model_complexity_results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "matplotlib.use('Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.backends.backend_pdf import PdfPages\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from scripts.parsing_utils import *\n",
    "from utils.utils import *\n",
    "from scripts.parse_results import *\n",
    "from IPython.core.display import HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skipping  /home/pari/learned-cardinalities/all_results/inl_fixed_scan_ops/nested_loop_index7/default/model_complexity_results/final_runs-nested_loop_index7-mscn-df:10-nn:2:512-loss:mse-0.0--D0.1-611\n",
      "skipping  /home/pari/learned-cardinalities/all_results/inl_fixed_scan_ops/nested_loop_index7/default/model_complexity_results/final_runs-nested_loop_index7-mscn-df:10-pr:2.0-nn:2:512-loss:mse-2.0--D0.1-366\n",
      "skipping  /home/pari/learned-cardinalities/all_results/inl_fixed_scan_ops/nested_loop_index7/default/model_complexity_results/model_complexity-nested_loop_index7-mscn-df:1-nn:2:512-loss:flow_loss2-0.0--D0.1-531\n",
      "skipping  /home/pari/learned-cardinalities/all_results/inl_fixed_scan_ops/nested_loop_index7/default/model_complexity_results/less_training_data-nested_loop_index7-mscn-df:1-nn:2:128-loss:mse-0.0--D0.1-143\n",
      "skipping  /home/pari/learned-cardinalities/all_results/inl_fixed_scan_ops/nested_loop_index7/default/model_complexity_results/model_complexity-nested_loop_index7-mscn-df:1-nn:2:512-loss:mse-0.0--D0.1-112\n",
      "skipping  /home/pari/learned-cardinalities/all_results/inl_fixed_scan_ops/nested_loop_index7/default/model_complexity_results/final_runs-nested_loop_index7-mscn-df:10-pr:2.0-nn:2:512-loss:mse-2.0--D0.1-108\n",
      "{'0'}\n"
     ]
    }
   ],
   "source": [
    "#RES_DIR = \"/home/pari/learned-cardinalities/all_results/inl_fixed_scan_ops/nested_loop_index7/test_diff/final_results2\"\n",
    "#RES_DIR = \"/home/pari/learned-cardinalities/all_results/inl_fixed_scan_ops/nested_loop_index7/test_less_data/final_results1\"\n",
    "RES_DIR = \"/home/pari/learned-cardinalities/all_results/inl_fixed_scan_ops/nested_loop_index7/default/model_complexity_results\"\n",
    "\n",
    "RES_DIRS = []\n",
    "#df = get_summary_df(RES_DIR)\n",
    "RES_DIRS.append(RES_DIR)\n",
    "\n",
    "dfs = []\n",
    "for res_dir in RES_DIRS:\n",
    "    df = get_summary_df(res_dir)\n",
    "    if \"final_results2\" in res_dir:\n",
    "        df = df[df.feat_rel_pg_ests_onehot == 1]\n",
    "    df[\"res_dir\"] = str(deterministic_hash(res_dir))[0:5]\n",
    "    print(set(df[\"partition\"]))\n",
    "    dfs.append(df)\n",
    "df = pd.concat(dfs, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'flow_loss2', 'mse'}\n",
      "{1}\n",
      "{1}\n",
      "{512, 256, 64, 128}\n",
      "{1, 10, 5}\n",
      "{0.5}\n"
     ]
    }
   ],
   "source": [
    "df = df[df[\"alg_name\"] != \"true\"]\n",
    "df = df[df[\"alg_name\"] != \"postgres\"]\n",
    "\n",
    "all_df = df[df[\"template\"] != \"all\"]\n",
    "df = df[df[\"template\"] == \"all\"]\n",
    "df = df[df[\"summary_type\"] == \"mean\"]\n",
    "df = df[df[\"flow_features\"] == 1]\n",
    "\n",
    "print(set(df[\"alg_name\"]))\n",
    "print(set(df[\"flow_features\"]))\n",
    "print(set(df[\"feat_rel_pg_ests_onehot\"]))\n",
    "print(set(df[\"hidden_layer_size\"]))\n",
    "print(set(df[\"max_discrete_featurizing_buckets\"]))\n",
    "print(set(df[\"test_size\"]))\n",
    "\n",
    "#ERRORS = [\"qerr\", \"plan_err\", \"jerr\", \"cm1_jerr\"]\n",
    "ERRORS = [\"qerr\", \"cm1_jerr\"]\n",
    "HUE_COLORS = {}\n",
    "\n",
    "HUE_COLORS[\"mse\"] = \"green\"\n",
    "HUE_COLORS[\"flow_loss2\"] = \"blue\"\n",
    "ORDER = [\"train\", \"test\"]\n",
    "df = df.rename(columns={\"max_discrete_featurizing_buckets\":\"buckets\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'job', 'test', 'train'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df[\"samples_type\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alg</th>\n",
       "      <th>samples_type</th>\n",
       "      <th>loss_func</th>\n",
       "      <th>loss_type</th>\n",
       "      <th>hidden_layer_size</th>\n",
       "      <th>buckets</th>\n",
       "      <th>loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>2.171557e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>1.623535e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>1.540184e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1.428375e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>1.351163e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>2.305323e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>3.393485e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>1.923166e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>1.834636e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>1.862567e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>1.795200e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>2.167877e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>1.188629e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>2.495135e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>3.326444e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1.857215e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>1.085424e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>2.682357e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>1.902419e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>1.564739e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>1.953202e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>1.489091e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>2.244888e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>1.560191e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>3.405681e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>4.542879e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>2.173684e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>2.304188e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>1.653361e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>3.223638e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>1.688564e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>1.379100e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>2.106765e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>3.626953e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>3.267251e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>4.568264e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>4.200661e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>4.560019e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>5.757745e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>3.464145e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>3.173914e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>6.921063e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>3.089969e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>3.902534e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>5.683380e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>2.842241e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>2.782754e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>7.045964e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>1.303860e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>2.774085e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>4.388453e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1.190511e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>2.679921e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>4.292026e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>2.864453e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>2.704481e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>3.024590e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>2.527854e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>2.871902e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>9.920062e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>1.849093e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>2.262638e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>6.232495e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1.154889e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>3.997775e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>7.966881e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>2.649186e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>3.284670e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