_NEU_a = BeautifulSoup_page_NEU.find_all('a')
# BeautifulSoup_page_NEU_a_href = [element.find_all('href') for element in BeautifulSoup_page_NEU_a]
#_______________________________ARTRES__________________________________________
#_______________________________ARTRES__________________________________________
#_______________________________ARTRES__________________________________________
#_______________________________ARTRES_______________________________________end
# _link = 'http://www.artres.com/C.aspx?VP3=ViewBox&VBID=2UN365FDE6XWG&VBIDL=&SMLS=1&RW=1296&RH=648&PN='
# link_ = '&IT=ThumbImageTemplate01_VForm&CT=Search&SH=1&SF=1&PPM=0&CTID=KRHEC0AOQE3R'
#
# #Think about how to generalize to n pages -> where in html is the current and total page specified?
# link_page_ARTRES = [_link + str(page_num) + link_ for page_num in range(1,7)]
# response_page_ARTRES = [requests.get(link) for link in link_page_ARTRES]
# BeautifulSoup_page_ARTRES = [BeautifulSoup(response.text, 'lxml') for response in response_page_ARTRES]
#
# page_container = []
# #form of web_container -> web_container[web_page][piece][0]
# web_container = []
# #form of link_page_ARTRES_piece -> link_page_ARTRES_piece[link]
# link_page_ARTRES_piece = []
# response_page_ARTRES_piece = []
# for html_page in BeautifulSoup_page_ARTRES:
# for piece in range(193, 263):
# try:
# print('a1.1.3.1.3.' + str(piece) + ':Image')
# page_container.append(html_page.findAll(id = 'a1.1.3.1.3.' + str(piece) + ':Image'))
# except:
# break
# web_container.append(page_container)
# page_container = []
# for webpage in range(0, len(web_container)):
# for piece in range(0, len(web_container[webpage])):
# link_page_ARTRES_piece.append('http://www.artres.com/' + web_container[webpage][piece][0].a['href'])
# response_page_ARTRES_piece = [requests.get(link) for link in link_page_ARTRES_piece]
# BeautifulSoup_page_ARTRES_piece = [BeautifulSoup(response.text, 'lxml') for response in response_page_ARTRES_piece]
#_______________________________Meal_Master_____________________________________
#_______________________________Meal_Master_____________________________________
#_______________________________Meal_Master_____________________________________
#_______________________________Meal_Master__________________________________end
# link_page_Meal_Master = 'http://www.garvick.com/recipes-fps/'
# response_page_Meal_Master = requests.get(link_page_Meal_Master)
# BeautifulSoup_page_Meal_Master = BeautifulSoup(response_page_Meal_Master.text, 'lxml')
# BeautifulSoup_page_Meal_Master_a = BeautifulSoup_page_Meal_Master.find_all('a')
# BeautifulSoup_page_Meal_Master_link = ['http://www.garvick.com/recipes-fps/' + tag['href'] for tag in BeautifulSoup_page_Meal_Master_a]
#_______________________________Epicurious_____________________________________
#_______________________________Epicurious_____________________________________
#_______________________________Epicurious_____________________________________
#_______________________________Epicurious__________________________________end
if False:
#!cuisine###https://www.epicurious.com/search?content=recipe
#1cuisine###https://www.epicurious.com/search/?cuisine=french&content=recipe
#forward and rear link
_link = 'https://www.epicurious.com/search/'
link_ = 'content=recipe'
#list of possible cuisine filters on https://www.epicurious.com/search/ page
EPI_cuisine = ['African','American','Asian','British','Cajun/Creole','Californian','Caribbean',
'Central/S. American','Chinese','Cuban','Eastern European','English','European',
'French','German','Greek','Indian','Irish','Italian','Italian American','Japanese',
'Jewish','Korean','Latin American','Mediterranean','Mexican','Middle Eastern',
'Moroccan','Nuevo Latino','Scandinavian','South American','South Asian','Southeast Asian',
'Southern','Southwestern','Spanish/Portuguese','Tex-Mex','Thai','Turkish','Vietnamese']
print('conversion of cuisine labels into requestable links. First N pages per category. Use first page to determine total number of pages per category.')
