{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import mhcflurry, seaborn, numpy, pandas, pickle, sklearn, collections, scipy, time\n",
    "import mhcflurry.data\n",
    "import mhcflurry.imputation\n",
    "import fancyimpute\n",
    "from matplotlib import pyplot\n",
    "import sklearn.metrics\n",
    "import sklearn.cross_validation\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "min_peptides_to_consider_allele = 50\n",
    "max_ic50 = 50000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_train_data = mhcflurry.data.load_allele_datasets(\"/Users/tim/sinai/git/mhcflurry/bdata.2009.mhci.public.1.txt\")\n",
    "all_validation_data = mhcflurry.data.load_allele_datasets(\"/Users/tim/sinai/git/mhcflurry/bdata.2013.mhci.public.blind.1.txt\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alleles = [allele for allele in all_train_data if len(all_train_data[allele].Y) >= min_peptides_to_consider_allele]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Gogo-B0101', 14),\n",
       " ('H-2-DB', 1482),\n",
       " ('H-2-DD', 201),\n",
       " ('H-2-KB', 1364),\n",
       " ('H-2-KD', 335),\n",
       " ('H-2-KK', 168),\n",
       " ('H-2-LD', 146),\n",
       " ('HLA-A0101', 3169),\n",
       " ('HLA-A0201', 6961),\n",
       " ('HLA-A0202', 2314),\n",
       " ('HLA-A0203', 3937),\n",
       " ('HLA-A0205', 36),\n",
       " ('HLA-A0206', 3223),\n",
       " ('HLA-A0207', 30),\n",
       " ('HLA-A0210', 18),\n",
       " ('HLA-A0211', 1038),\n",
       " ('HLA-A0212', 1143),\n",
       " ('HLA-A0216', 894),\n",
       " ('HLA-A0219', 1203),\n",
       " ('HLA-A0250', 132),\n",
       " ('HLA-A0301', 4601),\n",
       " ('HLA-A0302', 2),\n",
       " ('HLA-A11', 31),\n",
       " ('HLA-A1101', 3862),\n",
       " ('HLA-A2', 30),\n",
       " ('HLA-A2301', 1513),\n",
       " ('HLA-A2402', 1985),\n",
       " ('HLA-A2403', 1165),\n",
       " ('HLA-A2501', 519),\n",
       " ('HLA-A26', 4),\n",
       " ('HLA-A2601', 2395),\n",
       " ('HLA-A2602', 202),\n",
       " ('HLA-A2603', 205),\n",
       " ('HLA-A2902', 1839),\n",
       " ('HLA-A3001', 1949),\n",
       " ('HLA-A3002', 912),\n",
       " ('HLA-A3101', 3309),\n",
       " ('HLA-A3201', 575),\n",
       " ('HLA-A3301', 1616),\n",
       " ('HLA-A6601', 4),\n",
       " ('HLA-A6801', 1700),\n",
       " ('HLA-A6802', 3188),\n",
       " ('HLA-A6901', 2079),\n",
       " ('HLA-A8001', 782),\n",
       " ('HLA-B0702', 2974),\n",
       " ('HLA-B0801', 2084),\n",
       " ('HLA-B0802', 486),\n",
       " ('HLA-B0803', 217),\n",
       " ('HLA-B1402', 3),\n",
       " ('HLA-B1501', 3142),\n",
       " ('HLA-B1502', 164),\n",
       " ('HLA-B1503', 416),\n",
       " ('HLA-B1509', 346),\n",
       " ('HLA-B1517', 846),\n",
       " ('HLA-B1801', 1661),\n",
       " ('HLA-B2701', 1),\n",
       " ('HLA-B2702', 4),\n",
       " ('HLA-B2703', 433),\n",
       " ('HLA-B2705', 2324),\n",
       " ('HLA-B3501', 1993),\n",
       " ('HLA-B3503', 5),\n",
       " ('HLA-B3801', 136),\n",
       " ('HLA-B3901', 879),\n",
       " ('HLA-B4001', 2326),\n",
       " ('HLA-B4002', 474),\n",
       " ('HLA-B4201', 2),\n",
       " ('HLA-B4402', 1295),\n",
       " ('HLA-B4403', 502),\n",
       " ('HLA-B4501', 483),\n",
       " ('HLA-B4601', 1406),\n",
       " ('HLA-B4801', 861),\n",
       " ('HLA-B5101', 1336),\n",
       " ('HLA-B5301', 620),\n",
       " ('HLA-B5401', 621),\n",
       " ('HLA-B5701', 1719),\n",
       " ('HLA-B5801', 2444),\n",
       " ('HLA-B5802', 31),\n",
       " ('HLA-B7', 48),\n",
       " ('HLA-B7301', 115),\n",
       " ('HLA-E0101', 1),\n",
       " ('Mamu-A01', 823),\n",
       " ('Mamu-A02', 355),\n",
       " ('Mamu-A07', 33),\n",
       " ('Mamu-A11', 491),\n",
       " ('Mamu-A2201', 247),\n",
       " ('Mamu-A2601', 48),\n",
       " ('Mamu-B01', 237),\n",
       " ('Mamu-B03', 372),\n",
       " ('Mamu-B04', 1),\n",
       " ('Mamu-B08', 368),\n",
       " ('Mamu-B17', 678),\n",
       " ('Mamu-B52', 2),\n",
       " ('Patr-A0101', 203),\n",
       " ('Patr-A0301', 169),\n",
       " ('Patr-A0401', 144),\n",
       " ('Patr-A0602', 1),\n",
       " ('Patr-A0701', 287),\n",
       " ('Patr-A0901', 173),\n",
       " ('Patr-B0101', 454),\n",
       " ('Patr-B0901', 1),\n",
       " ('Patr-B1301', 97),\n",
       " ('Patr-B1701', 5),\n",
       " ('Patr-B2401', 193)]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted([(allele, len(all_train_data[allele].Y)) for allele in all_train_data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activation</th>\n",
       "      <th>allele</th>\n",
       "      <th>allele_size</th>\n",
       "      <th>embedding_input_dim</th>\n",
       "      <th>embedding_output_dim</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>impute</th>\n",
       "      <th>imputed_size</th>\n",
       "      <th>layer_sizes</th>\n",
       "      <th>model_params</th>\n",
       "      <th>test_auc</th>\n",
       "      <th>test_f1</th>\n",
       "      <th>test_tau</th>\n",
       "      <th>train_auc</th>\n",
       "      <th>train_f1</th>\n",
       "      <th>train_size</th>\n",
       "      <th>train_tau</th>\n",
       "      <th>layer0_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.516064</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.958791</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.365852</td>\n",
       "      <td>0.994641</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>323</td>\n",
       "      <td>0.423319</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.524809</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.966117</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.381375</td>\n",
       "      <td>0.996249</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>323</td>\n",
       "      <td>0.400697</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.586464</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.977106</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.328311</td>\n",
       "      <td>0.999732</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.416132</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.835091</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.946886</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.361160</td>\n",
       "      <td>0.995713</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>323</td>\n",
       "      <td>0.400371</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>1.178641</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.974359</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.328311</td>\n",
       "      <td>0.998928</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>323</td>\n",
       "      <td>0.413437</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.555257</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.966117</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.316398</td>\n",
       "      <td>0.998124</td>\n",
       "      <td>0.869565</td>\n",
       "      <td>323</td>\n",
       "      <td>0.408946</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.656072</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.974359</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.366213</td>\n",
       "      <td>0.998392</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.399064</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.790235</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.949634</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.326506</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.436141</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>1.058073</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.951465</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.298710</td>\n",
       "      <td>0.998392</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.420951</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>1.363177</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.310262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>323</td>\n",
       "      <td>0.411722</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>1.022370</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.966117</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.364409</td>\n",
       "      <td>0.998660</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.400861</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>1.102015</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.310623</td>\n",
       "      <td>0.998928</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.429199</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>1.200207</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.945971</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.319286</td>\n",
       "      <td>0.999464</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.429281</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>1.934082</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.856227</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.205578</td>\n",
       "      <td>0.998928</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.435079</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>2.577868</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.927656</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.269471</td>\n",
       "      <td>0.998660</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.435732</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>1.604558</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.942308</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.335530</td>\n",
       "      <td>0.999196</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.410824</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>1.712172</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.920330</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.333003</td>\n",
       "      <td>0.999196</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.395389</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>1.832423</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.905678</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.287520</td>\n",
       "      <td>0.999732</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.421441</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>2.786849</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.926740</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.262973</td>\n",
       "      <td>0.999196</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.429607</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>3.960335</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.891941</td>\n",
       "      <td>0.152174</td>\n",
       "      <td>0.239510</td>\n",
       "      <td>0.998660</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.433119</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>2.641378</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.340945</td>\n",
       "      <td>0.999464</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.406741</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>2.848119</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.923993</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.330476</td>\n",
       "      <td>0.999464</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.432956</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>3.030523</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.905678</td>\n",
       "      <td>0.325581</td>\n",
       "      <td>0.270915</td>\n",
       "      <td>0.999196</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.433446</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>4.795924</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.893773</td>\n",
       "      <td>0.208955</td>\n",
       "      <td>0.255032</td>\n",
       "      <td>0.999196</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.429934</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>7.112554</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.873626</td>\n",
       "      <td>0.164706</td>\n",
       "      <td>0.208466</td>\n",
       "      <td>0.998660</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.417766</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.268176</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.958791</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.346721</td>\n",
       "      <td>0.977760</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>323</td>\n",
       "      <td>0.319766</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.299982</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.978022</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.327950</td>\n",
       "      <td>0.995445</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>323</td>\n",
       "      <td>0.413764</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.309151</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.941392</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.315315</td>\n",
       "      <td>0.996249</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>323</td>\n",
       "      <td>0.396614</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.453042</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.343472</td>\n",
       "      <td>0.992497</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>323</td>\n",
       "      <td>0.415071</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.608721</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.953297</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.340584</td>\n",
       "      <td>0.993837</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>323</td>\n",
       "      <td>0.414826</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3057</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.779558</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3058</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.970431</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3059</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>1.269507</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3060</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>1.088002</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3061</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>1.183663</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3062</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>1.311969</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3063</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>2.065488</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3064</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>2.663677</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3065</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>1.884065</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3066</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>1.955619</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3067</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>1.958104</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3068</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>2.890013</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3069</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>4.137682</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3070</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>2.888799</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3071</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>3.009848</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3072</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>4.611519</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3073</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>4.610184</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3074</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>7.991142</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3075</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.295013</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.930464</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.409907</td>\n",
