survival analysis using KM.ipynb

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    "deepnote_cell_type": "code",
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   },
   "source": "# This command install the Xelatex typesetting engine on your Debian-based machine\n!chmod u+x dependencies.sh && sudo apt-get update && sudo bash dependencies.sh",
   "outputs": [
    {
     "name": "stdout",
     "text": "Hit:1 http://deb.debian.org/debian buster InRelease\nHit:2 http://deb.debian.org/debian buster-updates InRelease\nHit:3 http://security.debian.org/debian-security buster/updates InRelease\n\n\n\n\ngit-lfs is already the newest version (2.7.1-1+deb10u1).\nnano is already the newest version (3.2-3).\ntexlive-xetex is already the newest version (2018.20190227-2).\n0 upgraded, 0 newly installed, 0 to remove and 36 not upgraded.\n",
     "output_type": "stream"
    }
   ],
   "execution_count": null
  },
  {
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   "metadata": {
    "cell_id": "78796593-38d1-4ad9-ba3a-1422e7030b85",
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    "source_hash": "e260564e",
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    "execution_millis": 24811,
    "deepnote_cell_type": "code",
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   },
   "source": "import tikzplotlib\nimport pandas as pd\nimport numpy as np\n\nfrom pandas_profiling import ProfileReport\nfrom pysurvival.utils.display import correlation_matrix\nfrom matplotlib import pyplot as plt\n%pylab inline\n\nplt.style.use('tableau-colorblind10')",
   "outputs": [
    {
     "name": "stderr",
     "text": "Matplotlib is building the font cache; this may take a moment.\n/shared-libs/python3.7/py/lib/python3.7/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n  from .autonotebook import tqdm as notebook_tqdm\nPopulating the interactive namespace from numpy and matplotlib\n",
     "output_type": "stream"
    }
   ],
   "execution_count": null
  },
  {
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    "source_hash": "1846eb0d",
    "execution_start": 1654959036329,
    "execution_millis": 1033,
    "owner_user_id": "1d5e23fc-586a-4fb2-a64d-9f5bc0547ef6",
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 423.25
   },
   "source": "# Reading the dataset\nraw_dataset = pd.read_csv(\"processed_data/ds/user_features.csv\", sep=\"\\t\")\nprint(\"The raw_dataset has the following shape: {}.\".format(raw_dataset.shape))\nraw_dataset.head(3)",
   "outputs": [
    {
     "name": "stdout",
     "text": "The raw_dataset has the following shape: (100581, 19).\n",
     "output_type": "stream"
    },
    {
     "output_type": "execute_result",
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        {
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         "CreationDate": "2014-05-14 00:56:07",
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        }
       ]
      },
      "text/plain": "   UserId         CreationDate  CastDownVotes  CastUpVotes  ProfileViews  \\\n0      60  2014-05-14 00:56:07              0            0             1   \n1      67  2014-05-14 01:29:11              0            0             1   \n2      69  2014-05-14 01:40:28              0            1             1   \n\n        LastAccessDate  QuestionCount  AnswerCount  CommentCount  \\\n0  2014-05-14 00:56:07              0            0             0   \n1  2014-05-14 02:03:08              0            0             0   \n2  2014-05-14 04:26:39              0            0             0   \n\n   AvgQuestionViewCount  AvgQuestionFavouriteCount  AvgQuestionCommentCount  \\\n0                   0.0                        0.0                      0.0   \n1                   0.0                        0.0                      0.0   \n2                   0.0                        0.0                      0.0   \n\n   AvgQuestionScore  AvgAnswerScore  AvgAnswerCommentCount  AvgCommentScore  \\\n0               0.0             0.0                    0.0              0.0   \n1               0.0             0.0                    0.0              0.0   \n2               0.0             0.0                    0.0              0.0   \n\n   MonthsActive  MonthsSinceLastActivity  MonthsSinceJoined  \n0             0                       85                 85  \n1             0                       85                 85  \n2             0                       85                 85  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>UserId</th>\n      <th>CreationDate</th>\n      <th>CastDownVotes</th>\n      <th>CastUpVotes</th>\n      <th>ProfileViews</th>\n      <th>LastAccessDate</th>\n      <th>QuestionCount</th>\n      <th>AnswerCount</th>\n      <th>CommentCount</th>\n      <th>AvgQuestionViewCount</th>\n      <th>AvgQuestionFavouriteCount</th>\n      <th>AvgQuestionCommentCount</th>\n      <th>AvgQuestionScore</th>\n      <th>AvgAnswerScore</th>\n      <th>AvgAnswerCommentCount</th>\n      <th>AvgCommentScore</th>\n      <th>MonthsActive</th>\n      <th>MonthsSinceLastActivity</th>\n      <th>MonthsSinceJoined</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>60</td>\n      <td>2014-05-14 00:56:07</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2014-05-14 00:56:07</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>85</td>\n      <td>85</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>67</td>\n      <td>2014-05-14 01:29:11</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2014-05-14 02:03:08</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>85</td>\n      <td>85</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>69</td>\n      <td>2014-05-14 01:40:28</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2014-05-14 04:26:39</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>85</td>\n      <td>85</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00002-aa2c10a4-f0a8-4ef5-b6c4-263eb2b8e67d",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "66d778dc",
    "execution_start": 1654959037372,
    "execution_millis": 693370553,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 232.75,
    "deepnote_output_heights": [
     136
    ]
   },
   "source": "raw_dataset.columns",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 4,
     "data": {
      "text/plain": "Index(['UserId', 'CreationDate', 'CastDownVotes', 'CastUpVotes',\n       'ProfileViews', 'LastAccessDate', 'QuestionCount', 'AnswerCount',\n       'CommentCount', 'AvgQuestionViewCount', 'AvgQuestionFavouriteCount',\n       'AvgQuestionCommentCount', 'AvgQuestionScore', 'AvgAnswerScore',\n       'AvgAnswerCommentCount', 'AvgCommentScore', 'MonthsActive',\n       'MonthsSinceLastActivity', 'MonthsSinceJoined'],\n      dtype='object')"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00003-fc8872a6-619a-4080-8dc7-fefcba0d951b",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "1a2de8",
    "execution_start": 1654959037406,
    "execution_millis": 2309,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 521.75,
    "deepnote_output_heights": [
     371
    ]
   },
   "source": "features = ['CastDownVotes', 'CastUpVotes', 'QuestionCount', \n            'AnswerCount', 'CommentCount']\n\ncorrelation_matrix(raw_dataset[features], figure_size=(10, 4), text_fontsize=8)",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 720x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 434,
       "height": 369
      }
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00004-3c43a168-dcf1-402e-9bca-047fc4d7e3b9",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "7832fb47",
    "execution_start": 1654959039202,
    "execution_millis": 121,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 188.8333282470703
   },
   "source": "def f(x):\n    if x > 24:\n        return 1\n    else:\n        return 0\n    \nraw_dataset['disengaged'] = raw_dataset['MonthsSinceLastActivity'].apply(f)",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00005-646df3c1-de99-4b3f-87dc-6a857bf44b6a",
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      },
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      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>UserId</th>\n      <th>CreationDate</th>\n      <th>CastDownVotes</th>\n      <th>CastUpVotes</th>\n      <th>ProfileViews</th>\n      <th>LastAccessDate</th>\n      <th>QuestionCount</th>\n      <th>AnswerCount</th>\n      <th>CommentCount</th>\n      <th>AvgQuestionViewCount</th>\n      <th>AvgQuestionFavouriteCount</th>\n      <th>AvgQuestionCommentCount</th>\n      <th>AvgQuestionScore</th>\n      <th>AvgAnswerScore</th>\n      <th>AvgAnswerCommentCount</th>\n      <th>AvgCommentScore</th>\n      <th>MonthsActive</th>\n      <th>MonthsSinceLastActivity</th>\n      <th>MonthsSinceJoined</th>\n      <th>disengaged</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>60</td>\n      <td>2014-05-14 00:56:07</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2014-05-14 00:56:07</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>85</td>\n      <td>85</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>67</td>\n      <td>2014-05-14 