notebooks/10_capstone_projects_solutions.ipynb

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    "<center>\n",
    "    <h1>DatatableTon</h1>\n",
    "    💯 datatable exercises\n",
    "    <br>\n",
    "    <br>\n",
    "    <a href='https://github.com/vopani/datatableton/blob/master/LICENSE'>\n",
    "        <img src='https://img.shields.io/badge/license-Apache%202.0-blue.svg?logo=apache'>\n",
    "    </a>\n",
    "    <a href='https://github.com/vopani/datatableton'>\n",
    "        <img src='https://img.shields.io/github/stars/vopani/datatableton?color=yellowgreen&logo=github'>\n",
    "    </a>\n",
    "    <a href='https://twitter.com/vopani'>\n",
    "        <img src='https://img.shields.io/twitter/follow/vopani'>\n",
    "    </a>\n",
    "</center>"
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    "<center>\n",
    "    This is Set 10: Capstone Projects (Exercises 91-100) of <b>DatatableTon</b>: <i>💯 datatable exercises</i>\n",
    "    <br>\n",
    "    You can find all the exercises and solutions on <a href=\"https://github.com/vopani/datatableton#exercises-\">GitHub</a>\n",
    "</center>"
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   "source": [
    "**Prerequisites**\n",
    "* The `datatable` package should be upgraded to the latest version (or v1.0.0+).\n",
    "* The datasets from the [Avazu](https://www.kaggle.com/c/avazu-ctr-prediction/data) competition on kaggle will be used for the exercises and needs to be downloaded manually.\n",
    "* It is encouraged to use your own datasets for the last exercise but you can use the datasets from the [Tradeshift](https://www.kaggle.com/c/tradeshift-text-classification) competition on kaggle for practise."
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     "text": [
      "Requirement already satisfied: pip in /opt/conda/lib/python3.7/site-packages (21.1.2)\r\n",
      "Collecting pip\r\n",
      "  Downloading pip-21.2.2-py3-none-any.whl (1.6 MB)\r\n",
      "\u001b[K     |████████████████████████████████| 1.6 MB 603 kB/s \r\n",
      "\u001b[?25hInstalling collected packages: pip\r\n",
      "  Attempting uninstall: pip\r\n",
      "    Found existing installation: pip 21.1.2\r\n",
      "    Uninstalling pip-21.1.2:\r\n",
      "      Successfully uninstalled pip-21.1.2\r\n",
      "Successfully installed pip-21.2.2\r\n",
      "\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "Requirement already satisfied: datatable in /opt/conda/lib/python3.7/site-packages (1.0.0)\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n"
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    "!python3 -m pip install -U pip\n",
    "!python3 -m pip install -U datatable"
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       "<style type='text/css'>\n",
       ".datatable table.frame { margin-bottom: 0; }\n",
       ".datatable table.frame thead { border-bottom: none; }\n",
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   "source": [
    "## edit the paths to the downloaded datasets of Avazu\n",
    "PATH_TRAIN = '../input/avazu-ctr-prediction/train.gz'\n",
    "PATH_TEST = '../input/avazu-ctr-prediction/test.gz'\n",
    "\n",
    "import datatable as dt"
   ]
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   "source": [
    "**Exercise 91: Read `train.gz` and `test.gz` and assign it to `train` and `test` respectively**"
   ]
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    "train = dt.fread(PATH_TRAIN)\n",
    "train"
   ]
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       "    <tr><td class='row_index'>1</td><td>10000182526920855428</td><td>14103100</td><td>1005</td><td>0</td><td>1fbe01fe</td><td>f3845767</td><td>28905ebd</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td class=vellipsis>&hellip;</td><td>2616</td><td>0</td><td>35</td><td>100083</td><td>51</td></tr>\n",
       "    <tr><td class='row_index'>2</td><td>10000554139829213984</td><td>14103100</td><td>1005</td><td>0</td><td>1fbe01fe</td><td>f3845767</td><td>28905ebd</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td class=vellipsis>&hellip;</td><td>2616</td><td>0</td><td>35</td><td>100083</td><td>51</td></tr>\n",
       "    <tr><td class='row_index'>3</td><td>10001094637809798845</td><td>14103100</td><td>1005</td><td>0</td><td>85f751fd</td><td>c4e18dd6</td><td>50e219e0</td><td>51cedd4e</td><td>aefc06bd</td><td>0f2161f8</td><td class=vellipsis>&hellip;</td><td>1092</td><td>3</td><td>809</td><td>100156</td><td>61</td></tr>\n",
