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import nltk
import pickle
import argparse
from collections import Counter
import json
import os
from tqdm import *
import numpy as np
import re
def get_ingredient(det_ingr, replace_dict):
det_ingr_undrs = det_ingr['text'].lower()
det_ingr_undrs = ''.join(i for i in det_ingr_undrs if not i.isdigit())
for rep, char_list in replace_dict.items():
for c_ in char_list:
if c_ in det_ingr_undrs:
det_ingr_undrs = det_ingr_undrs.replace(c_, rep)
det_ingr_undrs = det_ingr_undrs.strip()
det_ingr_undrs = det_ingr_undrs.replace(' ', '_')
return det_ingr_undrs
def remove_plurals(counter_ingrs, ingr_clusters):
del_ingrs = []
for k, v in counter_ingrs.items():
if len(k) == 0:
del_ingrs.append(k)
continue
gotit = 0
if k[-2:] == 'es':
if k[:-2] in counter_ingrs.keys():
counter_ingrs[k[:-2]] += v
ingr_clusters[k[:-2]].extend(ingr_clusters[k])
del_ingrs.append(k)
gotit = 1
if k[-1] == 's' and gotit == 0:
if k[:-1] in counter_ingrs.keys():
counter_ingrs[k[:-1]] += v
ingr_clusters[k[:-1]].extend(ingr_clusters[k])
del_ingrs.append(k)
for item in del_ingrs:
del counter_ingrs[item]
del ingr_clusters[item]
return counter_ingrs, ingr_clusters
def cluster_ingredients(counter_ingrs):
mydict = dict()
mydict_ingrs = dict()
for k, v in counter_ingrs.items():
w1 = k.split('_')[-1]
w2 = k.split('_')[0]
lw = [w1, w2]
if len(k.split('_')) > 1:
w3 = k.split('_')[0] + '_' + k.split('_')[1]
w4 = k.split('_')[-2] + '_' + k.split('_')[-1]
lw = [w1, w2, w4, w3]
gotit = 0
for w in lw:
if w in counter_ingrs.keys():
# check if its parts are
parts = w.split('_')
if len(parts) > 0:
if parts[0] in counter_ingrs.keys():
w = parts[0]
elif parts[1] in counter_ingrs.keys():
w = parts[1]
if w in mydict.keys():
mydict[w] += v
mydict_ingrs[w].append(k)
else:
mydict[w] = v
mydict_ingrs[w] = [k]
gotit = 1
break
if gotit == 0:
mydict[k] = v
mydict_ingrs[k] = [k]
return mydict, mydict_ingrs
def update_counter(list_, counter_toks, istrain=False):
for sentence in list_:
tokens = nltk.tokenize.word_tokenize(sentence)
if istrain:
counter_toks.update(tokens)
def build_vocab_recipe1m(args):
print ("Loading data...")
dets = json.load(open(os.path.join(args.recipe1m_path, 'det_ingrs.json'), 'r'))
replace_dict_ingrs = {'and': ['&', "'n"], '': ['%', ',', '.', '#', '[', ']', '!', '?']}
replace_dict_instrs = {'and': ['&', "'n"], '': ['#', '[', ']']}
idx2ind = {}
for i, entry in enumerate(dets):
idx2ind[entry['id']] = i
ingrs_file = args.save_path + 'allingrs_count.pkl'
instrs_file = args.save_path + 'allwords_count.pkl'
# manually add missing entries for better clustering
base_words = ['peppers', 'tomato', 'spinach_leaves', 'turkey_breast', 'lettuce_leaf',
'chicken_thighs', 'milk_powder', 'bread_crumbs', 'onion_flakes',
'red_pepper', 'pepper_flakes', 'juice_concentrate', 'cracker_crumbs', 'hot_chili',
'seasoning_mix', 'dill_weed', 'pepper_sauce', 'sprouts', 'cooking_spray', 'cheese_blend',
'basil_leaves', 'pineapple_chunks', 'marshmallow', 'chile_powder',
'cheese_blend', 'corn_kernels', 'tomato_sauce', 'chickens', 'cracker_crust',
'lemonade_concentrate', 'red_chili', 'mushroom_caps', 'mushroom_cap', 'breaded_chicken',
'frozen_pineapple', 'pineapple_chunks', 'seasoning_mix', 'seaweed', 'onion_flakes',
'bouillon_granules', 'lettuce_leaf', 'stuffing_mix', 'parsley_flakes', 'chicken_breast',
'basil_leaves', 'baguettes', 'green_tea', 'peanut_butter', 'green_onion', 'fresh_cilantro',
'breaded_chicken', 'hot_pepper', 'dried_lavender', 'white_chocolate',
'dill_weed', 'cake_mix', 'cheese_spread', 'turkey_breast', 'chucken_thighs', 'basil_leaves',
'mandarin_orange', 'laurel', 'cabbage_head', 'pistachio', 'cheese_dip',
'thyme_leave', 'boneless_pork', 'red_pepper', 'onion_dip', 'skinless_chicken', 'dark_chocolate',
'canned_corn', 'muffin', 'cracker_crust', 'bread_crumbs', 'frozen_broccoli',
'philadelphia', 'cracker_crust', 'chicken_breast']
for base_word in base_words:
if base_word not in counter_ingrs.keys():
counter_ingrs[base_word] = 1
counter_ingrs, cluster_ingrs = cluster_ingredients(counter_ingrs)
counter_ingrs, cluster_ingrs = remove_plurals(counter_ingrs, cluster_ingrs)
# If the word frequency is less than 'threshold', then the word is discarded.
words = [word for word, cnt in counter_toks.items() if cnt >= args.threshold_words]
ingrs = {word: cnt for word, cnt in counter_ingrs.items() if cnt >= args.threshold_ingrs}
def main(args):
vocab_ingrs, vocab_toks, dataset = build_vocab_recipe1m(args)
with open(os.path.join(args.save_path, args.suff+'recipe1m_vocab_ingrs.pkl'), 'wb') as f:
pickle.dump(vocab_ingrs, f)
with open(os.path.join(args.save_path, args.suff+'recipe1m_vocab_toks.pkl'), 'wb') as f:
pickle.dump(vocab_toks, f)
for split in dataset.keys():
with open(os.path.join(args.save_path, args.suff+'recipe1m_' + split + '.pkl'), 'wb') as f:
pickle.dump(dataset[split], f)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--recipe1m_path', type=str,
default='path/to/recipe1m',
help='recipe1m path')
parser.add_argument('--save_path', type=str, default='../data/',
help='path for saving vocabulary wrapper')
parser.add_argument('--suff', type=str, default='')
parser.add_argument('--threshold_ingrs', type=int, default=10,
help='minimum ingr count threshold')
parser.add_argument('--threshold_words', type=int, default=10,
help='minimum word count threshold')
parser.add_argument('--maxnuminstrs', type=int, default=20,
help='max number of instructions (sentences)')
parser.add_argument('--maxnumingrs', type=int, default=20,
help='max number of ingredients')
parser.add_argument('--minnuminstrs', type=int, default=2,
help='max number of instructions (sentences)')
parser.add_argument('--minnumingrs', type=int, default=2,
help='max number of ingredients')
parser.add_argument('--minnumwords', type=int, default=20,
help='minimum number of characters in recipe')
parser.add_argument('--forcegen', dest='forcegen', action='store_true')
parser.set_defaults(forcegen=False)
args = parser.parse_args()
main(args)
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