README_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Usage of py-ciu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook's purpose is to illustrate the principles of the CIU method and how to use the py-ciu package. The notebook is also used for ensuring that all examples execute correctly (similarly to the \"R\" version of CIU, where the entire README is generated by R Studio). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For checking the version (and get other information), do \n",
    "#%pip show py-ciu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Minimal use example\n",
    "\n",
    "Import the library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If everything is set up correctly, then this should execute fine.\n",
    "import ciu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to keep these examples short, we import some helper/testing functions that import data, train a model and provide a test instance. We start with the well-known Titanic use case and produce a \"overlap\" CIU visualisation, where CI is shown by the length of the transparent bar, whereas the utility is indicated by the solid bar. CU=0 gives no bar, CU=1 gives a solid bar that covers the transparent bar entirely. In addition to feature importance and value utility, this plot also gives a kind of \"What if?\" explanation because it indicates what changes in input values would modify the outcome the most. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from ciu_tests import titanic_rf\n",
    "\n",
    "np.random.seed(26)\n",
    "CIU_titanic, titanic_model, titanic_instance = titanic_rf.get_titanic_rf()\n",
    "CIU_titanic.explain(titanic_instance, output_inds=1)\n",
    "CIU_titanic.plot_ciu(plot_mode='overlap', main=\"Why does this 8-year old boy have a rather good probability of survival?\", figsize=(7,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this graph we wee that \"Age\" is the most important feature and that Age=8 is a very favorable value for increasing the probability of survival. Gender (\"Sex\") is also important and being male is not favorable for survival. The number of siblings \"SibSp\" is also important and, in this case, it is good to not have any siblings, even though it could be even better (but also much wordse) with some other value that we could find out with an input-output plot (examples of input-output plots are shown later in this page). Traveling without parents (Parch=0) is the worst situation and having even one parent would increase the probability of survival quite significantly. Paying a slightly higher (or just different) Fare wouldn't significantly increase the probability of survival. The port of embarkment and the Pclass have little importance for the probability of survival in this case (/context).\n",
    "\n",
    "A textual explanation will say the same:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The explained value is Yes with the value 0.60 (CU=0.60), which is higher than average utility.\n",
      "Feature Pclass has very low importance (CI=0.00) and has value(s) 1.0, which is low utility (CU=0.00)\n",
      "Feature Sex has low importance (CI=0.31) and has value(s) 1.0, which is low utility (CU=0.00)\n",
      "Feature Age has high importance (CI=0.63) and has value(s) 8.0, which is high utility (CU=0.95)\n",
      "Feature SibSp has very low importance (CI=0.16) and has value(s) 0.0, which is high utility (CU=0.81)\n",
      "Feature Parch has very low importance (CI=0.12) and has value(s) 0.0, which is low utility (CU=0.00)\n",
      "Feature Fare has very low importance (CI=0.12) and has value(s) 72.0, which is higher than average utility (CU=0.71)\n",
      "Feature Embarked has very low importance (CI=0.03) and has value(s) 1.0, which is low utility (CU=0.00)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(CIU_titanic.textual_explanation())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The readability of the textual explanation can be improved by modifying the intervals and words used for CI and CU. It is important to point aout that since CI and CU values are in the range [0,1] they have absolute interpretations, which makes it possibly to produce this kind of textual explanations.\n",
    "\n",
    "We can also visualize the Contextual influence, which is more familiar to LIME/SHAP/etc users. Influence values are always relative to a baseline/reference value. Here we use the default `neutralCU=0.5` value (more about setting the `neutral.CU` value later). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pmx4cHCx8fX1fupwQQoSHhwsAYsSIEZoylUolQkNDhbW1teY4btu2TQAQX3zxhdby77zzjpBIJJpjZsjxT0hIEABETEyMpmzYsGEir0sMADF58mTN6549e4ry5cuLnJwcTdmdO3eEVCoVn3/+uaasXbt2ombNmlrvUZVKJZo1ayYqV678kqNi+P6q45NKpeL8+fMvXaeat7e3ACB+/fVXTdn9+/eFTCYTY8aM0ZRNmzZNyOVyceXKFa3lP/74Y2FhYSFu3LghhBBiy5YtAoBYsGCBZp7c3FzRtm1bneOs773yww8/6MQze/ZsveesOv7w8HDN6+J6n7+4XfV1JSgoSOt6+dFHHwkLCwuRnJysKQsPDxdyuTzfdU6ePFnv+/DFa9iTJ0+Es7OzzmfB3bt3hZOTk1a5+hx7/r0phBB169YV9evX17z++eefBQAxcuRIne2r9y8xMVFYWFiI6dOna00/d+6csLS01Cl/UdeuXYW1tbWIj4/XlN2+fVs4ODiIVq1a6eyvIcdWH0OvK+prgaOjo7h//77WOurUqSPKly8vHj16pCk7e/askEqlom/fvpoydZ116dJFa/mhQ4cKAOLs2bOaMkOvlepzdMuWLZqylJQU4e7uLurWrasp0/cZFh4ervM5+OI1LK/zKzk5WdjY2IgJEyZolY8cOVLI5XKRlpamE7/a/fv3hbW1tejQoYPIzc3VlH/99dcCgFi5cqWmTH3M8vucfPz4sQAgZs+e/dL5Xtw/tbzO2RYtWmhdv4Uw/Lr+4jl6+fJlnc9oIZ7Vv729vVadv1j/WVlZokaNGqJt27Yvjft53333nZBIJGLw4MF6p+elRHbhqFq1KsqWLavp23z27Fmkp6drWiiaNWumaT07fvw4cnNz9fZ/DgoKgkKh0LyuVasWHB0dcf369Ty3feDAATx58gQff/yxTh+pF39GtLe3x3vvvad5bW1tjUaNGr10/S+zadMmODk5oX379nj48KHmX/369WFvb681+oitra3m7ydPnuDhw4do2bIlMjIycOnSpQJt/2VkMhn69eunE2/VqlUREBCgFW/btm0BQBOvuvXlp59+0vmZ2xCG1OPzxyM7OxuPHj2Cn58fnJ2d8eeff2qmde/eHdnZ2Vo/++3fvx/Jycno3r07gGctnVu2bEHnzp0hhNDat+DgYKSkpGitU5/n40lJScHDhw8RGBiI69evIyUlxaD9fn74PnULYVZWFg4ePAjg2U1+FhYWGDlypNZyY8aMgRACe/bsAfDqx98Q3bt3x/3797V+Nt28eTNUKpXmuCYlJeHnn39GWFiY5j378OFDPHr0CMHBwbh69Spu3bqV5zYM3V+1wMBAVKtWzeB9qFatGlq2bKl57erqCn9/f6332aZNm9CyZUuUKVNG630RFBSE3Nxc/PrrrwCAvXv3wsrKCoMGDdIsK5VK9f768Px7JTMzEw8fPkSTJk0AIN/3WV6K632el/fff1/retmyZUvk5ubin3/+KdD6DHHgwAEkJyejZ8+eWvtiYWGBxo0b6x29afDgwVqvW7ZsqVXfW7ZsgUQi0fy8/jz1/m3duhUqlQphYWFa23Vzc0PlypX1blctNzcX+/fvR9euXeHr66spd3d3R69evXD06FGkpqZqLfOqxza/64pat27dtH7huHPnDs6cOYOIiAi4uLhoymvVqoX27dtj9+7dOtt68f0+YsQIANCa15hrpYeHh9avVI6Ojujbty9Onz6Nu3fvGrT/xnJycsJbb72FH374QdPVJTc3Fxs2bEDXrl1f2lf44MGDyMrKwqhRo7Ruth00aBAcHR2xa9cuo+NR3zcQGxur07XzVQwaNEjnfjBDruv6VKlSBXXq1NH6VSQ3NxebN29G586dter8+b8fP36MlJQUtGzZ0uBrz8WLF9G/f3+89dZbmm6HhiqRCbREIkGzZs00fZ3j4uJQvnx5zZ2pzyfQ6v/1JdBeXl46ZWXKlHnpmy4+Ph4ADBrj+Y033tBJqvNb/8tcvXoVKSkpKF++PFxdXbX+paWlaW4yAJ797Pe///0PTk5OcHR0hKurqyaZNzRBM0bFihV1biC5evUqzp8/rxNrlSpVAPx3U0T37t3RvHlzDBw4EBUqVECPHj2wceNGg5M5Q+rx6dOn+OyzzzT9UsuVKwdXV1ckJydrHY/atWsjICBA68TesGEDypUrp0n8Hzx4gOTkZCxbtkxn39RfIp6vC33i4uIQFBSk6Sfo6uqq6XNsSP1IpVKtD1QAmuOq7uf2zz//wMPDAw4ODlrzqbsDqT9QX/X4G6Jjx45wcnLSOa516tTRxH3t2jUIIfDpp5/qHFd1gvKy42ro/qpVqlTJqH0w5H129epV7N27Vyf+oKAgrfj/+ecfuLu769xoq+/u+qSkJHz44YeoUKECbG1t4erqqom9oOdycb3P8/LisSxTpgwAFOoH/ouuXr0KAGjbtq3O/uzfv19nX9T9mV+M8/kY4+Pj4eHhoZUw6tuuEAKVK1fW2e7FixdfegwfPHiAjIwM+Pv760yrWrUqVCqVTj/qVzm2hlxX1F48f9TnV16xPnz4EOnp6VrllStX1nqtUCgglUq1tmXMtdLPz0/nMzev+AtT3759cePGDRw5cgTAs8T43r176NOnz0uXy+uYWVtbw9fXt0BfKGUyGWbOnIk9e/agQoUKaNWqFWbNmvXKXyD0XS8Nua7npXv37oiLi9M0isTGxuL+/fs6iffOnTvRpEkT2NjYwMXFBa6uroiOjjb42rdhwwZkZWVhyZIlRo8IVCL7QAPPEuIdO3bg3Llzmv7Pas2aNdP0ezx69Cg8PDx0LgoA8hxdQ/0t8lUV9vpVKhXKly+PdevW6Z2uvtgnJycjMDAQjo6O+Pzzz6FQKGBjY4M///wTEyZMMCgxymvQ8xdvoFF7/lvi8/HWrFkT8+bN07uMp6enZtlff/0Vhw8fxq5du7B3715s2LABbdu2xf79+/MdBcWQ4zxixAjExMRg1KhRaNq0KZycnCCRSNCjRw+d49G9e3dMnz4dDx8+hIODA7Zv346ePXtq7iJWz//ee+/p7bMK4KX90+Lj49GuXTsEBARg3rx58PT0hLW1NXbv3o358+cXWStwXl71+BtCJpOha9eu+PHHH7FkyRLcu3cPcXFxWmP7qvd77Nixed54U5jDN+l7z76MIe8zlUqF9u3bY/z48Xrnze9DRZ+wsDAcO3YM48aNQ506dWBvbw+VSoWOHTu+0nulqN/nL1OY10ZDr1Xq/Vm7dq1Wv0u1F0cJKKzRl1QqFSQSCfbs2aN3nQW97yQvRf25pmbs+WOIF+vydbtW5iU4OBgVKlTAd999h1atWuG7776Dm5ub5otzcRs1ahQ6d+6Mbdu2Yd++ffj0008RFRWFn3/+GXXr1n3pssZ8xhtyXc9L9+7dERkZiU2bNmHUqFHYuHEjnJyc0LFjR808R44cQZcuXdCqVSssWbIE7u7usLKyQkxMjN5BG/RR32zt7u5u0PzPK9EJNPCs03lcXJzWzSz169eHTCZDbGwsfvvtN7z55puFtl11V4G///672MdiVCgUOHjwIJo3b/7Si1dsbCwePXqErVu3olWrVpryhIQEnXnz+vBRt1q8OGi8Md+IFQoFzp49i3bt2uX7FCKpVIp27dqhXbt2mDdvHr788ktMmjQJhw8fLpSL0ObNmxEeHo65c+dqyjIzM/UOit+9e3dMnToVW7ZsQYUKFZCamqp1U4OrqyscHByQm5tboNh27NgBpVKJ7du3a7UWveyn3BepVCpcv35dKyG7cuUKAGhuEvH29sbBgwfx5MkTrVZZdRceb29vTVlBjr+xT5bq3r07Vq9ejUOHDuHixYsQQmi1Nqi/5FpZWRXouBqzv0VFoVAgLS0t3/i9vb1x+PBhneEer127pjXf48ePcejQIUydOlXr5hd1a+rzClIfRfk+Ly7PX6uevxnvxWuV+tpdvnz5QtsfhUKBffv2ISkpKc9WaIVCASEEKlWqZPQXKFdXV9jZ2eHy5cs60y5dugSpVKppiCgMhlxX8qI+v/KKtVy5cjrdGa5evarVsnnt2jWoVCrNtoy9Vqp/xXr+XDA0/vy87PyysLBAr169sGrVKsycORPbtm3T2+XhRc8fs+cb+bKyspCQkPBK71OFQoExY8ZgzJgxuHr1KurUqYO5c+fiu+++A/DsvHnx8y8rKwt37twxajv5XdfzUqlSJTRq1AgbNmzA8OHDsXXrVnTt2lVr6MstW7bAxsYG+/bt0yqPiYkxOL5WrVoV+MtjiezCATy7k9zGxgbr1q3DrVu3tFqgZTIZ6tWrh8WLFyM9Pb1A4z/npUOHDnBwcEBUVJTOcDuF/Q3/RWFhYcjNzcW0adN0puXk5GhOBvVJ+3w86p8wXiSXy/X+FKL+sFH32QSefTM15ulHYWFhuHXrFpYvX64z7enTp5qf85KSknSmqx/b++JwdwVlYWGhUz+LFi3S+227atWqqFmzJjZs2IANGzbA3d1d64uIhYUFunXrhi1btmgNv6aW35BR+uonJSXFqIsCAHz99deav4UQ+Prrr2FlZYV27doBAN58803k5uZqzQcA8+fPh0Qi0YwEU9Djr/4wNPTJXEFBQXBxcdEc10aNGml9eJYvXx6tW7fG0qVL9V7E8zuuhu5vUQoLC8Px48exb98+nWnJycnIyckB8KzFKjs7W+vcUKlUOn309L1XAOh9eqix9VHU7/Piou9alZ6erjNMX3BwMBwdHfHll18iOztbZz0F2Z9u3bpBCIGpU6fqTFPX2dtvvw0LCwtMnTpVpx6FEFrDEb7IwsICHTp0wE8//aTVBeHevXv4/vvv0aJFCzg6Ohod98vkd13Ji7u7O+rUqYPVq1drvQf//vtv7N+/X29D1ovv90WLFgGA5lw19lp5+/ZtracUp6amYs2aNahTp47eXx2Mkd/51adPHzx+/BgffPAB0tLStO5/yktQUBCsra2xcOFCrX1csWIFUlJSjBplQi0jI0MnN1EoFHBwcNC6nisUCq1zBgCWLVuWZwv0y/bhZdf1l+nevTtOnDiBlStX4uHDhzqJt4WFBSQSiVZMiYmJmhG8DNGiRQutfv3GKLEt0NbW1mjYsCGOHDkCmUyG+vXra01v1qyZprWxMBNoR0dHzJ8/HwMHDkTDhg3Rq1cvlClTBmfPnkVGRobesVULS2BgID744ANERUXhzJkz6NChA6ysrHD16lVs2rQJX331Fd555x00a9YMZcqUQXh4OEaOHAmJRIK1a9fqTfDr16+PDRs2YPTo0WjYsCHs7e3RuXNnVK9eHU2aNEFkZKSmdWX9+vWaBMAQffr0wcaNGzF48GAcPnwYzZs3R25uLi5duoSNGzdi3759aNCgAT7//HP8+uuvCA0Nhbe3N+7fv48lS5bgjTfeKLS669SpE9auXQsnJydUq1YNx48fx8GDB/McrrB79+747LPPYGNjgwEDBuj0nZoxYwYOHz6Mxo0bY9CgQahWrRqSkpLw559/4uDBg3qTUrUOHTrA2toanTt31lxsly9fjvLlyxv87d/GxgZ79+5FeHg4GjdujD179mDXrl2YOHGipitP586d0aZNG0yaNAmJiYmoXbs29u/fj59++gmjRo3SJB4FPf7qc27kyJEIDg6GhYXFS5/2aWVlhbfffhvr169Heno65syZozPP4sWL0aJFC9SsWRODBg2Cr68v7t27h+PHj+Pff//F2bNn81y/oftblMaNG4ft27ejU6dOmiHu0tPTce7cOWzevBmJiYkoV64cunbtikaNGmHMmDG4du0aAgICsH37ds37Rt3a5ejoqOnDmJ2djYoVK2L//v16f01S18ekSZPQo0cPWFlZoXPnzi+9iako3+fFpUOHDvDy8sKAAQMwbtw4WFhYYOXKlXB1dcWNGzc08zk6OiI6Ohp9+vRBvXr10KNHD808u3btQvPmzXW+fOWnTZs26NOnDxYuXIirV69qutUcOXIEbdq0wfDhw6FQKPDFF18gMjISiYmJ6Nq1KxwcHJCQkIAff/wR77//PsaOHZvnNr744gvNOO1Dhw6FpaUlli5dCqVSme9zBYxlyHXlZWbPno2QkBA0bdoUAwYM0Axj5+TkpHfM4YSEBHTp0gUdO3bE8ePH8d1336FXr16acZWNvVZWqVIFAwYMwMmTJ1GhQgWsXLkS9+7dM7pxQp/8zq+6deuiRo0ampvn69Wrl+86XV1dERkZialTp6Jjx47o0qULLl++jCVLlqBhw4YGJeEvunLlCtq1a4ewsDBUq1YNlpaW+PHHH3Hv3j2t6/PAgQMxePBgdOvWDe3bt8fZs2exb98+zRB9hjLkup6XsLAwjB07FmPHjoWLi4tOi3toaCjmzZuHjh07olevXrh//z4WL14MPz8//PXXXwZtIzIyEqtXry5YA6dRY3aYmcjISAFANGvWTGfa1q1bBQDh4OCgM/SKEM+GcBk2bJhOeV5DuLw4dM327dtFs2bNhK2trXB0dBSNGjUSP/zwg2a6vmHohNA/XI4+eS0vhBDLli0T9evXF7a2tsLBwUHUrFlTjB8/Xty+fVszT1xcnGjSpImwtbUVHh4eYvz48WLfvn06w/ekpaWJXr16CWdnZwFAK7b4+HgRFBQkZDKZqFChgpg4caI4cOCA3mHs8oo1KytLzJw5U1SvXl3IZDJRpkwZUb9+fTF16lSRkpIihBDi0KFD4q233hIeHh7C2tpaeHh4iJ49e+oMBaaPofX4+PFj0a9fP1GuXDlhb28vgoODxaVLl/Ic+ubq1asCgAAgjh49qnfb9+7dE8OGDROenp7CyspKuLm5iXbt2olly5blG/f27dtFrVq1hI2NjfDx8REzZ84UK1euzHMYsueph/eKj48XHTp0EHZ2dqJChQpi8uTJWkMhCfFs6K6PPvpIeHh4CCsrK1G5cmUxe/ZsrWGuDDn++oaxy8nJESNGjBCurq5CIpFoDVOEPIZIUr9/JBKJuHnzpt79i4+PF3379hVubm7CyspKVKxYUXTq1Els3rz5pcfF0P1Vx6fvfZMXb29vERoaqlMeGBgoAgMDdWKIjIwUfn5+wtraWpQrV040a9ZMzJkzR2RlZWnme/DggejVq5dwcHAQTk5OIiIiQsTFxQkAYv369Zr5/v33X/G///1PODs7CycnJ/Huu++K27dv6z3G06ZNExUrVhRSqVTrvWSq93le19OTJ09qzZfX0GKGDGMnhBCnTp0SjRs3FtbW1sLLy0vMmzfvpUNxBgcHCycnJ2FjYyMUCoWIiIgQf/zxR77b1jdkXk5Ojpg9e7YICAgQ1tbWwtXVVYSEhIhTp05pzbdlyxbRokULIZfLhVwuFwEBAWLYsGHi8uXL+g6dlj///FMEBwcLe3t7YWdnJ9q0aSOOHTumNY8xx1YfQ68r6mtBXsOkHTx4UDRv3lzz2di5c2dx4cIFrXnUx/HChQvinXfeEQ4ODqJMmTJi+PDhOsPDGnqtVJ+j+/btE7Vq1RIymUwEBASITZs25Xs8DBnGToi8zy+1WbNmCQDiyy+/1Hts8vL111+LgIAAYWVlJSpUqCCGDBkiHj9+rDWPocPYPXz4UAwbNkwEBAQIuVwunJycROPGjcXGjRu15svNzRUTJkwQ5cqVE3Z2diI4OFhcu3bN4HP2efld1/MaalIIIZo3by4AiIEDB+qdvmLFClG5cmVNfcbExOhdX17XOPXwjAUhEaKI+xUQUbGKiIjA5s2bkZaWZupQqJBt27YN//vf/3D06FG9T04lKiq8rry6r776Ch999BESExP1jtpD5qXE9oEmIjJnT58+1Xqdm5uLRYsWwdHR0aCff4no9SGEwIoVKxAYGMjkuYQosX2giYjM2YgRI/D06VM0bdoUSqUSW7duxbFjx/Dll18WyRBhRFT40tPTsX37dhw+fBjnzp3DTz/9ZOqQqJAwgSYieg21bdsWc+fOxc6dO5GZmQk/Pz8sWrSowHeME1Hxe/DgAXr16gVnZ2dMnDgRXbp0MXVIVEjYB5qIiIiIyAjsA01EREREZAQm0ERERERERmAfaDOkUqlw+/ZtODg4GP14XiIiIiJjCCHw5MkTeHh46DxQqbRiAm2Gbt++DU9PT1OHQURERKXIzZs38cYbb5g6jNcCE2gz5ODgAAD45ZdfYG9vb+JoSg6JRAKFQoH4+PiCPdaTTIr1Z95Yf+aN9Wfe8qu/tLQ0BAYGavIPYgJtltTdNuzt7ZlAFyKpVApHR0c4ODhApVKZOhwyEuvPvLH+zBvrz7wZWn/sNvofdmQhIiIiIjICE2giIiIiIiMwgSYiIiIiMgL7QBNRiZGSkoKkpCT2wTRDUqkUd+7cYf2ZKdaf6clkMsjlclOHUWowgSaiEiE9PR3rN2xAbk6OqUMhIip2UqkFOnUKZRJdTJhAE1GJoFQqkZuTgyuSSngKG1OHQ0RUbGyRiSqqBCiVSibQxYQJNBGVKE9hg3QJP0CIqBTh0NvFjjcREhEREREZgQm0kY4fPw4LCwuEhoaaOhQiIiIiMgEm0EZasWIFRowYgV9//RW3b982dThEREREVMyYQBshLS0NGzZswJAhQxAaGopVq1ZpTd++fTsqV64MGxsbtGnTBqtXr4ZEIkFycrJmnqNHj6Jly5awtbWFp6cnRo4cifT09OLdESIiIiIqMN5EaISNGzciICAA/v7+eO+99zBq1ChERkZCIpEgISEB77zzDj788EMMHDgQp0+fxtixY7WWj4+PR8eOHfHFF19g5cqVePDgAYYPH47hw4cjJiYmz+0qlUoolUrN69TUVADPnkkvlfI7UGGRSCSa/3lciYjI3Eil0gJ9fuX3+aeeTv+RCCF476aBmjdvjrCwMHz44YfIycmBu7s7Nm3ahNatW+Pjjz/Grl27cO7cOc38n3zyCaZPn47Hjx/D2dkZAwcOhIWFBZYuXaqZ5+jRowgMDER6ejpsbPQPvTVlyhRMnTpVpzwlJQWOjo6Fv6NEZujOnTtYtmwZzkqqchQOIipV5CIdtcVFvP/++3B3dy/09aempsLJyYl5x3PYAm2gy5cv4/fff8ePP/4IALC0tET37t2xYsUKtG7dGpcvX0bDhg21lmnUqJHW67Nnz+Kvv/7CunXrNGVCCKhUKiQkJKBq1ap6tx0ZGYnRo0drXqempsLT0xPx8fFwcHAorF0s9SQSCRQKBeLj48HvleYnKSnJ1CEQEZnUzZs3C9QtNL/PvydPnhRGeCUKE2gDrVixAjk5OfDw8NCUCSEgk8nw9ddfG7SOtLQ0fPDBBxg5cqTONC8vrzyXk8lkkMlkOuXq5JsKh/pnKx5XIiIyRyqVqkCfX/l9/rFRSRcTaAPk5ORgzZo1mDt3Ljp06KA1rWvXrvjhhx/g7++P3bt3a007efKk1ut69erhwoUL8PPzK/KYiYiIiKhoMIE2wM6dO/H48WMMGDAATk5OWtO6deuGFStWYOPGjZg3bx4mTJiAAQMG4MyZM5pROtSd7ydMmIAmTZpg+PDhGDhwIORyOS5cuIADBw4Y3IpNRERERKbFoQYMsGLFCgQFBekkz8CzBPqPP/7AkydPsHnzZmzduhW1atVCdHQ0Jk2aBACa7he1atXCL7/8gitXrqBly5aoW7cuPvvsM61uIURERET0emMLtAF27NiR57RGjRpp+gbVqlULXbp00UybPn063njjDa3RNRo2bIj9+/cXXbBEREREVKSYQBeiJUuWoGHDhihbtizi4uIwe/ZsDB8+3NRhEZUqtsgEeL8LEZUitsg0dQilDhPoQnT16lV88cUXSEpKgpeXF8aMGYPIyEhTh0VUKshkMlhYWqJKToKpQyEiKnZSqYXeEbuoaDCBLkTz58/H/PnzTR0GUakkl8sxYvhwXL58mcMQmiGpVApPT0/cvHmT9WeGWH+mJ5PJIJfzIVLFhQk0EZUYTk5OcHFx4Qe4GZJKpXB3d0d6ejrrzwyx/qi0YQJNRCVGSkoKkpKSSuwHOFuYiIheD0ygiahESE9Px/oNG5Cbk2PqUIqMVGqBTp1CmUQTEZkYE2giKhGUSiVyc3JwRVIJT2GT/wJmxhaZqKJKgFKpZAJNRGRiTKCJqER5ChukS0pggsmh+YiIXht8EiERERERkRGYQL+ERCLBtm3bAACJiYmQSCQ4c+aMSWMiIiIiItMq1Qn0gwcPMGTIEHh5eUEmk8HNzQ3BwcGIi4sDANy5cwchISFGrfPHH39EkyZN4OTkBAcHB1SvXh2jRo0qguiJiIiIyBRKdR/obt26ISsrC6tXr4avry/u3buHQ4cO4dGjRwAANzc3o9Z36NAhdO/eHdOnT0eXLl0gkUhw4cIFHDhwoCjCJyIiIiITKLUt0MnJyThy5AhmzpyJNm3awNvbG40aNUJkZCS6dOkCQLsLh9qlS5fQrFkz2NjYoEaNGvjll18003bs2IHmzZtj3Lhx8Pf3R5UqVdC1a1csXrxYM8+UKVNQp04dLF26FJ6enrCzs0NYWBhSUlKKZb+JiIiI6NWU2hZoe3t72NvbY9u2bWjSpInBz48fN24cFixYgGrVqmHevHno3LkzEhISULZsWbi5ueH777/H33//jRo1auS5jmvXrmHjxo3YsWMHUlNTMWDAAAwdOhTr1q3TO79SqYRSqdS8Tk1NBfAswZdKS+13oEInkUg0//O40utKKpWWyPcnzz/zxvozb/nVn3o6/afUJtCWlpZYtWoVBg0ahG+++Qb16tVDYGAgevTogVq1auW53PDhw9GtWzcAQHR0NPbu3YsVK1Zg/PjxGDFiBI4cOYKaNWvC29sbTZo0QYcOHdC7d2+tBD0zMxNr1qxBxYoVAQCLFi1CaGgo5s6dq7fbSFRUFKZOnapTrlAo4Ojo+KqHgl6gUChMHQIVwJ07d0wdQrHw9PSEu7u7qcMoMjz/zBvrz7zlVX/qhjv6T6lNoIFnfaBDQ0Nx5MgRnDhxAnv27MGsWbPw7bffIiIiQu8yTZs21fxtaWmJBg0a4OLFiwAAuVyOXbt2IT4+HocPH8aJEycwZswYfPXVVzh+/Djs7OwAAF5eXprkWb1OlUqFy5cv602gIyMjMXr0aM3r1NRUeHp6Ij4+Hg4ODoVxKAjPvmErFArEx8dDCA66a26SkpJMHUKxuHnzJtLT000dRqHj+WfeWH/mLb/6e/LkiQmier2V6gQaAGxsbNC+fXu0b98en376KQYOHIjJkyfnmUAbQqFQQKFQYODAgZg0aRKqVKmCDRs2oF+/fgVan0wm09vFRAgBlUpV4DhJm/pnKx5Xep2pVKoS+f7k+WfeWH/mLb/645ciXeyo9IJq1aq9tHXnxIkTmr9zcnJw6tQpVK1aNc/5fXx8YGdnp7XOGzdu4Pbt21rrlEql8Pf3f8XoiYiIiKioldoW6EePHuHdd99F//79UatWLTg4OOCPP/7ArFmz8NZbb+W53OLFi1G5cmVUrVoV8+fPx+PHj9G/f38Az0bYyMjIwJtvvglvb28kJydj4cKFyM7ORvv27TXrsLGxQXh4OObMmYPU1FSMHDkSYWFhRg+bR0RERETFr9Qm0Pb29mjcuDHmz5+P+Ph4ZGdnw9PTE4MGDcLEiRPzXG7GjBmYMWMGzpw5Az8/P2zfvh3lypUDAAQGBmLx4sXo27cv7t27hzJlyqBu3brYv3+/Vuuyn58f3n77bbz55ptISkpCp06dsGTJkiLfZyIiIiJ6daU2gZbJZIiKikJUVFSe8zzf58fHx0fzumfPnnrnb9OmDdq0aWPQ9ocMGYIhQ4YYETERERERvQ5KbQJNRCWTLTKBEni/iy0yTR0CERH9PybQRFQiyGQyWFhaokpOgqlDKTJSqYXBD30iIqKiwwS6mE2ZMgVTpkwxdRhEJY5cLseI4cNx+fLlEjuMlkwmg1wuN3UYRESlHhNoIioxnJyc4OLiUmITaCIiej0wgSaiEiMlJQVJSUnFkkCzNZiIqPRiAk1EJUJ6ejrWb9iA3JycYtmeVGqBTp1CmUQTEZVCTKCJqERQKpXIzcnBFUklPIVNkW7LFpmookqAUqlkAk1EVAoVWgKdnJwMZ2fnwlodEVGBPIUN0iVFnNSWwGHyiIjIcNKCLDRz5kxs2LBB8zosLAxly5ZFxYoVcfbs2UILjoiIiIjodVOgBPqbb76Bp6cnAODAgQM4cOAA9uzZg5CQEIwbN65QAzS1iIgISCQSnX/Xrl0zdWhEREREZAIF6sJx9+5dTQK9c+dOhIWFoUOHDvDx8UHjxo0LNcDXQceOHRETE6NV5urqatQ6cnNzIZFIIJUW6DsLEREREb0mCpTNlSlTBjdv3gQA7N27F0FBQQAAIQRyc3MLL7rXhEwmg5ubm9a/r776CjVr1oRcLoenpyeGDh2KtLQ0zTKrVq2Cs7Mztm/fjmrVqkEmk+HGjRtQKpUYO3YsKlasCLlcjsaNGyM2NtZ0O0dERERERilQC/Tbb7+NXr16oXLlynj06BFCQkIAAKdPn4afn1+hBvi6kkqlWLhwISpVqoTr169j6NChGD9+PJYsWaKZJyMjAzNnzsS3336LsmXLonz58hg+fDguXLiA9evXw8PDAz/++CM6duyIc+fOoXLlynq3pVQqoVQqNa9TU1MBgC3ahUwikWj+53ElQ0ilUr5XCgnPP/PG+jNv+dWfejr9p0AJ9Pz58+Hj44ObN29i1qxZsLe3BwDcuXMHQ4cOLdQAXwc7d+7U7CMAhISEYNOmTZrXPj4++OKLLzB48GCtBDo7OxtLlixB7dq1AQA3btxATEwMbty4AQ8PDwDA2LFjsXfvXsTExODLL7/Uu/2oqChMnTpVp1yhUMDR0bFQ9pH+o1AoTB0CFcCdO3eKfZuenp5wd3cv9u2WZDz/zBvrz7zlVX/qhjv6T4ESaCsrK4wdO1an/KOPPnrlgF5Hbdq0QXR0tOa1XC7HwYMHERUVhUuXLiE1NRU5OTnIzMxERkYG7OzsAADW1taoVauWZrlz584hNzcXVapU0Vq/UqlE2bJl89x+ZGQkRo8erXmdmpoKT09PxMfHw8HBobB2s9STSCRQKBSIj4+HEBynzNwkJSUV+zZv3ryJ9PT0Yt9uScTzz7yx/sxbfvX35MkTE0T1eivwONBr167F0qVLcf36dRw/fhze3t5YsGABKlWqhLfeeqswYzQ5uVyu1TUlMTERnTp1wpAhQzB9+nS4uLjg6NGjGDBgALKysjQJtK2trdbPHmlpabCwsMCpU6dgYWGhtY3nW7hfJJPJIJPJdMqFEMXyyOLSQv2zFY8rGUqlUvG9Ukh4/pk31p95y6/++KVIV4E6KkVHR2P06NEICQlBcnKy5sZBZ2dnLFiwoDDjey2dOnUKKpUKc+fORZMmTVClShXcvn073+Xq1q2L3Nxc3L9/H35+flr/3NzciiFyIiIiInpVBUqgFy1ahOXLl2PSpElaLakNGjTAuXPnCi2415Wfnx+ys7OxaNEiXL9+HWvXrsU333yT73JVqlRB79690bdvX2zduhUJCQn4/fffERUVhV27dhVD5ERERET0qgqUQCckJKBu3bo65TKZrFT0B6xduzbmzZuHmTNnokaNGli3bh2ioqIMWjYmJgZ9+/bFmDFj4O/vj65du+LkyZPw8vIq4qiJiIiIqDAUqA90pUqVcObMGXh7e2uV7927F1WrVi2UwF4Xq1at0lv+0Ucf6dw02adPH83fERERiIiI0FnOysoKU6dO1TuqBhERERG9/gqUQI8ePRrDhg1DZmYmhBD4/fff8cMPPyAqKgrffvttYcdIRGQwW2QCRXy/iy0yi3YDRET0WitQAj1w4EDY2trik08+QUZGBnr16gUPDw989dVX6NGjR2HHSESUL5lMBgtLS1TJSSiW7UmlFnpHxyEiopLP6AQ6JycH33//PYKDg9G7d29kZGQgLS0N5cuXL4r4iIgMIpfLMWL4cFy+fLlYhtGSyWSQy+VFvh0iInr9GJ1AW1paYvDgwbh48SIAwM7OTjPuMRGRKTk5OcHFxYXj0BIRUZEq0CgcjRo1wunTpws7FiIiIiKi116B+kAPHToUY8aMwb///ov69evr/Iz5/OOriYiKw9OnTxEbG4uyZcuybzIRERWpAiXQ6hsFR44cqSmTSCQQQkAikWieTEhEVFyePn2KX375BSEhIUygiYioSBUogU5IKJ673ImIiIiIXjcFSqBffIAKEREREVFpUaAEes2aNS+d3rdv3wIF8zpr3bo16tSpgwULFpg6FCIiIiIyoQIl0B9++KHW6+zsbGRkZMDa2hp2dnavbQIdERGB1atXA3j2SG0vLy/07dsXEydOhKVlgQ4FEREREZUyBcoaHz9+rFN29epVDBkyBOPGjXvloIpSx44dERMTA6VSid27d2PYsGGwsrJCZGSkqUMjIiIiIjNQoHGg9alcuTJmzJih0zr9upHJZHBzc4O3tzeGDBmCoKAgbN++HQAQFxeH1q1bw87ODmXKlEFwcLDeLwsAsHbtWjRo0AAODg5wc3NDr169cP/+fc30x48fo3fv3nB1dYWtrS0qV66MmJgYAEBWVhaGDx8Od3d32NjYwNvbG1FRUUW/80RERET0ygq134KlpSVu375dmKsscra2tnj06BHOnDmDdu3aoX///vjqq69gaWmJw4cP5zkkX3Z2NqZNmwZ/f3/cv38fo0ePRkREBHbv3g0A+PTTT3HhwgXs2bMH5cqVw7Vr1/D06VMAwMKFC7F9+3Zs3LgRXl5euHnzJm7evJlnjEqlEkqlUvM6NTUVwLOhA6XSQvsOVOpJJBLN/zyu5o31Z354/pk31p95y6/+1NPpPwVKoNUttmpCCNy5cwdff/01mjdvXiiBFTUhBA4dOoR9+/ZhxIgRmDVrFho0aIAlS5Zo5qlevXqey/fv31/zt6+vLxYuXIiGDRsiLS0N9vb2uHHjBurWrYsGDRoAAHx8fDTz37hxA5UrV0aLFi0gkUjyHdUkKioKU6dO1SlXKBRwdHQ0dJfJQAqFwtQhUAHcuXMHAODp6Ql3d3cTR0MFxfPPvLH+zFte9aduuKP/FCiB7tq1q9ZriUQCV1dXtG3bFnPnzi2MuIrMzp07YW9vj+zsbKhUKvTq1QtTpkxBw4YN8e677xq8nlOnTmHKlCk4e/YsHj9+DJVKBeBZclytWjUMGTIE3bp1w59//okOHTqga9euaNasGYBnNzO2b98e/v7+6NixIzp16oQOHTrkua3IyEiMHj1a8zo1NRWenp6Ij4+Hg4NDAY8EvUgikUChUCA+Ph5CCFOHQ0ZKSkoCANy8eRPp6ekmjoaMxfPPvLH+zFt+9ffkyRMTRPV6K1ACrU4WzVGbNm0QHR0Na2treHh4aEbfsLW1NXgd6enpCA4ORnBwMNatWwdXV1fcuHEDwcHByMrKAgCEhITgn3/+we7du3HgwAG0a9cOw4YNw5w5c1CvXj0kJCRgz549OHjwIMLCwhAUFITNmzfr3Z5MJtP7ZDUhhFnXxetG/bMVj6v5Y/2ZH55/5o31Z97yqz9+KdJVoI5Kn3/+OTIyMnTKnz59is8///yVgypKcrkcfn5+8PLy0hq6rlatWjh06JBB67h06RIePXqEGTNmoGXLlggICNC6gVDN1dUV4eHh+O6777BgwQIsW7ZMM83R0RHdu3fH8uXLsWHDBmzZskXTgkZEREREr68CJdBTp05FWlqaTnlGRobevrrmIDIyEidPnsTQoUPx119/4dKlS4iOjsbDhw915vXy8oK1tTUWLVqE69evY/v27Zg2bZrWPJ999hl++uknXLt2DefPn8fOnTtRtWpVAMC8efPwww8/4NKlS7hy5Qo2bdoENzc3ODs7F8euEhEREdErKFACLYTQe0fm2bNn4eLi8spBmUKVKlWwf/9+nD17Fo0aNULTpk3x008/6X3AiqurK1atWoVNmzahWrVqmDFjBubMmaM1j7W1NSIjI1GrVi20atUKFhYWWL9+PQDAwcFBc9Niw4YNkZiYiN27d/POZSIiIiIzIBFGdGwpU6YMJBIJUlJS4OjoqJVE5+bmIi0tDYMHD8bixYuLJFh6JjU1FU5OTjh16hTs7e1NHU6JIZVK4efnh2vXrrEPnxlKTk7Gnj17EBISwl9zzBDPP/PG+jNv+dVfWloa6tevr8n/yMibCBcsWAAhBPr374+pU6fCyclJM83a2ho+Pj5o2rRpoQdJRERERPS6MCqBDg8PBwBUqlQJzZo1g5WVVZEERURkLFtbWwQGBho1og4REVFBFGgYu8DAQM3fmZmZmqHb1Ni8T0TFzdbWFq1bt+ZPyEREVOQKdNdaRkYGhg8fjvLly0Mul6NMmTJa/4iIiIiISqoCtUCPGzcOhw8fRnR0NPr06YPFixfj1q1bWLp0KWbMmFHYMRJRKZCeng6lUlng5aVSKVJSUgoxIiIiIv0KlEDv2LEDa9asQevWrdGvXz+0bNkSfn5+8Pb2xrp169C7d+/CjpOISrD09HTs2rEDua/4tKsD+/ejU+fO7AdNRERFqkAJdFJSEnx9fQE86++sfoJeixYtMGTIkMKLjohKBaVSiVwh4HPkCGwK2Iqc6eSExJYtoVQqmUATEVGRKlAC7evri4SEBHh5eSEgIAAbN25Eo0aNsGPHDo6/SkQFZpOSAjs+0p6IiF5zBbqJsF+/fjh79iwA4OOPP8bixYthY2ODjz76COPGjSvUAI01ZcoU1KlTp0jWHRsbC4lEguTk5EJbZ2JiIiQSCc6cOVNo6yQiIiKiolOgFuiPPvpI83dQUBAuXbqEU6dOwc/PD7Vq1TJ4PREREVi9erVOeXBwMPbu3VuQ0IiIiIiIilSBEujnZWZmwtvbG97e3gVavmPHjoiJidEqk8lkrxpWocvOzjZ1CERERET0GihQF47c3FxMmzYNFStWhL29Pa5fvw4A+PTTT7FixQqj1iWTyeDm5qb1Tz2WtEQiwdKlS9GpUyfY2dmhatWqOH78OK5du4bWrVtDLpejWbNmiI+P11nv0qVL4enpCTs7O4SFhWkNb3Xy5Em0b98e5cqVg5OTEwIDA/Hnn39qLS+RSBAdHY0uXbpALpdj+vTpOtvIyMhASEgImjdvrunW8e2336Jq1aqwsbFBQEAAlixZorXM77//jrp168LGxgYNGjTA6dOnjTpeRERERGRaBWqBnj59OlavXo1Zs2Zh0KBBmvIaNWpgwYIFGDBgQKEFOG3aNMybNw/z5s3DhAkT0KtXL/j6+iIyMhJeXl7o378/hg8fjj179miWuXbtGjZu3IgdO3YgNTUVAwYMwNChQ7Fu3ToAwJMnTxAeHo5FixZBCIG5c+fizTffxNWrV+Hg4KBZz5QpUzBjxgwsWLAAlpaWmi8KAJCcnIzQ0FDY29vjwIEDsLOzw7p16/DZZ5/h66+/Rt26dXH69GkMGjQIcrkc4eHhSEtLQ6dOndC+fXt89913SEhIwIcffpjvMVAqlVrj46ampgJ4luRLpQX6DkR6SCQSzf88rsWrsI8368/88Pwzb6w/85Zf/amn038KlECvWbMGy5YtQ7t27TB48GBNee3atXHp0iWj1rVz507Y29trlU2cOBETJ04E8OyGxbCwMADAhAkT0LRpU3z66acIDg4GAHz44Yfo16+f1vKZmZlYs2YNKlasCABYtGgRQkNDMXfuXLi5uaFt27Za8y9btgzOzs745Zdf0KlTJ015r169tNatTqDv3r2L7t27o3Llyvj+++9hbW0NAJg8eTLmzp2Lt99+GwBQqVIlXLhwAUuXLkV4eDi+//57qFQqrFixAjY2NqhevTr+/ffffIf+i4qKwtSpU3XKFQoFH5teBBQKhalDKHXu3LlTaOvy9PSEu7t7oa2PihfPP/PG+jNvedWfuuGO/lOgBPrWrVvw8/PTKVepVEb3FW7Tpg2io6O1ylxcXDR/P39TYoUKFQAANWvW1CrLzMxEamqqJpn08vLSJM8A0LRpU6hUKly+fBlubm64d+8ePvnkE8TGxuL+/fvIzc1FRkYGbty4oRVHgwYN9Mbcvn17NGrUCBs2bICFhQWAZw+CiI+Px4ABA7Ra5XNycuDk5AQAuHjxImrVqgUbGxut2PITGRmJ0aNHa16npqbC09MT8fHxWi3m9GokEgkUCgXi4+MhXvGBHmScpEIcuu7mzZtIT08vtPVR8eD5Z95Yf+Ytv/p78uSJCaJ6vRUoga5WrRqOHDmic+Pg5s2bUbduXaPWJZfL9SbjalZWVpq/1T8h6CtTqVQGbzM8PByPHj3CV199BW9vb8hkMjRt2hRZWVk6sekTGhqKLVu24MKFC5pkPi0tDQCwfPlyNG7cWGt+dZJdUDKZTO+NlUIIo/abXk79sxWPa/Er7OPN+jM/PP/MG+vPvOVXf/xSpKtACfRnn32G8PBw3Lp1CyqVClu3bsXly5exZs0a7Ny5s7BjNNqNGzdw+/ZteHh4AABOnDgBqVQKf39/AEBcXByWLFmCN998E8CzFquHDx8avP4ZM2bA3t4e7dq1Q2xsLKpVq4YKFSrAw8MD169fz/NR5lWrVsXatWuRmZmpaYU+ceLEq+wqERERERUzo3r6X79+HUIIvPXWW9ixYwcOHjwIuVyOzz77DBcvXsSOHTvQvn17owJQKpW4e/eu1j9jkll9bGxsEB4ejrNnz+LIkSMYOXIkwsLC4ObmBgCoXLky1q5di4sXL+K3335D7969jX7075w5c9C7d2+0bdtW0+976tSpiIqKwsKFC3HlyhWcO3cOMTExmDdvHoBnfaolEgkGDRqECxcuYPfu3ZgzZ84r7SsRERERFS+jWqArV66MO3fuoHz58mjZsiVcXFxw7tw5Td/kgti7d6/ODT/+/v5G34z4PD8/P7z99tt48803kZSUhE6dOmkNJ7dixQq8//77qFevHjw9PfHll19i7NixRm9n/vz5yM3NRdu2bREbG4uBAwfCzs4Os2fPxrhx4yCXy1GzZk2MGjUKAGBvb48dO3Zg8ODBqFu3LqpVq4aZM2eiW7duBd5XIiIiIipeEmFExxapVIq7d++ifPnyAABHR0ecOXMGvr6+RRYg6UpNTYWTkxNOnTqlM4IJFZxUKoWfnx+uXbvGPnzFLCkpCfv27UPAzp2wK+ANhRkuLrjUqRNCQkLg7OxcuAFSkeP5Z95Yf+Ytv/pLS0tD/fr1kZKSwtG//t8rDdbITuVEREREVNoY1YVDIpHoDKbNwbWJqLBk/v+Qj8W9LBERkTGMSqCFEIiIiNAMqZaZmYnBgwfrDPe2devWwouQiEo8mUwGC4kEiS1bvtJ6LC0s9A75SEREVJiMSqDDw8O1Xr/33nuFGgwRlU5yuRyhnTtrPbLeWOqhKh88eMA+mEREVKSMSqBjYmKKKg4iKuXkcnmeDy8yhFQqhZOTEx48eFCIUREREekq0INUiOj1kZ6e/kottyWFVCpFSkqKqcMgIqJSgAk0kRlLT0/Hrh07kMsRcQAAB/bvR6fOnY1+MBIREZExmEATmTGlUolcIeBz5AhsSnnra6aTExJbtoRSqWQCTURERYoJNFEJYJOSUuAHkBAREZFxXulBKmSc2NhYSCQSJCcnmzoUIiIiIiqgUp1AR0REaB4OY21tDT8/P3z++efIyckxdWhERERE9Joq9V04OnbsiJiYGCiVSuzevRvDhg2DlZUVIiMjjVpPbm4uJBIJpNJS/Z2EiIiIqMQr9dmeTCaDm5sbvL29MWTIEAQFBWH79u2YN28eatasCblcDk9PTwwdOhRpaWma5VatWgVnZ2ds374d1apVg0wmw40bN6BUKjFhwgR4enpCJpPBz88PK1as0NrmqVOn0KBBA9jZ2aFZs2a4fPlyce82ERERERVQqW+BfpGtrS0ePXoEqVSKhQsXolKlSrh+/TqGDh2K8ePHY8mSJZp5MzIyMHPmTHz77bcoW7Ysypcvj759++L48eNYuHAhateujYSEBDx8+FBrG5MmTcLcuXPh6uqKwYMHo3///oiLi8szJqVSqTXOb2pqKgCwxbuQSSQSzf/mclzNJc7ixuNifszx/KP/sP7MW371p55O/2EC/f+EEDh06BD27duHESNGYNSoUZppPj4++OKLLzB48GCtBDo7OxtLlixB7dq1AQBXrlzBxo0bceDAAQQFBQEAfH19dbY1ffp0BAYGAgA+/vhjhIaGIjMzEzY2Nnpji4qKwtSpU3XKFQoFHB0dC7zPpJ9CoTB1CAa7c+eOqUN47Xh6esLd3d3UYVABmdP5R7pYf+Ytr/pTN9zRf0p9Ar1z507Y29sjOzsbKpUKvXr1wpQpU3Dw4EFERUXh0qVLSE1NRU5ODjIzM5GRkQE7OzsAgLW1NWrVqqVZ15kzZ2BhYaFJjvPy/DLqD/r79+/Dy8tL7/yRkZEYPXq05nVqaio8PT0RHx8PBweHAu87aZNIJFAoFIiPj4cwkweTJHHoOh03b95Eenq6qcMgI5nj+Uf/Yf2Zt/zq78mTJyaI6vVW6hPoNm3aIDo6GtbW1vDw8IClpSUSExPRqVMnDBkyBNOnT4eLiwuOHj2KAQMGICsrS5NA29raav2sYejDG6ysrDR/q5dXqVR5zi+TySCTyXTKhRAvXY6Mo/7ZypyOq7nEWdx4XMyPOZ5/9B/Wn3nLr/74pUhXqe+oJJfL4efnBy8vL1haPvs+cerUKahUKsydOxdNmjRBlSpVcPv27XzXVbNmTahUKvzyyy9FHTYRERERmUipT6D18fPzQ3Z2NhYtWoTr169j7dq1+Oabb/JdzsfHB+Hh4ejfvz+2bduGhIQExMbGYuPGjcUQNREREREVBybQetSuXRvz5s3DzJkzUaNGDaxbtw5RUVEGLRsdHY133nkHQ4cORUBAAAYNGsT+mEREREQliESwY4vZSU1NhZOTE06dOgV7e3tTh1NiSKVS+Pn54dq1a2bThy8pKQn79u1DwM6dsCvlNxRmuLjgUqdOCAkJgbOzs6nDISOZ4/lH/2H9mbf86i8tLQ3169dHSkoKR//6f6X+JkKikiDTycnUIZgcjwERERUXJtBEZkwmk8FCIkFiy5amDuW1YGlhoXfEGiIiosLEBJrIjMnlcoR27qz1pMrSSiqVwt/fHw8ePOBPyEREVKSYQBOZOblcDrlcbuowTE4qlcLJyQkPHjwwdShERFTCMYEmvdLT00tdq6ZUKsWdO3eQlJTEFkwzJJVKkZKSYuowiIioFGACTTrS09Oxa8cO5HKAFjIzB/bvR6fOnQ1+KigREVFBMIEmHUqlErlCwOfIEdiwRY/MRKaTExJbtoRSqWQCTURERYoJNOXJJiWl1I8tTERERPQiPomQiIiIiMgITKCJiIiIiIzABLoAHjx4gCFDhsDLywsymQxubm4IDg5GXFycqUMjIiIioiLGPtAF0K1bN2RlZWH16tXw9fXFvXv3cOjQITx69MjUoRERERFREWMCbaTk5GQcOXIEsbGxCAwMBAB4e3ujUaNGWvOMHTsWP/30E5RKJRo0aID58+ejdu3aePDgAWrWrImRI0di4sSJAIBjx46hdevW2LNnD9q1a6ezTaVSqTUmc2pqKgBAIpFAKi38HxGKYp1ExYnvYfMjkUg0/7P+zA/rz7zlV3/q6fQfJtBGsre3h729PbZt24YmTZpAJpPpzPPuu+/C1tYWe/bsgZOTE5YuXYp27drhypUrcHV1xcqVK9G1a1d06NAB/v7+6NOnD4YPH643eQaAqKgoTJ06VadcoVDA0dGx0Pfxzp07hb5OouLi6ekJd3d3U4dBBaRQKEwdAr0C1p95y6v+1A139B+JEHxahrG2bNmCQYMG4enTp6hXrx4CAwPRo0cP1KpVC0ePHkVoaCju37+vlVz7+flh/PjxeP/99wEAw4YNw8GDB9GgQQOcO3cOJ0+e1JuMA/pboD09PfHnn3/CwcGh0PcvKSkJe/bsQcDOnRzGjsxGhosLLnXqhJCQELi4uJg6HDKSRCKBQqFAfHw8+LFkflh/5i2/+nvy5Anq1auHlJSUImm4M0dsgS6Abt26ITQ0FEeOHMGJEyewZ88ezJo1C99++y3S09ORlpaGsmXLai3z9OlTxMfHa17PmTMHNWrUwKZNm3Dq1Kk8k2cAkMlkeqcLIYrkkdN8jDWZO76HzY/6Z+Oiuq5R0WL9mbf86o9finQxgS4gGxsbtG/fHu3bt8enn36KgQMHYvLkyRg6dCjc3d0RGxurs4yzs7Pm7/j4eNy+fRsqlQqJiYmoWbNm8QVPRERERAXGBLqQVKtWDdu2bUO9evVw9+5dWFpawsfHR++8WVlZeO+999C9e3f4+/tj4MCBOHfuHMqXL1+8QRMRERGR0XirrJEePXqEtm3b4rvvvsNff/2FhIQEbNq0CbNmzcJbb72FoKAgNG3aFF27dsX+/fuRmJiIY8eOYdKkSfjjjz8AAJMmTUJKSgoWLlyICRMmoEqVKujfv7+J94yIiIiIDMEWaCPZ29ujcePGmD9/PuLj45GdnQ1PT08MGjQIEydOhEQiwe7duzFp0iT069cPDx48gJubG1q1aoUKFSogNjYWCxYswOHDhzUd8deuXYvatWsjOjoaQ4YMMfEeEhEREdHLMIE2kkwmQ1RUFKKiovKcx8HBAQsXLsTChQt1pnl6eiI7O1urzMfHBykpKYUeKxEREREVPibQlKdMJydTh0BkML5fiYiouDCBJh0ymQwWEgkSW7Y0dShERrG0sHjpkJBERESFgQk06ZDL5Qjt3Fnr4S2lgVQqhaenJ27evMlxTM2QVCqFv78/Hjx4wPojIqIixQSa9JLL5ZDL5aYOo1hJpVK4u7sjPT2dCZgZkkqlcHJywoMHD0wdChERlXAcxo6IiIiIyAhMoImIiIiIjMAEmoiIiIjICOwDbYaEEACAtLQ0E0dSskgkEqSmpuLJkyeaY0zmg/Vn3lh/5o31Z97yqz91vsG6/Q8TaDP05MkTAEBgYKCJIyEiIqLS4smTJ3DimPsAAIng1wmzo1KpcPv2bTg4OEAikZg6nBIjNTVVM4yd+jHrZD5Yf+aN9WfeWH/mLb/6E0LgyZMn8PDwgFTK3r8AW6DNklQqxRtvvGHqMEosR0dHfgCYMdafeWP9mTfWn3l7Wf2x5Vkbv0YQERERERmBCTQRERERkRGYQBP9P5lMhsmTJ0Mmk5k6FCoA1p95Y/2ZN9afeWP9GY83ERIRERERGYEt0ERERERERmACTURERERkBCbQRERERERGYAJNRERERGQEJtBUaiUlJaF3795wdHSEs7MzBgwYgLS0tJcus2zZMrRu3RqOjo6QSCRITk4unmAJALB48WL4+PjAxsYGjRs3xu+///7S+Tdt2oSAgADY2NigZs2a2L17dzFFSvoYU3/nz59Ht27d4OPjA4lEggULFhRfoKSXMfW3fPlytGzZEmXKlEGZMmUQFBSU7/lKRcuY+tu6dSsaNGgAZ2dnyOVy1KlTB2vXri3GaF9/TKCp1OrduzfOnz+PAwcOYOfOnfj111/x/vvvv3SZjIwMdOzYERMnTiymKEltw4YNGD16NCZPnow///wTtWvXRnBwMO7fv693/mPHjqFnz54YMGAATp8+ja5du6Jr1674+++/izlyAoyvv4yMDPj6+mLGjBlwc3Mr5mjpRcbWX2xsLHr27InDhw/j+PHj8PT0RIcOHXDr1q1ijpwA4+vPxcUFkyZNwvHjx/HXX3+hX79+6NevH/bt21fMkb/GBFEpdOHCBQFAnDx5UlO2Z88eIZFIxK1bt/Jd/vDhwwKAePz4cRFGSc9r1KiRGDZsmOZ1bm6u8PDwEFFRUXrnDwsLE6GhoVpljRs3Fh988EGRxkn6GVt/z/P29hbz588vwugoP69Sf0IIkZOTIxwcHMTq1auLKkR6iVetPyGEqFu3rvjkk0+KIjyzxBZoKpWOHz8OZ2dnNGjQQFMWFBQEqVSK3377zYSRkT5ZWVk4deoUgoKCNGVSqRRBQUE4fvy43mWOHz+uNT8ABAcH5zk/FZ2C1B+9Pgqj/jIyMpCdnQ0XF5eiCpPy8Kr1J4TAoUOHcPnyZbRq1aooQzUrTKCpVLp79y7Kly+vVWZpaQkXFxfcvXvXRFFRXh4+fIjc3FxUqFBBq7xChQp51tfdu3eNmp+KTkHqj14fhVF/EyZMgIeHh86XWip6Ba2/lJQU2Nvbw9raGqGhoVi0aBHat29f1OGaDSbQVKJ8/PHHkEgkL/136dIlU4dJRFRqzJgxA+vXr8ePP/4IGxsbU4dDBnJwcMCZM2dw8uRJTJ8+HaNHj0ZsbKypw3ptWJo6AKLCNGbMGERERLx0Hl9fX7i5uencPJGTk4OkpCTesPQaKleuHCwsLHDv3j2t8nv37uVZX25ubkbNT0WnIPVHr49Xqb85c+ZgxowZOHjwIGrVqlWUYVIeClp/UqkUfn5+AIA6derg4sWLiIqKQuvWrYsyXLPBFmgqUVxdXREQEPDSf9bW1mjatCmSk5Nx6tQpzbI///wzVCoVGjdubMI9IH2sra1Rv359HDp0SFOmUqlw6NAhNG3aVO8yTZs21ZofAA4cOJDn/FR0ClJ/9PooaP3NmjUL06ZNw969e7XuN6HiVVjnn0qlglKpLIoQzZOp72IkMpWOHTuKunXrit9++00cPXpUVK5cWfTs2VMz/d9//xX+/v7it99+05TduXNHnD59WixfvlwAEL/++qs4ffq0ePTokSl2oVRZv369kMlkYtWqVeLChQvi/fffF87OzuLu3btCCCH69OkjPv74Y838cXFxwtLSUsyZM0dcvHhRTJ48WVhZWYlz586ZahdKNWPrT6lUitOnT4vTp08Ld3d3MXbsWHH69Glx9epVU+1CqWZs/c2YMUNYW1uLzZs3izt37mj+PXnyxFS7UKoZW39ffvml2L9/v4iPjxcXLlwQc+bMEZaWlmL58uWm2oXXDhNoKrUePXokevbsKezt7YWjo6Po16+f1sU9ISFBABCHDx/WlE2ePFkA0PkXExNT/DtQCi1atEh4eXkJa2tr0ahRI3HixAnNtMDAQBEeHq41/8aNG0WVKlWEtbW1qF69uti1a1cxR0zPM6b+1Offi/8CAwOLP3ASQhhXf97e3nrrb/LkycUfOAkhjKu/SZMmCT8/P2FjYyPKlCkjmjZtKtavX2+CqF9fEiGEKPZmbyIiIiIiM8U+0ERERERERmACTURERERkBCbQRERERERGYAJNRERERGQEJtBEREREREZgAk1EREREZAQm0ERERERERmACTURERERkBCbQRESlRGJiIiQSCc6cOWPqUIiIzBoTaCIiIiIiIzCBJiIiIiIyAhNoIqISLiMjA/3790eNGjUAAM2aNUPPnj2RkpJi4siIiMwTE2giohJu3Lhx+PXXX7FmzRoAwPLly3HhwgUMHTrUxJEREZkniRBCmDoIIiIqGmlpaXBxccG6devQsGFDVKpUCadPn8bDhw/RoUMH3L59G25ubqYOk4jIrLAFmoioBIuPj0d2djYaN26sVd6oUSMIIXDlyhUTRUZEZL6YQBMRERERGYEJNBFRCaZQKGBlZYWTJ09qlZ88eRISiQRVqlQxUWREROaLCTQRUQlmb2+PQYMGITIyEqdPnwYAXLhwAaNHj0bPnj3Z/5mIqAB4EyERUQmXkZGBYcOGYdOmTUhPT4etrS26dOmCpUuXwsnJydThERGZHSbQRESlRGJiomYUjjp16pg6HCIis8UuHERERERERmACTURERERkBHbhICIiIiIyAlugiYiIiIiMwASaiIiIiMgITKCJiIiIiIzABJqIiIiIyAhMoImIiIiIjMAEmoiIiIjICEygiYiIiIiMwASaiIiIiMgI/wek7QzFU0vrkAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CIU_titanic.plot_influence(main=\"Which features have a positive or negative influence on probability of survival?\", figsize=(7,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Digging deeper into CIU capabilities\n",