       "    <tr><td class='row_index'>4</td><td>10001377041558670745</td><td>14103100</td><td>1005</td><td>0</td><td>85f751fd</td><td>c4e18dd6</td><td>50e219e0</td><td>9c13b419</td><td>2347f47a</td><td>f95efa07</td><td class=vellipsis>&hellip;</td><td>2667</td><td>0</td><td>47</td><td>&minus;1</td><td>221</td></tr>\n",
       "    <tr><td class='row_index'>5</td><td>10001521204153353724</td><td>14103100</td><td>1005</td><td>1</td><td>57fe1b20</td><td>5b626596</td><td>f028772b</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td class=vellipsis>&hellip;</td><td>572</td><td>2</td><td>39</td><td>&minus;1</td><td>32</td></tr>\n",
       "    <tr><td class='row_index'>6</td><td>10001911056707023378</td><td>14103100</td><td>1005</td><td>0</td><td>1fbe01fe</td><td>f3845767</td><td>28905ebd</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td class=vellipsis>&hellip;</td><td>2647</td><td>2</td><td>39</td><td>100148</td><td>23</td></tr>\n",
       "    <tr><td class='row_index'>7</td><td>10001982898844213216</td><td>14103100</td><td>1005</td><td>0</td><td>85f751fd</td><td>c4e18dd6</td><td>50e219e0</td><td>388d9bfb</td><td>2347f47a</td><td>cef3e649</td><td class=vellipsis>&hellip;</td><td>2675</td><td>3</td><td>939</td><td>100058</td><td>100</td></tr>\n",
       "    <tr><td class='row_index'>8</td><td>10002000217531288531</td><td>14103100</td><td>1005</td><td>0</td><td>543a539e</td><td>c7ca3108</td><td>3e814130</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td class=vellipsis>&hellip;</td><td>2709</td><td>3</td><td>39</td><td>&minus;1</td><td>23</td></tr>\n",
       "    <tr><td class='row_index'>9</td><td>10002107385290585663</td><td>14103100</td><td>1005</td><td>0</td><td>1fbe01fe</td><td>f3845767</td><td>28905ebd</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td class=vellipsis>&hellip;</td><td>1722</td><td>0</td><td>35</td><td>&minus;1</td><td>79</td></tr>\n",
       "    <tr><td class='row_index'>10</td><td>10002121728305927027</td><td>14103100</td><td>1005</td><td>0</td><td>1fbe01fe</td><td>f3845767</td><td>28905ebd</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td class=vellipsis>&hellip;</td><td>2545</td><td>0</td><td>431</td><td>100084</td><td>221</td></tr>\n",
       "    <tr><td class='row_index'>11</td><td>10002246330438763746</td><td>14103100</td><td>1005</td><td>0</td><td>85f751fd</td><td>c4e18dd6</td><td>50e219e0</td><td>2d869bee</td><td>d9b5648e</td><td>0f2161f8</td><td class=vellipsis>&hellip;</td><td>2736</td><td>0</td><td>33</td><td>100170</td><td>246</td></tr>\n",
       "    <tr><td class='row_index'>12</td><td>10002274319697022546</td><td>14103100</td><td>1010</td><td>7</td><td>85f751fd</td><td>c4e18dd6</td><td>50e219e0</td><td>643e0f88</td><td>7801e8d9</td><td>0f2161f8</td><td class=vellipsis>&hellip;</td><td>2513</td><td>3</td><td>1059</td><td>&minus;1</td><td>68</td></tr>\n",
       "    <tr><td class='row_index'>13</td><td>10002614093820007721</td><td>14103100</td><td>1005</td><td>0</td><td>85f751fd</td><td>c4e18dd6</td><td>50e219e0</td><td>03528b27</td><td>2347f47a</td><td>8ded1f7a</td><td class=vellipsis>&hellip;</td><td>1873</td><td>3</td><td>39</td><td>&minus;1</td><td>23</td></tr>\n",
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       "    <tr><td class='row_index'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22F1;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td></tr>\n",
       "    <tr><td class='row_index'>4,577,459</td><td>9998166651591969718</td><td>14103123</td><td>1005</td><td>0</td><td>93eaba74</td><td>7687a86e</td><td>3e814130</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td class=vellipsis>&hellip;</td><td>1994</td><td>2</td><td>39</td><td>&minus;1</td><td>33</td></tr>\n",
       "    <tr><td class='row_index'>4,577,460</td><td>9998249812366450951</td><td>14103123</td><td>1005</td><td>0</td><td>17d1b03f</td><td>f3845767</td><td>f028772b</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td class=vellipsis>&hellip;</td><td>2545</td><td>0</td><td>431</td><td>100084</td><td>221</td></tr>\n",
       "    <tr><td class='row_index'>4,577,461</td><td>99988023653614546</td><td>14103123</td><td>1005</td><td>0</td><td>5b08c53b</td><td>7687a86e</td><td>3e814130</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td class=vellipsis>&hellip;</td><td>1994</td><td>2</td><td>39</td><td>&minus;1</td><td>33</td></tr>\n",
       "    <tr><td class='row_index'>4,577,462</td><td>9999086574712596585</td><td>14103123</td><td>1005</td><td>0</td><td>85f751fd</td><td>c4e18dd6</td><td>50e219e0</td><td>92f5800b</td><td>ae637522</td><td>0f2161f8</td><td class=vellipsis>&hellip;</td><td>2734</td><td>1</td><td>175</td><td>100189</td><td>71</td></tr>\n",