>4.914379e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>2.158592e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>4.206668e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>3.800797e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>4.073042e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>4.690722e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>2.593077e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>4.593227e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>2.432284e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>6.092130e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>9.720260e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>1.242253e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>1.516965e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>6.432966e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>1.416668e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>1.195238e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>4.726542e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>5.614554e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>4.759294e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1.787424e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>6.042929e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>2.141979e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>5.905750e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>2.559609e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>2.250340e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>6.353154e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>1.106564e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>3.415730e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>7.387939e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>7.284878e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>1.585600e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1.198498e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>2.815695e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>1.253441e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>1.701468e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>1.534932e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>1.197260e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>1.536838e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>1.403916e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>1.195143e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>7.769888e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>4.357917e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>9.005375e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>7.692688e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>4.358122e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>1.131090e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>1.838085e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>1.308134e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>1.040454e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>1.194884e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>1.337514e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>1.602746e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>7.586691e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>2.169603e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>1.507856e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>2.819154e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>2.168727e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>1.645202e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>2.659783e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>2.548490e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>1.571345e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>2.093414e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>1.689106e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>2.403039e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>5.749911e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>3.062922e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>3.452572e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1.472249e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>1.707036e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>9.242217e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>3.752625e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>1.286041e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>2.426765e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>1.264565e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>4.038644e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>mscn</td>\n",
       "      <td>job</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>2.788881e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>7.542843e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>3.618452e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>1.022179e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>3.122189e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>1.499682e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>1.308488e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>1.088912e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>4.402633e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>1.379316e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>9.346114e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>8.610229e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>5.041346e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>7.404750e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>4.470951e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>3.654817e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>7.136326e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>4.635717e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>3.324206e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>6.773063e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>4.522264e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>3.486657e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>6.223349e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>4.196454e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>3.618093e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>1.204434e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>1.133951e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>8.216088e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1.115884e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>1.189427e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>1.094578e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>1.357550e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>1.284922e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>9.948561e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>1.417863e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>1.223201e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>8.838293e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>4.466593e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>3.556402e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>2.952323e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>4.531136e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>3.605179e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>2.454977e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>4.162360e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>3.872300e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>3.042539e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>4.333871e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>3.361893e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>2.772664e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>2.444348e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>1.509077e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>1.718607e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1.764982e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>2.102686e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>1.025918e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>3.382590e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>2.908957e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>5.317535e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>2.683938e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>1.780925e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