00_visualization.ipynb

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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 20.3.1; however, version 21.1.2 is available.\n",
      "You should consider upgrading via the 'c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    },
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 20.3.1; however, version 21.1.2 is available.\n",
      "You should consider upgrading via the 'c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "! pip install pm4py\n",
    "! pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pm4py\n",
    "import pandas as pd\n",
    "import datetime\n",
    "from math import ceil\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from importlib import reload\n",
    "import random\n",
    "import seaborn as sns\n",
    "from IPython import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is it about\n",
    "## Data\n",
    "We have data of processes - `bpi12`. It is a sequence of (`time_stamp`, `activity`,...) \n",
    "and other stuff which is not considered by community. Basicly here it is `BPI_Challenge_2012.xes`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <td>W_Completeren aanvraag</td>\n",
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       "    <tr>\n",
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       "      <td>112</td>\n",
       "      <td>SCHEDULE</td>\n",
       "      <td>W_Afhandelen leads</td>\n",
       "      <td>2012-02-29 23:52:01.287000+01:00</td>\n",
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       "      <td>COMPLETE</td>\n",
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       "      <td>2012-03-01 09:27:41.325000+01:00</td>\n",
       "      <td>2012-02-29 23:51:16.799000+01:00</td>\n",
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       "<p>262200 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "       org:resource lifecycle:transition            concept:name  \\\n",
       "0               112             COMPLETE             A_SUBMITTED   \n",
       "1               112             COMPLETE       A_PARTLYSUBMITTED   \n",
       "2               112             COMPLETE           A_PREACCEPTED   \n",
       "3               112             SCHEDULE  W_Completeren aanvraag   \n",
       "4               NaN                START  W_Completeren aanvraag   \n",
       "...             ...                  ...                     ...   \n",
       "262195          112             COMPLETE       A_PARTLYSUBMITTED   \n",
       "262196          112             SCHEDULE      W_Afhandelen leads   \n",
       "262197        11169                START      W_Afhandelen leads   \n",
       "262198        11169             COMPLETE              A_DECLINED   \n",
       "262199        11169             COMPLETE      W_Afhandelen leads   \n",
       "\n",
       "                          time:timestamp                     case:REG_DATE  \\\n",
       "0       2011-10-01 00:38:44.546000+02:00  2011-10-01 00:38:44.546000+02:00   \n",
       "1       2011-10-01 00:38:44.880000+02:00  2011-10-01 00:38:44.546000+02:00   \n",
       "2       2011-10-01 00:39:37.906000+02:00  2011-10-01 00:38:44.546000+02:00   \n",
       "3       2011-10-01 00:39:38.875000+02:00  2011-10-01 00:38:44.546000+02:00   \n",
       "4       2011-10-01 11:36:46.437000+02:00  2011-10-01 00:38:44.546000+02:00   \n",
       "...                                  ...                               ...   \n",
       "262195  2012-02-29 23:51:17.423000+01:00  2012-02-29 23:51:16.799000+01:00   \n",
       "262196  2012-02-29 23:52:01.287000+01:00  2012-02-29 23:51:16.799000+01:00   \n",
       "262197  2012-03-01 09:26:46.736000+01:00  2012-02-29 23:51:16.799000+01:00   \n",
       "262198  2012-03-01 09:27:37.118000+01:00  2012-02-29 23:51:16.799000+01:00   \n",
       "262199  2012-03-01 09:27:41.325000+01:00  2012-02-29 23:51:16.799000+01:00   \n",
       "\n",
       "       case:concept:name case:AMOUNT_REQ  \n",
       "0                 173688           20000  \n",
       "1                 173688           20000  \n",
       "2                 173688           20000  \n",
       "3                 173688           20000  \n",
       "4                 173688           20000  \n",
       "...                  ...             ...  \n",
       "262195            214376           15000  \n",
       "262196            214376           15000  \n",
       "262197            214376           15000  \n",
       "262198            214376           15000  \n",
       "262199            214376           15000  \n",
       "\n",
       "[262200 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = 'datasets/BPI_Challenge_2012.xes'\n",
    "event_log = pm4py.read_xes(file_path)\n",
    "df = pm4py.convert_to_dataframe(event_log)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We works only wuith activity (which is about completion of process) and time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pm4py.convert_to_dataframe(event_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pm4py.convert_to_dataframe(event_log)\n",
    "df = df[df['lifecycle:transition']=='COMPLETE']\n",
    "# work subprocess, starts with 'W_'\n",
    "is_work_sp = df['concept:name'].apply(lambda x: True if x[:2] == 'W_' else False)\n",
    "df = df[is_work_sp]\n",
    "\n",
    "df = df[['time:timestamp', 'case:concept:name', 'concept:name']]\n",
    "df = df.rename(columns={'time:timestamp': 'timestamp', 'case:concept:name': 'trace_id', 'concept:name': 'activity'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'W_Afhandelen leads',\n",