01:29:11</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2014-05-14 02:03:08</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>85</td>\n      <td>85</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>69</td>\n      <td>2014-05-14 01:40:28</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2014-05-14 04:26:39</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>85</td>\n      <td>85</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>93</td>\n      <td>2014-05-14 05:23:16</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2014-05-14 05:23:16</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>85</td>\n      <td>85</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>99</td>\n      <td>2014-05-14 05:44:39</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2014-05-14 09:24:43</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>85</td>\n      <td>85</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00006-25ba4fd8-66eb-4a26-843b-ba0b5b5f34ad",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e2ff3a61",
    "execution_start": 1654959039673,
    "execution_millis": 184,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 862.6666870117188
   },
   "source": "features = ['CastDownVotes', 'CastUpVotes', 'QuestionCount', 'AnswerCount', 'CommentCount']\n\ntime_column = \"MonthsActive\"\nevent_column = \"disengaged\"\n\nraw_dataset_cut = raw_dataset.copy()[features + [time_column, event_column]]\nraw_dataset_cut = raw_dataset_cut[((raw_dataset_cut['QuestionCount'] > 0) | \\\n                                  (raw_dataset_cut['AnswerCount'] > 0) |\n                                  (raw_dataset_cut['CommentCount'] > 0) |\n                                  (raw_dataset_cut['CastDownVotes'] > 0) |\n                                  (raw_dataset_cut['CastUpVotes'] > 0))|\n                                  True]\n\nraw_dataset_cut.reset_index(inplace=True)\nraw_dataset_cut",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 8,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 8,
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         "name": "MonthsActive",
         "dtype": "int64"
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         "name": "disengaged",
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       "rows": [
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        {
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        },
        {
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        },
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      },
      "text/plain": "         index  CastDownVotes  CastUpVotes  QuestionCount  AnswerCount  \\\n0            0              0            0              0            0   \n1            1              0            0              0            0   \n2            2              0            1              0            0   \n3            3              0            0              0            0   \n4            4              0            0              0            0   \n...        ...            ...          ...            ...          ...   \n100576  100576            717         1983              0           28   \n100577  100577              0            7              1            1   \n100578  100578              0            2              0            1   \n100579  100579              0            0              0            0   \n100580  100580              0         4815             24          345   \n\n        CommentCount  MonthsActive  disengaged  \n0                  0             0           1  \n1                  0             0           1  \n2                  0             0           1  \n3                  0             0           1  \n4                  0             0           1  \n...              ...           ...         ...  \n100576            91            50           0  \n100577             0            18           0  \n100578             0            28           0  \n100579             0            37           0  \n100580           882            52           0  \n\n[100581 rows x 8 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>index</th>\n      <th>CastDownVotes</th>\n      <th>CastUpVotes</th>\n      <th>QuestionCount</th>\n      <th>AnswerCount</th>\n      <th>CommentCount</th>\n      <th>MonthsActive</th>\n      <th>disengaged</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>100576</th>\n      <td>100576</td>\n      <td>717</td>\n      <td>1983</td>\n      <td>0</td>\n      <td>28</td>\n      <td>91</td>\n      <td>50</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>100577</th>\n      <td>100577</td>\n      <td>0</td>\n      <td>7</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>100578</th>\n      <td>100578</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>28</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>100579</th>\n      <td>100579</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>37</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>100580</th>\n      <td>100580</td>\n      <td>0</td>\n      <td>4815</td>\n      <td>24</td>\n      <td>345</td>\n      <td>882</td>\n      <td>52</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>100581 rows × 8 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00007-e429b876-7602-4360-bca1-b78c7b8faf38",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "562e291c",
    "execution_start": 1654959039867,
    "execution_millis": 65,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 134.8333282470703
   },
   "source": "raw_dataset_cut_original = raw_dataset_cut.copy() \nraw_dataset_cut = raw_dataset_cut[features].transform(lambda x: x)#np.log(x+1))\nraw_dataset_cut[time_column] = raw_dataset_cut_original[time_column].values\nraw_dataset_cut[event_column] = raw_dataset_cut_original[event_column].values",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00008-0258aa4a-cfbb-4968-88c7-69db0679f834",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "1ae644f0",
    "execution_start": 1654959040009,
    "execution_millis": 46,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 361.41668701171875
   },
   "source": "from sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import KFold\nfrom pysurvival.models.simulations import SimulationModel\nfrom pysurvival.models.multi_task import NeuralMultiTaskModel\nfrom pysurvival.utils.metrics import concordance_index\nfrom pysurvival.models.semi_parametric import CoxPHModel\nfrom pysurvival.utils.display import integrated_brier_score\nfrom pysurvival.models.survival_forest import ConditionalSurvivalForestModel\nfrom pysurvival.models.non_parametric import KaplanMeierModel\nfrom pysurvival.utils.display import display_non_parametric\n%pylab inline",
   "outputs": [
    {
     "name": "stdout",
     "text": "Populating the interactive namespace from numpy and matplotlib\n/shared-libs/python3.7/py-core/lib/python3.7/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['f']\n`%matplotlib` prevents importing * from pylab and numpy\n  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n",
     "output_type": "stream"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00009-59948348-1aad-4a4d-9a6e-d859175dada0",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "17199da4",
    "execution_start": 1654959040098,
    "execution_millis": 9,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 152.8333282470703
   },
   "source": "question_dataset = raw_dataset_cut.copy()\nanswer_dataset = raw_dataset_cut.copy()\ncomment_dataset = raw_dataset_cut.copy()\nupvote_dataset = raw_dataset_cut.copy()\ndownvote_dataset = raw_dataset_cut.copy()",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00010-b124b932-9bbc-465b-8aaa-8254f8d30fed",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "9c34eb6b",
    "execution_start": 1654959040173,
    "execution_millis": 118,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 314.83331298828125
   },
   "source": "question_dataset.sort_values('QuestionCount', ascending=False, inplace=True)\nquestion_dataset.reset_index(inplace=True)\n\nanswer_dataset.sort_values('AnswerCount', ascending=False, inplace=True)\nanswer_dataset.reset_index(inplace=True)\n\ncomment_dataset.sort_values('CommentCount', ascending=False, inplace=True)\ncomment_dataset.reset_index(inplace=True)\n\nupvote_dataset.sort_values('CastUpVotes', ascending=False, inplace=True)\nupvote_dataset.reset_index(inplace=True)\n\ndownvote_dataset.sort_values('CastDownVotes', ascending=False, inplace=True)\ndownvote_dataset.reset_index(inplace=True)",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00011-d87da2a5-3774-40f6-8e17-8b6e76477d0e",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "53dffc6e",
    "execution_start": 1654959040366,
    "execution_millis": 587,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 1088.8333740234375
   },
