data_viewer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # DSTC2 Data Viewer\n",
    "\n",
    "Firstly, we will load all data from the training & dev dataset.\n",
    "Each loaded data is a four tuple: (act_abstract, act_natural, best_asr, trans),\n",
    "corresponding to: system's abstracted action, system's reply in natural languge, the best asr hypothesis of the user's utterance, and the labeled user utterance.\n",
    "\n",
    "This may take some time (< 1 min on my laptop)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data ... This may take some time (< 1 min on my laptop) ...\n",
      "All data loaded.\n",
      "There are 11677 dialogs in the training data, and 3934 in the dev data.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from scripts.dataset_walker import *\n",
    "\n",
    "dataroot_path = \"data\"\n",
    "dataset_train = dataset_walker(\"dstc2_train\", dataroot=dataroot_path, \\\n",
    "                               labels=True)\n",
    "dataset_dev = dataset_walker(\"dstc2_dev\", dataroot=dataroot_path, \\\n",
    "                               labels=True)\n",
    "\n",
    "train_dial = []\n",
    "dev_dial = []\n",
    "\n",
    "act_rec = []\n",
    "\n",
    "dial_list = []\n",
    "\n",
    "def dialog_acts_to_str(acts):\n",
    "    global act_rec\n",
    "    res = \"\"\n",
    "    for act in acts:\n",
    "        act_rec.append(act[\"act\"])\n",
    "        res = res + act[\"act\"] + \"(\"\n",
    "        if len(act[\"slots\"]) > 0:\n",
    "            res = res + act[\"slots\"][0][0] + \", \" + act[\"slots\"][0][1]\n",
    "            for slot in act[\"slots\"][1:]:\n",
    "                res = res + \", \" + slot[0] + slot[1]\n",
    "        res = res + \")  \"\n",
    "    return res\n",
    "\n",
    "def acts_to_set(acts):\n",
    "    res = []\n",
    "    for act in acts:\n",
    "        s = act[\"act\"]\n",
    "        if len(act[\"slots\"]) > 0:\n",
    "            s = s + \"_\" + act[\"slots\"][0][0]\n",
    "        res.append(s)\n",
    "    return set(res)\n",
    "\n",
    "print \"Loading data ... This may take some time (< 1 min on my laptop) ...\"\n",
    "\n",
    "for call in dataset_train :\n",
    "    turn_list = []\n",
    "    for _turn, _label in call :\n",
    "        act_abstract = dialog_acts_to_str(_turn[\"output\"][\"dialog-acts\"])\n",
    "        act_natural = _turn[\"output\"][\"transcript\"]\n",
    "        best_asr = _turn[\"input\"][\"live\"][\"asr-hyps\"][0][\"asr-hyp\"]\n",
    "        trans = _label[\"transcription\"]\n",
    "        train_dial.append((act_abstract, act_natural, best_asr, trans))\n",
    "        turn_list.append(acts_to_set(_turn[\"output\"][\"dialog-acts\"]))\n",
    "    dial_list.append(turn_list)\n",
    "\n",
    "for call in dataset_dev :\n",
    "    turn_list = []\n",
    "    for _turn, _label in call :\n",
    "        act_abstract = dialog_acts_to_str(_turn[\"output\"][\"dialog-acts\"])\n",
    "        act_natural = _turn[\"output\"][\"transcript\"]\n",
    "        best_asr = _turn[\"input\"][\"live\"][\"asr-hyps\"][0][\"asr-hyp\"]\n",
    "        trans = _label[\"transcription\"]\n",
    "        dev_dial.append((act_abstract, act_natural, best_asr, trans))\n",
    "        turn_list.append(acts_to_set(_turn[\"output\"][\"dialog-acts\"]))\n",
    "    dial_list.append(turn_list)\n",
    "        \n",
    "print \"All data loaded.\"\n",
    "print \"There are %d dialogs in the training data, and %d in the dev data.\" % (len(train_dial), len(dev_dial))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next let's see the dialog actions (by the system), and the corresponding natural language.\n",
    "\n",
    "Explain:\n",
    "Each natural language generated by the system, may correspond to a set of different (abstracted) dialog actions.\n",
    "For example, the sentence \"pizza hut city centre is a nice restaurant in the centre of town serving italian food\" is generated by 4 dialog actions: \n",
    "{offer(name, pizza hut city center), inform(food, italian), inform(pricerange, cheap), inform(area, center)}\n",
    "\n",
