final_step4_scenario_discovery.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ludov\\anaconda3\\lib\\site-packages\\ema_workbench\\analysis\\prim.py:30: ImportWarning: altair based interactive inspection not available\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# import needed libriaries\n",
    "\n",
    "# ema_workbench components needed\n",
    "from ema_workbench.analysis import prim\n",
    "from ema_workbench import MultiprocessingEvaluator, ema_logging\n",
    "from ema_workbench.analysis import parcoords\n",
    "\n",
    "# other libraries needed\n",
    "import pickle # to read dictionaries\n",
    "import pandas as pd # to deal with csv files and manipulate dataframes\n",
    "import numpy as np # for conversion purposes\n",
    "import matplotlib.pyplot as plt # to plot\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scenario Discovery \n",
    "\n",
    "After having re-evaluated the policies for the different scenarios we aim to investigate when these policies have a poor performance. To achieve that we use the PRIM algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the outcomes' results of the policy re-evaluation (step2)\n",
    "\n",
    "outcomes_file = open(\"intermediate outputs/step2 - first re-evaluation - outcomes description.pkl\", \"rb\")\n",
    "outcomes = pickle.load(outcomes_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the experiments' results of the policy re-evaluation (step2)\n",
    "experiments = pd.read_csv('intermediate outputs/step2 - first re-evaluation - experiments description.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ema_logging.log_to_stderr(ema_logging.INFO);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# selecting the policies that perform bad in terms of the death number in the dikes which are on our interest\n",
    "#the 0.001 threshold was chosen by trial and error in an effort to have enough data for a meaningful analysis\n",
    "outcomes_prim = []\n",
    "for i in range(len(outcomes['A.4_Expected Number of Deaths'])):\n",
    "    if outcomes['A.4_Expected Number of Deaths'][i] > 0.001 and outcomes['A.5_Expected Number of Deaths'][i] > 0.001 :\n",
    "        outcomes_prim.append(True)\n",
    "    else:\n",
    "        outcomes_prim.append(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.02702702702702703\n"
     ]
    }
   ],
   "source": [
    "#count the number of True elements in the outcomes_prim list to check the amount of data that satisfy the defined threshold\n",
    "count = 0\n",
    "for i in outcomes_prim:\n",
    "    if i is True:\n",
    "        count =+ 1\n",
    "        \n",
    "print((count/len(outcomes_prim)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[MainProcess/INFO] model dropped from analysis because only a single category\n",
      "[MainProcess/INFO] 3700 points remaining, containing 323 cases of interest\n",
      "[MainProcess/INFO] mean: 0.7792792792792793, mass: 0.06, coverage: 0.5356037151702786, density: 0.7792792792792793 restricted_dimensions: 9\n"
     ]
    }
   ],
   "source": [
    "outcomes_prim = np.asarray(outcomes_prim) # convert the list into an array \n",
    "\n",
    "\n",
    "x = experiments.drop(columns=['0_RfR 0', '0_RfR 1', '0_RfR 2', #keep only the uncertainties' columns\n",
    "       '1_RfR 0', '1_RfR 1', '1_RfR 2', '2_RfR 0', '2_RfR 1', '2_RfR 2',\n",
    "       '3_RfR 0', '3_RfR 1', '3_RfR 2', '4_RfR 0', '4_RfR 1', '4_RfR 2',\n",
    "       'EWS_DaysToThreat', 'A.1_DikeIncrease 0', 'A.1_DikeIncrease 1',\n",
    "       'A.1_DikeIncrease 2', 'A.2_DikeIncrease 0', 'A.2_DikeIncrease 1',\n",
    "       'A.2_DikeIncrease 2', 'A.3_DikeIncrease 0', 'A.3_DikeIncrease 1',\n",
    "       'A.3_DikeIncrease 2', 'A.4_DikeIncrease 0', 'A.4_DikeIncrease 1',\n",
    "       'A.4_DikeIncrease 2', 'A.5_DikeIncrease 0', 'A.5_DikeIncrease 1',\n",
    "       'A.5_DikeIncrease 2','policy'])\n",
    "y = outcomes_prim                    \n",
    "\n",
    "#run the RPIM algorithm\n",
    "prim_alg = prim.Prim(x,y, threshold=0.5)\n",
    "box = prim_alg.find_box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot density vs coverage\n",
    "box.show_tradeoff()\n",
    "plt.savefig('output pictures/step4 - first prim scenario discovery results - density vs coverage.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coverage</th>\n",
       "      <th>density</th>\n",
       "      <th>id</th>\n",
       "      <th>mass</th>\n",
       "      <th>mean</th>\n",
       "      <th>res_dim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.087297</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.087297</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091892</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.091892</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.096997</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.096997</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.102703</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.102703</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.109122</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.109122</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.114865</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.114865</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.121246</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.121246</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.128378</