Fix test data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "base_dir = '../data/'\n",
    "\n",
    "with pd.get_store(base_dir + 'features_test.hd5') as store:\n",
    "    biz_images = store.select('features_test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "biz_ids = pd.read_csv(base_dir + \"test_photo_to_biz.csv\", delimiter=',',\n",
    "                      dtype={'business_id': 'string', 'photo_id':'int64'}, index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1190225\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>photo_id</th>\n",
       "      <th>business_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>330359</th>\n",
       "      <td>26227</td>\n",
       "      <td>9ym2z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569388</th>\n",
       "      <td>26227</td>\n",
       "      <td>gy3f7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832972</th>\n",
       "      <td>26227</td>\n",
       "      <td>p4fm1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996324</th>\n",
       "      <td>26227</td>\n",
       "      <td>ubjjg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131126</th>\n",
       "      <td>26227</td>\n",
       "      <td>yh8jz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         photo_id business_id\n",
       "330359      26227       9ym2z\n",
       "569388      26227       gy3f7\n",
       "832972      26227       p4fm1\n",
       "996324      26227       ubjjg\n",
       "1131126     26227       yh8jz"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There seem to be duplicates!\n",
    "print len(biz_ids)\n",
    "biz_ids[biz_ids.photo_id == 26227]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>photo_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>f_0</th>\n",
       "      <th>f_1</th>\n",
       "      <th>f_2</th>\n",
       "      <th>f_3</th>\n",
       "      <th>f_4</th>\n",
       "      <th>f_5</th>\n",
       "      <th>f_6</th>\n",
       "      <th>f_7</th>\n",
       "      <th>...</th>\n",
       "      <th>f_1014</th>\n",
       "      <th>f_1015</th>\n",
       "      <th>f_1016</th>\n",
       "      <th>f_1017</th>\n",
       "      <th>f_1018</th>\n",
       "      <th>f_1019</th>\n",
       "      <th>f_1020</th>\n",
       "      <th>f_1021</th>\n",
       "      <th>f_1022</th>\n",
       "      <th>f_1023</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>330359</th>\n",
       "      <td>26227</td>\n",
       "      <td>9ym2z</td>\n",
       "      <td>0.030472</td>\n",
       "      <td>0.24585</td>\n",
       "      <td>0.015396</td>\n",
       "      <td>0</td>\n",
       "      <td>1.865234</td>\n",
       "      <td>0</td>\n",
       "      <td>2.685547</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.134766</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.720215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569388</th>\n",
       "      <td>26227</td>\n",
       "      <td>gy3f7</td>\n",
       "      <td>0.030472</td>\n",
       "      <td>0.24585</td>\n",
       "      <td>0.015396</td>\n",
       "      <td>0</td>\n",
       "      <td>1.865234</td>\n",
       "      <td>0</td>\n",
       "      <td>2.685547</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.134766</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.720215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832972</th>\n",
       "      <td>26227</td>\n",
       "      <td>p4fm1</td>\n",
       "      <td>0.030472</td>\n",
       "      <td>0.24585</td>\n",
       "      <td>0.015396</td>\n",
       "      <td>0</td>\n",
       "      <td>1.865234</td>\n",
       "      <td>0</td>\n",
       "      <td>2.685547</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.134766</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.720215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996324</th>\n",
       "      <td>26227</td>\n",
       "      <td>ubjjg</td>\n",
       "      <td>0.030472</td>\n",
       "      <td>0.24585</td>\n",
       "      <td>0.015396</td>\n",
       "      <td>0</td>\n",
       "      <td>1.865234</td>\n",
       "      <td>0</td>\n",
       "      <td>2.685547</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.134766</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.720215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131126</th>\n",
       "      <td>26227</td>\n",
       "      <td>yh8jz</td>\n",
       "      <td>0.030472</td>\n",
       "      <td>0.24585</td>\n",
       "      <td>0.015396</td>\n",
       "      <td>0</td>\n",
       "      <td>1.865234</td>\n",
       "      <td>0</td>\n",
       "      <td>2.685547</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.134766</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.720215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1026 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         photo_id business_id       f_0      f_1       f_2  f_3       f_4  \\\n",
       "330359      26227       9ym2z  0.030472  0.24585  0.015396    0  1.865234   \n",
       "569388      26227       gy3f7  0.030472  0.24585  0.015396    0  1.865234   \n",
       "832972      26227       p4fm1  0.030472  0.24585  0.015396    0  1.865234   \n",
       "996324      26227       ubjjg  0.030472  0.24585  0.015396    0  1.865234   \n",
       "1131126     26227       yh8jz  0.030472  0.24585  0.015396    0  1.865234   \n",
       "\n",
       "         f_5       f_6  f_7   ...    f_1014  f_1015  f_1016  f_1017    f_1018  \\\n",
       "330359     0  2.685547    0   ...         0       0       0       0  3.134766   \n",
       "569388     0  2.685547    0   ...         0       0       0       0  3.134766   \n",
       "832972     0  2.685547    0   ...         0       0       0       0  3.134766   \n",
       "996324     0  2.685547    0   ...         0       0       0       0  3.134766   \n",
       "1131126    0  2.685547    0   ...         0       0       0       0  3.134766   \n",
       "\n",
       "         f_1019  f_1020    f_1021  f_1022  f_1023  \n",
       "330359        0       0  0.720215       0       0  \n",
       "569388        0       0  0.720215       0       0  \n",
       "832972        0       0  0.720215       0       0  \n",
