plot_training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import scipy.io as sio\n",
    "import scipy.sparse as sp\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib as mlp\n",
    "import re\n",
    "import io\n",
    "from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,\n",
    "                               AutoMinorLocator)\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from IPython.display import clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# outfile = './output/zoo2_gcn_l3_mwis_training.out'\n",
    "# outfile = './output/zoo4_gcn_l3_mwis_training.out'\n",
    "# outfile = './output/train_MWISTwinTr_l3_foo.out'\n",
    "\n",
    "outfile_dict = {\n",
    "    'ZOO2':    './output/zoo2_gcn_l3_mwis_training.out',\n",
    "    'ZOO11':   './output/zoo4_gcn_l3_mwis_training.out',\n",
    "    'Twin':    './output/train_MWISTwinTr_l3_foo.out'\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_out_file(outfile):\n",
    "    with open(outfile) as f:\n",
    "        firstline = f.readline().rstrip()\n",
    "    firstline = firstline.replace(\": \",\":\")\n",
    "    firstline\n",
    "\n",
    "    header_text = firstline.split(\":\")\n",
    "    header = []\n",
    "    for htext in header_text:\n",
    "        temp = htext.replace(\" \", \",\", 1)\n",
    "        header.append(temp.split(\",\")[-1])\n",
    "    header = header[0:-1]\n",
    "\n",
    "    # with open(outfile) as f:\n",
    "    #     text = f.read()\n",
    "    text = ''\n",
    "    with open(outfile) as f:\n",
    "        for line in f:\n",
    "            if not \"Actor loaded \" in line:\n",
    "                text += line\n",
    "    # print(text)\n",
    "    #     text = f.read()\n",
    "\n",
    "    for item in header:\n",
    "        text = text.replace(item+\":\", \",\")\n",
    "    text = text.replace(\" \",\"\")\n",
    "    textIO = io.StringIO(text)\n",
    "    df = pd.read_csv(textIO, names=header, header=None, index_col=0)\n",
    "    df.reset_index(drop=True, inplace=True)\n",
    "    \n",
    "    for item in ['Actor']:\n",
    "        item_index = df['Model'] == item\n",
    "        df_tmp = df[item_index].reset_index(drop=True)    \n",
    "        df_tmp['Best_Ratio'] = df_tmp['Test_Ratio'].cummax()\n",
    "\n",
    "    return df_tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Epoch</th>\n",
       "      <th>ID</th>\n",
       "      <th>Model</th>\n",
       "      <th>Train_Ratio</th>\n",
       "      <th>Test_Ratio</th>\n",
       "      <th>Loss</th>\n",
       "      <th>L_Avg</th>\n",
       "      <th>Track</th>\n",
       "      <th>runtime</th>\n",
       "      <th>z_avg</th>\n",
       "      <th>Best_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0094</td>\n",
       "      <td>1.0147</td>\n",
       "      <td>1.0094</td>\n",
       "      <td>1.0094</td>\n",
       "      <td>-1.0053</td>\n",
       "      <td>6.39</td>\n",
       "      <td>0.475</td>\n",
       "      <td>1.0147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0163</td>\n",
       "      <td>1.0146</td>\n",
       "      <td>1.0087</td>\n",
       "      <td>1.0090</td>\n",
       "      <td>-1.0058</td>\n",
       "      <td>7.73</td>\n",
       "      <td>0.479</td>\n",
       "      <td>1.0147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0094</td>\n",
       "      <td>1.0156</td>\n",
       "      <td>1.0104</td>\n",
       "      <td>1.0095</td>\n",
       "      <td>-1.0051</td>\n",
       "      <td>7.69</td>\n",
       "      <td>0.486</td>\n",
       "      <td>1.0156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0197</td>\n",
       "      <td>1.0194</td>\n",
       "      <td>1.0078</td>\n",
       "      <td>1.0091</td>\n",
       "      <td>-1.0115</td>\n",
       "      <td>8.32</td>\n",