       " 'W_Beoordelen fraude',\n",
       " 'W_Completeren aanvraag',\n",
       " 'W_Nabellen incomplete dossiers',\n",
       " 'W_Nabellen offertes',\n",
       " 'W_Valideren aanvraag'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df['activity'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "taces_with_nans = []\n",
    "traces = set(df['trace_id'].values)\n",
    "l = len(traces)\n",
    "for _i, t_id in enumerate(traces):\n",
    "    if df[df['trace_id'] == t_id].isna().sum().sum() > 0:\n",
    "        taces_with_nans.append(t_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(taces_with_nans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>timestamp</th>\n",
       "      <th>trace_id</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-10-01 11:45:13.917000+02:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>W_Completeren aanvraag</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-10-01 12:17:08.924000+02:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>W_Nabellen offertes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-10-08 16:32:00.886000+02:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>W_Nabellen offertes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-10-10 11:33:05.791000+02:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>W_Nabellen offertes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-10-13 10:37:37.026000+02:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>W_Valideren aanvraag</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72408</th>\n",
       "      <td>2012-03-01 09:28:21.228000+01:00</td>\n",
       "      <td>214370</td>\n",
       "      <td>W_Afhandelen leads</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72409</th>\n",
       "      <td>2012-03-01 09:27:52.388000+01:00</td>\n",
       "      <td>214373</td>\n",
       "      <td>W_Afhandelen leads</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72410</th>\n",
       "      <td>2012-03-01 20:22:41.157000+01:00</td>\n",
       "      <td>214373</td>\n",
       "      <td>W_Completeren aanvraag</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72411</th>\n",
       "      <td>2012-03-10 12:50:54.881000+01:00</td>\n",
       "      <td>214373</td>\n",
       "      <td>W_Nabellen offertes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72412</th>\n",
       "      <td>2012-03-01 09:27:41.325000+01:00</td>\n",
       "      <td>214376</td>\n",
       "      <td>W_Afhandelen leads</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72413 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              timestamp  trace_id                activity\n",
       "0      2011-10-01 11:45:13.917000+02:00    173688  W_Completeren aanvraag\n",
       "1      2011-10-01 12:17:08.924000+02:00    173688     W_Nabellen offertes\n",
       "2      2011-10-08 16:32:00.886000+02:00    173688     W_Nabellen offertes\n",
       "3      2011-10-10 11:33:05.791000+02:00    173688     W_Nabellen offertes\n",
       "4      2011-10-13 10:37:37.026000+02:00    173688    W_Valideren aanvraag\n",
       "...                                 ...       ...                     ...\n",
       "72408  2012-03-01 09:28:21.228000+01:00    214370      W_Afhandelen leads\n",
       "72409  2012-03-01 09:27:52.388000+01:00    214373      W_Afhandelen leads\n",
       "72410  2012-03-01 20:22:41.157000+01:00    214373  W_Completeren aanvraag\n",
       "72411  2012-03-10 12:50:54.881000+01:00    214373     W_Nabellen offertes\n",
       "72412  2012-03-01 09:27:41.325000+01:00    214376      W_Afhandelen leads\n",
       "\n",
       "[72413 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['trace_id'] = df['trace_id'].apply(lambda x: int(x))\n",
    "df.reset_index(inplace=True)\n",
    "df.drop(columns='index', inplace=True)\n",
    "df.to_csv('datasets/bpi_12.csv', index=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some defenitions must be mentioned:\n",
    "- event ($e_i$) - {`activity` ($a$), `time_stamp` ($t$)}\n",
    "- trace (t) - $[e_1,\\ e_2, \\dots]$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Purpose\n",
    "Main objective - predict next `time_stamp` and `activity`. \n",
    "\n",
    "For activity metric, used by community is accuracy, and in latest works `f1`. For `time_stamp` ussualy MAE, sometimes weighted MAE.\n",
    "\n",
    "Nevetheless some other objectives are discovered - whole trace prediction. The metric for activity prediction - Damerau-Levenshtein Similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization\n",
    "## Activity transition\n",
    "Firstly let's take a look on all activities transition  and try to estimate $P(a_{i+1} | a_i)$. It is a square matrix, which worth to be presented as `sns.heatmap`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "activities = list(set(df['activity'].values))\n",
    "n_act = len(activities)\n",
    "traces = list(set(df['trace_id'].values))\n",
    "_df = df.copy()\n",
    "_df['activity_prev'] = _df['activity'].shift(1)\n",
    "_df['trace_id_prev'] = _df['trace_id'].shift(1)\n",
    "_df = _df.dropna()\n",
    "_df = _df[_df['trace_id_prev'] == _df['trace_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "transitions_counts = np.zeros((n_act, n_act))\n",
    "nums_of_ac_next = []\n",
    "for _i_next, act_next in enumerate(activities):\n",
    "    _df_ac_next = _df[_df['activity'] == act_next]\n",
