OfflineDataPreparation.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3366, 18)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/serialTexts.csv\", sep=\";\", header=0)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3162, 18)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(subset=\"id_serial\", inplace=True)\n",
    "df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv(\"data/serialTexts.txt\", header=True, index=False, sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3162, 17)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/serialTexts.txt\", sep=\";\", header=0, index_col=0)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.fillna('', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df+\" \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
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   "outputs": [
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       "      <th>mergedText</th>\n",
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       "      <th>id_serial</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Skotsko Poznávací zájezdy Romantikou Anglie a ...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>Itálie Poznávací zájezdy PERLY TOSKÁNSKA  Vedl...</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Itálie Poznávací zájezdy Sicílie a jižní Itáli...</td>\n",
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       "      <th>6</th>\n",
       "      <td>Francie Poznávací zájezdy Královská Provence  ...</td>\n",
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      "text/plain": [
       "                                                  mergedText\n",
       "id_serial                                                   \n",
       "1          Skotsko Poznávací zájezdy Romantikou Anglie a ...\n",
       "2          Skotsko Poznávací zájezdy Okruh Skotskem s náv...\n",
       "3          Itálie Poznávací zájezdy PERLY TOSKÁNSKA  Vedl...\n",
       "5          Itálie Poznávací zájezdy Sicílie a jižní Itáli...\n",
       "6          Francie Poznávací zájezdy Královská Provence  ..."
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   "source": [
    "df['mergedText'] = df.fillna('').sum(axis=1)\n",
    "df[['mergedText']].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['mergedText'] = df['mergedText'].str.replace('<[^<]+?>', '')\n",
    "df['mergedText'] = df['mergedText'].str.replace('&nbsp;', ' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[['mergedText']].to_csv(\"data/slantour_data.txt\", header=False, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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   "outputs": [
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       "      <th>objectID</th>\n",
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       "      <th>userID</th>\n",
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      "text/plain": [
       "        objectID         lastModified\n",
       "userID                               \n",
       "186543      4927  2016-06-05 08:32:31\n",
       "531244      4805  2016-01-19 12:06:10\n",
       "531244         3  2016-06-23 11:42:13\n",
       "531244      2155  2016-06-23 11:44:32\n",
       "531244      4728  2016-07-26 07:18:13"
      ]
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     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "df2 = pd.read_csv(\"data/userEvents.csv\", sep=\";\", header=0, index_col=0)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2.lastModified = pd.to_datetime(df2.lastModified,infer_datetime_format=True)\n",
    "dfGB = df2.groupby(df2.index)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 13,
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       "      <td>41</td>\n",
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       "      <th>672696</th>\n",
       "      <td>10</td>\n",
       "      <td>2017-05-06 08:27:27</td>\n",
       "      <td>2016-02-15 17:04:57</td>\n",
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      "text/plain": [
       "        count                 max                 min\n",
       "userID                                               \n",
       "186543      1 2016-06-05 08:32:31 2016-06-05 08:32:31\n",
       "531244     41 2019-01-03 12:08:44 2016-01-19 12:06:10\n",
       "588117     70 2019-03-01 11:58:38 2016-04-15 09:42:17\n",
       "596178     60 2019-01-25 11:19:28 2016-01-04 16:37:41\n",
       "672696     10 2017-05-06 08:27:27 2016-02-15 17:04:57"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfCount = dfGB[\"lastModified\"].agg(['count', 'max', 'min'])\n",
    "dfCount.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 531244,  588117,  596178, ..., 3019733, 3019784, 3019813],\n",
       "      dtype=int64)"
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     "execution_count": 14,
     "metadata": {},
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   ],
   "source": [
    "validUsers = dfCount.loc[(dfCount[\"count\"] > 1) & (dfCount[\"count\"] <= 150)].index.values\n",
    "validUsers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "      <td>338471.000000</td>\n",
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       "      <td>2105.196882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
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       "      <th>25%</th>\n",
