notebook_plot_reward_evolution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from pylab import *\n",
    "from matplotlib import colors\n",
    "from matplotlib.colors import ListedColormap\n",
    "from argparse import Namespace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf = !python  parameters.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "configs = eval(conf[0])\n",
    "configs.et_normalize_coef_cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Experiment config\n",
    "case=\"E1000\"\n",
    "configs.name=\"E1000v9\"\n",
    "configs.n_devices=999\n",
    "configs.n_jobs=9\n",
    "\n",
    "#log_ppo_E1000v9_w5050"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0.         2111.7875       43.43549347 1074.75        529.5\n",
      "  2563.19995117 1620.         1660.375     ]]\n"
     ]
    }
   ],
   "source": [
    "train_log = pd.read_pickle('logs/log_ppo_' + str(configs.name) + '_w5050.pkl', 'infer')\n",
    "edge_log = pd.read_pickle('logs/log_edge2_' +  str(case) + '_' + str(configs.n_jobs)+ '_' + str(configs.n_jobs)+  \"_\"+ str(configs.n_devices) +'.pkl', 'infer')\n",
    "edge_log_cost = pd.read_pickle('logs/log_edge_PCost_' +  str(case) + '_' + str(configs.n_jobs)+ '_' + str(configs.n_jobs)+  \"_\"+ str(configs.n_devices) +'.pkl', 'infer')\n",
    "rnd_log = pd.read_pickle('logs/log_rnd_' +  str(case) + '_' + str(configs.n_jobs)+   '_' + str(configs.n_jobs)+  \"_\"+ str(configs.n_devices) +'.pkl', 'infer')\n",
    "train_log = np.array(train_log)\n",
    "edge_log = np.array(edge_log)\n",
    "edge_log_cost = np.array(edge_log_cost)\n",
    "rnd_log = np.array(rnd_log)\n",
    "#print(train_log.shape)\n",
    "train_log = np.abs(train_log)\n",
    "rnd_log = np.abs(rnd_log)\n",
    "edge_log = np.abs(edge_log)\n",
    "edge_log_cost = np.abs(edge_log_cost)\n",
    "\n",
    "print(train_log[:1])\n",
    "\n",
    "#[i_update, mean_rewards_all_env,v_loss,mean_all_init_rewards,init_times.mean(),time_all_env.mean(),init_costs.mean(),cost_all_env.mean()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "ax.plot(rnd_log[:,1],linewidth=1.6,linestyle=\":\",label=\"Random devices\")\n",
    "ax.plot(train_log[:,1],linewidth=2.5,label=\"DRL model\")\n",
    "ax.plot(train_log[:,3],linestyle=\"-.\",label=\"All-in-Cloud\")\n",
    "ax.plot(edge_log[:,1],linewidth=2.5,linestyle=\"dotted\",label=\"Greedy-Edge\",alpha=0.8,color=\"red\")\n",
    "ax.plot(edge_log_cost[:,1],linewidth=2.,label=\"Greedy-Cost\",alpha=0.5)\n",
    "\n",
    "# plt.legend(title='Allocations',fontsize=14)\n",
    "plt.legend(fontsize=12)\n",
    "# plt.title('Job times',fontsize=16)\n",
    "plt.xlabel('Episodes', fontsize=14)\n",
    "plt.ylabel('Average reward', fontsize=14)\n",
    "plt.savefig(\"images/reward_E1000v9_w5050.pdf\",dpi=300,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_log99' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m fig, ax \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39msubplots(figsize\u001b[39m=\u001b[39m(\u001b[39m8\u001b[39m,\u001b[39m4\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m ax\u001b[39m.\u001b[39mplot(train_log99[:,\u001b[39m1\u001b[39m],linewidth\u001b[39m=\u001b[39m\u001b[39m2.5\u001b[39m,label\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mTrained-policy (9x100)\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m      3\u001b[0m ax\u001b[39m.\u001b[39mplot(train_log99[:,\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m],linestyle\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m-.\u001b[39m\u001b[39m\"\u001b[39m,label\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mAll-in-Cloud (9x100)\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m      5\u001b[0m ax\u001b[39m.\u001b[39mplot(train_log999[:,\u001b[39m1\u001b[39m],linewidth\u001b[39m=\u001b[39m\u001b[39m2.5\u001b[39m,label\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mTrained-policy (9x1000)\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'train_log99' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "ax.plot(train_log99[:,1],linewidth=2.5,label=\"Trained-policy (9x100)\")\n",