       "      <td>0.977498</td>\n",
       "      <td>0.805195</td>\n",
       "      <td>346</td>\n",
       "      <td>0.467214</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3076</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.280043</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.795906</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.308759</td>\n",
       "      <td>0.987583</td>\n",
       "      <td>0.880952</td>\n",
       "      <td>346</td>\n",
       "      <td>0.484617</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3077</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.340199</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.886514</td>\n",
       "      <td>0.558140</td>\n",
       "      <td>0.377747</td>\n",
       "      <td>0.995334</td>\n",
       "      <td>0.917647</td>\n",
       "      <td>346</td>\n",
       "      <td>0.494710</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3078</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.474198</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.867249</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.371004</td>\n",
       "      <td>0.994130</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>346</td>\n",
       "      <td>0.490505</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3079</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.588388</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.874473</td>\n",
       "      <td>0.540541</td>\n",
       "      <td>0.368670</td>\n",
       "      <td>0.998119</td>\n",
       "      <td>0.953488</td>\n",
       "      <td>346</td>\n",
       "      <td>0.493869</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3080</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.287310</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.800722</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.314465</td>\n",
       "      <td>0.998420</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>346</td>\n",
       "      <td>0.492252</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3081</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.357486</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.907586</td>\n",
       "      <td>0.638298</td>\n",
       "      <td>0.399533</td>\n",
       "      <td>0.997968</td>\n",
       "      <td>0.965517</td>\n",
       "      <td>346</td>\n",
       "      <td>0.500209</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3082</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.439453</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.905780</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.395642</td>\n",
       "      <td>0.999624</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>346</td>\n",
       "      <td>0.505450</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3083</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>1.130689</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.921433</td>\n",
       "      <td>0.576923</td>\n",
       "      <td>0.402645</td>\n",
       "      <td>0.999699</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>346</td>\n",
       "      <td>0.502862</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3084</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.804340</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.914509</td>\n",
       "      <td>0.577778</td>\n",
       "      <td>0.402904</td>\n",
       "      <td>0.999398</td>\n",
       "      <td>0.942529</td>\n",
       "      <td>346</td>\n",
       "      <td>0.503833</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3085</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>0.593277</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.938892</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.419503</td>\n",
       "      <td>0.999849</td>\n",
       "      <td>0.988506</td>\n",
       "      <td>346</td>\n",
       "      <td>0.506291</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3086</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>0.683605</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.934979</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.419762</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>346</td>\n",
       "      <td>0.508232</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3087 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     activation     allele  allele_size  embedding_input_dim  \\\n",
       "0          tanh  HLA-B0802          486                   21   \n",
       "1          tanh  HLA-B0802          486                   21   \n",
       "2          tanh  HLA-B0802          486                   21   \n",
       "3          tanh  HLA-B0802          486                   21   \n",
       "4          tanh  HLA-B0802          486                   21   \n",
       "5          tanh  HLA-B0802          486                   21   \n",
       "6          tanh  HLA-B0802          486                   21   \n",
       "7          tanh  HLA-B0802          486                   21   \n",
       "8          tanh  HLA-B0802          486                   21   \n",
       "9          tanh  HLA-B0802          486                   21   \n",
       "10         tanh  HLA-B0802          486                   21   \n",
       "11         tanh  HLA-B0802          486                   21   \n",
       "12         tanh  HLA-B0802          486                   21   \n",
       "13         tanh  HLA-B0802          486                   21   \n",
       "14         tanh  HLA-B0802          486                   21   \n",
       "15         tanh  HLA-B0802          486                   21   \n",
       "16         tanh  HLA-B0802          486                   21   \n",
       "17         tanh  HLA-B0802          486                   21   \n",
       "18         tanh  HLA-B0802          486                   21   \n",
       "19         tanh  HLA-B0802          486                   21   \n",
       "20         tanh  HLA-B0802          486                   21   \n",
       "21         tanh  HLA-B0802          486                   21   \n",
       "22         tanh  HLA-B0802          486                   21   \n",
       "23         tanh  HLA-B0802          486                   21   \n",
       "24         tanh  HLA-B0802          486                   21   \n",
       "25         tanh  HLA-B0802          486                   21   \n",
       "26         tanh  HLA-B0802          486                   21   \n",
       "27         tanh  HLA-B0802          486                   21   \n",
       "28         tanh  HLA-B0802          486                   21   \n",
       "29         tanh  HLA-B0802          486                   21   \n",
       "...         ...        ...          ...                  ...   \n",
       "3057       tanh  HLA-A2501          519                   21   \n",
       "3058       tanh  HLA-A2501          519                   21   \n",
       "3059       tanh  HLA-A2501          519                   21   \n",
       "3060       tanh  HLA-A2501          519                   21   \n",
       "3061       tanh  HLA-A2501          519                   21   \n",
       "3062       tanh  HLA-A2501          519                   21   \n",
       "3063       tanh  HLA-A2501          519                   21   \n",
       "3064       tanh  HLA-A2501          519                   21   \n",
       "3065       tanh  HLA-A2501          519                   21   \n",
       "3066       tanh  HLA-A2501          519                   21   \n",
       "3067       tanh  HLA-A2501          519                   21   \n",
       "3068       tanh  HLA-A2501          519                   21   \n",
       "3069       tanh  HLA-A2501          519                   21   \n",
       "3070       tanh  HLA-A2501          519                   21   \n",
       "3071       tanh  HLA-A2501          519                   21   \n",
       "3072       tanh  HLA-A2501          519                   21   \n",
       "3073       tanh  HLA-A2501          519                   21   \n",
       "3074       tanh  HLA-A2501          519                   21   \n",
       "3075       tanh  HLA-A2501          519                   21   \n",
       "3076       tanh  HLA-A2501          519                   21   \n",
       "3077       tanh  HLA-A2501          519                   21   \n",
       "3078       tanh  HLA-A2501          519                   21   \n",
       "3079       tanh  HLA-A2501          519                   21   \n",
       "3080       tanh  HLA-A2501          519                   21   \n",
       "3081       tanh  HLA-A2501          519                   21   \n",
       "3082       tanh  HLA-A2501          519                   21   \n",
       "3083       tanh  HLA-A2501          519                   21   \n",
       "3084       tanh  HLA-A2501          519                   21   \n",
       "3085       tanh  HLA-A2501          519                   21   \n",
       "3086       tanh  HLA-A2501          519                   21   \n",
       "\n",
       "      embedding_output_dim  fit_time impute  imputed_size layer_sizes  \\\n",
       "0                        5  0.516064   True          9361         [4]   \n",
       "1                        5  0.524809   True          9361         [8]   \n",
       "2                        5  0.586464   True          9361        [16]   \n",
       "3                        5  0.835091   True          9361        [64]   \n",
       "4                        5  1.178641   True          9361       [128]   \n",
       "5                       10  0.555257   True          9361         [4]   \n",
       "6                       10  0.656072   True          9361         [8]   \n",
       "7                       10  0.790235   True          9361        [16]   \n",
       "8                       10  1.058073   True          9361        [64]   \n",
       "9                       10  1.363177   True          9361       [128]   \n",
       "10                      32  1.022370   True          9361         [4]   \n",
       "11                      32  1.102015   True          9361         [8]   \n",
       "12                      32  1.200207   True          9361        [16]   \n",
       "13                      32  1.934082   True          9361        [64]   \n",
       "14                      32  2.577868   True          9361       [128]   \n",
       "15                      64  1.604558   True          9361         [4]   \n",
       "16                      64  1.712172   True          9361         [8]   \n",
       "17                      64  1.832423   True          9361        [16]   \n",
       "18                      64  2.786849   True          9361        [64]   \n",
       "19                      64  3.960335   True          9361       [128]   \n",
       "20                     128  2.641378   True          9361         [4]   \n",
       "21                     128  2.848119   True          9361         [8]   \n",
       "22                     128  3.030523   True          9361        [16]   \n",
       "23                     128  4.795924   True          9361        [64]   \n",
       "24                     128  7.112554   True          9361       [128]   \n",
       "25                       5  0.268176  False           NaN         [4]   \n",
       "26                       5  0.299982  False           NaN         [8]   \n",
       "27                       5  0.309151  False           NaN        [16]   \n",
       "28                       5  0.453042  False           NaN        [64]   \n",
       "29                       5  0.608721  False           NaN       [128]   \n",
       "...                    ...       ...    ...           ...         ...   \n",
       "3057                    10  0.779558   True          9362        [16]   \n",
       "3058                    10  0.970431   True          9362        [64]   \n",
       "3059                    10  1.269507   True          9362       [128]   \n",
       "3060                    32  1.088002   True          9362         [4]   \n",
       "3061                    32  1.183663   True          9362         [8]   \n",
       "3062                    32  1.311969   True          9362        [16]   \n",
       "3063                    32  2.065488   True          9362        [64]   \n",
       "3064                    32  2.663677   True          9362       [128]   \n",
       "3065                    64  1.884065   True          9362         [4]   \n",
       "3066                    64  1.955619   True          9362         [8]   \n",
       "3067                    64  1.958104   True          9362        [16]   \n",
       "3068                    64  2.890013   True          9362        [64]   \n",
       "3069                    64  4.137682   True          9362       [128]   \n",
       "3070                   128  2.888799   True          9362         [4]   \n",
       "3071                   128  3.009848   True          9362         [8]   \n",
       "3072                   128  4.611519   True          9362        [16]   \n",
       "3073                   128  4.610184   True          9362        [64]   \n",
       "3074                   128  7.991142   True          9362       [128]   \n",
       "3075                     5  0.295013  False           NaN         [4]   \n",
       "3076                     5  0.280043  False           NaN         [8]   \n",
       "3077                     5  0.340199  False           NaN        [16]   \n",
       "3078                     5  0.474198  False           NaN        [64]   \n",
       "3079                     5  0.588388  False           NaN       [128]   \n",
       "3080                    10  0.287310  False           NaN         [4]   \n",
       "3081                    10  0.357486  False           NaN         [8]   \n",
       "3082                    10  0.439453  False           NaN        [16]   \n",
       "3083                    10  1.130689  False           NaN        [64]   \n",
       "3084                    10  0.804340  False           NaN       [128]   \n",
       "3085                    32  0.593277  False           NaN         [4]   \n",
       "3086                    32  0.683605  False           NaN         [8]   \n",
       "\n",
       "                                           model_params  test_auc   test_f1  \\\n",
       "0     {u'activation': u'tanh', u'embedding_input_dim...  0.958791  0.545455   \n",
       "1     {u'activation': u'tanh', u'embedding_input_dim...  0.966117  0.000000   \n",