       "    <tr><td class='row_index'>4,577,463</td><td>99992636456518364</td><td>14103123</td><td>1005</td><td>0</td><td>17d1b03f</td><td>f3845767</td><td>f028772b</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td class=vellipsis>&hellip;</td><td>2545</td><td>0</td><td>431</td><td>100084</td><td>221</td></tr>\n",
       "  </tbody>\n",
       "  </table>\n",
       "  <div class='footer'>\n",
       "    <div class='frame_dimensions'>4,577,464 rows &times; 23 columns</div>\n",
       "  </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<Frame#7f6b610b1720 4577464x23>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = dt.fread(PATH_TEST)\n",
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef6bb39",
   "metadata": {
    "papermill": {
     "duration": 0.030157,
     "end_time": "2021-07-31T10:26:52.780678",
     "exception": false,
     "start_time": "2021-07-31T10:26:52.750521",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Exercise 92: Drop the `id` column and update the `hour` column with the hour as an integer in `train`**"
   ]
  },
  {
   "cell_type": "code",
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   "id": "22d6ede4",
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       "  <thead>\n",
       "    <tr class='colnames'><td class='row_index'></td><th>click</th><th>hour</th><th>C1</th><th>banner_pos</th><th>site_id</th><th>site_domain</th><th>site_category</th><th>app_id</th><th>app_domain</th><th>app_category</th><th class='vellipsis'>&hellip;</th><th>C17</th><th>C18</th><th>C19</th><th>C20</th><th>C21</th></tr>\n",
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       "  </tbody>\n",
       "  </table>\n",
       "  <div class='footer'>\n",
       "    <div class='frame_dimensions'>10 rows &times; 23 columns</div>\n",
       "  </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<Frame#7f6b227a4b10 10x23>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[:, dt.update(hour=dt.as_type(dt.str.slice(dt.as_type(dt.f.hour, str), 6, 8), int))]\n",
    "del train['id']\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb1a320a",
   "metadata": {
    "papermill": {
     "duration": 0.030032,
     "end_time": "2021-07-31T10:26:52.986379",
     "exception": false,
     "start_time": "2021-07-31T10:26:52.956347",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Exercise 93: Assign the column `id` to `test_ids`, drop it from `test` and update the `hour` column with the hour as an integer in `test`**"
   ]
  },
  {
   "cell_type": "code",
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     "status": "completed"
    },
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   "outputs": [
    {
     "data": {
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       "    <tr class='colnames'><td class='row_index'></td><th>id</th></tr>\n",
       "    <tr class='coltypes'><td class='row_index'></td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td></tr>\n",
       "  </thead>\n",
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       "    <tr><td class='row_index'>4</td><td>10001377041558670745</td></tr>\n",
       "    <tr><td class='row_index'>5</td><td>10001521204153353724</td></tr>\n",
       "    <tr><td class='row_index'>6</td><td>10001911056707023378</td></tr>\n",
       "    <tr><td class='row_index'>7</td><td>10001982898844213216</td></tr>\n",
       "    <tr><td class='row_index'>8</td><td>10002000217531288531</td></tr>\n",
       "    <tr><td class='row_index'>9</td><td>10002107385290585663</td></tr>\n",
       "  </tbody>\n",
       "  </table>\n",
       "  <div class='footer'>\n",
       "    <div class='frame_dimensions'>10 rows &times; 1 column</div>\n",
       "  </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<Frame#7f6b227a4f30 10x1>"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_ids = test['id']\n",
    "test_ids.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8721f02e",
   "metadata": {
    "execution": {
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     "status": "completed"
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   },
   "outputs": [
    {
     "data": {
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       "<div class='datatable'>\n",
       "  <table class='frame'>\n",
       "  <thead>\n",
