lending.ipynb

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   "source": [
    "# Lending - Process Mining\n",
    "This data illustrates the steps within a loan application process. From an initial appointment request, the process navigates various stages, including information verification and underwriting, culminating in loan approval or denial. Additional steps may be required, such as co-signer enlistment or collateral assessment. Some cases experience outright appointment denial, indicating the process's variability, reflecting applicants' differing credit situations.\n",
    "The logs' attributes can aid in identifying influences on outcomes and detecting discrimination. Personal characteristics ('age', 'citizen', 'German speaking', and 'gender') and socio-economic indicators ('YearsOfEducation' and 'CreditScore') can impact the process. While 'yearsOfEducation' and 'CreditScore' can validly inform creditworthiness, 'age', 'citizen', 'language ability', and 'gender' should not bias loan decisions, ensuring these attributes are used responsibly fosters equitable loan processes."
   ],
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   "source": [
    "## Prequisites\n",
    "We are going to use :\n",
    "* pandas\n",
    "* graphviz\n",
    "* pm4py\n",
    "to extract revelant data from these logs using Process Mining.\n",
    "\n",
    "We first need to install our dependencies (librairies) and import our dataset and prepare it for Process Mining."
   ],
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   },
   "id": "e16e993918403719"
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  {
   "cell_type": "markdown",
   "source": [
    "### Importing our librairies\n",
    "We will import all librairies we are going to use in this application."
   ],
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    "collapsed": false
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  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import pm4py\n",
    "from pm4py.objects.log.importer.xes import importer as xes_importer\n",
    "from pm4py.algo.discovery.inductive import algorithm as inductive_miner\n",
    "from pm4py.algo.evaluation.replay_fitness import algorithm as replay_fitness_algorithm\n",
    "from pm4py.algo.evaluation.precision import algorithm as precision_algorithm\n",
    "from pm4py.algo.evaluation.generalization import algorithm as generalization_algorithm\n",
    "from pm4py.algo.evaluation.simplicity import algorithm as simplicity_algorithm\n",
    "from pm4py.objects.conversion.process_tree import converter as pt_converter\n",
    "from pm4py.visualization.petri_net import visualizer as pn_visualizer\n",
    "from pm4py.visualization.process_tree import visualizer as pt_visualizer\n",
    "from pm4py.visualization.bpmn import visualizer as bpmn_visualizer"
   ],
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    "ExecuteTime": {
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     "start_time": "2023-12-16T11:49:12.569969Z"
    }
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   "id": "64d335141723be74"
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  {
   "cell_type": "markdown",
   "source": [
    "### Importing our dataset, cleaning it (if needed) and transforming it.\n",
    "We must import our dataset."
   ],
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    "collapsed": false
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   "id": "1f6d0087fc4f19b3"
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  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/clementcombier/ProjectsDatabases/venv/lib/python3.9/site-packages/pm4py/util/dt_parsing/parser.py:77: UserWarning: ISO8601 strings are not fully supported with strpfromiso for Python versions below 3.11\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "parsing log, completed traces ::   0%|          | 0/10000 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0,
       "model_id": "bf63e00953ca44dcb9ce2ae34baeeeec"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 1: Import the XES log file\n",
    "log = xes_importer.apply(\"datasets/lending_log_high.xes\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-16T11:49:15.516205Z",
     "start_time": "2023-12-16T11:49:13.435123Z"
    }
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   "source": [],
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   "cell_type": "markdown",
   "source": [
    "---\n",
    "\n",
    "## Process Mining\n",
    "### Explaining our workflow\n",
    "\n",
    "For our Process Mining of the hiring dataset, we are going to split our workflow into three parts : \n",
    "1. Discover a process model using Inductive Miner\n",
    "2. Visualize the process model\n",
    "3. Quality measures\n",
    "\n",
    "### Step 1 : Discover a process model using Inductive Miner\n",
    "The first part is to create our model from the dataset using Inductive Miner in order to discover a model process."
