assignment 4 - scenario discovery.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notes from Q&A Session May 7, 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Carol\\Anaconda3\\lib\\site-packages\\ema_workbench\\em_framework\\optimization.py:48: ImportWarning: platypus based optimization not available\n",
      "  warnings.warn(\"platypus based optimization not available\", ImportWarning)\n"
     ]
    }
   ],
   "source": [
    "from lakemodel_function import lake_problem\n",
    "from ema_workbench import (Model, RealParameter, ScalarOutcome)\n",
    "# instantiate the model\n",
    "lake_model = Model('lakeproblem', function=lake_problem)\n",
    "lake_model.time_horizon = 100 # used to specifiy the number of timesteps\n",
    "#specify uncertainties\n",
    "lake_model.uncertainties =[RealParameter('b', 0.1, 0.45),\n",
    "                           RealParameter('q', 2.0, 4.5),\n",
    "                           RealParameter('mean', 0.01, 0.05),\n",
    "                           RealParameter('stdev', 0.001, 0.005),\n",
    "                           RealParameter('delta', 0.93, 0.99)]\n",
    "# set levers\n",
    "lake_model.levers = [RealParameter(f\"l{i}\", 0, 0.1) for i in range(lake_model.time_horizon)]\n",
    "#specify outcomes\n",
    "lake_model.outcomes = [ScalarOutcome('max_P'),\n",
    "                      ScalarOutcome('utility'),\n",
    "                      ScalarOutcome('inertia'),\n",
    "                      ScalarOutcome('reliability')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[MainProcess/INFO] pool started\n",
      "[MainProcess/INFO] performing 1000 scenarios * 1 policies * 1 model(s) = 1000 experiments\n",
      "[MainProcess/INFO] 100 cases completed\n",
      "[MainProcess/INFO] 200 cases completed\n",
      "[MainProcess/INFO] 300 cases completed\n",
      "[MainProcess/INFO] 400 cases completed\n",
      "[MainProcess/INFO] 500 cases completed\n",
      "[MainProcess/INFO] 600 cases completed\n",
      "[MainProcess/INFO] 700 cases completed\n",
      "[MainProcess/INFO] 800 cases completed\n",
      "[MainProcess/INFO] 900 cases completed\n",
      "[MainProcess/INFO] 1000 cases completed\n",
      "[MainProcess/INFO] experiments finished\n",
      "[MainProcess/INFO] terminating pool\n"
     ]
    }
   ],
   "source": [
    "from ema_workbench import Policy, ema_logging, MultiprocessingEvaluator, SequentialEvaluator\n",
    "ema_logging.log_to_stderr(ema_logging.INFO)\n",
    "policy = Policy(\"no release\", **{l.name:0 for l in lake_model.levers})\n",
    "n_scenarios = 1000\n",
    "with MultiprocessingEvaluator(lake_model) as evaluator:\n",
    "    experiments, outcomes = evaluator.perform_experiments(n_scenarios, policy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxp = outcomes['max_P']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Td//8NNQ4rok+gi5iCnQRCZdcZujrgEZ33+PuA8B/ANdPb1m5SY0T6ADFsYhaLiISKrkEeh1wYMT1psxtY/2pmb1kZveZ2eJsD2Rmt5hZg5k1tLS0nEa5o423ygWgOBohPqidoiISHrkEepa4ZOynL/8SWObuFwOPAndleyB33+ju9e5eX1tbe2qVZpFMOREDyzJDLymKaoYuIqGSS6A3ASNn3IuAQyMHuHubu8czV78DXDY15U0skfKs7RbIzNDVQxeREMkl0F8AVpnZcjMrBm4ENo0cYGYLRly9DtgxdSWOL5lKjRvoJbGIDoqKSKhMusrF3RNm9nngYSAKfM/dt5vZ14EGd98EfMHMrgMSwDHg49NY87BEyk9asjikWIEuIiEzaaADuPuDwINjbrttxOVbgVuntrTJJSdqucTUchGRcCn4naIT9dA1QxeRMCnoQE8mPeuSRYDqimJau+PZ7xQRCaCCDvSJZuhL55Sz71jv8OYjEZGgK+hAT6ZSJ324xZAlcyoYSKQ42tV/lqsSEcmPgg70RGr8lsuyOeUA7GvrPYsViYjkT0EH+kSrXJZWVwCwr63nbJYkIpI3BR3oE/XQF84qJRYxzdBFJDQKOtCTE2wsikUjLJpdxr5jCnQRCYfCD/RxZuiQPjCqlouIhEWgA31pdTn72npx19JFEQm+gg70xAQn54L0WvSu/gTtvYNnsSoRkfwo6ECfqIcOsHSOVrqISHgUZKAPfTh0IuVEJ5mhA+zXgVERCYGCC/SHXj7CJV/7NUc6+ic/KFqdDvS9rQp0EQm+ggv0JdXldMUTPNnYSmKCk3MBlBZFmV9Vyr5jarmISPDldD7015Pz5s9gTkUxTzW2kkw5sWj2RP/35/YD6VDfvLd9+PoH1y85a7WKiJxNBTdDj0SMK86p4cnGVgYnWeUCMKeymLaegbNUnYhI/hRcoANceU4NLV1x9rf1TthyAZhTUUx3PEE8kTw7xYlI6HX2j14q3djcxf+4dys98cS0Pm9BBvoVq2qAic/lMmTBzFIAtuxrn/a6JH8GkykeevkIHX0T7znoH0wG/s09lXI2vXiIX754iD0t3QX5mQAtXfFp2xDo7rx8sGPa/h48/9oxLvvbR4bbvAB//+BO7t9ykLuf2Tctzzmk4HroAHWzylhRU8Ge1p5JA/3ceTNYNbeSh7Yf4dx5M85ShfLkH1uJRY31y6uxcX5Hv9p2mG8+9kf+7r0XUr+s+rSfa/O+Y3zl/pd59WgXy2sq+M5HL+OcuenftbvzVGMbP35uH9sOdnDweB+zy4v58afWc/6CqlGP4+509ieYWVY0fNvOI500NndzzfnzKC2KjlvD3tYeuuMJimMR+geT7G3r5WB7H0VRo6IkRt2sMi5YWMWM0iKef+0Yz+5p483nzOHNK2tOquF47yDlJVFKYieeb09LN7uOdrGvrZeIGVeuquG8+TNO+tn2DyZ5/7efYdvBjuHbqkpjXL6yhq9ddwG/29XMT144QDLlXLp0NuuXV/NfVtVSUTI6CvoGkjzZ2Eprd5zjvYOcO6+SDavnEjG4f8tB/unXr3LVqlq+8q7zR/28stl5pJNfbD1ER98g7s5lS6u54bJFWce6O//2xB7+8aGd3Pzm5dz2njUA/G5XC1/btJ0/e8tK3l+/CDOjbyBJz0CCmsqS4T//VGMr+9p6eccF85gz4vYhg8kUX/vldn707H4uWTyLf/vIZcyrKp2w/ol09A7yibte4Orz5vK5t55DKuX87QOvMJh0/s/DO3nXRQvYd6yHx3Y2U14cZeMTu/no5UtP+nlPFcvlXdDMrgW+CUSB77r7P4y5vwS4G7gMaAM+4O57J3rM+vp6b2hoOM2y4bZfvMzdz+zjjctm87612f9yDOnoG+Sbj+1iXlUpj395w7gfijGewx19tHTFuXjRrNOut5B0xxNsP9jBkc5+egeSvOviBVSVnvyPNplyHtx2mI1P7MEMvvXhy6ibVcZ9m5v4n//5IgAraip439o61iysYtXcGSyaXUYkYtzz/H7+18+2YWaUFUW5+5PruHTJbNq64+xt62FlbSUzSov4zc5m7np6L4c6+lhRU8k5cyu5ZPEsLlk8i5eajnNvwwEe3dHMwpmlfOLK5Xz7d7vpH0zxgTcupq07zksHO9jT0kNFcZSVcyupqSyhYe8x3OGWq1bw529bRXc8wS+2HuSup/ey62g315w/l1uuWskjrxzhe0/tJZlyameUcPMVy1g+p4JIxEgknZ6BBAeO9fKrl4/Q2Nyd08+2OBphIHnis24/eeVyblq3hPu3NPHAS4c53NHHYNIpK4pyxTlzWFFbyc/+cJCWrpM/TnF+VSlvObeWDatrqZlRwuGOfr7/1Gts3X+cd1wwn1XzKjnY3sdLTR00tpyob/W8GcwqL+LFpuP0D6YojkW4YuUc1iysYsHMMva09HDf5gN09o9uD1RXFFNVWsTeth5Wza1kT2sPcyqKeX/9Io52xmnrjrN0TgWr58/AHQ609/L8a8fYvK+doqgxs6yYZCpFe+8gf/feC/nwm5bi7jz08hFaewZYUFXKg9sOc/8fDrJodhlN7X3864cu5dx5lbzvjqcZTKXoH0xxzflzqZ1RygMvHqJnIMGn37KSL1y9ijseb+T2xxsBiEaM+qWziZjRHU8wr6qUtUtm8fTuVp5qbOO6Nyzk0R1HqSiJ8ckrl9PWHac7nmDD6rlsWF076s10PMmU84kfvMDvdrUA8K0PXUrvQJIv/+eLfPqqFWz8/R4+ecVyXmvtYfP+du744KV86LvP8VfXnsdnNqzM6e9LNma22d3rs943WaCbWRTYBbwdaAJeAG5y91dGjPkscLG7/5mZ3Qi8z90/MNHjnmmgP7z9CJ/+4WbWL6/m+kvqJh2/ZV87921p4u1r5vHRy5eyeHY59zYc4Dc7m7l06WxueuMSYlHj51sP8vLBDt5ybi3/dc18fr71IN/67W7iiRRvXV3L/373GgzYfqiTaMS4YGEVi2aXc6Szn/1tvZQWRZhbVUoimeLFpg52N3ezal4l65fPYVZ5ES1dcbr6E9RUFjO7vJhkZkaWSKWYXV5MaVEUd6d/MMXB473sPNJFc2echbPKWDqnnLkzSphVXkw086bUP5jktdYedrd0k0w5VWVFVJXGiJhhZrT3DtDSGScSMS6qm8mS6nK27G/nycZWSmNR1i2v5uJFMymKRuiJJ/jB03v5/lOvjfrHPHdGCX9z3QVcuaqGPS097DzcyQt723lmdyuHOvpZUVNBS1ec0uIoH16/lG8+tosVtZVcsmgWz+89NmpjV1HUqK4o5mhnnHPnVfLuixfyg6f3MphIcfHimTyzu42hDkF5cZTegSQzy4qom1VGW0+c1u6B4Y1lADNKYtQvq+aqc2soiUU53jvAPc/v5+DxPmaWFVFdUczaJbO5qC79GgGOdvaz8Yk9lMQiVJUV0dTeS8ph4cxSVtRWsnlfO32D6f+Or1tWzfkLZvBUY9uoUBxilh4z9HsZTKaIRYzqyhKqy4tJudM/mKStZ4BDx/vo6k+wvKaCJdXlPLbzKM/uOZZ+HNL/m5w/s5SKkhjHeuK8eqSL472DLK+p4IK6mSyZXU51Rfo5dh3toj+R5Pd/bKVrxO+qvDjK+9bWccHCmaPqPHS8jx2HO1lZW8nSOeWYGYlUiv3HetlxqJOdR7po7x0g5RAxuLBuJvVLq6mdUUJJLMIfm7t5encrLV1xrjl/HuuWV3P4eD/3/6GJwx39VJXGqJtdzr62HnoH0j+7WMRYWVvJitoKLl0ym4qSGMmU86Nn97HraBdffc8aHt5+lGf2tI2q9Zrz53LVqlq+8/s9NHfFKS+OMph0PrNhJa8c6uTh7Ucwg4vq0q9xy/7jlMQixBMp6pfOZv2KOWxr6mB3SzexqFESi3CsZ4DW7gGiZrx3bR2XLZ3Nkc5+fvTsPo71DBCLGGXFUbr6E1SVxphTWUJH3yDlxVE+UL+Y966t45k9bdzz/H7c4aZ1i9nd0sPGJ/bw1fes4RdbD7HraBflxTHqZpXys89ewa33b+OnW5pIpJy/eMdqPvfWc7j5+8+z9cBxnvyrq097ln6mgX458Dfu/o7M9VsB3P3vR4x5ODPmGTOLAUeAWp/gwc800Dv7B1n79UdYv7yad1+8cNLx7s6jO5rZsr99uM8aMbh0yWy2HewgnkjPmmIRY1lNxagZ10V1M1k4s5Tf7moZHjeSGZxOuy8WMRJj+pvF0QiDqdSEj2cGJbEIA4kUp9sejUWMpHvW51mzoIo3LqtmdkURfQNJfvniIQ51jP4ov+qKYi5dMpu5M0pYs7CKlq44dz+zl/beQZbNqeDjb15GcSwdoH0DSZq7+mnuitPSFae5q5/ayhLeceF8YpEIx3sHuPPJ1wC4aNFMFs0qp60nTlv3ACvnVrJmQdXwG1gimeLQ8T4OtPdRXVHMufNmDN83UsonPr7S1N7LT144QEVJjOU1FZw3fwZLqtNBFx9M8lJTB/Nmlg5vTgNo7xmgP5HEPb3aqiQaoaw4OmErZjKNzd0c7ujjorqZzCovHnWfu5NI+fAbUTbJlNPU3ks8kWJmWRGzy4uHf+6nKuVOV3+CoohRnmPYuDtJd2KZc3CkMhOUiEFVWVHW38FAIsWdT+7hQHsfpUURrr1gAefNn0Fn/yCxaIT5mRbI8d4Bbn+8kXgixX+7cjlLMqfy6BtIEjEoyfzcdx3t4pFXjnJZpoU0XouvdyBByqFyxGtLppy+wSQVxVFSDrtbutl2sIOBRIry4ihtPQOjsmDV3ErMYNfR9G1DHYKOvkH+9fFGuuIJPn3VCpbOqaCzf5Bv/HoX0YjxF+9YzSeuXM7WA8d57x1P8ZfXruazG87J6Wc81pkG+g3Ate7+qcz1jwDr3f3zI8a8nBnTlLm+OzOmdcxj3QLckrm6Gnj1tF7RCTVA66SjCl9YXieE57XqdQbL2XydS929NtsdubwNZ3u7G/sukMsY3H0jsDGH58yJmTWM904VJGF5nRCe16rXGSyvl9eZy//NmoDFI64vAg6NNybTcpkJHJuKAkVEJDe5BPoLwCozW25mxcCNwKYxYzYBH8tcvgH4zUT9cxERmXqTtlzcPWFmnwceJr1s8Xvuvt3Mvg40uPsm4E7gh2bWSHpmfuN0Fj3ClLVvXufC8johPK9VrzNYXhevM6d16CIi8vpXkFv/RUTkZAp0EZGAKNhAN7NrzexVM2s0s7/Odz3TwcwWm9njZrbDzLab2RfzXdN0MrOomf3BzB7Idy3Txcxmmdl9ZrYz83u9PN81TQcz+++Zv7Mvm9k9Znb6J0x5nTGz75lZc2b/zdBt1Wb2iJn9MfN9dj5qK8hAz5yO4A7gncAa4CYzW5PfqqZFAviyu58PvAn4XEBf55AvAjvyXcQ0+ybwkLufB7yBAL5eM6sDvgDUu/uFpBdTnK2FEmfDD4Brx9z218Bj7r4KeCxz/awryEAH1gGN7r7H3QeA/wCuz3NNU87dD7v7lszlLtL/+Cc/cU0BMrNFwLuA7+a7luliZlXAVaRXheHuA+5+PL9VTZsYUJbZl1LOyXtXCpa7P8HJ+2yuB+7KXL4LeO9ZLSqjUAO9Djgw4noTAQ26IWa2DFgLPJffSqbN/wP+Ejj5ZDnBsQJoAb6faS1918wq8l3UVHP3g8A/AfuBw0CHu/86v1VNu3nufhjSEzFgbj6KKNRAz+lUA0FhZpXAT4EvuXtnvuuZamb2bqDZ3Tfnu5ZpFgMuBb7l7muBHvL0X/PplOkfXw8sBxYCFWb24fxWFQ6FGui5nI4gEMysiHSY/9jd7893PdPkCuA6M9tLun12tZn9KL8lTYsmoMndh/6XdR/pgA+aa4DX3L3F3QeB+4E357mm6XbUzBYAZL4356OIQg30XE5HUPAsfR7QO4Ed7v6NfNczXdz9Vndf5O7LSP8uf+PugZvRufsR4ICZrc7c9DbglQn+SKHaD7zJzMozf4ffRgAP/o4x8vQnHwN+kY8iCvIj6MY7HUGey5oOVwAfAbaZ2dbMbV9x9wfzWJOcmT8HfpyZiOwBbs5zPVPO3Z8zs/uALaRXav2B18nW+KlgZvcAG4AaM2sCvgr8A3CvmY3SO7sAAAA8SURBVH2S9Bva+/NSm7b+i4gEQ6G2XEREZAwFuohIQCjQRUQCQoEuIhIQCnQRkYBQoIuIBIQCXUQkIP4/nCCOurVvhGQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import altair as alt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "sns.distplot(maxp, ax = ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "227"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Example\n",