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.128378</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.136402</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.136402</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.145495</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.145495</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.153153</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.153153</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.161662</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.161662</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.171171</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.171171</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.181869</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.181869</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.193994</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.193994</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207851</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.207851</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.996904</td>\n",
       "      <td>0.223146</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.223146</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.990712</td>\n",
       "      <td>0.233747</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.233747</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.984520</td>\n",
       "      <td>0.245560</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.245560</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.978328</td>\n",
       "      <td>0.258804</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.258804</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.969040</td>\n",
       "      <td>0.272886</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.272886</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.956656</td>\n",
       "      <td>0.287978</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.287978</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.950464</td>\n",
       "      <td>0.307307</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.307307</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.328649</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.328649</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.928793</td>\n",
       "      <td>0.352526</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.352526</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.925697</td>\n",
       "      <td>0.384813</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.384813</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.910217</td>\n",
       "      <td>0.418208</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.418208</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.433934</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.433934</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.876161</td>\n",
       "      <td>0.449921</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.449921</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.873065</td>\n",
       "      <td>0.476351</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.476351</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.854489</td>\n",
       "      <td>0.497297</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.497297</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.525097</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.525097</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.553015</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.553015</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.798762</td>\n",
       "      <td>0.581081</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.581081</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.767802</td>\n",
       "      <td>0.609337</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.609337</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.733746</td>\n",
       "      <td>0.640541</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.640541</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.693498</td>\n",
       "      <td>0.672673</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.672673</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.650155</td>\n",
       "      <td>0.709459</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.709459</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.594427</td>\n",
       "      <td>0.741313</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.741313</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.535604</td>\n",
       "      <td>0.779279</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.779279</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    coverage   density    id  mass      mean  res_dim\n",
       "0   1.000000  0.087297   0.0  1.00  0.087297        0\n",
       "1   1.000000  0.091892   1.0  0.95  0.091892        1\n",
       "2   1.000000  0.096997   2.0  0.90  0.096997        2\n",
       "3   1.000000  0.102703   3.0  0.85  0.102703        2\n",
       "4   1.000000  0.109122   4.0  0.80  0.109122        2\n",
       "5   1.000000  0.114865   5.0  0.76  0.114865        2\n",
       "6   1.000000  0.121246   6.0  0.72  0.121246        2\n",
       "7   1.000000  0.128378   7.0  0.68  0.128378        2\n",
       "8   1.000000  0.136402   8.0  0.64  0.136402        2\n",
       "9   1.000000  0.145495   9.0  0.60  0.145495        3\n",