   ],
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  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/clementcombier/ProjectsDatabases/venv/lib/python3.9/site-packages/pm4py/utils.py:537: UserWarning: the EventLog class has been deprecated and will be removed in a future release.\n",
      "  warnings.warn(\"the EventLog class has been deprecated and will be removed in a future release.\")\n"
     ]
    }
   ],
   "source": [
    "# We are using one of pm4py's many available method in order to discover our process model.\n",
    "# Discover a process tree using the Inductive Miner algorithm\n",
    "tree = pm4py.discover_process_tree_inductive(log,\n",
    "                                                     activity_key='concept:name',\n",
    "                                                     timestamp_key='time:timestamp',\n",
    "                                                     case_id_key='case:concept:name')\n",
    "# Convert the ProcessTree to a Petri net\n",
    "net, initial_marking, final_marking = pt_converter.apply(tree)"
   ],
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    "ExecuteTime": {
     "end_time": "2023-12-16T11:49:15.547735Z",
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    }
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  {
   "cell_type": "markdown",
   "source": [
    "### Step 2 : Visualize the process model\n",
    "Now, we want to visualize our process model to see if our model is correct or not at a first glance.\n",
    "Thanks to pm4py many methods, we will use them to make three visualization of our model.\n",
    "1. A Petri Net\n",
    "2. A Process Tree\n",
    "3. A BPMN"
   ],
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   "id": "a8b37e38b24f6348"
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  {
   "cell_type": "markdown",
   "source": [
    "#### Petri net"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "b0ecb3810fbf58c9"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
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      "text/plain": "<IPython.core.display.Image object>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize as a Petri net\n",
    "gviz_petri = pn_visualizer.apply(net, initial_marking, final_marking)\n",
    "pn_visualizer.view(gviz_petri)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-16T11:49:15.861597Z",
     "start_time": "2023-12-16T11:49:15.547617Z"
    }
   },
   "id": "557b78a906f451d7"
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Process tree"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "595a8f3c99662b0c"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<IPython.core.display.Image object>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize as a process tree\n",
    "gviz_tree = pt_visualizer.apply(tree)\n",
    "pt_visualizer.view(gviz_tree)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-16T11:49:16.098018Z",
     "start_time": "2023-12-16T11:49:15.862160Z"
    }
   },
   "id": "4f6c7aeb80c0aeac"