       "      <td>1687.000000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>4322.000000</td>\n",
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       "      <th>75%</th>\n",
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       "            objectID\n",
       "count  338471.000000\n",
       "mean     3638.030924\n",
       "std      2105.196882\n",
       "min         1.000000\n",
       "25%      1687.000000\n",
       "50%      4322.000000\n",
       "75%      5353.000000\n",
       "max      6736.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
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   ],
   "source": [
    "df2Filtered = df2.loc[df2.index.isin(validUsers)]\n",
    "df2Filtered.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df2Filtered[\"strOID\"] = df2Filtered.objectID.astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
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   "outputs": [
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      "text/plain": [
       "        objectID        lastModified strOID\n",
       "userID                                     \n",
       "531244      4805 2016-01-19 12:06:10   4805\n",
       "531244         3 2016-06-23 11:42:13      3\n",
       "531244      2155 2016-06-23 11:44:32   2155\n",
       "531244      4728 2016-07-26 07:18:13   4728\n",
       "531244       160 2016-07-26 07:23:01    160"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2Filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userID\n",
       "531244    4805 3 2155 4728 160 4802 4687 4891 5088 5241 ...\n",
       "588117    4938 4668 571 4643 4234 160 4623 4540 1575 491...\n",
       "596178    4377 4384 4661 3661 1566 2384 2313 3007 3280 4...\n",
       "672696      4108 2067 3391 725 300 1687 5404 4467 5600 2331\n",
       "790363    2618 4536 4562 3270 4534 4623 4571 48 4894 132...\n",
       "Name: strOID, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfGB2 = df2Filtered.groupby(df2Filtered.index)\n",
    "dfConcat = dfGB2[\"strOID\"].agg(lambda x : ' '.join(x))\n",
    "dfConcat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfConcat.to_csv(\"data/user_data_wIndex.txt\", header=False, index=True)\n",
    "dfConcat.to_csv(\"data/user_data_woIndex.txt\", header=False, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
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   "outputs": [
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       "      <th></th>\n",
       "      <th>objectID</th>\n",
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       "      <th>userID</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>531244</th>\n",
       "      <td>4805</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>531244</th>\n",
       "      <td>3</td>\n",
       "      <td>2016-06-23 11:42:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531244</th>\n",
       "      <td>2155</td>\n",
       "      <td>2016-06-23 11:44:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531244</th>\n",
       "      <td>4728</td>\n",
       "      <td>2016-07-26 07:18:13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        objectID         lastModified\n",
       "userID                               \n",
       "186543      4927  2016-06-05 08:32:31\n",
       "531244      4805  2016-01-19 12:06:10\n",
       "531244         3  2016-06-23 11:42:13\n",
       "531244      2155  2016-06-23 11:44:32\n",
       "531244      4728  2016-07-26 07:18:13"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.read_csv(\"data/userEvents.csv\", sep=\";\", header=0, index_col=0)\n",
    "#pd.read_csv(\"data/testSetEvents.csv\", sep=\";\", header=0, index_col=0)\n",
    "\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df3[\"strOID\"] = df3.objectID.astype('str')\n",
    "dfGB = df3.groupby(df3.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userID\n",
       "186543                                                 4927\n",
       "531244    4805 3 2155 4728 160 4802 4687 4891 5088 5241 ...\n",
       "588117    4938 4668 571 4643 4234 160 4623 4540 1575 491...\n",
       "596178    4377 4384 4661 3661 1566 2384 2313 3007 3280 4...\n",
       "672696      4108 2067 3391 725 300 1687 5404 4467 5600 2331\n",
       "Name: strOID, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfConcat = dfGB[\"strOID\"].agg(lambda x : ' '.join(x))\n",
    "dfConcat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfConcat.to_csv(\"data/test_data_wIndex.txt\", header=True, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
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      "text/plain": [
       "        objectID         lastModified\n",
       "userID                               \n",
       "186543      4927  2016-06-05 08:32:31\n",
       "531244      4805  2016-01-19 12:06:10\n",
       "531244         3  2016-06-23 11:42:13\n",
       "531244      2155  2016-06-23 11:44:32\n",
       "531244      4728  2016-07-26 07:18:13"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.read_csv(\"data/userEvents.csv\", sep=\";\", header=0, index_col=0)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userID\n",
       "186543                                                 4927\n",
       "531244    4805 3 2155 4728 160 4802 4687 4891 5088 5241 ...\n",