    "\n",
    "The rest of this notebook showcases various capabilities of CIU for different data sets. We begin with a simple example using the well known Iris flower dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pylab as py_plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import datasets\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "np.random.seed(26)\n",
    "iris=datasets.load_iris()\n",
    "\n",
    "df = pd.DataFrame(data = np.c_[iris['data'], iris['target']],\n",
    "              columns = iris['feature_names'] + ['target'])\n",
    "df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)\n",
    "df.columns = ['s_length', 's_width', 'p_length', 'p_width', 'target', 'species']\n",
    "iris_outnames = df['species'].cat.categories.tolist()\n",
    "X = df[['s_length', 's_width', 'p_length', 'p_width']]\n",
    "y = df['species']\n",
    "np.random.seed(8)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then create and train a model, in this case an `LDA` model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LinearDiscriminantAnalysis()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearDiscriminantAnalysis()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We choose an instance from the test set to explain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instance 42 in the test set is a virginica and it is predicted to be a virginica\n",
      "The instance values are:\n",
      "     s_length  s_width  p_length  p_width\n",
      "143       6.8      3.2       5.9      2.3\n"
     ]
    }
   ],
   "source": [
    "inst_ind = 42\n",
    "instance = X_test.iloc[[inst_ind]]\n",
    "inst_class = y_test.iloc[inst_ind]\n",
    "pred = model.predict(instance)[0]\n",
    "print(f\"Instance {inst_ind} in the test set is a {inst_class} and it is predicted to be a {pred}\")\n",
    "print(f\"The instance values are:\\n{instance}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then get CIU values for this instance. The ``explain`` method returns a DataFrame with all relevant values from CIU calculations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>CI</th>\n",
       "      <th>CU</th>\n",
       "      <th>Cinfl</th>\n",
       "      <th>outname</th>\n",
       "      <th>outval</th>\n",
       "      <th>feature</th>\n",
       "      <th>ymin</th>\n",
       "      <th>ymax</th>\n",
       "      <th>inputs</th>\n",
       "      <th>invals</th>\n",
       "      <th>neutralCU</th>\n",
       "      <th>target_concept</th>\n",
       "      <th>target_inputs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>s_length</th>\n",
       "      <td>0.000074</td>\n",
       "      <td>0.953773</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>virginica</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>s_length</td>\n",
       "      <td>9.999257e-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[6.8]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s_width</th>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.985937</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>virginica</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>s_width</td>\n",
       "      <td>9.997626e-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[3.2]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.499997</td>\n",
       "      <td>virginica</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>p_length</td>\n",
       "      <td>6.464905e-19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[5.9]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_width</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.499997</td>\n",
       "      <td>virginica</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>p_width</td>\n",
       "      <td>4.378181e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[2.3]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                CI        CU     Cinfl    outname    outval   feature  \\\n",
       "s_length  0.000074  0.953773  0.000034  virginica  0.999997  s_length   \n",
       "s_width   0.000237  0.985937  0.000115  virginica  0.999997   s_width   \n",
       "p_length  1.000000  0.999997  0.499997  virginica  0.999997  p_length   \n",
       "p_width   1.000000  0.999997  0.499997  virginica  0.999997   p_width   \n",
       "\n",
       "                  ymin  ymax inputs invals  neutralCU target_concept  \\\n",
       "s_length  9.999257e-01   1.0    [0]  [6.8]        0.5           None   \n",
       "s_width   9.997626e-01   1.0    [1]  [3.2]        0.5           None   \n",
       "p_length  6.464905e-19   1.0    [2]  [5.9]        0.5           None   \n",
       "p_width   4.378181e-08   1.0    [3]  [2.3]        0.5           None   \n",
       "\n",
       "         target_inputs  \n",
       "s_length          None  \n",
       "s_width           None  \n",
       "p_length          None  \n",
       "p_width           None  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_ciu = ciu.CIU(model.predict_proba, iris_outnames, data=X_train)\n",
    "CIUres = iris_ciu.explain(instance, output_inds=2)\n",
    "display(CIUres)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was a **\"Why?\"** explanation, where we can see that Petal length and Petal width are the most(/only) relevant features with CI=1. This instance is a very typical Virginica, as shown by the close-to-one CU values for all features. \n",
    "\n",
    "We can also plot this explanation but it's now very interesting in the case of Iris:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_ciu.plot_ciu(CIUres, figsize=(6,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get a **\"Why not?\"** explanation for \"Why not a Versicolor?\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CIUres_versicolor = iris_ciu.explain(instance, output_inds=1)\n",
    "iris_ciu.plot_ciu(CIUres_versicolor, figsize=(6,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CIU plotting methods use the result from the last call to `explain` or `explain_voc` by default, so this can also be done without providing the CIU result explicitly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_ciu.plot_ciu(figsize=(6,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that Petal length and Petal width are also here the most(/only) relevant features with CI~=1. However, the CU values are almost zero so this is definitely not a Versicolor. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boston Housing example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by getting the Boston data and preparing it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
    "raw_df = pd.read_csv(data_url, sep=r\"\\s+\", skiprows=22, header=None)\n",
    "data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
    "target = raw_df.values[1::2, 2]\n",
    "\n",
    "data = pd.DataFrame(data)\n",
    "data.columns = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then train an XGB model for predicting Boston house prices based on the data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(alpha=10, base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.3, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=10, n_jobs=None,\n",
       "             num_parallel_tree=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(alpha=10, base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.3, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=10, n_jobs=None,\n",
       "             num_parallel_tree=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(alpha=10, base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.3, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=10, n_jobs=None,\n",
       "             num_parallel_tree=None, ...)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "np.random.seed(26)\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.3, random_state=123)\n",
    "xg_reg = xgb.XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.3, learning_rate = 0.1,max_depth = 5, alpha = 10, n_estimators = 10)\n",
    "xg_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We choose an expensive instance from the test set to explain:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instance 17 in the test set has a real price of $43 and a predicted price of $34\n",
      "The instance values are:\n",
      "        CRIM    ZN  INDUS  CHAS    NOX    RM   AGE     DIS  RAD    TAX  \\\n",
      "261  0.53412  20.0   3.97   0.0  0.647  7.52  89.4  2.1398  5.0  264.0   \n",
      "\n",
      "     PTRATIO       B  LSTAT  \n",
      "261     13.0  388.37   7.26  \n"
     ]
    }
   ],
   "source": [
    "inst_ind = np.argmax(xg_reg.predict(X_test)) # Test set instance with highest price estimate, so we expect a \"positive\" explanation.\n",
    "instance = X_test.iloc[[inst_ind]]\n",
    "inst_val = y_test[inst_ind]\n",
    "pred = xg_reg.predict(instance)[0]\n",
    "print(f\"Instance {inst_ind} in the test set has a real price of ${inst_val:.0f} and a predicted price of ${pred:.0f}\")\n",