    "    nums_of_ac_next.append(_df_ac_next.shape[0])\n",
    "    for _i_prev, act_prev in enumerate(activities):\n",
    "        transitions_counts[ _i_prev, _i_next] += _df_ac_next[_df_ac_next['activity_prev'] == act_prev].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transitions_counts[transitions_counts == 0] = np.nan\n",
    "sns.set(rc={'figure.facecolor':'white'})\n",
    "sns.heatmap(transitions_counts, xticklabels=list(activities), yticklabels=list(activities))\n",
    "plt.title('Total number of transition  $a_t -> a_{t+1}$')\n",
    "\n",
    "plt.xlabel('$a_i$')\n",
    "plt.ylabel('$a_{i+1}$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 17 not existing transitions\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {np.isnan(transitions_counts).sum()} not existing transitions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-14-6ef5bc085097>:1: RuntimeWarning: invalid value encountered in less\n",
      "  transitions_counts[transitions_counts < 10] = np.nan\n"
     ]
    }
   ],
   "source": [
    "transitions_counts[transitions_counts < 10] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.facecolor':'white'})\n",
    "sns.heatmap(transitions_counts\n",
    "            , xticklabels=list(activities), yticklabels=list(activities), annot=True)\n",
    "plt.title('Total number of transition  $a_t -> a_{t+1}$')\n",
    "\n",
    "plt.xlabel('$a_{i+1}$')\n",
    "plt.ylabel('$a_i$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 21 not existing transitions\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {np.isnan(transitions_counts).sum()} not existing transitions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cant say there are much rare transitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prob_matrix = transitions_counts / np.nan_to_num(transitions_counts, 0).sum(axis=1).reshape(-1, 1)\n",
    "sns.set(rc={'figure.facecolor':'white'})\n",
    "sns.heatmap(prob_matrix,\n",
    "            xticklabels=list(activities), yticklabels=list(activities), \n",
    "            annot=True, fmt=\".2f\"\n",
    "           )\n",
    "plt.title('Probability of transition  $a_t -> a_{t+1}$')\n",
    "plt.xlabel('$a_{i+1}$')\n",
    "plt.ylabel('$a_i$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dull baseline 0.7644911945174915\n"
     ]
    }
   ],
   "source": [
    "filled = np.nan_to_num(transitions_counts, 0)\n",
    "print(f'dull baseline {np.diagonal(filled).sum() / filled.sum()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can't say this dataset has uniform distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time features\n",
    "For leveraging predictive models the following features are created:\n",
    "- $t_e$ - time since previous event\n",
    "- $t_w$ - time since the beginning of week\n",
    "- $t_t$ - time since the beginning of trace\n",
    "\n",
    "let's visualize all these features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>timestamp</th>\n",
       "      <th>trace_id</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-10-01 11:45:13.917000+02:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>W_Completeren aanvraag</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-10-01 12:17:08.924000+02:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>W_Nabellen offertes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-10-08 16:32:00.886000+02:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>W_Nabellen offertes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-10-10 11:33:05.791000+02:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>W_Nabellen offertes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-10-13 10:37:37.026000+02:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>W_Valideren aanvraag</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72408</th>\n",
       "      <td>2012-03-01 09:28:21.228000+01:00</td>\n",
       "      <td>214370</td>\n",
       "      <td>W_Afhandelen leads</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72409</th>\n",
       "      <td>2012-03-01 09:27:52.388000+01:00</td>\n",
       "      <td>214373</td>\n",
       "      <td>W_Afhandelen leads</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72410</th>\n",
       "      <td>2012-03-01 20:22:41.157000+01:00</td>\n",
       "      <td>214373</td>\n",
       "      <td>W_Completeren aanvraag</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72411</th>\n",
       "      <td>2012-03-10 12:50:54.881000+01:00</td>\n",
       "      <td>214373</td>\n",
       "      <td>W_Nabellen offertes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72412</th>\n",
       "      <td>2012-03-01 09:27:41.325000+01:00</td>\n",
       "      <td>214376</td>\n",
       "      <td>W_Afhandelen leads</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72413 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              timestamp  trace_id                activity\n",
       "0      2011-10-01 11:45:13.917000+02:00    173688  W_Completeren aanvraag\n",
       "1      2011-10-01 12:17:08.924000+02:00    173688     W_Nabellen offertes\n",
       "2      2011-10-08 16:32:00.886000+02:00    173688     W_Nabellen offertes\n",
       "3      2011-10-10 11:33:05.791000+02:00    173688     W_Nabellen offertes\n",
       "4      2011-10-13 10:37:37.026000+02:00    173688    W_Valideren aanvraag\n",
       "...                                 ...       ...                     ...\n",
       "72408  2012-03-01 09:28:21.228000+01:00    214370      W_Afhandelen leads\n",