       "588117    4938 4668 571 4643 4234 160 4623 4540 1575 491...\n",
       "596178    4377 4384 4661 3661 1566 2384 2313 3007 3280 4...\n",
       "672696      4108 2067 3391 725 300 1687 5404 4467 5600 2331\n",
       "Name: strOID, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[\"strOID\"] = df3.objectID.astype('str')\n",
    "dfGB = df3.groupby(df3.index)\n",
    "dfConcat = dfGB[\"strOID\"].agg(lambda x : ' '.join(x))\n",
    "dfConcat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfConcat.to_csv(\"data/train_data_wIndex.txt\", header=True, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userID\n",
       "186543                                  2016-06-05 08:32:31\n",
       "531244    2016-01-19 12:06:10,2016-06-23 11:42:13,2016-0...\n",
       "588117    2016-04-15 09:42:17,2016-04-25 12:35:49,2016-0...\n",
       "596178    2016-01-04 16:37:41,2016-01-04 16:38:45,2016-0...\n",
       "672696    2016-02-15 17:04:57,2016-02-15 17:05:19,2016-0...\n",
       "Name: strDates, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[\"strDates\"] = df3.lastModified.astype('str')\n",
    "dfGB = df3.groupby(df3.index)\n",
    "dfConcat = dfGB[\"strDates\"].agg(lambda x : ','.join(x))\n",
    "dfConcat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfConcat.to_csv(\"data/train_data_datetime_wIndex.txt\", sep=\";\", header=True, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
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   "outputs": [
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      "text/plain": [
       "                                                   strOID\n",
       "userID                                                   \n",
       "186543                                               4927\n",
       "531244  4805 3 2155 4728 160 4802 4687 4891 5088 5241 ...\n",
       "588117  4938 4668 571 4643 4234 160 4623 4540 1575 491...\n",
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    "testSet = pd.read_csv(\"data/test_data_wIndex.txt\", sep=\",\", header=0, index_col=0)\n",
    "testSet.head()"
   ]
  },
  {
   "cell_type": "code",
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      "text/plain": [
       "                                                   strOID  \\\n",
       "userID                                                      \n",
       "186543                                               4927   \n",
       "531244  4805 3 2155 4728 160 4802 4687 4891 5088 5241 ...   \n",
       "588117  4938 4668 571 4643 4234 160 4623 4540 1575 491...   \n",
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       "672696    4108 2067 3391 725 300 1687 5404 4467 5600 2331   \n",
       "\n",
       "                                                     oids  \n",
       "userID                                                     \n",
       "186543                                             [4927]  \n",
       "531244  [4805, 3, 2155, 4728, 160, 4802, 4687, 4891, 5...  \n",
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       "672696  [4108, 2067, 3391, 725, 300, 1687, 5404, 4467,...  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
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   ],
   "source": [
    "testSet[\"oids\"] = testSet.strOID.str.split()\n",
    "testSet.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
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      "text/plain": [
       "                                                        1  \\\n",
       "0                                                           \n",
       "531244  4805 3 2155 4728 160 4802 4687 4891 5088 5241 ...   \n",
       "588117  4938 4668 571 4643 4234 160 4623 4540 1575 491...   \n",
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       "\n",
       "                                                     oids  \n",
       "0                                                          \n",
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      ]
     },
     "execution_count": 44,
     "metadata": {},
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   ],
   "source": [
    "trainSet = pd.read_csv(\"data/user_data_wIndex.txt\", sep=\",\", header=None, index_col=0)\n",
    "trainSet[\"oids\"] = trainSet[1].str.split()\n",
    "trainSet.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3588, 10)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df = pd.read_csv(\"data/serial_only_data.csv\", sep=\";\", header=0, index_col=0)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Slovensko</td>\n",
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       "      <td>377.7143</td>\n",
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       "    <tr>\n",
       "      <th>4083</th>\n",
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       "      <td>3</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id_typ  strava  doprava  ubytovani  ubytovani_kategorie  \\\n",
       "id_serial                                                            \n",
       "6130            3       3        1          0                  3.0   \n",
       "2798            1       1        1          7                  0.0   \n",
       "4083            7       3        1          6                  2.0   \n",
       "6700            2       1        3          7                  0.0   \n",