    "ax.plot(train_log99[:,-1],linestyle=\"-.\",label=\"All-in-Cloud (9x100)\")\n",
    "\n",
    "ax.plot(train_log999[:,1],linewidth=2.5,label=\"Trained-policy (9x1000)\")\n",
    "ax.plot(train_log999[:,-1],linestyle=\"-.\",label=\"All-in-Cloud (9x1000)\")\n",
    "\n",
    "\n",
    "# plt.legend(title='Allocations',fontsize=14)\n",
    "plt.legend(fontsize=12)\n",
    "# plt.title('Job times',fontsize=16)\n",
    "plt.xlabel('Episodes', fontsize=14)\n",
    "plt.ylabel('Average time', fontsize=14)\n",
    "plt.savefig(\"images/rewardstime_99-999_9.pdf\",dpi=300,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'trainlogs/log_ppo_train_9_99_3_20.pkl'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_log99 \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_pickle(\u001b[39m'\u001b[39;49m\u001b[39mtrainlogs/log_ppo_train_9_99_3_20.pkl\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39minfer\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m      2\u001b[0m train_log999 \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mread_pickle(\u001b[39m'\u001b[39m\u001b[39mtrainlogs/log_ppo_train_9_999_3_20.pkl\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39minfer\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m      3\u001b[0m train_log99 \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(train_log99)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/pandas/io/pickle.py:190\u001b[0m, in \u001b[0;36mread_pickle\u001b[0;34m(filepath_or_buffer, compression, storage_options)\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    125\u001b[0m \u001b[39mLoad pickled pandas object (or any object) from file.\u001b[39;00m\n\u001b[1;32m    126\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    187\u001b[0m \u001b[39m4    4    9\u001b[39;00m\n\u001b[1;32m    188\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    189\u001b[0m excs_to_catch \u001b[39m=\u001b[39m (\u001b[39mAttributeError\u001b[39;00m, \u001b[39mImportError\u001b[39;00m, \u001b[39mModuleNotFoundError\u001b[39;00m, \u001b[39mTypeError\u001b[39;00m)\n\u001b[0;32m--> 190\u001b[0m \u001b[39mwith\u001b[39;00m get_handle(\n\u001b[1;32m    191\u001b[0m     filepath_or_buffer,\n\u001b[1;32m    192\u001b[0m     \u001b[39m\"\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    193\u001b[0m     compression\u001b[39m=\u001b[39;49mcompression,\n\u001b[1;32m    194\u001b[0m     is_text\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m    195\u001b[0m     storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[1;32m    196\u001b[0m ) \u001b[39mas\u001b[39;00m handles:\n\u001b[1;32m    197\u001b[0m \n\u001b[1;32m    198\u001b[0m     \u001b[39m# 1) try standard library Pickle\u001b[39;00m\n\u001b[1;32m    199\u001b[0m     \u001b[39m# 2) try pickle_compat (older pandas version) to handle subclass changes\u001b[39;00m\n\u001b[1;32m    200\u001b[0m     \u001b[39m# 3) try pickle_compat with latin-1 encoding upon a UnicodeDecodeError\u001b[39;00m\n\u001b[1;32m    202\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    203\u001b[0m         \u001b[39m# TypeError for Cython complaints about object.__new__ vs Tick.