       "    <tr class='colnames'><td class='row_index'></td><th>hour</th><th>C1</th><th>banner_pos</th><th>site_id</th><th>site_domain</th><th>site_category</th><th>app_id</th><th>app_domain</th><th>app_category</th><th>device_id</th><th class='vellipsis'>&hellip;</th><th>C17</th><th>C18</th><th>C19</th><th>C20</th><th>C21</th></tr>\n",
       "    <tr class='coltypes'><td class='row_index'></td><td class='int' title='int64'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td></td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><td class='row_index'>0</td><td>0</td><td>1005</td><td>0</td><td>235ba823</td><td>f6ebf28e</td><td>f028772b</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td>a99f214a</td><td class=vellipsis>&hellip;</td><td>761</td><td>3</td><td>175</td><td>100075</td><td>23</td></tr>\n",
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       "    <tr><td class='row_index'>2</td><td>0</td><td>1005</td><td>0</td><td>1fbe01fe</td><td>f3845767</td><td>28905ebd</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td>a99f214a</td><td class=vellipsis>&hellip;</td><td>2616</td><td>0</td><td>35</td><td>100083</td><td>51</td></tr>\n",
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       "    <tr><td class='row_index'>5</td><td>0</td><td>1005</td><td>1</td><td>57fe1b20</td><td>5b626596</td><td>f028772b</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td>a99f214a</td><td class=vellipsis>&hellip;</td><td>572</td><td>2</td><td>39</td><td>&minus;1</td><td>32</td></tr>\n",
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       "    <tr><td class='row_index'>7</td><td>0</td><td>1005</td><td>0</td><td>85f751fd</td><td>c4e18dd6</td><td>50e219e0</td><td>388d9bfb</td><td>2347f47a</td><td>cef3e649</td><td>3772665a</td><td class=vellipsis>&hellip;</td><td>2675</td><td>3</td><td>939</td><td>100058</td><td>100</td></tr>\n",
       "    <tr><td class='row_index'>8</td><td>0</td><td>1005</td><td>0</td><td>543a539e</td><td>c7ca3108</td><td>3e814130</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td>a99f214a</td><td class=vellipsis>&hellip;</td><td>2709</td><td>3</td><td>39</td><td>&minus;1</td><td>23</td></tr>\n",
       "    <tr><td class='row_index'>9</td><td>0</td><td>1005</td><td>0</td><td>1fbe01fe</td><td>f3845767</td><td>28905ebd</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td>a99f214a</td><td class=vellipsis>&hellip;</td><td>1722</td><td>0</td><td>35</td><td>&minus;1</td><td>79</td></tr>\n",
       "  </tbody>\n",
       "  </table>\n",
       "  <div class='footer'>\n",
       "    <div class='frame_dimensions'>10 rows &times; 22 columns</div>\n",
       "  </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<Frame#7f6b23e33330 10x22>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[:, dt.update(hour=dt.as_type(dt.str.slice(dt.as_type(dt.f.hour, str), 6, 8), int))]\n",
    "del test['id']\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6f4f229",
   "metadata": {
    "papermill": {
     "duration": 0.031108,
     "end_time": "2021-07-31T10:26:53.223732",
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     "start_time": "2021-07-31T10:26:53.192624",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Exercise 94: Split `train` into `valid_data`, `valid_target` for rows where `hour=23` and rest as `train_data`, `train_target`**"
   ]
  },
  {
   "cell_type": "code",
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   "id": "a2168279",
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='datatable'>\n",
       "  <table class='frame'>\n",
       "  <thead>\n",
       "    <tr class='colnames'><td class='row_index'></td><th>hour</th><th>C1</th><th>banner_pos</th><th>site_id</th><th>site_domain</th><th>site_category</th><th>app_id</th><th>app_domain</th><th>app_category</th><th>device_id</th><th class='vellipsis'>&hellip;</th><th>C17</th><th>C18</th><th>C19</th><th>C20</th><th>C21</th></tr>\n",
       "    <tr class='coltypes'><td class='row_index'></td><td class='int' title='int64'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td></td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><td class='row_index'>0</td><td>23</td><td>1005</td><td>0</td><td>85f751fd</td><td>c4e18dd6</td><td>50e219e0</td><td>7358e05e</td><td>b9528b13</td><td>cef3e649</td><td>c6b5fe99</td><td class=vellipsis>&hellip;</td><td>2443</td><td>2</td><td>39</td><td>&minus;1</td><td>23</td></tr>\n",
       "    <tr><td class='row_index'>1</td><td>23</td><td>1005</td><td>0</td><td>85f751fd</td><td>c4e18dd6</td><td>50e219e0</td><td>85b28c93</td><td>ae637522</td><td>8ded1f7a</td><td>a99f214a</td><td class=vellipsis>&hellip;</td><td>2348</td><td>3</td><td>427</td><td>100003</td><td>61</td></tr>\n",
