AS_9_graphtools.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre_id</th>\n",
       "      <th>ts_listen</th>\n",
       "      <th>media_id</th>\n",
       "      <th>album_id</th>\n",
       "      <th>context_type</th>\n",
       "      <th>release_date</th>\n",
       "      <th>platform_name</th>\n",
       "      <th>platform_family</th>\n",
       "      <th>media_duration</th>\n",
       "      <th>listen_type</th>\n",
       "      <th>user_gender</th>\n",
       "      <th>user_id</th>\n",
       "      <th>artist_id</th>\n",
       "      <th>user_age</th>\n",
       "      <th>is_listened</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   genre_id   ts_listen  media_id  album_id  context_type  release_date  \\\n",
       "0     25471  1480597215    222606     41774            12      20040704   \n",
       "1     25571  1480544735    250467     43941             0      20060301   \n",
       "2        16  1479563953    305197     48078             1      20140714   \n",
       "3         7  1480152098    900502     71521             0      20001030   \n",
       "4         7  1478368974    542335     71718             0      20080215   \n",
       "\n",
       "   platform_name  platform_family  media_duration  listen_type  user_gender  \\\n",
       "0              1                0             223            0            0   \n",
       "1              2                1             171            0            0   \n",
       "2              2                1             149            1            1   \n",
       "3              0                0             240            0            1   \n",
       "4              0                0             150            0            1   \n",
       "\n",
       "   user_id  artist_id  user_age  is_listened  \n",
       "0     9241      55164        29            0  \n",
       "1    16547      55830        30            1  \n",
       "2     7665       2704        29            1  \n",
       "3     1580        938        30            0  \n",
       "4     1812       2939        24            1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from graph_tool.all import *\n",
    "import pandas as pd\n",
    "\n",
    "path_to_train = './data/train.csv'\n",
    "\n",
    "train = pd.read_csv(path_to_train, encoding='utf-8')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "data": {
      "text/plain": [
       "0          None\n",
       "1          None\n",
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       "3          None\n",
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       "28         None\n",
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       "           ... \n",
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       "7558833    None\n",
       "Length: 7558834, dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = Graph(directed=False)\n",
    "artists = {}\n",
    "art_to_album = {}\n",
    "albums = {}\n",
    "alb_to_media = {}\n",
    "medias = {}\n",
    "users = {}\n",
    "\n",
    "vprop_id = g.new_vertex_property(\"int\")\n",
    "g.vertex_properties[\"id\"] = vprop_id\n",
    "\n",
    "vprop_label = g.new_vertex_property(\"string\")\n",
    "g.vertex_properties[\"label\"] = vprop_label\n",
    "\n",
    "eprop_label = g.new_edge_property(\"string\")\n",
    "g.edge_properties[\"label\"] = eprop_label\n",
    "\n",
    "eprop_ts = g.new_edge_property(\"int\")\n",
    "g.edge_properties[\"ts\"] = eprop_ts\n",
    "\n",
    "processed = 0\n",
    "\n",
    "def convert_row_to_graph(row):\n",
    "    global g, artists, albums, medias, users, vprop_id, vprop_label, eprop_label, art_to_album, alb_to_media, processed\n",
    "    \n",
    "    if(row['user_id'] not in users):\n",
    "        u = g.add_vertex()\n",
    "        users[row['user_id']] = u\n",
    "        vprop_id[u] = row['user_id']\n",
    "        vprop_label[u] = 'USER'\n",
    "        \n",
    "    user = users[row['user_id']]\n",
    "    \n",
    "    if(row['artist_id'] not in artists):\n",
    "        art = g.add_vertex()\n",
    "        artists[row['artist_id']] = art\n",
    "        vprop_id[art] = row['artist_id']\n",
    "        vprop_label[art] = 'ARTIST'\n",
    "    \n",
    "    artist = artists[row['artist_id']]\n",
    "    \n",
    "    if(row['album_id'] not in albums):\n",
    "        alb = g.add_vertex()\n",
    "        albums[row['album_id']] = alb\n",
    "        vprop_id[alb] = row['album_id']\n",
    "        vprop_label[alb] = 'ALBUM'\n",
    "        \n",
    "    album = albums[row['album_id']]\n",
    "    \n",
    "    if((row['artist_id'], row['album_id']) not in art_to_album):\n",
    "        a_to_a = g.add_edge(artist, album)\n",
    "        art_to_album[(row['artist_id'], row['album_id'])] = a_to_a\n",
    "        eprop_label[a_to_a] = 'CREATION'\n",
    "    \n",
    "    if(row['media_id'] not in medias):\n",
    "        m = g.add_vertex()\n",
    "        medias[row['media_id']] = m\n",
    "        vprop_id[m] = row['media_id']\n",
    "        vprop_label[m] = 'MEDIA'\n",
    "    \n",
    "    media = medias[row['media_id']]\n",
    "    \n",
    "    if((row['album_id'], row['media_id']) not in alb_to_media):\n",
    "        a_to_m = g.add_edge(album, media)\n",
    "        alb_to_media[(row['album_id'], row['media_id'])] = a_to_m\n",
    "        eprop_label[a_to_m] = 'CREATION'\n",
    "        \n",
    "    if(row['is_listened']==1):\n",
    "        is_listened = g.add_edge(user, media)\n",
    "        eprop_label[is_listened] = 'LISTENED'\n",
    "        eprop_ts[is_listened] = row['ts_listen']\n",
    "    \n",
    "    processed += 1\n",
    "    if(processed%10000 == 0):\n",
    "        print(\"Done: %d/%d\"%(processed, len(train)))\n",
    "        \n",
    "train.apply(convert_row_to_graph, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "g.save(\"./data/graph.xml.gz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from graph_tool.all import *\n",
    "g = load_graph(\"./data/graph.xml.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre_id</th>\n",
       "      <th>ts_listen</th>\n",
       "      <th>media_id</th>\n",
       "      <th>album_id</th>\n",
       "      <th>context_type</th>\n",
       "      <th>release_date</th>\n",
       "      <th>platform_name</th>\n",
       "      <th>platform_family</th>\n",
       "      <th>media_duration</th>\n",
       "      <th>listen_type</th>\n",
       "      <th>user_gender</th>\n",
       "      <th>user_id</th>\n",
       "      <th>artist_id</th>\n",
       "      <th>user_age</th>\n",
       "      <th>is_listened</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25471</td>\n",
       "      <td>1480597215</td>\n",
       "      <td>222606</td>\n",
       "      <td>41774</td>\n",
       "      <td>12</td>\n",
       "      <td>20040704</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>223</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9241</td>\n",