   "source": "from lifelines import KaplanMeierFitter\nfrom lifelines.statistics import logrank_test\n\npercent = 5\n\nx = math.ceil((question_dataset.shape[0]*percent)/100.0)\n\ntop_q = question_dataset.head(x)\nbottom_q = question_dataset.tail(x)\nT_top_q, T_bottom_q = top_q[time_column], bottom_q[time_column]\nE_top_q, E_bottom_q = top_q[event_column], bottom_q[event_column]\n\ntop_a = answer_dataset.head(x)\nbottom_a = answer_dataset.tail(x)\nT_top_a, T_bottom_a = top_a[time_column], bottom_a[time_column]\nE_top_a, E_bottom_a = top_a[event_column], bottom_a[event_column]\n\ntop_c = comment_dataset.head(x)\nbottom_c = comment_dataset.tail(x)\nT_top_c, T_bottom_c = top_c[time_column], bottom_c[time_column]\nE_top_c, E_bottom_c = top_c[event_column], bottom_c[event_column]\n\ntop_uv = upvote_dataset.head(x)\nbottom_uv = upvote_dataset.tail(x)\nT_top_uv, T_bottom_uv = top_uv[time_column], bottom_uv[time_column]\nE_top_uv, E_bottom_uv = top_uv[event_column], bottom_uv[event_column]\n\ntop_dv = downvote_dataset.head(x)\nbottom_dv = downvote_dataset.tail(x)\nT_top_dv, T_bottom_dv = top_dv[time_column], bottom_dv[time_column]\nE_top_dv, E_bottom_dv = top_dv[event_column], bottom_dv[event_column]\n\n# User behavioural attributes importance analysis for predicting the probability of disengagement over time  \none_or_more_q = question_dataset[(question_dataset['QuestionCount'] > 0)]\nno_q = question_dataset[(question_dataset['QuestionCount'] == 0)]\nT_one_or_more_q, T_no_q = one_or_more_q[time_column], no_q[time_column]\nE_one_or_more_q, E_no_q = one_or_more_q[event_column], no_q[event_column]\n\none_or_more_a = answer_dataset[(answer_dataset['AnswerCount'] > 0)]\nno_a = answer_dataset[(answer_dataset['AnswerCount'] == 0)]\nT_one_or_more_a, T_no_a = one_or_more_a[time_column], no_a[time_column]\nE_one_or_more_a, E_no_a = one_or_more_a[event_column], no_a[event_column]\n\none_or_more_c = comment_dataset[(comment_dataset['CommentCount'] > 0)]\nno_c = comment_dataset[(comment_dataset['CommentCount'] == 0)]\nT_one_or_more_c, T_no_c = one_or_more_c[time_column], no_c[time_column]\nE_one_or_more_c, E_no_c = one_or_more_c[event_column], no_c[event_column]\n\none_or_more_uv = upvote_dataset[(upvote_dataset['CastUpVotes'] > 0)]\nno_uv = upvote_dataset[(upvote_dataset['CastUpVotes'] == 0)]\nT_one_or_more_uv, T_no_uv = one_or_more_uv[time_column], no_uv[time_column]\nE_one_or_more_uv, E_no_uv = one_or_more_uv[event_column], no_uv[event_column]\n\none_or_more_dv = downvote_dataset[(downvote_dataset['CastDownVotes'] > 0)]\nno_dv = downvote_dataset[(downvote_dataset['CastDownVotes'] == 0)]\nT_one_or_more_dv, T_no_dv = one_or_more_dv[time_column], no_dv[time_column]\nE_one_or_more_dv, E_no_dv = one_or_more_dv[event_column], no_dv[event_column]",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00012-5a2fa92e-b086-4eb6-817f-1ee42be7897f",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "13b3a00b",
    "execution_start": 1654959041015,
    "execution_millis": 1705,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 792.75,
    "deepnote_output_heights": [
     264
    ]
   },
   "source": "kmf = KaplanMeierFitter()\n\nax = plt.subplot(111)\n# ax = kmf.fit(T_top_q, E_top_q, label=\"User with top-{0}% of questions\".format(percent)).plot(ax=ax)\n# ax = kmf.fit(T_top_a, E_top_a, label=\"User with top-{0}% of answers\".format(percent)).plot(ax=ax)\n# ax = kmf.fit(T_top_c, E_top_c, label=\"User with top-{0}% of comments\".format(percent)).plot(ax=ax)\n# ax = kmf.fit(T_top_uv, E_top_uv, label=\"User with top-{0}% of upvotes\".format(percent)).plot(ax=ax)\n# ax = kmf.fit(T_top_dv, E_top_dv, label=\"User with top-{0}% of downvotes\".format(percent)).plot(ax=ax)\n\nax = kmf.fit(T_one_or_more_q, E_one_or_more_q, label=\"Users who posted at least one question\".format(percent)).plot(ax=ax)\nax = kmf.fit(T_no_q, E_no_q, label=\"Users who asked no question\".format(percent)).plot(ax=ax)\n\n#ax = kmf.fit(T_one_or_more_a, E_one_or_more_a, label=\"Users who posted at least one answer\".format(percent)).plot(ax=ax)\n#ax = kmf.fit(T_no_a, E_no_a, label=\"Users who answered no question\".format(percent)).plot(ax=ax)\n\n#ax = kmf.fit(T_one_or_more_c, E_one_or_more_c, label=\"Users who posted at least one comment\".format(percent)).plot(ax=ax)\n#ax = kmf.fit(T_no_c, E_no_c, label=\"Users who posted no comments\".format(percent)).plot(ax=ax)\n\n#ax = kmf.fit(T_one_or_more_uv, E_one_or_more_uv, label=\"Users who upvoted at least once\".format(percent)).plot(ax=ax)\n#ax = kmf.fit(T_no_uv, E_no_uv, label=\"Users who did not upvote\".format(percent)).plot(ax=ax)\n\n#ax = kmf.fit(T_one_or_more_dv, E_one_or_more_dv, label=\"Users who downvoted at least once\".format(percent)).plot(ax=ax)\n#ax = kmf.fit(T_no_dv, E_no_dv, label=\"Users who did not downvote\".format(percent)).plot(ax=ax)\n\ntikzplotlib.save(\"figures/tikz/cs/36/survial_curve_questions_36.tikz\")",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 372,