__new__\u001b[39;00m\n\u001b[1;32m    204\u001b[0m         \u001b[39mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/pandas/io/common.py:865\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    856\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(\n\u001b[1;32m    857\u001b[0m             handle,\n\u001b[1;32m    858\u001b[0m             ioargs\u001b[39m.\u001b[39mmode,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    861\u001b[0m             newline\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m    862\u001b[0m         )\n\u001b[1;32m    863\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    864\u001b[0m         \u001b[39m# Binary mode\u001b[39;00m\n\u001b[0;32m--> 865\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(handle, ioargs\u001b[39m.\u001b[39;49mmode)\n\u001b[1;32m    866\u001b[0m     handles\u001b[39m.\u001b[39mappend(handle)\n\u001b[1;32m    868\u001b[0m \u001b[39m# Convert BytesIO or file objects passed with an encoding\u001b[39;00m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'trainlogs/log_ppo_train_9_99_3_20.pkl'"
     ]
    }
   ],
   "source": [
    "train_log99 = pd.read_pickle('trainlogs/log_ppo_train_9_99_3_20.pkl', 'infer')\n",
    "train_log999 = pd.read_pickle('trainlogs/log_ppo_train_9_999_3_20.pkl', 'infer')\n",
    "train_log99 = np.array(train_log99)\n",
    "train_log999 = np.array(train_log999)\n",
    "train_log999 = np.abs(train_log999)\n",
    "train_log99 = np.abs(train_log99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_log99' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m fig, ax \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39msubplots(figsize\u001b[39m=\u001b[39m(\u001b[39m8\u001b[39m,\u001b[39m4\u001b[39m))\n\u001b[1;32m      4\u001b[0m axins1 \u001b[39m=\u001b[39m zoomed_inset_axes(ax, zoom \u001b[39m=\u001b[39m \u001b[39m3\u001b[39m, loc\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mcenter right\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m axins1\u001b[39m.\u001b[39mplot(train_log99[:,\u001b[39m1\u001b[39m],color\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mgreen\u001b[39m\u001b[39m'\u001b[39m,linewidth\u001b[39m=\u001b[39m\u001b[39m2.5\u001b[39m)\n\u001b[1;32m      6\u001b[0m axins1\u001b[39m.\u001b[39mplot(train_log999[:,\u001b[39m1\u001b[39m],color\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mblue\u001b[39m\u001b[39m'\u001b[39m,linewidth\u001b[39m=\u001b[39m\u001b[39m2.5\u001b[39m)\n\u001b[1;32m      7\u001b[0m axins1\u001b[39m.\u001b[39mplot(train_log99[:,\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m],color\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mred\u001b[39m\u001b[39m'\u001b[39m,linestyle\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m-.\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'train_log99' is not defined"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, mark_inset\n",
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "axins1 = zoomed_inset_axes(ax, zoom = 3, loc='center right')\n",
    "axins1.plot(train_log99[:,1],color='green',linewidth=2.5)\n",
    "axins1.plot(train_log999[:,1],color='blue',linewidth=2.5)\n",
    "axins1.plot(train_log99[:,-1],color='red',linestyle=\"-.\")\n",
    "axins1.plot(train_log999[:,-1],color='orange',linestyle=\"-.\")\n",
    "axins1.tick_params(labelleft=False, labelbottom=False)\n",
    "#axins1.plot(y,datapts.T,'mo')\n",
    "x1, x2, y1, y2 = 80,100,200,350 \n",
    "axins1.set_xlim(x1, x2)\n",
    "axins1.set_ylim(y1, y2)\n",
    "\n",
    "ax.plot(train_log99[:,1],linewidth=2.5,label=\"Trained-policy (81x100)\",color='green')\n",
    "ax.plot(train_log99[:,-1],linestyle=\"-.\",label=\"All-in-Cloud (81x100)\",color='red')\n",
