data_stat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"\\nsession_data = [{\\n'sid':\\n'qids':[q1,q2,...],\\n'docs':[[(d1,c1),(d2,c2),...],[]]\\n},]\\n\""
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os,tqdm\n",
    "import cPickle,json\n",
    "import numpy as np\n",
    "prefix = '/ivi/ilps/personal/xli/knowledge_intent/data/'\n",
    "session_data = json.load(open(prefix + 'session.json'))\n",
    "qdid2text_dict = json.load(open(prefix + 'qdid2text_dict.json'))\n",
    "(qid2eid,did2eid) = cPickle.load(open(prefix + 'qdid2eid-xlore.pkl'))\n",
    "\n",
    "print 'loaded'\n",
    "'''\n",
    "session_data = [{\n",
    "'sid':\n",
    "'qids':[q1,q2,...],\n",
    "'docs':[[(d1,c1),(d2,c2),...],[]]\n",
    "},]\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#nodes:  2136189\n",
      "#edges:  7556097\n"
     ]
    }
   ],
   "source": [
    "ent_graph = cPickle.load(open(prefix + '../entity/ent-graph.pkl'))\n",
    "print '#nodes: ',  ent_graph.number_of_nodes()\n",
    "print '#edges: ',  ent_graph.number_of_edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'ent_graph' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-39fee7b16fe8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mquery_nodes_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ment_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0meid\u001b[0m \u001b[0;32min\u001b[0m \u001b[0ment_list\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0meid\u001b[0m \u001b[0;32min\u001b[0m \u001b[0ment_graph\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m             \u001b[0mcount\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ent_graph' is not defined"
     ]
    }
   ],
   "source": [
    "count = 0;total = 0\n",
    "query_nodes_list = set()\n",
    "nodes_list = set()\n",
    "for qid,ent_list in tqdm.tqdm(qid2eid.iteritems()):\n",
    "    total += len(ent_list)\n",
    "    nodes_list.update(ent_list)\n",
    "    query_nodes_list.update(ent_list)\n",
    "    for eid in ent_list:\n",
    "        if eid in ent_graph:\n",
    "            count += 1\n",
    "\n",
    "for qid,ent_list in tqdm.tqdm(did2eid.iteritems()):\n",
    "    total += len(ent_list)\n",
    "    nodes_list.update(ent_list)\n",
    "    for eid in ent_list:\n",
    "        if eid in ent_graph:\n",
    "            count += 1\n",
    "           \n",
    "print 'Count: ',count\n",
    "print 'Total: ', total\n",
    "print 'Cover: ', float(count)/ total\n",
    "print 'Query size: ', len(query_nodes_list)\n",
    "print 'size: ', len(nodes_list)\n",
    "\n",
    "'''\n",
    "node_file = open('nodes.txt','w')\n",
    "for node in nodes_list:\n",
    "    print >> node_file, node\n",
    "'''\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ent_graph' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-62261b12f05c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ment_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot_degree_dist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;31m#degrees = [G.degree(n) for n in G.nodes()]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ent_graph' is not defined"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import networkx as nx\n",
    "import collections\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "print nx.info(ent_graph)\n",
    "def plot_degree_dist(G):\n",
    "    #degrees = [G.degree(n) for n in G.nodes()]\n",
    "    degree_sequence = sorted([d for n, d in G.degree()], reverse=True)  # degree sequence\n",
    "    # print \"Degree sequence\", degree_sequence\n",
    "    degreeCount = collections.Counter(degree_sequence)\n",
    "    deg, cnt = zip(*degreeCount.items())\n",
    "    count = 0;mid = 2000\n",
    "    \n",
    "    for d in deg:\n",
    "        if d > mid:         \n",
    "            count += 1\n",
    "            \n",
    "    print count\n",
    "    fig, ax = plt.subplots()\n",
    "    cnt = np.log(cnt)\n",
    "    plt.bar(deg, cnt, color='b')\n",
    "    \n",
    "    plt.title(\"Degree Histogram\")\n",
    "    plt.ylabel(\"Log of Count\")\n",
    "    plt.xlabel(\"Degree\")\n",
    "    \n",
    "    ax.set_xlim(0,mid)\n",
    "    #ax.set_ylim(0,2000)\n",
    "    #ax.set_xticks([d + 0.4 for d in deg])\n",
    "    #ax.set_xticklabels(deg)\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "plot_degree_dist(ent_graph)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'query_nodes_list' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-9a723d1baab5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     33\u001b[0m '''\n\u001b[1;32m     34\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery_nodes_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnodes_exits\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'query_nodes_list' is not defined"
     ]
    }
   ],
   "source": [
    "#building sparse matrix for \n",
    "big_degree_neigb_dict = {}\n",
    "big_degree_threshold = 200\n",
    "\n",
    "error = 0\n",
    "def collect_neigbs(node,cur,step,new_neibs):\n",
    "    if cur >= step:\n",
    "        return []\n",
    "    #print node,ent_graph.degree(node)\n",
    "    if node not in ent_graph:\n",
    "        global error\n",
    "        error += 1\n",
    "        return []\n",
    "    neibs = [nid for nid in ent_graph.neighbors(node)][:]\n",
    "    #print len(neibs)  \n",
    "    for n in neibs:\n",
    "        if n in new_neibs:\n",
    "            continue\n",
    "        neis = collect_neigbs(n,cur+1,step,new_neibs)\n",
    "        new_neibs.update(neis)\n",
    "    new_neibs.update(neibs)\n",
    "    return new_neibs\n",
    "\n",
    "\n",
    "neib_dict = set()\n",
    "max_step = 2\n",
    "\n",
    "nodes_exits = set()\n",
    "'''\n",
    "if os.path.exists('./nodes_neib_set.pkl'):\n",
    "    nodes_exits = cPickle.load(open('./nodes_neib_set.pkl'))\n",
    "    neib_dict = cPickle.load(open('%s%s%d%s' % (prefix,'neig_dict_',max_step,'.pkl')))\n",
    "'''\n",
    "i = 0\n",
    "for node in tqdm.tqdm(list(query_nodes_list)[:1]):\n",
    "    if node in nodes_exits:\n",
    "        continue\n",
    "    new_neibs = set()\n",
    "    neibs = collect_neigbs(node,0,max_step,new_neibs)\n",
    "    for n in neibs:\n",
    "        if (node,n) not in neib_dict or (n,node) not in neib_dict and n in nodes_list:\n",
    "            neib_dict.add((node,n))\n",
    "    i += 1\n",
    "    nodes_exits.add(node)\n",
    "    '''\n",
    "    if (i + 1) % 100 == 0:\n",
    "        cPickle.dump(nodes_exits, open('./nodes_neib_set.pkl','w'))\n",
    "        cPickle.dump(neib_dict,open('%s%s%d%s' % (prefix,'neig_dict_',max_step,'.pkl'), 'w'))\n",
    "    '''\n",
    "\n",
    "print 'Error: ', error\n",
    "print ('#neig_size: ', len(neib_dict))\n",
    "prefix = '/ivi/ilps/personal/xli/knowledge_intent/data/'\n",
    "#cPickle.dump(nodes_exits, open('./nodes_neib_set.pkl','w'))\n",
    "#cPickle.dump(neib_dict,open('%s%s%d%s' % (prefix,'neig_dict_',max_step,'.pkl'), 'w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#system_df.assign()\n",
    "def session_calculation(cal_function,filter_click=False):\n",
    "    match_ratio_ranks = [[] for i in range(10)]\n",
    "    for session in tqdm.tqdm(session_data[:]):\n",
    "        qids = session['qids']\n",
    "        docs = session['docs']\n",
    "        for qid,dids in zip(qids,docs):\n",
    "            qid_entlist = qid2eid[qid]\n",
    "            q_size = len(qid_entlist)\n",
    "            #print dids\n",
    "            for pos,(did,click) in enumerate(dids):\n",
    "                #print 'pos: ',pos\n",
    "                if filter_click and click == 0:\n",
    "                    continue\n",
    "                did_entlist = did2eid[did]\n",
    "                d_size = len(qid_entlist)\n",
    "                match = cal_function(qid_entlist,did_entlist)\n",
    "                match_ratio_ranks[pos].append(match)\n",
    "    return match_ratio_ranks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class PDtable:\n",
    "    '''\n",
    "    .add(data, “colname”)\n",
    "    .to_pandas\n",
    "    '''\n",
    "    def __init__(self):\n",
    "        self.column_datas = {}\n",
    "        self.column_names = []\n",
    "\n",
    "    def add(self, data_list, colname):\n",
    "        if colname not in self.column_datas:\n",
    "            #self.column_datas[colname] = []\n",
    "            self.column_names.append(colname)\n",
    "        self.column_datas[colname] = data_list\n",
    "\n",
    "    def to_pandas(self):\n",
    "        return pd.DataFrame(self.column_datas)[self.column_names]\n",
    "\n",
    "def add_to_df(table,rank_data,name):\n",
    "    for i,data_list in enumerate(rank_data):\n",
    "        col_name = name + '_' + str(i)\n",
    "        table.add(data_list,col_name)\n",
    "        \n",
    "system_table = PDtable()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "def getExactMatchRatio(qid_entlist,did_entlist):\n",
    "    did_entlist = set(did_entlist)\n",
    "    match = 0;total = 1\n",
    "    for ent in did_entlist:\n",
    "        if ent in qid_entlist:\n",
    "            match += 1\n",
    "        total += 1\n",
    "    return float(match) / total \n",
    "\n",
    "def getBinaryMatch(qid_entlist,did_entlist):\n",
    "    did_entlist = set(did_entlist)\n",
    "    match = 0;total = 0\n",
    "    for ent in qid_entlist:\n",
    "        if ent in did_entlist:\n",
    "            return 1\n",
    "    return 0\n",
    "\n",
    "def printRanks(match_ratio_ranks):\n",
    "    avgs = []\n",
    "    for pos in range(len(match_ratio_ranks)):\n",
    "        print 'Position: ',pos\n",
    "        print 'Total size: ', len(match_ratio_ranks[pos])\n",
    "        print 'Match rate: ', np.mean(match_ratio_ranks[pos])\n",
    "        avgs.append(np.mean(match_ratio_ranks[pos]))\n",
    "        if pos > 0:\n",
    "            stat_val, p_val = stats.ttest_ind(match_ratio_ranks[0], match_ratio_ranks[pos])\n",
    "            print ('Two-sample t-statistic D = %6.3f, p-value = %6.4f' % (stat_val, p_val))\n",
    "    return avgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:05<00:00, 15711.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Position:  0\n",
      "Total size:  231142\n",
      "Match rate:  0.22058402771168079\n",
      "Position:  1\n",
      "Total size:  231142\n",
      "Match rate:  0.2021509144450886\n",
      "Two-sample t-statistic D = 29.743, p-value = 0.0000\n",
      "Position:  2\n",
      "Total size:  231141\n",
      "Match rate:  0.21362710784038028\n",
      "Two-sample t-statistic D = 11.127, p-value = 0.0000\n",
      "Position:  3\n",
      "Total size:  231125\n",
      "Match rate:  0.22140594916170903\n",
      "Two-sample t-statistic D = -1.300, p-value = 0.1937\n",
      "Position:  4\n",
      "Total size:  231068\n",
      "Match rate:  0.178019385346882\n",
      "Two-sample t-statistic D = 69.022, p-value = 0.0000\n",
      "Position:  5\n",
      "Total size:  230762\n",
      "Match rate:  0.15553058995848532\n",
      "Two-sample t-statistic D = 106.247, p-value = 0.0000\n",
      "Position:  6\n",
      "Total size:  229758\n",
      "Match rate:  0.16345858105775063\n",
      "Two-sample t-statistic D = 93.211, p-value = 0.0000\n",
      "Position:  7\n",
      "Total size:  226711\n",
      "Match rate:  0.1825160667104816\n",
      "Two-sample t-statistic D = 61.727, p-value = 0.0000\n",
      "Position:  8\n",
      "Total size:  212893\n",
      "Match rate:  0.19635262784591317\n",
      "Two-sample t-statistic D = 38.738, p-value = 0.0000\n",
      "Position:  9\n",
      "Total size:  159081\n",
      "Match rate:  0.19600193192985543\n",
      "Two-sample t-statistic D = 36.288, p-value = 0.0000\n"
     ]
    }
   ],
   "source": [
    "Exactmatch_ratio_ranks = session_calculation(getExactMatchRatio)\n",
    "#add_to_df(system_table, Exactmatch_ratio_ranks, 'ExactMatchRatio')\n",
    "avg_list = printRanks(Exactmatch_ratio_ranks)\n",
    "system_table.add(avg_list,'ExactMatchRatio')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:04<00:00, 22220.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Position:  0\n",
      "Total size:  231142\n",
      "Match rate:  0.6448114146282372\n",
      "Position:  1\n",
      "Total size:  231142\n",
      "Match rate:  0.603226587984875\n",
      "Two-sample t-statistic D = 29.213, p-value = 0.0000\n",
      "Position:  2\n",
      "Total size:  231141\n",
      "Match rate:  0.6279846500620834\n",
      "Two-sample t-statistic D = 11.894, p-value = 0.0000\n",
      "Position:  3\n",
      "Total size:  231125\n",
      "Match rate:  0.6388534342888048\n",
      "Two-sample t-statistic D =  4.224, p-value = 0.0000\n",
      "Position:  4\n",
      "Total size:  231068\n",
      "Match rate:  0.5345352883133969\n",
      "Two-sample t-statistic D = 76.691, p-value = 0.0000\n",
      "Position:  5\n",
      "Total size:  230762\n",
      "Match rate:  0.46196080810532064\n",
      "Two-sample t-statistic D = 127.157, p-value = 0.0000\n",
      "Position:  6\n",
      "Total size:  229758\n",
      "Match rate:  0.5003090207957939\n",
      "Two-sample t-statistic D = 100.232, p-value = 0.0000\n",
      "Position:  7\n",
      "Total size:  226711\n",
      "Match rate:  0.5598052145683271\n",
      "Two-sample t-statistic D = 58.993, p-value = 0.0000\n",
      "Position:  8\n",
      "Total size:  212893\n",
      "Match rate:  0.603918400323167\n",
      "Two-sample t-statistic D = 28.147, p-value = 0.0000\n",
      "Position:  9\n",
      "Total size:  159081\n",
      "Match rate:  0.6099659921675121\n",