    "print(f\"The instance values are:\\n{instance}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to know the minimal and maximal price values in order to know how cheap or expensive the instance is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The minimal price in the training set is $5 and the maximal price is $50\n"
     ]
    }
   ],
   "source": [
    "out_minmaxs = pd.DataFrame({'mins': [min(y_train)], 'maxs': max(y_train)})\n",
    "print(f\"The minimal price in the training set is ${min(y_train):.0f} and the maximal price is ${max(y_train):.0f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then get CIU Object and show tabular results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>CI</th>\n",
       "      <th>CU</th>\n",
       "      <th>Cinfl</th>\n",
       "      <th>outname</th>\n",
       "      <th>outval</th>\n",
       "      <th>feature</th>\n",
       "      <th>ymin</th>\n",
       "      <th>ymax</th>\n",
       "      <th>inputs</th>\n",
       "      <th>invals</th>\n",
       "      <th>neutralCU</th>\n",
       "      <th>target_concept</th>\n",
       "      <th>target_inputs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>0.056011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028006</td>\n",
       "      <td>Price</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>CRIM</td>\n",
       "      <td>31.502943</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[0.53412]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>0.009911</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004956</td>\n",
       "      <td>Price</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>ZN</td>\n",
       "      <td>33.577450</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[20.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>0.057076</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028538</td>\n",
       "      <td>Price</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>INDUS</td>\n",
       "      <td>31.455057</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[3.97]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>Price</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>CHAS</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[0.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>0.048834</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024417</td>\n",
       "      <td>Price</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>NOX</td>\n",
       "      <td>31.825907</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>[4]</td>\n",
       "      <td>[0.647]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>0.074986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.037493</td>\n",
       "      <td>Price</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>RM</td>\n",
       "      <td>30.649109</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>[5]</td>\n",
       "      <td>[7.52]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.046000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.023000</td>\n",
       "      <td>Price</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>AGE</td>\n",
       "      <td>31.953468</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>[6]</td>\n",
       "      <td>[89.4]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>0.051620</td>\n",
       "      <td>0.904325</td>\n",
       "      <td>0.020871</td>\n",
       "      <td>Price</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>DIS</td>\n",
       "      <td>31.922781</td>\n",
       "      <td>34.245701</td>\n",
       "      <td>[7]</td>\n",
       "      <td>[2.1398]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>Price</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>RAD</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>[8]</td>\n",
       "      <td>[5.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>0.043273</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021636</td>\n",
       "      <td>Price</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>TAX</td>\n",
       "      <td>32.076191</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>[9]</td>\n",
       "      <td>[264.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>0.040333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020166</td>\n",
       "      <td>Price</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>PTRATIO</td>\n",
       "      <td>32.208485</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>[10]</td>\n",
       "      <td>[13.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.046000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.023000</td>\n",
       "      <td>Price</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>B</td>\n",
       "      <td>31.953468</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>[11]</td>\n",
       "      <td>[388.37]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>0.107049</td>\n",
       "      <td>0.792038</td>\n",
       "      <td>0.031262</td>\n",
       "      <td>Price</td>\n",
       "      <td>34.023457</td>\n",
       "      <td>LSTAT</td>\n",
       "      <td>30.208048</td>\n",
       "      <td>35.025249</td>\n",
       "      <td>[12]</td>\n",
       "      <td>[7.26]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               CI        CU     Cinfl outname     outval  feature       ymin  \\\n",
       "CRIM     0.056011  1.000000  0.028006   Price  34.023457     CRIM  31.502943   \n",
       "ZN       0.009911  1.000000  0.004956   Price  34.023457       ZN  33.577450   \n",
       "INDUS    0.057076  1.000000  0.028538   Price  34.023457    INDUS  31.455057   \n",
       "CHAS     0.000000  0.000000 -0.000000   Price  34.023457     CHAS  34.023457   \n",
       "NOX      0.048834  1.000000  0.024417   Price  34.023457      NOX  31.825907   \n",
       "RM       0.074986  1.000000  0.037493   Price  34.023457       RM  30.649109   \n",
       "AGE      0.046000  1.000000  0.023000   Price  34.023457      AGE  31.953468   \n",
       "DIS      0.051620  0.904325  0.020871   Price  34.023457      DIS  31.922781   \n",
       "RAD      0.000000  0.000000 -0.000000   Price  34.023457      RAD  34.023457   \n",
       "TAX      0.043273  1.000000  0.021636   Price  34.023457      TAX  32.076191   \n",
       "PTRATIO  0.040333  1.000000  0.020166   Price  34.023457  PTRATIO  32.208485   \n",
       "B        0.046000  1.000000  0.023000   Price  34.023457        B  31.953468   \n",
       "LSTAT    0.107049  0.792038  0.031262   Price  34.023457    LSTAT  30.208048   \n",
       "\n",
       "              ymax inputs     invals  neutralCU target_concept target_inputs  \n",
       "CRIM     34.023457    [0]  [0.53412]        0.5           None          None  \n",
       "ZN       34.023457    [1]     [20.0]        0.5           None          None  \n",
       "INDUS    34.023457    [2]     [3.97]        0.5           None          None  \n",
       "CHAS     34.023457    [3]      [0.0]        0.5           None          None  \n",
       "NOX      34.023457    [4]    [0.647]        0.5           None          None  \n",
       "RM       34.023457    [5]     [7.52]        0.5           None          None  \n",
       "AGE      34.023457    [6]     [89.4]        0.5           None          None  \n",
       "DIS      34.245701    [7]   [2.1398]        0.5           None          None  \n",
       "RAD      34.023457    [8]      [5.0]        0.5           None          None  \n",
       "TAX      34.023457    [9]    [264.0]        0.5           None          None  \n",
       "PTRATIO  34.023457   [10]     [13.0]        0.5           None          None  \n",
       "B        34.023457   [11]   [388.37]        0.5           None          None  \n",
       "LSTAT    35.025249   [12]     [7.26]        0.5           None          None  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boston_ciu = ciu.CIU(xg_reg.predict, ['Price'], data=X_train, out_minmaxs=out_minmaxs)\n",
    "CIUres = boston_ciu.explain(instance)\n",
    "display(CIUres)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can also plot the CI/CU values using the CIU Object's ``plot_ciu`` function. Due to the relatively small CI values, we reduce the extent of the X-axis for readability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boston_ciu.plot_ciu(CIUres, CImax=min(1.0,(max(CIUres.CI)+0.01)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several plot options available using the following parameters, such as:\n",
    "\n",
    "* ``plot_mode``: defines the type plot to use between 'color_CU', 'overlap' and 'combined'.                                                 \n",
    "* ``sort``: defines the order of the plot bars by the 'ci' (default), 'cu' values or unsorted if None.\n",
    "* ``color_blind``: defines accessible color maps to use for the plots, such as 'protanopia', 'deuteranopia' and 'tritanopia'.                           \n",
    "* ``color_edge_cu``: defines the hex or named color for the CU edge in the overlap plot mode.         \n",
    "* ``color_fill_cu``: defines the hex or named color for the CU fill in the overlap plot mode.         \n",
    "* ``color_edge_ci``: defines the hex or named color for the CI edge in the overlap plot mode.         \n",
    "* ``color_fill_ci``: defines the hex or named color for the CI fill in the overlap plot mode.         \n",
    "\n",
    "Here's an example using some of these parameters to create a modified version of the above plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boston_ciu.plot_ciu(CIUres, plot_mode=\"combined\", color_blind='tritanopia', sort='cu', CImax=min(1.0,(max(CIUres.CI)+0.01)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contextual influence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Contextual influence is calculated from CI and CU as follows:\n",
    "\n",
    "$$\n",
    "\\phi_{j,\\{i\\},\\{I\\}}(x)=\\omega_{j,\\{i\\},\\{I\\}}(x)(CU_{j,\\{i\\}}(x) - \\phi_{0}),\n",