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### BPMN"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "79f356544e47a95"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "image/png": 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YPn06v7GhoeHRo0cPHjwoLi4uLi4uKioqLi62sbE5f/78wYMHQ0NDDQ0Nc3Jy2ik9g48qqWFra+vu7m5hYREYGBgeHi7Fkh0AHQ+VNAAAxLG2tl63bt1PP/1UX19PCBkxYoSNjQ31VF1dnb+/v5ubm5GREVUZY+3atQkJCefOnfP09BRZroHFYj158mTDhg2VlZUHDx7kt1+/fn327NnR0dFJSUl9+/YdMWJEQUGBmHZKRERESEiInZ1dUFAQlYTw7t27tWvX9u/fn9rWyclpyZIlp0+fHjZs2NChQx89etQkyLS0tHnz5nl6ep4/f37IkCGGhoZZWVnv3r2bMWOGtrZ2VFRUc3sqSedtLFgh0sePHysElJaWlgi4e/du3j9EFqbgWJp4QQAAIABJREFU8XjUXN7i4uJffvnF0NDQ3Nw8Njb22bNnksfg6uoaLkBPT4//lLKy8smTJ1+8eLFy5coLFy7wi2xQZT2oZV1d3aqqKkLIxYsX6+rqqBeLEKKiosJPShZDZFeCdHR0CCFPnjwR04mMjIy2tja1XFhYKC8vr6qqqqqqqq2tnZ2dbWNjIyY2WVmJ0jsLCgrKysp27tyZkJDw5s0bOp0uWEyDTqfzA7C3tyeEULlPzbUL4gcg8mjn5eXp6elRB0FBQeGXX37h74VwD1evXuVyufxXcMyYMTweLycnh0hwnL8wvr6+tbW1e/fuJYRkZGTY2tqSZs4N8r/nDxF7Sjg6OqqqqiYnJxNC9u7d6+7u3tyaTCYzICBg69at+fn5hoaGU6ZMoVICxLxGTcIQfChmK2dn51u3blHvTg8fPqyurv7rr78IIe/fv+/Zs6eEez1w4MDhw4dPmDDB39+/b9++1IZiSHI6Sb6nsrKy/NKTPXv25PF41OWpqalJCKmqqmrujUgQrgIpCg4OjoiIqK2tba5QDAAAAAAAAAAAAAAAAAAAAECXpaCgYGRk9P333y9btiwhIeHSpUtlZWWHDx+2t7dv7wIawlBSA74kqKQBACDOtm3bbt26FR0dfezYse3bt0+aNElDQ4N6auHChUFBQWZmZidOnLCzs7Ozs1uzZs21a9cGDRq0efNmkb1Rn10YDIa1tXV8fLyrqyvVXl9ff/r06erqaktLy8WLF//yyy+Ojo537txprl1GRmb9+vUyMjLh4eEsFsvS0nLBggW7d++ur6+vrKwsKysjhCgoKNy4cUNTU9PY2DglJcXZ2TksLGzPnj2CQRYWFoaHhyspKVlZWWVkZLi7uy9evJjJZP7666/79+9ftWrVvHnz+vTpI7ynknT+yWNbUlIyYsQIwYm/PB6vsrJScJ0PHz40d+v0TxJTUYTH47HZbBqNduPGjfv37xcUFNTV1SkpKbVuIEHbt2/Pzs6Oj4/fvn07ldBCCKHRaPwkB/7ys2fPuFxuY2OjhGkPYroSZGRkRKPR7ty5I2EnlZWVJSUlgwcP5p/YhJA///yzudhEDiosPj4+MDBQXV2deujk5JSSkhIVFSVcc0BLS0tBQUG4wEJz7YIBCB/tDx8+PHv2rLy8XEtL65O7T1VEuX//PvVXhK6uLvln8vonj3NzBJOvpOvGjRvm5uYin5KTk2tsbOzduzeTyXRzcxsxYkRLIzExMbGysoqPj588ebKhoSE1NV/kuUGEDoiY4yMnJ7do0aLNmzcvXrz47t27a9as+WQkcnJyP//8c3p6+o4dO8aOHSvha9TkoZitGAyGhYXFwYMHHRwczMzMKioqUlNTdXR0xowZQ20r4V6fPXs2KCgoLi7ur7/++vvvvwWTtYRJcjq1bk+Fe6MeinwjEhmS1K+COXPmtFM9maioqJ9//lnkU9QlMGzYMA8PDycnJ345i6NHj6anp/v6+v755587duyYPHmy1KM6deqUkpKSp6dncXFxdHT0woULJ0yYIPVRCCFlZWW+vr5HjhyhPmzo6uo2NDQ0V5oGAAAAAAAAAAAAAAAAAAAAoHU4HE5MTEyHFdAQhpIa8MVAkgYAgDg9e/bMz8/38/NLTk62trZetGjRr7/+KiMj8/jx48uXL8+fP//06dNcLpfL5R45csTHx0d8b8eOHUtJSSGEeHl5OTk5FRYWGhsbE0LGjx+vp6dnYGCwevVqQoi6urq3t/eFCxcmTpwosn348OEhISH37t0jhMjLywcEBLi6ugYHBxsaGlpYWGzdupUQYmJiwmAw9PT0vLy8CCFTp06l7kwvyNjY+KuvvlJRUXFzcyOEzJ49OyAgYPXq1TQabdGiRWvWrHn06BGLxRK5p5/s/JNUVFQsLS2bzIRWU1MTTK7o1q2bvLw8/6GioqJgKoW8vHy3bt34D+l0upqaGrWcnJy8YcMGDocjPC6NRpOVlWWz2aNHj3Z3d587d66KigqTyWxp/MKuXbsWGBj4/v17RUVFQohwKQ9B1GTowsLCUaNGUS2tTkcR1L9//2+//fbSpUsvX77s06eP4FPnz58fN25ck7yL/v37czicv//+m5pXzeFwTp482cbYqqurX716RVVjoCgrK+/bty8tLY1fS4H/0jQ0NLBYLENDQ/HtwkQe7QEDBvB4vKysrFmzZhFC6urqsrOzm5uZbWBgQAjhp3rX1dURQiwsLCTf084lLy/PYrG6d+/u5OTk4uJiZWUleTKJsEWLFs2dO9fHxycxMZFqEXluzJw5s0Xdent7r1u3btWqVePHj5dwE6rcDVUdqHWvkfitnJ2dd+zYwWKxli9f3qdPn9mzZ/fp0ycsLEzyvX716hWXy92yZYutra2Tk1NiYiJ/81aT4tnYojeiz/oqoC4BBoPh7Ow8b948BoMhvA6Tyfz66699fX1tbGw8PT1jYmJUVVWlFcDbt2+3bNly5MgRQsiaNWtSU1N9fX3z8/MFf21JBZVqoqKiMnjw4KNHjx49epRqp34nKioqakhAW1sb/7AAAAAAAAAAAAAAAAAAAAAAMYqKitzd3fPz8zs9NYIqqUGli/