      "Two-sample t-statistic D = 22.176, p-value = 0.0000\n"
     ]
    }
   ],
   "source": [
    "Binarymatch_ratio_ranks = session_calculation(getBinaryMatch)\n",
    "#add_to_df(system_table, Binarymatch_ratio_ranks, 'BinaryMatch')\n",
    "avg_list = printRanks(Binarymatch_ratio_ranks)\n",
    "system_table.add(avg_list,'BinaryMatchRatio')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def loadNeigbor(step):\n",
    "    print 'run'\n",
    "    addr = '/ivi/ilps/personal/xli/knowledge_intent/data/neib_%d.txt' % step\n",
    "    neib_dict = {};\n",
    "    for line in tqdm.tqdm(open(addr)):\n",
    "        elements = line.strip().split('\\t')\n",
    "        neib_dict[(elements[0],elements[1])] = int(elements[2])\n",
    "    return neib_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "25119it [00:00, 251187.84it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "135814it [00:00, 511223.13it/s]\n"
     ]
    }
   ],
   "source": [
    "neib_dict_1 = loadNeigbor(1)\n",
    "match_count = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def getNeibhorBinaryMatchRatio1(qid_entlist,did_entlist):\n",
    "    did_entlist = set(did_entlist)\n",
    "    match = 0;total = 0\n",
    "    for ent in qid_entlist:\n",
    "        if ent in did_entlist:\n",
    "            return 1\n",
    "        for d_ent in did_entlist:\n",
    "            if (ent,d_ent) in neib_dict_1 or (d_ent,ent) in neib_dict_1:\n",
    "                global match_count\n",
    "                match_count += 1\n",
    "                return 1\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:07<00:00, 12739.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Position:  0\n",
      "Total size:  231142\n",
      "Match rate:  0.6514783120332955\n",
      "Position:  1\n",
      "Total size:  231142\n",
      "Match rate:  0.6065535471701379\n",
      "Two-sample t-statistic D = 31.650, p-value = 0.0000\n",
      "Position:  2\n",
      "Total size:  231141\n",
      "Match rate:  0.6310174309187898\n",
      "Two-sample t-statistic D = 14.506, p-value = 0.0000\n",
      "Position:  3\n",
      "Total size:  231125\n",
      "Match rate:  0.6427387777176853\n",
      "Two-sample t-statistic D =  6.217, p-value = 0.0000\n",
      "Position:  4\n",
      "Total size:  231068\n",
      "Match rate:  0.5412562535703775\n",
      "Two-sample t-statistic D = 76.854, p-value = 0.0000\n",
      "Position:  5\n",
      "Total size:  230762\n",
      "Match rate:  0.4779729764865966\n",
      "Two-sample t-statistic D = 120.786, p-value = 0.0000\n",
      "Position:  6\n",
      "Total size:  229758\n",
      "Match rate:  0.5136709059096963\n",
      "Two-sample t-statistic D = 95.805, p-value = 0.0000\n",
      "Position:  7\n",
      "Total size:  226711\n",
      "Match rate:  0.5658481502882524\n",
      "Two-sample t-statistic D = 59.598, p-value = 0.0000\n",
      "Position:  8\n",
      "Total size:  212893\n",
      "Match rate:  0.6099824794615135\n",
      "Two-sample t-statistic D = 28.664, p-value = 0.0000\n",
      "Position:  9\n",
      "Total size:  159081\n",
      "Match rate:  0.6129330341146962\n",
      "Two-sample t-statistic D = 24.607, p-value = 0.0000\n",
      "match_count:  16393\n"
     ]
    }
   ],
   "source": [
    "NeibhorBinaryMatchRatio_ranks = session_calculation(getNeibhorBinaryMatchRatio1)\n",
    "avg_list = printRanks(NeibhorBinaryMatchRatio_ranks)\n",
    "print 'match_count: ', match_count\n",
    "system_table.add(avg_list,'Neibhor1BinaryMatchRatio')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "32081it [00:00, 320806.48it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "42119584it [00:54, 776109.52it/s]\n"
     ]
    }
   ],
   "source": [
    "neib_dict_2 = loadNeigbor(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "match_count = 0\n",
    "def getNeibhorBinaryMatchRatio2(qid_entlist,did_entlist):\n",
    "    did_entlist = set(did_entlist)\n",
    "    match = 0;total = 0\n",
    "    for ent in qid_entlist:\n",
    "        if ent in did_entlist:\n",
    "            return 1\n",
    "        for d_ent in did_entlist:\n",
    "            if (ent,d_ent) in neib_dict_1 or (d_ent,ent) in neib_dict_1 or\\\n",
    "                    (ent,d_ent) in neib_dict_2 or (d_ent,ent) in neib_dict_2:\n",
    "                global match_count\n",
    "                match_count += 1\n",
    "                return 1\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:06<00:00, 12918.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Position:  0\n",
      "Total size:  231142\n",
      "Match rate:  0.7115755682653953\n",
      "Position:  1\n",
      "Total size:  231142\n",
      "Match rate:  0.6882565695546461\n",
      "Two-sample t-statistic D = 17.303, p-value = 0.0000\n",
      "Position:  2\n",
      "Total size:  231141\n",
      "Match rate:  0.702203416961941\n",
      "Two-sample t-statistic D =  7.000, p-value = 0.0000\n",
      "Position:  3\n",
      "Total size:  231125\n",
      "Match rate:  0.6952904272579773\n",
      "Two-sample t-statistic D = 12.123, p-value = 0.0000\n",
      "Position:  4\n",
      "Total size:  231068\n",
      "Match rate:  0.6618657711149964\n",
      "Two-sample t-statistic D = 36.484, p-value = 0.0000\n",
      "Position:  5\n",
      "Total size:  230762\n",
      "Match rate:  0.694819771019492\n",
      "Two-sample t-statistic D = 12.466, p-value = 0.0000\n",
      "Position:  6\n",
      "Total size:  229758\n",
      "Match rate:  0.6621880413304433\n",
      "Two-sample t-statistic D = 36.202, p-value = 0.0000\n",
      "Position:  7\n",
      "Total size:  226711\n",
      "Match rate:  0.6813740841864753\n",
      "Two-sample t-statistic D = 22.237, p-value = 0.0000\n",
      "Position:  8\n",
      "Total size:  212893\n",
      "Match rate:  0.6927376663394287\n",
      "Two-sample t-statistic D = 13.721, p-value = 0.0000\n",
      "Position:  9\n",
      "Total size:  159081\n",
      "Match rate:  0.6702811775133423\n",
      "Two-sample t-statistic D = 27.552, p-value = 0.0000\n",
      "match_count:  290072\n"
     ]
    }
   ],
   "source": [
    "NeibhorBinaryMatchRatio_ranks = session_calculation(getNeibhorBinaryMatchRatio2)\n",
    "avg_list = printRanks(NeibhorBinaryMatchRatio_ranks)\n",
    "print 'match_count: ', match_count\n",
    "system_table.add(avg_list,'Neibhor2BinaryMatchRatio')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "system_table.add(range(10),'Position')\n",
    "df = system_table.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'Position'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "df.rename(columns={'BinaryMatchRatio':'ExactMatch',\n",
    "                   'Neibhor1BinaryMatchRatio':'Within 1-st order nodes',\n",
    "                   'Neibhor2BinaryMatchRatio':'Within 2-nd order nodes'},\n",
    "          inplace=True)\n",
    "\n",
    "print df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['Within 2-nd order nodes', 'Within 1-st order nodes', 'ExactMatch'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-e8e79b04c3e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mtidy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Position'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'ExactMatch'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Within 1-st order nodes'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Within 2-nd order nodes'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m    \u001b[0;34m.\u001b[0m\u001b[0mmelt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mid_vars\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Position'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbarplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Position'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Value'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Variable'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtidy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"muted\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/xli/.virtualenvs/deeper/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2932\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2933\u001b[0m             indexer = self.loc._convert_to_indexer(key, axis=1,\n\u001b[0;32m-> 2934\u001b[0;31m                                                    raise_missing=True)\n\u001b[0m\u001b[1;32m   2935\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2936\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/xli/.virtualenvs/deeper/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[0;34m(self, obj, axis, is_setter, raise_missing)\u001b[0m\n\u001b[1;32m   1352\u001b[0m                 kwargs = {'raise_missing': True if is_setter else\n\u001b[1;32m   1353\u001b[0m                           raise_missing}\n\u001b[0;32m-> 1354\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1355\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1356\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/xli/.virtualenvs/deeper/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1159\u001b[0m         self._validate_read_indexer(keyarr, indexer,\n\u001b[1;32m   1160\u001b[0m                                     \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1161\u001b[0;31m                                     raise_missing=raise_missing)\n\u001b[0m\u001b[1;32m   1162\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/xli/.virtualenvs/deeper/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1250\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'loc'\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1251\u001b[0m                 \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1252\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{} not in index\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnot_found\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1254\u001b[0m             \u001b[0;31m# we skip the warning on Categorical/Interval\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['Within 2-nd order nodes', 'Within 1-st order nodes', 'ExactMatch'] not in index\""
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 150 #分辨率\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "\n",
    "tidy = df[['Position','ExactMatch','Within 1-st order nodes','Within 2-nd order nodes']]\\\n",
    "    .melt(id_vars='Position').rename(columns=str.title)\n",
    "sns.barplot(x='Position', y='Value', hue='Variable', data=tidy, ax=ax1,palette=\"muted\")\n",
    "\n",
    "ax1.set(ylim=(0.4, 0.9))\n",
    "ax1.set_ylabel('Match ratio',fontsize=15)    \n",
    "ax1.set_xlabel('Position',fontsize=15)\n",
    "ax1.legend().set_title('')\n",
    "sns.despine(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:00<00:00, 100491.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Position:  0\n",
      "Total size:  123632\n",
      "Match rate:  0.6609777403908373\n",
      "Position:  1\n",
      "Total size:  36636\n",
      "Match rate:  0.6594333442515559\n",
      "Two-sample t-statistic D =  0.548, p-value = 0.5835\n",
      "Position:  2\n",
      "Total size:  18670\n",
      "Match rate:  0.6797000535618639\n",
      "Two-sample t-statistic D = -5.047, p-value = 0.0000\n",
      "Position:  3\n",
      "Total size:  11536\n",
      "Match rate:  0.6893203883495146\n",
      "Two-sample t-statistic D = -6.162, p-value = 0.0000\n",
      "Position:  4\n",
      "Total size:  7716\n",
      "Match rate:  0.6762571280456194\n",
      "Two-sample t-statistic D = -2.753, p-value = 0.0059\n",
      "Position:  5\n",
      "Total size:  4544\n",
      "Match rate:  0.6294014084507042\n",
      "Two-sample t-statistic D =  4.413, p-value = 0.0000\n",
      "Position:  6\n",
      "Total size:  3482\n",
      "Match rate:  0.641298104537622\n",
      "Two-sample t-statistic D =  2.418, p-value = 0.0156\n",
      "Position:  7\n",
      "Total size:  2818\n",
      "Match rate:  0.6689141234918382\n",
      "Two-sample t-statistic D = -0.880, p-value = 0.3788\n",
      "Position:  8\n",
      "Total size:  2277\n",
      "Match rate:  0.6855511638120334\n",
      "Two-sample t-statistic D = -2.455, p-value = 0.0141\n",
      "Position:  9\n",
      "Total size:  1476\n",
      "Match rate:  0.7079945799457995\n",
      "Two-sample t-statistic D = -3.795, p-value = 0.0001\n"
     ]
    }
   ],
   "source": [
    "click_table = PDtable()\n",
    "Exactmatch_ratio_ranks = session_calculation(getBinaryMatch,filter_click=True)\n",
    "#add_to_df(system_table, Exactmatch_ratio_ranks, 'ExactMatchRatio')\n",
    "avg_list = printRanks(Exactmatch_ratio_ranks)\n",
    "click_table.add(avg_list,'ExactMatch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:01<00:00, 77642.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Position:  0\n",
      "Total size:  123632\n",
      "Match rate:  0.6663566067037661\n",
      "Position:  1\n",
      "Total size:  36636\n",
      "Match rate:  0.6615896931979474\n",
      "Two-sample t-statistic D =  1.698, p-value = 0.0895\n",
      "Position:  2\n",
      "Total size:  18670\n",
      "Match rate:  0.6829673272629887\n",
      "Two-sample t-statistic D = -4.494, p-value = 0.0000\n",
      "Position:  3\n",
      "Total size:  11536\n",
      "Match rate:  0.6916608876560333\n",
      "Two-sample t-statistic D = -5.522, p-value = 0.0000\n",
      "Position:  4\n",
      "Total size:  7716\n",
      "Match rate:  0.6797563504406429\n",
      "Two-sample t-statistic D = -2.423, p-value = 0.0154\n",
      "Position:  5\n",
      "Total size:  4544\n",
      "Match rate:  0.6362235915492958\n",
      "Two-sample t-statistic D =  4.228, p-value = 0.0000\n",
      "Position:  6\n",