>3.166241e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>1.451615e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>7.376277e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>4.243051e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>2.713994e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>1.242414e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>1.312903e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>1.489890e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>7.069569e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>5.604244e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>6.360796e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>8.475872e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>4.678772e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>1.401848e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>1.747702e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>1.353300e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>2.001684e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>1.667181e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>1.491654e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>1.401698e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>1.572413e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>1.342251e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>1.518671e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>1.528274e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>1.142455e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>6.753658e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>5.517727e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>4.462419e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>6.547776e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>5.192634e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>4.869318e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>6.696531e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>5.441670e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>4.262077e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>7.020437e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>5.932711e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>4.550811e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>1.356829e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>1.166098e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>8.723228e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1.176747e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>1.111173e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>7.523889e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>1.177933e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>1.279513e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>7.217568e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>1.205067e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>1.147694e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>7.996633e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>4.739258e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>4.699724e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>3.266083e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>4.235122e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>4.344400e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>3.201951e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>4.291836e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>4.392067e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>3.006242e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>4.149620e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>4.382126e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>3.315719e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>3.300309e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>2.277560e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>1.539640e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>2.588894e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>2.059128e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>1.087599e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>2.388510e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>2.102355e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>8.515164e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>2.278058e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>2.048424e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>6.733664e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>3.297815e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>1.238516e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>1.719698e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>3.334147e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>1.222243e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>2.159322e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>3.168983e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>1.651144e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>2.298201e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>2.702962e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>2.078766e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>mscn</td>\n",
       "      <td>test</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>2.055390e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>8.803667e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>5.931431e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>1.249980e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>4.027534e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>1.494087e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>1.649176e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>1.380633e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>4.812399e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>8.691603e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>8.089419e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>9.448707e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>4.623824e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>7.436454e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>3.851460e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>2.950171e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>7.172701e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>3.600614e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>2.343989e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>6.827178e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>3.425456e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>2.242204e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>6.521696e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>3.283284e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>1.979062e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>1.294209e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>1.007408e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>7.737647e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1.135841e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>1.009392e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>8.193724e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>1.322134e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>9.736488e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>6.516503e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>1.377523e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>8.576785e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>6.544766e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>4.973681e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>3.174493e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>2.329019e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>5.015472e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>2.857470e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>1.875022e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>3.986516e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>3.021812e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>1.743940e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>4.116398e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>2.642826e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>1.319179e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>2.781467e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>1.844571e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