       "    <tr><td class='row_index'>2</td><td>23</td><td>1005</td><td>0</td><td>89a490f5</td><td>ce307e01</td><td>3e814130</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td>a99f214a</td><td class=vellipsis>&hellip;</td><td>2227</td><td>0</td><td>679</td><td>100077</td><td>48</td></tr>\n",
       "    <tr><td class='row_index'>3</td><td>23</td><td>1005</td><td>0</td><td>d9750ee7</td><td>98572c79</td><td>f028772b</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td>a99f214a</td><td class=vellipsis>&hellip;</td><td>1722</td><td>0</td><td>35</td><td>100084</td><td>79</td></tr>\n",
       "    <tr><td class='row_index'>4</td><td>23</td><td>1005</td><td>0</td><td>85f751fd</td><td>c4e18dd6</td><td>50e219e0</td><td>54c5d545</td><td>2347f47a</td><td>0f2161f8</td><td>9af87478</td><td class=vellipsis>&hellip;</td><td>2480</td><td>3</td><td>297</td><td>100111</td><td>61</td></tr>\n",
       "    <tr><td class='row_index'>5</td><td>23</td><td>1005</td><td>0</td><td>1fbe01fe</td><td>f3845767</td><td>28905ebd</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td>a99f214a</td><td class=vellipsis>&hellip;</td><td>2502</td><td>0</td><td>35</td><td>&minus;1</td><td>221</td></tr>\n",
       "    <tr><td class='row_index'>6</td><td>23</td><td>1005</td><td>1</td><td>5b4d2eda</td><td>16a36ef3</td><td>f028772b</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td>a99f214a</td><td class=vellipsis>&hellip;</td><td>2227</td><td>0</td><td>679</td><td>100074</td><td>48</td></tr>\n",
       "    <tr><td class='row_index'>7</td><td>23</td><td>1005</td><td>0</td><td>85f751fd</td><td>c4e18dd6</td><td>50e219e0</td><td>45aff1a2</td><td>5c620f04</td><td>d1327cf5</td><td>a3b3a891</td><td class=vellipsis>&hellip;</td><td>2036</td><td>3</td><td>47</td><td>&minus;1</td><td>156</td></tr>\n",
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       "    <tr><td class='row_index'>9</td><td>23</td><td>1005</td><td>0</td><td>1fbe01fe</td><td>f3845767</td><td>28905ebd</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td>a99f214a</td><td class=vellipsis>&hellip;</td><td>2502</td><td>0</td><td>35</td><td>&minus;1</td><td>221</td></tr>\n",
       "  </tbody>\n",
       "  </table>\n",
       "  <div class='footer'>\n",
       "    <div class='frame_dimensions'>10 rows &times; 22 columns</div>\n",
       "  </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<Frame#7f6b23e332a0 10x22>"
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_data = train[dt.f.hour == 23, [col for col in train.names if col != 'click']]\n",
    "valid_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9978de72",
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='datatable'>\n",
       "  <table class='frame'>\n",
       "  <thead>\n",
       "    <tr class='colnames'><td class='row_index'></td><th>click</th></tr>\n",
       "    <tr class='coltypes'><td class='row_index'></td><td class='bool' title='bool8'>&#x25AA;</td></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><td class='row_index'>0</td><td>0</td></tr>\n",
       "    <tr><td class='row_index'>1</td><td>0</td></tr>\n",
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       "    <tr><td class='row_index'>3</td><td>0</td></tr>\n",
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       "    <tr><td class='row_index'>9</td><td>0</td></tr>\n",
       "  </tbody>\n",
       "  </table>\n",
       "  <div class='footer'>\n",
       "    <div class='frame_dimensions'>10 rows &times; 1 column</div>\n",
       "  </div>\n",
       "</div>\n"
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       "<Frame#7f6b227a4d80 10x1>"
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     "execution_count": 9,
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   ],
   "source": [
    "valid_target = train[dt.f.hour == 23, 'click']\n",
    "valid_target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "edd11ae8",
   "metadata": {
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     "status": "completed"
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    "tags": []
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   "outputs": [
    {
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       "    <tr class='colnames'><td class='row_index'></td><th>hour</th><th>C1</th><th>banner_pos</th><th>site_id</th><th>site_domain</th><th>site_category</th><th>app_id</th><th>app_domain</th><th>app_category</th><th>device_id</th><th class='vellipsis'>&hellip;</th><th>C17</th><th>C18</th><th>C19</th><th>C20</th><th>C21</th></tr>\n",