      "Total size:  3482\n",
      "Match rate:  0.6450315910396324\n",
      "Two-sample t-statistic D =  2.631, p-value = 0.0085\n",
      "Position:  7\n",
      "Total size:  2818\n",
      "Match rate:  0.6710432931156849\n",
      "Two-sample t-statistic D = -0.522, p-value = 0.6018\n",
      "Position:  8\n",
      "Total size:  2277\n",
      "Match rate:  0.6877470355731226\n",
      "Two-sample t-statistic D = -2.146, p-value = 0.0319\n",
      "Position:  9\n",
      "Total size:  1476\n",
      "Match rate:  0.709349593495935\n",
      "Two-sample t-statistic D = -3.484, p-value = 0.0005\n",
      "match_count:  291063\n"
     ]
    }
   ],
   "source": [
    "NeibhorBinaryMatchRatio_ranks = session_calculation(getNeibhorBinaryMatchRatio1,filter_click=True)\n",
    "avg_list = printRanks(NeibhorBinaryMatchRatio_ranks)\n",
    "print 'match_count: ', match_count\n",
    "click_table.add(avg_list,'Neibhor1BinaryMatchRatio')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:01<00:00, 65462.75it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Position:  0\n",
      "Total size:  123632\n",
      "Match rate:  0.720274686165394\n",
      "Position:  1\n",
      "Total size:  36636\n",
      "Match rate:  0.73070204170761\n",
      "Two-sample t-statistic D = -3.916, p-value = 0.0001\n",
      "Position:  2\n",
      "Total size:  18670\n",
      "Match rate:  0.744617032672737\n",
      "Two-sample t-statistic D = -6.932, p-value = 0.0000\n",
      "Position:  3\n",
      "Total size:  11536\n",
      "Match rate:  0.7413314840499307\n",
      "Two-sample t-statistic D = -4.829, p-value = 0.0000\n",
      "Position:  4\n",
      "Total size:  7716\n",
      "Match rate:  0.7387247278382582\n",
      "Two-sample t-statistic D = -3.507, p-value = 0.0005\n",
      "Position:  5\n",
      "Total size:  4544\n",
      "Match rate:  0.7372359154929577\n",
      "Two-sample t-statistic D = -2.503, p-value = 0.0123\n",
      "Position:  6\n",
      "Total size:  3482\n",
      "Match rate:  0.7352096496266514\n",
      "Two-sample t-statistic D = -1.937, p-value = 0.0527\n",
      "Position:  7\n",
      "Total size:  2818\n",
      "Match rate:  0.7377572746628814\n",
      "Two-sample t-statistic D = -2.045, p-value = 0.0408\n",
      "Position:  8\n",
      "Total size:  2277\n",
      "Match rate:  0.7505489679402723\n",
      "Two-sample t-statistic D = -3.191, p-value = 0.0014\n",
      "Position:  9\n",
      "Total size:  1476\n",
      "Match rate:  0.7479674796747967\n",
      "Two-sample t-statistic D = -2.357, p-value = 0.0184\n",
      "match_count:  311290\n"
     ]
    }
   ],
   "source": [
    "NeibhorBinaryMatchRatio_ranks = session_calculation(getNeibhorBinaryMatchRatio2,filter_click=True)\n",
    "avg_list = printRanks(NeibhorBinaryMatchRatio_ranks)\n",
    "print 'match_count: ', match_count\n",
    "click_table.add(avg_list,'Neibhor2BinaryMatchRatio')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "click_table.add(range(10),'Position')\n",
    "df = click_table.to_pandas()\n",
    "df.rename(columns={'BinaryMatch':'ExactMatch',\n",
    "                   'Neibhor1BinaryMatchRatio':'Within 1-st order nodes',\n",
    "                   'Neibhor2BinaryMatchRatio':'Within 2-nd order nodes'},\n",
    "          inplace=True)\n",
    "\n",
    "tidy = df[['Position','ExactMatch','Within 1-st order nodes','Within 2-nd order nodes']]\\\n",
    "    .melt(id_vars='Position').rename(columns=str.title)\n",
    "sns.barplot(x='Position', y='Value', hue='Variable', data=tidy, ax=ax1,palette=\"muted\")\n",
    "\n",
    "ax1.set(ylim=(0.4, 0.9))\n",
    "ax1.set_ylabel('Match ratio',fontsize=15)    \n",
    "ax1.set_xlabel('Position',fontsize=15)\n",
    "ax1.legend().set_title('')\n",
    "sns.despine(fig)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "UnicodeEncodeError",
     "evalue": "'ascii' codec can't encode characters in position 5-6: ordinal not in range(128)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUnicodeEncodeError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-eec372d80235>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mqid\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt_sess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0;34m'len: '\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqdid2text_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mqid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m     \u001b[0;32mprint\u001b[0m \u001b[0mqdid2text_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mqid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'max_ent_delta: '\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmax_growth_e\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mUnicodeEncodeError\u001b[0m: 'ascii' codec can't encode characters in position 5-6: ordinal not in range(128)"
     ]
    }
   ],
   "source": [
    "#delta entity\n",
    "delta_entity = []\n",
    "delta_text = []\n",
    "frist_query_entity_contains = []\n",
    "last_query_entity_contains = []\n",
    "first_query_text = []\n",
    "last_query_text = []\n",
    "\n",
    "\n",
    "max_growth_t = 0;t_sess = []\n",
    "max_growth_e = 0;r_sess = []\n",
    "\n",
    "def remove_space(text):\n",
    "    return ''.join(text.split(' '))\n",
    "\n",
    "def get_words(text):\n",
    "    return text.split(' ')\n",
    "\n",
    "def contain_percent(last_list,cur_list):\n",
    "    count = 0\n",
    "    for ent in cur_list:\n",
    "        if ent in last_list:\n",
    "            count += 1\n",
    "    return float(count) / (len(cur_list) + 1)\n",
    "\n",
    "\n",
    "for session in tqdm.tqdm(session_data[:]):\n",
    "    qids = session['qids']\n",
    "    docs = session['docs']\n",
    "\n",
    "    for i in range(1,len(qids)):\n",
    "        first_query_text.append(contain_percent(get_words(qdid2text_dict[qids[0]]),get_words(qdid2text_dict[qids[i]])))\n",
    "        last_query_text.append(contain_percent(get_words(qdid2text_dict[qids[i-1]]),get_words(qdid2text_dict[qids[i]])))\n",
    "        \n",
    "        frist_query_entity_contains.append(contain_percent(qid2eid[qids[0]],qid2eid[qids[i]]))\n",
    "        last_query_entity_contains.append(contain_percent(qid2eid[qids[i-1]],qid2eid[qids[i]]))\n",
    "        growth_t = len(remove_space(qdid2text_dict[qids[i]]))\\\n",
    "                            - len(remove_space(qdid2text_dict[qids[i-1]]))\n",
    "        growth_e = len(qid2eid[qids[i]]) - len(qid2eid[qids[i-1]])\n",
    "        \n",
    "        if growth_t > max_growth_t:\n",
    "            max_growth_t = growth_t\n",
    "            t_sess= qids\n",
    "        \n",
    "        if growth_e > max_growth_e:\n",
    "            max_growth_e = growth_e\n",
    "            e_sess= qids\n",
    "            \n",
    "        delta_text.append(growth_t)\n",
    "        delta_entity.append(growth_e)\n",
    "\n",
    "print 'max_text_delta: ',max_growth_t\n",
    "print 'session, '\n",
    "for qid in t_sess:\n",
    "    print 'len: ',len(qdid2text_dict[qid].split())\n",
    "    print qdid2text_dict[qid]\n",
    "\n",
    "print 'max_ent_delta: ',max_growth_e\n",
    "print 'session, '\n",
    "for qid in e_sess:\n",
    "    print qdid2text_dict[qid]\n",
    "    \n",
    "from scipy import stats\n",
    "print 'Avg Entity: ', np.mean(delta_entity)\n",
    "print stats.ttest_1samp(delta_entity,0)\n",
    "\n",
    "#print delta_text\n",
    "print 'Avg Text: ', np.mean(delta_text)\n",
    "print stats.ttest_1samp(delta_text,0)\n",
    "\n",
    "print 'Avg Frist query entity: ', np.mean(frist_query_entity_contains)\n",
    "print 'Avg Frist query text: ', np.mean(first_query_text)\n",
    "print stats.ttest_1samp(frist_query_entity_contains,0)\n",
    "\n",
    "print 'Avg Last query entity: ', np.mean(last_query_entity_contains)\n",
    "print 'Avg Last query text: ', np.mean(last_query_text)\n",
    "print stats.ttest_1samp(last_query_entity_contains,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:01<00:00, 76865.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg Common query entity:  0.30367128386284364\n",
      "Avg Common query text:  0.3571381804088713\n",
      "lowest_text_r:  0.9\n",
      "session, \n",
      "喜羊羊 与 灰 太 狼 之 妈妈 乐 疯狂\n",
      "喜羊羊 与 灰 太 狼 之 妈妈 乐 疯狂 全集\n",
      "--------\n",
      "lowest_ent_r:  0.833333333333\n",
      "session, \n",
      "4 月 “ 兰 的 秘密 基地\n",
      "4 月 + 兰 的 秘密 基地 + 限免 商城\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "common_text_in_session = []\n",
    "common_entity_in_session = []\n",
    "\n",
    "lowest_text_r = 0;low_sess = []\n",
    "lowest_ent_r = 0;low_sess_ent = []\n",
    "for session in tqdm.tqdm(session_data[:]):\n",
    "    qids = session['qids']\n",
    "    docs = session['docs']\n",
    "    words_lists = map(lambda qid:get_words(qdid2text_dict[qid]),qids)\n",
    "    entities_lists = map(lambda qid:qid2eid[qid],qids)\n",
    "    \n",
    "    common_words = set.intersection(*map(set, words_lists))\n",
    "    common_entities = set.intersection(*map(set, entities_lists))\n",
    "    \n",
    "    for qid in qids[:1]:\n",
    "        rate_text = float(len(common_words)) / (len(get_words(qdid2text_dict[qid]))+1)\n",
    "        rate_entity = float(len(common_entities)) / (len(qid2eid[qid])+1)\n",
    "        \n",
    "        if rate_text > lowest_text_r:\n",
    "            lowest_text_r = rate_text\n",
    "            low_sess= qids\n",
    "        \n",
    "        if rate_entity > lowest_ent_r:\n",
    "            lowest_ent_r = rate_entity\n",
    "            low_sess_ent= qids\n",
    "            \n",
    "        common_text_in_session.append(rate_text)\n",
    "        common_entity_in_session.append(rate_entity)\n",
    "\n",
    "print 'Avg Common query entity: ', np.mean(common_entity_in_session)\n",
    "print 'Avg Common query text: ', np.mean(common_text_in_session)\n",
    "\n",
    "print 'lowest_text_r: ',lowest_text_r\n",
    "print 'session, '\n",
    "for qid in low_sess:\n",
    "    print qdid2text_dict[qid]\n",
    "print '--------'\n",
    "print 'lowest_ent_r: ', lowest_ent_r\n",
    "print 'session, '\n",
    "for qid in low_sess_ent:\n",
    "    print qdid2text_dict[qid]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'neib_dict_1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-a6d3bc9e7c2e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mn2_neig_list_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mp1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mp2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mneib_dict_1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mn1_neig_list_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'neib_dict_1' is not defined"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "n1_neig_list_dict = defaultdict(list)\n",
    "n2_neig_list_dict = defaultdict(list)\n",
    "\n",
    "for (p1,p2) in tqdm.tqdm(neib_dict_1):\n",
    "    n1_neig_list_dict[p1].append(p2)\n",
    "\n",
    "for (p1,p2) in tqdm.tqdm(neib_dict_2):\n",
    "    n2_neig_list_dict[p1].append(p2)\n",
    "\n",
    "print 'n1_neig_list_dict: ',len(n1_neig_list_dict)\n",
    "print 'n2_neig_list_dict: ',len(n2_neig_list_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "multi_step_precision = [[],[],[]] #0,1,2\n",
    "multi_step_recall = [[],[],[]]\n",
    "\n",
    "def cal_pr(ent1s,ent2s,step):\n",
    "    ent1s = list(set(ent1s))\n",
    "    ent2s = list(set(ent2s))\n",
    "    if step == 0:\n",
    "        common = float(len(set.intersection(*map(set, [ent1s,ent2s]))))\n",
    "        prec = common / (len(ent2s) + 1)\n",
    "        recall = common / (len(ent1s) + 1)\n",
    "    else:\n",
    "        p_count = 0;r_total = len(ent1s)\n",
    "        for ent in ent1s:\n",
    "            if ent in ent2s:\n",
    "                p_count += 1\n",
    "                continue\n",
    "            for d_ent in ent2s:\n",
    "                if step == 1:\n",
    "                    if (ent,d_ent) in neib_dict_1 or (d_ent,ent) in neib_dict_1:\n",
    "                        p_count += 1\n",
    "                if step == 2:\n",
    "                    if (ent,d_ent) in neib_dict_1 or (d_ent,ent) in neib_dict_1 or\\\n",
    "                            (ent,d_ent) in neib_dict_2 or (d_ent,ent) in neib_dict_2:\n",
    "                        p_count += 1\n",
    "                    \n",
    "        for ent in ent1s:\n",
    "            if step >= 1 and ent in n1_neig_list_dict:\n",
    "                r_total += len(n1_neig_list_dict[ent])\n",
    "            if step >= 2 and ent in n2_neig_list_dict:\n",
    "                r_total += len(n2_neig_list_dict[ent])\n",
    "        prec = float(p_count) / (len(ent2s) + 1)\n",
    "        recall = float(p_count) / (r_total + 1)\n",
    "    return (prec,recall)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:00<00:00, 94358.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step:  0\n",
      "Precision:  0.31552031399172586\n",
      "Recall:  0.3458851054300296\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "def cal_session_spread(step):\n",
    "    pres = [];recalls = []\n",
    "    for session in tqdm.tqdm(session_data[:]):\n",
    "        qids = session['qids']\n",
    "        docs = session['docs'] \n",
    "        entities_lists = map(lambda qid:qid2eid[qid],qids)\n",
    "        \n",
    "        for i in range(1,len(qids)):\n",
    "            ent1s = entities_lists[i-1]\n",
    "            ent2s = entities_lists[i]\n",
    "            (prec,recall) = cal_pr(ent1s,ent2s,step)\n",
    "            pres.append(prec)\n",