    "#ID conditions under which we fail: a high max_P is a form of faiulre\n",
    "#lets see whre max pollution is higher than 0.5\n",
    "import numpy as np\n",
    "np.sum(maxp > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[MainProcess/INFO] policy dropped from analysis because only a single category\n",
      "[MainProcess/INFO] model dropped from analysis because only a single category\n",
      "[MainProcess/INFO] 1000 points remaining, containing 227 cases of interest\n",
      "[MainProcess/INFO] mean: 0.923728813559322, mass: 0.118, coverage: 0.4801762114537445, density: 0.923728813559322 restricted_dimensions: 3\n"
     ]
    }
   ],
   "source": [
    "from ema_workbench.analysis import prim\n",
    "x = experiments\n",
    "y = maxp > 0.5\n",
    "\n",
    "# primalf = prim.Prim?\n",
    "\n",
    "primlg = prim.Prim(x, y, threshold = 0.8)\n",
    "box1 = primlg.find_box()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PRIM\n",
    "\n",
    "Aim of PRIM is to find orthogonal subspace that has a high concentratiton of points that we are interested in \n",
    "\n",
    "Makes candidate boxes with concentration of points of interst and chooses the best box (stepwise iterative process)\n",
    "\n",
    "Each of these steps is saved - i.e. peeling trajectory, so you can go back and see them (box1.show_)tradeoff(), plt.show() )\n",
    "\n",
    " --------------------\n",
    " \n",
    "*peel_apha = controls how much data is removed at each step (default is 0.05 - i.e. 5% of data is being removed at the time)\n",
    "BUT, îf you remove a small amount of data, you have less data, so you need  a stopping condition -> controlled by a max/min \n",
    "\n",
    "*max/min = Remove data that you have little to no interest in (default is 0.05 - i.e. 5% of the data is left, which means you can do at most 95 steps assuming everyting else is a continuous variable)\n",
    "\n",
    "*threshold = concetration of cases of interest left in the final box after the interative removing of data (0.08  default - i e. you want a concentration or density of 80% in the final box)\n",
    "^^Note: May need to set a lower threshold because you cannot make a box that meets the threshold critereia\n",
    "\n",
    "*paste_alhpa = Exact opposite of peel alpha (default is 0.05) (after you hit the stopping conditon of min/max PRIM tries to make the box a bit larger by trying to glue back a small bit of data (0.05 deafulty - i e adds 5% of data back))\n",
    "\n",
    "(Note: ^^Can basically forget about past_alpha because it rarely makes a difference)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Peeling Trajectory\n",
    "#Each box outcome is saved (peeling trajectory - so we can go back and look at these because they are also saved)\n",
    "box1.show_tradeoff()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coverage    0.612335\n",
      "density     0.827381\n",
      "id                34\n",
      "mass           0.168\n",
      "mean        0.827381\n",
      "res_dim            3\n",
      "Name: 34, dtype: object\n",
      "\n",
      "        box 34                                         \n",
      "           min       max                      qp values\n",
      "b     0.100006  0.201061  [-1.0, 1.240715789098592e-39]\n",
      "q     2.000374  4.138306   [-1.0, 0.042599154343923515]\n",
      "mean  0.023137  0.049973  [4.327111525465287e-07, -1.0]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Inspect different points on the peeling trajectory to see the perfect balanace of tradeoffs (i.e. use box1.inspect(candiate box number))\n",
    "#Example\n",
    "box1.inspect(34)\n",
    "\n",
    "#Note -1 on left hand means no restriction on lower end, and -1 on right hand means no restriction on higher end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Note, same info as box1.inspect() above, just a different visualisation\n",
