{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "31eb9c1d",
   "metadata": {},
   "source": [
    "Event Feature Extraction\n",
    "========================\n",
    "\n",
    "In this tutorial, we introduce a few feature extraction techniques. Let's start by loading some data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "64f434b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BPI17 Event Log\n",
       "    Cases: 31,509\n",
       "    Events: 1,202,267\n",
       "    Activities: 26"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from skpm.event_logs import split, BPI17\n",
    "from skpm.feature_extraction import TimestampExtractor\n",
    "\n",
    "# download the dataset\n",
    "log = BPI17()\n",
    "log"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c2a4194",
   "metadata": {},
   "source": [
    "We'll use the unbiased split strategy in this tutorial and extract features accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b599d555",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = split.unbiased(log.dataframe, **log.unbiased_split_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65b2483b",
   "metadata": {},
   "source": [
    "## Time-related features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a1535e98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((805809, 33), (294882, 33))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "te = TimestampExtractor().fit(train)\n",
    "train[te.get_feature_names_out()] = te.transform(train)\n",
    "test[te.get_feature_names_out()] = te.transform(test)\n",
    "\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "612c3540",
   "metadata": {},
   "source": [
    "In the literature, features like the weekday are usually extracted as a categorical features, but we currently implement it as a numerical by normalizing the values between `[-0.5, 0.5]`. In the future, we intend to provide a parameter to choose between the two options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "94241d23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "accumulated_time                0.0\n",
       "execution_time                0.048\n",
       "remaining_time          1144676.119\n",
       "day_of_month                   -0.5\n",
       "day_of_week                0.166667\n",
       "day_of_year                    -0.5\n",
       "hour_of_day               -0.108696\n",
       "min_of_hour                0.364407\n",
       "month_of_year                  -0.5\n",
       "numerical_timestamp    1451641875.0\n",
       "sec_of_min                -0.245763\n",
       "secs_since_sunday         -0.441344\n",
       "secs_within_day            -0.08941\n",
       "week_of_year                    0.5\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[0, te.get_feature_names_out()].T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f80c51",
   "metadata": {},
   "source": [
    "## Resource-related features\n",
    "\n",
    "The resource pool extractor is a feature extractor that identifies\n",
    "resource roles based on the correlation between activities and resources.\n",
    "You can easily use this function as shown below:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "63435dde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "resource_role\n",
       "2    710158\n",
       "1     94110\n",
       "3      1522\n",
       "4        17\n",
       "5         2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from skpm.feature_extraction import ResourcePoolExtractor\n",
    "\n",
    "re = ResourcePoolExtractor(threshold=0.7).fit(train)\n",
    "train[\"resource_role\"] = re.transform(train)\n",
    "\n",
    "train[\"resource_role\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "365697f1",
   "metadata": {},
   "source": [
    "The `threshold` defines the minimum correlation required to assign a role between a resource and an activity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "aad20b0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "resource_roles\n",
       "2                 407870\n",
       "3                 301954\n",
       "1                  94110\n",
       "4                   1522\n",
       "9                    266\n",
       "5                     36\n",
       "8                     32\n",
       "6                     17\n",
       "7                      2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ResourcePoolExtractor(threshold=0.9).fit_transform(train).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c68ed41",
   "metadata": {},
   "source": [
    "From the machine learning perspective, it can be seen as a nice way\n",
    "to encode the resource information and reduce the dimensionality of the\n",
    "data. In this example (`threshold=0.7`), we grouped 133 resource labels into 5 roles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "72df461d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 2 artists>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from skpm.config import EventLogConfig as elc\n",
    "plt.style.use(\"ggplot\")\n",
    "\n",
    "features = train[[elc.resource, \"resource_role\"]].nunique().index.values\n",
    "values = train[[elc.resource, \"resource_role\"]].nunique().values\n",
    "\n",
    "plt.figure(figsize=(8,2))\n",
    "plt.bar(features, values, edgecolor=\"black\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebf1c128",
   "metadata": {},
   "source": [
    "## Inter-case features\n",
    "\n",
    "Inter-case features refer to features that are computed based on the\n",