       "2     {u'activation': u'tanh', u'embedding_input_dim...  0.977106  0.727273   \n",
       "3     {u'activation': u'tanh', u'embedding_input_dim...  0.946886  0.250000   \n",
       "4     {u'activation': u'tanh', u'embedding_input_dim...  0.974359  0.444444   \n",
       "5     {u'activation': u'tanh', u'embedding_input_dim...  0.966117  0.545455   \n",
       "6     {u'activation': u'tanh', u'embedding_input_dim...  0.974359  0.727273   \n",
       "7     {u'activation': u'tanh', u'embedding_input_dim...  0.949634  0.250000   \n",
       "8     {u'activation': u'tanh', u'embedding_input_dim...  0.951465  0.400000   \n",
       "9     {u'activation': u'tanh', u'embedding_input_dim...  0.964286  0.500000   \n",
       "10    {u'activation': u'tanh', u'embedding_input_dim...  0.966117  0.500000   \n",
       "11    {u'activation': u'tanh', u'embedding_input_dim...  0.928571  0.461538   \n",
       "12    {u'activation': u'tanh', u'embedding_input_dim...  0.945971  0.600000   \n",
       "13    {u'activation': u'tanh', u'embedding_input_dim...  0.856227  0.250000   \n",
       "14    {u'activation': u'tanh', u'embedding_input_dim...  0.927656  0.304348   \n",
       "15    {u'activation': u'tanh', u'embedding_input_dim...  0.942308  0.444444   \n",
       "16    {u'activation': u'tanh', u'embedding_input_dim...  0.920330  0.470588   \n",
       "17    {u'activation': u'tanh', u'embedding_input_dim...  0.905678  0.444444   \n",
       "18    {u'activation': u'tanh', u'embedding_input_dim...  0.926740  0.318182   \n",
       "19    {u'activation': u'tanh', u'embedding_input_dim...  0.891941  0.152174   \n",
       "20    {u'activation': u'tanh', u'embedding_input_dim...  0.923077  0.588235   \n",
       "21    {u'activation': u'tanh', u'embedding_input_dim...  0.923993  0.444444   \n",
       "22    {u'activation': u'tanh', u'embedding_input_dim...  0.905678  0.325581   \n",
       "23    {u'activation': u'tanh', u'embedding_input_dim...  0.893773  0.208955   \n",
       "24    {u'activation': u'tanh', u'embedding_input_dim...  0.873626  0.164706   \n",
       "25    {u'activation': u'tanh', u'embedding_input_dim...  0.958791  0.000000   \n",
       "26    {u'activation': u'tanh', u'embedding_input_dim...  0.978022  0.250000   \n",
       "27    {u'activation': u'tanh', u'embedding_input_dim...  0.941392  0.600000   \n",
       "28    {u'activation': u'tanh', u'embedding_input_dim...  0.952381  0.250000   \n",
       "29    {u'activation': u'tanh', u'embedding_input_dim...  0.953297  0.250000   \n",
       "...                                                 ...       ...       ...   \n",
       "3057  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3058  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3059  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3060  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3061  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3062  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3063  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3064  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3065  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3066  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3067  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3068  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3069  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3070  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3071  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3072  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3073  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3074  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3075  {u'activation': u'tanh', u'embedding_input_dim...  0.930464  0.750000   \n",
       "3076  {u'activation': u'tanh', u'embedding_input_dim...  0.795906  0.470588   \n",
       "3077  {u'activation': u'tanh', u'embedding_input_dim...  0.886514  0.558140   \n",
       "3078  {u'activation': u'tanh', u'embedding_input_dim...  0.867249  0.590909   \n",
       "3079  {u'activation': u'tanh', u'embedding_input_dim...  0.874473  0.540541   \n",
       "3080  {u'activation': u'tanh', u'embedding_input_dim...  0.800722  0.444444   \n",
       "3081  {u'activation': u'tanh', u'embedding_input_dim...  0.907586  0.638298   \n",
       "3082  {u'activation': u'tanh', u'embedding_input_dim...  0.905780  0.583333   \n",
       "3083  {u'activation': u'tanh', u'embedding_input_dim...  0.921433  0.576923   \n",
       "3084  {u'activation': u'tanh', u'embedding_input_dim...  0.914509  0.577778   \n",
       "3085  {u'activation': u'tanh', u'embedding_input_dim...  0.938892  0.680851   \n",
       "3086  {u'activation': u'tanh', u'embedding_input_dim...  0.934979  0.636364   \n",
       "\n",
       "      test_tau  train_auc  train_f1  train_size  train_tau  layer0_size  \n",
       "0     0.365852   0.994641  0.666667         323   0.423319            4  \n",
       "1     0.381375   0.996249  0.761905         323   0.400697            8  \n",
       "2     0.328311   0.999732  0.909091         323   0.416132           16  \n",
       "3     0.361160   0.995713  0.800000         323   0.400371           64  \n",
       "4     0.328311   0.998928  0.761905         323   0.413437          128  \n",
       "5     0.316398   0.998124  0.869565         323   0.408946            4  \n",
       "6     0.366213   0.998392  0.909091         323   0.399064            8  \n",
       "7     0.326506   1.000000  0.909091         323   0.436141           16  \n",
       "8     0.298710   0.998392  0.909091         323   0.420951           64  \n",
       "9     0.310262   1.000000  0.857143         323   0.411722          128  \n",
       "10    0.364409   0.998660  0.909091         323   0.400861            4  \n",
       "11    0.310623   0.998928  0.956522         323   0.429199            8  \n",
       "12    0.319286   0.999464  0.909091         323   0.429281           16  \n",
       "13    0.205578   0.998928  0.909091         323   0.435079           64  \n",
       "14    0.269471   0.998660  0.909091         323   0.435732          128  \n",
       "15    0.335530   0.999196  0.956522         323   0.410824            4  \n",
       "16    0.333003   0.999196  0.956522         323   0.395389            8  \n",
       "17    0.287520   0.999732  0.956522         323   0.421441           16  \n",
       "18    0.262973   0.999196  0.956522         323   0.429607           64  \n",
       "19    0.239510   0.998660  0.909091         323   0.433119          128  \n",
       "20    0.340945   0.999464  0.956522         323   0.406741            4  \n",
       "21    0.330476   0.999464  0.956522         323   0.432956            8  \n",
       "22    0.270915   0.999196  0.956522         323   0.433446           16  \n",
       "23    0.255032   0.999196  0.956522         323   0.429934           64  \n",
       "24    0.208466   0.998660  0.956522         323   0.417766          128  \n",
       "25    0.346721   0.977760  0.555556         323   0.319766            4  \n",
       "26    0.327950   0.995445  0.761905         323   0.413764            8  \n",
       "27    0.315315   0.996249  0.700000         323   0.396614           16  \n",
       "28    0.343472   0.992497  0.666667         323   0.415071           64  \n",
       "29    0.340584   0.993837  0.700000         323   0.414826          128  \n",
       "...        ...        ...       ...         ...        ...          ...  \n",
       "3057       NaN   0.432646  0.000000         346        NaN           16  \n",
       "3058       NaN   0.432646  0.000000         346        NaN           64  \n",
       "3059       NaN   0.432646  0.000000         346        NaN          128  \n",
       "3060       NaN   0.432646  0.000000         346        NaN            4  \n",
       "3061       NaN   0.432646  0.000000         346        NaN            8  \n",
       "3062       NaN   0.432646  0.000000         346        NaN           16  \n",
       "3063       NaN   0.432646  0.000000         346        NaN           64  \n",
       "3064       NaN   0.432646  0.000000         346        NaN          128  \n",
       "3065       NaN   0.432646  0.000000         346        NaN            4  \n",
       "3066       NaN   0.432646  0.000000         346        NaN            8  \n",
       "3067       NaN   0.432646  0.000000         346        NaN           16  \n",
       "3068       NaN   0.432646  0.000000         346        NaN           64  \n",
       "3069       NaN   0.432646  0.000000         346        NaN          128  \n",
       "3070       NaN   0.432646  0.000000         346        NaN            4  \n",
       "3071       NaN   0.432646  0.000000         346        NaN            8  \n",
       "3072       NaN   0.432646  0.000000         346        NaN           16  \n",
       "3073       NaN   0.432646  0.000000         346        NaN           64  \n",
       "3074       NaN   0.432646  0.000000         346        NaN          128  \n",
       "3075  0.409907   0.977498  0.805195         346   0.467214            4  \n",
       "3076  0.308759   0.987583  0.880952         346   0.484617            8  \n",
       "3077  0.377747   0.995334  0.917647         346   0.494710           16  \n",
       "3078  0.371004   0.994130  0.928571         346   0.490505           64  \n",
       "3079  0.368670   0.998119  0.953488         346   0.493869          128  \n",
       "3080  0.314465   0.998420  0.941176         346   0.492252            4  \n",
       "3081  0.399533   0.997968  0.965517         346   0.500209            8  \n",
       "3082  0.395642   0.999624  0.952381         346   0.505450           16  \n",
       "3083  0.402645   0.999699  0.976744         346   0.502862           64  \n",
       "3084  0.402904   0.999398  0.942529         346   0.503833          128  \n",
       "3085  0.419503   0.999849  0.988506         346   0.506291            4  \n",
       "3086  0.419762   1.000000  0.976744         346   0.508232            8  \n",
       "\n",
       "[3087 rows x 18 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_df = pandas.read_csv(\"../data/cv.csv\", converters={'model_params': eval, 'layer_sizes': eval})\n",
    "cv_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activation</th>\n",
       "      <th>allele</th>\n",
       "      <th>allele_size</th>\n",
       "      <th>embedding_input_dim</th>\n",
       "      <th>embedding_output_dim</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>impute</th>\n",
       "      <th>imputed_size</th>\n",
       "      <th>layer_sizes</th>\n",
       "      <th>model_params</th>\n",
       "      <th>test_auc</th>\n",
       "      <th>test_f1</th>\n",
       "      <th>test_tau</th>\n",
       "      <th>train_auc</th>\n",
       "      <th>train_f1</th>\n",
       "      <th>train_size</th>\n",
       "      <th>train_tau</th>\n",
       "      <th>layer0_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.516064</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.958791</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.365852</td>\n",
       "      <td>0.994641</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>323</td>\n",
       "      <td>0.423319</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.524809</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.966117</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.381375</td>\n",
       "      <td>0.996249</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>323</td>\n",
       "      <td>0.400697</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.586464</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.977106</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.328311</td>\n",
       "      <td>0.999732</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.416132</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.835091</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.946886</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.361160</td>\n",
       "      <td>0.995713</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>323</td>\n",
       "      <td>0.400371</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>1.178641</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.974359</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.328311</td>\n",
       "      <td>0.998928</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>323</td>\n",
       "      <td>0.413437</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.555257</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.966117</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.316398</td>\n",
       "      <td>0.998124</td>\n",
       "      <td>0.869565</td>\n",
       "      <td>323</td>\n",
       "      <td>0.408946</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.656072</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.974359</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.366213</td>\n",
       "      <td>0.998392</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.399064</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.790235</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.949634</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.326506</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.436141</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>1.058073</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.951465</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.298710</td>\n",
       "      <td>0.998392</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.420951</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>1.363177</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.310262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>323</td>\n",
       "      <td>0.411722</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>1.022370</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.966117</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.364409</td>\n",
       "      <td>0.998660</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.400861</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>1.102015</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.310623</td>\n",
       "      <td>0.998928</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.429199</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>1.200207</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.945971</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.319286</td>\n",
       "      <td>0.999464</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.429281</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>1.934082</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.856227</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.205578</td>\n",
       "      <td>0.998928</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.435079</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>2.577868</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.927656</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.269471</td>\n",
       "      <td>0.998660</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.435732</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>1.604558</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.942308</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.335530</td>\n",
