data_notebook.ipynb

{
 "cells": [
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   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries and define functions\n",
    "import csv\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [15, 5]\n",
    "plt.rcParams.update({'font.size': 22})\n",
    "\n",
    "def set_style():\n",
    "    plt.minorticks_on()\n",
    "    plt.grid(b=True, which='minor', color='white', linestyle='-', alpha=0.4)\n",
    "    plt.style.use('ggplot')\n",
    "    plt.rcParams.update({'font.size': 22})\n",
    "    legend = plt.gca().legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode=\"expand\", borderaxespad=0., frameon = 1)\n",
    "    frame = legend.get_frame()\n",
    "    frame.set_facecolor('white')\n",
    "    frame.set_edgecolor('white')\n",
    "\n",
    "\n",
    "def save_fig(file_name):\n",
    "    plt.savefig(file_name, dpi=300, facecolor='w', edgecolor='w',\n",
    "        orientation='portrait', papertype=None, format=None,\n",
    "        transparent=False, bbox_inches='tight', pad_inches=0.02,\n",
    "        frameon=None, metadata=None)\n",
    "\n",
    "\n",
    "def read_file(path):\n",
    "  with open(path, 'r') as fd:\n",
    "    cwriter = csv.reader(fd)\n",
    "    data = []\n",
    "    skipped_first_row = False\n",
    "    for row in cwriter:\n",
    "      if not skipped_first_row:\n",
    "        skipped_first_row = True\n",
    "        continue\n",
    "      row_tmp = row\n",
    "      for idx in range(len(row_tmp)):\n",
    "        row_tmp[idx] = float(row_tmp[idx])\n",
    "      data.append(row_tmp)\n",
    "    config = data[0]\n",
    "    data = data[1]\n",
    "  return data, config\n",
    "\n",
    "\n",
    "def read_data_folder(path):\n",
    "  data_max = []\n",
    "  data_all = []\n",
    "  configs = []\n",
    "  for idx in range(1, DESIGNS+1):\n",
    "    data, config = read_file(path + '{}.csv'.format(idx))\n",
    "    for d in data:\n",
    "        data_all.append(d)\n",
    "    data_max.append(np.amax(data))\n",
    "    data = np.array(data)\n",
    "    data_all.append(data)\n",
    "    configs.append(config)\n",
    "  return data_all, data_max, configs\n",
    "    \n",
    "\n",
    "def compute_mean_std(paths):\n",
    "  data_max_ar = []\n",
    "  data_top_mean_ar = []\n",
    "  configs_ar = []\n",
    "  data_all_ar = []\n",
    "  for p in paths:\n",
    "    data_all, data_max, configs = read_data_folder(p)\n",
    "    data_all_ar.append(data_all)\n",
    "    data_max_ar.append(data_max)\n",
    "    configs_ar.append(configs)\n",
    "  data_max_mean = np.mean(data_max_ar, axis=0)\n",
    "  data_max_std = np.std(data_max_ar, axis=0)\n",
    "  return data_max_mean, data_max_std\n",
    "\n",
    "\n",
    "def plot_data_means(mean_data, std_data, color, label='', plot_random_std=False):\n",
    "  x = np.arange(1, len(mean_data) + 1)\n",
    "  init_data_mean = mean_data[0:INITIAL_DESIGNS+1]\n",
    "  init_data_std = std_data[0:INITIAL_DESIGNS+1]\n",
    "  init_x = x[0:INITIAL_DESIGNS+1]\n",
    "  optim_data_mean = mean_data[INITIAL_DESIGNS::2]\n",
    "  optim_data_std = std_data[INITIAL_DESIGNS::2]\n",
    "  optim_x = x[INITIAL_DESIGNS::2]\n",
    "  random_data_mean = mean_data[INITIAL_DESIGNS+1::2]\n",
    "  random_data_std = std_data[INITIAL_DESIGNS+1::2]\n",
    "  random_x = x[INITIAL_DESIGNS+1::2]\n",
    "  plt.plot(init_x, init_data_mean, color=color, alpha=0.3, linewidth=2.0)\n",
    "  plt.fill_between(init_x, init_data_mean - init_data_std, init_data_mean + init_data_std, facecolor=color, alpha=0.1)\n",
    "  plt.plot(optim_x, optim_data_mean, color=color, label=label, linewidth=2.0)\n",
    "  plt.fill_between(optim_x, optim_data_mean - optim_data_std, optim_data_mean + optim_data_std, facecolor=color, alpha=0.2)\n",
    "  #plt.plot(random_x, random_data_mean, color=color, linestyle='--')\n",
    "  if plot_random_std:\n",
    "    plt.fill_between(random_x, random_data_mean - random_data_std, random_data_mean + random_data_std, facecolor=color, alpha=0.2)\n",
    "  set_style()\n",
    "\n",
    "    \n",