    "relationship between different cases. It aims to quantify and module\n",
    "the resource sharing between cases, for instance. In the current version\n",
    "of our library, we only have a simple example of such feature: the number of\n",
    "cases in progress simultaneously. This feature is commonly called\n",
    "work in progress.\n",
    "\n",
    "Let's see how it works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e9c939c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average daily \\nWork in Progress (WIP) over time')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from skpm.feature_extraction import WorkInProgress\n",
    "\n",
    "wip = WorkInProgress()\n",
    "wip.fit(train)\n",
    "train[\"wip\"] = wip.transform(train)\n",
    "\n",
    "data = (\n",
    "    train\n",
    "    .set_index(elc.timestamp)\n",
    "    .resample(\"D\")[[\"wip\"]]\n",
    "    .mean()\n",
    "    .reset_index()\n",
    ")\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.plot(pd.to_datetime(data[elc.timestamp]), data[\"wip\"])\n",
    "plt.title(\"Average daily \\nWork in Progress (WIP) over time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26bd38d9",
   "metadata": {},
   "source": [
    "In this tutorial, we showed how to extract features from timestamps,\n",
    "resources, and the inter-case perspective. We hope you find it useful\n",
    "for your projects. If you have any questions or suggestions, please\n",
    "open an issue on our GitHub repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8d9aa048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>Created</td>\n",
       "      <td>statechange</td>\n",
       "      <td>Created</td>\n",
       "      <td>Deleted</td>\n",
       "      <td>Created</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org:resource</th>\n",
       "      <td>User_1</td>\n",
       "      <td>User_1</td>\n",
       "      <td>User_1</td>\n",
       "      <td>User_1</td>\n",
       "      <td>User_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concept:name</th>\n",
       "      <td>A_Create Application</td>\n",
       "      <td>A_Submitted</td>\n",
       "      <td>W_Handle leads</td>\n",
       "      <td>W_Handle leads</td>\n",
       "      <td>W_Complete application</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EventOrigin</th>\n",
       "      <td>Application</td>\n",
       "      <td>Application</td>\n",
       "      <td>Workflow</td>\n",
       "      <td>Workflow</td>\n",
       "      <td>Workflow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EventID</th>\n",
       "      <td>Application_652823628</td>\n",
       "      <td>ApplState_1582051990</td>\n",
       "      <td>Workitem_1298499574</td>\n",
       "      <td>Workitem_1673366067</td>\n",
       "      <td>Workitem_1493664571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lifecycle:transition</th>\n",
       "      <td>complete</td>\n",
       "      <td>complete</td>\n",
       "      <td>schedule</td>\n",
       "      <td>withdraw</td>\n",
       "      <td>schedule</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time:timestamp</th>\n",
       "      <td>2016-01-01 09:51:15.304000</td>\n",
       "      <td>2016-01-01 09:51:15.352000</td>\n",
       "      <td>2016-01-01 09:51:15.774000</td>\n",
       "      <td>2016-01-01 09:52:36.392000</td>\n",
       "      <td>2016-01-01 09:52:36.403000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case:LoanGoal</th>\n",
       "      <td>Existing loan takeover</td>\n",
       "      <td>Existing loan takeover</td>\n",
       "      <td>Existing loan takeover</td>\n",
       "      <td>Existing loan takeover</td>\n",
       "      <td>Existing loan takeover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case:ApplicationType</th>\n",
       "      <td>New credit</td>\n",
       "      <td>New credit</td>\n",
       "      <td>New credit</td>\n",
       "      <td>New credit</td>\n",
       "      <td>New credit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case:concept:name</th>\n",
       "      <td>Application_652823628</td>\n",
       "      <td>Application_652823628</td>\n",
       "      <td>Application_652823628</td>\n",
       "      <td>Application_652823628</td>\n",
       "      <td>Application_652823628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case:RequestedAmount</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FirstWithdrawalAmount</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTerms</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accepted</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCost</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Selected</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CreditScore</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OfferedAmount</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OfferID</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accumulated_time</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.47</td>\n",
       "      <td>81.088</td>\n",
       "      <td>81.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>execution_time</th>\n",
       "      <td>0.048</td>\n",
       "      <td>0.422</td>\n",
       "      <td>80.618</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>remaining_time</th>\n",
       "      <td>1144676.119</td>\n",
       "      <td>1144676.071</td>\n",
       "      <td>1144675.649</td>\n",
       "      <td>1144595.031</td>\n",