    "            recalls.append(recall)\n",
    "    \n",
    "    print 'Step: ', step\n",
    "    print 'Precision: ', np.mean(pres)\n",
    "    print 'Recall: ', np.mean(recalls)\n",
    "    return (np.mean(pres),np.mean(recalls)),(pres,recalls)\n",
    "\n",
    "(p0,r0),(pl0,rl0) = cal_session_spread(0)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:00<00:00, 108149.43it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step:  1\n",
      "Precision:  0.3158677793100197\n",
      "Recall:  0.12682054108205215\n",
      "Precision T-test:  Ttest_indResult(statistic=-0.3398128041467359, pvalue=0.7339977571592263)\n",
      "Recall T-test:  Ttest_indResult(statistic=252.49709421387107, pvalue=0.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "(p1,r1),(pl1,rl1) = cal_session_spread(1)\n",
    "print 'Precision T-test: ', stats.ttest_ind(pl0,pl1)\n",
    "print 'Recall T-test: ', stats.ttest_ind(rl0,rl1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:01<00:00, 88857.14it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step:  2\n",
      "Precision:  0.3329934113598057\n",
      "Recall:  0.011490477242076889\n",
      "Precision T-test:  Ttest_indResult(statistic=-16.92769994044636, pvalue=3.0245765940476027e-64)\n",
      "Recall T-test:  Ttest_indResult(statistic=425.65744523657224, pvalue=0.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "(p2,r2),(pl2,rl2) = cal_session_spread(2)\n",
    "print 'Precision T-test: ', stats.ttest_ind(pl0,pl2)\n",
    "print 'Recall T-test: ', stats.ttest_ind(rl0,rl2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  labels  values_a  values_b  values_x  values_y\n",
      "0  date1         1         3       150       170\n",
      "1  date2         2         6       200       180\n"
     ]
    }
   ],
   "source": [
    "columns = [\"labels\", \"values_a\", \"values_b\", \"values_x\", \"values_y\"]\n",
    "test_data = pd.DataFrame.from_records([(\"date1\", 1, 3, 150, 170),\\\n",
    "                                       (\"date2\", 2, 6, 200, 180)],\\\n",
    "                                       columns=columns)\n",
    "print test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Hops  Precision    Recall\n",
      "0     0   0.345885  0.315520\n",
      "1     1   0.126821  0.315868\n",
      "2     2   0.011490  0.332993\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = [\"labels\", \"values_a\", \"values_b\", \"values_x\", \"values_y\"]\n",
    "df = pd.DataFrame({\"Hops\": [0,1,2],\n",
    "                   \"Recall\": [p0,p1,p2], \n",
    "                   \"Precision\": [r0,r1,r2]})\n",
    "print df\n",
    "import seaborn as sns\n",
    "fig, ax = plt.subplots()\n",
    "tidy = df.melt(id_vars='Hops').rename(columns=str.title)\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "ax.set(ylim=(0, 0.5))\n",
    "g = sns.barplot(x='Hops', y='Value', hue='Variable', data=tidy, ax=ax,\n",
    "\tpalette=\"muted\")\n",
    "sns.despine(fig)\n",
    "\n",
    "\n",
    "#for index, row in df.iterrows():\n",
    "#    print index,row\n",
    "#    #g.text(row.name,row.Value, round(row.total_bill,2), color='black', ha=\"center\")\n",
    "for p in ax.patches:\n",
    "\tax.annotate(\"%.4f\" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()-0.012),\n",
    "\t\tha='center', va='center', fontsize=11, color='black', xytext=(0, 20),\n",
    "\t\ttextcoords='offset points')\n",
    "    \n",
    "# Create a second y-axis with the scaled ticks\n",
    "ax.legend().set_title('')\n",
    "ax.set_ylabel('Value')\n",
    "'''\n",
    "ax2 = ax.twinx()\n",
    "\n",
    "# Ensure ticks occur at the same positions, then modify labels\n",
    "ax2.set_ylim(ax.get_ylim())\n",
    "#ax2.set_yticklabels(np.round(ax1.get_yticks()/scale,1))\n",
    "ax2.set_ylabel('Recall')\n",
    "'''\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "def cal_context_spread(step):\n",
    "    pres = [];recalls = []\n",
    "    for session in tqdm.tqdm(session_data[:]):\n",
    "        qids = session['qids']\n",
    "        docs = session['docs'] \n",
    "        entities_lists = map(lambda qid:qid2eid[qid],qids)\n",
    "        \n",
    "        ent1s = list(itertools.chain(*entities_lists[:-1]))\n",
    "        ent1s = list(set(ent1s))\n",
    "        \n",
    "        ent2s = entities_lists[-1]\n",
    "\n",
    "        (prec,recall) = cal_pr(ent1s,ent2s,step)\n",
    "        pres.append(prec)\n",
    "        recalls.append(recall)\n",
    "    \n",
    "    print 'Step: ', step\n",
    "    print 'Precision: ', np.mean(pres)\n",
    "    print 'Recall: ', np.mean(recalls)\n",
    "    return (np.mean(pres),np.mean(recalls)),(pres,recalls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:00<00:00, 113415.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step:  0\n",
      "Precision:  0.3598656815657607\n",
      "Recall:  0.3624701000903828\n"
     ]
    }
   ],
   "source": [
    "(pc0,rc0),(plc0,rlc0) = cal_context_spread(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:00<00:00, 94659.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step:  1\n",
      "Precision:  0.36039630624305846\n",
      "Recall:  0.12848285766049047\n",
      "Precision T-test:  Ttest_indResult(statistic=-0.42660559132227277, pvalue=0.6696671362176705)\n",
      "Recall T-test:  Ttest_indResult(statistic=230.23790529102055, pvalue=0.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "(pc1,rc1),(plc1,rlc1) = cal_context_spread(1)\n",
    "print 'Precision T-test: ', stats.ttest_ind(plc0,plc1)\n",
    "print 'Recall T-test: ', stats.ttest_ind(rlc0,rlc1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:01<00:00, 81925.48it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step:  2\n",
      "Precision:  0.3859069651685852\n",
      "Recall:  0.009741235179461039\n",
      "Precision T-test:  Ttest_indResult(statistic=-0.42660559132227277, pvalue=0.6696671362176705)\n",
      "Recall T-test:  Ttest_indResult(statistic=230.23790529102055, pvalue=0.0)\n"
     ]
    }
   ],
   "source": [
    "(pc2,rc2),(plc2,rlc2) = cal_context_spread(2)\n",
    "print 'Precision T-test: ', stats.ttest_ind(plc0,plc1)\n",
    "print 'Recall T-test: ', stats.ttest_ind(rlc0,rlc1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Hops  Precision    Recall\n",
      "0     0   0.362470  0.359866\n",
      "1     1   0.128483  0.360396\n",
      "2     2   0.009741  0.385907\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Hops\": [0,1,2],\n",
    "                   \"Recall\": [pc0,pc1,pc2], \n",
    "                   \"Precision\": [rc0,rc1,rc2]})\n",
    "print df\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "tidy = df.melt(id_vars='Hops').rename(columns=str.title)\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "ax.set(ylim=(0, 0.6))\n",
    "g = sns.barplot(x='Hops', y='Value', hue='Variable', data=tidy, ax=ax,\n",
    "\tpalette=\"muted\")\n",
    "sns.despine(fig)\n",
    "\n",
    "\n",
    "#for index, row in df.iterrows():\n",
    "#    print index,row\n",
    "#    #g.text(row.name,row.Value, round(row.total_bill,2), color='black', ha=\"center\")\n",
    "for p in ax.patches:\n",
    "\tax.annotate(\"%.4f\" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()-0.01),\n",
    "\t\tha='center', va='center', fontsize=11, color='black', xytext=(0, 20),\n",
    "\t\ttextcoords='offset points')\n",
    "    \n",
    "# Create a second y-axis with the scaled ticks\n",
    "ax.legend().set_title('')\n",
    "ax.set_ylabel('Value')\n",
    "'''\n",
    "ax2 = ax.twinx()\n",
    "\n",
    "# Ensure ticks occur at the same positions, then modify labels\n",
    "ax2.set_ylim(ax.get_ylim())\n",
    "#ax2.set_yticklabels(np.round(ax1.get_yticks()/scale,1))\n",
    "ax2.set_ylabel('Recall')\n",
    "'''\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "from collections import defaultdict\n",
    "\n",
    "def cal_context_spread_ses_len(step,compare_prec = None):\n",
    "    pres = defaultdict(list)\n",
    "    recalls = defaultdict(list)\n",
    "    \n",
    "    for session in tqdm.tqdm(session_data[:]):\n",
    "        qids = session['qids']\n",
    "        docs = session['docs'] \n",
    "        entities_lists = map(lambda qid:qid2eid[qid],qids)\n",
    "        \n",
    "        ent1s = list(itertools.chain(*entities_lists[:1]))\n",
    "        ent1s = list(set(ent1s))\n",
    "        \n",
    "        ent2s = entities_lists[-1]\n",
    "\n",
    "        (prec,recall) = cal_pr(ent1s,ent2s,step)\n",
    "        pres[len(qids)].append(prec)\n",
    "        recalls[len(qids)].append(recall)\n",
    "    \n",
    "    print 'Step: ', step\n",
    "    pre_list = [];rec_list = []\n",
    "    \n",
    "    for k,v in pres.iteritems():\n",
    "        print 'len: ', k\n",
    "        print 'Precision: ', np.mean(pres[k])\n",
    "        print 'Recall: ', np.mean(recalls[k])\n",
    "        if compare_prec != None:\n",
    "            print 'Precision T-test: ', stats.ttest_ind(pres[k],compare_prec[k])\n",
    "        pre_list.append(np.mean(pres[k]))\n",
    "        rec_list.append(np.mean(recalls[k]))\n",
    "    \n",
    "    return (pres,recalls),(pre_list,rec_list)\n"
   ]
  },
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     "text": [
      "100%|██████████| 89737/89737 [00:00<00:00, 106349.91it/s]"
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      "len:  3\n",
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      "len:  4\n",
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      "Recall:  0.2621458829759068\n",
      "len:  6\n",
      "Precision:  0.2250262329485834\n",
      "Recall:  0.2481112277019937\n",
      "len:  7\n",
      "Precision:  0.24579475308641976\n",
      "Recall:  0.2640046296296296\n",
      "len:  8\n",
      "Precision:  0.2325795644891122\n",
      "Recall:  0.26113902847571185\n",
      "len:  9\n",
      "Precision:  0.22098765432098766\n",
      "Recall:  0.21496913580246915\n",
      "len:  10\n",
      "Precision:  0.20425531914893616\n",
      "Recall:  0.22269503546099287\n"
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     "name": "stderr",
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   "source": [
    "(pcs0,rcs0),(pre_list0,rec_list0) = cal_context_spread_ses_len(0)"
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   "execution_count": 27,
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:00<00:00, 96343.15it/s] "
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     "text": [
      "100%|██████████| 89737/89737 [00:01<00:00, 86414.86it/s]"
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      "Precision T-test:  Ttest_indResult(statistic=13.708439729828285, pvalue=9.777477704729179e-43)\n",
      "len:  3\n",
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      "len:  5\n",
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    "(pcs2,rcs2),(pre_list2,rec_list2) = cal_context_spread_ses_len(2,pcs0)"
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   ],
   "source": [
    "%config InlineBackend.figure_format='svg'\n",
    "df = pd.DataFrame({\"Session\": range(2,11),\n",
    "                   \"0-hop\": pre_list0, \n",
    "                   \"1-hop\": pre_list1,\n",
    "                  \"2-hop\": pre_list2})\n",
    "print df\n",
    "import seaborn as sns\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "tidy = df.melt(id_vars='Session').rename(columns=str.title)\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "ax.set(ylim=(0, 0.4))\n",
    "g = sns.barplot(x='Session', y='Value', hue='Variable', data=tidy, ax=ax,\n",
    "\tpalette=\"muted\")\n",
    "sns.despine(fig)\n",
    "\n",
    "\n",
    "#for index, row in df.iterrows():\n",
    "#    print index,row\n",
    "#    #g.text(row.name,row.Value, round(row.total_bill,2), color='black', ha=\"center\")\n",
    "'''\n",
    "for p in ax.patches:\n",
    "\tax.annotate(\"%.4f\" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()-0.01),\n",
    "\t\tha='center', va='center', fontsize=11, color='black', xytext=(0, 20),\n",
    "\t\ttextcoords='offset points')\n",
    "'''\n",
    "# Create a second y-axis with the scaled ticks\n",
    "ax.legend().set_title('')\n",
    "ax.set_xlabel('Session length')\n",
    "ax.set_ylabel('Recall')\n",
    "plt.show()"
   ]
  },
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   "execution_count": 29,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "#Precision From the current query\n",
    "#using the function cal_pr\n",
    "def click_calculation(step):\n",
    "    click_ratio = [[],[]];unclick_ratio = [[],[]]\n",
    "    for session in tqdm.tqdm(session_data[:]):\n",
    "        qids = session['qids']\n",
    "        docs = session['docs']\n",
    "        for qid,dids in zip(qids,docs):\n",
    "            qid_entlist = qid2eid[qid]\n",
    "            q_size = len(qid_entlist)\n",
    "            for pos,(did,click) in enumerate(dids):\n",
    "                did_entlist = did2eid[did]\n",
    "                (p,r) = cal_pr(qid_entlist,did_entlist,step)\n",
    "                if click == 0:\n",
    "                    unclick_ratio[0].append(p)\n",
    "                    unclick_ratio[1].append(r)\n",
    "                else:\n",
    "                    click_ratio[0].append(p)\n",
    "                    click_ratio[1].append(r)\n",
    "    \n",
    "    print 'Step: ', step\n",
    "    print 'Click precision: ', np.mean(click_ratio[0])\n",
    "    print 'Click recall: ', np.mean(click_ratio[1])\n",
    "    print '--------------------------'\n",
    "    print 'UnClick precision: ', np.mean(unclick_ratio[0])\n",
    "    print 'UnClick recall: ', np.mean(unclick_ratio[1])  \n",
