Ingredient Parsing - Spacy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "import ast\n",
    "import pymongo\n",
    "import tensorflow as tf\n",
    "\n",
    "import spacy\n",
    "sp = spacy.load('en_core_web_sm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MongoDB\n",
    "client = pymongo.MongoClient(\"mongodb://localhost:27017/\")\n",
    "db = client['food_analysis']\n",
    "recipes = db['recipes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(list(recipes.find({})))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyt = pd.read_csv('nyt_ingredients_training.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text Processing in Spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "unit_map_dict = {'cup':['cup', 'cups', 'c.', 'c'],\n",
    "            'tbsp':['tbsp', 'tbsp.', 'tablespoon', 'tablespoons'],\n",
    "            'tsp':['tsp', 'tsp.', 'teaspoon', 'teaspoons'],\n",
    "            'lb':['lb', 'pound', 'lbs', 'lb.', 'lbs.', 'pounds'],\n",
    "            'oz':['ounce', 'oz.', 'oz', 'ozs', 'ozs.', 'ounces'],\n",
    "            'g':['g', 'gram', 'grams'],\n",
    "            'quart':['quart', 'qt', 'qrt', 'quarts'],\n",
    "            'pint':['pint', 'pints', 'pt'],\n",
    "            'gallon':['gallon', 'gallons']}\n",
    "\n",
    "def map_units(x):\n",
    "    \n",
    "    out = x\n",
    "    for i, token in enumerate(x):\n",
    "        for key in unit_map_dict:\n",
    "            if token in unit_map_dict[key]:\n",
    "                out[i] = key\n",
    "        \n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def text_proc_pipeline(df, col):\n",
    "    \n",
    "    odf = df.copy()\n",
    "    odf[col] = odf[col].str.lower()\n",
    "    \n",
    "    print(\"Replacing bad characters\")\n",
    "    replacements = {\n",
    "                # Bad fractions\n",
    "                \"↉\": \"0\", \"⅒\": \"1/10\", \"⅑\": \"1/9\", \"⅛\": \"1/8\",\n",
    "                     \"⅐\": \"1/7\", \"⅙\": \"1/6\", \"⅕\": \"1/5\", \"¼\": \"1/4\",\n",
    "                     \"⅓\": \"1/3\", \"½\": \"1/2\", \"⅖\": \"2/3\", \"⅔\": \"2/3\",\n",
    "                     \"⅜\": \"3/8\", \"⅗\": \"3/5\", \"¾\": \"3/4\", \"⅘\": \"4/5\",\n",
    "                     \"⅝\": \"5/8\", \"⅚\": \"5/6\", \"⅞\": \"7/8\", \n",
    "                \n",
    "                # Bad punnctuation\n",
    "                \"⁄\":\"/\", \"-\":\"\", \"'\":\"\", '\"':\"\"}\n",
    "    \n",
    "    for key in replacements:\n",
    "        odf[col] = odf[col].str.replace(key, replacements[key])\n",
    "        \n",
    "    print(\"Tokenizing and Stemming (Saves as token list)\")\n",
    "    odf[col] = odf[col].apply(lambda x: [y.lemma_ for y in sp(str(x))])\n",
    "    \n",
    "    print(\"Cleaning units\")\n",
    "    odf[col] = odf[col].apply(map_units)\n",
    "    \n",
    "    return odf[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_proc_pipeline(df, proc_cols, keep_cols):\n",
    "    \n",
    "    odf = df[keep_cols].copy()\n",
    "    \n",
    "    for col in proc_cols:\n",
    "        print(f\"Processing {col}\")\n",
    "        odf[col+'_sp'] = text_proc_pipeline(df, col)\n",
    "        print()\n",
    "        \n",
    "    return odf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing input\n",
      "Replacing bad characters\n",
      "Tokenizing and Stemming (Saves as token list)\n",
      "Cleaning units\n",
      "\n",
      "Processing unit\n",
      "Replacing bad characters\n",
      "Tokenizing and Stemming (Saves as token list)\n",