       "height": 262
      }
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00013-1e309738-52ef-4ed9-822d-62fa21db1d9f",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "7a392f3a",
    "execution_start": 1654959042134,
    "execution_millis": 600,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 327.75,
    "deepnote_output_heights": [
     177
    ]
   },
   "source": "# logrank_test for questions\nresults = logrank_test(T_one_or_more_q, T_no_q, \n                       event_observed_A=E_one_or_more_q, event_observed_B=E_no_q)\nresults.print_summary()",
   "outputs": [
    {
     "data": {
      "text/plain": "<lifelines.StatisticalResult: logrank_test>\n               t_0 = -1\n null_distribution = chi squared\ndegrees_of_freedom = 1\n         test_name = logrank_test\n\n---\n test_statistic      p  -log2(p)\n        1528.57 <0.005       inf",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <tbody>\n    <tr>\n      <th>t_0</th>\n      <td>-1</td>\n    </tr>\n    <tr>\n      <th>null_distribution</th>\n      <td>chi squared</td>\n    </tr>\n    <tr>\n      <th>degrees_of_freedom</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>test_name</th>\n      <td>logrank_test</td>\n    </tr>\n  </tbody>\n</table>\n</div><table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>test_statistic</th>\n      <th>p</th>\n      <th>-log2(p)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1528.57</td>\n      <td>&lt;0.005</td>\n      <td>inf</td>\n    </tr>\n  </tbody>\n</table>",
      "text/latex": "\\begin{tabular}{lrrr}\n\\toprule\n{} &  test\\_statistic &    p &  -log2(p) \\\\\n\\midrule\n0 &     1528.570198 &  0.0 &       inf \\\\\n\\bottomrule\n\\end{tabular}\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00014-90e0a8cb-0845-4da3-a99a-84ab52d0db47",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ee192581",
    "execution_start": 1654959042437,
    "execution_millis": 325,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 327.75,
    "deepnote_output_heights": [
     177
    ]
   },
   "source": "# logrank_test for answers\nresults = logrank_test(T_one_or_more_a, T_no_a, \n                       event_observed_A=E_one_or_more_a, event_observed_B=E_no_a)\nresults.print_summary()",
   "outputs": [
    {
     "data": {
      "text/plain": "<lifelines.StatisticalResult: logrank_test>\n               t_0 = -1\n null_distribution = chi squared\ndegrees_of_freedom = 1\n         test_name = logrank_test\n\n---\n test_statistic      p  -log2(p)\n        1300.58 <0.005    943.67",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <tbody>\n    <tr>\n      <th>t_0</th>\n      <td>-1</td>\n    </tr>\n    <tr>\n      <th>null_distribution</th>\n      <td>chi squared</td>\n    </tr>\n    <tr>\n      <th>degrees_of_freedom</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>test_name</th>\n      <td>logrank_test</td>\n    </tr>\n  </tbody>\n</table>\n</div><table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>test_statistic</th>\n      <th>p</th>\n      <th>-log2(p)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1300.58</td>\n      <td>&lt;0.005</td>\n      <td>943.67</td>\n    </tr>\n  </tbody>\n</table>",
      "text/latex": "\\begin{tabular}{lrrr}\n\\toprule\n{} &  test\\_statistic &              p &    -log2(p) \\\\\n\\midrule\n0 &      1300.57686 &  8.469583e-285 &  943.667216 \\\\\n\\bottomrule\n\\end{tabular}\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00015-b2be3473-4b73-45ec-ad38-30ed63379799",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "fdaa1140",
    "execution_start": 1654959042684,
    "execution_millis": 213,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 327.83331298828125,
    "deepnote_output_heights": [
     177
    ]
   },
   "source": "# logrank_test for comments\nresults = logrank_test(T_one_or_more_c, T_no_c, \n                       event_observed_A=E_one_or_more_c, event_observed_B=E_no_c)\nresults.print_summary()",
   "outputs": [
    {
     "data": {