       "      <td>0.484</td>\n",
       "      <td>1.0194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0164</td>\n",
       "      <td>1.0190</td>\n",
       "      <td>1.0186</td>\n",
       "      <td>1.0110</td>\n",
       "      <td>-1.0004</td>\n",
       "      <td>7.31</td>\n",
       "      <td>0.484</td>\n",
       "      <td>1.0194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>3</td>\n",
       "      <td>1600</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0313</td>\n",
       "      <td>1.0441</td>\n",
       "      <td>1.0492</td>\n",
       "      <td>1.0399</td>\n",
       "      <td>-0.9951</td>\n",
       "      <td>7.56</td>\n",
       "      <td>0.549</td>\n",
       "      <td>1.0469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>3</td>\n",
       "      <td>1700</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0668</td>\n",
       "      <td>1.0437</td>\n",
       "      <td>1.0406</td>\n",
       "      <td>1.0399</td>\n",
       "      <td>-1.0030</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.546</td>\n",
       "      <td>1.0469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>3</td>\n",
       "      <td>1800</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0243</td>\n",
       "      <td>1.0437</td>\n",
       "      <td>1.0385</td>\n",
       "      <td>1.0398</td>\n",
       "      <td>-1.0050</td>\n",
       "      <td>7.84</td>\n",
       "      <td>0.537</td>\n",
       "      <td>1.0469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>3</td>\n",
       "      <td>1900</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0365</td>\n",
       "      <td>1.0427</td>\n",
       "      <td>1.0564</td>\n",
       "      <td>1.0407</td>\n",
       "      <td>-0.9870</td>\n",
       "      <td>7.46</td>\n",
       "      <td>0.537</td>\n",
       "      <td>1.0469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>3</td>\n",
       "      <td>2000</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0346</td>\n",
       "      <td>1.0397</td>\n",
       "      <td>1.0397</td>\n",
       "      <td>1.0407</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>8.03</td>\n",
       "      <td>0.536</td>\n",
       "      <td>1.0469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>197 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Epoch    ID  Model  Train_Ratio  Test_Ratio    Loss   L_Avg   Track  \\\n",
       "0        0   100  Actor       1.0094      1.0147  1.0094  1.0094 -1.0053   \n",
       "1        0   200  Actor       1.0163      1.0146  1.0087  1.0090 -1.0058   \n",
       "2        0   300  Actor       1.0094      1.0156  1.0104  1.0095 -1.0051   \n",
       "3        0   400  Actor       1.0197      1.0194  1.0078  1.0091 -1.0115   \n",
       "4        0   500  Actor       1.0164      1.0190  1.0186  1.0110 -1.0004   \n",
       "..     ...   ...    ...          ...         ...     ...     ...     ...   \n",
       "192      3  1600  Actor       1.0313      1.0441  1.0492  1.0399 -0.9951   \n",
       "193      3  1700  Actor       1.0668      1.0437  1.0406  1.0399 -1.0030   \n",
       "194      3  1800  Actor       1.0243      1.0437  1.0385  1.0398 -1.0050   \n",
       "195      3  1900  Actor       1.0365      1.0427  1.0564  1.0407 -0.9870   \n",
       "196      3  2000  Actor       1.0346      1.0397  1.0397  1.0407 -1.0000   \n",
       "\n",
       "     runtime  z_avg  Best_Ratio  \n",
       "0       6.39  0.475      1.0147  \n",
       "1       7.73  0.479      1.0147  \n",
       "2       7.69  0.486      1.0156  \n",
       "3       8.32  0.484      1.0194  \n",
       "4       7.31  0.484      1.0194  \n",
       "..       ...    ...         ...  \n",
       "192     7.56  0.549      1.0469  \n",
       "193     7.30  0.546      1.0469  \n",