    "box1.inspect(34, style = 'graph')\n",
    "plt.show()\n",
    "\n",
    "#Note, p-values greater than 0.05 are not statisticall significant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Note: For final box, put no number in for candidate box (i.e. for me, last box is 43)\n",
    "box1.inspect(style = 'graph')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-6cec38388f344820b450233f73254a3b\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-6cec38388f344820b450233f73254a3b\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-6cec38388f344820b450233f73254a3b\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"vconcat\": [{\"data\": {\"name\": \"data-680a3835b1c189b086c26e9a03b91b8b\"}, \"mark\": {\"type\": \"circle\", \"size\": 75}, \"encoding\": {\"color\": {\"type\": \"ordinal\", \"field\": \"res_dim\", \"scale\": {\"range\": [\"#eff9b6\", \"#d0edb3\", \"#97d6b9\", \"#5dc0c0\", \"#31a5c2\", \"#1f80b8\", \"#2354a3\", \"#21318d\"]}}, \"opacity\": {\"condition\": {\"value\": 1, \"selection\": \"selector001\"}, \"value\": 0.4}, \"tooltip\": [{\"type\": \"ordinal\", \"field\": \"id\"}, {\"type\": \"quantitative\", \"field\": \"coverage\", \"format\": \".2\"}, {\"type\": \"quantitative\", \"field\": \"density\", \"format\": \".2\"}, {\"type\": \"ordinal\", \"field\": \"res_dim\"}], \"x\": {\"type\": \"quantitative\", \"field\": \"coverage\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"density\"}}, \"height\": 400, \"selection\": {\"selector001\": {\"type\": \"single\", \"fields\": [\"id\"]}}, \"width\": 400}, {\"layer\": [{\"data\": {\"name\": \"data-8a77354323b2b39b61d335556e3e88bd\"}, \"mark\": \"rule\", \"encoding\": {\"x\": {\"type\": \"quantitative\", \"axis\": {\"grid\": false, \"labels\": false, \"title\": \"box limits\"}, \"field\": \"x_lower\", \"scale\": {\"domain\": [0, 1], \"padding\": 0.1}}, \"x2\": {\"field\": \"x_upper\"}, \"y\": {\"type\": \"nominal\", \"field\": \"name\", \"scale\": {\"padding\": 1.0}}}, \"transform\": [{\"calculate\": \"(datum.x1-datum.minimum)/(datum.maximum-datum.minimum)\", \"as\": \"x_lower\"}, {\"calculate\": \"(datum.x2-datum.minimum)/(datum.maximum-datum.minimum)\", \"as\": \"x_upper\"}, {\"filter\": {\"selection\": \"selector001\"}}], \"width\": 400}, {\"data\": {\"name\": \"data-8a77354323b2b39b61d335556e3e88bd\"}, \"mark\": {\"type\": \"text\", \"align\": \"left\", \"baseline\": \"top\", \"dy\": 5}, \"encoding\": {\"text\": {\"type\": \"ordinal\", \"field\": \"text\"}, \"x\": {\"type\": \"quantitative\", \"axis\": {\"grid\": 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-1.0, \"qp_upper\": 2.91738240705306e-42}, {\"x1\": 0.022085921068461814, \"x2\": 0.04997296380019461, \"name\": \"mean\", \"id\": 43, \"minimum\": 0.010029656680151443, \"maximum\": 0.04997296380019461, \"qp_lower\": 7.755314743767659e-07, \"qp_upper\": -1.0}, {\"x1\": 2.000374089339239, \"x2\": 4.149702832929322, \"name\": \"q\", \"id\": 43, \"minimum\": 2.000374089339239, \"maximum\": 4.49764233550658, \"qp_lower\": -1.0, \"qp_upper\": 0.009531662735886029}], \"data-bb8d07f6df3d47a6a475317795287b23\": [{\"start\": 0, \"end\": 1}], \"data-d751713988987e9331980363e24189ce\": []}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Interactive way to look at both the graph and the chart\n",
    "#Gives same peeling trajectory as before, but interactive (requires that you have altair installed - note, the moment you import PRIM, altair is also imported, you do not need to do it manually - just make sure you have it)\n",
    "#Note, sometimes this won't work for partiucalry 'nasty' datasets - but should be fine for the data used in this courss\n",
    "box1.inspect_tradeoff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#What do we do with the variables that are not significant?\n",
    "#Drop the non-significant dimension (i.e. the dimension with a p-value that is greater than 0.05)\n",
    "box1.inspect(style = 'graph')\n",
    "box1.drop_restriction('delta in Jan example which was greater than 0.05')\n",
    "box1.inspect(style = 'graph')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Same Process but for Multiple Policies Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[MainProcess/INFO] pool started\n",
      "[MainProcess/INFO] performing 1000 scenarios * 4 policies * 1 model(s) = 4000 experiments\n",
      "[MainProcess/INFO] 400 cases completed\n",