       "6630            4       1        1          7                  0.0   \n",
       "\n",
       "                              zeme        destinace  AVG(prumerna_cena)  \\\n",
       "id_serial                                                                 \n",
       "6130                     Slovensko  Dunajská Streda             20667.0   \n",
       "2798                     Slovensko  Jižní Slovensko              2099.0   \n",
       "4083                     Slovensko     Vysoké Tatry              1995.0   \n",
       "6700       Spojené Arabské Emiráty              NaN                 1.0   \n",
       "6630                         Sport              NaN                 1.0   \n",
       "\n",
       "           AVG(prumerna_cena_noc)  AVG(delka)  \n",
       "id_serial                                      \n",
       "6130                    2198.5000     11.0000  \n",
       "2798                     377.7143      7.5714  \n",
       "4083                     997.6000      3.0000  \n",
       "6700                       1.0000      2.0000  \n",
       "6630                       1.0000      2.0000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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      "text/plain": [
       "          id_typ strava doprava ubytovani  ubytovani_kategorie  \\\n",
       "id_serial                                                        \n",
       "6130           3      3       1         0                  3.0   \n",
       "2798           1      1       1         7                  0.0   \n",
       "4083           7      3       1         6                  2.0   \n",
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       "\n",
       "                              zeme        destinace  AVG(prumerna_cena)  \\\n",
       "id_serial                                                                 \n",
       "6130                     Slovensko  Dunajská Streda             20667.0   \n",
       "2798                     Slovensko  Jižní Slovensko              2099.0   \n",
       "4083                     Slovensko     Vysoké Tatry              1995.0   \n",
       "6700       Spojené Arabské Emiráty              NaN                 1.0   \n",
       "6630                         Sport              NaN                 1.0   \n",
       "\n",
       "           AVG(prumerna_cena_noc)  AVG(delka)  \n",
       "id_serial                                      \n",
       "6130                    2198.5000     11.0000  \n",
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       "4083                     997.6000      3.0000  \n",
       "6700                       1.0000      2.0000  \n",
       "6630                       1.0000      2.0000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.id_typ = df.id_typ.astype(str)\n",
    "df.strava = df.strava.astype(str)\n",
    "df.doprava = df.doprava.astype(str)\n",
    "df.ubytovani = df.ubytovani.astype(str)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
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   "outputs": [
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      "text/plain": [
       "          id_typ strava doprava ubytovani  ubytovani_kategorie  \\\n",
       "id_serial                                                        \n",
       "6130           3      3       1         0                  3.0   \n",
       "2798           1      1       1         7                  0.0   \n",
       "4083           7      3       1         6                  2.0   \n",
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       "\n",
       "                              zeme        destinace  AVG(prumerna_cena)  \\\n",
       "id_serial                                                                 \n",
       "6130                     Slovensko  Dunajská Streda             20667.0   \n",
       "2798                     Slovensko  Jižní Slovensko              2099.0   \n",
       "4083                     Slovensko     Vysoké Tatry              1995.0   \n",
       "6700       Spojené Arabské Emiráty                                  1.0   \n",
       "6630                         Sport                                  1.0   \n",
       "\n",
       "           AVG(prumerna_cena_noc)  AVG(delka)  \n",
       "id_serial                                      \n",
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     "execution_count": 48,
     "metadata": {},
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   ],
   "source": [
    "df.fillna(\"\", inplace=True)\n",
    "df.zeme = df.zeme.str.replace(r':.+', \"\")\n",
    "df.destinace = df.destinace.str.replace(r':.+', \"\")\n",
    "df.head()"
   ]
  },
  {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 314 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           typ_1  typ_2  typ_29  typ_3  typ_30  typ_4  typ_5  typ_6  typ_7  \\\n",
       "id_serial                                                                    \n",
       "6130           0      0       0      1       0      0      0      0      0   \n",
       "2798           1      0       0      0       0      0      0      0      0   \n",
       "4083           0      0       0      0       0      0      0      0      1   \n",
       "6700           0      1       0      0       0      0      0      0      0   \n",