    "We randomly shuffle the data, and print the first 5 from traing/dev."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 examples from train data:\n",
      "request(slot, food)   :\n",
      "   What kind of food would you like?\n",
      "welcomemsg()   :\n",
      "   Hello , welcome to the Cambridge restaurant system? You can ask for restaurants by area , price range or food type . How may I help you?\n",
      "welcomemsg()   :\n",
      "   Hello , welcome to the Cambridge restaurant system? You can ask for restaurants by area , price range or food type . How may I help you?\n",
      "offer(name, restaurant alimentum)  inform(pricerange, moderate)  inform(area, south)   :\n",
      "   restaurant alimentum is a nice place in the south of town and the prices are moderate\n",
      "canthelp(food, basque)   :\n",
      "   I'm sorry but there is no restaurant serving basque food\n",
      "\n",
      "5 examples from dev data:\n",
      "welcomemsg()   :\n",
      "   Hello , welcome to the Cambridge restaurant system? You can ask for restaurants by area , price range or food type . How may I help you?\n",
      "canthelp(food, spanish, areaeast)   :\n",
      "   I'm sorry but there is no spanish restaurant in the east of town\n",
      "offer(name, restaurant alimentum)  inform(pricerange, moderate)  inform(area, south)   :\n",
      "   restaurant alimentum is a nice place in the south of town and the prices are moderate\n",
      "offer(name, rajmahal)  inform(pricerange, moderate)  inform(area, east)   :\n",
      "   rajmahal is a nice place in the east of town and the prices are moderate\n",
      "request(slot, pricerange)   :\n",
      "   Would you like something in the cheap , moderate , or expensive price range?\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "random.shuffle(train_dial)\n",
    "random.shuffle(dev_dial)\n",
    "\n",
    "print \"5 examples from train data:\"\n",
    "for k in range(5):\n",
    "    print train_dial[k][0], \":\\n  \", train_dial[k][1]\n",
    "    \n",
    "print\n",
    "print \"5 examples from dev data:\"\n",
    "for k in range(5):\n",
    "    print dev_dial[k][0], \":\\n  \", dev_dial[k][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The followings are some conversations between the system and the user (in natural language)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System: What kind of food would you like?\n",
      "User: anything\n",
      "\n",
      "System: Hello , welcome to the Cambridge restaurant system? You can ask for restaurants by area , price range or food type . How may I help you?\n",
      "User: looking for an expensive restaurant that serves indonesian food\n",
      "\n",
      "System: Hello , welcome to the Cambridge restaurant system? You can ask for restaurants by area , price range or food type . How may I help you?\n",
      "User: looking for a moderately priced restaurant in the north of town\n",
      "\n",
      "System: restaurant alimentum is a nice place in the south of town and the prices are moderate\n",
      "User: phone number\n",
      "\n",
      "System: I'm sorry but there is no restaurant serving basque food\n",
      "User: how about asian oriental\n",
      "\n",
      "System: The phone number of la margherita is 01223 315232 .\n",
      "User: thank you good bye\n",
      "\n",
      "System: What kind of food would you like?\n",
      "User: basque\n",
      "\n",
      "System: the nirala is a nice restaurant in the north of town serving indian food\n",
      "User: what is the address\n",
      "\n",
      "System: Hello , welcome to the Cambridge restaurant system? You can ask for restaurants by area , price range or food type . How may I help you?\n",
      "User: i want to find a moderately priced restaurant that serves fusion food\n",
      "\n",
      "System: gourmet burger kitchen is a nice place in the centre of town serving tasty north american food\n",
      "User: whats the phone number\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for k in range(10):\n",
    "    print \"System:\", train_dial[k][1]\n",
    "    print \"User:\", train_dial[k][3]\n",