      "[MainProcess/INFO] 800 cases completed\n",
      "[MainProcess/INFO] 1200 cases completed\n",
      "[MainProcess/INFO] 1600 cases completed\n",
      "[MainProcess/INFO] 2000 cases completed\n",
      "[MainProcess/INFO] 2400 cases completed\n",
      "[MainProcess/INFO] 2800 cases completed\n",
      "[MainProcess/INFO] 3200 cases completed\n",
      "[MainProcess/INFO] 3600 cases completed\n",
      "[MainProcess/INFO] 4000 cases completed\n",
      "[MainProcess/INFO] experiments finished\n",
      "[MainProcess/INFO] terminating pool\n"
     ]
    }
   ],
   "source": [
    "#Question about what to do if yo have multiple policies also in your dataset:\n",
    "#Example\n",
    "\n",
    "n_scenarios = 1000\n",
    "n_policies = 4\n",
    "\n",
    "with MultiprocessingEvaluator(lake_model) as evaluator:\n",
    "    experiments, outcomes = evaluator.perform_experiments(n_scenarios, n_policies)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>b</th>\n",
       "      <th>delta</th>\n",
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       "      <th>policy</th>\n",
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       "      <th>1</th>\n",
       "      <td>0.245702</td>\n",
       "      <td>0.967512</td>\n",
       "      <td>0.025659</td>\n",
       "      <td>2.221150</td>\n",
       "      <td>0.001010</td>\n",
       "      <td>0.015453</td>\n",
       "      <td>0.085259</td>\n",
       "      <td>0.0511</td>\n",
       "      <td>0.05027</td>\n",
       "      <td>0.031991</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037806</td>\n",
       "      <td>0.033794</td>\n",
       "      <td>0.027304</td>\n",
       "      <td>0.073189</td>\n",
       "      <td>0.003451</td>\n",
       "      <td>0.002681</td>\n",
       "      <td>0.012395</td>\n",
       "      <td>1001</td>\n",
       "      <td>0</td>\n",
       "      <td>lakeproblem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.346298</td>\n",
       "      <td>0.931431</td>\n",
       "      <td>0.034188</td>\n",
       "      <td>4.346887</td>\n",
       "      <td>0.002180</td>\n",
       "      <td>0.015453</td>\n",
       "      <td>0.085259</td>\n",
       "      <td>0.0511</td>\n",
       "      <td>0.05027</td>\n",
       "      <td>0.031991</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037806</td>\n",
       "      <td>0.033794</td>\n",
       "      <td>0.027304</td>\n",
       "      <td>0.073189</td>\n",
       "      <td>0.003451</td>\n",
       "      <td>0.002681</td>\n",
       "      <td>0.012395</td>\n",
       "      <td>1002</td>\n",
       "      <td>0</td>\n",
       "      <td>lakeproblem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.155935</td>\n",
       "      <td>0.966157</td>\n",
       "      <td>0.025305</td>\n",
       "      <td>3.139417</td>\n",
       "      <td>0.001023</td>\n",
       "      <td>0.015453</td>\n",
       "      <td>0.085259</td>\n",
       "      <td>0.0511</td>\n",
       "      <td>0.05027</td>\n",
       "      <td>0.031991</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037806</td>\n",
       "      <td>0.033794</td>\n",
       "      <td>0.027304</td>\n",
       "      <td>0.073189</td>\n",
       "      <td>0.003451</td>\n",
       "      <td>0.002681</td>\n",
       "      <td>0.012395</td>\n",
       "      <td>1003</td>\n",
       "      <td>0</td>\n",
       "      <td>lakeproblem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.310590</td>\n",
       "      <td>0.944015</td>\n",
       "      <td>0.038212</td>\n",
       "      <td>2.527866</td>\n",
       "      <td>0.002185</td>\n",
       "      <td>0.015453</td>\n",
       "      <td>0.085259</td>\n",
       "      <td>0.0511</td>\n",
       "      <td>0.05027</td>\n",
       "      <td>0.031991</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037806</td>\n",
       "      <td>0.033794</td>\n",
       "      <td>0.027304</td>\n",
       "      <td>0.073189</td>\n",
       "      <td>0.003451</td>\n",
       "      <td>0.002681</td>\n",
       "      <td>0.012395</td>\n",
       "      <td>1004</td>\n",
       "      <td>0</td>\n",
       "      <td>lakeproblem</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          b     delta      mean         q     stdev        l0        l1  \\\n",
       "0  0.205231  0.975762  0.038587  4.296666  0.003841  0.015453  0.085259   \n",