      "text/plain": "<lifelines.StatisticalResult: logrank_test>\n               t_0 = -1\n null_distribution = chi squared\ndegrees_of_freedom = 1\n         test_name = logrank_test\n\n---\n test_statistic      p  -log2(p)\n        2070.12 <0.005       inf",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <tbody>\n    <tr>\n      <th>t_0</th>\n      <td>-1</td>\n    </tr>\n    <tr>\n      <th>null_distribution</th>\n      <td>chi squared</td>\n    </tr>\n    <tr>\n      <th>degrees_of_freedom</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>test_name</th>\n      <td>logrank_test</td>\n    </tr>\n  </tbody>\n</table>\n</div><table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>test_statistic</th>\n      <th>p</th>\n      <th>-log2(p)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2070.12</td>\n      <td>&lt;0.005</td>\n      <td>inf</td>\n    </tr>\n  </tbody>\n</table>",
      "text/latex": "\\begin{tabular}{lrrr}\n\\toprule\n{} &  test\\_statistic &    p &  -log2(p) \\\\\n\\midrule\n0 &     2070.123068 &  0.0 &       inf \\\\\n\\bottomrule\n\\end{tabular}\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00016-c5cfbc7a-e937-48bb-b2bc-d8a5530d56f5",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "52abec7e",
    "execution_start": 1654959042930,
    "execution_millis": 231,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 327.75,
    "deepnote_output_heights": [
     177
    ]
   },
   "source": "# logrank_test for upvotes\nresults = logrank_test(T_one_or_more_uv, T_no_uv, \n                       event_observed_A=E_one_or_more_uv, event_observed_B=E_no_uv)\nresults.print_summary()",
   "outputs": [
    {
     "data": {
      "text/plain": "<lifelines.StatisticalResult: logrank_test>\n               t_0 = -1\n null_distribution = chi squared\ndegrees_of_freedom = 1\n         test_name = logrank_test\n\n---\n test_statistic      p  -log2(p)\n        4897.03 <0.005       inf",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <tbody>\n    <tr>\n      <th>t_0</th>\n      <td>-1</td>\n    </tr>\n    <tr>\n      <th>null_distribution</th>\n      <td>chi squared</td>\n    </tr>\n    <tr>\n      <th>degrees_of_freedom</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>test_name</th>\n      <td>logrank_test</td>\n    </tr>\n  </tbody>\n</table>\n</div><table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>test_statistic</th>\n      <th>p</th>\n      <th>-log2(p)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4897.03</td>\n      <td>&lt;0.005</td>\n      <td>inf</td>\n    </tr>\n  </tbody>\n</table>",
      "text/latex": "\\begin{tabular}{lrrr}\n\\toprule\n{} &  test\\_statistic &    p &  -log2(p) \\\\\n\\midrule\n0 &     4897.032106 &  0.0 &       inf \\\\\n\\bottomrule\n\\end{tabular}\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00017-6a4ebaf6-fcd7-4819-ac45-e151089835ff",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "6081d05",
    "execution_start": 1654959043190,
    "execution_millis": 239,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 327.75,
    "deepnote_output_heights": [
     177
    ]
   },
   "source": "# logrank_test for downvotes\nresults = logrank_test(T_one_or_more_dv, T_no_dv, \n                       event_observed_A=E_one_or_more_dv, event_observed_B=E_no_dv)\nresults.print_summary()",
   "outputs": [
    {
     "data": {
      "text/plain": "<lifelines.StatisticalResult: logrank_test>\n               t_0 = -1\n null_distribution = chi squared\ndegrees_of_freedom = 1\n         test_name = logrank_test\n\n---\n test_statistic      p  -log2(p)\n         370.55 <0.005    271.89",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <tbody>\n    <tr>\n      <th>t_0</th>\n      <td>-1</td>\n    </tr>\n    <tr>\n      <th>null_distribution</th>\n      <td>chi squared</td>\n    </tr>\n    <tr>\n      <th>degrees_of_freedom</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>test_name</th>\n      <td>logrank_test</td>\n    </tr>\n  </tbody>\n</table>\n</div><table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>test_statistic</th>\n      <th>p</th>\n      <th>-log2(p)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>370.55</td>\n      <td>&lt;0.005</td>\n      <td>271.89</td>\n    </tr>\n  </tbody>\n</table>",
      "text/latex": "\\begin{tabular}{lrrr}\n\\toprule\n{} &  test\\_statistic &             p &    -log2(p) \\\\\n\\midrule\n0 &      370.550372 &  1.420238e-82 &  271.891971 \\\\\n\\bottomrule\n\\end{tabular}\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00018-8fd537b4-904a-4e6d-8bf7-4be065ff8b85",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ec5b6325",
    "execution_start": 1654959043456,
    "execution_millis": 7,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 117.75,
    "deepnote_output_heights": [
     21
    ]
   },
   "source": "one_or_more_q.shape[0], no_q.shape[0]",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 20,
     "data": {