       "6630           0      0       0      0       0      1      0      0      0   \n",
       "\n",
       "           typ_8           ...             destinace_České Švýcarsko  \\\n",
       "id_serial                  ...                                         \n",
       "6130           0           ...                                     0   \n",
       "2798           0           ...                                     0   \n",
       "4083           0           ...                                     0   \n",
       "6700           0           ...                                     0   \n",
       "6630           0           ...                                     0   \n",
       "\n",
       "           destinace_Český Krumlov  destinace_Český Ráj  destinace_Čína  \\\n",
       "id_serial                                                                 \n",
       "6130                             0                    0               0   \n",
       "2798                             0                    0               0   \n",
       "4083                             0                    0               0   \n",
       "6700                             0                    0               0   \n",
       "6630                             0                    0               0   \n",
       "\n",
       "           destinace_Řím  destinace_Španělsko  destinace_Španělská liga  \\\n",
       "id_serial                                                                 \n",
       "6130                   0                    0                         0   \n",
       "2798                   0                    0                         0   \n",
       "4083                   0                    0                         0   \n",
       "6700                   0                    0                         0   \n",
       "6630                   0                    0                         0   \n",
       "\n",
       "           destinace_Štúrovo  destinace_Šumava  destinace_Žďárské Vrchy  \n",
       "id_serial                                                                \n",
       "6130                       0                 0                        0  \n",
       "2798                       0                 0                        0  \n",
       "4083                       0                 0                        0  \n",
       "6700                       0                 0                        0  \n",
       "6630                       0                 0                        0  \n",
       "\n",
       "[5 rows x 314 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfDummy = pd.get_dummies(df[[\"id_typ\",\"strava\",\"doprava\",\"ubytovani\",\"zeme\",\"destinace\"]], prefix=[\"typ\",\"strava\",\"doprava\",\"ubytovani\",\"zeme\",\"destinace\"])\n",
    "dfDummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>ubytovani_kategorie</th>\n",
       "      <th>zeme</th>\n",
       "      <th>destinace</th>\n",
       "      <th>AVG(prumerna_cena)</th>\n",
       "      <th>AVG(prumerna_cena_noc)</th>\n",
       "      <th>AVG(delka)</th>\n",
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       "      <th>id_serial</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
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       "      <td>Slovensko</td>\n",
       "      <td>Jižní Slovensko</td>\n",
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       "      <td>0.310520</td>\n",
       "      <td>Slovensko</td>\n",
       "      <td>Vysoké Tatry</td>\n",
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       "      <td>-0.359893</td>\n",
       "      <td>-0.655730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6700</th>\n",
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       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>-1.155944</td>\n",
       "      <td>Spojené Arabské Emiráty</td>\n",
       "      <td></td>\n",
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       "      <td>-0.532468</td>\n",
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       "    <tr>\n",
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       "      <td>4</td>\n",
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       "      <td>7</td>\n",
       "      <td>-1.155944</td>\n",
       "      <td>Sport</td>\n",
       "      <td></td>\n",
       "      <td>-0.679945</td>\n",
       "      <td>-0.532468</td>\n",
       "      <td>-1.008950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id_typ strava doprava ubytovani  ubytovani_kategorie  \\\n",
       "id_serial                                                        \n",
       "6130           3      3       1         0             1.043752   \n",
       "2798           1      1       1         7            -1.155944   \n",
       "4083           7      3       1         6             0.310520   \n",
       "6700           2      1       3         7            -1.155944   \n",
       "6630           4      1       1         7            -1.155944   \n",
       "\n",
       "                              zeme        destinace  AVG(prumerna_cena)  \\\n",
       "id_serial                                                                 \n",
       "6130                     Slovensko  Dunajská Streda            0.726227   \n",