       "      <td>0.999196</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.410824</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>1.712172</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.920330</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.333003</td>\n",
       "      <td>0.999196</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.395389</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>1.832423</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.905678</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.287520</td>\n",
       "      <td>0.999732</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.421441</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>2.786849</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.926740</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.262973</td>\n",
       "      <td>0.999196</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.429607</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>3.960335</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.891941</td>\n",
       "      <td>0.152174</td>\n",
       "      <td>0.239510</td>\n",
       "      <td>0.998660</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>323</td>\n",
       "      <td>0.433119</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>2.641378</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.340945</td>\n",
       "      <td>0.999464</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.406741</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>2.848119</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.923993</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.330476</td>\n",
       "      <td>0.999464</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.432956</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>3.030523</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.905678</td>\n",
       "      <td>0.325581</td>\n",
       "      <td>0.270915</td>\n",
       "      <td>0.999196</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.433446</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>4.795924</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.893773</td>\n",
       "      <td>0.208955</td>\n",
       "      <td>0.255032</td>\n",
       "      <td>0.999196</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.429934</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>7.112554</td>\n",
       "      <td>True</td>\n",
       "      <td>9361</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.873626</td>\n",
       "      <td>0.164706</td>\n",
       "      <td>0.208466</td>\n",
       "      <td>0.998660</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>323</td>\n",
       "      <td>0.417766</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.268176</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.958791</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.346721</td>\n",
       "      <td>0.977760</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>323</td>\n",
       "      <td>0.319766</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.299982</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.978022</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.327950</td>\n",
       "      <td>0.995445</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>323</td>\n",
       "      <td>0.413764</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.309151</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.941392</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.315315</td>\n",
       "      <td>0.996249</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>323</td>\n",
       "      <td>0.396614</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.453042</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.343472</td>\n",
       "      <td>0.992497</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>323</td>\n",
       "      <td>0.415071</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.608721</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.953297</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.340584</td>\n",
       "      <td>0.993837</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>323</td>\n",
       "      <td>0.414826</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3057</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.779558</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3058</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.970431</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3059</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>1.269507</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3060</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>1.088002</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3061</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>1.183663</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3062</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>1.311969</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3063</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>2.065488</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3064</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>2.663677</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3065</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>1.884065</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3066</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>1.955619</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3067</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>1.958104</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3068</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>2.890013</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3069</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>4.137682</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3070</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>2.888799</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3071</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>3.009848</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3072</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>4.611519</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3073</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>4.610184</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3074</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>7.991142</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3075</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.295013</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.930464</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.409907</td>\n",
       "      <td>0.977498</td>\n",
       "      <td>0.805195</td>\n",
       "      <td>346</td>\n",
       "      <td>0.467214</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3076</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.280043</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.795906</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.308759</td>\n",
       "      <td>0.987583</td>\n",
       "      <td>0.880952</td>\n",
       "      <td>346</td>\n",
       "      <td>0.484617</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3077</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.340199</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.886514</td>\n",
       "      <td>0.558140</td>\n",
       "      <td>0.377747</td>\n",
       "      <td>0.995334</td>\n",
       "      <td>0.917647</td>\n",
       "      <td>346</td>\n",
       "      <td>0.494710</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3078</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.474198</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.867249</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.371004</td>\n",
       "      <td>0.994130</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>346</td>\n",
       "      <td>0.490505</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3079</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0.588388</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.874473</td>\n",
       "      <td>0.540541</td>\n",
       "      <td>0.368670</td>\n",
       "      <td>0.998119</td>\n",
       "      <td>0.953488</td>\n",
       "      <td>346</td>\n",
       "      <td>0.493869</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3080</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.287310</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.800722</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.314465</td>\n",
       "      <td>0.998420</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>346</td>\n",
       "      <td>0.492252</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3081</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.357486</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.907586</td>\n",
       "      <td>0.638298</td>\n",
       "      <td>0.399533</td>\n",
       "      <td>0.997968</td>\n",
       "      <td>0.965517</td>\n",
       "      <td>346</td>\n",
       "      <td>0.500209</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3082</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.439453</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.905780</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.395642</td>\n",
       "      <td>0.999624</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>346</td>\n",
       "      <td>0.505450</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3083</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>1.130689</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.921433</td>\n",
       "      <td>0.576923</td>\n",
       "      <td>0.402645</td>\n",
       "      <td>0.999699</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>346</td>\n",
       "      <td>0.502862</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3084</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.804340</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.914509</td>\n",
       "      <td>0.577778</td>\n",
       "      <td>0.402904</td>\n",
       "      <td>0.999398</td>\n",
       "      <td>0.942529</td>\n",
       "      <td>346</td>\n",
       "      <td>0.503833</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3085</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>0.593277</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.938892</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.419503</td>\n",
       "      <td>0.999849</td>\n",
       "      <td>0.988506</td>\n",
       "      <td>346</td>\n",
       "      <td>0.506291</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3086</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>0.683605</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.934979</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.419762</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>346</td>\n",
       "      <td>0.508232</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3087 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     activation     allele  allele_size  embedding_input_dim  \\\n",
       "0          tanh  HLA-B0802          486                   21   \n",
       "1          tanh  HLA-B0802          486                   21   \n",
       "2          tanh  HLA-B0802          486                   21   \n",
       "3          tanh  HLA-B0802          486                   21   \n",
       "4          tanh  HLA-B0802          486                   21   \n",
       "5          tanh  HLA-B0802          486                   21   \n",
       "6          tanh  HLA-B0802          486                   21   \n",
       "7          tanh  HLA-B0802          486                   21   \n",
       "8          tanh  HLA-B0802          486                   21   \n",
       "9          tanh  HLA-B0802          486                   21   \n",
       "10         tanh  HLA-B0802          486                   21   \n",
       "11         tanh  HLA-B0802          486                   21   \n",
       "12         tanh  HLA-B0802          486                   21   \n",
       "13         tanh  HLA-B0802          486                   21   \n",
       "14         tanh  HLA-B0802          486                   21   \n",
       "15         tanh  HLA-B0802          486                   21   \n",
       "16         tanh  HLA-B0802          486                   21   \n",
       "17         tanh  HLA-B0802          486                   21   \n",
       "18         tanh  HLA-B0802          486                   21   \n",
       "19         tanh  HLA-B0802          486                   21   \n",
       "20         tanh  HLA-B0802          486                   21   \n",
       "21         tanh  HLA-B0802          486                   21   \n",
       "22         tanh  HLA-B0802          486                   21   \n",
       "23         tanh  HLA-B0802          486                   21   \n",
       "24         tanh  HLA-B0802          486                   21   \n",
       "25         tanh  HLA-B0802          486                   21   \n",
       "26         tanh  HLA-B0802          486                   21   \n",
       "27         tanh  HLA-B0802          486                   21   \n",
       "28         tanh  HLA-B0802          486                   21   \n",
       "29         tanh  HLA-B0802          486                   21   \n",
       "...         ...        ...          ...                  ...   \n",
       "3057       tanh  HLA-A2501          519                   21   \n",
       "3058       tanh  HLA-A2501          519                   21   \n",
       "3059       tanh  HLA-A2501          519                   21   \n",
       "3060       tanh  HLA-A2501          519                   21   \n",
       "3061       tanh  HLA-A2501          519                   21   \n",
       "3062       tanh  HLA-A2501          519                   21   \n",
       "3063       tanh  HLA-A2501          519                   21   \n",
       "3064       tanh  HLA-A2501          519                   21   \n",
       "3065       tanh  HLA-A2501          519                   21   \n",
       "3066       tanh  HLA-A2501          519                   21   \n",
       "3067       tanh  HLA-A2501          519                   21   \n",
       "3068       tanh  HLA-A2501          519                   21   \n",
       "3069       tanh  HLA-A2501          519                   21   \n",
       "3070       tanh  HLA-A2501          519                   21   \n",
       "3071       tanh  HLA-A2501          519                   21   \n",
       "3072       tanh  HLA-A2501          519                   21   \n",
       "3073       tanh  HLA-A2501          519                   21   \n",
       "3074       tanh  HLA-A2501          519                   21   \n",
       "3075       tanh  HLA-A2501          519                   21   \n",
       "3076       tanh  HLA-A2501          519                   21   \n",
       "3077       tanh  HLA-A2501          519                   21   \n",
       "3078       tanh  HLA-A2501          519                   21   \n",
       "3079       tanh  HLA-A2501          519                   21   \n",
       "3080       tanh  HLA-A2501          519                   21   \n",