       "996324        0       0  0.720215       0       0  \n",
       "1131126       0       0  0.720215       0       0  \n",
       "\n",
       "[5 rows x 1026 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biz_images[biz_images.photo_id == 26227]\n",
    "#Images all look the same though, so that's good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "photo_count = biz_ids.groupby('photo_id').agg(['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbc08325c10>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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gwACMGDEC06ZNw3333dfRcXrllVeivLwcw4cPx/nnn4/vf//7Hdtt3boVF154IQYOHIjR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\nLrCLgprCS8UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc08275790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Indeed, each image is duplicated 5x\n",
    "#https://www.kaggle.com/c/yelp-restaurant-photo-classification/forums/t/18675/duplicate-photo-ids-in-test-data-test-photo-to-biz-csv/112917#post112917\n",
    "%matplotlib inline\n",
    "import matplotlib.image as mpimg\n",
    "import matplotlib.pyplot as plt\n",
    "photo_count[0:500].plot()\n",
    "# We could probably find out which businesses are real and which are not!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot copy sequence with size 237152 to array axis with dimension 1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-25-cd975c40813f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mphoto_count\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mphoto_count\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbusiness_id\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/home/fcarey/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1961\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1962\u001b[0m             \u001b[1;31m# either boolean or fancy integer index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1963\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1964\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1965\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/fcarey/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_array\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1988\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1989\u001b[0m         \u001b[1;31m# also raises Exception if object array with NA values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1990\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1991\u001b[0m             \u001b[1;31m# warning here just in case -- previously __setitem__ was\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1992\u001b[0m             \u001b[1;31m# reindexing but __getitem__ was not; it seems more reasonable to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/fcarey/anaconda2/lib/python2.7/site-packages/pandas/core/common.pyc\u001b[0m in \u001b[0;36mis_bool_indexer\u001b[1;34m(key)\u001b[0m\n\u001b[0;32m   1745\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1746\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1747\u001b[1;33m             \u001b[0marr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1748\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbool_\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1749\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pragma: no cover\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/fcarey/anaconda2/lib/python2.7/site-packages/numpy/core/numeric.pyc\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m    472\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    473\u001b[0m     \"\"\"\n\u001b[1;32m--> 474\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    475\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    476\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: cannot copy sequence with size 237152 to array axis with dimension 1"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "photo_id        468146\n",
       "business_id      003sg\n",
       "f_0            4.66797\n",
       "f_1            6.21484\n",
       "f_2            2.52344\n",
       "f_3            1.30566\n",
       "f_4             4.5625\n",
       "f_5            3.36523\n",
       "f_6            7.42188\n",
       "f_7            3.53906\n",
       "f_8            5.47656\n",
       "f_9            2.22656\n",
       "f_10            3.0332\n",
       "f_11           2.66797\n",
       "f_12           3.55664\n",
       "f_13           3.72852\n",
       "f_14           5.02734\n",
       "f_15           3.32031\n",
       "f_16             5.125\n",
       "f_17           4.82422\n",
       "f_18           4.60938\n",
       "f_19           2.11133\n",
       "f_20           4.59766\n",
       "f_21           4.00391\n",
       "f_22           5.35156\n",
       "f_23           4.60938\n",
       "f_24           4.53906\n",
       "f_25           2.72266\n",
       "f_26           6.01172\n",
       "f_27             9.875\n",
       "                ...   \n",
       "f_994          7.33984\n",
       "f_995            6.375\n",
       "f_996          2.41602\n",
       "f_997           3.8418\n",
       "f_998          9.14844\n",
       "f_999          7.60547\n",
       "f_1000         3.18945\n",
       "f_1001         3.08008\n",
       "f_1002         1.66406\n",
       "f_1003          3.0625\n",
       "f_1004         3.49805\n",
       "f_1005         8.59375\n",
       "f_1006         3.88867\n",
       "f_1007         6.00391\n",
       "f_1008         4.83594\n",
       "f_1009               5\n",
       "f_1010         4.58984\n",
       "f_1011         6.60547\n",
       "f_1012         7.33984\n",
       "f_1013         2.19336\n",
       "f_1014         4.10156\n",
       "f_1015         2.89453\n",
       "f_1016         7.31641\n",
       "f_1017         4.14062\n",
       "f_1018         9.25781\n",
       "f_1019         4.79297\n",
       "f_1020         6.42969\n",
       "f_1021         8.21875\n",
       "f_1022         13.3047\n",
       "f_1023         7.67578\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biz_images[biz_ids.business_id == '003sg'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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