      "text/plain": "(16041, 84540)"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00019-231ff540-4579-4c87-b947-5356d146c2a9",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b3f50f51",
    "execution_start": 1654959043550,
    "execution_millis": 693368462,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 117.75,
    "deepnote_output_heights": [
     21
    ]
   },
   "source": "one_or_more_a.shape[0], no_a.shape[0]",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 21,
     "data": {
      "text/plain": "(7226, 93355)"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00020-d16f599f-7b7f-4bb9-a2c6-e05379745b4b",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b8f9cd12",
    "execution_start": 1654959043595,
    "execution_millis": 693368488,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 117.75,
    "deepnote_output_heights": [
     21
    ]
   },
   "source": "one_or_more_c.shape[0], no_c.shape[0]",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 22,
     "data": {
      "text/plain": "(12056, 88525)"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00021-1663f30e-af3b-4976-a3e9-db2b6d67bd93",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "d34615e2",
    "execution_start": 1654959043596,
    "execution_millis": 693368406,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 117.75,
    "deepnote_output_heights": [
     21
    ]
   },
   "source": "one_or_more_uv.shape[0], no_uv.shape[0]",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 23,
     "data": {
      "text/plain": "(16328, 84253)"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00022-d4e4f0f5-a16e-461f-b378-01826da3a309",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "f6a1510",
    "execution_start": 1654959043642,
    "execution_millis": 693368447,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 117.75,
    "deepnote_output_heights": [
     21
    ]
   },
   "source": "one_or_more_dv.shape[0], no_dv.shape[0]",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 24,
     "data": {
      "text/plain": "(732, 99849)"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": "<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=5b875ec7-933b-4a3a-92bc-cefd5e08f9f6' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPHN2ZyB3aWR0aD0iODBweCIgaGVpZ2h0PSI4MHB4IiB2aWV3Qm94PSIwIDAgODAgODAiIHZlcnNpb249IjEuMSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIiB4bWxuczp4bGluaz0iaHR0cDovL3d3dy53My5vcmcvMTk5OS94bGluayI+CiAgICA8IS0tIEdlbmVyYXRvcjogU2tldGNoIDU0LjEgKDc2NDkwKSAtIGh0dHBzOi8vc2tldGNoYXBwLmNvbSAtLT4KICAgIDx0aXRsZT5Hcm91cCAzPC90aXRsZT4KICAgIDxkZXNjPkNyZWF0ZWQgd2l0aCBTa2V0Y2guPC9kZXNjPgogICAgPGcgaWQ9IkxhbmRpbmciIHN0cm9rZT0ibm9uZSIgc3Ryb2tlLXdpZHRoPSIxIiBmaWxsPSJub25lIiBmaWxsLXJ1bGU9ImV2ZW5vZGQiPgogICAgICAgIDxnIGlkPSJBcnRib2FyZCIgdHJhbnNmb3JtPSJ0cmFuc2xhdGUoLTEyMzUuMDAwMDAwLCAtNzkuMDAwMDAwKSI+CiAgICAgICAgICAgIDxnIGlkPSJHcm91cC0zIiB0cmFuc2Zvcm09InRyYW5zbGF0ZSgxMjM1LjAwMDAwMCwgNzkuMDAwMDAwKSI+CiAgICAgICAgICAgICAgICA8cG9seWdvbiBpZD0iUGF0aC0yMCIgZmlsbD0iIzAyNjVCNCIgcG9pbnRzPSIyLjM3NjIzNzYyIDgwIDM4LjA0NzY2NjcgODAgNTcuODIxNzgyMiA3My44MDU3NTkyIDU3LjgyMTc4MjIgMzIuNzU5MjczOSAzOS4xNDAyMjc4IDMxLjY4MzE2ODMiPjwvcG9seWdvbj4KICAgICAgICAgICAgICAgIDxwYXRoIGQ9Ik0zNS4wMDc3MTgsODAgQzQyLjkwNjIwMDcsNzYuNDU0OTM1OCA0Ny41NjQ5MTY3LDcxLjU0MjI2NzEgNDguOTgzODY2LDY1LjI2MTk5MzkgQzUxLjExMjI4OTksNTUuODQxNTg0MiA0MS42NzcxNzk1LDQ5LjIxMjIyODQgMjUuNjIzOTg0Niw0OS4yMTIyMjg0IEMyNS40ODQ5Mjg5LDQ5LjEyNjg0NDggMjkuODI2MTI5Niw0My4yODM4MjQ4IDM4LjY0NzU4NjksMzEuNjgzMTY4MyBMNzIuODcxMjg3MSwzMi41NTQ0MjUgTDY1LjI4MDk3Myw2Ny42NzYzNDIxIEw1MS4xMTIyODk5LDc3LjM3NjE0NCBMMzUuMDA3NzE4LDgwIFoiIGlkPSJQYXRoLTIyIiBmaWxsPSIjMDAyODY4Ij48L3BhdGg+CiAgICAgICAgICAgICAgICA8cGF0aCBkPSJNMCwzNy43MzA0NDA1IEwyNy4xMTQ1MzcsMC4yNTcxMTE0MzYgQzYyLjM3MTUxMjMsLTEuOTkwNzE3MDEgODAsMTAuNTAwMzkyNyA4MCwzNy43MzA0NDA1IEM4MCw2NC45NjA0ODgyIDY0Ljc3NjUwMzgsNzkuMDUwMzQxNCAzNC4zMjk1MTEzLDgwIEM0Ny4wNTUzNDg5LDc3LjU2NzA4MDggNTMuNDE4MjY3Nyw3MC4zMTM2MTAzIDUzLjQxODI2NzcsNTguMjM5NTg4NSBDNTMuNDE4MjY3Nyw0MC4xMjg1NTU3IDM2LjMwMzk1NDQsMzcuNzMwNDQwNSAyNS4yMjc0MTcsMzcuNzMwNDQwNSBDMTcuODQzMDU4NiwzNy43MzA0NDA1IDkuNDMzOTE5NjYsMzcuNzMwNDQwNSAwLDM3LjczMDQ0MDUgWiIgaWQ9IlBhdGgtMTkiIGZpbGw9IiMzNzkzRUYiPjwvcGF0aD4KICAgICAgICAgICAgPC9nPgogICAgICAgIDwvZz4KICAgIDwvZz4KPC9zdmc+' > </img>\nCreated in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>",
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    "created_in_deepnote_cell": true,
    "deepnote_cell_type": "markdown"
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  }
 ],
 "nbformat": 4,
 "nbformat_minor": 5,
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 }
}