structure.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from string import digits\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "base_dir = 'data/jurik_data/e2e_aligned'\n",
    "\n",
    "data = pd.read_csv(base_dir+'/devset_e2e [aligned].csv') \n",
    "ref = data.loc[:, 'ref']\n",
    "align = data.loc[:, 'alignment']\n",
    "mr = data.loc[:,'mr']\n",
    "stop = ref.str.find('.')\n",
    "align[1]\n",
    "\n",
    "align = align.str.replace(')','')\n",
    "align = align.str.replace('(',',')\n",
    "align = align.str.strip(',')\n",
    "split = align.str.split(',') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "a = [0 for i in range(len(ref))]\n",
    "b = [0 for i in range(len(ref))]\n",
    "new = [0 for i in range(len(ref))]\n",
    "\n",
    "len(ref)\n",
    "for i in range(len(ref)):\n",
    "    a[i] = [m.start() for m in re.finditer('\\.', ref[i])]\n",
    "for i in range(len(mr)):\n",
    "    b[i] = mr[i]\n",
    "for k in range(len(split[i])):\n",
    "    print(k)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(ref)):\n",
    "    for j in range(len(a[i])):\n",
    "        for k in range(len(split[i])):\n",
    "            if re.sub('\\D','',split[i][k])=='':\n",
    "                break\n",
    "            if a[i][j] < int(re.sub('\\D','',split[i][k])):\n",
    "                split[i].insert(k,'period')\n",
    "                j += j\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "['13: near ', '21: name ', '36: food ', '44: eattype ', '60: pricerange ', '76: customerrating']\n",
      "['0: name ', '14: eattype ', '52: customerrating ', '73: pricerange ', '107: near']\n",
      "['2: pricerange ', '13: food ', '30: eattype ', '42: name ', 'period', '73: near']\n",
      "['0: name ', '14: eattype ', '31: pricerange ', '54: near ', 'period', 'period', '72: food ', '112: customerrating']\n",
      "['0: name ', '15: customerrating ', '23: eattype ', '68: food ', '86: near']\n",
      "['4: name ', '18: pricerange ', '29: eattype ', '61: near ', '81: food']\n",
      "['0: name ', '14: customerrating ', '21: eattype ', '53: near ', '73: food ', '92: pricerange']\n",
      "['5: near ', '23: food ', '31: eattype ', '48: customerrating ', '68: pricerange ', '87: name']\n",
      "['0: name ', '14: eattype ', '51: near ', '70: pricerange']\n",
      "['11: pricerange ', '18: eattype ', '30: name ', '56: near']\n",
      "['5: near ', '25: customerrating ', '33: eattype ', '78: food ', 'period', '106: name']\n",
      "['0: name ', '9: eattype ', '46: near']\n",
      "['3: food ', '11: eattype ', '55: customerrating ', '83: name ', '116: near']\n",
      "['0: name ', '15: food ', '23: eattype ', '43: customerrating ', 'period', '106: near']\n",
      "['9: eattype ', '43: near ', 'period', 'period', '51: name ', '90: customerrating']\n",
      "['4: name ', '18: customerrating ', '29: food ', '37: eattype ', '54: near ', '83: pricerange']\n",
      "['9: near ', '28: pricerange ', '34: eattype ', '53: name']\n",
      "['0: name ', '14: eattype ', '37: pricerange ', '59: food ', '81: near ', '94: customerrating']\n",
      "['0: name ', '14: pricerange ', '14: customerrating ', '25: eattype ', '66: near']\n",
      "['13: near ', '19: name ', '28: eattype ', '47: food ', '67: customerrating ', '98: pricerange']\n",
      "['0: name ', '14: eattype ', '33: pricerange ', '33: customerrating ', 'period', '70: near']\n",
      "['25: eattype ', '41: pricerange ', '60: near ', '72: name']\n",
      "['15: name ', '24: eattype ', '43: food ', 'period', '76: near']\n",
      "['0: name ', '13: eattype ', '42: near ', '57: pricerange']\n",
      "['0: name ', '15: pricerange ', '27: eattype ', '48: near']\n",
      "['0: pricerange ', '20: name ', '29: eattype ', '48: food ', '70: near']\n",
      "['0: name ', '14: pricerange ', '32: eattype ', '52: food ', 'period', 'period', '86: near ', '101: customerrating']\n",
      "['0: name ', '15: food ', '23: eattype ', '48: near ', '59: pricerange ', '94: customerrating']\n",
      "['4: name ', '30: eattype ', '53: near']\n",
      "['2: customerrating ', '12: food ', '26: eattype ', '45: near ', '58: name ', '71: pricerange']\n",
      "['0: name ', '15: food ', '23: eattype ', '40: near ', '55: pricerange ', '85: customerrating']\n",
      "['0: name ', '23: eattype ', 'period', 'period', '43: customerrating ', '78: near']\n",
      "['0: name ', '11: food ', '19: eattype ', '50: near ', '64: pricerange ', 'period', '95: customerrating']\n",
      "['0: name ', '18: food ', '37: pricerange ', 'period', 'period', 'period', '62: customerrating ', '103: near ', '119: eattype']\n",
      "['0: name ', '27: eattype ', '80: near']\n",
      "['0: name ', '12: eattype ', '51: customerrating ', 'period', '88: near']\n",
      "['13: eattype ', '25: name ', '46: near ', '64: customerrating']\n",
      "['11: pricerange ', '29: eattype ', '48: name ', '66: near ', '80: food ', 'period', '113: customerrating']\n",
      "['0: name ', '9: eattype ', '26: near ', '37: pricerange ', '66: food ', 'period', '109: customerrating']\n",
      "['0: near ', '38: eattype ', '57: name']\n",
      "['0: name ', '25: eattype ', '53: near']\n",
      "['17: customerrating ', '30: name ', '39: eattype ', '58: food ', '80: pricerange ', 'period', '121: near']\n",
      "['0: name ', '14: eattype ', '39: near ', '56: customerrating ', '87: pricerange ', '105: food']\n",
      "['2: eattype ', '21: name ', '69: customerrating ', 'period', '108: near']\n",
      "['0: name ', '14: eattype ', '31: pricerange ', '49: food ', '67: near ', '84: customerrating']\n",
      "['0: name ', '14: pricerange ', '32: food ', '40: eattype ', '69: near ', 'period', '95: customerrating']\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(ref)):\n",
    "    print(split[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Wildwood is a medium scale coffee shop that serves all-American food located in Ranch.'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref[4660]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a dictionary of attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "mr_attr_dict = []\n",
    "for i in range(0,len(mr)):\n",
    "    mr_attr_dict.insert(i, {})\n",
    "    for attr_val in mr[i].split(','):\n",
    "        #print ('Attr:', attr_val)\n",
    "        attr_val = attr_val.strip()\n",
    "        attr_key = attr_val[0:attr_val.find('[')]\n",
    "        #print ('Key:', attr_key)\n",
    "        mr_attr_dict[i][attr_key] = attr_val\n",
    "        \n",
    "mr_attr_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4672"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mr_attr_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reconstruct MR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = ['name', 'area', 'familyfriendly', 'period', 'near', 'period']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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