code/paper-Figure-2-manifold-informed-backward-elimination-algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 📄 Generate illustrative plots showing iterative manifold topology changes in the manifold-informed backward elimination algorithm (**Figure 2**)\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run -i imports.py\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaling = 'level'\n",
    "n_components = 2\n",
    "sample_percentage = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing N2.\n",
      "\n",
      "The data set has 9800 observations.\n",
      "\n",
      "Using: T, H2, O2, OH, H2O, CO, CO2 as target variables.\n"
     ]
    }
   ],
   "source": [
    "data_tag = 'lightweight-flamelet-CO-H2-10-1'\n",
    "%run -i load-SLFM-data.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Run the manifold-informed backward elimination algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizing...\n",
      "\n",
      "PC source terms will be assessed in the original-and-continuous-symlog space.\n",
      "\n",
      "Iteration No.1\n",
      "Currently eliminating variable from the following list: \n",
      "['T', 'H2', 'O2', 'O', 'OH', 'H2O', 'H', 'HO2', 'CO', 'CO2', 'HCO']\n",
      "\tCurrently eliminated variable: T\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, O2, O, OH, H2O, H, HO2, CO, CO2, HCO\n",
      "\tCost:\t25.2808\n",
      "\tCurrently eliminated variable: H2\n",
      "\tRunning PCA for a subset:\n",
      "\tT, O2, O, OH, H2O, H, HO2, CO, CO2, HCO\n",
      "\tCost:\t28.7130\n",
      "\tCurrently eliminated variable: O2\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O, OH, H2O, H, HO2, CO, CO2, HCO\n",
      "\tCost:\t29.1466\n",
      "\tCurrently eliminated variable: O\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, OH, H2O, H, HO2, CO, CO2, HCO\n",
      "\tCost:\t30.9018\n",
      "\tCurrently eliminated variable: OH\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, H2O, H, HO2, CO, CO2, HCO\n",
      "\tCost:\t29.6438\n",
      "\tCurrently eliminated variable: H2O\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, OH, H, HO2, CO, CO2, HCO\n",
      "\tCost:\t25.2830\n",
      "\tCurrently eliminated variable: H\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, OH, H2O, HO2, CO, CO2, HCO\n",
      "\tCost:\t29.7679\n",
      "\tCurrently eliminated variable: HO2\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, OH, H2O, H, CO, CO2, HCO\n",
      "\tCost:\t21.9436\n",
      "\tCurrently eliminated variable: CO\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, OH, H2O, H, HO2, CO2, HCO\n",
      "\tCost:\t26.7706\n",
      "\tCurrently eliminated variable: CO2\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, OH, H2O, H, HO2, CO, HCO\n",
      "\tCost:\t25.6119\n",
      "\tCurrently eliminated variable: HCO\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, OH, H2O, H, HO2, CO, CO2\n",
      "\tCost:\t27.8456\n",
      "\n",
      "\tVariable HO2 is removed.\n",
      "\tCost:\t21.9436\n",
      "\n",
      "\tIteration time: 4.9 minutes.\n",
      "--------------------------------------------------\n",
      "Iteration No.2\n",
      "Currently eliminating variable from the following list: \n",
      "['T', 'H2', 'O2', 'O', 'OH', 'H2O', 'H', 'CO', 'CO2', 'HCO']\n",
      "\tCurrently eliminated variable: T\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, O2, O, OH, H2O, H, CO, CO2, HCO\n",
      "\tCost:\t22.1896\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: H2\n",
      "\tRunning PCA for a subset:\n",
      "\tT, O2, O, OH, H2O, H, CO, CO2, HCO\n",
      "\tCost:\t25.4358\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: O2\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O, OH, H2O, H, CO, CO2, HCO\n",
      "\tCost:\t34.5693\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: O\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, OH, H2O, H, CO, CO2, HCO\n",
      "\tCost:\t21.1740\n",
      "\u001b[32m\tSAME OR BETTER\u001b[0m\n",
      "\tCurrently eliminated variable: OH\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, H2O, H, CO, CO2, HCO\n",
      "\tCost:\t22.5975\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: H2O\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, OH, H, CO, CO2, HCO\n",
      "\tCost:\t22.1896\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: H\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, OH, H2O, CO, CO2, HCO\n",
      "\tCost:\t23.4068\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: CO\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, OH, H2O, H, CO2, HCO\n",
      "\tCost:\t23.6848\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: CO2\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, OH, H2O, H, CO, HCO\n",
      "\tCost:\t22.2702\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: HCO\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, OH, H2O, H, CO, CO2\n",
      "\tCost:\t17.1101\n",
      "\u001b[32m\tSAME OR BETTER\u001b[0m\n",
      "\n",
      "\tVariable HCO is removed.\n",
      "\tCost:\t17.1101\n",
      "\n",
      "\tIteration time: 4.5 minutes.\n",