       "194     7.84  0.537      1.0469  \n",
       "195     7.46  0.537      1.0469  \n",
       "196     8.03  0.536      1.0469  \n",
       "\n",
       "[197 rows x 11 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for item in ['Actor']:\n",
    "    item_index = df['Model'] == item\n",
    "    df_tmp = df[item_index].reset_index(drop=True)    \n",
    "    df_tmp['Best_Ratio'] = df_tmp['Test_Ratio'].cummax()\n",
    "df_tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples_dict = {\n",
    "    'ZOO2': 2,\n",
    "    'ZOO11': 11,\n",
    "    'Twin': 1\n",
    "}\n",
    "\n",
    "label_dict = {\n",
    "    'ZOO2': 'ZOO-2 points',\n",
    "    'ZOO11': 'ZOO-11 points',\n",
    "    'Twin': 'GDPG-Twin-1 point'\n",
    "}\n",
    "\n",
    "training_set_size=5900\n",
    "\n",
    "df_mwis = pd.DataFrame([])\n",
    "for key in outfile_dict:\n",
    "    outfile = outfile_dict[key]\n",
    "    df_tmp = read_out_file(outfile)\n",
    "    df_tmp['sample'] = samples_dict[key]\n",
    "    df_tmp['train'] = label_dict[key]\n",
    "    df_mwis = pd.concat([df_mwis, df_tmp], axis=0, ignore_index=True)\n",
    "\n",
    "df_mwis['calls'] = (df_mwis['Epoch']*training_set_size+df_mwis['ID'])*df_mwis['sample']\n"
   ]
  },
  {
   "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",
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       "\n",
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       "\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>Epoch</th>\n",
       "      <th>ID</th>\n",
       "      <th>Model</th>\n",
       "      <th>Train_Ratio</th>\n",
       "      <th>Test_Ratio</th>\n",
       "      <th>Loss</th>\n",
       "      <th>L_Avg</th>\n",
       "      <th>Track</th>\n",
       "      <th>runtime</th>\n",
       "      <th>z_avg</th>\n",
       "      <th>Best_Ratio</th>\n",
       "      <th>sample</th>\n",
       "      <th>train</th>\n",
       "      <th>Corr</th>\n",
       "      <th>calls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0094</td>\n",
       "      <td>1.0147</td>\n",
       "      <td>1.0094</td>\n",
       "      <td>1.0094</td>\n",
       "      <td>-1.0053</td>\n",
       "      <td>6.39</td>\n",
       "      <td>0.475</td>\n",
       "      <td>1.0147</td>\n",
       "      <td>2</td>\n",
       "      <td>ZOO-2 points</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0163</td>\n",
       "      <td>1.0146</td>\n",
       "      <td>1.0087</td>\n",
       "      <td>1.0090</td>\n",
       "      <td>-1.0058</td>\n",
       "      <td>7.73</td>\n",
       "      <td>0.479</td>\n",
       "      <td>1.0147</td>\n",
       "      <td>2</td>\n",
       "      <td>ZOO-2 points</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0094</td>\n",
       "      <td>1.0156</td>\n",
       "      <td>1.0104</td>\n",
       "      <td>1.0095</td>\n",
       "      <td>-1.0051</td>\n",
       "      <td>7.69</td>\n",
       "      <td>0.486</td>\n",
       "      <td>1.0156</td>\n",
       "      <td>2</td>\n",
       "      <td>ZOO-2 points</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0197</td>\n",
       "      <td>1.0194</td>\n",
       "      <td>1.0078</td>\n",
       "      <td>1.0091</td>\n",
       "      <td>-1.0115</td>\n",
       "      <td>8.32</td>\n",
       "      <td>0.484</td>\n",
       "      <td>1.0194</td>\n",
       "      <td>2</td>\n",
       "      <td>ZOO-2 points</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0164</td>\n",
       "      <td>1.0190</td>\n",
       "      <td>1.0186</td>\n",
       "      <td>1.0110</td>\n",
       "      <td>-1.0004</td>\n",
       "      <td>7.31</td>\n",
       "      <td>0.484</td>\n",
       "      <td>1.0194</td>\n",
       "      <td>2</td>\n",