z111/Jyckdny4C0EZI0gAA+ARVVdWdO3fa29v7+Pj89ttvOjo6ISEhhYWF8vLy1FxPVVXV7Ozsnj17iu+noKCgrKxs586dhBAWi0Wn0+Pj4/kVJ+h0ura2NrVsb2/v7e1dXFw8ceJEke319fVcLpd/7/YxY8bweLycnBxDQ0PBKfh0Op3/OUlXV1ewZgWfrKysjIwMtdyzZ08ej0fN89bU1CSEVFVVNbenknQuXs+ePSMjI1u6leSdCzdSM3r19fWdnZ3d3d379+/foj6pKh+1tbWCjRUVFfzqEM+fP29oaHj79q2KisqVK1fq6+vLysrU1dXr6uqoeeeEEA6Hw2KxCCHW1tbq6upr1649cuQInU5/9uyZyGPYZFCRXTWxYcOGqVOn2tvbZ2Vl8e+znpOT8/79e+r04HA4/A7HjBmjr6+/dOnS7t276+vrJyYmLlmyRElJqbnYWCwWm82mzhMVFZWXL1+y2ezS0tJ+/frxA0hMTJwxY4ZgSObm5v369YuIiJg3bx512pSWllJPXbhwwcLCYvTo0dRDke0sFquxsZEalB+AyKNtaWmpp6e3cuVKBoOhrKyclJQUGhpKCBEMld+DtbW1gYHBqVOnPD09CSG5ubk2Njb6+voSHufOIisry+FwlJWVHR0dnZ2dqXeJtndrb29PXTX8xBiR5wb53/OH/O8pQb01lZeX85/V0tJycXGJi4vjv7JNNCmew2KxoqKiZGVlXVxcCCFiXqMmYQg+FLMVIcTJySkwMNDW1rZbt27Tpk3r1q3b27dv+VexJHv95s2bnTt3xsXFffPNN46Ojk0yzYjEl63g4ZJ8T1ksFv99nsPhUNks5J9MKhaL1dwb0RdwFVD7SNXN6NOnj5ub25w5c4YOHSp+Kx0dncOHD1N5DqdPn5ZWSY3a2loHB4f4+HjqPU1FRSUyMtLV1XX16tUbNmxoe/8UfgENT0/P2NhYOp1eIaS+vr6uro7/sKSkhFp48+ZNk0xFMekcwskevXr14n88AAAAAAAAAAAAAAAAAAAAgC9bpxfQEIaSGvC5Q5IGAIA4V69eHTNmDCHk22+/vXHjxujRo+Pj40NCQiorK0tKSgYPHiw8Pbc58fHxgYGB6urq1EMnJ6eUlJSoqCj+5GA+LS0tBQUF/vR64faSkhJCyP3796kPQ1TdA2raLo1G499Nv7llQeLXodFo1IxP4T2VpPNORKfT+fePp2b09ujRY+7cua6urubm5q3rMzY2lhCSkJCgoaFhbW395s2bXbt2lZSUlJWV7dixw8HBYfr06UOHDjUxMZk8efK0adMOHz6clJRkYmKSl5f34sWLrKwsNTW1P/7449mzZ/v27Zs7d25KSoqzs/OwYcP69++vqqpaX18fFxfn5+fHH/HWrVvUZN8DBw4wGAw6nd5cV4JxDhky5Nq1ax4eHgYGBpaWlkOGDOFwOKampg4ODoSQP/74Izc3l0ajHTt2zM7OTl5e/sCBA/b29tOmTaPT6dHR0VT9DZGxfffdd2lpaWw2e9OmTYsXL545c6a7u7ubmxuVekSh8iK+/fZbKyurwYMHU43Hjh2rr69/9uyZs7MzlZlz7ty5DRs2KCsrHz16dNeuXfzNhdufPn2anp7e2Ni4adOmadOm8QPw8PAQPtrBwcEHDhxwcHAwNTUdMmRIdHQ0dR3xQw0JCRHchYyMDDs7uwULFjAYjFu3bqWnpxNCTpw4Iclx7hTy8vLffffdvHnzpk2bJvwW0caeFy5cKFi6ROS50eT8efz4Mf94MpnMbdu2EUKSkpJ69uw5bdo0qh8fH5+PHz/y3/oEXbp0iTofQkJCDh06VFNTc+3atX79+p05c+brr78mhNDpdJGvUZMwmjxsbiuKjo6OjY2Nh4cHIUROTm7OnDnffvtti/aaELJ79246nW5hYVFTU7Nw4ULBnZLwsrW2tqaS9PiHS5I9PXfu3LVr1xQVFc+cOdOnT5/U1FQ2m71x48Z58+ZRl+G+ffvWrl0r8tL4Aq6CxsZGLS0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      "text/plain": "<IPython.core.display.Image object>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert to BPMN and visualize\n",
    "bpmn_graph = pm4py.convert_to_bpmn(tree)\n",
    "gviz_bpmn = bpmn_visualizer.apply(bpmn_graph)\n",
    "bpmn_visualizer.view(gviz_bpmn)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-16T11:49:16.245497Z",
     "start_time": "2023-12-16T11:49:16.038381Z"
    }
   },
   "id": "ea26ca91dc1eb2e5"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Step 3 : Quality measures\n",
    "We do want to check if our model is fitting the log and is correct. For these, we are going to calculate :\n",
    "1. Fitness\n",
    "2. Precision\n",
    "3. Generalization\n",