    "$$\n",
    "\n",
    "where $\\phi_{0}$ is the *baseline/reference* value ($y(u(0))$ in the\n",
    "plot). For instance, $\\phi_{0}=0.5$ signifies using the average utility\n",
    "value $0.5$ as the baseline.\n",
    "An explanation using Contextual influence on the titanic dataset can be obtained as follows:\n",
    "\n",
    "*Note*: the dataset and model used are not identical to the R version, \n",
    "therefore the results will see a slight variance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "data = pd.read_csv(\"https://raw.githubusercontent.com/KaryFramling/py-ciu/master/ciu_tests/data/titanic.csv\")\n",
    "data = data.drop(data.columns[0], axis=1)\n",
    "unused = ['PassengerId','Cabin','Name','Ticket']\n",
    "\n",
    "for col in unused:\n",
    "    data = data.drop(col, axis=1)\n",
    "\n",
    "np.random.seed(26)\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "data = data.dropna().apply(LabelEncoder().fit_transform)\n",
    "train = data.drop('Survived', axis=1)\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=100)\n",
    "model.fit(train, data.Survived)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new instance to explain (the 8-year old boy used in te book https://ema.drwhy.ai):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create test instance (8-year old boy)\n",
    "new_passenger = pd.DataFrame.from_dict({\"Pclass\" : [1], \"Sex\": [1], \"Age\": [8.0], \"SibSp\": [0], \"Parch\": [0], \"Fare\": [72.0], \"Embarked\": [2]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "res = model.predict_proba(new_passenger)\n",
    "print(type(res))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check out some basic things about the instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The estimated probability of survival is 0.61.\n",
      "(712, 2)\n",
      "The mean probability of survival in the training set is 0.41.\n"
     ]
    }
   ],
   "source": [
    "surv_prob = model.predict_proba(new_passenger)\n",
    "print(f\"The estimated probability of survival is {surv_prob[0,1]:.2f}.\")\n",
    "titanic_train_probs = model.predict_proba(train)\n",
    "print(titanic_train_probs.shape)\n",
    "mean_surv_prob = np.mean(titanic_train_probs[:,1])\n",
    "print(f\"The mean probability of survival in the training set is {mean_surv_prob:.2f}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to make a distinction between numerical (floating point) features and integer/symbolic features, then we need to tell CIU what to use as integer/symbolic values using a \"category mapping\" dictionary. If the values are strings, then they are replaced with the corresponding index for passing to the model. If the values are integers, they are used as such as input values to the model. \n",
    "\n",
    "It is important to make this distinction between features also because the perturbation strategy used by CIU depends on it. If no mapping is provided, then all features will be perturbed using random float values within the given min-max range of each feature. \n",
    "\n",
    "*Remark*: This is not needed in \"R\" because ML models and (correctly implemented) XAI methods know how to use the \"factor\" type for this purpose. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_mapping = {\n",
    "    'Sex': ['female','male'],\n",
    "    'Pclass': list(range(max(data.Pclass))),\n",
    "    'SibSp': list(range(max(data.SibSp))),\n",
    "    'Parch': list(range(max(data.Parch))),\n",
    "    'Embarked': [\"Belfast\",\"Cherbourg\",\"Queenstown\",\"Southampton\"]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the mean probability of survival as the `normalCU` value of Contextual influence for consistency with the SHAP example further down. Here we show a bar chart using Contextual Influence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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79zf87OjoiLCwMJw7dw4AoFAo8PXXX0OtVuPgwYM4fvw4XnnlFbz77rs4duwYXF1dAQB+fn6GcFS9Tr1ejwsXLtQakBITE5GQkGB4r9FooFKpoFar4ebmZolW3JMgCAgMDIRarYYo8jkVUigoKLD6NnJzc6HVaq2+neaA33HpsefSYr+rFBcX12s+uw5IAODs7IyoqChERUXhrbfewtSpUzF//vw6A1J9BAYGIjAwEFOnTsXcuXMRHByM7du3Y9KkSWatTy6X13oIUBRF6PV6s+tsiOrhWCm3Sdan1+v5+/wPfselx55Li/2uUt9waL8HIevQpUuXu/6L+vjx44afKyoqkJGRgc6dO9c5f0BAAFxdXY3WmZOTg7y8PKN1ymQyhISENLJ6IiIisgSLjSAVFhbC09PTUquzups3b+Lpp5/G5MmTERoaCjc3N/z8889YsmQJnnzyyTqXe//999GxY0d07twZK1aswK1btzB58mQAVVeolZaW4oknnoC/vz8KCwuxatUqlJeXIyoqyrAOZ2dnxMbGYunSpdBoNJg1axbGjBnT4NsKEBERkXWYFZAWL16MgIAAPPPMMwCAMWPG4PPPP4e3tzf27NmD7t27W7RIa1Aqlejbty9WrFgBtVqN8vJyqFQqTJs2DW+88Uady6WkpCAlJQUnT55EUFAQdu3ahfvuuw8AEBERgffffx8TJkzA1atX0bp1a/Ts2RN79+41Gh0KCgrCU089hSeeeAIFBQUYPnw41qxZY/V9JiIiovoxKyB98MEHhkvS9+3bh3379iE9PR07duzA7NmzsXfvXosWaQ1yuRzJyclITk6uc57/PU4ZEBBgeD927Nha5x88eDAGDx5cr+3HxcUhLi6uARUTERGRVMwKSFeuXIFKpQIA7N69G2PGjMGQIUMQEBCAvn37WrRAIiIiIqmZdZJ269atkZubCwD45ptvEBkZCaBqxKWystJy1RERERHZgFkB6amnnsK4ceMQFRWFmzdvGh7ompmZiaCgIIsW2NIkJSXh5MmTti6DiIiI7sKsQ2wrVqxAQEAAcnNzsWTJEiiVSgBVd/6Nj4+3aIFEREREUjMrILVq1Qqvvvpqjel/+9vfGl0QERERka2ZfR+kLVu2YO3atbh8+TKOHTsGf39/rFy5Eu3bt7/rfYSIqGFcUAZY+KkALiiz7AqJiFoYswJSamoq5s2bh5dffhmLFi0ynJjt6emJlStXMiARWYBcLoeDoyOCK7Kssn6ZzKHWR9gQEZGZAWn16tVYt24dRo0ahZSUFMP0sLCwWg+9EVHDKRQKvDRzJi5cuGCV5ybJ5XIoFAqLr5eIqCUwKyBlZWWhZ8+eNabL5XI+GZzIgjw8PODl5WXXD5YkIrIFsy7zb9++fa2Xqn/zzTd3fXArERERUXNg1ghSQkICZsyYgbKyMoiiiJ9++gmffvopkpOTsX79ekvXSERERCQpswLS1KlT4eLigjfffBOlpaUYN24cfH198e677+LZZ5+1dI1EdquoqAgFBQVGh9h47hARkfU1OCBVVFTgk08+wdChQzF+/HiUlpaipKQE7dq1s0Z9RHZLq9Vi2/btqKyoMJoukzlg+PAYhiQiIitqcEBydHTE9OnTce7cOQCAq6srXF1dLV4Ykb3T6XSorKjARaE9bsMZQNX9i4L1WdDpdAxIRERWZNZJ2uHh4cjMzLR0LURUi9twhlZQQCsoDEGJiIisy6xzkOLj4/HKK6/gjz/+QO/evWv8SzY0NNQixRERERHZglkBqfpE7FmzZhmmCYIAURQhCILhztpEREREzZHZN4okIiIiaqnMCkj+/v6WroOIiIioyTArIG3evPmun0+YMMGsYpqyQYMGoUePHli5cqWtSyEiIiIrMysg/fWvfzV6X15ejtLSUjg5OcHV1bXJBqSJEyfio48+AgC0atUKfn5+mDBhAt544w04OprVCiIiImqBzEoFt27dqjHt999/R1xcHGbPnt3ooqwpOjoaGzduhE6nw549ezBjxgy0atUKiYmJti6NiIiImgiz7oNUm44dOyIlJaXG6FJTI5fL4e3tDX9/f8TFxSEyMhK7du0CABw9ehSDBg2Cq6srWrdujaFDh9YaBgFgy5YtCAsLg5ubG7y9vTFu3Dhcu3bN8PmtW7cwfvx4tG3bFi4uLujYsSM2btwIALhz5w5mzpwJHx8fODs7w9/fH8nJydbfeSIiIqoXix5XcnR0RF5eniVXaXUuLi64efMmTp48iccffxyTJ0/Gu+++C0dHRxw8eLDOWxaUl5dj4cKFCAkJwbVr15CQkICJEydiz549AIC33noLZ8+eRXp6Ou677z5cunQJt2/fBgCsWrUKu3btwo4dO+Dn54fc3Fzk5ubWWaNOp4NOpzO812g0AKpurSCTWSzj3pUgCJJvk+omk8n4e7Awfselx55Li/2uUt2HezErIFWPuFQTRRH5+fl477338PDDD5uzSsmJoogDBw7g22+/xUsvvYQlS5YgLCwMa9asMczz0EMP1bn85MmTDT936NABq1atQp8+fVBSUgKlUomcnBz07NkTYWFhAICAgADD/Dk5OejYsSMeeeQRCIJwz6sCk5OTsWDBghrTAwMD4e7uXt9dtojAwEBJt2fP8vPz6/xMpVLBx8dHwmrsB7/j0mPPpWXv/a4eZLgXswLSqFGjjN4LgoC2bdvisccew7Jly8xZpWR2794NpVKJ8vJy6PV6jBs3DklJSejTpw+efvrpeq8nIyMDSUlJOHXqFG7dumV42npOTg66dOmCuLg4jB49Gr/88guGDBmCUaNGYcCAAQCqThaPiopCSEgIoqOjMXz4cAwZMqTObSUmJiIhIcHwXqPRQKVSQa1Ww83NzcxONIwgCAgMDIRarYYoipJs094VFBTU+Vlubi60Wq2E1bR8/I5Ljz2XFvtdpbi4uF7zmRWQqsNAczR48GCkpqbCyckJvr6+hqvXXFxc6r0OrVaLoUOHYujQodi6dSvatm2LnJwcDB06FHfu3AEADBs2DP/3f/+HPXv2YN++fXj88ccxY8YMLF26FL169UJWVhbS09Oxf/9+jBkzBpGRkfjss89q3Z5cLodcLq8xXRRFyX4X1cOxUm6T6qbX6/l7sDB+x6XHnkuL/a5S33Bo1kHIt99+G6WlpTWm3759G2+//bY5q5SMQqFAUFAQ/Pz8jC7tDw0NxYEDB+q1jvPnz+PmzZtISUnBwIED0alTJ6MTtKu1bdsWsbGx+Pjjj7Fy5Up8+OGHhs/c3d3xzDPPYN26ddi+fTs+//zzu44YEBERkXTMCkgLFixASUlJjemlpaW1nivTHCQmJuLEiROIj4/H6dOncf78eaSmpuLGjRs15vXz84OTkxNWr16Ny5cvY9euXVi4cKHRPPPmzcO//vUvXLp0Cb/99ht2796Nzp07AwCWL1+OTz/9FOfPn8fFixexc+dOeHt7w9PTU4pdJSIionswKyBVP5TW1KlTp+Dl5dXoomwhODgYe/fuxalTpxAeHo7+/fvjX//6V603kGzbti02bdqEnTt3okuXLkhJScHSpUuN5nFyckJiYiJCQ0Px6KOPwsHBAdu2bQMAuLm5GU4K79OnD7Kzs7Fnzx67vqqAiIioKRHEBpyp1bp1awiCgKKiIri7uxuFpMrKSpSUlGD69Ol4//33rVIsVdFoNPDw8EBGRgaUSqUk25TJZAgKCsKlS5fs+ti1lAoLC5Geno5TQmdoBQUAQCFq0V08h6FDhzbbf4w0VfyOS489lxb7XaWkpAS9e/c2ZJm6NOgk7ZUrV0IURUyePBkLFiyAh4eH4TMnJycEBASgf//+5ldNRERE1AQ0KCDFxsYCANq3b48BAwagVatWVimKiIiIyJbMusw/IiLC8HNZWZnh0vZqUt+8kIiIiMiSzDoruLS0FDNnzkS7du2gUCjQunVroxcRERFRc2ZWQJo9ezb+/e9/IzU1FXK5HOvXr8eCBQvg6+uLzZs3W7pGIiIiIkmZdYgtLS0NmzdvxqBBgzBp0iQMHDgQQUFB8Pf3x9atWzF+/HhL10lkt1xQBoj/8zMREVmdWQGpoKAAHTp0AFB1vlH1HaAfeeQRxMXFWa46Ijsml8vh4OiI4Ioso+kymUOtj54hIiLLMSsgdejQAVlZWfDz80OnTp2wY8cOhIeHIy0tjXeDJrIQhUKBl2bOxIULF4zuWSKXy6FQKGxYGRFRy2dWQJo0aRJOnTqFiIgIvP766xgxYgTee+89lJeXY/ny5ZaukchueXh4wMvLy65v6kZEZAtmBaS//e1vhp8jIyNx/vx5ZGRkICgoCKGhoRYrjoiIiMgWzApI/6usrAz+/v7w9/e3RD1ERERENmfWZf6VlZVYuHAhHnjgASiVSly+fBkA8NZbb2HDhg0WLZDIHmm1WhQUFKCoqMjWpRAR2SWzAtKiRYuwadMmLFmyBE5OTobpXbt2xfr16y1WHJE90mq1+DotDenp6Xhv9WpotVpbl0REZHfMCkibN2/Ghx9+iPHjx8PBwcEwvXv37jh//rzFiiOyRzqdDpWiCO/Tp1FRWQmdTmfrkoiI7I5ZAenPP/9EUFBQjel6vR7l5eWNLoqIAKeSEluXQERkt8wKSF26dMHhw4drTP/ss8/Qs2fPRhdFREREZEtmXcU2b948xMbG4s8//4Rer8cXX3yBCxcuYPPmzdi9e7elayQiIiKSVINGkC5fvgxRFPHkk08iLS0N+/fvh0KhwLx583Du3DmkpaUhKirKWrUSERERSaJBI0gdO3ZEfn4+2rVrh4EDB8LLywtnzpzB/fffb636iIiIiCTXoBEkURSN3qenpze5S5CTkpLQo0cPq6z70KFDEAQBhYWFFltndnY2BEHAyZMnLbZOIiIiahyzTtKuZhqYGmrixIkQBKHGKzo6ulHrJSIiImqMBh1iqw4wptMaIzo6Ghs3bjSaJpfLG7VOa+DtC4iIiOxHgw+xTZw4EU899RSeeuoplJWVYfr06Yb31a+GkMvl8Pb2Nnq1bt0aQFX4Wrt2LYYPHw5XV1d07twZx44dw6VLlzBo0CAoFAoMGDAAarW6xnrXrl0LlUoFV1dXjBkzxuiRDSdOnEBUVBTuu+8+eHh4ICIiAr/88ovR8oIgIDU1FSNHjoRCocCiRYtqbKO0tBTDhg3Dww8/bDjstn79enTu3BnOzs7o1KkT1qxZY7TMTz/9hJ49e8LZ2RlhYWHIzMxsUL+IiIjI+ho0ghQbG2v0/rnnnrNoMbVZuHAhli9fjuXLl2POnDkYN24cOnTogMTERPj5+WHy5MmYOXMm0tPTDctcunQJO3bsQFpaGjQaDaZMmYL4+Hhs3boVAFBcXIzY2FisXr0aoihi2bJleOKJJ/D777/Dzc3NsJ6kpCSkpKRg5cqVcHR0NDxzDgAKCwsRExMDpVKJffv2wdXVFVu3bsW8efPw3nvvoWfPnsjMzMS0adOgUCgQGxuLkpISDB8+HFFRUfj444+RlZWFv/71r/fsgU6nM7qbskajAVAV4mSyRh0lrbfqkUIpt2mvausve259/I5Ljz2XFvtdpd5HvkQbio2NFR0cHESFQmH0WrRokShWneAkvvnmm4b5jx07JgIQN2zYYJj26aefis7Ozob38+fPFx0cHMQ//vjDMC09PV2UyWRifn5+rXVUVlaKbm5uYlpammEaAPHll182mu/gwYMiAPHcuXNiaGioOHr0aFGn0xk+DwwMFD/55BOjZRYuXCj2799fFEVRXLt2rdimTRvx9u3bhs9TU1NFAGJmZmadfZo/f74IoMarqKiozmWo+crLyxOTkpLEfw4ZIiYlJYl5eXm2LomIqMUoKiqq199Qs24UaUmDBw9Gamqq0TQvLy/Dz6GhoYafq28n0K1bN6NpZWVl0Gg0cHd3BwD4+fnhgQceMMzTv39/6PV6XLhwAd7e3rh69SrefPNNHDp0CNeuXUNlZSVKS0uRk5NjVEdYWFitNUdFRSE8PBzbt283PItOq9VCrVZjypQpmDZtmmHeiooKeHh4AADOnTuH0NBQODs7G9V2L4mJiUhISDC812g0UKlUUKvVRiNe1iQIAgIDA6FWqxt9cj7dXUFBgdH73NzcJne1aEvE77j02HNpsd9ViouL6zWfzQOSQqGo9blu1Vq1amX4uXpYrLZper2+3tuMjY3FzZs38e6778Lf3x9yuRz9+/fHnTt3atRWm5iYGHz++ec4e/asIayV/Oe5WevWrUPfvn2N5v/fB/qaQy6X13riuiiKDdrvxqgejpVym/aqtv6y59bH77j02HNpsd9V6hsObR6QrCEnJwd5eXnw9fUFABw/fhwymQwhISEAgKNHj2LNmjV44oknAFT9C/3GjRv1Xn9KSgqUSiUef/xxHDp0CF26dMH9998PX19fXL58GePHj691uc6dO2PLli0oKyszjCIdP368MbtKREREVmDzgKTT6XDlyhWjaY6OjrjvvvvMXqezszNiY2OxdOlSaDQazJo1C2PGjIG3tzeAqjuCb9myBWFhYdBoNJg9ezZcXFwatI2lS5eisrISjz32GA4dOoROnTphwYIFmDVrFjw8PBAdHQ2dToeff/4Zt27dQkJCAsaNG4e5c+di2rRpSExMRHZ2NpYuXWr2fhIREZF12Pw09m+++QY+Pj5Gr0ceeaRR6wwKCsJTTz2FJ554AkOGDEFoaKjR5fYbNmzArVu30KtXLzz//POYNWsW2rVr1+DtrFixAmPGjMFjjz2GixcvYurUqVi/fj02btyIbt26ISIiAps2bUL79u0BAEqlEmlpaThz5gx69uyJuXPnYvHixY3aVyIiIrI8QbTnM7WaKY1GAw8PD2RkZECpVEqyTZlMhqCgIFy6dMmuj11LoaCgAN9++y38fvgBOQMGYNiwYfD09LR1WS0ev+PSY8+lxX5XKSkpQe/evVFUVGS4uKs2Nh9BIiIiImpqGJCIiIiITDAgEREREZlgQCIiIiIywYBE1ETdkegEfCIiqokBiaiJkcvlcBAEXAkNhaODQ613USciIuuy+Y0iiciYQqFAzIgRKC8vR0hICK5fv27Xl+QSEdkCR5CImiCFQgEvLy/Dg46JiEhaDEhEREREJhiQiIiIiEwwIBERERGZ4EnaZFNarRY6nc7WZTRJMpkMRUVFti6DiMguMSCRzWi1WnydloZKPi+5Tvv27sXwESPg4uJi61KIiOwKAxLZjE6nQ6UoIuDwYThzpKSGMg8PZA8cCJ1Ox4BERCQxBiSyOeeiIrgWFNi6DCIiIgOepE1ERERkggGJiIiIyAQDEhEREZEJBiQiIiIiEwxIEjp06BAEQUBhYaGtSyEiIqK7sOuANHHiRAiCAEEQ4OTkhKCgILz99tuoqKiwdWlERERkQ3Z/mX90dDQ2btwInU6HPXv2YMaMGWjVqhUSExMbtJ7KykoIggCZzK4zJxERUYtg93/N5XI5vL294e/vj7i4OERGRmLXrl1Yvnw5unXrBoVCAZVKhfj4eJSUlBiW27RpEzw9PbFr1y506dIFcrkcOTk50Ol0mDNnDlQqFeRyOYKCgrBhwwajbWZkZCAsLAyurq4YMGAALly4IPVuExER0V3Y/QiSKRcXF9y8eRMymQyrVq1C+/btcfnyZcTHx+O1117DmjVrDPOWlpZi8eLFWL9+Pdq0aYN27dphwoQJOHbsGFatWoXu3bsjKysLN27cMNrG3LlzsWzZMrRt2xbTp0/H5MmTcfTo0Tpr0ul0Rs8r02g0ACDpiJUgCBbfJkfb6o+9sj5rfMfp7thzabHfVar7cC8MSP8hiiIOHDiAb7/9Fi+99BJefvllw2cBAQH4+9//junTpxsFpPLycqxZswbdu3cHAFy8eBE7duzAvn37EBkZCQDo0KFDjW0tWrQIERERAIDXX38dMTExKCsrg7Ozc621JScnY8GCBTWmBwYGwt3d3ex9NkdgYKDF1pWfn2+xdbVkKpUKPj4+ti7DbljyO071w55Ly977XT3IcC92H5B2794NpVKJ8vJy6PV6jBs3DklJSdi/fz+Sk5Nx/vx5aDQaVFRUoKysDKWlpXB1dQUAODk5ITQ01LCukydPwsHBwRB+6vK/y1T/4bt27Rr8/PxqnT8xMREJCQmG9xqNBiqVCmq1Gm5ubmbve0MIgoDAwECo1WqIFnq4bAEfL1Ivubm50Gq1ti6jxbPGd5zujj2XFvtdpbi4uF7z2X1AGjx4MFJTU+Hk5ARfX184OjoiOzsbw4cPR1xcHBYtWgQvLy8cOXIEU6ZMwZ07dwwBycXFxWiorr4PFG3VqpXh5+rl9Xp9nfPL5XLI5fIa00VRvOtyllQ9HGvJbUpVe0vAXlmfNb7jdHfsubTY7yr1DYf2exDyPxQKBYKCguDn5wdHx6q8mJGRAb1ej2XLlqFfv34IDg5GXl7ePdfVrVs36PV6fPfdd9Yum4iIiKzI7gNSbYKCglBeXo7Vq1fj8uXL2LJlCz744IN7LhcQEIDY2FhMnjwZX331FbKysnDo0CHs2LFDgqqJiIjIUhiQatG9e3csX74cixcvRteuXbF161YkJyfXa9nU1FT85S9/QXx8PDp16oRp06bx/BEiIqJmRhDt+UytZkqj0cDDwwMZGRlQKpWSbFMmkyEoKAiXLl2y2LHrgoICfPvtt+i0ezdcecJ2DaVeXjg/fDiGDRsGT09PW5fT4lnjO053x55Li/2uUlJSgt69e6OoqOiuV4JzBImIiIjIBAMSERERkQkGJCIiIiITDEhEREREJuz+RpFke2UeHrYuoUliX4iIbIcBiWxGLpfDQRCQPXCgrUtpshwdHGq9izoREVkXAxLZjEKhQMyIEdDpdLYupUmSyWQICQnB9evX7fqSXCIiW2BAIptSKBRQKBS2LqNJkslk8PDwwPXr121dChGR3eFJ2kREREQmGJCIiIiITDAgEREREZngOUhkoNVq6zxhWiaTIT8/HwUFBTxhWCIymQxFRUW2LoOIyC4xIBGAqnD0dVoaKvns4iZl3969GD5iBFxcXGxdChGRXWFAIgCATqdDpSgi4PBhOHPUokko8/BA9sCB0Ol0DEhERBJjQCIjzkVFcC0osHUZRERENsWTtImIiIhMMCARERERmWBAIiIiIjLBgERERERkggGJiIiIyAQDkhmuX7+OuLg4+Pn5QS6Xw9vbG0OHDsXRo0dtXRoRERFZAC/zN8Po0aNx584dfPTRR+jQoQOuXr2KAwcO4ObNm7YujYiIiCyAI0gNVFhYiMOHD2Px4sUYPHgw/P39ER4ejsTERIwcOdIwz9SpU9G2bVu4u7vjsccew6lTpwBUjT55e3vjnXfeMazzhx9+gJOTEw4cOGCTfSIiIiJjHEFqIKVSCaVSia+++gr9+vWDXC6vMc/TTz8NFxcXpKenw8PDA2vXrsXjjz+Oixcvom3btvjnP/+JUaNGYciQIQgJCcHzzz+PmTNn4vHHH691mzqdzugZaRqNBgAgCAJkMstkXEuth6yDvx/rEwTB8L/stzTYc2mx31Wq+3DP+USRD99qqM8//xzTpk3D7du30atXL0RERODZZ59FaGgojhw5gpiYGFy7ds0oPAUFBeG1117DCy+8AACYMWMG9u/fj7CwMJw5cwYnTpyoNWwBQFJSEhYsWFBjelFREdzd3S2yT/n5+fjwww/Rafdu3km7iSj18sL54cPxwgsvwMfHx9blEBG1CBqNBh4eHvf8G8oRJDOMHj0aMTExOHz4MI4fP4709HQsWbIE69evh1arRUlJCdq0aWO0zO3bt6FWqw3vly5diq5du2Lnzp3IyMioMxwBQGJiIhISEgzvNRoNVCoV1Go13NzcLLJPBQxFTVZubi60Wq2ty2jxBEFAYGAg1Go1+O9GabDn0mK/qxQXF9drPgYkMzk7OyMqKgpRUVF46623MHXqVMyfPx/x8fHw8fHBoUOHaizj6elp+FmtViMvLw96vR7Z2dno1q1bnduSy+W1BihRFKHX6y2xOxZbD1kHfz/WV33IwZL/XdHdsefSYr+r1DccMiBZSJcuXfDVV1+hV69euHLlChwdHREQEFDrvHfu3MFzzz2HZ555BiEhIZg6dSrOnDmDdu3aSVs0ERER1cp+z9Iy082bN/HYY4/h448/xunTp5GVlYWdO3diyZIlePLJJxEZGYn+/ftj1KhR2Lt3L7Kzs/HDDz9g7ty5+PnnnwEAc+fORVFREVatWoU5c+YgODgYkydPtvGeERERUTWOIDWQUqlE3759sWLFCqjVapSXl0OlUmHatGl44403IAgC9uzZg7lz52LSpEmGy/offfRR3H///Th06BBWrlyJgwcPGk4O27JlC7p3747U1FTExcXZeA+JiIiIAamB5HI5kpOTkZycXOc8bm5uWLVqFVatWlXjM5VKhfLycqNpAQEBKCoqsnitREREZB4eYiMiIiIywYBEREREZIIBiYiIiMgEAxIRERGRCZ6kTUbKPDxsXQL9B38XRES2w4BEAKquznMQBGQPHGjrUuh/ODo43PUxNEREZB0MSAQAUCgUiBkxAjqdrtbPZTIZVCoVcnNz7foW9VKSyWQICQnB9evX2XMiIokxIJGBQqGAQqGo9TOZTAYfHx9otVr+sZaITCaDh4cHrl+/butSiIjsDk/SJiIiIjLBgERERERkggGJiIiIyAQDEhEREZEJBiQiIiIiEwxIRERERCYYkIiIiIhM8D5IzZAoigCAkpISybYpCAI0Gg2Ki4sN2yfrYs+lxX5Ljz2XFvtdpfpv5716wIDUDBUXFwMAIiIibFwJERFR81RcXAyPuzzzUhDtOUY2U3q9Hnl5eXBzc4MgCJJsU6PRGB414u7uLsk27R17Li32W3rsubTY7yqiKKK4uBi+vr6Qyeo+04gjSM2QTCbDgw8+aJNtu7u72/V/WLbAnkuL/ZYeey4t9ht3HTmqxpO0iYiIiEwwIBERERGZYECiepHL5Zg/fz7kcrmtS7Eb7Lm02G/psefSYr8bhidpExEREZngCBIRERGRCQYkIiIiIhMMSEREREQmGJCIiIiITDAgUZ0KCgowfvx4uLu7w9PTE1OmTLnn898+/PBDDBo0CO7u7hAEAYWFhdIU20y9//77CAgIgLOzM/r27YuffvrprvPv3LkTnTp1grOzM7p164Y9e/ZIVGnL0JB+//bbbxg9ejQCAgIgCAJWrlwpXaEtSEN6vm7dOgwcOBCtW7dG69atERkZec//JshYQ/r9xRdfICwsDJ6enlAoFOjRowe2bNkiYbVNGwMS1Wn8+PH47bffsG/fPuzevRvff/89XnjhhbsuU1paiujoaLzxxhsSVdl8bd++HQkJCZg/fz5++eUXdO/eHUOHDsW1a9dqnf+HH37A2LFjMWXKFGRmZmLUqFEYNWoUfv31V4krb54a2u/S0lJ06NABKSkp8Pb2lrjalqGhPT906BDGjh2LgwcP4tixY1CpVBgyZAj+/PNPiStvnhraby8vL8ydOxfHjh3D6dOnMWnSJEyaNAnffvutxJU3USJRLc6ePSsCEE+cOGGYlp6eLgqCIP7555/3XP7gwYMiAPHWrVtWrLJ5Cw8PF2fMmGF4X1lZKfr6+orJycm1zj9mzBgxJibGaFrfvn3FF1980ap1thQN7ff/8vf3F1esWGHF6lqmxvRcFEWxoqJCdHNzEz/66CNrldiiNLbfoiiKPXv2FN98801rlNfscASJanXs2DF4enoiLCzMMC0yMhIymQw//vijDStrGe7cuYOMjAxERkYapslkMkRGRuLYsWO1LnPs2DGj+QFg6NChdc5P/2VOv6lxLNHz0tJSlJeXw8vLy1plthiN7bcoijhw4AAuXLiARx991JqlNhsMSFSrK1euoF27dkbTHB0d4eXlhStXrtioqpbjxo0bqKysxP333280/f7776+zv1euXGnQ/PRf5vSbGscSPZ8zZw58fX1r/MOAajK330VFRVAqlXByckJMTAxWr16NqKgoa5fbLDAg2ZnXX38dgiDc9XX+/Hlbl0lEdi4lJQXbtm3Dl19+CWdnZ1uX02K5ubnh5MmTOHHiBBYtWoSEhAQcOnTI1mU1CY62LoCk9corr2DixIl3nadDhw7w9vaucWJfRUUFCgoKeMKqBdx3331wcHDA1atXjaZfvXq1zv56e3s3aH76L3P6TY3TmJ4vXboUKSkp2L9/P0JDQ61ZZothbr9lMhmCgoIAAD169MC5c+eQnJyMQYMGWbPcZoEjSHambdu26NSp011fTk5O6N+/PwoLC5GRkWFY9t///jf0ej369u1rwz1oGZycnNC7d28cOHDAME2v1+PAgQPo379/rcv079/faH4A2LdvX53z03+Z029qHHN7vmTJEixcuBDffPON0TmQdHeW+o7r9XrodDprlNj82PoscWq6oqOjxZ49e4o//vijeOTIEbFjx47i2LFjDZ//8ccfYkhIiPjjjz8apuXn54uZmZniunXrRADi999/L2ZmZoo3b960xS40adu2bRPlcrm4adMm8ezZs+ILL7wgenp6ileuXBFFURSff/558fXXXzfMf/ToUdHR0VFcunSpeO7cOXH+/Pliq1atxDNnzthqF5qVhvZbp9OJmZmZYmZmpujj4yO++uqrYmZmpvj777/baheanYb2PCUlRXRychI/++wzMT8/3/AqLi621S40Kw3t9zvvvCPu3btXVKvV4tmzZ8WlS5eKjo6O4rp162y1C00KAxLV6ebNm+LYsWNFpVIpuru7i5MmTTL6P6qsrCwRgHjw4EHDtPnz54sAarw2btwo/Q40A6tXrxb9/PxEJycnMTw8XDx+/Ljhs4iICDE2NtZo/h07dojBwcGik5OT+NBDD4lff/21xBU3bw3pd/X32/QVEREhfeHNWEN67u/vX2vP58+fL33hzVRD+j137lwxKChIdHZ2Flu3bi32799f3LZtmw2qbpoEURRFyYetiIiIiJownoNEREREZIIBiYiIiMgEAxIRERGRCQYkIiIiIhMMSEREREQmGJCIiIiITDAgEREREZlgQCIisqDs7GwIgoCTJ0/auhQiagQGJCIiIiITDEhEREREJhiQiIgsoLS0FJMnT0bXrl0BAAMGDMDYsWNRVFRk48qIyBwMSEREFjB79mx8//332Lx5MwBg3bp1OHv2LOLj421cGRGZgw+rJSJqpJKSEnh5eWHr1q3o06cP2rdvj8zMTNy4cQNDhgxBXl4evL29bV0mETUAR5CIiBpJrVajvLwcffv2NZoeHh4OURRx8eJFG1VGROZiQCIiIiIywYBERNRIgYGBaNWqFU6cOGE0/cSJExAEAcHBwTaqjIjMxYBERNRISqUS06ZNQ2JiIjIzMwEAZ8+eRUJCAsaOHcvzj4iaIZ6kTURkAaWlpZgxYwZ27twJrVYLFxcXjBw5EmvXroWHh4etyyOiBmJAIiKyoOzsbMNVbD169LB1OURkJh5iIyIiIjLBgERERERkgofYiIiIiExwBImIiIjIBAMSERERkQkGJCIiIiITDEhEREREJhiQiIiIiEwwIBERERGZYEAiIiIiMsGARERERGSCAYmIiIjIxP8D8HISPKxmP9AAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CIU_titanic = ciu.CIU(model.predict_proba, ['No', 'Yes'], data=train, category_mapping=category_mapping)\n",
    "CIUres = CIU_titanic.explain(new_passenger, output_inds=1, neutralCU=mean_surv_prob)\n",
    "CIU_titanic.plot_influence(CIUres, figsize=(6,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Remark:** The Equation for Contextual influence is similar to the\n",
    "definition of Shapley values for linear models, except that the input\n",
    "value $x_{i}$ is replaced by its utility value(s) $CU_{j,\\{i\\}}(x)$. In\n",
    "practice, **all *Additive Feature Attribution (AFA)* methods estimate\n",
    "influence values, not feature importance. Most state-of-the-art methods\n",
    "such as *Shapley values*, *LIME*,** are AFA methods.\n",
    "\n",
    "Influence values give no counter-factual information and are easily\n",
    "misinterpreted. Below, we create a Shapley value explanation using the\n",
    "shap package. In that explanation, for instance the close-to-zero Shapley\n",
    "value for $Parch$ gives the impression that it’s a non-important\n",
    "feature, which is clearly wrong based on the CIU explanation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Uncomment this if you have shap installed and want to run it. At the moment of writing, shap doesn't work with Python 3.12.\n",
    "# import shap\n",
    "# explainer = shap.Explainer(model, train)\n",
    "# shap_values = explainer(new_passenger)\n",
    "# shap.plots.bar(shap_values[0,:,1], order=np.argsort(shap_values[0,:,1].values)[::-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intermediate Concepts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CIU can use named feature coalitions and structured vocabularies. \n",
    "Such vocabularies allow explanations at any abstraction level and can make explanations interactive.\n",
    "\n",
    "The following code snippet plots the joint effect of features $age$ and\n",
    "$parch$ for the studied Titanic case (applicable for numeric features).\n",
    "It therefore shows how the coalition of those two features affects the\n",
    "output value and how CI and CU can be deduced in the same way as for a\n",