    "\n",
    "ax.plot(train_log999[:,1],linewidth=2.5,label=\"Trained-policy (81x1000)\",color='blue')\n",
    "ax.plot(train_log999[:,-1],linestyle=\"-.\",label=\"All-in-Cloud (81x1000)\",color='orange')\n",
    "\n",
    "mark_inset(ax, axins1, loc1=3, loc2=4, fc=\"none\", ec=\"0.5\")\n",
    "# plt.legend(title='Allocations',fontsize=14)\n",
    "ax.legend(fontsize=12,ncol=2)\n",
    "# plt.title('Job times',fontsize=16)\n",
    "ax.set_xlabel('Episodes', fontsize=14)\n",
    "ax.set_ylabel('Average time', fontsize=14)\n",
    "plt.savefig(\"images/rewardstime_99-999_81.pdf\",dpi=300,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_log15 = pd.read_pickle('trainlogs/log_ppo_c15_train_9_99_3_20.pkl', 'infer')\n",
    "train_log15 = np.array(train_log15)\n",
    "train_log15 = np.abs(train_log15)\n",
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "ax.plot(train_log15[:,1],linewidth=2.5,label=\"Trained-policy (81x100-15L)\")\n",
    "ax.plot(train_log15[:,-1],linestyle=\"-.\",label=\"All-in-Cloud (81x100-15L)\")\n",
    "\n",
    "\n",
    "# plt.legend(title='Allocations',fontsize=14)\n",
    "plt.legend(fontsize=12)\n",
    "# plt.title('Job times',fontsize=16)\n",
    "plt.xlabel('Episodes', fontsize=14)\n",
    "plt.ylabel('Average time', fontsize=14)\n",
    "plt.savefig(\"images/rewardstime_81_100_L15.pdf\",dpi=300,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'logs/log_ppo_E1000v9_9_999.pkl'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_log  \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_pickle(\u001b[39m'\u001b[39;49m\u001b[39mlogs/log_ppo_\u001b[39;49m\u001b[39m'\u001b[39;49m  \u001b[39m+\u001b[39;49m \u001b[39mstr\u001b[39;49m(configs\u001b[39m.\u001b[39;49mname) \u001b[39m+\u001b[39;49m \u001b[39m\"\u001b[39;49m\u001b[39m_\u001b[39;49m\u001b[39m\"\u001b[39;49m \u001b[39m+\u001b[39;49m \u001b[39mstr\u001b[39;49m(configs\u001b[39m.\u001b[39;49mn_jobs) \u001b[39m+\u001b[39;49m \u001b[39m'\u001b[39;49m\u001b[39m_\u001b[39;49m\u001b[39m'\u001b[39;49m \u001b[39m+\u001b[39;49m \u001b[39mstr\u001b[39;49m(configs\u001b[39m.\u001b[39;49mn_devices)\u001b[39m+\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39m.pkl\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39minfer\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/pandas/io/pickle.py:190\u001b[0m, in \u001b[0;36mread_pickle\u001b[0;34m(filepath_or_buffer, compression, storage_options)\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    125\u001b[0m \u001b[39mLoad pickled pandas object (or any object) from file.\u001b[39;00m\n\u001b[1;32m    126\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    187\u001b[0m \u001b[39m4    4    9\u001b[39;00m\n\u001b[1;32m    188\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    189\u001b[0m excs_to_catch \u001b[39m=\u001b[39m (\u001b[39mAttributeError\u001b[39;00m, \u001b[39mImportError\u001b[39;00m, \u001b[39mModuleNotFoundError\u001b[39;00m, \u001b[39mTypeError\u001b[39;00m)\n\u001b[0;32m--> 190\u001b[0m \u001b[39mwith\u001b[39;00m get_handle(\n\u001b[1;32m    191\u001b[0m     filepath_or_buffer,\n\u001b[1;32m    192\u001b[0m     \u001b[39m\"\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    193\u001b[0m     compression\u001b[39m=\u001b[39;49mcompression,\n\u001b[1;32m    194\u001b[0m     is_text\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m    195\u001b[0m     storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[1;32m    196\u001b[0m ) \u001b[39mas\u001b[39;00m handles:\n\u001b[1;32m    197\u001b[0m \n\u001b[1;32m    198\u001b[0m     \u001b[39m# 1) try standard library Pickle\u001b[39;00m\n\u001b[1;32m    199\u001b[0m     \u001b[39m# 2) try pickle_compat (older pandas version) to handle subclass changes\u001b[39;00m\n\u001b[1;32m    200\u001b[0m     \u001b[39m# 3) try pickle_compat with latin-1 encoding upon a UnicodeDecodeError\u001b[39;00m\n\u001b[1;32m    202\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    203\u001b[0m         \u001b[39m# TypeError for Cython complaints about object.