src/build_vocab.py

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.

import nltk
import pickle
import argparse
from collections import Counter
import json
import os
from tqdm import *
import numpy as np
import re


class Vocabulary(object):
    """Simple vocabulary wrapper."""
    def __init__(self):
        self.word2idx = {}
        self.idx2word = {}
        self.idx = 0

    def add_word(self, word, idx=None):
        if idx is None:
            if not word in self.word2idx:
                self.word2idx[word] = self.idx
                self.idx2word[self.idx] = word
                self.idx += 1
            return self.idx
        else:
            if not word in self.word2idx:
                self.word2idx[word] = idx
                if idx in self.idx2word.keys():
                    self.idx2word[idx].append(word)
                else:
                    self.idx2word[idx] = [word]

                return idx

    def __call__(self, word):
        if not word in self.word2idx:
            return self.word2idx['<pad>']
        return self.word2idx[word]

    def __len__(self):
        return len(self.idx2word)


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 get_instruction(instruction, replace_dict, instruction_mode=True):
    instruction = instruction.lower()

    for rep, char_list in replace_dict.items():
        for c_ in char_list:
            if c_ in instruction:
                instruction = instruction.replace(c_, rep)
        instruction = instruction.strip()
    # remove sentences starting with "1.", "2.", ... from the targets
    if len(instruction) > 0 and instruction[0].isdigit() and instruction_mode:
        instruction = ''
    return instruction


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'))
    layer1 = json.load(open(os.path.join(args.recipe1m_path, 'layer1.json'), 'r'))
    layer2 = json.load(open(os.path.join(args.recipe1m_path, 'layer2.json'), 'r'))

    id2im = {}

    for i, entry in enumerate(layer2):
        id2im[entry['id']] = i

    print("Loaded data.")
    print("Found %d recipes in the dataset." % (len(layer1)))
    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'

    #####
    # 1. Count words in dataset and clean
    #####
    if os.path.exists(ingrs_file) and os.path.exists(instrs_file) and not args.forcegen:
        print ("loading pre-extracted word counters")
        counter_ingrs = pickle.load(open(args.save_path + 'allingrs_count.pkl', 'rb'))
        counter_toks = pickle.load(open(args.save_path + 'allwords_count.pkl', 'rb'))
    else:
        counter_toks = Counter()
        counter_ingrs = Counter()
        counter_ingrs_raw = Counter()

        for i, entry in tqdm(enumerate(layer1)):

            # get all instructions for this recipe
            instrs = entry['instructions']

            instrs_list = []
            ingrs_list = []

            # retrieve pre-detected ingredients for this entry
            det_ingrs = dets[idx2ind[entry['id']]]['ingredients']

            valid = dets[idx2ind[entry['id']]]['valid']
            det_ingrs_filtered = []

            for j, det_ingr in enumerate(det_ingrs):
                if len(det_ingr) > 0 and valid[j]:
                    det_ingr_undrs = get_ingredient(det_ingr, replace_dict_ingrs)
                    det_ingrs_filtered.append(det_ingr_undrs)
                    ingrs_list.append(det_ingr_undrs)

            # get raw text for instructions of this entry
            acc_len = 0
            for instr in instrs:
                instr = instr['text']
                instr = get_instruction(instr, replace_dict_instrs)
                if len(instr) > 0:
                    instrs_list.append(instr)
                    acc_len += len(instr)

            # discard recipes with too few or too many ingredients or instruction words
            if len(ingrs_list) < args.minnumingrs or len(instrs_list) < args.minnuminstrs \
                    or len(instrs_list) >= args.maxnuminstrs or len(ingrs_list) >= args.maxnumingrs \
                    or acc_len < args.minnumwords:
                continue