#conversion of cuisine labels into requestable links. First on N pages per category. Use first page to determine total number of pages per category.
link_page_EPI_cuisine = [(cuisine ,_link + '?cuisine=' + cuisine.lower().replace(' ', '-') + '&' + link_) for cuisine in EPI_cuisine]
response_page_EPI_cuisine = [requests.get(link[1]) for link in link_page_EPI_cuisine]
BeautifulSoup_page_EPI_cuisine = [BeautifulSoup(response.text, 'lxml') for response in response_page_EPI_cuisine]
BeautifulSoup_page_EPI_cuisine_data_total_pages = []
for Beautiful in BeautifulSoup_page_EPI_cuisine:
try:
BeautifulSoup_page_EPI_cuisine_data_total_pages.append(Beautiful.find_all('nav', class_ = 'common-pagination')[0]['data-total-pages'])
except:
BeautifulSoup_page_EPI_cuisine_data_total_pages.append('1')
print('reconstruction of of cuisine labels with all page numbers assosiated to each category.')
#reconstruction of of cuisine labels with all page numbers assosiated to each category.
link_page_EPI_cuisine_with_page_number = []
for link, page in list(zip(link_page_EPI_cuisine, BeautifulSoup_page_EPI_cuisine_data_total_pages)):
for itt in range(1, int(page) + 1):
print(link[0],link[1] + '&page=' + str(itt))
link_page_EPI_cuisine_with_page_number.append((link[0],link[1] + '&page=' + str(itt)))
response_page_EPI_cuisine = [(link[0],requests.get(link[1])) for link in link_page_EPI_cuisine_with_page_number]
BeautifulSoup_page_EPI_cuisine = [(response[0], BeautifulSoup(response[1].text, 'lxml')) for response in response_page_EPI_cuisine]
print('locating <a, class = "photo-link"> to extract recipe link. Construction of recipe link.')
#locating <a, class = 'photo-link'> to extract recipe link. Construction of recipe link.
BeautifulSoup_page_EPI_cuisine_a = [(Beautiful[0],Beautiful[1].find_all('a', class_ = 'photo-link')) for Beautiful in BeautifulSoup_page_EPI_cuisine]
BeautifulSoup_page_EPI_cuisine_recipe_link = []
for Beautiful in BeautifulSoup_page_EPI_cuisine_a:
for tag in Beautiful[1]:
print('https://www.epicurious.com' + tag['href'])
BeautifulSoup_page_EPI_cuisine_recipe_link.append((Beautiful[0],'https://www.epicurious.com' + tag['href']))
recipe_df = pd.DataFrame(data={'cuisine': BeautifulSoup_page_EPI_cuisine_recipe_link[0],'recipe_link':BeautifulSoup_page_EPI_cuisine_recipe_link[1]})
recipe_df.to_csv("./recipe_cuisine.csv", sep=',',index=False)
#############GENERALIZE
#############GENERALIZE
#############GENERALIZE
#############GENERALIZE
recipe_cuisine = pd.read_csv('recipe_cuisine.csv')
# response_page_EPI_recipe = [requests.get(recipe_cuisine['recipe_link'][_recipe_link]) for _recipe_link in range(0,len(recipe_cuisine))]
# BeautifulSoup_page_EPI_recipe = [BeautifulSoup(response.text, 'lxml') for response in response_page_EPI_recipe]
for a in range(173, 174):
print(datetime.datetime.now())
print('----------',a*100, a*100 + 100)
response_page_EPI_recipe = [(recipe_cuisine['recipe_link'][_recipe_link], requests.get(recipe_cuisine['recipe_link'][_recipe_link])) for _recipe_link in range(a*100,a*100 + 100)]
print('post_Request')
BeautifulSoup_page_EPI_recipe = [(response[0], BeautifulSoup(response[1].text, 'lxml')) for response in response_page_EPI_recipe]
print('beautiful_Soup')
EPI_dictionary = {'link': [],'name': [], 'ingredients': [], 'recipe_category': [], 'recipe_cuisine': [], 'rating': [], 'rating_count': [], 'keywords': []}
for Beautiful in BeautifulSoup_page_EPI_recipe:
print('--')
EPI_dictionary['link'].append(Beautiful[0])
try:
EPI_dictionary['name'].append(Beautiful[1].find_all('h1', itemprop = 'name')[0].text)
except:
EPI_dictionary['name'].append('unknown')
try:
EPI_dictionary['rating'].append(Beautiful[1].find_all('span', class_ = 'rating')[0].text)
except:
EPI_dictionary['rating'].append(0)