       "10  1.000000  0.153153  10.0  0.57  0.153153        3\n",
       "11  1.000000  0.161662  11.0  0.54  0.161662        3\n",
       "12  1.000000  0.171171  12.0  0.51  0.171171        4\n",
       "13  1.000000  0.181869  13.0  0.48  0.181869        4\n",
       "14  1.000000  0.193994  14.0  0.45  0.193994        4\n",
       "15  1.000000  0.207851  15.0  0.42  0.207851        4\n",
       "16  0.996904  0.223146  16.0  0.39  0.223146        4\n",
       "17  0.990712  0.233747  17.0  0.37  0.233747        4\n",
       "18  0.984520  0.245560  18.0  0.35  0.245560        5\n",
       "19  0.978328  0.258804  19.0  0.33  0.258804        5\n",
       "20  0.969040  0.272886  20.0  0.31  0.272886        6\n",
       "21  0.956656  0.287978  21.0  0.29  0.287978        6\n",
       "22  0.950464  0.307307  22.0  0.27  0.307307        6\n",
       "23  0.941176  0.328649  23.0  0.25  0.328649        6\n",
       "24  0.928793  0.352526  24.0  0.23  0.352526        7\n",
       "25  0.925697  0.384813  25.0  0.21  0.384813        7\n",
       "26  0.910217  0.418208  26.0  0.19  0.418208        8\n",
       "27  0.894737  0.433934  27.0  0.18  0.433934        8\n",
       "28  0.876161  0.449921  28.0  0.17  0.449921        8\n",
       "29  0.873065  0.476351  29.0  0.16  0.476351        9\n",
       "30  0.854489  0.497297  30.0  0.15  0.497297        9\n",
       "31  0.842105  0.525097  31.0  0.14  0.525097        9\n",
       "32  0.823529  0.553015  32.0  0.13  0.553015        9\n",
       "33  0.798762  0.581081  33.0  0.12  0.581081        9\n",
       "34  0.767802  0.609337  34.0  0.11  0.609337        9\n",
       "35  0.733746  0.640541  35.0  0.10  0.640541        9\n",
       "36  0.693498  0.672673  36.0  0.09  0.672673        9\n",
       "37  0.650155  0.709459  37.0  0.08  0.709459        9\n",
       "38  0.594427  0.741313  38.0  0.07  0.741313        9\n",
       "39  0.535604  0.779279  39.0  0.06  0.779279        9"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "box.peeling_trajectory #investigating the coverage and density values for all boxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coverage     0.693498\n",
      "density      0.672673\n",
      "id          36.000000\n",
      "mass         0.090000\n",
      "mean         0.672673\n",
      "res_dim      9.000000\n",
      "Name: 36, dtype: float64\n",
      "\n",
      "                             box 36              \\\n",
      "                                min         max   \n",
      "A.5_pfail                  0.007751    0.466340   \n",
      "A.3_Bmax                  31.652959  328.831854   \n",
      "A.0_ID flood wave shape    0.000000  110.000000   \n",
      "A.1_pfail                  0.162936    0.998602   \n",
      "A.4_pfail                  0.066726    0.499678   \n",
      "A.2_pfail                  0.206623    0.990317   \n",
      "A.1_Bmax                 100.122000  292.146732   \n",
      "A.4_Bmax                  36.410216  339.244047   \n",
      "A.5_Bmax                  70.436244  331.882111   \n",
      "\n",
      "                                                                       \n",
      "                                                            qp values  \n",
      "A.5_pfail                               [-1.0, 6.769571617673648e-30]  \n",
      "A.3_Bmax                                 [-1.0, 0.008675583501053699]  \n",
      "A.0_ID flood wave shape                  [-1.0, 0.030240899455215433]  \n",
      "A.1_pfail                                [0.006585876550882027, -1.0]  \n",
      "A.4_pfail                [0.006585876550882027, 0.006585876550882027]  \n",
      "A.2_pfail                  [0.008675583501053699, 0.5259465220936456]  \n",
      "A.1_Bmax                   [0.08520097862276793, 0.12161084331274558]  \n",
      "A.4_Bmax                     [0.5259465220936456, 0.5259465220936456]  \n",
      "A.5_Bmax                     [0.5259465220936456, 0.5259465220936456]  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#explore the characteristics of the chosen box\n",
    "# box 36 was chosen as it offers a good combination of relatively high density and coverage at the same time\n",
    "box.inspect(36) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#explore the characteristics of the chosen box\n",
    "box.select(36)\n",
    "box.inspect(style='graph')\n",
    "plt.savefig('output pictures/step4 - first prim scenario discovery results - box 36 description.png')\n",
    "#plt.rcParams[\"figure.figsize\"]=(20,15) # fix the size of the picture\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the results\n",
    "scens_in_box = experiments.iloc[box.yi]\n",
    "outcomes_in_box = {k:v[box.yi] for k,v in outcomes.items()}\n",
    "\n",
    "scens_in_box.to_csv('intermediate outputs/step4 - prim results - scens in box.csv')\n",
    "a_file = open(\"intermediate outputs/step4 - prim results - outcomes in box.pkl\", \"wb\")\n",
    "pickle.dump(outcomes_in_box, a_file)\n",
    "a_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot and highlight the death outcomes of the policies that perform badly \n",
    "data = pd.DataFrame({k:v[y] for k,v in outcomes.items()})\n",
    "all_data = pd.DataFrame({k:v for k,v in outcomes.items()})\n",
    "\n",
    "selected_data = all_data[['A.4_Expected Number of Deaths','A.5_Expected Number of Deaths','Other.Dikes_Expected Number of Deaths']]\n",