       "      <td>1144595.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_month</th>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_week</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_year</th>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour_of_day</th>\n",
       "      <td>-0.108696</td>\n",
       "      <td>-0.108696</td>\n",
       "      <td>-0.108696</td>\n",
       "      <td>-0.108696</td>\n",
       "      <td>-0.108696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_of_hour</th>\n",
       "      <td>0.364407</td>\n",
       "      <td>0.364407</td>\n",
       "      <td>0.364407</td>\n",
       "      <td>0.381356</td>\n",
       "      <td>0.381356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_of_year</th>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numerical_timestamp</th>\n",
       "      <td>1451641875.0</td>\n",
       "      <td>1451641875.0</td>\n",
       "      <td>1451641875.0</td>\n",
       "      <td>1451641956.0</td>\n",
       "      <td>1451641956.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sec_of_min</th>\n",
       "      <td>-0.245763</td>\n",
       "      <td>-0.245763</td>\n",
       "      <td>-0.245763</td>\n",
       "      <td>0.110169</td>\n",
       "      <td>0.110169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secs_since_sunday</th>\n",
       "      <td>-0.441344</td>\n",
       "      <td>-0.441344</td>\n",
       "      <td>-0.441344</td>\n",
       "      <td>-0.44121</td>\n",
       "      <td>-0.44121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secs_within_day</th>\n",
       "      <td>-0.08941</td>\n",
       "      <td>-0.08941</td>\n",
       "      <td>-0.08941</td>\n",
       "      <td>-0.088472</td>\n",
       "      <td>-0.088472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week_of_year</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resource_role</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wip</th>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                0                           1  \\\n",
       "Action                                    Created                 statechange   \n",
       "org:resource                               User_1                      User_1   \n",
       "concept:name                 A_Create Application                 A_Submitted   \n",
       "EventOrigin                           Application                 Application   \n",
       "EventID                     Application_652823628        ApplState_1582051990   \n",
       "lifecycle:transition                     complete                    complete   \n",
       "time:timestamp         2016-01-01 09:51:15.304000  2016-01-01 09:51:15.352000   \n",
       "case:LoanGoal              Existing loan takeover      Existing loan takeover   \n",
       "case:ApplicationType                   New credit                  New credit   \n",
       "case:concept:name           Application_652823628       Application_652823628   \n",
       "case:RequestedAmount                      20000.0                     20000.0   \n",
       "FirstWithdrawalAmount                        None                        None   \n",
       "NumberOfTerms                                None                        None   \n",
       "Accepted                                     None                        None   \n",
       "MonthlyCost                                  None                        None   \n",
       "Selected                                     None                        None   \n",
       "CreditScore                                  None                        None   \n",
       "OfferedAmount                                None                        None   \n",
       "OfferID                                      None                        None   \n",
       "accumulated_time                              0.0                       0.048   \n",
       "execution_time                              0.048                       0.422   \n",
       "remaining_time                        1144676.119                 1144676.071   \n",
       "day_of_month                                 -0.5                        -0.5   \n",
       "day_of_week                              0.166667                    0.166667   \n",
       "day_of_year                                  -0.5                        -0.5   \n",
       "hour_of_day                             -0.108696                   -0.108696   \n",
       "min_of_hour                              0.364407                    0.364407   \n",
       "month_of_year                                -0.5                        -0.5   \n",
       "numerical_timestamp                  1451641875.0                1451641875.0   \n",
       "sec_of_min                              -0.245763                   -0.245763   \n",
       "secs_since_sunday                       -0.441344                   -0.441344   \n",
       "secs_within_day                          -0.08941                    -0.08941   \n",
       "week_of_year                                  0.5                         0.5   \n",
       "resource_role                                   1                           1   \n",
       "wip                                          19.0                        19.0   \n",
       "\n",
       "                                                2                           3  \\\n",
       "Action                                    Created                     Deleted   \n",