    "    print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The followings are some statistics of interests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of system actions in train/dev: 26302\n",
      "Unique action type: 12\n"
     ]
    },
    {
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lDo4jQbObSPtatS/Ta1fnjGKevIfGLKTSjWSNn+qMnP1r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19d3462810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "print \"About sys actions\"\n",
    "print \"Total number of system actions in train/dev:\", len(act_rec)\n",
    "uni_act = list(set(act_rec))\n",
    "print \"Unique action type:\", len(uni_act)\n",
    "\n",
    "y_pos = np.arange(len(uni_act)) * 4\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(15, 9))\n",
    "plt.bar(y_pos, [act_rec.count(a) for a in uni_act])\n",
    "plt.xticks(y_pos, uni_act)\n",
    "plt.show()\n",
    "\n",
    "print \"About user actions\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Information about dialogs (each dialog is a list of turns, and each turn is an unorder set of system actions without considering the last argument)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num of all dialog sequences: 2118\n",
      "Num of unique dialog sequences: 1642\n",
      "\n",
      "Examples of dialogs:\n",
      "Dialog 0:\n",
      "Turn 0: set([u'welcomemsg'])\n",
      "Turn 1: set([u'request_slot'])\n",
      "Turn 2: set([u'canthelp_pricerange'])\n",
      "Turn 3: set([u'inform_food', u'offer_name', u'inform_pricerange'])\n",
      "Turn 4: set([u'reqmore'])\n",
      "Turn 5: set([u'offer_name', u'inform_addr'])\n",
      "Turn 6: set([u'offer_name', u'inform_phone'])\n",
      "\n",
      "Dialog 1:\n",
      "Turn 0: set([u'welcomemsg'])\n",
      "Turn 1: set([u'request_slot'])\n",
      "Turn 2: set([u'request_slot'])\n",
      "Turn 3: set([u'request_slot'])\n",
      "Turn 4: set([u'request_slot'])\n",
      "Turn 5: set([u'request_slot'])\n",
      "Turn 6: set([u'offer_name', u'inform_pricerange', u'inform_area'])\n",
      "Turn 7: set([u'offer_name', u'inform_pricerange', u'inform_area'])\n",
      "Turn 8: set([u'offer_name', u'inform_phone'])\n",
      "Turn 9: set([u'offer_name', u'inform_pricerange'])\n",
      "Turn 10: set([u'offer_name', u'inform_phone'])\n",
      "\n",
      "Dialog 2:\n",
      "Turn 0: set([u'welcomemsg'])\n",
      "Turn 1: set([u'request_slot'])\n",
      "Turn 2: set([u'expl-conf_pricerange'])\n",
      "Turn 3: set([u'request_slot'])\n",
      "Turn 4: set([u'canthelp_food'])\n",
      "Turn 5: set([u'select_food'])\n",
      "Turn 6: set([u'inform_food', u'offer_name'])\n",
      "Turn 7: set([u'inform_food', u'offer_name'])\n",
      "Turn 8: set([u'offer_name', u'inform_addr'])\n",
      "\n",
      "Dialog 3:\n",
      "Turn 0: set([u'welcomemsg'])\n",
      "Turn 1: set([u'request_slot'])\n",
      "Turn 2: set([u'request_slot'])\n",
      "Turn 3: set([u'offer_name', u'inform_pricerange', u'inform_area'])\n",
      "Turn 4: set([u'offer_name', u'inform_pricerange', u'inform_area'])\n",
      "Turn 5: set([u'offer_name', u'inform_phone'])\n",
      "\n",
      "Dialog 4:\n",
      "Turn 0: set([u'welcomemsg'])\n",
      "Turn 1: set([u'request_slot'])\n",
      "Turn 2: set([u'request_slot'])\n",
      "Turn 3: set([u'request_slot'])\n",
      "Turn 4: set([u'offer_name', u'inform_pricerange', u'inform_area'])\n",
      "Turn 5: set([u'offer_name', u'inform_pricerange', u'inform_area'])\n",
      "Turn 6: set([u'offer_name', u'inform_phone'])\n",
      "Turn 7: set([u'inform_food', u'offer_name'])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "uni_dial_list = []\n",
    "\n",
    "for dial in dial_list:\n",
    "    if dial not in uni_dial_list:\n",
    "        uni_dial_list.append(dial)\n",
    "\n",
    "print \"Num of all dialog sequences:\", len(dial_list)\n",
    "print \"Num of unique dialog sequences:\", len(uni_dial_list)\n",
    "print\n",
    "\n",
    "print \"Examples of dialogs:\"\n",
    "for k in range(5):\n",
    "    print \"Dialog %d:\" % k\n",
    "    for (tk, turn) in enumerate(uni_dial_list[k]):\n",
    "        print \"Turn %d: %s\" % (tk, turn)\n",
    "    print "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}