       "      <td>ZOO-2 points</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1435</th>\n",
       "      <td>0</td>\n",
       "      <td>5900</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0603</td>\n",
       "      <td>1.0455</td>\n",
       "      <td>-0.2067</td>\n",
       "      <td>0.2856</td>\n",
       "      <td>5.0571</td>\n",
       "      <td>11.53</td>\n",
       "      <td>1.110</td>\n",
       "      <td>1.0493</td>\n",
       "      <td>1</td>\n",
       "      <td>GDPG-Twin-1 point</td>\n",
       "      <td>0.3806</td>\n",
       "      <td>5900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0433</td>\n",
       "      <td>1.0463</td>\n",
       "      <td>-0.1156</td>\n",
       "      <td>0.2885</td>\n",
       "      <td>9.0517</td>\n",
       "      <td>9.74</td>\n",
       "      <td>0.937</td>\n",
       "      <td>1.0493</td>\n",
       "      <td>1</td>\n",
       "      <td>GDPG-Twin-1 point</td>\n",
       "      <td>0.4162</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1437</th>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0330</td>\n",
       "      <td>1.0427</td>\n",
       "      <td>-0.2239</td>\n",
       "      <td>0.2840</td>\n",
       "      <td>4.6567</td>\n",
       "      <td>11.07</td>\n",
       "      <td>1.036</td>\n",
       "      <td>1.0493</td>\n",
       "      <td>1</td>\n",
       "      <td>GDPG-Twin-1 point</td>\n",
       "      <td>0.4244</td>\n",
       "      <td>6100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1438</th>\n",
       "      <td>1</td>\n",
       "      <td>300</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0302</td>\n",
       "      <td>1.0459</td>\n",
       "      <td>-0.1330</td>\n",
       "      <td>0.2788</td>\n",
       "      <td>7.8621</td>\n",
       "      <td>12.28</td>\n",
       "      <td>1.060</td>\n",
       "      <td>1.0493</td>\n",
       "      <td>1</td>\n",
       "      <td>GDPG-Twin-1 point</td>\n",
       "      <td>0.4062</td>\n",
       "      <td>6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439</th>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0352</td>\n",
       "      <td>1.0442</td>\n",
       "      <td>-0.1841</td>\n",
       "      <td>0.2664</td>\n",
       "      <td>5.6727</td>\n",
       "      <td>12.21</td>\n",
       "      <td>1.055</td>\n",
       "      <td>1.0493</td>\n",
       "      <td>1</td>\n",
       "      <td>GDPG-Twin-1 point</td>\n",
       "      <td>0.3848</td>\n",
       "      <td>6300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1440 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Epoch    ID  Model  Train_Ratio  Test_Ratio    Loss   L_Avg   Track  \\\n",
       "0         0   100  Actor       1.0094      1.0147  1.0094  1.0094 -1.0053   \n",
       "1         0   200  Actor       1.0163      1.0146  1.0087  1.0090 -1.0058   \n",
       "2         0   300  Actor       1.0094      1.0156  1.0104  1.0095 -1.0051   \n",
       "3         0   400  Actor       1.0197      1.0194  1.0078  1.0091 -1.0115   \n",
       "4         0   500  Actor       1.0164      1.0190  1.0186  1.0110 -1.0004   \n",
       "...     ...   ...    ...          ...         ...     ...     ...     ...   \n",
       "1435      0  5900  Actor       1.0603      1.0455 -0.2067  0.2856  5.0571   \n",
       "1436      1   100  Actor       1.0433      1.0463 -0.1156  0.2885  9.0517   \n",
       "1437      1   200  Actor       1.0330      1.0427 -0.2239  0.2840  4.6567   \n",
       "1438      1   300  Actor       1.0302      1.0459 -0.1330  0.2788  7.8621   \n",
       "1439      1   400  Actor       1.0352      1.0442 -0.1841  0.2664  5.6727   \n",
       "\n",
       "      runtime  z_avg  Best_Ratio  sample              train    Corr  calls  \n",
       "0        6.39  0.475      1.0147       2       ZOO-2 points     NaN    200  \n",