      "--------------------------------------------------\n",
      "Iteration No.3\n",
      "Currently eliminating variable from the following list: \n",
      "['T', 'H2', 'O2', 'O', 'OH', 'H2O', 'H', 'CO', 'CO2']\n",
      "\tCurrently eliminated variable: T\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, O2, O, OH, H2O, H, CO, CO2\n",
      "\tCost:\t17.1653\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: H2\n",
      "\tRunning PCA for a subset:\n",
      "\tT, O2, O, OH, H2O, H, CO, CO2\n",
      "\tCost:\t22.7411\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: O2\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O, OH, H2O, H, CO, CO2\n",
      "\tCost:\t26.3267\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: O\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, OH, H2O, H, CO, CO2\n",
      "\tCost:\t15.6053\n",
      "\u001b[32m\tSAME OR BETTER\u001b[0m\n",
      "\tCurrently eliminated variable: OH\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, H2O, H, CO, CO2\n",
      "\tCost:\t18.4672\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: H2O\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, OH, H, CO, CO2\n",
      "\tCost:\t16.7184\n",
      "\u001b[32m\tSAME OR BETTER\u001b[0m\n",
      "\tCurrently eliminated variable: H\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, OH, H2O, CO, CO2\n",
      "\tCost:\t21.2004\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: CO\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, OH, H2O, H, CO2\n",
      "\tCost:\t20.2098\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: CO2\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, O, OH, H2O, H, CO\n",
      "\tCost:\t17.2389\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\n",
      "\tVariable O is removed.\n",
      "\tCost:\t15.6053\n",
      "\n",
      "\tIteration time: 4.0 minutes.\n",
      "--------------------------------------------------\n",
      "Iteration No.4\n",
      "Currently eliminating variable from the following list: \n",
      "['T', 'H2', 'O2', 'OH', 'H2O', 'H', 'CO', 'CO2']\n",
      "\tCurrently eliminated variable: T\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, O2, OH, H2O, H, CO, CO2\n",
      "\tCost:\t15.7344\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: H2\n",
      "\tRunning PCA for a subset:\n",
      "\tT, O2, OH, H2O, H, CO, CO2\n",
      "\tCost:\t20.0399\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: O2\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, OH, H2O, H, CO, CO2\n",
      "\tCost:\t22.5615\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: OH\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, H2O, H, CO, CO2\n",
      "\tCost:\t18.5735\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: H2O\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, OH, H, CO, CO2\n",
      "\tCost:\t15.5608\n",
      "\u001b[32m\tSAME OR BETTER\u001b[0m\n",
      "\tCurrently eliminated variable: H\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, OH, H2O, CO, CO2\n",
      "\tCost:\t18.8170\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: CO\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, OH, H2O, H, CO2\n",
      "\tCost:\t18.3612\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: CO2\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, OH, H2O, H, CO\n",
      "\tCost:\t15.8994\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\n",
      "\tVariable H2O is removed.\n",
      "\tCost:\t15.5608\n",
      "\n",
      "\tIteration time: 3.6 minutes.\n",
      "--------------------------------------------------\n",
      "Iteration No.5\n",
      "Currently eliminating variable from the following list: \n",
      "['T', 'H2', 'O2', 'OH', 'H', 'CO', 'CO2']\n",
      "\tCurrently eliminated variable: T\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, O2, OH, H, CO, CO2\n",
      "\tCost:\t15.7000\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: H2\n",
      "\tRunning PCA for a subset:\n",
      "\tT, O2, OH, H, CO, CO2\n",
      "\tCost:\t19.0479\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: O2\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, OH, H, CO, CO2\n",
      "\tCost:\t23.0848\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: OH\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, H, CO, CO2\n",
      "\tCost:\t18.1291\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: H\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, OH, CO, CO2\n",
      "\tCost:\t18.9804\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: CO\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, OH, H, CO2\n",
      "\tCost:\t17.7492\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: CO2\n",
      "\tRunning PCA for a subset:\n",
      "\tT, H2, O2, OH, H, CO\n",
      "\tCost:\t15.8402\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\n",
      "\tVariable T is removed.\n",
      "\tCost:\t15.7000\n",
      "\n",
      "\tIteration time: 3.1 minutes.\n",
      "--------------------------------------------------\n",
      "Iteration No.6\n",
      "Currently eliminating variable from the following list: \n",