       "2798                     Slovensko  Jižní Slovensko           -0.537191   \n",
       "4083                     Slovensko     Vysoké Tatry           -0.544267   \n",
       "6700       Spojené Arabské Emiráty                            -0.679945   \n",
       "6630                         Sport                            -0.679945   \n",
       "\n",
       "           AVG(prumerna_cena_noc)  AVG(delka)  \n",
       "id_serial                                      \n",
       "6130                    -0.151941    2.170034  \n",
       "2798                    -0.467235    0.958982  \n",
       "4083                    -0.359893   -0.655730  \n",
       "6700                    -0.532468   -1.008950  \n",
       "6630                    -0.532468   -1.008950  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "vals = scaler.fit_transform(df[[\"ubytovani_kategorie\", \"AVG(prumerna_cena)\",\"AVG(prumerna_cena_noc)\",\"AVG(delka)\"]].values)\n",
    "df[[\"ubytovani_kategorie\", \"AVG(prumerna_cena)\",\"AVG(prumerna_cena_noc)\",\"AVG(delka)\"]] = vals\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>5 rows × 318 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           typ_1  typ_2  typ_29  typ_3  typ_30  typ_4  typ_5  typ_6  typ_7  \\\n",
       "id_serial                                                                    \n",
       "6130           0      0       0      1       0      0      0      0      0   \n",
       "2798           1      0       0      0       0      0      0      0      0   \n",
       "4083           0      0       0      0       0      0      0      0      1   \n",
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       "6630           0      0       0      0       0      1      0      0      0   \n",
       "\n",
       "           typ_8     ...      destinace_Řím  destinace_Španělsko  \\\n",
       "id_serial            ...                                           \n",
       "6130           0     ...                  0                    0   \n",
       "2798           0     ...                  0                    0   \n",
       "4083           0     ...                  0                    0   \n",
       "6700           0     ...                  0                    0   \n",
       "6630           0     ...                  0                    0   \n",
       "\n",
       "           destinace_Španělská liga  destinace_Štúrovo  destinace_Šumava  \\\n",
       "id_serial                                                                  \n",
       "6130                              0                  0                 0   \n",
       "2798                              0                  0                 0   \n",
       "4083                              0                  0                 0   \n",
       "6700                              0                  0                 0   \n",
       "6630                              0                  0                 0   \n",
       "\n",
       "           destinace_Žďárské Vrchy  ubytovani_kategorie  AVG(prumerna_cena)  \\\n",
       "id_serial                                                                     \n",
       "6130                             0             1.043752            0.726227   \n",
       "2798                             0            -1.155944           -0.537191   \n",
       "4083                             0             0.310520           -0.544267   \n",
       "6700                             0            -1.155944           -0.679945   \n",
       "6630                             0            -1.155944           -0.679945   \n",
       "\n",
       "           AVG(prumerna_cena_noc)  AVG(delka)  \n",
       "id_serial                                      \n",
       "6130                    -0.151941    2.170034  \n",
       "2798                    -0.467235    0.958982  \n",
       "4083                    -0.359893   -0.655730  \n",
       "6700                    -0.532468   -1.008950  \n",
       "6630                    -0.532468   -1.008950  \n",
       "\n",
       "[5 rows x 318 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfDummy[[\"ubytovani_kategorie\", \"AVG(prumerna_cena)\",\"AVG(prumerna_cena_noc)\",\"AVG(delka)\"]] = df[[\"ubytovani_kategorie\", \"AVG(prumerna_cena)\",\"AVG(prumerna_cena_noc)\",\"AVG(delka)\"]]\n",
    "dfDummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfDummy.to_csv(\"data/serialCBFeatures.txt\", header=True, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>588117</th>\n",
       "      <td>2016-04-15 09:42:17,2016-04-25 12:35:49,2016-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596178</th>\n",
       "      <td>2016-01-04 16:37:41,2016-01-04 16:38:45,2016-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672696</th>\n",
       "      <td>2016-02-15 17:04:57,2016-02-15 17:05:19,2016-0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 strDates\n",
       "userID                                                   \n",
       "186543                                2016-06-05 08:32:31\n",
       "531244  2016-01-19 12:06:10,2016-06-23 11:42:13,2016-0...\n",
       "588117  2016-04-15 09:42:17,2016-04-25 12:35:49,2016-0...\n",
       "596178  2016-01-04 16:37:41,2016-01-04 16:38:45,2016-0...\n",
       "672696  2016-02-15 17:04:57,2016-02-15 17:05:19,2016-0..."