       "1        7.73  0.479      1.0147       2       ZOO-2 points     NaN    400  \n",
       "2        7.69  0.486      1.0156       2       ZOO-2 points     NaN    600  \n",
       "3        8.32  0.484      1.0194       2       ZOO-2 points     NaN    800  \n",
       "4        7.31  0.484      1.0194       2       ZOO-2 points     NaN   1000  \n",
       "...       ...    ...         ...     ...                ...     ...    ...  \n",
       "1435    11.53  1.110      1.0493       1  GDPG-Twin-1 point  0.3806   5900  \n",
       "1436     9.74  0.937      1.0493       1  GDPG-Twin-1 point  0.4162   6000  \n",
       "1437    11.07  1.036      1.0493       1  GDPG-Twin-1 point  0.4244   6100  \n",
       "1438    12.28  1.060      1.0493       1  GDPG-Twin-1 point  0.4062   6200  \n",
       "1439    12.21  1.055      1.0493       1  GDPG-Twin-1 point  0.3848   6300  \n",
       "\n",
       "[1440 rows x 15 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mwis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Epoch</th>\n",
       "      <th>ID</th>\n",
       "      <th>Model</th>\n",
       "      <th>Train_Ratio</th>\n",
       "      <th>Test_Ratio</th>\n",
       "      <th>Loss</th>\n",
       "      <th>L_Avg</th>\n",
       "      <th>Track</th>\n",
       "      <th>runtime</th>\n",
       "      <th>z_avg</th>\n",
       "      <th>Best_Ratio</th>\n",
       "      <th>sample</th>\n",
       "      <th>train</th>\n",
       "      <th>Corr</th>\n",
       "      <th>calls</th>\n",
       "      <th>ratio</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0094</td>\n",
       "      <td>1.0147</td>\n",
       "      <td>1.0094</td>\n",
       "      <td>1.0094</td>\n",
       "      <td>-1.0053</td>\n",
       "      <td>6.39</td>\n",
       "      <td>0.475</td>\n",
       "      <td>1.0147</td>\n",
       "      <td>2</td>\n",
       "      <td>ZOO-2 points</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0094</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0163</td>\n",
       "      <td>1.0146</td>\n",
       "      <td>1.0087</td>\n",
       "      <td>1.0090</td>\n",
       "      <td>-1.0058</td>\n",
       "      <td>7.73</td>\n",
       "      <td>0.479</td>\n",
       "      <td>1.0147</td>\n",
       "      <td>2</td>\n",
       "      <td>ZOO-2 points</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0163</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0094</td>\n",
       "      <td>1.0156</td>\n",
       "      <td>1.0104</td>\n",
       "      <td>1.0095</td>\n",
       "      <td>-1.0051</td>\n",
       "      <td>7.69</td>\n",
       "      <td>0.486</td>\n",
       "      <td>1.0156</td>\n",
       "      <td>2</td>\n",
       "      <td>ZOO-2 points</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600</td>\n",
       "      <td>1.0094</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0197</td>\n",
       "      <td>1.0194</td>\n",
       "      <td>1.0078</td>\n",
       "      <td>1.0091</td>\n",
       "      <td>-1.0115</td>\n",
       "      <td>8.32</td>\n",
       "      <td>0.484</td>\n",
       "      <td>1.0194</td>\n",
       "      <td>2</td>\n",
       "      <td>ZOO-2 points</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800</td>\n",
       "      <td>1.0197</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0164</td>\n",
       "      <td>1.0190</td>\n",
       "      <td>1.0186</td>\n",
       "      <td>1.0110</td>\n",
       "      <td>-1.0004</td>\n",
       "      <td>7.31</td>\n",
       "      <td>0.484</td>\n",
       "      <td>1.0194</td>\n",
       "      <td>2</td>\n",
       "      <td>ZOO-2 points</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.0164</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2875</th>\n",