      "Cleaning units\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nyt_sp = df_proc_pipeline(nyt, ['input', 'unit'], ['qty', 'comment', 'input'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unit Parser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"counts\":{\",\":65971,\"1\":60198,\"cup\":43041,\"2\":36133,\"tbsp\":32974,\"1\\\\/2\":28840,\"tsp\":25756,\"or\":20132,\"and\":18944,\"to\":16241,\"chop\":16092,\"(\":15843,\")\":15662,\"salt\":15324,\"pepper\":13924,\"1\\\\/4\":13247,\"lb\":12487,\"3\":12141,\"oil\":11695,\"fresh\":10933,\"4\":10918,\"ground\":10663,\"taste\":10484,\"freshly\":9204,\"olive\":7753,\"oz\":7657,\"peel\":7309,\"garlic\":7091,\"finely\":6943,\"large\":6642,\"mince\":6621,\"butter\":6290,\"cut\":6095,\"of\":6078,\"clove\":6049,\"dry\":5932,\"black\":5926,\"sugar\":5910,\"onion\":5787,\"for\":5695,\"slice\":5685,\"about\":5648,\"6\":5244,\"juice\":5119,\"white\":4953,\"into\":4855,\"lemon\":4813,\"3\\\\/4\":4795,\"red\":4602,\"egg\":4501,\"leave\":4442,\"small\":4182,\"in\":4174,\"grate\":3833,\"flour\":3781,\"tomato\":3698,\"8\":3611,\"a\":3598,\"water\":3583,\"dice\":3523,\"parsley\":3508,\"chicken\":3398,\"plus\":3341,\"medium\":3129,\"vinegar\":3122,\"seed\":3088,\"more\":2972,\"wine\":2970,\"1\\\\/3\":2838,\"cream\":2779,\"green\":2720,\"optional\":2680,\"unsalted\":2592,\"5\":2502,\"sauce\":2502,\"kosher\":2430,\"piece\":2403,\"whole\":2318,\"g\":2261,\"vegetable\":2242,\"thyme\":2202,\"milk\":2189,\"if\":2117,\"cheese\":2092,\"extra\":2055,\"see\":2036,\"thinly\":2003,\"coarsely\":1992,\"virgin\":1932,\"the\":1929,\"powder\":1901,\"as\":1866,\">\":1821,\"ginger\":1808,\"garnish\":1789,\"stock\":1775,\"rice\":1766,\"sprig\":1766,\"12\":1735,\"can\":1715,\"heavy\":1683,\"trim\":1652,\"preferably\":1639,\"with\":1629,\"potato\":1622,\"carrot\":1619,\"lime\":1568,\"bread\":1567,\"scallion\":1550,\"pinch\":1539,\"orange\":1508,\"bay\":1495,\"shallot\":1457,\"basil\":1456,\"desire\":1430,\"celery\":1428,\"allpurpose\":1417,\"ripe\":1414,\"extravirgin\":1411,\"bean\":1391,\"stem\":1360,\"mustard\":1351,\"mushroom\":1334,\"vanilla\":1327,\"10\":1325,\"corn\":1322,\"broth\":1290,\"1\\\\/8\":1261,\"thin\":1249,\"cilantro\":1245,\"cinnamon\":1232,\"remove\":1230,\"sea\":1221,\"cube\":1210,\"crush\":1205,\"sliced\":1176,\"sweet\":1173,\"parmesan\":1169,\"brown\":1163,\"half\":1156,\"drain\":1149,\"\\\\/\":1138,\"hot\":1129,\"grind\":1123,\"cumin\":1121,\"1\\\\/2inch\":1111,\"coarse\":1102,\"each\":1085,\"soy\":1075,\"peanut\":1066,\"leaf\":1054,\"mint\":1040,\"zest\":1037,\"2\\\\/3\":1019,\"other\":1014,\"cook\":1005,\"toast\":1005,\"fillet\":1004,\"coriander\":999,\"thick\":996,\"need\":987,\"serve\":983,\"fine\":978,\"lightly\":975,\"stick\":973,\"bunch\":962,\"inch\":962,\"flake\":959,\"halve\":957,\"note\":951,\"<\":940,\"from\":939,\"cayenne\":937,\"=\":933,\"like\":933,\"href\":924,\"core\":924,\"italian\":924,\"rinse\":923,\"chili\":919,\"at\":914,\"extract\":914,\"apple\":910,\"yellow\":905,\"quarter\":900,\"sesame\":880,\"wash\":868,\"paste\":867,\"rosemary\":854,\"sherry\":852,\"yolk\":851,\"nutmeg\":848,\"head\":846,\"light\":846,\"http:\\\\/\\\\/cooking.nytimes.com\":831,\"chive\":822,\"boneless\":820,\"beat\":817,\"almond\":799,\"beef\":793,\"1inch\":783,\"baking\":775,\"yogurt\":775,\"recipe<\\\\/a\":762,\"bell\":761,\"pork\":755,\"honey\":743,\"dark\":732,\"seeded\":730,\"room\":729,\"bake\":729,\"mediumsize\":724,\"temperature\":723,\"shell\":717,\"crumb\":715,\"fish\":714,\"roughly\":713,\"canola\":706,\"strip\":695,\"very\":693,\"pack\":690,\"7\":686,\"1\\\\/4inch\":685,\"part\":680,\"stalk\":679,\"clean\":677,\"shrimp\":676,\"walnut\":674,\"cold\":669,\"dijon\":666,\"-PRON-\":663,\"plum\":654,\"lengthwise\":649,\"fennel\":628,\"skin\":626,\"quart\":613,\"syrup\":607,\"chunk\":606,\"leek\":604,\"shred\":600,\".