       "1  0.245702  0.967512  0.025659  2.221150  0.001010  0.015453  0.085259   \n",
       "2  0.346298  0.931431  0.034188  4.346887  0.002180  0.015453  0.085259   \n",
       "3  0.155935  0.966157  0.025305  3.139417  0.001023  0.015453  0.085259   \n",
       "4  0.310590  0.944015  0.038212  2.527866  0.002185  0.015453  0.085259   \n",
       "\n",
       "      l10      l11       l12  ...       l93       l94       l95       l96  \\\n",
       "0  0.0511  0.05027  0.031991  ...  0.037806  0.033794  0.027304  0.073189   \n",
       "1  0.0511  0.05027  0.031991  ...  0.037806  0.033794  0.027304  0.073189   \n",
       "2  0.0511  0.05027  0.031991  ...  0.037806  0.033794  0.027304  0.073189   \n",
       "3  0.0511  0.05027  0.031991  ...  0.037806  0.033794  0.027304  0.073189   \n",
       "4  0.0511  0.05027  0.031991  ...  0.037806  0.033794  0.027304  0.073189   \n",
       "\n",
       "        l97       l98       l99  scenario  policy        model  \n",
       "0  0.003451  0.002681  0.012395      1000       0  lakeproblem  \n",
       "1  0.003451  0.002681  0.012395      1001       0  lakeproblem  \n",
       "2  0.003451  0.002681  0.012395      1002       0  lakeproblem  \n",
       "3  0.003451  0.002681  0.012395      1003       0  lakeproblem  \n",
       "4  0.003451  0.002681  0.012395      1004       0  lakeproblem  \n",
       "\n",
       "[5 rows x 108 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ID number of unique policies\n",
    "experiments.policy.unique() #Gives us 4 unique policies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Scenario discovery in this dataset:\n",
    "\n",
    "#In this particular case, the policy ID already gives us info so drop levers from experiments column\n",
    "\n",
    "# Basically, cleaning up experiements first can help eliminate redundency \n",
    "#(reduncy is that you have only 4 policies, so the 100 columns with all of the levers with the timesteps are not intervesting, \n",
    "#you only care about the policy as a whole) (i.e. why he drops the levers for \"cleaned\")\n",
    "\n",
    "cleaned = experiments.drop(columns=[l.name for l in lake_model.levers])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>delta</th>\n",
       "      <th>mean</th>\n",
       "      <th>q</th>\n",
       "      <th>stdev</th>\n",
       "      <th>scenario</th>\n",
       "      <th>policy</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.205231</td>\n",
       "      <td>0.975762</td>\n",
       "      <td>0.038587</td>\n",
       "      <td>4.296666</td>\n",
       "      <td>0.003841</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>lakeproblem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.245702</td>\n",
       "      <td>0.967512</td>\n",
       "      <td>0.025659</td>\n",
       "      <td>2.221150</td>\n",
       "      <td>0.001010</td>\n",
       "      <td>1001</td>\n",
       "      <td>0</td>\n",
       "      <td>lakeproblem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.346298</td>\n",
       "      <td>0.931431</td>\n",
       "      <td>0.034188</td>\n",
       "      <td>4.346887</td>\n",
       "      <td>0.002180</td>\n",
       "      <td>1002</td>\n",
       "      <td>0</td>\n",
       "      <td>lakeproblem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.155935</td>\n",
       "      <td>0.966157</td>\n",
       "      <td>0.025305</td>\n",
       "      <td>3.139417</td>\n",
       "      <td>0.001023</td>\n",
       "      <td>1003</td>\n",
       "      <td>0</td>\n",
       "      <td>lakeproblem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.310590</td>\n",
       "      <td>0.944015</td>\n",
       "      <td>0.038212</td>\n",
       "      <td>2.527866</td>\n",
       "      <td>0.002185</td>\n",
       "      <td>1004</td>\n",
       "      <td>0</td>\n",
       "      <td>lakeproblem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3995</th>\n",
       "      <td>0.385489</td>\n",
       "      <td>0.948255</td>\n",
       "      <td>0.040476</td>\n",
       "      <td>3.229883</td>\n",
       "      <td>0.001615</td>\n",
       "      <td>1995</td>\n",
       "      <td>3</td>\n",
       "      <td>lakeproblem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3996</th>\n",
       "      <td>0.285103</td>\n",
       "      <td>0.936989</td>\n",
       "      <td>0.049409</td>\n",