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       "    <tr><td class='row_index'>3</td><td>0</td><td>1005</td><td>0</td><td>1fbe01fe</td><td>f3845767</td><td>28905ebd</td><td>ecad2386</td><td>7801e8d9</td><td>07d7df22</td><td>a99f214a</td><td class=vellipsis>&hellip;</td><td>1722</td><td>0</td><td>35</td><td>100084</td><td>79</td></tr>\n",
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     },
     "execution_count": 10,
     "metadata": {},
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    }
   ],
   "source": [
    "train_data = train[dt.f.hour != 23, [col for col in train.names if col != 'click']]\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
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   "id": "fbcb63b4",
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    {
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train_target = train[dt.f.hour != 23, 'click']\n",
    "train_target.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52ef28d8",
   "metadata": {
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     "start_time": "2021-07-31T10:27:55.845511",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Exercise 95: Train a FTRL model `model_ftrl_1` using `train_data` and `train_target` and assign the predictions of `valid_data` to `preds_valid_1` and of `test` to `preds_test_1`**"
   ]
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  {
   "cell_type": "code",
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   "id": "fac07cbc",
   "metadata": {
    "execution": {
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     "status": "completed"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<datatable.models.Ftrl at 0x7f6b227c05d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datatable.models import Ftrl\n",
    "\n",
    "model_ftrl_1 = Ftrl()\n",
    "model_ftrl_1.fit(train_data, train_target)\n",
    "model_ftrl_1"
   ]
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  {
   "cell_type": "code",
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   "source": [
    "preds_valid_1 = model_ftrl_1.predict(valid_data)\n",
    "preds_valid_1.head()"
   ]
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       "  <div class='footer'>\n",
       "    <div class='frame_dimensions'>10 rows &times; 2 columns</div>\n",
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "preds_test_1 = model_ftrl_1.predict(test)\n",
    "preds_test_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f17852",
   "metadata": {
    "papermill": {
     "duration": 0.033982,
     "end_time": "2021-07-31T10:28:48.233759",
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     "start_time": "2021-07-31T10:28:48.199777",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Exercise 96: Display the feature importances of `model_ftrl_1` in descending order and calculate the logloss of `valid_target` and `preds_valid_1`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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     "status": "completed"
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    "tags": []
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   "outputs": [
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       "    <tr><td class='row_index'>20</td><td>device_id</td><td>0.142158</td></tr>\n",
       "    <tr><td class='row_index'>21</td><td>device_ip</td><td>0.0405234</td></tr>\n",
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    "model_ftrl_1.feature_importances[:, :, dt.sort(-dt.f.feature_importance)]"
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   "source": [
    "preds = dt.cbind(valid_target, preds_valid_1)\n",
    "preds[:, -dt.mean(dt.f.click * dt.math.log(dt.f['True']) + (1-dt.f.click) * dt.math.log(dt.f['False']))][0, 0]"
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   },
   "source": [
    "**Exercise 97: Create `submission_1` in the submission format of the competition, write it as `submission_1.csv` and submit it on Kaggle**"
   ]
  },
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    "model_ftrl_2 = Ftrl(nepochs=3, nbins=10**8)\n",
    "model_ftrl_2.fit(train_data, train_target)\n",
    "model_ftrl_2"
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_2 = dt.Frame(id=test_ids, click=preds_test_2['True'])\n",
    "submission_2.to_csv('submission_2.csv')\n",
    "submission_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a1edc9c",
   "metadata": {
    "papermill": {
     "duration": 0.03683,