       "      <td>0</td>\n",
       "      <td>5900</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0603</td>\n",
       "      <td>1.0455</td>\n",
       "      <td>-0.2067</td>\n",
       "      <td>0.2856</td>\n",
       "      <td>5.0571</td>\n",
       "      <td>11.53</td>\n",
       "      <td>1.110</td>\n",
       "      <td>1.0493</td>\n",
       "      <td>1</td>\n",
       "      <td>GDPG-Twin-1 point</td>\n",
       "      <td>0.3806</td>\n",
       "      <td>5900</td>\n",
       "      <td>1.0455</td>\n",
       "      <td>Validation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2876</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0433</td>\n",
       "      <td>1.0463</td>\n",
       "      <td>-0.1156</td>\n",
       "      <td>0.2885</td>\n",
       "      <td>9.0517</td>\n",
       "      <td>9.74</td>\n",
       "      <td>0.937</td>\n",
       "      <td>1.0493</td>\n",
       "      <td>1</td>\n",
       "      <td>GDPG-Twin-1 point</td>\n",
       "      <td>0.4162</td>\n",
       "      <td>6000</td>\n",
       "      <td>1.0463</td>\n",
       "      <td>Validation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2877</th>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0330</td>\n",
       "      <td>1.0427</td>\n",
       "      <td>-0.2239</td>\n",
       "      <td>0.2840</td>\n",
       "      <td>4.6567</td>\n",
       "      <td>11.07</td>\n",
       "      <td>1.036</td>\n",
       "      <td>1.0493</td>\n",
       "      <td>1</td>\n",
       "      <td>GDPG-Twin-1 point</td>\n",
       "      <td>0.4244</td>\n",
       "      <td>6100</td>\n",
       "      <td>1.0427</td>\n",
       "      <td>Validation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2878</th>\n",
       "      <td>1</td>\n",
       "      <td>300</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0302</td>\n",
       "      <td>1.0459</td>\n",
       "      <td>-0.1330</td>\n",
       "      <td>0.2788</td>\n",
       "      <td>7.8621</td>\n",
       "      <td>12.28</td>\n",
       "      <td>1.060</td>\n",
       "      <td>1.0493</td>\n",
       "      <td>1</td>\n",
       "      <td>GDPG-Twin-1 point</td>\n",
       "      <td>0.4062</td>\n",
       "      <td>6200</td>\n",
       "      <td>1.0459</td>\n",
       "      <td>Validation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2879</th>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1.0352</td>\n",
       "      <td>1.0442</td>\n",
       "      <td>-0.1841</td>\n",
       "      <td>0.2664</td>\n",
       "      <td>5.6727</td>\n",
       "      <td>12.21</td>\n",
       "      <td>1.055</td>\n",
       "      <td>1.0493</td>\n",
       "      <td>1</td>\n",
       "      <td>GDPG-Twin-1 point</td>\n",
       "      <td>0.3848</td>\n",
       "      <td>6300</td>\n",
       "      <td>1.0442</td>\n",
       "      <td>Validation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2880 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Epoch    ID  Model  Train_Ratio  Test_Ratio    Loss   L_Avg   Track  \\\n",
       "0         0   100  Actor       1.0094      1.0147  1.0094  1.0094 -1.0053   \n",
       "1         0   200  Actor       1.0163      1.0146  1.0087  1.0090 -1.0058   \n",
       "2         0   300  Actor       1.0094      1.0156  1.0104  1.0095 -1.0051   \n",
       "3         0   400  Actor       1.0197      1.0194  1.0078  1.0091 -1.0115   \n",
       "4         0   500  Actor       1.0164      1.0190  1.0186  1.0110 -1.0004   \n",
       "...     ...   ...    ...          ...         ...     ...     ...     ...   \n",
       "2875      0  5900  Actor       1.0603      1.0455 -0.2067  0.2856  5.0571   \n",
       "2876      1   100  Actor       1.0433      1.0463 -0.1156  0.2885  9.0517   \n",
       "2877      1   200  Actor       1.0330      1.0427 -0.2239  0.2840  4.6567   \n",