    "\n",
    "limits = parcoords.get_limits(selected_data)\n",
    "plt.rcParams[\"figure.figsize\"] = (15,7)\n",
    "axes = parcoords.ParallelAxes(limits)\n",
    "axes.plot(all_data, color='lightgrey')\n",
    "axes.plot(data, color='blue')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A.4_Expected Number of Deaths             887\n",
      "A.5_Expected Number of Deaths            2476\n",
      "Other.Dikes_Expected Number of Deaths     863\n",
      "dtype: int64\n",
      "A.4_Expected Number of Deaths            0\n",
      "A.5_Expected Number of Deaths            0\n",
      "Other.Dikes_Expected Number of Deaths    1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# print the indices of the scenarios with the maximum and minimum death outcomes respectively\n",
    "print(selected_data.idxmax())\n",
    "print(selected_data.idxmin())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>A.0_ID flood wave shape</th>\n",
       "      <th>A.1_Bmax</th>\n",
       "      <th>A.1_Brate</th>\n",
       "      <th>A.1_pfail</th>\n",
       "      <th>A.2_Bmax</th>\n",
       "      <th>A.2_Brate</th>\n",
       "      <th>A.2_pfail</th>\n",
       "      <th>A.3_Bmax</th>\n",
       "      <th>A.3_Brate</th>\n",
       "      <th>A.3_pfail</th>\n",
       "      <th>A.4_Bmax</th>\n",
       "      <th>A.4_Brate</th>\n",
       "      <th>A.4_pfail</th>\n",
       "      <th>A.5_Bmax</th>\n",
       "      <th>A.5_Brate</th>\n",
       "      <th>A.5_pfail</th>\n",
       "      <th>discount rate 0</th>\n",
       "      <th>discount rate 1</th>\n",
       "      <th>discount rate 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>25</td>\n",
       "      <td>40.770650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.742590</td>\n",
       "      <td>98.410441</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.067249</td>\n",
       "      <td>209.668458</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.719802</td>\n",
       "      <td>45.128570</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000821</td>\n",
       "      <td>135.083179</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.156476</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2476</th>\n",
       "      <td>45</td>\n",
       "      <td>117.176284</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.946148</td>\n",
       "      <td>132.559754</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.412481</td>\n",
       "      <td>304.418095</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.652990</td>\n",
       "      <td>249.033968</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.473411</td>\n",
       "      <td>243.549286</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033733</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>23</td>\n",
       "      <td>68.676375</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.802261</td>\n",
       "      <td>140.824284</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.761561</td>\n",
       "      <td>265.662429</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.003890</td>\n",
       "      <td>294.052841</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.269397</td>\n",
       "      <td>42.535374</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.389347</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A.0_ID flood wave shape    A.1_Bmax  A.1_Brate  A.1_pfail    A.2_Bmax  \\\n",
       "887                        25   40.770650        1.0   0.742590   98.410441   \n",
       "2476                       45  117.176284        1.0   0.946148  132.559754   \n",
       "863                        23   68.676375       10.0   0.802261  140.824284   \n",
       "\n",
       "      A.2_Brate  A.2_pfail    A.3_Bmax  A.3_Brate  A.3_pfail    A.4_Bmax  \\\n",
       "887         1.0   0.067249  209.668458       10.0   0.719802   45.128570   \n",
       "2476        1.5   0.412481  304.418095        1.5   0.652990  249.033968   \n",
       "863        10.0   0.761561  265.662429        1.5   0.003890  294.052841   \n",
       "\n",
       "      A.4_Brate  A.4_pfail    A.5_Bmax  A.5_Brate  A.5_pfail  discount rate 0  \\\n",
       "887         1.0   0.000821  135.083179       10.0   0.156476              4.5   \n",
       "2476        1.5   0.473411  243.549286        1.0   0.033733              1.5   \n",
       "863        10.0   0.269397   42.535374        1.0   0.389347              1.5   \n",
       "\n",
       "      discount rate 1  discount rate 2  \n",
       "887               2.5              1.5  \n",
       "2476              3.5              2.5  \n",
       "863               2.5              2.5  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the worst case scenarios (those with the maximum death outcomes)\n",
    "selected = experiments.loc[[887,2476,863], ['A.0_ID flood wave shape', 'A.1_Bmax', 'A.1_Brate',\n",
    "       'A.1_pfail', 'A.2_Bmax', 'A.2_Brate', 'A.2_pfail', 'A.3_Bmax',\n",
    "       'A.3_Brate', 'A.3_pfail', 'A.4_Bmax', 'A.4_Brate', 'A.4_pfail',\n",
    "       'A.5_Bmax', 'A.5_Brate', 'A.5_pfail', 'discount rate 0',\n",
    "       'discount rate 1', 'discount rate 2']] #keep only the uncertainty columns\n",
    "selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save the results\n",
    "selected.to_csv('intermediate outputs/step4 - prim results - worst case scenarios.csv')"
   ]
  }
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