>2.744191e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>2.162503e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>2.194476e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>9.052303e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>3.823970e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>2.237322e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>2.721270e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>3.169831e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>1.537323e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>7.941305e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>9.167322e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>6.615197e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>3.622197e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1.647836e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>1.078656e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>1.142073e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>1.012916e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>6.016527e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>4.670348e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>4.779344e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>6.885158e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>flow_loss2</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>3.976157e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>2.169667e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>1.936007e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>1.170422e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1.796997e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>1.598039e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>1.246743e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>1.689528e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>1.270849e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>1.581290e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>1.479077e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>1.225477e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>9.070270e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>7.201030e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>5.077808e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>3.781648e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>6.921596e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>4.487953e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>3.523490e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>6.908220e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>4.433368e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>2.936228e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>7.129976e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>3.930518e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>flow_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>2.237066e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>1.338673e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>1.137431e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>7.678796e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1.220790e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>1.022254e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>6.831177e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>1.168526e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>1.051540e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>5.499657e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>1.308031e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>1.042508e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>jerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>4.744634e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>4.637054e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>4.486444e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>2.747946e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>4.160681e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>3.864820e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>2.489215e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>3.950957e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>4.023026e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>2.559288e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>4.151856e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>4.006480e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>2.209078e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>3.429015e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>2.719933e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>1.075167e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>2.919609e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>1.879218e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>1.070128e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>2.919415e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>2.721023e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>1.639291e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>2.710247e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>2.511390e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>9.738447e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>1.511516e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>6.013798e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>4.415566e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1.533071e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>5</td>\n",
       "      <td>4.777388e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>3.120400e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>1.346458e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>5</td>\n",
       "      <td>4.150846e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "      <td>2.443587e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>1.200916e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>5</td>\n",
       "      <td>3.330891e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>mscn</td>\n",
       "      <td>train</td>\n",
       "      <td>mse</td>\n",
       "      <td>qerr</td>\n",
       "      <td>512</td>\n",
       "      <td>10</td>\n",
       "      <td>2.012897e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 500)\n",
    "\n",
    "HTML(df.groupby([\"alg\", \"samples_type\", \"loss_func\", \"loss_type\", \"hidden_layer_size\", \"buckets\"])[\"loss\"].mean().reset_index().to_html())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df[df.samples_type == \"test\"]\n",
    "df2 = df2[df2.max_discrete_featurizing_buckets==5]\n",
    "df2 = df2[df2.hidden_layer_size==64]\n",
    "df2 = df2[df2.loss_type==\"qerr\"]\n",
    "#df2 = df2[df2.num_tables==\"all\"]\n",
    "#print(df2[\"loss\"])\n",
    "#print(df2.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1527.38x1080 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1527.38x1080 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "construct_summary_final_model_complexity(df, \"errors\", ERRORS, HUE_COLORS=HUE_COLORS, ORDER=ORDER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1527.38x1080 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1527.38x1080 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "construct_summary_final_model_complexity(df, \"errors\", ERRORS, HUE_COLORS=HUE_COLORS, ORDER=[\"job\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df[df.samples_type == \"job\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>loss_type</th>\n",
       "      <th>loss</th>\n",
       "      <th>summary_type</th>\n",
       "      <th>template</th>\n",
       "      <th>num_samples</th>\n",
       "      <th>samples_type</th>\n",
       "      <th>query_directory</th>\n",
       "      <th>cost_model</th>\n",
       "      <th>join_loss_data_file</th>\n",
       "      <th>exp_prefix</th>\n",
       "      <th>...</th>\n",
       "      <th>use_svd</th>\n",
       "      <th>num_singular_vals</th>\n",
       "      <th>num_bins</th>\n",
       "      <th>cl_recompute</th>\n",
       "      <th>pgm_sampling_percentage</th>\n",
       "      <th>pgm_merge_aliases</th>\n",
       "      <th>alg</th>\n",
       "      <th>alg_name</th>\n",
       "      <th>partition</th>\n",
       "      <th>res_dir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>qerr</td>\n",
       "      <td>4.038644e+06</td>\n",
       "      <td>mean</td>\n",
       "      <td>all</td>\n",
       "      <td>31512</td>\n",