       "72409  2012-03-01 09:27:52.388000+01:00    214373      W_Afhandelen leads\n",
       "72410  2012-03-01 20:22:41.157000+01:00    214373  W_Completeren aanvraag\n",
       "72411  2012-03-10 12:50:54.881000+01:00    214373     W_Nabellen offertes\n",
       "72412  2012-03-01 09:27:41.325000+01:00    214376      W_Afhandelen leads\n",
       "\n",
       "[72413 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rl4pm_lib.preprocessing as preprocessing\n",
    "\n",
    "\n",
    "prepro = preprocessing.DfPreprocesser()\n",
    "prepro.fit(df)\n",
    "df_preprocessed = prepro.transform(df)\n",
    "df_preprocessed.to_csv('bpi_12_preprocessed.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>tt</th>\n",
       "      <th>te</th>\n",
       "      <th>tw</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>trace_id</th>\n",
       "      <th>W_Afhandelen leads</th>\n",
       "      <th>W_Beoordelen fraude</th>\n",
       "      <th>W_Completeren aanvraag</th>\n",
       "      <th>W_Nabellen incomplete dossiers</th>\n",
       "      <th>W_Nabellen offertes</th>\n",
       "      <th>W_Valideren aanvraag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>474313.917</td>\n",
       "      <td>2011-10-01 11:45:13.917000+02:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1915.007</td>\n",
       "      <td>1915.007</td>\n",
       "      <td>476228.924</td>\n",
       "      <td>2011-10-01 12:17:08.924000+02:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>622006.969</td>\n",
       "      <td>620091.962</td>\n",
       "      <td>491520.886</td>\n",
       "      <td>2011-10-08 16:32:00.886000+02:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>776871.874</td>\n",
       "      <td>154864.905</td>\n",
       "      <td>41585.791</td>\n",
       "      <td>2011-10-10 11:33:05.791000+02:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1032743.109</td>\n",
       "      <td>255871.235</td>\n",
       "      <td>297457.026</td>\n",
       "      <td>2011-10-13 10:37:37.026000+02:00</td>\n",
       "      <td>173688</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72408</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>293301.228</td>\n",
       "      <td>2012-03-01 09:28:21.228000+01:00</td>\n",
       "      <td>214370</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72409</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>293272.388</td>\n",
       "      <td>2012-03-01 09:27:52.388000+01:00</td>\n",
       "      <td>214373</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72410</th>\n",
       "      <td>39288.769</td>\n",
       "      <td>39288.769</td>\n",
       "      <td>332561.157</td>\n",
       "      <td>2012-03-01 20:22:41.157000+01:00</td>\n",
       "      <td>214373</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72411</th>\n",
       "      <td>789782.493</td>\n",
       "      <td>750493.724</td>\n",
       "      <td>478254.881</td>\n",
       "      <td>2012-03-10 12:50:54.881000+01:00</td>\n",
       "      <td>214373</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72412</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>293261.325</td>\n",
       "      <td>2012-03-01 09:27:41.325000+01:00</td>\n",
       "      <td>214376</td>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72413 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tt          te          tw                         timestamp  \\\n",
       "0            0.000       0.000  474313.917  2011-10-01 11:45:13.917000+02:00   \n",
       "1         1915.007    1915.007  476228.924  2011-10-01 12:17:08.924000+02:00   \n",
       "2       622006.969  620091.962  491520.886  2011-10-08 16:32:00.886000+02:00   \n",
       "3       776871.874  154864.905   41585.791  2011-10-10 11:33:05.791000+02:00   \n",
       "4      1032743.109  255871.235  297457.026  2011-10-13 10:37:37.026000+02:00   \n",
       "...            ...         ...         ...                               ...   \n",
       "72408        0.000       0.000  293301.228  2012-03-01 09:28:21.228000+01:00   \n",
       "72409        0.000       0.000  293272.388  2012-03-01 09:27:52.388000+01:00   \n",
       "72410    39288.769   39288.769  332561.157  2012-03-01 20:22:41.157000+01:00   \n",
       "72411   789782.493  750493.724  478254.881  2012-03-10 12:50:54.881000+01:00   \n",
       "72412        0.000       0.000  293261.325  2012-03-01 09:27:41.325000+01:00   \n",
       "\n",
       "       trace_id  W_Afhandelen leads  W_Beoordelen fraude  \\\n",
       "0        173688                   0                    0   \n",
       "1        173688                   0                    0   \n",
       "2        173688                   0                    0   \n",
       "3        173688                   0                    0   \n",
       "4        173688                   0                    0   \n",
       "...         ...                 ...                  ...   \n",
       "72408    214370                   1                    0   \n",
       "72409    214373                   1                    0   \n",
       "72410    214373                   0                    0   \n",
       "72411    214373                   0                    0   \n",
       "72412    214376                   1                    0   \n",
       "\n",
       "       W_Completeren aanvraag  W_Nabellen incomplete dossiers  \\\n",
       "0                           1                               0   \n",