       "      <td>2.000678</td>\n",
       "      <td>0.002278</td>\n",
       "      <td>1996</td>\n",
       "      <td>3</td>\n",
       "      <td>lakeproblem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3997</th>\n",
       "      <td>0.211717</td>\n",
       "      <td>0.969398</td>\n",
       "      <td>0.033792</td>\n",
       "      <td>2.056013</td>\n",
       "      <td>0.001096</td>\n",
       "      <td>1997</td>\n",
       "      <td>3</td>\n",
       "      <td>lakeproblem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3998</th>\n",
       "      <td>0.323670</td>\n",
       "      <td>0.941601</td>\n",
       "      <td>0.044181</td>\n",
       "      <td>4.366671</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>1998</td>\n",
       "      <td>3</td>\n",
       "      <td>lakeproblem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3999</th>\n",
       "      <td>0.246203</td>\n",
       "      <td>0.973332</td>\n",
       "      <td>0.024711</td>\n",
       "      <td>2.963553</td>\n",
       "      <td>0.004090</td>\n",
       "      <td>1999</td>\n",
       "      <td>3</td>\n",
       "      <td>lakeproblem</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             b     delta      mean         q     stdev scenario policy  \\\n",
       "0     0.205231  0.975762  0.038587  4.296666  0.003841     1000      0   \n",
       "1     0.245702  0.967512  0.025659  2.221150  0.001010     1001      0   \n",
       "2     0.346298  0.931431  0.034188  4.346887  0.002180     1002      0   \n",
       "3     0.155935  0.966157  0.025305  3.139417  0.001023     1003      0   \n",
       "4     0.310590  0.944015  0.038212  2.527866  0.002185     1004      0   \n",
       "...        ...       ...       ...       ...       ...      ...    ...   \n",
       "3995  0.385489  0.948255  0.040476  3.229883  0.001615     1995      3   \n",
       "3996  0.285103  0.936989  0.049409  2.000678  0.002278     1996      3   \n",
       "3997  0.211717  0.969398  0.033792  2.056013  0.001096     1997      3   \n",
       "3998  0.323670  0.941601  0.044181  4.366671  0.002585     1998      3   \n",
       "3999  0.246203  0.973332  0.024711  2.963553  0.004090     1999      3   \n",
       "\n",
       "            model  \n",
       "0     lakeproblem  \n",
       "1     lakeproblem  \n",
       "2     lakeproblem  \n",
       "3     lakeproblem  \n",
       "4     lakeproblem  \n",
       "...           ...  \n",
       "3995  lakeproblem  \n",
       "3996  lakeproblem  \n",
       "3997  lakeproblem  \n",
       "3998  lakeproblem  \n",
       "3999  lakeproblem  \n",
       "\n",
       "[4000 rows x 8 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[MainProcess/INFO] model dropped from analysis because only a single category\n",
      "[MainProcess/INFO] 4000 points remaining, containing 2818 cases of interest\n",
      "[MainProcess/INFO] mean: 1.0, mass: 0.386, coverage: 0.5479063165365508, density: 1.0 restricted_dimensions: 3\n"
     ]
    }
   ],
   "source": [
    "x = cleaned\n",
    "y = outcomes['max_P'] > 0.5\n",
    "primalg = prim.Prim(x, y, threshold = 0.8, peel_alpha = 0.1)\n",
    "box1 = primalg.find_box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
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0.04996398599052446, \"name\": \"mean\", \"id\": 8, \"minimum\": 0.010024929916871149, \"maximum\": 0.04996398599052446, \"qp_lower\": 4.7601328817951526e-05, \"qp_upper\": -1.0}, {\"x1\": 0.1000927427287997, \"x2\": 0.26513771607396125, \"name\": \"b\", \"id\": 9, \"minimum\": 0.1000927427287997, \"maximum\": 0.4499124879026535, \"qp_lower\": -1.0, \"qp_upper\": 4.0893910863374354e-179}, {\"x1\": 0.014024084740223848, \"x2\": 0.04996398599052446, \"name\": \"mean\", \"id\": 9, \"minimum\": 0.010024929916871149, \"maximum\": 0.04996398599052446, \"qp_lower\": 1.104181231789602e-06, \"qp_upper\": -1.0}, {\"x1\": 2.0006776376144013, \"x2\": 4.2033501581624275, \"name\": \"q\", \"id\": 9, \"minimum\": 2.0006776376144013, \"maximum\": 4.4984223517672675, \"qp_lower\": -1.0, \"qp_upper\": 1.9695267392759556e-05}], \"data-bb8d07f6df3d47a6a475317795287b23\": [{\"start\": 0, \"end\": 1}], \"data-d751713988987e9331980363e24189ce\": []}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Inspect the tradeoff (i.e. peeling trajectory)\n",