    "    return click_ratio,unclick_ratio"
   ]
  },
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   "execution_count": 23,
   "metadata": {
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     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:11<00:00, 8077.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step:  0\n",
      "Click precision:  0.23074349780140077\n",
      "Click recall:  0.31544267271966814\n",
      "--------------------------\n",
      "UnClick precision:  0.18886011540252026\n",
      "UnClick recall:  0.27425952879968196\n"
     ]
    }
   ],
   "source": [
    "c,unc = click_calculation(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:14<00:00, 6032.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step:  1\n",
      "Click precision:  0.23185423294969457\n",
      "Click recall:  0.11445429558506043\n",
      "--------------------------\n",
      "UnClick precision:  0.19066753378394122\n",
      "UnClick recall:  0.10012888712789052\n"
     ]
    }
   ],
   "source": [
    "c1,unc1 = click_calculation(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:20<00:00, 4411.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step:  2\n",
      "Click precision:  0.2688178005861887\n",
      "Click recall:  0.009424625624199774\n",
      "--------------------------\n",
      "UnClick precision:  0.23784797909061914\n",
      "UnClick recall:  0.009769023871826803\n"
     ]
    }
   ],
   "source": [
    "c2,unc2 = click_calculation(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------c0 vs c1-------------\n",
      "Click Precision T-test:  Ttest_indResult(statistic=-1.6773954680251932, pvalue=0.09346590935921194)\n",
      "UnClick Precision T-test:  Ttest_indResult(statistic=-8.720925610652538, pvalue=2.7603896389208056e-18)\n",
      "--------c1 vs c2-------------\n",
      "Click Precision T-test:  Ttest_indResult(statistic=-54.104999225195236, pvalue=0.0)\n",
      "UnClick Precision T-test:  Ttest_indResult(statistic=-220.67355463003892, pvalue=0.0)\n"
     ]
    }
   ],
   "source": [
    "print '--------c0 vs c1-------------'\n",
    "print 'Click Precision T-test: ', stats.ttest_ind(c[0],c1[0])\n",
    "print 'UnClick Precision T-test: ', stats.ttest_ind(unc[0],unc1[0])\n",
    "\n",
    "print '--------c1 vs c2-------------'\n",
    "print 'Click Precision T-test: ', stats.ttest_ind(c1[0],c2[0])\n",
    "print 'UnClick Precision T-test: ', stats.ttest_ind(unc1[0],unc2[0])"
   ]
  },
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   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "click_precision = map(lambda t:np.mean(t[0]), [c,c1,c2])\n",
    "unclick_precision = map(lambda t:np.mean(t[0]), [unc,unc1,unc2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Click  Hops   Unclick\n",
      "0  0.230743     0  0.188860\n",
      "1  0.231854     1  0.190668\n",
      "2  0.268818     2  0.237848\n"
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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Hops\": [0,1,2],\n",
    "                   \"Click\": click_precision, \n",
    "                   \"Unclick\": unclick_precision})\n",
    "print df\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "tidy = df.melt(id_vars='Hops').rename(columns=str.title)\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "ax.set(ylim=(0, 0.5))\n",
    "g = sns.barplot(x='Hops', y='Value', hue='Variable', data=tidy, ax=ax,\n",
    "\tpalette=\"muted\")\n",
    "sns.despine(fig)\n",
    "\n",
    "\n",
    "#for index, row in df.iterrows():\n",
    "#    print index,row\n",
    "#    #g.text(row.name,row.Value, round(row.total_bill,2), color='black', ha=\"center\")\n",
    "for p in ax.patches:\n",
    "\tax.annotate(\"%.4f\" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()-0.01),\n",
    "\t\tha='center', va='center', fontsize=11, color='black', xytext=(0, 20),\n",
    "\t\ttextcoords='offset points')\n",
    "    \n",
    "# Create a second y-axis with the scaled ticks\n",
    "ax.legend().set_title('')\n",
    "ax.set_ylabel('Ratio')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def click_context_calculation(step,use_click=False):\n",
    "    click_ratio = [[],[]];unclick_ratio = [[],[]]\n",
    "    for session in tqdm.tqdm(session_data[:]):\n",
    "        qids = session['qids']\n",
    "        docs = session['docs']\n",
    "        \n",
    "        entities_lists = map(lambda qid:qid2eid[qid],qids)\n",
    "        \n",
    "        if use_click:\n",
    "            for dids in docs[:-1]:\n",
    "                for pos,(did,click) in enumerate(dids):\n",
    "                    if click != 0:\n",
    "                        did_entlist = did2eid[did]\n",
    "                        entities_lists.append(did_entlist)\n",
    "        \n",
    "        ent1s = list(itertools.chain(*entities_lists))\n",
    "        ent1s = list(set(ent1s))\n",
    "        \n",
    "        dids = docs[-1]\n",
    "        for pos,(did,click) in enumerate(dids):\n",
    "            did_entlist = did2eid[did]\n",
    "            (p,r) = cal_pr(ent1s,did_entlist,step)\n",
    "            if click == 0:\n",
    "                unclick_ratio[0].append(p)\n",
    "                unclick_ratio[1].append(r)\n",
    "            else:\n",
    "                click_ratio[0].append(p)\n",
    "                click_ratio[1].append(r)\n",
    "    \n",
    "    print 'Step: ', step\n",
    "    print 'Click precision: ', np.mean(click_ratio[0])\n",
    "    print 'Click recall: ', np.mean(click_ratio[1])\n",
    "    print '--------------------------'\n",
    "    print 'UnClick precision: ', np.mean(unclick_ratio[0])\n",
    "    print 'UnClick recall: ', np.mean(unclick_ratio[1])  \n",
    "    return click_ratio,unclick_ratio"
   ]
  },
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   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:05<00:00, 15339.84it/s]\n",
      "  0%|          | 0/89737 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step:  0\n",
      "Click precision:  0.26285389990074587\n",
      "Click recall:  0.2781512353240508\n",
      "--------------------------\n",
      "UnClick precision:  0.22426101187007547\n",
      "UnClick recall:  0.24873174552225244\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:09<00:00, 9062.38it/s]\n",
      "  0%|          | 0/89737 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step:  1\n",
      "Click precision:  0.2642798180013562\n",
      "Click recall:  0.08736240501080564\n",
      "--------------------------\n",
      "UnClick precision:  0.22659538352598813\n",
      "UnClick recall:  0.07731037071753465\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:15<00:00, 5723.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step:  2\n",
      "Click precision:  0.33417191599760226\n",
      "Click recall:  0.004026843236155516\n",
      "--------------------------\n",
      "UnClick precision:  0.31074955235582674\n",
      "UnClick recall:  0.003764030112744063\n"
     ]
    }
   ],
   "source": [
    "c_cxtq,unc_cxtq = click_context_calculation(0)\n",
    "c_cxtq1,unc_cxtq1 = click_context_calculation(1)\n",
    "c_cxtq2,unc_cxtq2 = click_context_calculation(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------c0 vs c1-------------\n",
      "Click Precision T-test:  Ttest_indResult(statistic=-1.423975578847097, pvalue=0.15445507471117728)\n",
      "UnClick Precision T-test:  Ttest_indResult(statistic=-6.595408451038376, pvalue=4.24225580168053e-11)\n",
      "--------c1 vs c2-------------\n",
      "Click Precision T-test:  Ttest_indResult(statistic=-63.9911772894142, pvalue=0.0)\n",
      "UnClick Precision T-test:  Ttest_indResult(statistic=-214.96710717861285, pvalue=0.0)\n"
     ]
    }
   ],
   "source": [
    "print '--------c0 vs c1-------------'\n",
    "print 'Click Precision T-test: ', stats.ttest_ind(c_cxtq[0],c_cxtq1[0])\n",
    "print 'UnClick Precision T-test: ', stats.ttest_ind(unc_cxtq[0],unc_cxtq1[0])\n",
    "\n",
    "print '--------c1 vs c2-------------'\n",
    "print 'Click Precision T-test: ', stats.ttest_ind(c_cxtq1[0],c_cxtq2[0])\n",
    "print 'UnClick Precision T-test: ', stats.ttest_ind(unc_cxtq1[0],unc_cxtq2[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "click_precision = map(lambda t:np.mean(t[0]), [c_cxtq,c_cxtq1,c_cxtq2])\n",
    "unclick_precision = map(lambda t:np.mean(t[0]), [unc_cxtq,unc_cxtq1,unc_cxtq2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Click  Hops   Unclick\n",
      "0  0.262854     0  0.224261\n",
      "1  0.264280     1  0.226595\n",
      "2  0.334172     2  0.310750\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Hops\": [0,1,2],\n",
    "                   \"Click\": click_precision, \n",
    "                   \"Unclick\": unclick_precision})\n",
    "print df\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "tidy = df.melt(id_vars='Hops').rename(columns=str.title)\n",
    "\n",
    "#print tidy\n",
    "sns.set(style=\"white\")\n",
    "ax.set(ylim=(0, 0.5))\n",
    "g = sns.barplot(x='Hops', y='Value', hue='Variable', data=tidy, ax=ax,\n",
    "\tpalette=\"muted\")\n",
    "sns.despine(fig)\n",
    "\n",
    "\n",
    "#for index, row in df.iterrows():\n",
    "#    print index,row\n",
    "#    #g.text(row.name,row.Value, round(row.total_bill,2), color='black', ha=\"center\")\n",
    "for p in ax.patches:\n",
    "\tax.annotate(\"%.4f\" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()-0.01),\n",
    "\t\tha='center', va='center', fontsize=11, color='black', xytext=(0, 20),\n",
    "\t\ttextcoords='offset points')\n",
    "    \n",
    "# Create a second y-axis with the scaled ticks\n",
    "ax.legend().set_title('')\n",
    "ax.set_ylabel('Ratio')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'click_context_calculation' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
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      "\u001b[0;31mNameError\u001b[0m: name 'click_context_calculation' is not defined"
     ]
    }
   ],
   "source": [
    "c_cxtqc,unc_cxtqc = click_context_calculation(0,use_click=True)\n",
    "c_cxtqc1,unc_cxtqc1 = click_context_calculation(1,use_click=True)\n",
    "c_cxtqc2,unc_cxtqc2 = click_context_calculation(2,use_click=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "print '--------c0 vs c1-------------'\n",
    "print 'Click Precision T-test: ', stats.ttest_ind(c_cxtqc[0],c_cxtqc1[0])\n",
    "print 'UnClick Precision T-test: ', stats.ttest_ind(unc_cxtqc[0],unc_cxtqc1[0])\n",
    "\n",
    "print '--------c1 vs c2-------------'\n",
    "print 'Click Precision T-test: ', stats.ttest_ind(c_cxtqc1[0],c_cxtqc2[0])\n",
    "print 'UnClick Precision T-test: ', stats.ttest_ind(unc_cxtqc1[0],unc_cxtqc2[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'c_cxtqc' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-52-fa70721eb303>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mclick_precision\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mc_cxtqc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc_cxtqc1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc_cxtqc2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0munclick_precision\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0munc_cxtqc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munc_cxtqc1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munc_cxtqc2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'c_cxtqc' is not defined"
     ]
    }
   ],
   "source": [
    "click_precision = map(lambda t:np.mean(t[0]), [c_cxtqc,c_cxtqc1,c_cxtqc2])\n",
    "unclick_precision = map(lambda t:np.mean(t[0]), [unc_cxtqc,unc_cxtqc1,unc_cxtqc2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'click_precision' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-51-664482acc881>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m df = pd.DataFrame({\"Hops\": [0,1,2],\n\u001b[0;32m----> 2\u001b[0;31m                    \u001b[0;34m\"Click\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mclick_precision\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m                    \"Unclick\": unclick_precision})\n\u001b[1;32m      4\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'click_precision' is not defined"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Hops\": [0,1,2],\n",
    "                   \"Click\": click_precision, \n",
    "                   \"Unclick\": unclick_precision})\n",
    "print df\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "tidy = df.melt(id_vars='Hops').rename(columns=str.title)\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "ax.set(ylim=(0, 0.6))\n",
    "g = sns.barplot(x='Hops', y='Value', hue='Variable', data=tidy, ax=ax,\n",
    "\tpalette=\"muted\")\n",
    "sns.despine(fig)\n",
    "\n",
    "\n",
    "#for index, row in df.iterrows():\n",
    "#    print index,row\n",
    "#    #g.text(row.name,row.Value, round(row.total_bill,2), color='black', ha=\"center\")\n",
    "for p in ax.patches:\n",
    "\tax.annotate(\"%.4f\" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()-0.01),\n",
    "\t\tha='center', va='center', fontsize=11, color='black', xytext=(0, 20),\n",
    "\t\ttextcoords='offset points')\n",
    "    \n",
    "# Create a second y-axis with the scaled ticks\n",
    "ax.legend().set_title('')\n",
    "ax.set_ylabel('Ratio')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'R'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#understanding the entity in different reformulation type\n",
    "import re\n",
    "def keepwordsonly(words):\n",
    "    #print 'words: ', words\n",
    "    line = ''.join(words)\n",