       "org:resource                               User_1                      User_1   \n",
       "concept:name                       W_Handle leads              W_Handle leads   \n",
       "EventOrigin                              Workflow                    Workflow   \n",
       "EventID                       Workitem_1298499574         Workitem_1673366067   \n",
       "lifecycle:transition                     schedule                    withdraw   \n",
       "time:timestamp         2016-01-01 09:51:15.774000  2016-01-01 09:52:36.392000   \n",
       "case:LoanGoal              Existing loan takeover      Existing loan takeover   \n",
       "case:ApplicationType                   New credit                  New credit   \n",
       "case:concept:name           Application_652823628       Application_652823628   \n",
       "case:RequestedAmount                      20000.0                     20000.0   \n",
       "FirstWithdrawalAmount                        None                        None   \n",
       "NumberOfTerms                                None                        None   \n",
       "Accepted                                     None                        None   \n",
       "MonthlyCost                                  None                        None   \n",
       "Selected                                     None                        None   \n",
       "CreditScore                                  None                        None   \n",
       "OfferedAmount                                None                        None   \n",
       "OfferID                                      None                        None   \n",
       "accumulated_time                             0.47                      81.088   \n",
       "execution_time                             80.618                       0.011   \n",
       "remaining_time                        1144675.649                 1144595.031   \n",
       "day_of_month                                 -0.5                        -0.5   \n",
       "day_of_week                              0.166667                    0.166667   \n",
       "day_of_year                                  -0.5                        -0.5   \n",
       "hour_of_day                             -0.108696                   -0.108696   \n",
       "min_of_hour                              0.364407                    0.381356   \n",
       "month_of_year                                -0.5                        -0.5   \n",
       "numerical_timestamp                  1451641875.0                1451641956.0   \n",
       "sec_of_min                              -0.245763                    0.110169   \n",
       "secs_since_sunday                       -0.441344                    -0.44121   \n",
       "secs_within_day                          -0.08941                   -0.088472   \n",
       "week_of_year                                  0.5                         0.5   \n",
       "resource_role                                   1                           1   \n",
       "wip                                          19.0                        19.0   \n",
       "\n",
       "                                                4  \n",
       "Action                                    Created  \n",
       "org:resource                               User_1  \n",
       "concept:name               W_Complete application  \n",
       "EventOrigin                              Workflow  \n",
       "EventID                       Workitem_1493664571  \n",
       "lifecycle:transition                     schedule  \n",
       "time:timestamp         2016-01-01 09:52:36.403000  \n",
       "case:LoanGoal              Existing loan takeover  \n",
       "case:ApplicationType                   New credit  \n",
       "case:concept:name           Application_652823628  \n",
       "case:RequestedAmount                      20000.0  \n",
       "FirstWithdrawalAmount                        None  \n",
       "NumberOfTerms                                None  \n",
       "Accepted                                     None  \n",
       "MonthlyCost                                  None  \n",
       "Selected                                     None  \n",
       "CreditScore                                  None  \n",
       "OfferedAmount                                None  \n",
       "OfferID                                      None  \n",
       "accumulated_time                           81.099  \n",
       "execution_time                               0.01  \n",
       "remaining_time                         1144595.02  \n",
       "day_of_month                                 -0.5  \n",
       "day_of_week                              0.166667  \n",
       "day_of_year                                  -0.5  \n",
       "hour_of_day                             -0.108696  \n",
       "min_of_hour                              0.381356  \n",
       "month_of_year                                -0.5  \n",
       "numerical_timestamp                  1451641956.0  \n",
       "sec_of_min                               0.110169  \n",
       "secs_since_sunday                        -0.44121  \n",
       "secs_within_day                         -0.088472  \n",
       "week_of_year                                  0.5  \n",
       "resource_role                                   1  \n",
       "wip                                          19.0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head().T"
   ]
  }
 ],
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