Critiquing Energy Plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'figure.facecolor': 'white',\n",
       " 'axes.labelcolor': '.15',\n",
       " 'xtick.direction': 'out',\n",
       " 'ytick.direction': 'out',\n",
       " 'xtick.color': '.15',\n",
       " 'ytick.color': '.15',\n",
       " 'axes.axisbelow': True,\n",
       " 'grid.linestyle': '-',\n",
       " 'text.color': '.15',\n",
       " 'font.family': ['sans-serif'],\n",
       " 'font.sans-serif': ['Arial',\n",
       "  'DejaVu Sans',\n",
       "  'Liberation Sans',\n",
       "  'Bitstream Vera Sans',\n",
       "  'sans-serif'],\n",
       " 'lines.solid_capstyle': 'round',\n",
       " 'patch.edgecolor': 'w',\n",
       " 'patch.force_edgecolor': True,\n",
       " 'image.cmap': 'rocket',\n",
       " 'xtick.top': False,\n",
       " 'ytick.right': False,\n",
       " 'axes.grid': False,\n",
       " 'axes.facecolor': 'white',\n",
       " 'axes.edgecolor': '.15',\n",
       " 'grid.color': '.8',\n",
       " 'axes.spines.left': True,\n",
       " 'axes.spines.bottom': True,\n",
       " 'axes.spines.right': True,\n",
       " 'axes.spines.top': True,\n",
       " 'xtick.bottom': False,\n",
       " 'ytick.left': False}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from utils.io import load_yaml\n",
    "from ast import literal_eval\n",
    "sns.axes_style(\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandas_bar_plot(df, x, y, hue, x_name, y_name, folder='figures', name='unknown', save=True):\n",
    "    fig, ax = plt.subplots(figsize=(6, 3))\n",
    "    sns.barplot(ax=ax, x=x, y=y, hue=hue, data=df, errwidth=1, edgecolor='black', facecolor=(1, 1, 1, 0)) #, errwidth=0.5\n",
    "\n",
    "    num_category = len(df[x].unique())\n",
    "    hatch = None\n",
    "    hatches = itertools.cycle(['//', '\\\\\\\\\\\\\\\\', '**', '////', '----', 'xxxx', ' ', '\\\\', '...', 'OOO', \"++++++++\"])\n",
    "    for i, bar in enumerate(ax.patches):\n",
    "        if i % num_category == 0:\n",
    "            hatch = next(hatches)\n",
    "        bar.set_hatch(hatch)\n",
    "\n",
    "    plt.xlabel(x_name)\n",
    "    plt.ylabel(y_name)\n",
    "    #plt.xticks(rotation=15)\n",
    "    plt.legend(loc='upper left', ncol=5)\n",
    "    # if 'Precision' not in y:\n",
    "    ax.legend_.remove()\n",
    "    plt.tight_layout()\n",
    "    if save:\n",
    "        fig_path = load_yaml('config/global.yml', key='path')['figs']\n",
    "        plt.savefig(\"{2}/{0}/{1}_bar.pdf\".format(folder, name, fig_path), format=\"pdf\")\n",
    "        plt.savefig(\"{2}/{0}/{1}_bar.png\".format(folder, name, fig_path), format=\"png\")\n",
    "\n",
    "        fig_leg = plt.figure(figsize=(5, 0.7))\n",
    "        ax_leg = fig_leg.add_subplot(111)\n",
    "        ax_leg.legend(*ax.get_legend_handles_labels(), loc='center', ncol=3)\n",
    "        ax_leg.axis('off')\n",
    "        fig_leg.savefig('figs/bar_legend.pdf', format='pdf')\n",
    "\n",
    "    else:\n",
    "        plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zero-out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CDsVinyl Beta 1e-4"
   ]
  },
  {
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Energy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.291216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.283083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.273586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.263645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.253837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time    Energy\n",
       "0     1  0.291216\n",
       "1     2  0.283083\n",
       "2     3  0.273586\n",
       "3     4  0.263645\n",
       "4     5  0.253837"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zero_out = pd.read_csv(\"tables/CDsVinyl_fmap_energy/zero_out/CDsVinyl_fmap_energy_beta_1e-4.csv\")\n",
    "df_zero_out['Energy'] /= 40\n",
    "df_zero_out.groupby('Time', as_index=False).agg({'Energy': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pandas_bar_plot(df=df_zero_out, x='Time', y='Energy', hue=None, x_name='Number of Critiquing Loops', y_name='Average Score of Keyphrase Prediction', folder='CDsVinyl', name='energy', save=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Redistribute"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CDsVinyl Beta 0.0001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Energy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.290266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.288896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.288139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.287721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.287336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time    Energy\n",