     "end_time": "2021-07-31T10:31:29.701951",
     "exception": false,
     "start_time": "2021-07-31T10:31:29.665121",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Exercise 99: Submit a ensemble of `model_ftrl_1` and `model_ftrl_2` by averaging the predictions as `submission_ensemble`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "23e9fcf3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T10:31:30.301161Z",
     "iopub.status.busy": "2021-07-31T10:31:30.300557Z",
     "iopub.status.idle": "2021-07-31T10:31:30.309390Z",
     "shell.execute_reply": "2021-07-31T10:31:30.308925Z",
     "shell.execute_reply.started": "2021-07-30T05:36:25.948007Z"
    },
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     "exception": false,
     "start_time": "2021-07-31T10:31:29.738714",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DatatableWarning: Duplicate column names found, and were assigned unique names: 'id' -> 'id.0', 'click' -> 'click.0'\n"
     ]
    },
    {
     "data": {
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       "    <div class='frame_dimensions'>10 rows &times; 2 columns</div>\n",
       "  </div>\n",
       "</div>\n"
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       "<Frame#7f6b227c0870 10x2>"
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     },
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     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "submission_ensemble = dt.cbind(submission_1, submission_2)\n",
    "submission_ensemble[:, dt.update(click = 0.5 * dt.f.click + 0.5 * dt.f['click.0'])]\n",
    "del submission_ensemble[:, ['id.0', 'click.0']]\n",
    "submission_ensemble.to_csv('submission_ensemble.csv')\n",
    "submission_ensemble.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "260c6a74",
   "metadata": {
    "papermill": {
     "duration": 0.038657,
     "end_time": "2021-07-31T10:31:30.390762",
     "exception": false,
     "start_time": "2021-07-31T10:31:30.352105",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Exercise 100: Use datatable on your own datasets and projects**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4905e97b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T10:31:30.471723Z",
     "iopub.status.busy": "2021-07-31T10:31:30.471169Z",
     "iopub.status.idle": "2021-07-31T10:31:30.474107Z",
     "shell.execute_reply": "2021-07-31T10:31:30.473700Z",
     "shell.execute_reply.started": "2021-07-31T09:48:50.091503Z"
    },
    "papermill": {
     "duration": 0.045884,
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     "exception": false,
     "start_time": "2021-07-31T10:31:30.428345",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import datatable as dt\n",
    "from datatable.models import Ftrl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9446b07d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T10:31:30.553808Z",
     "iopub.status.busy": "2021-07-31T10:31:30.553211Z",
     "iopub.status.idle": "2021-07-31T10:31:56.175025Z",
     "shell.execute_reply": "2021-07-31T10:31:56.175528Z",
     "shell.execute_reply.started": "2021-07-31T09:52:06.453992Z"
    },
    "papermill": {
     "duration": 25.663715,
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     "exception": false,
     "start_time": "2021-07-31T10:31:30.512037",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "  <div class='footer'>\n",
       "    <div class='frame_dimensions'>10 rows &times; 146 columns</div>\n",
       "  </div>\n",
       "</div>\n"
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     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = dt.fread('../input/tradeshift-text-classification/train.csv.gz')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "51ca863f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T10:31:56.294681Z",
     "iopub.status.busy": "2021-07-31T10:31:56.293979Z",
     "iopub.status.idle": "2021-07-31T10:32:05.457411Z",
     "shell.execute_reply": "2021-07-31T10:32:05.457999Z",
     "shell.execute_reply.started": "2021-07-31T09:49:25.309969Z"
    },
    "papermill": {
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     "exception": false,
     "start_time": "2021-07-31T10:31:56.233494",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "    <tr><td class='row_index'>6</td><td>1700007</td><td></td><td></td><td></td><td></td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td class=vellipsis>&hellip;</td><td>NO</td><td>YES</td><td>3</td><td>0.926009</td><td>0.255943</td></tr>\n",