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/train_data_datetime_wIndex.txt\", sep=\";\", header=0, index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>672696</th>\n",
       "      <td>2016-02-15 17:04:57,2016-02-15 17:05:19,2016-0...</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 strDates  \\\n",
       "userID                                                      \n",
       "186543                                2016-06-05 08:32:31   \n",
       "531244  2016-01-19 12:06:10,2016-06-23 11:42:13,2016-0...   \n",
       "588117  2016-04-15 09:42:17,2016-04-25 12:35:49,2016-0...   \n",
       "596178  2016-01-04 16:37:41,2016-01-04 16:38:45,2016-0...   \n",
       "672696  2016-02-15 17:04:57,2016-02-15 17:05:19,2016-0...   \n",
       "\n",
       "                                                    dates  \n",
       "userID                                                     \n",
       "186543                              [2016-06-05 08:32:31]  \n",
       "531244  [2016-01-19 12:06:10, 2016-06-23 11:42:13, 201...  \n",
       "588117  [2016-04-15 09:42:17, 2016-04-25 12:35:49, 201...  \n",
       "596178  [2016-01-04 16:37:41, 2016-01-04 16:38:45, 201...  \n",
       "672696  [2016-02-15 17:04:57, 2016-02-15 17:05:19, 201...  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"dates\"] = df.strDates.str.split(\",\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2018, 7, 1, 0, 0)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "now = datetime.datetime(2018, 7, 1, 00, 00)\n",
    "now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 strDates  \\\n",
       "userID                                                      \n",
       "186543                                2016-06-05 08:32:31   \n",
       "531244  2016-01-19 12:06:10,2016-06-23 11:42:13,2016-0...   \n",
       "588117  2016-04-15 09:42:17,2016-04-25 12:35:49,2016-0...   \n",
       "596178  2016-01-04 16:37:41,2016-01-04 16:38:45,2016-0...   \n",
       "672696  2016-02-15 17:04:57,2016-02-15 17:05:19,2016-0...   \n",
       "\n",
       "                                                    dates logDays  \n",
       "userID                                                             \n",
       "186543                              [2016-06-05 08:32:31]          \n",
       "531244  [2016-01-19 12:06:10, 2016-06-23 11:42:13, 201...          \n",
       "588117  [2016-04-15 09:42:17, 2016-04-25 12:35:49, 201...          \n",
       "596178  [2016-01-04 16:37:41, 2016-01-04 16:38:45, 201...          \n",
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      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"logDays\"] = \"\"\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "                                                 strDates  \\\n",
       "userID                                                      \n",
       "186543                                2016-06-05 08:32:31   \n",
       "531244  2016-01-19 12:06:10,2016-06-23 11:42:13,2016-0...   \n",
       "588117  2016-04-15 09:42:17,2016-04-25 12:35:49,2016-0...   \n",
       "596178  2016-01-04 16:37:41,2016-01-04 16:38:45,2016-0...   \n",
       "672696  2016-02-15 17:04:57,2016-02-15 17:05:19,2016-0...   \n",
       "\n",
       "                                                    dates logDays  \n",
       "userID                                                             \n",
       "186543                              [2016-06-05 08:32:31]          \n",
       "531244  [2016-01-19 12:06:10, 2016-06-23 11:42:13, 201...          \n",
       "588117  [2016-04-15 09:42:17, 2016-04-25 12:35:49, 201...          \n",
       "596178  [2016-01-04 16:37:41, 2016-01-04 16:38:45, 201...          \n",
       "672696  [2016-02-15 17:04:57, 2016-02-15 17:05:19, 201...          "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.logDays.astype(object, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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      ],
      "text/plain": [
       "                                                 strDates  \\\n",
       "userID                                                      \n",
       "186543                                2016-06-05 08:32:31   \n",
       "531244  2016-01-19 12:06:10,2016-06-23 11:42:13,2016-0...   \n",
       "588117  2016-04-15 09:42:17,2016-04-25 12:35:49,2016-0...   \n",
       "596178  2016-01-04 16:37:41,2016-01-04 16:38:45,2016-0...   \n",
       "672696  2016-02-15 17:04:57,2016-02-15 17:05:19,2016-0...   \n",
       "\n",
       "                                                    dates  \\\n",
       "userID                                                      \n",
       "186543                              [2016-06-05 08:32:31]   \n",
       "531244  [2016-01-19 12:06:10, 2016-06-23 11:42:13, 201...   \n",
       "588117  [2016-04-15 09:42:17, 2016-04-25 12:35:49, 201...   \n",
       "596178  [2016-01-04 16:37:41, 2016-01-04 16:38:45, 201...   \n",
       "672696  [2016-02-15 17:04:57, 2016-02-15 17:05:19, 201...   \n",
       "\n",
       "                                                  logDays  \n",
       "userID                                                     \n",
       "186543                              [0.14866091268830786]  \n",
       "531244  [0.14504132891357085, 0.14919610396535354, 0.1...  \n",
       "588117  [0.14723022240461198, 0.14750134522524172, 0.1...  \n",
       "596178  [0.1446917436599719, 0.1446917436599719, 0.144...  \n",
       "672696  [0.1456900885317203, 0.1456900885317203, 0.146...  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "for idx,val in df.dates.iteritems():\n",
    "    v2 = [1/(math.log(max([(now - datetime.datetime.strptime(i, '%Y-%m-%d %H:%M:%S')).days,1]) )+0.1) for i in val]\n",
    "    df.logDays[idx] = v2\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df[[\"logDays\"]]\n",
    "df.to_csv(\"data/serialLogDays.txt\", header=True, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "source": []
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