       "3081       tanh  HLA-A2501          519                   21   \n",
       "3082       tanh  HLA-A2501          519                   21   \n",
       "3083       tanh  HLA-A2501          519                   21   \n",
       "3084       tanh  HLA-A2501          519                   21   \n",
       "3085       tanh  HLA-A2501          519                   21   \n",
       "3086       tanh  HLA-A2501          519                   21   \n",
       "\n",
       "      embedding_output_dim  fit_time impute  imputed_size layer_sizes  \\\n",
       "0                        5  0.516064   True          9361         [4]   \n",
       "1                        5  0.524809   True          9361         [8]   \n",
       "2                        5  0.586464   True          9361        [16]   \n",
       "3                        5  0.835091   True          9361        [64]   \n",
       "4                        5  1.178641   True          9361       [128]   \n",
       "5                       10  0.555257   True          9361         [4]   \n",
       "6                       10  0.656072   True          9361         [8]   \n",
       "7                       10  0.790235   True          9361        [16]   \n",
       "8                       10  1.058073   True          9361        [64]   \n",
       "9                       10  1.363177   True          9361       [128]   \n",
       "10                      32  1.022370   True          9361         [4]   \n",
       "11                      32  1.102015   True          9361         [8]   \n",
       "12                      32  1.200207   True          9361        [16]   \n",
       "13                      32  1.934082   True          9361        [64]   \n",
       "14                      32  2.577868   True          9361       [128]   \n",
       "15                      64  1.604558   True          9361         [4]   \n",
       "16                      64  1.712172   True          9361         [8]   \n",
       "17                      64  1.832423   True          9361        [16]   \n",
       "18                      64  2.786849   True          9361        [64]   \n",
       "19                      64  3.960335   True          9361       [128]   \n",
       "20                     128  2.641378   True          9361         [4]   \n",
       "21                     128  2.848119   True          9361         [8]   \n",
       "22                     128  3.030523   True          9361        [16]   \n",
       "23                     128  4.795924   True          9361        [64]   \n",
       "24                     128  7.112554   True          9361       [128]   \n",
       "25                       5  0.268176  False           NaN         [4]   \n",
       "26                       5  0.299982  False           NaN         [8]   \n",
       "27                       5  0.309151  False           NaN        [16]   \n",
       "28                       5  0.453042  False           NaN        [64]   \n",
       "29                       5  0.608721  False           NaN       [128]   \n",
       "...                    ...       ...    ...           ...         ...   \n",
       "3057                    10  0.779558   True          9362        [16]   \n",
       "3058                    10  0.970431   True          9362        [64]   \n",
       "3059                    10  1.269507   True          9362       [128]   \n",
       "3060                    32  1.088002   True          9362         [4]   \n",
       "3061                    32  1.183663   True          9362         [8]   \n",
       "3062                    32  1.311969   True          9362        [16]   \n",
       "3063                    32  2.065488   True          9362        [64]   \n",
       "3064                    32  2.663677   True          9362       [128]   \n",
       "3065                    64  1.884065   True          9362         [4]   \n",
       "3066                    64  1.955619   True          9362         [8]   \n",
       "3067                    64  1.958104   True          9362        [16]   \n",
       "3068                    64  2.890013   True          9362        [64]   \n",
       "3069                    64  4.137682   True          9362       [128]   \n",
       "3070                   128  2.888799   True          9362         [4]   \n",
       "3071                   128  3.009848   True          9362         [8]   \n",
       "3072                   128  4.611519   True          9362        [16]   \n",
       "3073                   128  4.610184   True          9362        [64]   \n",
       "3074                   128  7.991142   True          9362       [128]   \n",
       "3075                     5  0.295013  False           NaN         [4]   \n",
       "3076                     5  0.280043  False           NaN         [8]   \n",
       "3077                     5  0.340199  False           NaN        [16]   \n",
       "3078                     5  0.474198  False           NaN        [64]   \n",
       "3079                     5  0.588388  False           NaN       [128]   \n",
       "3080                    10  0.287310  False           NaN         [4]   \n",
       "3081                    10  0.357486  False           NaN         [8]   \n",
       "3082                    10  0.439453  False           NaN        [16]   \n",
       "3083                    10  1.130689  False           NaN        [64]   \n",
       "3084                    10  0.804340  False           NaN       [128]   \n",
       "3085                    32  0.593277  False           NaN         [4]   \n",
       "3086                    32  0.683605  False           NaN         [8]   \n",
       "\n",
       "                                           model_params  test_auc   test_f1  \\\n",
       "0     {u'activation': u'tanh', u'embedding_input_dim...  0.958791  0.545455   \n",
       "1     {u'activation': u'tanh', u'embedding_input_dim...  0.966117  0.000000   \n",
       "2     {u'activation': u'tanh', u'embedding_input_dim...  0.977106  0.727273   \n",
       "3     {u'activation': u'tanh', u'embedding_input_dim...  0.946886  0.250000   \n",
       "4     {u'activation': u'tanh', u'embedding_input_dim...  0.974359  0.444444   \n",
       "5     {u'activation': u'tanh', u'embedding_input_dim...  0.966117  0.545455   \n",
       "6     {u'activation': u'tanh', u'embedding_input_dim...  0.974359  0.727273   \n",
       "7     {u'activation': u'tanh', u'embedding_input_dim...  0.949634  0.250000   \n",
       "8     {u'activation': u'tanh', u'embedding_input_dim...  0.951465  0.400000   \n",
       "9     {u'activation': u'tanh', u'embedding_input_dim...  0.964286  0.500000   \n",
       "10    {u'activation': u'tanh', u'embedding_input_dim...  0.966117  0.500000   \n",
       "11    {u'activation': u'tanh', u'embedding_input_dim...  0.928571  0.461538   \n",
       "12    {u'activation': u'tanh', u'embedding_input_dim...  0.945971  0.600000   \n",
       "13    {u'activation': u'tanh', u'embedding_input_dim...  0.856227  0.250000   \n",
       "14    {u'activation': u'tanh', u'embedding_input_dim...  0.927656  0.304348   \n",
       "15    {u'activation': u'tanh', u'embedding_input_dim...  0.942308  0.444444   \n",
       "16    {u'activation': u'tanh', u'embedding_input_dim...  0.920330  0.470588   \n",
       "17    {u'activation': u'tanh', u'embedding_input_dim...  0.905678  0.444444   \n",
       "18    {u'activation': u'tanh', u'embedding_input_dim...  0.926740  0.318182   \n",
       "19    {u'activation': u'tanh', u'embedding_input_dim...  0.891941  0.152174   \n",
       "20    {u'activation': u'tanh', u'embedding_input_dim...  0.923077  0.588235   \n",
       "21    {u'activation': u'tanh', u'embedding_input_dim...  0.923993  0.444444   \n",
       "22    {u'activation': u'tanh', u'embedding_input_dim...  0.905678  0.325581   \n",
       "23    {u'activation': u'tanh', u'embedding_input_dim...  0.893773  0.208955   \n",
       "24    {u'activation': u'tanh', u'embedding_input_dim...  0.873626  0.164706   \n",
       "25    {u'activation': u'tanh', u'embedding_input_dim...  0.958791  0.000000   \n",
       "26    {u'activation': u'tanh', u'embedding_input_dim...  0.978022  0.250000   \n",
       "27    {u'activation': u'tanh', u'embedding_input_dim...  0.941392  0.600000   \n",
       "28    {u'activation': u'tanh', u'embedding_input_dim...  0.952381  0.250000   \n",
       "29    {u'activation': u'tanh', u'embedding_input_dim...  0.953297  0.250000   \n",
       "...                                                 ...       ...       ...   \n",
       "3057  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3058  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3059  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3060  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3061  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3062  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3063  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3064  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3065  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3066  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3067  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3068  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3069  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3070  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3071  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3072  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3073  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3074  {u'activation': u'tanh', u'embedding_input_dim...  0.000000  0.000000   \n",
       "3075  {u'activation': u'tanh', u'embedding_input_dim...  0.930464  0.750000   \n",
       "3076  {u'activation': u'tanh', u'embedding_input_dim...  0.795906  0.470588   \n",
       "3077  {u'activation': u'tanh', u'embedding_input_dim...  0.886514  0.558140   \n",
       "3078  {u'activation': u'tanh', u'embedding_input_dim...  0.867249  0.590909   \n",
       "3079  {u'activation': u'tanh', u'embedding_input_dim...  0.874473  0.540541   \n",
       "3080  {u'activation': u'tanh', u'embedding_input_dim...  0.800722  0.444444   \n",
       "3081  {u'activation': u'tanh', u'embedding_input_dim...  0.907586  0.638298   \n",
       "3082  {u'activation': u'tanh', u'embedding_input_dim...  0.905780  0.583333   \n",
       "3083  {u'activation': u'tanh', u'embedding_input_dim...  0.921433  0.576923   \n",
       "3084  {u'activation': u'tanh', u'embedding_input_dim...  0.914509  0.577778   \n",
       "3085  {u'activation': u'tanh', u'embedding_input_dim...  0.938892  0.680851   \n",
       "3086  {u'activation': u'tanh', u'embedding_input_dim...  0.934979  0.636364   \n",
       "\n",
       "      test_tau  train_auc  train_f1  train_size  train_tau  layer0_size  \n",
       "0     0.365852   0.994641  0.666667         323   0.423319            4  \n",
       "1     0.381375   0.996249  0.761905         323   0.400697            8  \n",
       "2     0.328311   0.999732  0.909091         323   0.416132           16  \n",
       "3     0.361160   0.995713  0.800000         323   0.400371           64  \n",
       "4     0.328311   0.998928  0.761905         323   0.413437          128  \n",
       "5     0.316398   0.998124  0.869565         323   0.408946            4  \n",
       "6     0.366213   0.998392  0.909091         323   0.399064            8  \n",
       "7     0.326506   1.000000  0.909091         323   0.436141           16  \n",
       "8     0.298710   0.998392  0.909091         323   0.420951           64  \n",
       "9     0.310262   1.000000  0.857143         323   0.411722          128  \n",
       "10    0.364409   0.998660  0.909091         323   0.400861            4  \n",
       "11    0.310623   0.998928  0.956522         323   0.429199            8  \n",
       "12    0.319286   0.999464  0.909091         323   0.429281           16  \n",
       "13    0.205578   0.998928  0.909091         323   0.435079           64  \n",
       "14    0.269471   0.998660  0.909091         323   0.435732          128  \n",
       "15    0.335530   0.999196  0.956522         323   0.410824            4  \n",
       "16    0.333003   0.999196  0.956522         323   0.395389            8  \n",
       "17    0.287520   0.999732  0.956522         323   0.421441           16  \n",
       "18    0.262973   0.999196  0.956522         323   0.429607           64  \n",
       "19    0.239510   0.998660  0.909091         323   0.433119          128  \n",
       "20    0.340945   0.999464  0.956522         323   0.406741            4  \n",
       "21    0.330476   0.999464  0.956522         323   0.432956            8  \n",
       "22    0.270915   0.999196  0.956522         323   0.433446           16  \n",
       "23    0.255032   0.999196  0.956522         323   0.429934           64  \n",
       "24    0.208466   0.998660  0.956522         323   0.417766          128  \n",
       "25    0.346721   0.977760  0.555556         323   0.319766            4  \n",
       "26    0.327950   0.995445  0.761905         323   0.413764            8  \n",
       "27    0.315315   0.996249  0.700000         323   0.396614           16  \n",
       "28    0.343472   0.992497  0.666667         323   0.415071           64  \n",
       "29    0.340584   0.993837  0.700000         323   0.414826          128  \n",
       "...        ...        ...       ...         ...        ...          ...  \n",
       "3057       NaN   0.432646  0.000000         346        NaN           16  \n",
       "3058       NaN   0.432646  0.000000         346        NaN           64  \n",
       "3059       NaN   0.432646  0.000000         346        NaN          128  \n",
       "3060       NaN   0.432646  0.000000         346        NaN            4  \n",
       "3061       NaN   0.432646  0.000000         346        NaN            8  \n",
       "3062       NaN   0.432646  0.000000         346        NaN           16  \n",
       "3063       NaN   0.432646  0.000000         346        NaN           64  \n",
       "3064       NaN   0.432646  0.000000         346        NaN          128  \n",
       "3065       NaN   0.432646  0.000000         346        NaN            4  \n",
       "3066       NaN   0.432646  0.000000         346        NaN            8  \n",
       "3067       NaN   0.432646  0.000000         346        NaN           16  \n",
       "3068       NaN   0.432646  0.000000         346        NaN           64  \n",
       "3069       NaN   0.432646  0.000000         346        NaN          128  \n",
       "3070       NaN   0.432646  0.000000         346        NaN            4  \n",
       "3071       NaN   0.432646  0.000000         346        NaN            8  \n",
       "3072       NaN   0.432646  0.000000         346        NaN           16  \n",
       "3073       NaN   0.432646  0.000000         346        NaN           64  \n",
       "3074       NaN   0.432646  0.000000         346        NaN          128  \n",
       "3075  0.409907   0.977498  0.805195         346   0.467214            4  \n",
       "3076  0.308759   0.987583  0.880952         346   0.484617            8  \n",
       "3077  0.377747   0.995334  0.917647         346   0.494710           16  \n",
       "3078  0.371004   0.994130  0.928571         346   0.490505           64  \n",