       "      <td>55164</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25571</td>\n",
       "      <td>1480544735</td>\n",
       "      <td>250467</td>\n",
       "      <td>43941</td>\n",
       "      <td>0</td>\n",
       "      <td>20060301</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16547</td>\n",
       "      <td>55830</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>1479563953</td>\n",
       "      <td>305197</td>\n",
       "      <td>48078</td>\n",
       "      <td>1</td>\n",
       "      <td>20140714</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7665</td>\n",
       "      <td>2704</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>1480152098</td>\n",
       "      <td>900502</td>\n",
       "      <td>71521</td>\n",
       "      <td>0</td>\n",
       "      <td>20001030</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1580</td>\n",
       "      <td>938</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>1478368974</td>\n",
       "      <td>542335</td>\n",
       "      <td>71718</td>\n",
       "      <td>0</td>\n",
       "      <td>20080215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1812</td>\n",
       "      <td>2939</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   genre_id   ts_listen  media_id  album_id  context_type  release_date  \\\n",
       "0     25471  1480597215    222606     41774            12      20040704   \n",
       "1     25571  1480544735    250467     43941             0      20060301   \n",
       "2        16  1479563953    305197     48078             1      20140714   \n",
       "3         7  1480152098    900502     71521             0      20001030   \n",
       "4         7  1478368974    542335     71718             0      20080215   \n",
       "\n",
       "   platform_name  platform_family  media_duration  listen_type  user_gender  \\\n",
       "0              1                0             223            0            0   \n",
       "1              2                1             171            0            0   \n",
       "2              2                1             149            1            1   \n",
       "3              0                0             240            0            1   \n",
       "4              0                0             150            0            1   \n",
       "\n",
       "   user_id  artist_id  user_age  is_listened  \n",
       "0     9241      55164        29            0  \n",
       "1    16547      55830        30            1  \n",
       "2     7665       2704        29            1  \n",
       "3     1580        938        30            0  \n",
       "4     1812       2939        24            1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "path_to_train = './data/train.csv'\n",
    "\n",
    "train = pd.read_csv(path_to_train, encoding='utf-8')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# first create indexes\n",
    "artists = {}\n",
    "albums = {}\n",
    "medias = {}\n",
    "users = {}\n",
    "\n",
    "vprop_id = g.vertex_properties[\"id\"]\n",
    "vprop_label = g.vertex_properties[\"label\"]\n",
    "\n",
    "for v in g.vertices():\n",
    "    label = vprop_label[v]\n",
    "    id = vprop_id[v]\n",
    "    if label == \"USER\":\n",
    "        users[id] = v\n",
    "    elif label == \"MEDIA\":\n",
    "        medias[id] = v\n",
    "    elif label == \"ALBUM\":\n",
    "        albums[id] = v\n",
    "    elif label == \"ARTIST\":\n",
    "        artists[id] = v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "('unorderable types: int() > PropertyMap()', 'occurred at index 0')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-24fcc2748089>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mdistance_to_media\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mshortest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\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[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, broadcast, raw, reduce, args, **kwds)\u001b[0m\n\u001b[1;32m   4358\u001b[0m                         \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4359\u001b[0m                         \u001b[0mreduce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4360\u001b[0;31m                         ignore_failures=ignore_failures)\n\u001b[0m\u001b[1;32m   4361\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4362\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_broadcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_apply_standard\u001b[0;34m(self, func, axis, ignore_failures, reduce)\u001b[0m\n\u001b[1;32m   4454\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4455\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4456\u001b[0;31m                     \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4457\u001b[0m                     \u001b[0mkeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4458\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-7-24fcc2748089>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mdistance_to_media\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mshortest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\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[0;32m<ipython-input-7-24fcc2748089>\u001b[0m in \u001b[0;36mshortest\u001b[0;34m(g, row)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0muser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0musers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ts_listen'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_edge_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meprop_ts\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Start search\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mdist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshortest_distance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muser\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmedia\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_dist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: ('unorderable types: int() > PropertyMap()', 'occurred at index 0')"
     ]
    }
   ],
   "source": [
    "# first lets try general shortest distance\n",
    "eprop_label = g.edge_properties[\"label\"]\n",
    "eprop_ts = g.edge_properties[\"ts\"]\n",
    "\n",
    "def shortest(g, row):\n",
    "    media = medias[row['media_id']]\n",
    "    user = users[row['user_id']]\n",
    "    ts = row['ts_listen']\n",
    "    g.set_edge_filter(eprop_ts < ts)\n",
    "    print(\"Start search\")\n",
    "    dist = shortest_distance(g, user, media, max_dist=20)\n",
    "    print(dist)\n",
    "    return len(dist)\n",
    "    \n",
    "\n",
    "distance_to_media = train.apply(lambda row: shortest(g, row),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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