__new__ vs Tick.__new__\u001b[39;00m\n\u001b[1;32m    204\u001b[0m         \u001b[39mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/pandas/io/common.py:865\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    856\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(\n\u001b[1;32m    857\u001b[0m             handle,\n\u001b[1;32m    858\u001b[0m             ioargs\u001b[39m.\u001b[39mmode,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    861\u001b[0m             newline\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m    862\u001b[0m         )\n\u001b[1;32m    863\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    864\u001b[0m         \u001b[39m# Binary mode\u001b[39;00m\n\u001b[0;32m--> 865\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(handle, ioargs\u001b[39m.\u001b[39;49mmode)\n\u001b[1;32m    866\u001b[0m     handles\u001b[39m.\u001b[39mappend(handle)\n\u001b[1;32m    868\u001b[0m \u001b[39m# Convert BytesIO or file objects passed with an encoding\u001b[39;00m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'logs/log_ppo_E1000v9_9_999.pkl'"
     ]
    }
   ],
   "source": [
    "train_log  = pd.read_pickle('logs/log_ppo_'  + str(configs.name) + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'.pkl', 'infer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 2.11178750e+03, 4.34354935e+01, 1.07475000e+03,\n",
       "        5.29500000e+02, 2.56319995e+03, 1.62000000e+03, 1.66037500e+03],\n",
       "       [1.00000000e+00, 1.88507500e+03, 2.34550076e+01, 1.07912500e+03,\n",
       "        5.38250000e+02, 2.37437500e+03, 1.62000000e+03, 1.39577502e+03],\n",
       "       [2.00000000e+00, 1.84513750e+03, 5.81674671e+00, 1.07850000e+03,\n",
       "        5.37000000e+02, 2.33100000e+03, 1.62000000e+03, 1.35927502e+03],\n",
       "       [3.00000000e+00, 2.04530000e+03, 7.31672096e+00, 1.07225000e+03,\n",
       "        5.24500000e+02, 2.52625000e+03, 1.62000000e+03, 1.56434998e+03],\n",
       "       [4.00000000e+00, 2.02073750e+03, 5.41378260e+00, 1.07725000e+03,\n",
       "        5.34500000e+02, 2.49662500e+03, 1.62000000e+03, 1.54484998e+03]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_log = np.array(train_log)\n",
    "train_log[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "reward = train_log[:,1]\n",
    "initTime = train_log[:,4]\n",
    "Time = train_log[:,5]\n",
    "initCost = train_log[:,6]\n",
    "Cost = train_log[:,7]\n",
    "meanV = train_log[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "ax.plot(initTime,linewidth=2.5,label=\"Init time\")\n",
    "ax.plot(Time,linestyle=\"-.\",label=\"Time\")\n",
    "\n",
    "ax.plot(initCost,linewidth=2.5,label=\"Init cost\")\n",
    "ax.plot(Cost,linestyle=\"-.\",label=\"Cost\")\n",
    "\n",
    "vx = meanV.min()\n",
    "ax.vlines(x=vx, ymin=40, ymax=100, colors='purple', ls='-', lw=2, label='Best result')\n",
    "# plt.legend(title='Allocations',fontsize=14)\n",
    "plt.legend(fontsize=12)\n",
    "# plt.title('Job times',fontsize=16)\n",
    "plt.xlabel('Episodes', fontsize=14)\n",
    "plt.ylabel('Average time', fontsize=14)\n",
    "# plt.savefig(\"images/rewardstime_99-999_9.pdf\",dpi=300,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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