    "def plot_data_means_optim_random(mean_data, std_data, color, label=''):\n",
    "  x = np.arange(1, len(mean_data) + 1)\n",
    "  init_data_mean = mean_data[0:INITIAL_DESIGNS+1]\n",
    "  init_data_std = std_data[0:INITIAL_DESIGNS+1]\n",
    "  init_x = x[0:INITIAL_DESIGNS+1]\n",
    "  optim_data_mean = mean_data[INITIAL_DESIGNS::2]\n",
    "  optim_data_std = std_data[INITIAL_DESIGNS::2]\n",
    "  optim_x = x[INITIAL_DESIGNS::2]\n",
    "  random_data_mean = mean_data[INITIAL_DESIGNS+1::2]\n",
    "  random_data_std = std_data[INITIAL_DESIGNS+1::2]\n",
    "  random_x = x[INITIAL_DESIGNS+1::2]\n",
    "  plt.plot(init_x, init_data_mean, color=color, alpha=0.3, linewidth=2.0)\n",
    "  plt.fill_between(init_x, init_data_mean - init_data_std, init_data_mean + init_data_std, facecolor=color, alpha=0.1)\n",
    "  plt.plot(optim_x, optim_data_mean, color=color, linewidth=2.0, label=label)\n",
    "  plt.fill_between(optim_x, optim_data_mean - optim_data_std, optim_data_mean + optim_data_std, facecolor=color, alpha=0.2)\n",
    "  plt.plot(random_x, random_data_mean, color=color, label=label + ' Random Exploration', linestyle='--', linewidth=2.0)\n",
    "  plt.fill_between(random_x, random_data_mean - random_data_std, random_data_mean + random_data_std, facecolor=color, alpha=0.2)\n",
    "  set_style()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change these parameters as required\n",
    "INITIAL_DESIGNS = 5\n",
    "DESIGNS = 50\n",
    "\n",
    "# Proposed method using data-driven design optimization\n",
    "# Change folder names\n",
    "EXPERIMENT_FOLDERS_1 = [\n",
    "    'data_exp_sac_pso_batch/Mon_Oct_21_05:47:55_2019__e37381f2',\n",
    "    'data_exp_sac_pso_batch/Mon_Oct_21_05:48:30_2019__0041f8c5',\n",
    "    'data_exp_sac_pso_batch/Mon_Oct_21_05:49:00_2019__5e2e3460',\n",
    "    'data_exp_sac_pso_batch/Sun_Oct_20_23:36:49_2019__5db36c30',\n",
    "    'data_exp_sac_pso_batch/Sun_Oct_20_23:37:06_2019__30302df7',\n",
    "]\n",
    "\n",
    "# Baseline using simulations for the evaluation of design candidates\n",
    "# Change folder names\n",
    "EXPERIMENTS_FOLDERS_2 = [\n",
    "    'data_exp_sac_pso_sim/Wed_Oct_23_05:49:05_2019__32509883',\n",
    "    'data_exp_sac_pso_sim/Wed_Oct_23_05:49:22_2019__6878474d',\n",
    "    'data_exp_sac_pso_sim/Wed_Oct_23_05:51:21_2019__36c0fafc',\n",
    "    'data_exp_sac_pso_sim/Wed_Oct_23_05:51:33_2019__b21f861b',\n",
    "    'data_exp_sac_pso_sim/Wed_Oct_23_05:51:45_2019__cc74a513',\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: TkAgg\n"
     ]
    }
   ],
   "source": [
    "%matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_files = ['{}/data_design_'.format(folder) for folder in EXPERIMENT_FOLDERS] # Novelty Search + PSO on Q\n",
    "exp_mean, exp_std = compute_mean_std(exp_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot performance of optimized designs and randomly selected designs\n",
    "plot_data_means_optim_random(exp_mean, exp_std, color='red', label='Proposed Method')\n",
    "plt.ylabel('Cum. Episodic Reward')\n",
    "plt.xlabel('Designs')\n",
    "plt.ylim([150,600])\n",
    "plt.xlim([1,DESIGNS])\n",
    "plt.show()\n",
    "#save_fig('plots_HalfCheetah_random_vs_novelty_search.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compare two experiments/methods against each other\n",
    "exp_files = ['{}/data_design_'.format(folder) for folder in EXPERIMENT_FOLDERS_1] # Novelty Search + PSO on Q\n",
    "exp_mean, exp_std = compute_mean_std(exp_files)\n",
    "plot_data_means(exp_mean, exp_std, color='red', label='Proposed Method')\n",
    "\n",
    "exp_files = ['{}/data_design_'.format(folder) for folder in EXPERIMENTS_FOLDERS_2] # Novelty Search + PSO on Q\n",
    "exp_mean, exp_std = compute_mean_std(exp_files)\n",
    "plot_data_means(exp_mean, exp_std, color='blue', label='Using Simulations')\n",
    "plt.ylabel('Cum. Episodic Reward')\n",
    "plt.xlabel('Designs')\n",
    "plt.ylim([150,600])\n",
    "plt.xlim([1,DESIGNS])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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