try:
EPI_dictionary['rating_count'].append(Beautiful[1].find_all('span', class_ = 'reviews-count')[0].text)
except:
EPI_dictionary['rating_count'].append(0)
ingredient_string = ''
for ingredient in Beautiful[1].find_all('li', class_ = 'ingredient'):
ingredient_string += ingredient.text + ' $ '
EPI_dictionary['ingredients'].append(ingredient_string)
recipe_category_string = ''
for recipe_category in Beautiful[1].find_all('dt', itemprop = "recipeCategory"):
recipe_category_string += recipe_category.text + ' $ '
EPI_dictionary['recipe_category'].append(recipe_category_string)
recipe_cuisine_string = ''
for recipe_cuisine_ in Beautiful[1].find_all('dt', itemprop = "recipeCuisine"):
recipe_cuisine_string += recipe_cuisine_.text + ' $ '
EPI_dictionary['recipe_cuisine'].append(recipe_cuisine_string)
EPI_dictionary['keywords'].append(recipe_category_string + recipe_cuisine_string)
recipe_info = pd.DataFrame(EPI_dictionary)
recipe_info.to_csv("./recipe_cuisine_recipe_info"+ str(a) +".csv", sep=',',index=False)
df = pd.concat([pd.read_csv('recipe_cuisine_recipe_info' + str(i) + '.csv')for i in range(0,174)], axis = 0)
df.to_csv('./recipe.csv', sep = ',', index = False)
recipe_cuisine = pd.read_csv('recipe_cuisine.csv')
df = pd.read_csv('recipe.csv')
recipe_cuisine.columns = ['col1', 'col2']
df1 = pd.concat([df, recipe_cuisine['col1']], axis = 1)
df = pd.read_csv('allrecipes.csv')
name_count_vectorizer = CountVectorizer(stop_words='english')
name_count_vectorizer.fit(np.array(df['name'].fillna('unknown')))
name_count_vectorizer_transform = name_count_vectorizer.transform(df['name'].fillna(' '))
name_count_vectorizer_OHE = pd.DataFrame(name_count_vectorizer_transform.toarray(), columns = name_count_vectorizer.get_feature_names())
ingredients_count_vectorizer = CountVectorizer(stop_words='english')
ingredients_count_vectorizer.fit(np.array(df['ingredients'].fillna('unknown')))
ingredients_count_vectorizer_transform = ingredients_count_vectorizer.transform(df['ingredients'].fillna(' '))
ingredients_count_vectorizer_OHE = pd.DataFrame(ingredients_count_vectorizer_transform.toarray(), columns = ingredients_count_vectorizer.get_feature_names())
keywords_count_vectorizer = CountVectorizer(stop_words='english')
keywords_count_vectorizer.fit(np.array(df['keywords'].fillna('unknown')))
keywords_count_vectorizer_transform = keywords_count_vectorizer.transform(df['keywords'].fillna(' '))
keywords_count_vectorizer_OHE = pd.DataFrame(keywords_count_vectorizer_transform.toarray(), columns = keywords_count_vectorizer.get_feature_names())
name_tfidf_vectorizer = TfidfVectorizer(stop_words='english')
name_tfidf_vectorizer.fit(np.array(df['name'].fillna('unknown')))
name_tfidf_vectorizer_transform = name_tfidf_vectorizer.transform(df['name'].fillna(' '))
name_tfidf_vectorizer_OHE = pd.DataFrame(name_tfidf_vectorizer_transform.toarray(), columns = name_tfidf_vectorizer.get_feature_names())
ingredients_tfidf_vectorizer = TfidfVectorizer(stop_words='english')
ingredients_tfidf_vectorizer.fit(np.array(df['ingredients'].fillna('unknown')))
ingredients_tfidf_vectorizer_transform = ingredients_tfidf_vectorizer.transform(df['ingredients'].fillna(' '))
ingredients_tfidf_vectorizer_OHE = pd.DataFrame(ingredients_tfidf_vectorizer_transform.toarray(), columns = ingredients_tfidf_vectorizer.get_feature_names())
keywords_tfidf_vectorizer = TfidfVectorizer(stop_words='english')
keywords_tfidf_vectorizer.fit(np.array(df['keywords'].fillna('unknown')))
keywords_tfidf_vectorizer_transform = keywords_tfidf_vectorizer.transform(df['keywords'].fillna(' '))
keywords_tfidf_vectorizer_OHE = pd.DataFrame(keywords_tfidf_vectorizer_transform.toarray(), columns = keywords_tfidf_vectorizer.get_feature_names())