       "1                           0                               0   \n",
       "2                           0                               0   \n",
       "3                           0                               0   \n",
       "4                           0                               0   \n",
       "...                       ...                             ...   \n",
       "72408                       0                               0   \n",
       "72409                       0                               0   \n",
       "72410                       1                               0   \n",
       "72411                       0                               0   \n",
       "72412                       0                               0   \n",
       "\n",
       "       W_Nabellen offertes  W_Valideren aanvraag  \n",
       "0                        0                     0  \n",
       "1                        1                     0  \n",
       "2                        1                     0  \n",
       "3                        1                     0  \n",
       "4                        0                     1  \n",
       "...                    ...                   ...  \n",
       "72408                    0                     0  \n",
       "72409                    0                     0  \n",
       "72410                    0                     0  \n",
       "72411                    1                     0  \n",
       "72412                    0                     0  \n",
       "\n",
       "[72413 rows x 11 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preprocessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tts = []\n",
    "for _tid in set(df_preprocessed['trace_id'].values):\n",
    "    tts.append(df_preprocessed[df_preprocessed['trace_id']==_tid]['tt'].values[-1])\n",
    "    \n",
    "plt.hist(np.array(tts) / 3600 / 24, bins=50)\n",
    "plt.title('$t_t$ distribution')\n",
    "plt.xlabel('$t_t$, days')\n",
    "plt.ylabel('count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5204848279682104"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preprocessed['te'].mean()  /3600 / 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12482668981481482"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preprocessed['te'].median()  /3600 / 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_preprocessed['te'].values /3600 / 24, bins=50)\n",
    "plt.title('$t_e$ distribution')\n",
    "plt.xlabel('$t_e$, days')\n",
    "plt.ylabel('count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "low_bond = 24 *  60 * 60\n",
    "plt.hist(df_preprocessed[df_preprocessed['te'] > low_bond]['te'].values  /3600 / 24, bins=50)\n",
    "plt.title(f'$t_e >$ {low_bond / 60 / 60: .1f} h distribution')\n",
    "plt.xlabel('$t_e$, days')\n",
    "plt.ylabel('count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This distribution is far from uniform too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90.0 % of $te$ are less then  140.73 h\n",
      "95.0 % of $te$ are less then  173.25 h\n",
      "97.0 % of $te$ are less then  193.57 h\n",
      "99.0 % of $te$ are less then  307.72 h\n"
     ]
    }
   ],
   "source": [
    "for quant in [0.9, 0.95, 0.97, 0.99]:\n",
    "    print(f'{quant * 100} % of $te$ are less then {np.quantile(df_preprocessed[\"te\"].values, quant) / (60 * 60): .2f} h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is wrong with this visualization\n",
    "- transition matrix has a lot of `nan`\n",
    "- Whant to see which transitions lead to high $t_e$ \n",
    "### SberProcessMining\n",
    "Let's use [this](https://github.com/SberProcessMining/Sber_Process_Mining) repo to got a nice visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sberpm in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (1.1.0)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from sberpm) (3.3.2)\n",
      "Requirement already satisfied: graphviz>=0.13.2 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from sberpm) (0.16)\n",
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      "Requirement already satisfied: IPython in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from sberpm) (7.18.1)\n",
      "Requirement already satisfied: numpy>=1.18.1 in c:\\users\\pc\\appdata\\roaming\\python\\python38\\site-packages (from sberpm) (1.18.5)\n",
      "Requirement already satisfied: scikit-learn>=0.23.2 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from sberpm) (0.23.2)\n",
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      "Requirement already satisfied: backcall in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from IPython->sberpm) (0.2.0)\n",
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      "Requirement already satisfied: pickleshare in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from IPython->sberpm) (0.7.5)\n",
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      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib->sberpm) (7.2.0)\n",
      "Requirement already satisfied: numpy>=1.18.1 in c:\\users\\pc\\appdata\\roaming\\python\\python38\\site-packages (from sberpm) (1.18.5)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib->sberpm) (2020.6.20)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib->sberpm) (2.4.7)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib->sberpm) (0.10.0)\n",
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      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib->sberpm) (1.2.0)\n",