    "    line = line.decode(\"utf8\")\n",
    "    string = re.sub(\"[\\s+\\.\\!\\/_,$%^*(+\\\"\\']+|[+——！，。？?、~@#￥%……&*（）]+\".decode(\"utf8\"), \"\".decode(\"utf8\"),line)\n",
    "    return string\n",
    "\n",
    "def reformulationClassfier(query1,query2):\n",
    "    words1 = map(str,query1)\n",
    "    words2 = map(str,query2)\n",
    "    \n",
    "    common_words = set.intersection(*map(set, [words1,words2]))\n",
    "    \n",
    "    raw_words1 = keepwordsonly(words1)\n",
    "    raw_words2 = keepwordsonly(words2)\n",
    "    if raw_words1 == raw_words2:\n",
    "        return 'R'#Repeat\n",
    "    \n",
    "    if len(common_words) > 0:\n",
    "        if len(words1) > len(words2):\n",
    "            return 'G' #Generalization\n",
    "        elif len(words1) < len(words2):\n",
    "            return 'S' #Specialization\n",
    "        else:\n",
    "            for w in words2:\n",
    "                if w not in words1:\n",
    "                    return 'WR' #word substitution\n",
    "        \n",
    "    return 'N'#new\n",
    "\n",
    "reformulationClassfier(['虎牙','直播'], ['虎牙',',','直播'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "31352it [00:00, 313519.55it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "135814it [00:00, 138481.84it/s]\n",
      "36497it [00:00, 364969.48it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "42119584it [03:01, 232471.06it/s]\n"
     ]
    }
   ],
   "source": [
    "def loadNeigborRelation(step):\n",
    "    print 'run'\n",
    "    addr = '/ivi/ilps/personal/xli/knowledge_intent/data/neib_%d_relation.txt' % step\n",
    "    neib_dict = {};\n",
    "    for line in tqdm.tqdm(open(addr)):\n",
    "        elements = line.strip().split('\\t')\n",
    "        relations = map(int,elements[-1].split('#'))\n",
    "        neib_dict[(elements[0],elements[1])] = (int(elements[2]),relations)\n",
    "        #break\n",
    "    return neib_dict\n",
    "\n",
    "relation_dict1 = loadNeigborRelation(1)\n",
    "relation_dict2 = loadNeigborRelation(2)\n",
    "#print relation_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<function <lambda> at 0x7f4f161f5f50>, {1: 113614, 2: 1644, 3: 20298, 4: 258})\n"
     ]
    }
   ],
   "source": [
    "rel_count = defaultdict(lambda:0)\n",
    "for k,v in relation_dict1.iteritems():\n",
    "    for r in v[1]:\n",
    "        rel_count[r] += 1\n",
    "print rel_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<function <lambda> at 0x7f4f161f56e0>, {1: 83213162, 2: 210337, 3: 800562, 4: 15104})\n"
     ]
    }
   ],
   "source": [
    "rel_count = defaultdict(lambda:0)\n",
    "for k,v in relation_dict2.iteritems():\n",
    "    for r in v[1]:\n",
    "        rel_count[r] += 1\n",
    "print rel_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gethops(ent1s,ent2s):\n",
    "    hops = []\n",
    "    derivation = 0\n",
    "    equal = 0\n",
    "    has_same = 0\n",
    "    for e1 in ent1s:\n",
    "        for e2 in ent2s:\n",
    "            if e1 == e2:\n",
    "                #continue\n",
    "                has_same = 1\n",
    "                hops.append(0)\n",
    "            else:\n",
    "                if (e1,e2) in relation_dict1 or (e2,e1) in relation_dict1:\n",
    "                    ent1 = e1;ent2 = e2\n",
    "                    if (e1,e2) not in relation_dict1:\n",
    "                        ent2 = e1;ent1 = e2s\n",
    "                        \n",
    "                    hops.append(1)\n",
    "                    for r in relation_dict1[(ent1,ent2)][1]:\n",
    "                        #print 'runhere'\n",
    "                        if r == 1 or r == 2:\n",
    "                            derivation += 1\n",
    "                        else:\n",
    "                            equal += 1\n",
    "                elif (e1,e2) in relation_dict2 or (e2,e1) in relation_dict2:\n",
    "                    ent1 = e1;ent2 = e2\n",
    "                    if (e1,e2) not in relation_dict2:\n",
    "                        ent2 = e1;ent1 = e2s\n",
    "                        \n",
    "                    hops.append(2)\n",
    "                    for r in relation_dict2[(ent1,ent2)][1]:\n",
    "                        if r == 1 or r == 2:\n",
    "                            derivation += 1\n",
    "                        else:\n",
    "                            equal += 1\n",
    "                #else:\n",
    "                    #hops.append(0)\n",
    "    total_r = derivation + equal\n",
    "    if len(hops) == 0:\n",
    "        avg_hops = 0\n",
    "    else:\n",
    "        avg_hops = np.mean(hops)\n",
    "    if total_r == 0:\n",
    "        return avg_hops,0,0,has_same\n",
    "    return avg_hops,float(derivation)/total_r, float(equal)/total_r,has_same\n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "global name 'get_words' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-586583a7e5dd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mdocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'docs'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mwords_lists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mqid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mget_words\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqdid2text_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mqid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mqids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0mentities_lists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mqid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mqid2eid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mqid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mqids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-4-586583a7e5dd>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(qid)\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mdocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'docs'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mwords_lists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mqid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mget_words\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqdid2text_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mqid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mqids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0mentities_lists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mqid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mqid2eid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mqid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mqids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: global name 'get_words' is not defined"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "reload(sys)\n",
    "sys.setdefaultencoding('utf8')\n",
    "\n",
    "#query Reformulation spread in terms of relation types\n",
    "from collections import defaultdict\n",
    "\n",
    "type_count = defaultdict(lambda:0)\n",
    "delta_e_count = defaultdict(list)\n",
    "ehops_count = defaultdict(list)\n",
    "rel_count = defaultdict(list)\n",
    "\n",
    "for session in tqdm.tqdm(session_data):\n",
    "    qids = session['qids']\n",
    "    docs = session['docs']\n",
    "    \n",
    "    words_lists = map(lambda qid:get_words(qdid2text_dict[qid]),qids)\n",
    "    entities_lists = map(lambda qid:qid2eid[qid],qids)\n",
    "    \n",
    "    #entities_lists = map(lambda qid:qid2eid[qid],qids)\n",
    "    \n",
    "    for i in range(1,len(qids)):\n",
    "        #print words_lists[i-1]\n",
    "        reformulate_type = reformulationClassfier(words_lists[i-1],words_lists[i])\n",
    "        type_count[reformulate_type] += 1\n",
    "        \n",
    "        delta_e = 0\n",
    "        for e in entities_lists[i]:\n",
    "            if e not in entities_lists[i-1]:\n",
    "                delta_e += 1\n",
    "        delta_e = float(delta_e) / (len(entities_lists[i])+ 1)\n",
    "        #delta_e = len(entities_lists[i]) - len(entities_lists[i-1])\n",
    "        delta_e_count[reformulate_type].append(delta_e)\n",
    "        \n",
    "        hops, dev, eq = gethops(entities_lists[i-1],entities_lists[i])\n",
    "        if hops > 0:\n",
    "            ehops_count[reformulate_type].append(hops)\n",
    "            rel_count[str(reformulate_type) + 'dev'].append(dev)\n",
    "            rel_count[str(reformulate_type) + 'eq'].append(eq)\n",
    "print type_count\n",
    "\n",
    "for qtype in delta_e_count:\n",
    "    print 'qtype:', qtype\n",
    "    print 'Delta E:', np.mean(delta_e_count[qtype])\n",
    "    print stats.ttest_1samp(delta_e_count[qtype],0)\n",
    "    \n",
    "    print '--------------'\n",
    "    print 'Avg hops: ',np.mean(ehops_count[qtype])\n",
    "    dev = np.mean(rel_count[str(qtype)+'dev'])\n",
    "    eq = np.mean(rel_count[str(qtype)+'eq'])\n",
    "    total_r = dev + eq\n",
    "    if total_r == 0:\n",
    "        print 'dev:0,eq:0'\n",
    "    else:\n",
    "        print 'dev: ', float(dev)# / total_r\n",
    "        print 'eq: ', float(eq)# / total_r\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cPickle\n",
    "prefix_data_addr = '/ivi/ilps/personal/xli/knowledge_intent/entity/'\n",
    "(ent2id, id2ent) = cPickle.load(open(prefix_data_addr + 'raw-xlore.ent_dict.pkl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#document clicking spread in terms of relation types\n",
    "from collections import defaultdict\n",
    "import scipy.stats as stats\n",
    "click_count = defaultdict(list)\n",
    "unclick_count = defaultdict(list)\n",
    "click_hops = [];unclick_hops = []\n",
    "click_num = 0;unclick_num = 0\n",
    "\n",
    "click_percent = defaultdict(list)\n",
    "click_e_rate = defaultdict(list)\n",
    "\n",
    "print_times = 0;cur_print = 0\n",
    "comments = ''\n",
    "for session in tqdm.tqdm(session_data):\n",
    "    qids = session['qids']\n",
    "    docs = session['docs']\n",
    "    \n",
    "    #words_lists = map(lambda qid:get_words(qdid2text_dict[qid]),qids)\n",
    "    #entities_lists = map(lambda qid:qid2eid[qid],qids)\n",
    "    \n",
    "    #entities_lists = map(lambda qid:qid2eid[qid],qids)\n",
    "\n",
    "    for i,qid in enumerate(qids):\n",
    "        query_ents = qid2eid[qid]\n",
    "        if len(query_ents) == 0:\n",
    "            continue\n",
    "        click_ents = []\n",
    "        \n",
    "        #if cur_print == print_times:\n",
    "        #    break\n",
    "        for pos,(did,click) in enumerate(docs[i]):\n",
    "            did_entlist = did2eid[did]\n",
    "            if len(did_entlist) == 0:\n",
    "                continue\n",
    "            hops, dev, eq, has_same = gethops(query_ents,did_entlist)\n",
    "            common_es = set.intersection(*map(set, [query_ents,did_entlist]))\n",
    "            \n",
    "            if click != 0:\n",
    "                click_hops.append(hops)\n",
    "                if hops>0:\n",
    "                    click_count['dev'].append(dev)\n",
    "                    click_count['eq'].append(eq)\n",
    "                click_num += 1\n",
    "                click_percent['click'].append(has_same)\n",
    "                click_e_rate['click'].append(float(len(common_es)) / (len(query_ents) + 1))\n",
    "                \n",
    "                distances = []\n",
    "                rela_list = []\n",
    "                '''\n",
    "                for e1 in qid2eid[qid]:\n",
    "                    for e2 in did2eid[did]:\n",
    "                        if e1 == e2:\n",
    "                            distances.append(0)\n",
    "                        elif (e1,e2) in relation_dict1 or (e2,e1) in relation_dict1:\n",
    "                            distances.append(1)\n",
    "                            comments =  'E1: %s E2: %s' % (id2ent[e1],id2ent[e2])\n",
    "                            rela = relation_dict1[(e1,e2)]\n",
    "                            rela_list += rela[1]\n",
    "                        elif (e1,e2) in relation_dict2 or (e2,e1) in relation_dict2:\n",
    "                            distances.append(2)\n",
    "                            comments =  'E1: %s E2: %s' % (id2ent[e1],id2ent[e2])\n",
    "                            rela = relation_dict2[(e1,e2)]\n",
    "                            rela_list += rela[1]\n",
    "                        else:\n",
    "                            distances.append(-1)\n",
    "                '''              \n",
    "                if cur_print < print_times and has_same == 0 and (2 in distances) and (3 in rela_list or 4 in rela_list):#(2 in distances):\n",
    "                    print 'Query: ', qdid2text_dict[qid]\n",
    "                    for i,e in enumerate(map(lambda e:id2ent[e],qid2eid[qid])):\n",
    "                        print ('E %d: %s' % (i,e))\n",
    "                    print 'Doc: ', qdid2text_dict[did]\n",
    "                    for i,e in enumerate(map(lambda e:id2ent[e],did2eid[did])):\n",
    "                        print ('E %d: %s' % (i,e))\n",
    "                    \n",
    "                    print comments\n",
    "                    print rela\n",
    "                    \n",
    "                    print distances\n",
    "                    cur_print += 1\n",
    "            else:\n",
    "                unclick_hops.append(hops)\n",
    "                if hops>0:   \n",
    "                    unclick_count['dev'].append(dev)\n",
    "                    unclick_count['eq'].append(eq)\n",
    "                unclick_num += 1\n",
    "                click_percent['unclick'].append(has_same)\n",