       "      <td>job</td>\n",
       "      <td>./our_dataset/queries</td>\n",
       "      <td>nested_loop_index7</td>\n",
       "      <td>None</td>\n",
       "      <td>model_complexity</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0</td>\n",
       "      <td>mscn</td>\n",
       "      <td>mse</td>\n",
       "      <td>0</td>\n",
       "      <td>41514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>jerr</td>\n",
       "      <td>1.403916e+06</td>\n",
       "      <td>mean</td>\n",
       "      <td>all</td>\n",
       "      <td>105</td>\n",
       "      <td>job</td>\n",
       "      <td>./our_dataset/queries</td>\n",
       "      <td>nested_loop_index7</td>\n",
       "      <td>None</td>\n",
       "      <td>model_complexity</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0</td>\n",
       "      <td>mscn</td>\n",
       "      <td>mse</td>\n",
       "      <td>0</td>\n",
       "      <td>41514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>cm1_jerr</td>\n",
       "      <td>1.416668e+06</td>\n",
       "      <td>mean</td>\n",
       "      <td>all</td>\n",
       "      <td>105</td>\n",
       "      <td>job</td>\n",
       "      <td>./our_dataset/queries</td>\n",
       "      <td>nested_loop_index7</td>\n",
       "      <td>None</td>\n",
       "      <td>model_complexity</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0</td>\n",
       "      <td>mscn</td>\n",
       "      <td>mse</td>\n",
       "      <td>0</td>\n",
       "      <td>41514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>plan_err</td>\n",
       "      <td>1.337514e+06</td>\n",
       "      <td>mean</td>\n",
       "      <td>all</td>\n",
       "      <td>105</td>\n",
       "      <td>job</td>\n",
       "      <td>./our_dataset/queries</td>\n",
       "      <td>nested_loop_index7</td>\n",
       "      <td>None</td>\n",
       "      <td>model_complexity</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0</td>\n",
       "      <td>mscn</td>\n",
       "      <td>mse</td>\n",
       "      <td>0</td>\n",
       "      <td>41514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1424</th>\n",
       "      <td>plan_pg_err</td>\n",
       "      <td>1.689106e+06</td>\n",
       "      <td>mean</td>\n",
       "      <td>all</td>\n",
       "      <td>105</td>\n",
       "      <td>job</td>\n",
       "      <td>./our_dataset/queries</td>\n",
       "      <td>nested_loop_index7</td>\n",
       "      <td>None</td>\n",
       "      <td>model_complexity</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0</td>\n",
       "      <td>mscn</td>\n",
       "      <td>mse</td>\n",
       "      <td>0</td>\n",
       "      <td>41514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 142 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        loss_type          loss summary_type template  num_samples  \\\n",
       "272          qerr  4.038644e+06         mean      all        31512   \n",
       "560          jerr  1.403916e+06         mean      all          105   \n",
       "848      cm1_jerr  1.416668e+06         mean      all          105   \n",
       "1136     plan_err  1.337514e+06         mean      all          105   \n",
       "1424  plan_pg_err  1.689106e+06         mean      all          105   \n",
       "\n",
       "     samples_type        query_directory          cost_model  \\\n",
       "272           job  ./our_dataset/queries  nested_loop_index7   \n",
       "560           job  ./our_dataset/queries  nested_loop_index7   \n",
       "848           job  ./our_dataset/queries  nested_loop_index7   \n",
       "1136          job  ./our_dataset/queries  nested_loop_index7   \n",
       "1424          job  ./our_dataset/queries  nested_loop_index7   \n",
       "\n",
       "     join_loss_data_file        exp_prefix  ... use_svd  num_singular_vals  \\\n",
       "272                 None  model_complexity  ...       0                  5   \n",
       "560                 None  model_complexity  ...       0                  5   \n",
       "848                 None  model_complexity  ...       0                  5   \n",
       "1136                None  model_complexity  ...       0                  5   \n",
       "1424                None  model_complexity  ...       0                  5   \n",
       "\n",
       "      num_bins  cl_recompute  pgm_sampling_percentage  pgm_merge_aliases  \\\n",
       "272        100             0                    0.001                  0   \n",
       "560        100             0                    0.001                  0   \n",
       "848        100             0                    0.001                  0   \n",
       "1136       100             0                    0.001                  0   \n",
       "1424       100             0                    0.001                  0   \n",
       "\n",
       "       alg alg_name  partition  res_dir  \n",
       "272   mscn      mse          0    41514  \n",
       "560   mscn      mse          0    41514  \n",
       "848   mscn      mse          0    41514  \n",
       "1136  mscn      mse          0    41514  \n",
       "1424  mscn      mse          0    41514  \n",
       "\n",
       "[5 rows x 142 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alg   hidden_layer_size  buckets  loss_type  \n",
       "mscn  64                 1        cm1_jerr       3.439214e+05\n",
       "                                  flow_err       3.151424e+08\n",
       "                                  jerr           6.060520e+05\n",
       "                                  plan_err       6.580146e+05\n",
       "                                  plan_pg_err    9.403993e+05\n",
       "                                                     ...     \n",
       "      512                10       flow_err       1.707943e+09\n",
       "                                  jerr           5.998555e+07\n",
       "                                  plan_err       1.153671e+06\n",
       "                                  plan_pg_err    1.697523e+06\n",
       "                                  qerr           1.394460e+07\n",
       "Name: loss, Length: 72, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby([\"alg\", \"hidden_layer_size\", \"buckets\", \"loss_type\"])[\"loss\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2[df2.loss_type == \"cm1_jerr\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alg   loss_func   hidden_layer_size  buckets\n",
       "mscn  flow_loss2  64                 1          2.171557e+05\n",
       "                                     5          1.623535e+05\n",
       "                                     10         1.540184e+05\n",
       "                  128                1          1.428375e+05\n",
       "                                     5          1.351163e+05\n",
       "                                     10         2.305323e+05\n",
       "                  256                1          3.393485e+05\n",
       "                                     5          1.923166e+05\n",
       "                                     10         1.834636e+05\n",
       "                  512                1          1.862567e+05\n",
       "                                     5          1.795200e+05\n",
       "                                     10         2.167877e+05\n",
       "      mse         64                 1          4.073042e+05\n",
       "                                     5          4.690722e+05\n",
       "                                     10         2.593077e+05\n",
       "                  128                1          4.593227e+05\n",
       "                                     5          2.432284e+06\n",
       "                                     10         6.092130e+05\n",
       "                  256                1          9.720260e+05\n",
       "                                     5          1.242253e+07\n",
       "                                     10         1.516965e+06\n",
       "                  512                1          6.432966e+05\n",
       "                                     5          1.416668e+06\n",
       "                                     10         1.195238e+07\n",
       "Name: loss, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby([\"alg\", \"loss_func\", \"hidden_layer_size\", \"buckets\"])[\"loss\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{20}\n"
     ]
    }
   ],
   "source": [
    "print(set(df[\"max_epochs\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}