      "Requirement already satisfied: six in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from cycler>=0.10->matplotlib->sberpm) (1.15.0)\n",
      "Requirement already satisfied: jupyter-core in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from nbformat>=4.2.0->sberpm) (4.6.3)\n",
      "Requirement already satisfied: ipython-genutils in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from nbformat>=4.2.0->sberpm) (0.2.0)\n",
      "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from nbformat>=4.2.0->sberpm) (3.2.0)\n",
      "Requirement already satisfied: traitlets>=4.2 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from IPython->sberpm) (5.0.5)\n",
      "Requirement already satisfied: attrs>=17.4.0 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->sberpm) (20.2.0)\n",
      "Requirement already satisfied: setuptools>=18.5 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from IPython->sberpm) (51.0.0)\n",
      "Requirement already satisfied: six in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from cycler>=0.10->matplotlib->sberpm) (1.15.0)\n",
      "Requirement already satisfied: pyrsistent>=0.14.0 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->sberpm) (0.17.3)\n",
      "Requirement already satisfied: pywin32>=1.0 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from jupyter-core->nbformat>=4.2.0->sberpm) (228)\n",
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      "Requirement already satisfied: numpy>=1.18.1 in c:\\users\\pc\\appdata\\roaming\\python\\python38\\site-packages (from sberpm) (1.18.5)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from pandas>=1.1.0->sberpm) (2020.1)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib->sberpm) (2.8.1)\n",
      "Requirement already satisfied: retrying>=1.3.3 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from plotly>=4.9.0->sberpm) (1.3.3)\n",
      "Requirement already satisfied: six in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from cycler>=0.10->matplotlib->sberpm) (1.15.0)\n",
      "Requirement already satisfied: wcwidth in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->IPython->sberpm) (0.2.5)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib->sberpm) (2.4.7)\n",
      "Requirement already satisfied: six in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from cycler>=0.10->matplotlib->sberpm) (1.15.0)\n",
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      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from scikit-learn>=0.23.2->sberpm) (2.1.0)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from scikit-learn>=0.23.2->sberpm) (0.17.0)\n",
      "Requirement already satisfied: scipy>=1.4.1 in c:\\users\\pc\\appdata\\roaming\\python\\python38\\site-packages (from sberpm) (1.5.4)\n",
      "Requirement already satisfied: numpy>=1.18.1 in c:\\users\\pc\\appdata\\roaming\\python\\python38\\site-packages (from sberpm) (1.18.5)\n",
      "Requirement already satisfied: numpy>=1.18.1 in c:\\users\\pc\\appdata\\roaming\\python\\python38\\site-packages (from sberpm) (1.18.5)\n",
      "Requirement already satisfied: ipython-genutils in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from nbformat>=4.2.0->sberpm) (0.2.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 20.3.1; however, version 21.1.2 is available.\n",
      "You should consider upgrading via the 'c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "! pip install sberpm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sberpm import DataHolder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sberpm\\_holder.py:307: UserWarning: DataHolder: 'time_format' is not set, recommended to specify it for correct time conversion, e.g., time_format='%d-%m-%Y %H:%M:%S'\n",
      "  warnings.warn(\"DataHolder: 'time_format' is not set, \"\n",
      "c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sberpm\\_holder.py:339: UserWarning: DataHolder: timestamp auto conversion will be done. 'dayfirst' is not set, in ambiguous cases it will be considered as False.\n",
      "  warnings.warn(f\"DataHolder: timestamp auto conversion will be done. '{argument}' \"\n",
      "c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sberpm\\_holder.py:339: UserWarning: DataHolder: timestamp auto conversion will be done. 'yearfirst' is not set, in ambiguous cases it will be considered as False.\n",
      "  warnings.warn(f\"DataHolder: timestamp auto conversion will be done. '{argument}' \"\n"
     ]
    }
   ],
   "source": [
    "data_holder = DataHolder(data=df, \n",
    "                         id_column='trace_id', \n",
    "                         activity_column='activity', \n",
    "                         start_timestamp_column='timestamp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strange warnings. It read all right, enven pandas can read datetime with no additional formats, so why does it nedd it, dunno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>trace_id</th>\n",
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       "      <th>0</th>\n",
       "      <td>2011-10-01 09:45:13.917000+00:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>W_Completeren aanvraag</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-10-01 10:17:08.924000+00:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>W_Nabellen offertes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-10-08 14:32:00.886000+00:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>W_Nabellen offertes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         timestamp trace_id                activity\n",