       "3079  0.368670   0.998119  0.953488         346   0.493869          128  \n",
       "3080  0.314465   0.998420  0.941176         346   0.492252            4  \n",
       "3081  0.399533   0.997968  0.965517         346   0.500209            8  \n",
       "3082  0.395642   0.999624  0.952381         346   0.505450           16  \n",
       "3083  0.402645   0.999699  0.976744         346   0.502862           64  \n",
       "3084  0.402904   0.999398  0.942529         346   0.503833          128  \n",
       "3085  0.419503   0.999849  0.988506         346   0.506291            4  \n",
       "3086  0.419762   1.000000  0.976744         346   0.508232            8  \n",
       "\n",
       "[3087 rows x 18 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cv_df[\"label\"] = [\"%s (%d)\" % (row.allele, row.allele_size) for (_, row) in cv_df.iterrows()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cv_df.sort(\"allele_size\", ascending=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Patr-A0101', 'Mamu-B01', 'Patr-A0701', 'Mamu-A02', 'Mamu-B08',\n",
       "       'HLA-B4501', 'HLA-B0802', 'HLA-B4403', 'HLA-A2501', 'HLA-B5401',\n",
       "       'HLA-A8001', 'Mamu-A01', 'HLA-A2403', 'HLA-B4402', 'HLA-B1801',\n",
       "       'HLA-A6801', 'HLA-A2402', 'HLA-B2705', 'HLA-B0702', 'HLA-A6802',\n",
       "       'HLA-A3101'], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "min() arg is an empty sequence",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-74-70f7bf28b543>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mallele\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallele_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msub_cv_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mseaborn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstripplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcv_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcv_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallele\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mallele\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"embedding_output_dim\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"test_tau\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"impute\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjitter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mallele\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/tim/venvs/analysis-venv-2.7/lib/python2.7/site-packages/seaborn/categorical.pyc\u001b[0m in \u001b[0;36mstripplot\u001b[0;34m(x, y, hue, data, order, hue_order, jitter, split, orient, color, palette, size, edgecolor, linewidth, ax, **kwargs)\u001b[0m\n\u001b[1;32m   2499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2500\u001b[0m     plotter = _StripPlotter(x, y, hue, data, order, hue_order,\n\u001b[0;32m-> 2501\u001b[0;31m                             jitter, split, orient, color, palette)\n\u001b[0m\u001b[1;32m   2502\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2503\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/tim/venvs/analysis-venv-2.7/lib/python2.7/site-packages/seaborn/categorical.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, hue, data, order, hue_order, jitter, split, orient, color, palette)\u001b[0m\n\u001b[1;32m   1107\u001b[0m         \u001b[0;34m\"\"\"Initialize the plotter.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1108\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1109\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1111\u001b[0m         \u001b[0;31m# Set object attributes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/tim/venvs/analysis-venv-2.7/lib/python2.7/site-packages/seaborn/categorical.pyc\u001b[0m in \u001b[0;36mestablish_colors\u001b[0;34m(self, color, palette, saturation)\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0;31m# Determine the gray color to use for the lines framing the plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m         \u001b[0mlight_vals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcolorsys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrgb_to_hls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrgb_colors\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m         \u001b[0ml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlight_vals\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m.6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    303\u001b[0m         \u001b[0mgray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrgb2hex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: min() arg is an empty sequence"
     ]
    }
   ],
   "source": [
    "for allele in [x[1] for x in sorted([(row.allele_size, row.label) for (_, row) in sub_cv_df.iterrows()])]:\n",
    "    seaborn.stripplot(data=cv_df.ix[cv_df.allele == allele], x=\"embedding_output_dim\", y=\"test_tau\", hue=\"impute\", jitter=.2)\n",
    "    pyplot.title(allele)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>allele</th>\n",
       "      <th>allele_size</th>\n",
       "      <th>impute</th>\n",
       "      <th>embedding_output_dim</th>\n",
       "      <th>activation</th>\n",
       "      <th>embedding_input_dim</th>\n",
       "      <th>layer0_size</th>\n",
       "      <th>test_tau</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HLA-A2402</td>\n",
       "      <td>1985</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>0.410680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>HLA-A2403</td>\n",
       "      <td>1165</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>0.519384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>HLA-A2501</td>\n",
       "      <td>519</td>\n",
       "      <td>False</td>\n",
       "      <td>64</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>0.399642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>False</td>\n",
       "      <td>32</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>0.539537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>HLA-A6801</td>\n",
       "      <td>1700</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>0.592004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>HLA-A6802</td>\n",
       "      <td>3188</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>0.481201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>HLA-A8001</td>\n",
       "      <td>782</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>0.511992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>HLA-B0702</td>\n",
       "      <td>2974</td>\n",
       "      <td>False</td>\n",
       "      <td>64</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>0.474471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>HLA-B0802</td>\n",
       "      <td>486</td>\n",
       "      <td>False</td>\n",
       "      <td>64</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>0.391766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>HLA-B1801</td>\n",
       "      <td>1661</td>\n",
       "      <td>False</td>\n",
       "      <td>128</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>0.361516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>HLA-B2705</td>\n",
       "      <td>2324</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>0.516798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>HLA-B4402</td>\n",
       "      <td>1295</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>0.304112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>HLA-B4403</td>\n",
       "      <td>502</td>\n",
       "      <td>False</td>\n",
       "      <td>32</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>0.513881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>HLA-B4501</td>\n",
       "      <td>483</td>\n",
       "      <td>False</td>\n",
       "      <td>32</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>0.551053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>HLA-B5401</td>\n",
       "      <td>621</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>0.539538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>Mamu-A01</td>\n",
       "      <td>823</td>\n",
       "      <td>False</td>\n",
       "      <td>32</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>0.520938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>Mamu-A02</td>\n",
       "      <td>355</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>0.551511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>Mamu-B01</td>\n",
       "      <td>237</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>0.582210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>Mamu-B08</td>\n",
       "      <td>368</td>\n",
       "      <td>False</td>\n",
       "      <td>64</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>0.571033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>961</th>\n",
       "      <td>Patr-A0101</td>\n",
       "      <td>203</td>\n",
       "      <td>False</td>\n",
       "      <td>32</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>0.512013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>Patr-A0701</td>\n",
       "      <td>287</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>tanh</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>0.294510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          allele  allele_size impute  embedding_output_dim activation  \\\n",
       "6      HLA-A2402         1985  False                    10       tanh   \n",
       "52     HLA-A2403         1165  False                     5       tanh   \n",
       "115    HLA-A2501          519  False                    64       tanh   \n",
       "162    HLA-A3101         3309  False                    32       tanh   \n",
       "205    HLA-A6801         1700  False                    10       tanh   \n",
       "253    HLA-A6802         3188  False                     5       tanh   \n",
       "300    HLA-A8001          782  False                     5       tanh   \n",
       "366    HLA-B0702         2974  False                    64       tanh   \n",
       "415    HLA-B0802          486  False                    64       tanh   \n",
       "471    HLA-B1801         1661  False                   128       tanh   \n",
       "506    HLA-B2705         2324  False                    10       tanh   \n",
       "557    HLA-B4402         1295  False                    10       tanh   \n",
       "612    HLA-B4403          502  False                    32       tanh   \n",
       "663    HLA-B4501          483  False                    32       tanh   \n",
       "704    HLA-B5401          621  False                     5       tanh   \n",
       "764     Mamu-A01          823  False                    32       tanh   \n",
       "828     Mamu-A02          355   True                     5       tanh   \n",
       "856     Mamu-B01          237  False                    10       tanh   \n",
       "916     Mamu-B08          368  False                    64       tanh   \n",
       "961   Patr-A0101          203  False                    32       tanh   \n",
       "1005  Patr-A0701          287  False                    10       tanh   \n",
       "\n",
       "      embedding_input_dim  layer0_size  test_tau  \n",
       "6                      21            8  0.410680  \n",
       "52                     21           16  0.519384  \n",
       "115                    21            4  0.399642  \n",
       "162                    21           16  0.539537  \n",
       "205                    21            4  0.592004  \n",
       "253                    21           64  0.481201  \n",
       "300                    21            4  0.511992  \n",
       "366                    21            8  0.474471  \n",
       "415                    21            4  0.391766  \n",
       "471                    21            8  0.361516  \n",
       "506                    21            8  0.516798  \n",
       "557                    21           16  0.304112  \n",
       "612                    21           16  0.513881  \n",
       "663                    21           64  0.551053  \n",
       "704                    21          128  0.539538  \n",
       "764                    21          128  0.520938  \n",
       "828                    21           64  0.551511  \n",
       "856                    21            8  0.582210  \n",
       "916                    21            8  0.571033  \n",
       "961                    21            8  0.512013  \n",
       "1005                   21            4  0.294510  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_params_explored = set.union(*[set(x) for x in cv_df.model_params])\n",
    "models_params_explored\n",
    "\n",
    "group_columns = [\"allele\", \"allele_size\", \"impute\"]\n",
    "group_columns.extend(models_params_explored)\n",
    "group_columns.append(\"layer0_size\")\n",
    "group_columns.remove(\"layer_sizes\")\n",
    "\n",
    "def best_by(score):\n",
    "    means = cv_df.groupby(group_columns)[score].mean().reset_index()\n",
    "    max_rows = []\n",
    "    for allele in means.allele.unique():\n",
    "        max_rows.append(means.ix[means.allele == allele][score].argmax())\n",
    "    return means.ix[max_rows]\n",
    "best_by('test_tau')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Patr-A0101 (203)', 'Mamu-B01 (237)', 'Patr-A0701 (287)',\n",
       "       'Mamu-A02 (355)', 'Mamu-B08 (368)', 'HLA-B4501 (483)',\n",
       "       'HLA-B0802 (486)', 'HLA-B4403 (502)', 'HLA-A2501 (519)',\n",
       "       'HLA-B5401 (621)', 'HLA-A8001 (782)', 'Mamu-A01 (823)',\n",
       "       'HLA-A2403 (1165)', 'HLA-B4402 (1295)', 'HLA-B1801 (1661)',\n",
       "       'HLA-A6801 (1700)', 'HLA-A2402 (1985)', 'HLA-B2705 (2324)',\n",
       "       'HLA-B0702 (2974)', 'HLA-A6802 (3188)', 'HLA-A3101 (3309)'], dtype=object)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_df.label.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activation</th>\n",
       "      <th>allele</th>\n",
       "      <th>allele_size</th>\n",
       "      <th>embedding_input_dim</th>\n",
       "      <th>embedding_output_dim</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>impute</th>\n",
       "      <th>imputed_size</th>\n",
       "      <th>layer_sizes</th>\n",
       "      <th>model_params</th>\n",
       "      <th>test_auc</th>\n",
       "      <th>test_f1</th>\n",
       "      <th>test_tau</th>\n",
       "      <th>train_auc</th>\n",
       "      <th>train_f1</th>\n",
       "      <th>train_size</th>\n",
       "      <th>train_tau</th>\n",
       "      <th>layer0_size</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>1.868021</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.935570</td>\n",
       "      <td>0.710112</td>\n",
       "      <td>0.555839</td>\n",
       "      <td>0.953386</td>\n",
       "      <td>0.769964</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.573294</td>\n",
       "      <td>16</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>13.486147</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>1.629664</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.939689</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.568165</td>\n",
       "      <td>0.954817</td>\n",
       "      <td>0.789100</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.570262</td>\n",
       "      <td>8</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>1.576970</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.930989</td>\n",