       "    <tr><td class='row_index'>7</td><td>1700008</td><td>NO</td><td>NO</td><td>of++PFL1bbB+kD+ut5T+Iwizb/9eFwjag8LUngoW8Gg=</td><td>EI0dNKKW6d1fYeK0fkGEp7/pY1RFj2bw/Pk47eN1Ls4=</td><td>1.4148</td><td>0</td><td>1</td><td>0</td><td>0.213946</td><td class=vellipsis>&hellip;</td><td>NO</td><td>NO</td><td>0</td><td>0.029148</td><td>0.206022</td></tr>\n",
       "    <tr><td class='row_index'>8</td><td>1700009</td><td>NO</td><td>NO</td><td>AyUKdwJjCuwoVZ3jPrVyvnZeNS7UA/Ax6wpkgjegCGQ=</td><td>LLq6/7mBuo5pBB3xZCtooTsWqpSRfGhCln/WAHBwmNQ=</td><td>1.41368</td><td>0</td><td>1</td><td>0</td><td>0.680412</td><td class=vellipsis>&hellip;</td><td>NO</td><td>YES</td><td>0</td><td>0.0997758</td><td>0.670103</td></tr>\n",
       "    <tr><td class='row_index'>9</td><td>1700010</td><td>NO</td><td>NO</td><td>EVM/jSDSOjHBhtk7JQOLFeUb1FSAvyoL18jVgRSrcvg=</td><td>pHy5/m37WXgicL2NksCPoQ54Fpgz+pPWudDtbA2ZrTM=</td><td>1.4148</td><td>0</td><td>1</td><td>0</td><td>0.160063</td><td class=vellipsis>&hellip;</td><td>NO</td><td>NO</td><td>0</td><td>0.213004</td><td>0.14897</td></tr>\n",
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       "  </table>\n",
       "  <div class='footer'>\n",
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       "  </div>\n",
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     "execution_count": 27,
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   ],
   "source": [
    "test = dt.fread('../input/tradeshift-text-classification/test.csv.gz')\n",
    "test.head()"
   ]
  },
  {
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    "train_labels = dt.fread('../input/tradeshift-text-classification/trainLabels.csv.gz')\n",
    "train_labels.head()"
   ]
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   "cell_type": "code",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model for target y1\n",
      "Model for target y2\n",
      "Model for target y3\n",
      "Model for target y4\n",
      "Model for target y5\n",
      "Model for target y6\n",
      "Model for target y7\n",
      "Model for target y8\n",
      "Model for target y9\n",
      "Model for target y10\n",
      "Model for target y11\n",
      "Model for target y12\n",
      "Model for target y13\n",
      "Model for target y14\n",
      "Model for target y15\n",
      "Model for target y16\n",
      "Model for target y17\n",
      "Model for target y18\n",
      "Model for target y19\n",
      "Model for target y20\n",
      "Model for target y21\n",
      "Model for target y22\n",
      "Model for target y23\n",
      "Model for target y24\n",
      "Model for target y25\n",
      "Model for target y26\n",
      "Model for target y27\n",
      "Model for target y28\n",
      "Model for target y29\n",
      "Model for target y30\n",
      "Model for target y31\n",
      "Model for target y32\n",
      "Model for target y33\n"
     ]
    }
   ],
   "source": [
    "test_ids = test['id']\n",
    "\n",
    "del train['id']\n",
    "del test['id']\n",
    "\n",
    "submission = dt.Frame()\n",
    "\n",
    "for target in train_labels.names[1:]:\n",
    "    print(f'Model for target {target}')\n",
    "\n",
    "    model_ftrl = Ftrl(nepochs=5, nbins=10**8, lambda1=0.1)\n",
    "    model_ftrl.fit(train, train_labels[target])\n",
    "\n",
    "    preds_test = model_ftrl.predict(test)\n",
    "    submission_target = dt.Frame(id_label=test_ids[:, dt.as_type(dt.f.id, str) + f'_{target}'],\n",
    "                                 pred=preds_test['True'])\n",
    "    submission.rbind(submission_target)"
   ]
  },
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   "outputs": [
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   ],
   "source": [
    "submission.to_csv('submission.csv')\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6af53391",
   "metadata": {
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   "source": [
    "✅ This completes Set 10: Capstone Projects (Exercises 91-100) and concludes **DatatableTon**: *💯 datatable exercises*\n",
    "\n",
    "Hope these exercises enabled you to learn more about the Python datatable library. If this project made you go from an amateur to a datatable ninja or helped you get a deeper understanding of datatable or was useful in teaching datatable to others, please share your thoughts and any other feedback or suggestions on our [GitHub Repository](https://github.com/vopani/datatableton) where you can also find all the exercises and solutions along with future updates."
   ]
  }
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