       "0 2011-10-01 09:45:13.917000+00:00   173688  W_Completeren aanvraag\n",
       "1 2011-10-01 10:17:08.924000+00:00   173688     W_Nabellen offertes\n",
       "2 2011-10-08 14:32:00.886000+00:00   173688     W_Nabellen offertes"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_holder.data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sberpm.autoinsights import AutoInsights\n",
    "from sberpm.miners import SimpleMiner\n",
    "from sberpm.visual import GraphvizPainter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the longest-lasting transitions are colored red"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: graphviz in c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (0.16)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 20.3.1; however, version 21.1.2 is available.\n",
      "You should consider upgrading via the 'c:\\users\\pc\\appdata\\local\\programs\\python\\python38\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "! pip install graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "auto_i = AutoInsights(data_holder, time_unit='day')\n",
    "simple_miner = SimpleMiner(data_holder)\n",
    "simple_miner.apply()\n",
    "auto_i.apply(miner=simple_miner, mode='time')\n",
    "graph = auto_i.get_graph()\n",
    "\n",
    "painter = GraphvizPainter()\n",
    "painter.apply_insights(graph)\n",
    "painter.show()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "all transitions are given a status:\n",
    "- Optimal (positive insights)\n",
    "- (negative insights)"
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  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_i = AutoInsights(data_holder, time_unit='day')\n",
    "simple_miner = SimpleMiner(data_holder)\n",
    "simple_miner.apply()\n",
    "auto_i.apply(miner=simple_miner, mode='overall')\n",
    "graph = auto_i.get_graph()\n",
    "\n",
    "painter = GraphvizPainter()\n",
    "painter.apply_insights(graph)\n",
    "painter.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Max $t_e$ & trace len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1915.007</td>\n",
       "      <td>1915.007</td>\n",
       "      <td>476228.924</td>\n",
       "      <td>2011-10-01 12:17:08.924000+02:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>622006.969</td>\n",
       "      <td>620091.962</td>\n",
       "      <td>491520.886</td>\n",
       "      <td>2011-10-08 16:32:00.886000+02:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>776871.874</td>\n",
       "      <td>154864.905</td>\n",
       "      <td>41585.791</td>\n",
       "      <td>2011-10-10 11:33:05.791000+02:00</td>\n",
       "      <td>173688</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1032743.109</td>\n",
       "      <td>255871.235</td>\n",
       "      <td>297457.026</td>\n",
       "      <td>2011-10-13 10:37:37.026000+02:00</td>\n",
       "      <td>173688</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72408</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>293301.228</td>\n",
       "      <td>2012-03-01 09:28:21.228000+01:00</td>\n",
       "      <td>214370</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72409</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>293272.388</td>\n",
       "      <td>2012-03-01 09:27:52.388000+01:00</td>\n",
       "      <td>214373</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72410</th>\n",
       "      <td>39288.769</td>\n",
       "      <td>39288.769</td>\n",
       "      <td>332561.157</td>\n",
       "      <td>2012-03-01 20:22:41.157000+01:00</td>\n",
       "      <td>214373</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72411</th>\n",
       "      <td>789782.493</td>\n",
       "      <td>750493.724</td>\n",
       "      <td>478254.881</td>\n",
       "      <td>2012-03-10 12:50:54.881000+01:00</td>\n",
       "      <td>214373</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72412</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>293261.325</td>\n",
       "      <td>2012-03-01 09:27:41.325000+01:00</td>\n",
       "      <td>214376</td>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72413 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tt          te          tw                         timestamp  \\\n",
       "0            0.000       0.000  474313.917  2011-10-01 11:45:13.917000+02:00   \n",
       "1         1915.007    1915.007  476228.924  2011-10-01 12:17:08.924000+02:00   \n",
       "2       622006.969  620091.962  491520.886  2011-10-08 16:32:00.886000+02:00   \n",
       "3       776871.874  154864.905   41585.791  2011-10-10 11:33:05.791000+02:00   \n",
       "4      1032743.109  255871.235  297457.026  2011-10-13 10:37:37.026000+02:00   \n",
       "...            ...         ...         ...                               ...   \n",
       "72408        0.000       0.000  293301.228  2012-03-01 09:28:21.228000+01:00   \n",
       "72409        0.000       0.000  293272.388  2012-03-01 09:27:52.388000+01:00   \n",
       "72410    39288.769   39288.769  332561.157  2012-03-01 20:22:41.157000+01:00   \n",
       "72411   789782.493  750493.724  478254.881  2012-03-10 12:50:54.881000+01:00   \n",
       "72412        0.000       0.000  293261.325  2012-03-01 09:27:41.325000+01:00   \n",
       "\n",
       "       trace_id  W_Afhandelen leads  W_Beoordelen fraude  \\\n",
       "0        173688                   0                    0   \n",