    "4. Simplicity"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "56cfae0440136d5c"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "replaying log with TBR, completed variants ::   0%|          | 0/41 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "957d2227bd184e7089b63a7a6e4866e7"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "computing precision with alignments, completed variants ::   0%|          | 0/52 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "6f66c132f0794a34829ad8cb14eb6603"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "replaying log with TBR, completed variants ::   0%|          | 0/41 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "bc0919c452d6484b802b5f48649b4ff0"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitness: {'perc_fit_traces': 100.0, 'average_trace_fitness': 1.0, 'log_fitness': 1.0, 'percentage_of_fitting_traces': 100.0}\n",
      "Precision: 0.8945986468076685\n",
      "Generalization: 0.9784947808129482\n",
      "Simplicity: 0.6774193548387096\n"
     ]
    }
   ],
   "source": [
    "# Replay Fitness Evaluation (using token-based replay)\n",
    "fitness = replay_fitness_algorithm.apply(log, net, initial_marking, final_marking, variant=replay_fitness_algorithm.Variants.TOKEN_BASED)\n",
    "\n",
    "# Precision Evaluation\n",
    "precision = precision_algorithm.apply(log, net, initial_marking, final_marking)\n",
    "\n",
    "# Generalization Evaluation\n",
    "generalization = generalization_algorithm.apply(log, net, initial_marking, final_marking)\n",
    "\n",
    "# Simplicity Evaluation\n",
    "simplicity = simplicity_algorithm.apply(net)\n",
    "\n",
    "\n",
    "# Print the quality measures\n",
    "print(f\"Fitness: {fitness}\")\n",
    "print(f\"Precision: {precision}\")\n",
    "print(f\"Generalization: {generalization}\")\n",
    "print(f\"Simplicity: {simplicity}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-16T11:49:16.471026Z",
     "start_time": "2023-12-16T11:49:16.258599Z"
    }
   },
   "id": "8b8c12d0b2e1b96"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Step 4 : Analysing our result\n",
    "#### Analysing the Fitness of our model\n",
    "* **Percentage of traces that fit the model** = 100%; It seems that every traces in our log can be exactly replicated by our modal.\n",
    "* **Log fitness** = 1; Perfect fitting!\n",
    "* **Average fitness of each traces in the log** = 1; It seems our traces conform perfectly to our model.\n",
    "* **Percentage of fitting traces** = 100%; Similar to the percentage of traces that fit the model. A result of 100% tells us traces fit our model!\n",
    "\n",
    "#### Analysing the Precision of our model\n",
    "* **Precision** = 0.894 ≃ 90%; A good result for the precision of our model. It is estimated that it's precise at 90% for our traces.\n",
    "\n",
    "#### Analysing the Generalization of our moddel\n",
    "* **Generalization** = 0.978 ≃ 98%; Another good result, it seems that in 98% of cases our model is able to handle cases that are similar but still different from what's it used to.\n",
    "\n",
    "#### Analysing the Simplicity of our moddel\n",
    "* **Simplicity** = 0.677 ≃ 68%; This result could be higher. Our model is moderatly simple. You may need some times or prior knowledge to correctly understand our model."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a17e9bac2863e68d"
  }
 ],
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