      "['H2', 'O2', 'OH', 'H', 'CO', 'CO2']\n",
      "\tCurrently eliminated variable: H2\n",
      "\tRunning PCA for a subset:\n",
      "\tO2, OH, H, CO, CO2\n",
      "\tCost:\t19.0526\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: O2\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, OH, H, CO, CO2\n",
      "\tCost:\t21.9033\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: OH\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, O2, H, CO, CO2\n",
      "\tCost:\t18.2277\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: H\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, O2, OH, CO, CO2\n",
      "\tCost:\t18.6680\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: CO\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, O2, OH, H, CO2\n",
      "\tCost:\t17.8506\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: CO2\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, O2, OH, H, CO\n",
      "\tCost:\t15.9725\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\n",
      "\tVariable CO2 is removed.\n",
      "\tCost:\t15.9725\n",
      "\n",
      "\tIteration time: 2.7 minutes.\n",
      "--------------------------------------------------\n",
      "Iteration No.7\n",
      "Currently eliminating variable from the following list: \n",
      "['H2', 'O2', 'OH', 'H', 'CO']\n",
      "\tCurrently eliminated variable: H2\n",
      "\tRunning PCA for a subset:\n",
      "\tO2, OH, H, CO\n",
      "\tCost:\t19.1332\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: O2\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, OH, H, CO\n",
      "\tCost:\t21.1422\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: OH\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, O2, H, CO\n",
      "\tCost:\t18.4225\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: H\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, O2, OH, CO\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tCost:\t18.6739\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: CO\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, O2, OH, H\n",
      "\tCost:\t18.0693\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\n",
      "\tVariable CO is removed.\n",
      "\tCost:\t18.0693\n",
      "\n",
      "\tIteration time: 2.2 minutes.\n",
      "--------------------------------------------------\n",
      "Iteration No.8\n",
      "Currently eliminating variable from the following list: \n",
      "['H2', 'O2', 'OH', 'H']\n",
      "\tCurrently eliminated variable: H2\n",
      "\tRunning PCA for a subset:\n",
      "\tO2, OH, H\n",
      "\tCost:\t32.0315\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: O2\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, OH, H\n",
      "\tCost:\t25.1526\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: OH\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, O2, H\n",
      "\tCost:\t21.4873\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: H\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, O2, OH\n",
      "\tCost:\t21.5799\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\n",
      "\tVariable OH is removed.\n",
      "\tCost:\t21.4873\n",
      "\n",
      "\tIteration time: 1.8 minutes.\n",
      "--------------------------------------------------\n",
      "Iteration No.9\n",
      "Currently eliminating variable from the following list: \n",
      "['H2', 'O2', 'H']\n",
      "\tCurrently eliminated variable: H2\n",
      "\tRunning PCA for a subset:\n",
      "\tO2, H\n",
      "\tCost:\t47.0227\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: O2\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, H\n",
      "\tCost:\t27.3060\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\tCurrently eliminated variable: H\n",
      "\tRunning PCA for a subset:\n",
      "\tH2, O2\n",
      "\tCost:\t21.1694\n",
      "\u001b[31m\tWORSE\u001b[0m\n",
      "\n",
      "\tVariable H is removed.\n",
      "\tCost:\t21.1694\n",
      "\n",
      "\tIteration time: 1.4 minutes.\n",
      "--------------------------------------------------\n",
      "Ordered variables:\n",
      "O2, H2, H, OH, CO, CO2, T, H2O, O, HCO, HO2\n",
      "[2, 1, 6, 4, 8, 9, 0, 5, 3, 10, 7]\n",
      "Final cost: 21.1694\n",
      "\n",
      "Selected variables:\n",
      "O2, H2, H, OH, CO, CO2, T\n",
      "[2, 1, 6, 4, 8, 9, 0]\n",
      "Lowest cost: 15.5608\n",
      "\n",
      "Optimization time: 28.2 minutes.\n",
      "--------------------------------------------------\n",
      "['O2' 'H2' 'H' 'OH' 'CO' 'CO2' 'T']\n"
     ]
    }
   ],
   "source": [
    "ordered_variables, selected_variables, optimized_cost, costs = analysis.manifold_informed_backward_elimination(state_space,\n",
    "                                                                                            state_space_sources,\n",
    "                                                                                            list(state_space_names),\n",
    "                                                                                            scaling=scaling,\n",
    "                                                                                            bandwidth_values=bandwidth_values,\n",