       "1        173688                   0                    0   \n",
       "2        173688                   0                    0   \n",
       "3        173688                   0                    0   \n",
       "4        173688                   0                    0   \n",
       "...         ...                 ...                  ...   \n",
       "72408    214370                   1                    0   \n",
       "72409    214373                   1                    0   \n",
       "72410    214373                   0                    0   \n",
       "72411    214373                   0                    0   \n",
       "72412    214376                   1                    0   \n",
       "\n",
       "       W_Completeren aanvraag  W_Nabellen incomplete dossiers  \\\n",
       "0                           1                               0   \n",
       "1                           0                               0   \n",
       "2                           0                               0   \n",
       "3                           0                               0   \n",
       "4                           0                               0   \n",
       "...                       ...                             ...   \n",
       "72408                       0                               0   \n",
       "72409                       0                               0   \n",
       "72410                       1                               0   \n",
       "72411                       0                               0   \n",
       "72412                       0                               0   \n",
       "\n",
       "       W_Nabellen offertes  W_Valideren aanvraag  \n",
       "0                        0                     0  \n",
       "1                        1                     0  \n",
       "2                        1                     0  \n",
       "3                        1                     0  \n",
       "4                        0                     1  \n",
       "...                    ...                   ...  \n",
       "72408                    0                     0  \n",
       "72409                    0                     0  \n",
       "72410                    0                     0  \n",
       "72411                    1                     0  \n",
       "72412                    0                     0  \n",
       "\n",
       "[72413 rows x 11 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preprocessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "traces_te_max = {}\n",
    "traces_len = {}\n",
    "for t_id in set(df_preprocessed['trace_id'].values):\n",
    "    _d = df_preprocessed[df_preprocessed['trace_id'] == t_id]\n",
    "    traces_len[t_id] = _d.shape[0]\n",
    "    traces_te_max[t_id] = _d['te'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.array(list(traces_te_max.values())) / 3600 / 24, traces_len.values(), s=0.1)\n",
    "plt.xlabel('max $t_e, days$')\n",
    "plt.ylabel('max trace length')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loops\n",
    "На рисунке были видны циклы.\n",
    "Это плохо. Если мы использаем для обучения окна длиннйо 2, то возникает проблема:\n",
    "\n",
    "$A \\rightarrow B \\rightarrow A \\rightarrow B \\rightarrow A \\dots A \\rightarrow B \\rightarrow C$\n",
    "\n",
    "сформируют признаки\n",
    "\n",
    "$$[A,\\ B],\\ [A]$$\n",
    "$$[B,\\ A],\\ [B]$$\n",
    "$$[A,\\ B],\\ [A]$$\n",
    "$$\\dots$$\n",
    "\n",
    "и не получится обучить что после $[A,\\ B]$ идёт $[C]$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Какие циклы?\n",
    "Чтобы сильно не париться, будем искать циклы $A \\rightarrow B \\rightarrow A$. Отметим `trace_id` с циклами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rl4pm_lib.preprocessing import is_there_cycle, number_of_cycles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert is_there_cycle([1, 2, 3, 2]) == True\n",
    "assert is_there_cycle([1, 2, 3]) == False\n",
    "assert is_there_cycle([1, 2, 3, 3, 5, 6]) == True\n",
    "assert is_there_cycle([1, 2, 3, 4, 5, 6]) == False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "traces_is_cycle = {}\n",
    "traces_num_cycles = {}\n",
    "for t_id in set(df_preprocessed['trace_id'].values):\n",
    "    _d = df[df['trace_id'] == t_id]\n",
    "    traces_is_cycle[t_id] = is_there_cycle(_d['activity'].values)\n",
    "    traces_num_cycles[t_id] = number_of_cycles(_d['activity'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 64.57% of all traces got cycles\n"
     ]
    }
   ],
   "source": [
    "print(f'{np.array(list(traces_is_cycle.values())).sum() / len(traces_is_cycle) * 100: .2f}% of all traces got cycles')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Хотелось бы както это исправить"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.array(list(traces_te_max.values())) / 3600 / 24, traces_num_cycles.values(), s=0.1)\n",
    "plt.xlabel('max $t_e, days$')\n",
    "plt.ylabel('number of cycles len 2 or 1 ')\n",
    "plt.title('Max te vs number of cycles')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(list(traces_len.values()), traces_num_cycles.values(), s=0.2)\n",
    "plt.xlabel('length of trace')\n",
    "plt.ylabel('number of cycles, len 2 or 1 ')\n",
    "plt.title('Trace length vs number of cycles')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "тоесть если есть цикл, то он занимает $\\frac{2}{3}$ процесса. Ужс"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}