            # tokenize sentences and update counter
            update_counter(instrs_list, counter_toks, istrain=entry['partition'] == 'train')
            title = nltk.tokenize.word_tokenize(entry['title'].lower())
            if entry['partition'] == 'train':
                counter_toks.update(title)
            if entry['partition'] == 'train':
                counter_ingrs.update(ingrs_list)

        pickle.dump(counter_ingrs, open(args.save_path + 'allingrs_count.pkl', 'wb'))
        pickle.dump(counter_toks, open(args.save_path + 'allwords_count.pkl', 'wb'))
        pickle.dump(counter_ingrs_raw, open(args.save_path + 'allingrs_raw_count.pkl', 'wb'))

    # 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}

    # Recipe vocab
    # Create a vocab wrapper and add some special tokens.
    vocab_toks = Vocabulary()
    vocab_toks.add_word('<start>')
    vocab_toks.add_word('<end>')
    vocab_toks.add_word('<eoi>')

    # Add the words to the vocabulary.
    for i, word in enumerate(words):
        vocab_toks.add_word(word)
    vocab_toks.add_word('<pad>')

    # Ingredient vocab
    # Create a vocab wrapper for ingredients
    vocab_ingrs = Vocabulary()
    idx = vocab_ingrs.add_word('<end>')
    # this returns the next idx to add words to
    # Add the ingredients to the vocabulary.
    for k, _ in ingrs.items():
        for ingr in cluster_ingrs[k]:
            idx = vocab_ingrs.add_word(ingr, idx)
        idx += 1
    _ = vocab_ingrs.add_word('<pad>', idx)

    print("Total ingr vocabulary size: {}".format(len(vocab_ingrs)))
    print("Total token vocabulary size: {}".format(len(vocab_toks)))

    dataset = {'train': [], 'val': [], 'test': []}

    ######
    # 2. Tokenize and build dataset based on vocabularies.
    ######
    for i, entry in tqdm(enumerate(layer1)):

        # get all instructions for this recipe
        instrs = entry['instructions']

        instrs_list = []
        ingrs_list = []
        images_list = []

        # retrieve pre-detected ingredients for this entry
        det_ingrs = dets[idx2ind[entry['id']]]['ingredients']
        valid = dets[idx2ind[entry['id']]]['valid']
        labels = []

        for j, det_ingr in enumerate(det_ingrs):
            if len(det_ingr) > 0 and valid[j]:
                det_ingr_undrs = get_ingredient(det_ingr, replace_dict_ingrs)
                ingrs_list.append(det_ingr_undrs)
                label_idx = vocab_ingrs(det_ingr_undrs)
                if label_idx is not vocab_ingrs('<pad>') and label_idx not in labels:
                    labels.append(label_idx)

        # get raw text for instructions of this entry
        acc_len = 0
        for instr in instrs:
            instr = instr['text']
            instr = get_instruction(instr, replace_dict_instrs)
            if len(instr) > 0:
                acc_len += len(instr)
                instrs_list.append(instr)

        # we discard recipes with too many or too few ingredients or instruction words
        if len(labels) < args.minnumingrs or len(instrs_list) < args.minnuminstrs \
                or len(instrs_list) >= args.maxnuminstrs or len(labels) >= args.maxnumingrs \
                or acc_len < args.minnumwords:
            continue

        if entry['id'] in id2im.keys():
            ims = layer2[id2im[entry['id']]]

            # copy image paths for this recipe
            for im in ims['images']:
                images_list.append(im['id'])

        # tokenize sentences
        toks = []

        for instr in instrs_list:
            tokens = nltk.tokenize.word_tokenize(instr)
            toks.append(tokens)

        title = nltk.tokenize.word_tokenize(entry['title'].lower())

        newentry = {'id': entry['id'], 'instructions': instrs_list, 'tokenized': toks,
                    'ingredients': ingrs_list, 'images': images_list, 'title': title}
        dataset[entry['partition']].append(newentry)

    print('Dataset size:')
    for split in dataset.keys():
        print(split, ':', len(dataset[split]))

    return vocab_ingrs, vocab_toks, dataset


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)