       "0     1  0.290266\n",
       "1     2  0.288896\n",
       "2     3  0.288139\n",
       "3     4  0.287721\n",
       "4     5  0.287336"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_redistribute = pd.read_csv(\"tables/CDsVinyl_fmap_energy/redistribute/CDsVinyl_fmap_energy_beta_1e-4.csv\")\n",
    "df_redistribute['Energy'] /= 40\n",
    "df_redistribute.groupby('Time', as_index=False).agg({'Energy': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pandas_bar_plot(df=df_redistribute, x='Time', y='Energy', hue=None, x_name='Number of Critiquing Loops', y_name='Average Score of Keyphrase Prediction', folder='CDsVinyl', name='energy', save=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_zero_out['Critiquing Function'] = \"Zero Out\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Time</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Critiquing Function</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2732</td>\n",
       "      <td>1</td>\n",
       "      <td>0.197472</td>\n",
       "      <td>Zero Out</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2732</td>\n",
       "      <td>2</td>\n",
       "      <td>0.195186</td>\n",
       "      <td>Zero Out</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2732</td>\n",
       "      <td>3</td>\n",
       "      <td>0.188077</td>\n",
       "      <td>Zero Out</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2732</td>\n",
       "      <td>4</td>\n",
       "      <td>0.175828</td>\n",
       "      <td>Zero Out</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2732</td>\n",
       "      <td>5</td>\n",
       "      <td>0.166811</td>\n",
       "      <td>Zero Out</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User  Time    Energy Critiquing Function\n",
       "0  2732     1  0.197472            Zero Out\n",
       "1  2732     2  0.195186            Zero Out\n",
       "2  2732     3  0.188077            Zero Out\n",
       "3  2732     4  0.175828            Zero Out\n",
       "4  2732     5  0.166811            Zero Out"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zero_out.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_redistribute['Critiquing Function'] = \"Energy Redistribution\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Time</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Critiquing Function</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2732</td>\n",
       "      <td>1</td>\n",
       "      <td>0.194655</td>\n",
       "      <td>Energy Redistribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2732</td>\n",
       "      <td>2</td>\n",
       "      <td>0.196230</td>\n",
       "      <td>Energy Redistribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2732</td>\n",
       "      <td>3</td>\n",
       "      <td>0.196776</td>\n",
       "      <td>Energy Redistribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2732</td>\n",
       "      <td>4</td>\n",
       "      <td>0.195197</td>\n",
       "      <td>Energy Redistribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2732</td>\n",
       "      <td>5</td>\n",
       "      <td>0.194054</td>\n",
       "      <td>Energy Redistribution</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User  Time    Energy    Critiquing Function\n",
       "0  2732     1  0.194655  Energy Redistribution\n",
       "1  2732     2  0.196230  Energy Redistribution\n",
       "2  2732     3  0.196776  Energy Redistribution\n",
       "3  2732     4  0.195197  Energy Redistribution\n",
       "4  2732     5  0.194054  Energy Redistribution"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_redistribute.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined = df_redistribute.append(df_zero_out)\n",
    "df_combined['Time'] -= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pandas_bar_plot(df=df_combined, x='Time', y='Energy', hue='Critiquing Function', x_name='Number of Critiquing Loops', y_name='Average Score of\\n Keyphrase Prediction', folder='Energy', name='energy_critiquing', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf-gpu",
   "language": "python",
   "name": "tf-gpu"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}