\":599,\"use\":598,\"such\":594,\"pit\":591,\"pan\":586,\"melt\":586,\"oregano\":585,\"approximately\":582,\"wedge\":578,\"bacon\":575,\"spinach\":569,\"paprika\":568,\"dill\":567,\"tarragon\":567,\"plain\":559,\"canned\":554,\"chocolate\":553,\"breast\":552,\"on\":552,\"pea\":550,\"crumble\":547,\"lamb\":534,\"granulate\":530,\"sage\":525,\"sliver\":523,\"squeeze\":519,\"soda\":517,\"cucumber\":511,\"roast\":504,\"skinless\":503,\"chile\":503,\"squash\":500,\"be\":498,\"sour\":491,\"one\":484,\"frozen\":483,\"peppercorn\":475,\"homemade\":471,\"16\":470,\"not\":469,\"cornstarch\":465,\"grease\":461,\"bone\":460,\"baby\":458,\"flatleaf\":457,\"cider\":455,\"smoke\":452,\"balsamic\":450,\"zucchini\":450,\"cherry\":443,\"lettuce\":442,\"only\":441,\"fat\":439,\"rib\":427,\"coconut\":426,\"well\":425,\"2inch\":423,\"raisin\":421,\"separate\":420,\"caper\":417,\"cabbage\":410,\"pint\":404,\"recipe\":398,\"julienne\":398,\"soak\":398,\"package\":397,\"salmon\":395,\"meat\":392,\"turkey\":390,\"anchovy\":386,\"ice\":385,\"mix\":381,\"rind\":381,\"lowfat\":373,\"veal\":371,\"available\":369,\"eggplant\":368,\"soften\":366,\"firm\":362,\"long\":361,\"raw\":357,\"sausage\":353,\";\":349,\"buttermilk\":349,\"unsweetened\":347,\"nut\":344,\"allspice\":344,\"redwine\":341,\"steak\":338,\"round\":335,\"dash\":334,\"lean\":331,\"chilie\":327,\"cornmeal\":327,\"mayonnaise\":321,\"additional\":317,\"pasta\":316,\"jalapen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      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_vocab(nyt, 'input_sp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_vocab(df, colname, encoding='descending', vocab_cap=5000):\n",
    "    \"\"\"\n",
    "    Function to take dataframe's token columns and generate a vocab dict.\n",
    "    Returned vocab dict will have unique mapping from token to int.\n",
    "    \n",
    "    :param df: pandas DataFrame, df that has the tokens\n",
    "    :param colname: str, column in dataframe to gen vocab from\n",
    "    :param encoding: str, encoding type, one of ['arbitrary', 'descending']\n",
    "    :returns: \n",
    "    \"\"\"\n",
    "    \n",
    "    unique_tokens = {}\n",
    "    for x in nyt[colname]:\n",
    "        for token in x:\n",
    "\n",
    "            token_ = token\n",
    "\n",
    "            # check if a unit needs mapping to simplified version\n",
    "            for k in unit_map_dict:\n",
    "                if token in unit_map_dict[k]:\n",
    "                    token_ = k\n",
    "\n",
    "            if token_ not in unique_tokens.keys():\n",
    "                unique_tokens[token_] = 1\n",
    "            else:\n",
    "                unique_tokens[token_] += 1\n",
    "                \n",
    "    df = (pd.DataFrame.from_dict(unique_tokens, orient='index')\n",
    "                  .rename(columns={0:'counts'})\n",
    "                  .sort_values('counts', ascending=False))\n",
    "        \n",
    "    return df[:vocab_cap].to_json()['counts']\n",
    "\n",
    "def gen_training_data(vocab, tokens, split=.3):\n",
    "    \"\"\"\n",
    "    Loading data function for training\n",
    "    \"\"\"\n",
    "    # "
   ]
  }
 ],
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