    "                click_e_rate['unclick'].append(float(len(common_es)) / (len(query_ents) + 1))\n",
    "                \n",
    "print '-------Click---------'\n",
    "print 'Percentage: ', (float(click_num)/(click_num + unclick_num))\n",
    "print 'Avg hops: ', np.mean(click_hops)\n",
    "print 'Has at least one entity Percent: ', np.mean(click_percent['click'])\n",
    "print 'Query ent coverage: ', np.mean(click_e_rate['click'])\n",
    "#print click_count\n",
    "dev = np.mean(click_count['dev'])\n",
    "eq = np.mean(click_count['eq'])\n",
    "total_r = dev + eq\n",
    "if total_r == 0:\n",
    "    print 'dev:0,eq:0'\n",
    "else:\n",
    "    print 'dev: ', float(dev) #/ total_r\n",
    "    print 'eq: ', float(eq) #/ total_r\n",
    "\n",
    "print '-------UnClick---------'\n",
    "print 'Percentage: ', (float(unclick_num)/(click_num + unclick_num))\n",
    "print 'Avg hops: ', np.mean(unclick_hops)\n",
    "print 'Avg hop T-test: ', stats.ttest_ind(click_hops,unclick_hops)\n",
    "\n",
    "print 'Has at least one entity Percent: ', np.mean(click_percent['unclick'])\n",
    "print 'Query ent coverage: ', np.mean(click_e_rate['unclick'])\n",
    "dev = np.mean(unclick_count['dev'])\n",
    "eq = np.mean(unclick_count['eq'])\n",
    "total_r = dev + eq\n",
    "if total_r == 0:\n",
    "    print 'dev:0,eq:0'\n",
    "else:\n",
    "    print 'Dev: ', float(dev) #/ total_r\n",
    "    print 'Dev T-test: ', stats.ttest_ind(click_count['dev'],unclick_count['dev'])\n",
    "    print 'Eq: ', float(eq) #/ total_r \n",
    "    print 'Eq T-test: ', stats.ttest_ind(click_count['eq'],unclick_count['eq'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:00<00:00, 112055.14it/s]"
     ]
    },
    {
     "name": "stdout",
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     "text": [
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
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   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "ent_count = defaultdict(lambda:defaultdict(list))\n",
    "    \n",
    "for session in tqdm.tqdm(session_data[:]):\n",
    "    qids = session['qids']\n",
    "    docs = session['docs']\n",
    "    \n",
    "    words_lists = map(lambda qid:get_words(qdid2text_dict[qid]),qids)\n",
    "    entities_lists = map(lambda qid:qid2eid[qid],qids)\n",
    "\n",
    "    for i in range(len(qids)):\n",
    "        ent_count[len(qids)][i].append(len(entities_lists[i]))\n",
    "\n",
    "result = {}\n",
    "for length in ent_count:\n",
    "    result[length] = []\n",
    "    for num in ent_count[length]:\n",
    "        result[length].append(np.mean(ent_count[length][num]))\n",
    "\n",
    "\n",
    "entity_in_session = result    \n",
    "print entity_in_session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
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   ],
   "source": [
    "%config InlineBackend.figure_format='svg'\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "data = entity_in_session\n",
    "data['Session'] = list(range(1,11))\n",
    "df = pd.DataFrame(dict([ (str(k),pd.Series(v)) for k,v in data.iteritems() ]))\n",
    "\n",
    "#print df\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "tidy = df.melt(id_vars='Session').rename(columns=str.title)\n",
    "\n",
    "\n",
    "tidy = tidy.dropna()\n",
    "#tidy.dropna()\n",
    "#print tidy\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "ax.set(ylim=(1.45, 2.7))\n",
    "ax.set(xlim=(0.5, 10.5))\n",
    "g = sns.lineplot(x='Session', y='Value', hue='Variable', data=tidy, ax=ax,hue_order=map(str,range(2,11)),\n",
    "\tpalette=\"muted\",marker = 'o',legend=False)\n",
    "\n",
    "sns.despine(fig)\n",
    "\n",
    "font1 = {\n",
    "'size'   : 10,\n",
    "}\n",
    "#'weight' : 'bold',\n",
    "\n",
    "\n",
    "# Create a second y-axis with the scaled ticks\n",
    "#ax.legend(prop=font1).set_title('')\n",
    "plt.xticks(range(1, 11))\n",
    "ax.set_xlabel('Session Position',weight='bold')\n",
    "ax.set_ylabel('Average Entity Number',weight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ent_count = defaultdict(lambda:defaultdict(list))\n",
    "import itertools\n",
    "\n",
    "def getNew(ent1s,ent2s):\n",
    "    num = 0\n",
    "    for e in ent2s:\n",
    "        if e not in ent1s:\n",
    "            num += 1\n",
    "    return num\n",
    "for session in tqdm.tqdm(session_data[:]):\n",
    "    qids = session['qids']\n",
    "    docs = session['docs']\n",
    "    \n",
    "    words_lists = map(lambda qid:get_words(qdid2text_dict[qid]),qids)\n",
    "    entities_lists = map(lambda qid:qid2eid[qid],qids)\n",
    "\n",
    "    for i in range(0,len(qids)):\n",
    "        if i == 0:\n",
    "            ent_count[len(qids)][i].append(0)\n",
    "        else:\n",
    "            comp = list(itertools.chain(*entities_lists[:i]))\n",
    "            ent_count[len(qids)][i].append(getNew(comp,entities_lists[i]))\n",
    "\n",
    "result = {}\n",
    "for length in ent_count:\n",
    "    result[length] = []\n",
    "    for num in ent_count[length]:\n",
    "        result[length].append(np.mean(ent_count[length][num]))\n",
    "\n",
    "\n",
    "newentity_in_session = result    \n",
    "print newentity_in_session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "%config InlineBackend.figure_format='svg'\n",
    "%matplotlib inline\n",
    "\n",
    "data = newentity_in_session\n",
    "data['Session'] = list(range(1,11))\n",
    "df = pd.DataFrame(dict([ (str(k),pd.Series(v)) for k,v in data.iteritems() ]))\n",
    "\n",
    "#print df\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "tidy = df.melt(id_vars='Session').rename(columns=str.title)\n",
    "\n",
    "\n",
    "tidy = tidy.dropna()\n",
    "#tidy.dropna()\n",
    "#print tidy\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "ax.set(ylim=(0, 1.4))\n",
    "ax.set(xlim=(0.5, 10.5))\n",
    "g = sns.lineplot(x='Session', y='Value', hue='Variable', data=tidy, ax=ax,hue_order=map(str,range(2,11)),\n",
    "\tpalette=\"muted\",marker = 'o',legend=False)\n",
    "\n",
    "sns.despine(fig)\n",
    "\n",
    "font1 = {\n",
    "'size'   : 10,\n",
    "}\n",
    "#'weight' : 'bold',\n",
    "\n",
    "\n",
    "# Create a second y-axis with the scaled ticks\n",
    "#ax.legend(prop=font1).set_title('')\n",
    "plt.xticks(range(1, 11))\n",
    "ax.set_xlabel('Session Position',weight='bold')\n",
    "ax.set_ylabel('Average New Entity Number',weight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:00<00:00, 222864.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Click ratio:  0.712501406062\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#calculate the click ratio\n",
    "click_count = 0;total = 0\n",
    "for session in tqdm.tqdm(session_data[:]):\n",
    "    qids = session['qids']\n",
    "    docs = session['docs']\n",
    "\n",
    "    #entities_lists = map(lambda qid:qid2eid[qid],qids)\n",
    "\n",
    "    for s_i,dids in enumerate(docs):\n",
    "        is_click = 0\n",
    "        for pos,(did,click) in enumerate(dids):\n",
    "            if click != 0:\n",
    "                is_click = 1\n",
    "                break\n",
    "        click_count += is_click\n",
    "        total += 1\n",
    "\n",
    "print 'Click ratio: ', (float(click_count)/total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:00<00:00, 111825.29it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "qc_rates: 0.2987447641737126\n",
      "qq_rates: 0.3412738823051317\n",
      "Ttest_indResult(statistic=-28.829464046655072, pvalue=3.1036479108823284e-182)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "def get_comp(comp_e,cur_e):\n",
    "    num = 0\n",
    "    for e in cur_e:\n",
    "        if e in comp_e:\n",
    "            num += 1\n",
    "    #return 1 if num > 0 else 0\n",
    "    #if len(comp_e) == 0 or len(cur_e) == 0:\n",
    "    #    return -1\n",
    "    return float(num) / (len(cur_e)+1)\n",
    "\n",
    "qc_rates = [];qq_rates = []\n",
    "qq_result_list = defaultdict(lambda:defaultdict(list))\n",
    "qc_result_list = defaultdict(lambda:defaultdict(list))\n",
    "\n",
    "result_dict = defaultdict(lambda:defaultdict())\n",
    "result_list = defaultdict(lambda:defaultdict(list))\n",
    "\n",
    "session_len_dict = defaultdict(lambda:0)\n",
    "\n",
    "for session in tqdm.tqdm(session_data[:]):\n",
    "    qids = session['qids']\n",
    "    docs = session['docs']\n",
    "\n",
    "    entities_lists = map(lambda qid:qid2eid[qid],qids)\n",
    "    \n",
    "    session_len = len(docs)\n",
    "    \n",
    "    for i,dids in enumerate(docs[:-1]):\n",
    "        is_click = 0\n",
    "        qq = get_comp(entities_lists[i],entities_lists[i+1])\n",
    "        \n",
    "        for pos,(did,click) in enumerate(dids):\n",
    "            #qq = get_comp(entities_lists[i],entities_lists[i+1])\n",
    "            #qq = cal_pr(entities_lists[i],entities_lists[i+1],2)[0]\n",
    "            if click != 0:\n",
    "                is_click = 1\n",
    "                break    \n",
    "        \n",
    "        if is_click != 0:\n",
    "            #qc = get_comp(did_entlist,entities_lists[i+1])\n",
    "            qc_rates.append(qq)\n",
    "            qc_result_list[session_len][i].append(qq)\n",
    "        elif is_click == 0:\n",
    "            qq_rates.append(qq)\n",
    "            qq_result_list[session_len][i].append(qq)\n",
    "    \n",
    "\n",
    "#print qc_rates\n",
    "print 'qc_rates:',  np.mean(qc_rates)\n",
    "print 'qq_rates:',  np.mean(qq_rates)\n",
    "print stats.ttest_ind(qc_rates,qq_rates)\n",
    "\n",
    "for k in qc_result_list:\n",
    "    for kk in qc_result_list[k]:\n",
    "        #result_dict[k][kk] = np.mean(qq_result_list[k][kk])\n",
    "        result_dict[k][kk] = np.mean(qc_result_list[k][kk]) / np.mean(qq_result_list[k][kk]) #np.mean(qq_result_list[k][kk]) / \n",
    "#print result_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "result = {}\n",
    "for length in result_dict:\n",
    "    result[length] = []\n",
    "    for pos,v in result_dict[length].iteritems():\n",
    "        #if length < 10:\n",
    "        result[length].append(v)\n",
    "\n",
    "qc_rate_in_session = result\n",
    "print qc_rate_in_session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Click Session  Unclick\n",
      "0    NaN   Short      NaN\n",
      "1    NaN  Medium      NaN\n",
      "2    NaN    Long      NaN\n"
     ]
    },
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "\n",
    "qc_data = [[],[],[]]#short, medium, long\n",
    "qq_data = [[],[],[]]\n",
    "for k in result_list:\n",
    "    for kk in result_list[k]:\n",
    "        index = 0\n",
    "        if 3 < k <= 5:\n",
    "            index = 1\n",
    "        elif k > 5:\n",
    "            index = 2           \n",
    "        qc_data[index].append(np.mean(qc_result_list[k][kk]))\n",
    "        qq_data[index].append(np.mean(qq_result_list[k][kk]))\n",
    "\n",
    "\n",
    "\n",
    "df = pd.DataFrame({\"Session\": ['Short','Medium','Long'],\n",
    "                   \"Unclick\": map(lambda l:np.mean(l),qq_data), \n",
    "                   \"Click\": map(lambda l:np.mean(l),qc_data)})\n",
    "print df\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "tidy = df.melt(id_vars='Session').rename(columns=str.title)\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "ax.set(ylim=(0.25, 0.4))\n",
    "g = sns.barplot(x='Session', y='Value', hue='Variable', data=tidy, ax=ax,\n",
    "\tpalette=\"muted\")\n",
    "sns.despine(fig)\n",
    "\n",
    "\n",
    "#for index, row in df.iterrows():\n",
    "#    print index,row\n",
    "#    #g.text(row.name,row.Value, round(row.total_bill,2), color='black', ha=\"center\")\n",
    "for p in ax.patches:\n",
    "\tax.annotate(\"%.4f\" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()-0.01),\n",
    "\t\tha='center', va='center', fontsize=11, color='black', xytext=(0, 20),\n",
    "\t\ttextcoords='offset points')\n",
    "    \n",
    "# Create a second y-axis with the scaled ticks\n",
    "ax.legend().set_title('')\n",
    "ax.set_ylabel('Ratio')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "session:  Short\n",
      "Value:  0.8733909098522413\n",
      "session:  Medium\n",
      "Value:  0.9183983424225041\n",
      "session:  Long\n",
      "Value:  0.9124303638545647\n"
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   ],
   "source": [
    "qc_rate_data = {'Session':[],'Value':[]}#short, medium, long\n",
    "#print qc_rate_in_session\n",
    "\n",
    "data_list = defaultdict(list)\n",
    "for k,l_data in qc_rate_in_session.iteritems():\n",
    "    if k >= 10:\n",
    "        continue\n",
    "    index = 'Short'\n",
    "    if k <= 3:\n",
    "        index = 'Short'\n",
    "    elif 3 < k <= 5:\n",
    "        index = 'Medium'\n",
    "    elif k < 10:\n",
    "        index = 'Long'\n",
    "    for d in l_data:\n",
    "        qc_rate_data['Session'].append(index)\n",
    "        qc_rate_data['Value'].append(d)\n",
    "    \n",
    "    data_list[index] += l_data\n",
    "\n",
    "for k,v in data_list.iteritems():\n",
    "    print 'session: ',k\n",
    "    print 'Value: ', np.mean(v)\n",
    "    \n",
    "#print qc_rate_data\n",
    "\n",
    "tidy = pd.DataFrame(qc_rate_data)\n",
    "#print tidy\n",
    "fig, ax = plt.subplots()\n",
    "g = sns.boxplot(x='Session', y='Value', data=tidy, ax=ax,showmeans=False)\n",
    "ax.axhline(1, ls='--',color='red')\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('Cov(q|c=1)/Cov(q|c=0)',weight='bold')\n",