       "      <td>0.674757</td>\n",
       "      <td>0.547016</td>\n",
       "      <td>0.954279</td>\n",
       "      <td>0.805936</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.571380</td>\n",
       "      <td>4</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>24.451036</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>16.921623</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>11.111831</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>10.251438</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>9.658818</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>5.399939</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>6.645087</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>6.370398</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>8.495607</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>5.994825</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>4.056105</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>3.800774</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>3.579236</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>4.614510</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>9.744167</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>2.540756</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.940790</td>\n",
       "      <td>0.739040</td>\n",
       "      <td>0.566896</td>\n",
       "      <td>0.954801</td>\n",
       "      <td>0.788863</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.578530</td>\n",
       "      <td>64</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>4.967129</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.931049</td>\n",
       "      <td>0.724696</td>\n",
       "      <td>0.542662</td>\n",
       "      <td>0.973771</td>\n",
       "      <td>0.840708</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.616580</td>\n",
       "      <td>4</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>1.715692</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.938789</td>\n",
       "      <td>0.706294</td>\n",
       "      <td>0.557485</td>\n",
       "      <td>0.961590</td>\n",
       "      <td>0.813212</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.591506</td>\n",
       "      <td>4</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>3.316780</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>24.718327</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.926357</td>\n",
       "      <td>0.716157</td>\n",
       "      <td>0.530217</td>\n",
       "      <td>0.992517</td>\n",
       "      <td>0.901639</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.673750</td>\n",
       "      <td>128</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>15.078671</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.928238</td>\n",
       "      <td>0.722330</td>\n",
       "      <td>0.545203</td>\n",
       "      <td>0.996735</td>\n",
       "      <td>0.939189</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.698979</td>\n",
       "      <td>64</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>9.691375</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.929415</td>\n",
       "      <td>0.718631</td>\n",
       "      <td>0.535749</td>\n",
       "      <td>0.996518</td>\n",
       "      <td>0.954796</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.702057</td>\n",
       "      <td>16</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>9.539105</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.921741</td>\n",
       "      <td>0.714876</td>\n",
       "      <td>0.528658</td>\n",
       "      <td>0.994681</td>\n",
       "      <td>0.935939</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.683223</td>\n",
       "      <td>8</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>8.566430</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.922656</td>\n",
       "      <td>0.730612</td>\n",
       "      <td>0.513425</td>\n",
       "      <td>0.984851</td>\n",
       "      <td>0.879908</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.633644</td>\n",
       "      <td>4</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>12.400123</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.938572</td>\n",
       "      <td>0.721973</td>\n",
       "      <td>0.557326</td>\n",
       "      <td>0.975941</td>\n",
       "      <td>0.854142</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.618043</td>\n",
       "      <td>128</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>8.800901</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.936365</td>\n",
       "      <td>0.736243</td>\n",
       "      <td>0.560585</td>\n",
       "      <td>0.979745</td>\n",
       "      <td>0.862876</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.628978</td>\n",
       "      <td>64</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>3.458234</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>1.726367</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.911769</td>\n",
       "      <td>0.677656</td>\n",
       "      <td>0.549271</td>\n",
       "      <td>0.968196</td>\n",
       "      <td>0.819747</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.584648</td>\n",
       "      <td>4</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>2.303598</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.595212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>3.510116</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.864834</td>\n",
       "      <td>0.593886</td>\n",
       "      <td>0.454865</td>\n",
       "      <td>0.963047</td>\n",
       "      <td>0.793466</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.576880</td>\n",
       "      <td>128</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>1.843633</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.875898</td>\n",
       "      <td>0.619329</td>\n",
       "      <td>0.490314</td>\n",
       "      <td>0.963191</td>\n",
       "      <td>0.805755</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.576035</td>\n",
       "      <td>16</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>3.601491</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>1.967647</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>2.331174</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>2.447505</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>3.381274</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>4.639203</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>3.600294</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>3.849825</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>4.064893</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>6.032371</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>8.455435</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>5.447830</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>5.885132</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>6.447617</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>9.614197</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>13.323382</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>9.620890</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>10.234888</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>10.958013</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[16]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>16.790324</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>128</td>\n",
       "      <td>24.410361</td>\n",
       "      <td>True</td>\n",
       "      <td>9362</td>\n",
       "      <td>[128]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.653018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.521762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>1.557381</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[4]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.908213</td>\n",
       "      <td>0.658744</td>\n",
       "      <td>0.526577</td>\n",
       "      <td>0.963035</td>\n",
       "      <td>0.804651</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.576558</td>\n",
       "      <td>4</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>1.582523</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.886031</td>\n",
       "      <td>0.641921</td>\n",
       "      <td>0.493376</td>\n",
       "      <td>0.962878</td>\n",
       "      <td>0.803632</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.574186</td>\n",
       "      <td>8</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>2.540075</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[64]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.919382</td>\n",
       "      <td>0.712687</td>\n",
       "      <td>0.534931</td>\n",
       "      <td>0.963956</td>\n",
       "      <td>0.812207</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.577430</td>\n",
       "      <td>64</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>tanh</td>\n",
       "      <td>HLA-A3101</td>\n",
       "      <td>3309</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>2.175294</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[8]</td>\n",
       "      <td>{u'activation': u'tanh', u'embedding_input_dim...</td>\n",
       "      <td>0.941927</td>\n",
       "      <td>0.735084</td>\n",
       "      <td>0.514062</td>\n",
       "      <td>0.959710</td>\n",
       "      <td>0.784000</td>\n",
       "      <td>2206</td>\n",
       "      <td>0.601219</td>\n",
       "      <td>8</td>\n",
       "      <td>HLA-A3101 (3309)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    activation     allele  allele_size  embedding_input_dim  \\\n",
       "427       tanh  HLA-A3101         3309                   21   \n",
       "419       tanh  HLA-A3101         3309                   21   \n",
       "426       tanh  HLA-A3101         3309                   21   \n",
       "425       tanh  HLA-A3101         3309                   21   \n",
       "424       tanh  HLA-A3101         3309                   21   \n",
       "423       tanh  HLA-A3101         3309                   21   \n",
       "422       tanh  HLA-A3101         3309                   21   \n",
       "421       tanh  HLA-A3101         3309                   21   \n",
       "420       tanh  HLA-A3101         3309                   21   \n",
       "415       tanh  HLA-A3101         3309                   21   \n",
       "417       tanh  HLA-A3101         3309                   21   \n",
       "416       tanh  HLA-A3101         3309                   21   \n",
       "414       tanh  HLA-A3101         3309                   21   \n",
       "413       tanh  HLA-A3101         3309                   21   \n",
       "412       tanh  HLA-A3101         3309                   21   \n",
       "411       tanh  HLA-A3101         3309                   21   \n",
       "410       tanh  HLA-A3101         3309                   21   \n",
       "409       tanh  HLA-A3101         3309                   21   \n",
       "418       tanh  HLA-A3101         3309                   21   \n",
       "428       tanh  HLA-A3101         3309                   21   \n",
       "440       tanh  HLA-A3101         3309                   21   \n",
       "430       tanh  HLA-A3101         3309                   21   \n",
       "408       tanh  HLA-A3101         3309                   21   \n",
       "449       tanh  HLA-A3101         3309                   21   \n",
       "448       tanh  HLA-A3101         3309                   21   \n",
       "447       tanh  HLA-A3101         3309                   21   \n",
       "446       tanh  HLA-A3101         3309                   21   \n",
       "445       tanh  HLA-A3101         3309                   21   \n",
       "444       tanh  HLA-A3101         3309                   21   \n",
       "443       tanh  HLA-A3101         3309                   21   \n",
       "..         ...        ...          ...                  ...   \n",
       "404       tanh  HLA-A3101         3309                   21   \n",
       "380       tanh  HLA-A3101         3309                   21   \n",
       "406       tanh  HLA-A3101         3309                   21   \n",
       "379       tanh  HLA-A3101         3309                   21   \n",
       "377       tanh  HLA-A3101         3309                   21   \n",
       "354       tanh  HLA-A3101         3309                   21   \n",
       "355       tanh  HLA-A3101         3309                   21   \n",
       "356       tanh  HLA-A3101         3309                   21   \n",
       "357       tanh  HLA-A3101         3309                   21   \n",
       "358       tanh  HLA-A3101         3309                   21   \n",
       "359       tanh  HLA-A3101         3309                   21   \n",
       "360       tanh  HLA-A3101         3309                   21   \n",
       "361       tanh  HLA-A3101         3309                   21   \n",
       "362       tanh  HLA-A3101         3309                   21   \n",
       "363       tanh  HLA-A3101         3309                   21   \n",
       "364       tanh  HLA-A3101         3309                   21   \n",
       "365       tanh  HLA-A3101         3309                   21   \n",
       "366       tanh  HLA-A3101         3309                   21   \n",
       "367       tanh  HLA-A3101         3309                   21   \n",
       "368       tanh  HLA-A3101         3309                   21   \n",
       "369       tanh  HLA-A3101         3309                   21   \n",
       "370       tanh  HLA-A3101         3309                   21   \n",
       "371       tanh  HLA-A3101         3309                   21   \n",
       "372       tanh  HLA-A3101         3309                   21   \n",
       "373       tanh  HLA-A3101         3309                   21   \n",
       "374       tanh  HLA-A3101         3309                   21   \n",
       "375       tanh  HLA-A3101         3309                   21   \n",
       "376       tanh  HLA-A3101         3309                   21   \n",
       "378       tanh  HLA-A3101         3309                   21   \n",
       "331       tanh  HLA-A3101         3309                   21   \n",
       "\n",
       "     embedding_output_dim   fit_time impute  imputed_size layer_sizes  \\\n",
       "427                     5   1.868021  False           NaN        [16]   \n",
       "419                    64  13.486147   True          9362       [128]   \n",
       "426                     5   1.629664  False           NaN         [8]   \n",
       "425                     5   1.576970  False           NaN         [4]   \n",
       "424                   128  24.451036   True          9362       [128]   \n",
       "423                   128  16.921623   True          9362        [64]   \n",
       "422                   128  11.111831   True          9362        [16]   \n",
       "421                   128  10.251438   True          9362         [8]   \n",
       "420                   128   9.658818   True          9362         [4]   \n",
       "415                    64   5.399939   True          9362         [4]   \n",
       "417                    64   6.645087   True          9362        [16]   \n",
       "416                    64   6.370398   True          9362         [8]   \n",
