deepmind-research/side_effects_penalties/plot_results.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rINYEKJlYpQU"
      },
      "source": [
        "Copyright 2019 DeepMind Technologies Limited.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        "https://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "KbCarv91XChI"
      },
      "outputs": [],
      "source": [
        "from __future__ import absolute_import\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "\n",
        "from google.colab import files\n",
        "import io\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "irahOycBZM1E"
      },
      "source": [
        "### Plot parameters (edit as needed)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "VeaB-Y9TYWCP"
      },
      "outputs": [],
      "source": [
        "# Make a bar plot for average results from the final 100 episodes (True),\n",
        "# or make a learning curve plot (False)\n",
        "bar_plot = False\n",
        "\n",
        "# Compare different penalties using the best beta value for each penalty (True),\n",
        "# or compare different beta values for the same penalty (False):\n",
        "compare_penalties = False\n",
        "\n",
        "# If compare_penalties is False, specify the penalty parameters:\n",
        "dev_measure = 'rel_reach'\n",
        "dev_fun = 'truncation'\n",
        "value_discount = 0.99\n",
        "\n",
        "# Environment name\n",
        "env_name = 'box'\n",
        "\n",
        "# Filename suffix\n",
        "suffix = '' "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "t8hYO3f2ZaHq"
      },
      "source": [
        "### Plot settings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "foyP_qrUeTsx"
      },
      "outputs": [],
      "source": [
        "final_str = '_final' if bar_plot else ''\n",
        "if compare_penalties:\n",
        "  var = 'label'\n",
        "  x_label = 'deviation_measure'\n",
        "  legend_title = 'penalty'\n",
        "  palette = sns.color_palette()\n",
        "  filename = ('df_summary_penalties_' + env_name + final_str + suffix\n",
        "              + '.csv')\n",
        "else:\n",
        "  var = 'beta'\n",
        "  x_label = 'beta'\n",
        "  legend_title = 'beta'\n",
        "  palette = sns.cubehelix_palette()\n",
        "  filename = ('df_summary_betas_' + env_name + '_' + dev_measure + '_' + dev_fun \n",
        "              + '_' + str(value_discount) + final_str + suffix + '.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5RakwQsFZc0V"
      },
      "source": [
        "### Load summary data output by results_summary.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "PQ615uuGYLF3"
      },
      "outputs": [],
      "source": [
        "uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "-FoI7u8BtYol"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(io.BytesIO(uploaded[filename]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "A7BGierpZi6t"
      },
      "source": [
        "### Make bar plots"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "ch06DzzeXlGK"
      },
      "outputs": [],
      "source": [
        "plot = sns.catplot(data=df, col='baseline', x=var, y='performance_smooth',\n",
        "                   kind='bar', height=4, aspect=1.3)\n",
        "axes = plot.axes.flatten()\n",
        "for ax in axes:\n",
        "  title = ax.get_title().split()\n",
        "  ax.set_title(title[2] + ' baseline')\n",
        "  ax.set_ylabel('performance')\n",
        "  ax.set_xlabel(x_label)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "BU9PYyzOZlRu"
      },
      "source": [
        "### Make learning curve plots"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "RRfU_2iIX-jo"
      },
      "outputs": [],
      "source": [
        "plot = sns.FacetGrid(df, col='baseline', size=5, aspect=1.3,\n",
        "                     sharey=False, sharex=False)\n",
        "plot.map_dataframe(sns.tsplot, time='episode', unit='seed', condition=var,\n",
        "                   value='performance_smooth', n_boot=100, color=palette,\n",
        "                   alpha=1.0, linewidth=1)\n",
        "plot.add_legend(title=legend_title)\n",
        "axes = plot.axes.flatten()\n",
        "for ax in axes:\n",
        "  title = ax.get_title().split()\n",
        "  ax.set_title(title[2] + ' baseline')\n",
        "  ax.set_ylabel('performance')\n",
        "  ax.set_xlabel('episode')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "plot_results.ipynb",
      "provenance": [
        {
          "file_id": "1a8ub19XYD4M-r5mGm0lKYTrNwTo1zF7Z",
          "timestamp": 1569850224175
        }
      ]
    },
    "kernelspec": {
      "display_name": "Python 2",
      "name": "python2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}