    "sns.despine(fig)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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      "         10         2         3         4         5         6         7  \\\n",
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      "8  2.104401       NaN       NaN       NaN       NaN       NaN       NaN   \n",
      "\n",
      "          8         9  Session  \n",
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      "text/plain": [
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     },
     "metadata": {
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     "output_type": "display_data"
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   ],
   "source": [
    "%config InlineBackend.figure_format='svg'\n",
    "%matplotlib inline\n",
    "\n",
    "data = qc_rate_in_session\n",
    "data['Session'] = list(range(2,10))\n",
    "df = pd.DataFrame(dict([ (str(k),pd.Series(v)) for k,v in data.iteritems() ]))\n",
    "\n",
    "print df\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "tidy = df.melt(id_vars='Session').rename(columns=str.title)\n",
    "\n",
    "\n",
    "tidy = tidy.dropna()\n",
    "#tidy.dropna()\n",
    "#print tidy\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "#ax.set(ylim=(0.6, 1.4))\n",
    "ax.set(xlim=(1.5, 10.5))\n",
    "\n",
    "marks = {}\n",
    "for i,m in enumerate(['o','+','>','^','*','s','v','<','.']):\n",
    "    marks[str(i+2)] = m\n",
    "    \n",
    "#filled_markers = ('o', 'v', '^', '<', '>', '8', 's', 'p', '*', 'h', 'H', 'D', 'd', 'P', 'X')\n",
    "g = sns.lineplot(x='Session', y='Value', hue='Variable', data=tidy, ax=ax,hue_order=map(str,range(2,11)),\n",
    "\tpalette=\"muted\",marker='o')\n",
    "\n",
    "sns.despine(fig)\n",
    "\n",
    "font1 = {\n",
    "'size'   : 10,\n",
    "}\n",
    "#'weight' : 'bold',\n",
    "\n",
    "\n",
    "# Create a second y-axis with the scaled ticks\n",
    "#ax.legend(prop=font1).set_title('')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "#ax.legend(handles=handles[1:], labels=labels[1:])\n",
    "plt.xticks(range(1, 11))\n",
    "ax.set_xlabel('Session Position',weight='bold')\n",
    "ax.set_ylabel('Cov(q|c=1)/Cov(q|c=0)',weight='bold')\n",
    "ax.axhline(1, ls='--',color='red')\n",
    "labels = ['length '+ str(i) for i in range(1,10)]\n",
    "g.legend(loc='center right', bbox_to_anchor=(1.25, 0.5), ncol=1,handles=handles[1:], labels=labels[1:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:01<00:00, 86562.91it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "click exists rate: 0.06962978678264559\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "def checkexist(ent1,ent2,click):\n",
    "    for e in ent2:\n",
    "        if e not in ent1 and e in click:\n",
    "            return 1\n",
    "    return 0\n",
    "\n",
    "click_list = [];\n",
    "result_list = defaultdict(lambda:defaultdict(list))\n",
    "result_dict = defaultdict(lambda:defaultdict())\n",
    "\n",
    "for session in tqdm.tqdm(session_data[:]):\n",
    "    qids = session['qids']\n",
    "    docs = session['docs']\n",
    "\n",
    "    entities_lists = map(lambda qid:qid2eid[qid],qids)\n",
    "    \n",
    "    session_len = len(docs)\n",
    "    for i,dids in enumerate(docs[:-1]):\n",
    "        \n",
    "        clicks_ents = set()\n",
    "        for pos,(did,click) in enumerate(dids):\n",
    "            if click != 0:\n",
    "                #if session_len == 3 and i == :         \n",
    "                did_entlist = did2eid[did]\n",
    "                clicks_ents.update(did_entlist)\n",
    "        clicks_ents = list(clicks_ents)\n",
    "        is_exist = checkexist(entities_lists[i],entities_lists[i+1],clicks_ents)\n",
    "        \n",
    "        click_list.append(is_exist)\n",
    "        result_list[session_len][i].append(is_exist)\n",
    "print 'click exists rate:', np.mean(click_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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   ],
   "source": [
    "for k in result_list:\n",
    "    for kk in result_list[k]:\n",
    "        #result_dict[k][kk] = np.mean(qq_result_list[k][kk])\n",
    "        result_dict[k][kk] = np.mean(result_list[k][kk])\n",
    "result = {}\n",
    "for length in result_dict:\n",
    "    result[length] = []\n",
    "    for pos,v in result_dict[length].iteritems():\n",
    "        if length < 10:\n",
    "            result[length].append(v)\n",
    "\n",
    "click_e_in_session = result\n",
    "print click_e_in_session\n",
    "\n",
    "%config InlineBackend.figure_format='svg'\n",
    "%matplotlib inline\n",
    "\n",
    "data = click_e_in_session\n",
    "data['Session'] = list(range(2,10))\n",
    "df = pd.DataFrame(dict([ (str(k),pd.Series(v)) for k,v in data.iteritems() ]))\n",
    "\n",
    "#print df\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "tidy = df.melt(id_vars='Session').rename(columns=str.title)\n",
    "\n",
    "\n",
    "tidy = tidy.dropna()\n",
    "#tidy.dropna()\n",
    "#print tidy\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "#ax.set(ylim=(0.6, 1.4))\n",
    "ax.set(xlim=(1.5, 10.5))\n",
    "\n",
    "marks = {}\n",
    "for i,m in enumerate(['o','+','>','^','*','s','v','<','.']):\n",
    "    marks[str(i+2)] = m\n",
    "    \n",
    "#filled_markers = ('o', 'v', '^', '<', '>', '8', 's', 'p', '*', 'h', 'H', 'D', 'd', 'P', 'X')\n",
    "g = sns.lineplot(x='Session', y='Value', hue='Variable', data=tidy, ax=ax,hue_order=map(str,range(2,10)),\n",
    "\tpalette=\"muted\",marker='o')\n",
    "\n",
    "sns.despine(fig)\n",
    "\n",
    "font1 = {\n",
    "'size'   : 10,\n",
    "}\n",
    "#'weight' : 'bold',\n",
    "\n",
    "\n",
    "# Create a second y-axis with the scaled ticks\n",
    "#ax.legend(prop=font1).set_title('')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "#ax.legend(handles=handles[1:], labels=labels[1:])\n",
    "plt.xticks(range(1, 11))\n",
    "ax.set_xlabel('Session Position',weight='bold')\n",
    "ax.set_ylabel('Cov(q|c=1)/Cov(q|c=0)',weight='bold')\n",
    "#ax.axhline(1, ls='--',color='red')\n",
    "labels = ['length '+ str(i) for i in range(1,10)]\n",
    "g.legend(loc='center right', bbox_to_anchor=(1.25, 0.5), ncol=1,handles=handles[1:], labels=labels[1:])\n",
    "plt.show()"
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   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:00<00:00, 115503.51it/s]\n"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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   ],
   "source": [
    "#Useless\n",
    "%config InlineBackend.figure_format='svg'\n",
    "%matplotlib inline\n",
    "\n",
    "result_dict = defaultdict(lambda:defaultdict(lambda:0))\n",
    "total_dict = defaultdict(lambda:defaultdict(lambda:0))\n",
    "\n",
    "def checkNew(ent1,ent2):\n",
    "    for e in ent2:\n",
    "        if e not in ent1:\n",
    "            return 1\n",
    "    return 0\n",
    "\n",
    "for session in tqdm.tqdm(session_data[:]):\n",
    "    qids = session['qids']\n",
    "    docs = session['docs']\n",
    "    words_lists = map(lambda qid:get_words(qdid2text_dict[qid]),qids)\n",
    "    entities_lists = map(lambda qid:qid2eid[qid],qids)\n",
    "    \n",
    "    #common_words = set.intersection(*map(set, words_lists))\n",
    "    #common_entities = set.intersection(*map(set, entities_lists))\n",
    "    session_length = len(qids)\n",
    "    \n",
    "    for i in range(len(qids) - 1):        \n",
    "        common_entities = set.intersection(*map(set, entities_lists[i:i+2]))\n",
    "        if len(common_entities) > 0:\n",
    "        #if checkNew(entities_lists[i],entities_lists[i+1]):\n",
    "            result_dict[session_length][i] += 1\n",
    "        total_dict[session_length][i] += 1\n",
    "\n",
    "print result_dict\n",
    "result = {}\n",
    "for length in result_dict:\n",
    "    result[length] = [0]\n",
    "    for pos,v in result_dict[length].iteritems():\n",
    "        result[length].append(float(v)/total_dict[length][pos])            \n",
    "#print result   \n",
    "\n",
    "\n",
    "max_x = 10\n",
    "data = result\n",
    "data['Session'] = list(range(1,max_x+1))\n",
    "df = pd.DataFrame(dict([ (str(k),pd.Series(v)) for k,v in data.iteritems() ]))\n",
    "\n",
    "#print df\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "tidy = df.melt(id_vars='Session').rename(columns=str.title)\n",
    "\n",
    "\n",
    "tidy = tidy.dropna()\n",
    "#tidy.dropna()\n",
    "#print tidy\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "#ax.set(ylim=(0.6, 1.4))\n",
    "ax.set(xlim=(1.5, 10.5))\n",
    "\n",
    "marks = {}\n",
    "for i,m in enumerate(['o','+','>','^','*','s','v','<','.']):\n",
    "    marks[str(i+2)] = m\n",
    "    \n",
    "#filled_markers = ('o', 'v', '^', '<', '>', '8', 's', 'p', '*', 'h', 'H', 'D', 'd', 'P', 'X')\n",
    "g = sns.lineplot(x='Session', y='Value', hue='Variable', data=tidy, ax=ax,hue_order=map(str,range(2,max_x+1)),\n",
    "\tpalette=\"muted\",marker='o')\n",
    "\n",
    "sns.despine(fig)\n",
    "\n",
    "font1 = {\n",
    "'size'   : 10,\n",
    "}\n",
    "#'weight' : 'bold',\n",
    "\n",
    "\n",
    "# Create a second y-axis with the scaled ticks\n",
    "#ax.legend(prop=font1).set_title('')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "#ax.legend(handles=handles[1:], labels=labels[1:])\n",
    "plt.xticks(range(1, max_x+1))\n",
    "ax.set_xlabel('Session Position',weight='bold')\n",
    "ax.set_ylabel('P(E($q_i$) $\\cap$ E($q_{i+1}$) != $\\emptyset$)',weight='bold')\n",
    "\n",
    "#ax.ticklabel_format(style='sci', scilimits=(0,0), axis='y')\n",
    "#ax.axhline(1, ls='--',color='red')\n",
    "labels = ['length '+ str(i) for i in range(1,max_x+1)]\n",
    "g.legend(loc='center right', bbox_to_anchor=(1.25, 0.5), ncol=1,handles=handles[1:], labels=labels[1:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89737/89737 [00:01<00:00, 89157.56it/s]\n"
     ]
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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   "source": [
    "#Useless\n",
    "%config InlineBackend.figure_format='svg'\n",
    "%matplotlib inline\n",
    "\n",
    "result_dict = defaultdict(lambda:defaultdict(lambda:0))\n",
    "total_dict = defaultdict(lambda:defaultdict(list))\n",
    "\n",
    "def checkNew(ent1,ent2):\n",
    "    for e in ent2:\n",
    "        if e not in ent1:\n",
    "            return 1\n",
    "    return 0\n",
    "\n",
    "for session in tqdm.tqdm(session_data[:]):\n",
    "    qids = session['qids']\n",
    "    docs = session['docs']\n",
    "    words_lists = map(lambda qid:get_words(qdid2text_dict[qid]),qids)\n",
    "    entities_lists = map(lambda qid:qid2eid[qid],qids)\n",
    "    \n",
    "    #common_words = set.intersection(*map(set, words_lists))\n",
    "    #common_entities = set.intersection(*map(set, entities_lists))\n",
    "    session_length = len(qids)\n",
    "    \n",
    "    for i in range(len(qids) - 1):        \n",
    "        common_entities = set.intersection(*map(set, entities_lists[i:i+2]))\n",
    "        if len(common_entities) > 0:\n",
    "        #if checkNew(entities_lists[i],entities_lists[i+1]):\n",
    "            result_dict[session_length][i] += 1\n",
    "        old_rate = float(len(common_entities)) / (len(entities_lists[i+1]) + 1)\n",
    "        new_rate = float(len(entities_lists[i+1]) - len(common_entities)) / (len(entities_lists[i+1]) + 1)\n",
    "        total_dict[session_length][i].append(new_rate / (old_rate + 1))\n",
    "\n",
    "print result_dict\n",
    "result = {}\n",
    "for length in result_dict:\n",
    "    result[length] = [0]\n",
    "    for pos,v in result_dict[length].iteritems():\n",
    "        result[length].append(np.mean(total_dict[length][pos]))     \n",
    "        #result[length].append(float(v)/total_dict[length][pos])            \n",
    "#print result   \n",
    "\n",
    "\n",
    "max_x = 10\n",
    "start = 1\n",
    "data = result\n",
    "data['Session'] = list(range(start,max_x+1))\n",
    "df = pd.DataFrame(dict([ (str(k),pd.Series(v)) for k,v in data.iteritems() ]))\n",
    "\n",
    "#print df\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "tidy = df.melt(id_vars='Session').rename(columns=str.title)\n",
    "\n",
    "\n",
    "tidy = tidy.dropna()\n",
    "#tidy.dropna()\n",
    "#print tidy\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "#ax.set(ylim=(0.6, 1.4))\n",
    "ax.set(xlim=(1.5, 10.5))\n",
    "\n",
    "    \n",
    "#filled_markers = ('o', 'v', '^', '<', '>', '8', 's', 'p', '*', 'h', 'H', 'D', 'd', 'P', 'X')\n",
    "g = sns.lineplot(x='Session', y='Value', hue='Variable', data=tidy, ax=ax,hue_order=map(str,range(2,max_x+1)),\n",
    " palette=\"muted\",marker='o')\n",
    "\n",
    "sns.despine(fig)\n",
    "\n",
    "font1 = {\n",
    "'size'   : 10,\n",
    "}\n",
    "#'weight' : 'bold',\n",
    "\n",
    "\n",
    "# Create a second y-axis with the scaled ticks\n",
    "#ax.legend(prop=font1).set_title('')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "#ax.legend(handles=handles[1:], labels=labels[1:])\n",
    "plt.xticks(range(1, max_x+1))\n",
    "ax.set_xlabel('Session Position',weight='bold')\n",
    "ax.set_ylabel('$E_{new}$ / $E_{old}$',weight='bold')\n",
    "\n",
    "#ax.ticklabel_format(style='sci', scilimits=(0,0), axis='y')\n",
    "#ax.axhline(1, ls='--',color='red')\n",
    "labels = ['length '+ str(i) for i in range(1,max_x+1)]\n",
    "g.legend(loc='center right', bbox_to_anchor=(1.25, 0.5), ncol=1,handles=handles[1:], labels=labels[1:])\n",
    "plt.show()"
   ]
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