drl/Ejik Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze Trained Ejik Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_path = r'C:\\git\\unity\\ejik\\drl\\eval\\6_frames_3_interval_net2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_brain = pd.read_csv(os.path.join(out_path, 'brain.csv'))\n",
    "df_random = pd.read_csv(os.path.join(out_path, 'random.csv'))\n",
    "df_shooting = pd.read_csv(os.path.join(out_path, 'shooting.csv'))\n",
    "df = pd.concat({'random': df_random, 'brain':df_brain, 'shooting': df_shooting}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">brain</th>\n",
       "      <th colspan=\"2\" halign=\"left\">random</th>\n",
       "      <th colspan=\"2\" halign=\"left\">shooting</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>reward</th>\n",
       "      <th>length</th>\n",
       "      <th>reward</th>\n",
       "      <th>length</th>\n",
       "      <th>reward</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>167</td>\n",
       "      <td>0.11</td>\n",
       "      <td>126</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>167</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>167</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>121</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>167</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>167</td>\n",
       "      <td>0.28</td>\n",
       "      <td>167</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>167</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>125</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>86</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>167</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>167</td>\n",
       "      <td>0.24</td>\n",
       "      <td>167</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>167</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   brain        random        shooting       \n",
       "  length reward length reward   length reward\n",
       "0    167   0.11    126  -0.98      167   0.25\n",
       "1    167  -0.20    121  -0.91      167   0.38\n",
       "2    167   0.28    167  -0.16      167   0.10\n",
       "3    125  -0.94     86  -0.94      167  -0.01\n",
       "4    167   0.24    167  -0.24      167   0.58"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_episode = df.loc[:, ('random', 'length')]\n",
    "brain_episode = df.loc[:, ('brain', 'length')]\n",
    "shooting_episode = df.loc[:, ('shooting', 'length')]\n",
    "brain_reward = df.loc[:, ('brain', 'reward')]\n",
    "random_reward = df.loc[:, ('random', 'reward')]\n",
    "shooting_reward = df.loc[:, ('shooting', 'reward')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26b70441198>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 2, figsize=(20, 10))\n",
    "sns.distplot(random_episode, hist=True, kde=False, color=\"b\", ax=axes[0], label=\"random\")\n",
    "sns.distplot(brain_episode, hist=True,kde=False, color=\"g\", ax=axes[0], label=\"brain\")\n",
    "sns.distplot(shooting_episode, hist=True,kde=False, color=\"r\", ax=axes[0], label=\"shooting\")\n",
    "sns.distplot(random_reward, hist=True, color=\"b\", ax=axes[1], label=\"random\")\n",
    "sns.distplot(brain_reward, hist=True, color=\"g\", ax=axes[1], label=\"brain\")\n",
    "sns.distplot(shooting_reward, hist=True, color=\"r\", ax=axes[1], label=\"shooting\")\n",
    "axes[1].set_title(\"Episode reward distribution\")\n",
    "axes[0].set_title(\"Episode length distribution\")\n",
    "axes[1].legend()\n",
    "axes[0].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean trained reward: -0.0002, std: 0.316, episode length: 163.675\n",
      "Mean shooting reward: 0.0592, std: 0.294, episode length: 165.329\n",
      "Mean random reward: -0.6317, std: 0.367, episode length: 131.582\n"
     ]
    }
   ],
   "source": [
    "print(f\"Mean trained reward: {brain_reward.mean():.4f}, std: {brain_reward.std():.3f}, episode length: {brain_episode.mean():.3f}\")\n",
    "print(f\"Mean shooting reward: {shooting_reward.mean():.4f}, std: {shooting_reward.std():.3f}, episode length: {shooting_episode.mean():.3f}\")\n",
    "print(f\"Mean random reward: {random_reward.mean():.4f}, std: {random_reward.std():.3f}, episode length: {random_episode.mean():.3f}\")\n"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}