    "single feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CIU_titanic.plot_3D(ind_inputs=[2,4], ind_output=1, azim=-50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Titanic Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define a small vocabulary for Titanic as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_voc = {\n",
    "    \"Wealth\":['Pclass', 'Fare'],\n",
    "    \"Family\":['SibSp', 'Parch'],\n",
    "    \"Gender\":['Sex'],\n",
    "    \"Age\":['Age'],\n",
    "    \"Embarked\":['Embarked']\n",
    "}\n",
    "CIU_titanic.vocabulary = titanic_voc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we produce a barplot explanation using this vocabulary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_res = CIU_titanic.explain_voc(new_passenger, output_inds=1, nsamples=1000)\n",
    "CIU_titanic.plot_ciu(top_res, plot_mode='overlap', figsize=(6,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then explain the values of intermediate concepts FAMILY and WEALTH using their constituent features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = CIU_titanic.explain(new_passenger, output_inds=1, target_concept=\"Family\", target_ciu=top_res, nsamples=1000)\n",
    "CIU_titanic.plot_ciu(res, figsize=(6,1.5), main=\"Explained concept is Family\", plot_mode=\"overlap\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = CIU_titanic.explain(new_passenger, output_inds=1, target_concept=\"Wealth\", target_ciu=top_res, nsamples=1000)\n",
    "CIU_titanic.plot_ciu(res, figsize=(6,1.5), main=\"Explained concept is Wealth\", plot_mode=\"overlap\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same thing can be done using textual explanations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The explained value is Yes with the value 0.61 (CU=0.61), which is higher than average utility.\n",
      "Feature Wealth has low importance (CI=0.37) and has value(s) [ 1. 72.], which is lower than average utility (CU=0.43)\n",
      "Feature Family has low importance (CI=0.37) and has value(s) [0. 0.], which is low utility (CU=0.24)\n",
      "Feature Sex has low importance (CI=0.27) and has value(s) 1.0, which is low utility (CU=0.00)\n",
      "Feature Age has high importance (CI=0.67) and has value(s) 8.0, which is high utility (CU=0.91)\n",
      "Feature Embarked has very low importance (CI=0.03) and has value(s) 2.0, which is lower than average utility (CU=0.33)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(CIU_titanic.textual_explanation(top_res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The explained value is Wealth for output Yes.\n",
      "Feature Pclass has very low importance (CI=0.03) and has value(s) 1.0, which is high utility (CU=1.00)\n",
      "Feature Fare has normal importance (CI=0.51) and has value(s) 72.0, which is higher than average utility (CU=0.68)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(CIU_titanic.textual_explanation(res))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ames Housing Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ames housing is a data set about properties in the town Ames in the US. It contains over 80 features that can be used for learning to estimate the sales price. The following code imports the data set (make sure that you have the path to the CSV file set correctly!), does some pre-processing and trains a Gradient Boosting model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(alpha=10, base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=15, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(alpha=10, base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=15, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(alpha=10, base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=15, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, ...)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('ciu_tests/data/AmesHousing.csv')\n",
    "\n",
    "#Checking for missing data\n",
    "missing_data_count = df.isnull().sum()\n",
    "missing_data_percent = df.isnull().sum() / len(df) * 100\n",
    "\n",
    "missing_data = pd.DataFrame({\n",
    "    'Count': missing_data_count,\n",
    "    'Percent': missing_data_percent\n",
    "})\n",
    "\n",
    "missing_data = missing_data[missing_data.Count > 0]\n",
    "missing_data.sort_values(by='Count', ascending=False, inplace=True)\n",
    "\n",
    "#This one has spaces for some reason\n",
    "df.columns = df.columns.str.replace(' ', '')\n",
    "\n",
    "\n",
    "#Taking care of missing values\n",
    "from sklearn.impute import SimpleImputer\n",
    "# Group 1:\n",
    "group_1 = [\n",
    "    'PoolQC', 'MiscFeature', 'Alley', 'Fence', 'FireplaceQu', 'GarageType',\n",
    "    'GarageFinish', 'GarageQual', 'GarageCond', 'BsmtQual', 'BsmtCond',\n",
    "    'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'MasVnrType'\n",
    "]\n",
    "df[group_1] = df[group_1].fillna(\"None\")\n",
    "\n",
    "# Group 2:\n",
    "group_2 = [\n",
    "    'GarageArea', 'GarageCars', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF',\n",
    "    'TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath', 'MasVnrArea'\n",
    "]\n",
    "\n",
    "df[group_2] = df[group_2].fillna(0)\n",
    "\n",
    "# Group 3:\n",
    "group_3a = [\n",
    "    'Functional', 'MSZoning', 'Electrical', 'KitchenQual', 'Exterior1st',\n",
    "    'Exterior2nd', 'SaleType', 'Utilities'\n",
    "]\n",
    "\n",
    "imputer = SimpleImputer(strategy='most_frequent')\n",
    "df[group_3a] = pd.DataFrame(imputer.fit_transform(df[group_3a]), index=df.index)\n",
    "\n",
    "df.LotFrontage = df.LotFrontage.fillna(df.LotFrontage.mean())\n",
    "df.GarageYrBlt = df.GarageYrBlt.fillna(df.YearBuilt)\n",
    "\n",
    "#Label encoding\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "df = df.apply(LabelEncoder().fit_transform)\n",
    "\n",
    "data = df.drop(columns=['SalePrice'])\n",
    "data = data.astype(float) # This is a \"quick fix\" to make everything into float. Some of these would rather need a category mapping. \n",
    "target = df.SalePrice\n",
    "\n",
    "#Splitting and training\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.3, random_state=123)\n",
    "xg_reg = xgb.XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.5, learning_rate = 0.1, max_depth = 15, alpha = 10)\n",
    "\n",
    "xg_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start with an “explanation” using all 80 basic features, which is not very readable and overly detailed for “ordinary” humans to understand:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The minimal price in the training set is $0 and the maximal price is $1031\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ames_instance = X_test.iloc[[345]]\n",
    "\n",
    "out_minmaxs = pd.DataFrame({'mins': [float(min(y_train))], 'maxs': [float(max(y_train))]})\n",
    "print(f\"The minimal price in the training set is ${min(y_train):.0f} and the maximal price is ${max(y_train):.0f}\")\n",
    "\n",
    "CIU_ames = ciu.CIU(xg_reg.predict, ['Price'], data=X_train, out_minmaxs=out_minmaxs) #, category_mapping=category_mapping)\n",
    "CIUres = CIU_ames.explain(ames_instance)\n",
    "CIU_ames.plot_ciu(CIUres, plot_mode='overlap', figsize=(6,9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we create our vocabulary of intermediate concepts, in this case a list containing dictionaries of a `concept->[columns]` structure as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "ames_voc = {\n",
    "    \"Garage\":[c for c in df.columns if 'Garage' in c],\n",
    "    \"Basement\":[c for c in df.columns if 'Bsmt' in c],\n",
    "    \"Lot\":list(df.columns[[3,4,7,8,9,10,11]]),\n",
    "    \"Access\":list(df.columns[[13,14]]),\n",
    "    \"House_type\":list(df.columns[[1,15,16,21]]),\n",
    "    \"House_aesthetics\":list(df.columns[[22,23,24,25,26]]),\n",
    "    \"House_condition\":list(df.columns[[20,18,21,28,19,29]]),\n",
    "    \"First_floor_surface\":list(df.columns[[43]]),\n",
    "    \"Above_ground_living area\":[c for c in df.columns if 'GrLivArea' in c]\n",
    "}\n",
    "CIU_ames.vocabulary = ames_voc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can produce an explanation using the highest-level concepts of the vocabulary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_res = CIU_ames.explain_voc(ames_instance, nsamples=10000)\n",
    "CIU_ames.plot_ciu(top_res, figsize=(6,3), plot_mode='overlap')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then explain further some intermediate concepts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tconcept = \"House_condition\"\n",
    "res = CIU_ames.explain(ames_instance, nsamples=1000, target_concept=tconcept, target_ciu=top_res)\n",
    "CIU_ames.plot_ciu(res, plot_mode=\"overlap\", figsize=(6,1.5), main=f\"Explained concept is {tconcept}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tconcept = \"Basement\"\n",
    "res = CIU_ames.explain(ames_instance, target_concept=tconcept, target_ciu=top_res, nsamples=1000)\n",
    "CIU_ames.plot_ciu(res, plot_mode=\"overlap\", figsize=(6,2.5), main=f\"Explained concept is {tconcept}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tconcept = \"Garage\"\n",
    "res = CIU_ames.explain(ames_instance, target_concept=tconcept, target_ciu=top_res, nsamples=1000)\n",
    "CIU_ames.plot_ciu(res, plot_mode=\"overlap\", figsize=(6,2.5), main=f\"Explained concept is {tconcept}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This vocabulary is just an example of what kind of concepts a human typically deals with. Vocabularies can be built freely (or learned, if possible) and used freely, even so that different vocabularies can be used with different users.\n",
    "\n",
    "Furthermore, it is possible to switch between different kinds of bar plots, textual explanations and other explanation modalities on the fly. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py-ciu",
   "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": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}