       "2878      1   300  Actor       1.0302      1.0459 -0.1330  0.2788  7.8621   \n",
       "2879      1   400  Actor       1.0352      1.0442 -0.1841  0.2664  5.6727   \n",
       "\n",
       "      runtime  z_avg  Best_Ratio  sample              train    Corr  calls  \\\n",
       "0        6.39  0.475      1.0147       2       ZOO-2 points     NaN    200   \n",
       "1        7.73  0.479      1.0147       2       ZOO-2 points     NaN    400   \n",
       "2        7.69  0.486      1.0156       2       ZOO-2 points     NaN    600   \n",
       "3        8.32  0.484      1.0194       2       ZOO-2 points     NaN    800   \n",
       "4        7.31  0.484      1.0194       2       ZOO-2 points     NaN   1000   \n",
       "...       ...    ...         ...     ...                ...     ...    ...   \n",
       "2875    11.53  1.110      1.0493       1  GDPG-Twin-1 point  0.3806   5900   \n",
       "2876     9.74  0.937      1.0493       1  GDPG-Twin-1 point  0.4162   6000   \n",
       "2877    11.07  1.036      1.0493       1  GDPG-Twin-1 point  0.4244   6100   \n",
       "2878    12.28  1.060      1.0493       1  GDPG-Twin-1 point  0.4062   6200   \n",
       "2879    12.21  1.055      1.0493       1  GDPG-Twin-1 point  0.3848   6300   \n",
       "\n",
       "       ratio        type  \n",
       "0     1.0094    Training  \n",
       "1     1.0163    Training  \n",
       "2     1.0094    Training  \n",
       "3     1.0197    Training  \n",
       "4     1.0164    Training  \n",
       "...      ...         ...  \n",
       "2875  1.0455  Validation  \n",
       "2876  1.0463  Validation  \n",
       "2877  1.0427  Validation  \n",
       "2878  1.0459  Validation  \n",
       "2879  1.0442  Validation  \n",
       "\n",
       "[2880 rows x 17 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp = df_mwis[df_mwis['Model']=='Actor'].reset_index(drop=True)    \n",
    "df_tmp1 = df_tmp.copy()\n",
    "df_tmp2 = df_tmp.copy()\n",
    "df_tmp1['ratio'] = df_tmp1['Train_Ratio']\n",
    "df_tmp1['type'] = 'Training'\n",
    "df_tmp2['ratio'] = df_tmp2['Test_Ratio']\n",
    "df_tmp2['type'] = 'Validation'\n",
    "df_tmp = pd.concat([df_tmp1,df_tmp2], axis=0, ignore_index=True)\n",
    "df_tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,sharey=True, figsize=(8, 6))\n",
    "for item in ['Actor']:\n",
    "    sns.lineplot(x=\"calls\", y=\"ratio\", hue=\"train\", style='type', \n",
    "                 hue_order=['GDPG-Twin-1 point','ZOO-2 points','ZOO-11 points'],\n",
    "                 style_order=['Validation','Training'],\n",
    "                 size='type',\n",
    "                 sizes={'Validation':1.5,'Training':0.5},\n",
    "                 data=df_tmp,\n",
    "                 ci=None, ax=ax,\n",
    "                 linewidth=1.0, markersize=10, \n",
    "                 palette=\"tab10\", markers=False, dashes=True)\n",
    "    \n",
    "ax.set_xlim([0, 40000])\n",
    "ax.set_xticks(np.arange(0,40000,5000))\n",
    "\n",
    "ax.grid(True)\n",
    "ax.set_ylim([0.985, 1.075])\n",
    "ax.set_yticks(np.arange(0.99,1.07,0.01))\n",
    "ax.set_xlabel(\"Evaluations of $h(\\cdot)$\", fontsize=14)\n",
    "ax.set_ylabel(\"Approx. Ratio to Vanilla LGS-MWIS\", fontsize=14)\n",
    "\n",
    "ax.legend(fontsize=14, framealpha=0.3, ncol=2)\n",
    "fig.set_size_inches(6, 4)\n",
    "fname='./output/convergence_sampling.pdf'\n",
    "# pp = PdfPages(fname)\n",
    "plt.tight_layout(pad=0.1)\n",
    "plt.subplots_adjust(left=0.11, right=0.995, top=0.995, bottom=0.115)\n",
    "fig.savefig(fname,\n",
    "            dpi=300, #facecolor='w', edgecolor='w',\n",
    "            orientation='portrait',\n",
    "            format='pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}