    "                                                                                            target_variables=target_variables,\n",
    "                                                                                            add_transformed_source=True,\n",
    "                                                                                            source_space='original-and-continuous-symlog',\n",
    "                                                                                            target_manifold_dimensionality=n_components,\n",
    "                                                                                            penalty_function=penalty_function,\n",
    "                                                                                            norm=norm,\n",
    "                                                                                            integrate_to_peak=False,\n",
    "                                                                                            verbose=True)\n",
    "\n",
    "print(state_space_names[selected_variables])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = reduction.PCA(state_space, scaling=scaling, n_components=n_components)\n",
    "PCs = pca.transform(state_space)\n",
    "PC_sources = pca.transform(state_space_sources, nocenter=True)\n",
    "symlog_PC_sources = preprocess.log_transform(PC_sources, method='continuous-symlog', threshold=1.e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tTime it took: 1.7 minutes.\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "tic = time.perf_counter()\n",
    "\n",
    "depvars = np.hstack((PC_sources, symlog_PC_sources, target_variables))\n",
    "depvar_names = ['SZ' + str(i+1) for i in range(0,n_components)] + ['contsymlog-SZ' + str(i) for i in range(1,n_components+1)] + target_variables_names\n",
    "\n",
    "variance_data = analysis.compute_normalized_variance(PCs,\n",
    "                                                     depvars,\n",
    "                                                     depvar_names=depvar_names,\n",
    "                                                     bandwidth_values=bandwidth_values)\n",
    "\n",
    "cost = analysis.cost_function_normalized_variance_derivative(variance_data, \n",
    "                                                             penalty_function=penalty_function, \n",
    "                                                             norm=norm,\n",
    "                                                             integrate_to_peak=False)\n",
    "\n",
    "toc = time.perf_counter()\n",
    "print(f'\\tTime it took: {(toc - tic)/60:0.1f} minutes.\\n' + '-'*40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25.8672563654024\n"
     ]
    }
   ],
   "source": [
    "print(cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## ✏️ Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset_indices = [2, 1, 6, 4, 8, 9, 0, 5, 3, 10, 7]\n",
    "ordered_variables = [2, 1, 6, 4, 8, 9, 0, 5, 3, 10, 7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial state vector:\n",
      "[2, 1, 6, 4, 8, 9, 0, 5, 3, 10, 7]\n",
      "['O2' 'H2' 'H' 'OH' 'CO' 'CO2' 'T' 'H2O' 'O' 'HCO' 'HO2']\n",
      "\n",
      "Iteration 2\n",
      "Remove HO2\n",
      "[2, 1, 6, 4, 8, 9, 0, 5, 3, 10]\n",
      "['O2' 'H2' 'H' 'OH' 'CO' 'CO2' 'T' 'H2O' 'O' 'HCO']\n",
      "\n",
      "Iteration 3\n",
      "Remove HCO\n",
      "[2, 1, 6, 4, 8, 9, 0, 5, 3]\n",
      "['O2' 'H2' 'H' 'OH' 'CO' 'CO2' 'T' 'H2O' 'O']\n",
      "\n",
      "Iteration 4\n",
      "Remove O\n",
      "[2, 1, 6, 4, 8, 9, 0, 5]\n",
      "['O2' 'H2' 'H' 'OH' 'CO' 'CO2' 'T' 'H2O']\n",
      "\n",
      "Iteration 5\n",
      "Remove H2O\n",
      "[2, 1, 6, 4, 8, 9, 0]\n",
      "['O2' 'H2' 'H' 'OH' 'CO' 'CO2' 'T']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for index, i in enumerate([11, 10, 9, 8, 7]):\n",
    "    if index > 0:\n",
    "        print('\\nIteration ' + str(index+1) + '\\nRemove ' + state_space_names[ordered_variables][-index])\n",
    "    else:\n",
    "        print('Initial state vector:')\n",
    "\n",
    "    subset_indices = ordered_variables[0:i]\n",
    "    print(subset_indices)\n",
    "    print(state_space_names[subset_indices])\n",
    "    \n",
    "    subset_pca = reduction.SubsetPCA(state_space, X_source=state_space_sources, full_sequence=False, subset_indices=subset_indices, scaling=scaling, n_components=2)\n",
    "    PCs = subset_pca.principal_components\n",
    "\n",
    "    figure = plt.figure(figsize=(3, 3))\n",
    "    scat_b = plt.scatter(PCs[:,0], PCs[:,1], c=state_space[:,0], s=10, cmap='inferno', vmin=np.min(state_space[:,0]), vmax=np.max(state_space[:,0]))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.gca().spines[\"top\"].set_visible(False)\n",
    "    plt.gca().spines[\"right\"].set_visible(False)\n",
    "    plt.gca().spines[\"bottom\"].set_visible(False)\n",
    "    plt.gca().spines[\"left\"].set_visible(False)\n",
    "    plt.savefig('../figures/Figure-2-' + str(index) + '.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}