    "box1.inspect_tradeoff()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note, Jan usualy removes levers before testing in order to make interpreting results easier, but this is just his preference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answers to questions posed at the end of Q&A\n",
    "\n",
    "- Threshold applies to the density of the final box - (i.e. says that the density of the final box should be higher than 0.8)\n",
    "\n",
    "- Cleaning up experiements first can help eliminate redundency (reduncy is that you have only 4 policies, so the 100 columns with all of the levers with the timesteps are not intervesting, you only care about the policy as a whole) (i.e. why he drops the levers for \"cleaned\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------- New Topic -------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dimensional Stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[MainProcess/INFO] model dropped from analysis because only a single category\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ema_workbench.analysis import dimensional_stacking\n",
    "\n",
    "x= experiments\n",
    "y = outcomes['max_P'] > 2\n",
    "\n",
    "dimensional_stacking.create_pivot_plot(x,y, nr_levels =2) #NoteL nr_levels sets number of dimensions that we are stacking, so set to 3 gives us 6 uncertainties\n",
    "plt.show()\n",
    "\n",
    "#Bright yellow, all points in particular bin are of interet\n",
    "#Dark blue, no points of interst in a bin\n",
    "#White means that you have not sampled anything in a region - (Note: could mean you have not sampled enough)\n",
    "#q, b, delta, and mean are split into 3 intervals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Basically, dimensional stacking gives us a plot that is basically a fancy column plot\n",
    "#The way dimensional stacking works is comparable to PRIM - but the difference is that dimensional stacking is a purely visual way of doing it\n",
    "\n",
    "#Imagine that we scatter our experiemtns against one and another on the b and q dimension\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(experiments.b[y], experiments.q[y])\n",
    "ax.scatter(experiments.b[y==False], experiments.q[y==False])\n",
    "plt.show()\n",
    "\n",
    "#Note: blue = points of interest, orange = points not of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make scatter plot for 2 dimensions for the full range then with conditional of other dimension as well\n",
    "#(basically we do a scatter plot of subset of the data and then doing a binning overlay) --> Basically this gives us dimensional stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[MainProcess/INFO] model dropped from analysis because only a single category\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Example, with a lot of yellow space, may be better to make dimensions more restrcitve\n",
    "#Example:\n",
    "\n",
    "from ema_workbench.analysis import dimensional_stacking\n",
    "\n",
    "x= experiments\n",
    "y = outcomes['max_P'] > 4\n",
    "\n",
    "dimensional_stacking.create_pivot_plot(x,y, nr_levels =2, nbins=5) #NoteL nr_levels sets number of dimensions that we are stacking, so set to 3 gives us 6 uncertainties\n",
    "plt.show()\n",
    "\n",
    "#This plot gives a lot of white space -> so may be better to run more experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------- New Topic -------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sub Space Partitioning vs Global Sensitivity Analysis (Use both - they are complimentary and can help check one another)\n",
    "\n",
    "Sub Space Partitioning: \n",
    "- Try to partition model input space into distinct regions that are characeristic of success or failure\n",
    "- Basically given what is in my model input space, do I get a particular type of outcome?\n",
    "- PRIM, scenario discorery (e.g. PRIM, CART) and n-dimensional stacking\n",
    "\n",
    "Global Sensitivity Analysis\n",
    "- (Reasons other way around from Sub Space Partitioning)\n",
    "- Understanding how variation in model outputs is explained by varaition in model inputs\n",
    "- Gives a different understanding of how and why different outcomes occur\n",
    "- (Will not give you easy rules of subspaces like PRIM and CART do, but gives you a rank order of relevant importance of different model outcomes that you get)"
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