       "414                    32   8.495607   True          9362       [128]   \n",
       "413                    32   5.994825   True          9362        [64]   \n",
       "412                    32   4.056105   True          9362        [16]   \n",
       "411                    32   3.800774   True          9362         [8]   \n",
       "410                    32   3.579236   True          9362         [4]   \n",
       "409                    10   4.614510   True          9362       [128]   \n",
       "418                    64   9.744167   True          9362        [64]   \n",
       "428                     5   2.540756  False           NaN        [64]   \n",
       "440                    64   4.967129  False           NaN         [4]   \n",
       "430                    10   1.715692  False           NaN         [4]   \n",
       "408                    10   3.316780   True          9362        [64]   \n",
       "449                   128  24.718327  False           NaN       [128]   \n",
       "448                   128  15.078671  False           NaN        [64]   \n",
       "447                   128   9.691375  False           NaN        [16]   \n",
       "446                   128   9.539105  False           NaN         [8]   \n",
       "445                   128   8.566430  False           NaN         [4]   \n",
       "444                    64  12.400123  False           NaN       [128]   \n",
       "443                    64   8.800901  False           NaN        [64]   \n",
       "..                    ...        ...    ...           ...         ...   \n",
       "404                     5   3.458234   True          9362       [128]   \n",
       "380                    10   1.726367  False           NaN         [4]   \n",
       "406                    10   2.303598   True          9362         [8]   \n",
       "379                     5   3.510116  False           NaN       [128]   \n",
       "377                     5   1.843633  False           NaN        [16]   \n",
       "354                     5   3.601491   True          9362       [128]   \n",
       "355                    10   1.967647   True          9362         [4]   \n",
       "356                    10   2.331174   True          9362         [8]   \n",
       "357                    10   2.447505   True          9362        [16]   \n",
       "358                    10   3.381274   True          9362        [64]   \n",
       "359                    10   4.639203   True          9362       [128]   \n",
       "360                    32   3.600294   True          9362         [4]   \n",
       "361                    32   3.849825   True          9362         [8]   \n",
       "362                    32   4.064893   True          9362        [16]   \n",
       "363                    32   6.032371   True          9362        [64]   \n",
       "364                    32   8.455435   True          9362       [128]   \n",
       "365                    64   5.447830   True          9362         [4]   \n",
       "366                    64   5.885132   True          9362         [8]   \n",
       "367                    64   6.447617   True          9362        [16]   \n",
       "368                    64   9.614197   True          9362        [64]   \n",
       "369                    64  13.323382   True          9362       [128]   \n",
       "370                   128   9.620890   True          9362         [4]   \n",
       "371                   128  10.234888   True          9362         [8]   \n",
       "372                   128  10.958013   True          9362        [16]   \n",
       "373                   128  16.790324   True          9362        [64]   \n",
       "374                   128  24.410361   True          9362       [128]   \n",
       "375                     5   1.557381  False           NaN         [4]   \n",
       "376                     5   1.582523  False           NaN         [8]   \n",
       "378                     5   2.540075  False           NaN        [64]   \n",
       "331                    10   2.175294  False           NaN         [8]   \n",
       "\n",
       "                                          model_params  test_auc   test_f1  \\\n",
       "427  {u'activation': u'tanh', u'embedding_input_dim...  0.935570  0.710112   \n",
       "419  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "426  {u'activation': u'tanh', u'embedding_input_dim...  0.939689  0.733333   \n",
       "425  {u'activation': u'tanh', u'embedding_input_dim...  0.930989  0.674757   \n",
       "424  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "423  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "422  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "421  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "420  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "415  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "417  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "416  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "414  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "413  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "412  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "411  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "410  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "409  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "418  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "428  {u'activation': u'tanh', u'embedding_input_dim...  0.940790  0.739040   \n",
       "440  {u'activation': u'tanh', u'embedding_input_dim...  0.931049  0.724696   \n",
       "430  {u'activation': u'tanh', u'embedding_input_dim...  0.938789  0.706294   \n",
       "408  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "449  {u'activation': u'tanh', u'embedding_input_dim...  0.926357  0.716157   \n",
       "448  {u'activation': u'tanh', u'embedding_input_dim...  0.928238  0.722330   \n",
       "447  {u'activation': u'tanh', u'embedding_input_dim...  0.929415  0.718631   \n",
       "446  {u'activation': u'tanh', u'embedding_input_dim...  0.921741  0.714876   \n",
       "445  {u'activation': u'tanh', u'embedding_input_dim...  0.922656  0.730612   \n",
       "444  {u'activation': u'tanh', u'embedding_input_dim...  0.938572  0.721973   \n",
       "443  {u'activation': u'tanh', u'embedding_input_dim...  0.936365  0.736243   \n",
       "..                                                 ...       ...       ...   \n",
       "404  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "380  {u'activation': u'tanh', u'embedding_input_dim...  0.911769  0.677656   \n",
       "406  {u'activation': u'tanh', u'embedding_input_dim...  0.428520  0.000000   \n",
       "379  {u'activation': u'tanh', u'embedding_input_dim...  0.864834  0.593886   \n",
       "377  {u'activation': u'tanh', u'embedding_input_dim...  0.875898  0.619329   \n",
       "354  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "355  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "356  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "357  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "358  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "359  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "360  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "361  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "362  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "363  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "364  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "365  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "366  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "367  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "368  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "369  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "370  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "371  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "372  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "373  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "374  {u'activation': u'tanh', u'embedding_input_dim...  0.653018  0.000000   \n",
       "375  {u'activation': u'tanh', u'embedding_input_dim...  0.908213  0.658744   \n",
       "376  {u'activation': u'tanh', u'embedding_input_dim...  0.886031  0.641921   \n",
       "378  {u'activation': u'tanh', u'embedding_input_dim...  0.919382  0.712687   \n",
       "331  {u'activation': u'tanh', u'embedding_input_dim...  0.941927  0.735084   \n",
       "\n",
       "     test_tau  train_auc  train_f1  train_size  train_tau  layer0_size  \\\n",
       "427  0.555839   0.953386  0.769964        2206   0.573294           16   \n",
       "419       NaN   0.595212  0.000000        2206        NaN          128   \n",
       "426  0.568165   0.954817  0.789100        2206   0.570262            8   \n",
       "425  0.547016   0.954279  0.805936        2206   0.571380            4   \n",
       "424       NaN   0.595212  0.000000        2206        NaN          128   \n",
       "423       NaN   0.595212  0.000000        2206        NaN           64   \n",
       "422       NaN   0.595212  0.000000        2206        NaN           16   \n",
       "421       NaN   0.595212  0.000000        2206        NaN            8   \n",
       "420       NaN   0.595212  0.000000        2206        NaN            4   \n",
       "415       NaN   0.595212  0.000000        2206        NaN            4   \n",
       "417       NaN   0.595212  0.000000        2206        NaN           16   \n",
       "416       NaN   0.595212  0.000000        2206        NaN            8   \n",
       "414       NaN   0.595212  0.000000        2206        NaN          128   \n",
       "413       NaN   0.595212  0.000000        2206        NaN           64   \n",
       "412       NaN   0.595212  0.000000        2206        NaN           16   \n",
       "411       NaN   0.595212  0.000000        2206        NaN            8   \n",
       "410       NaN   0.595212  0.000000        2206        NaN            4   \n",
       "409       NaN   0.595212  0.000000        2206        NaN          128   \n",
       "418       NaN   0.595212  0.000000        2206        NaN           64   \n",
       "428  0.566896   0.954801  0.788863        2206   0.578530           64   \n",
       "440  0.542662   0.973771  0.840708        2206   0.616580            4   \n",
       "430  0.557485   0.961590  0.813212        2206   0.591506            4   \n",
       "408       NaN   0.595212  0.000000        2206        NaN           64   \n",
       "449  0.530217   0.992517  0.901639        2206   0.673750          128   \n",
       "448  0.545203   0.996735  0.939189        2206   0.698979           64   \n",
       "447  0.535749   0.996518  0.954796        2206   0.702057           16   \n",
       "446  0.528658   0.994681  0.935939        2206   0.683223            8   \n",
       "445  0.513425   0.984851  0.879908        2206   0.633644            4   \n",
       "444  0.557326   0.975941  0.854142        2206   0.618043          128   \n",
       "443  0.560585   0.979745  0.862876        2206   0.628978           64   \n",
       "..        ...        ...       ...         ...        ...          ...   \n",
       "404       NaN   0.595212  0.000000        2206        NaN          128   \n",
       "380  0.549271   0.968196  0.819747        2206   0.584648            4   \n",
       "406       NaN   0.595212  0.000000        2206        NaN            8   \n",
       "379  0.454865   0.963047  0.793466        2206   0.576880          128   \n",
       "377  0.490314   0.963191  0.805755        2206   0.576035           16   \n",
       "354       NaN   0.521762  0.000000        2206        NaN          128   \n",
       "355       NaN   0.521762  0.000000        2206        NaN            4   \n",
       "356       NaN   0.521762  0.000000        2206        NaN            8   \n",
       "357       NaN   0.521762  0.000000        2206        NaN           16   \n",
       "358       NaN   0.521762  0.000000        2206        NaN           64   \n",
       "359       NaN   0.521762  0.000000        2206        NaN          128   \n",
       "360       NaN   0.521762  0.000000        2206        NaN            4   \n",
       "361       NaN   0.521762  0.000000        2206        NaN            8   \n",
       "362       NaN   0.521762  0.000000        2206        NaN           16   \n",
       "363       NaN   0.521762  0.000000        2206        NaN           64   \n",
       "364       NaN   0.521762  0.000000        2206        NaN          128   \n",
       "365       NaN   0.521762  0.000000        2206        NaN            4   \n",
       "366       NaN   0.521762  0.000000        2206        NaN            8   \n",
       "367       NaN   0.521762  0.000000        2206        NaN           16   \n",
       "368       NaN   0.521762  0.000000        2206        NaN           64   \n",
       "369       NaN   0.521762  0.000000        2206        NaN          128   \n",
       "370       NaN   0.521762  0.000000        2206        NaN            4   \n",
       "371       NaN   0.521762  0.000000        2206        NaN            8   \n",
       "372       NaN   0.521762  0.000000        2206        NaN           16   \n",
       "373       NaN   0.521762  0.000000        2206        NaN           64   \n",
       "374       NaN   0.521762  0.000000        2206        NaN          128   \n",
       "375  0.526577   0.963035  0.804651        2206   0.576558            4   \n",
       "376  0.493376   0.962878  0.803632        2206   0.574186            8   \n",
       "378  0.534931   0.963956  0.812207        2206   0.577430           64   \n",
       "331  0.514062   0.959710  0.784000        2206   0.601219            8   \n",
       "\n",
       "                label  \n",
       "427  HLA-A3101 (3309)  \n",
       "419  HLA-A3101 (3309)  \n",
       "426  HLA-A3101 (3309)  \n",
       "425  HLA-A3101 (3309)  \n",
       "424  HLA-A3101 (3309)  \n",
       "423  HLA-A3101 (3309)  \n",
       "422  HLA-A3101 (3309)  \n",
       "421  HLA-A3101 (3309)  \n",
       "420  HLA-A3101 (3309)  \n",
       "415  HLA-A3101 (3309)  \n",
       "417  HLA-A3101 (3309)  \n",
       "416  HLA-A3101 (3309)  \n",
       "414  HLA-A3101 (3309)  \n",
       "413  HLA-A3101 (3309)  \n",
       "412  HLA-A3101 (3309)  \n",
       "411  HLA-A3101 (3309)  \n",
       "410  HLA-A3101 (3309)  \n",
       "409  HLA-A3101 (3309)  \n",
       "418  HLA-A3101 (3309)  \n",
       "428  HLA-A3101 (3309)  \n",
       "440  HLA-A3101 (3309)  \n",
       "430  HLA-A3101 (3309)  \n",
       "408  HLA-A3101 (3309)  \n",
       "449  HLA-A3101 (3309)  \n",
       "448  HLA-A3101 (3309)  \n",
       "447  HLA-A3101 (3309)  \n",
       "446  HLA-A3101 (3309)  \n",
       "445  HLA-A3101 (3309)  \n",
       "444  HLA-A3101 (3309)  \n",
       "443  HLA-A3101 (3309)  \n",
       "..                ...  \n",
       "404  HLA-A3101 (3309)  \n",
       "380  HLA-A3101 (3309)  \n",
       "406  HLA-A3101 (3309)  \n",
       "379  HLA-A3101 (3309)  \n",
       "377  HLA-A3101 (3309)  \n",
       "354  HLA-A3101 (3309)  \n",
       "355  HLA-A3101 (3309)  \n",
       "356  HLA-A3101 (3309)  \n",
       "357  HLA-A3101 (3309)  \n",
       "358  HLA-A3101 (3309)  \n",
       "359  HLA-A3101 (3309)  \n",
       "360  HLA-A3101 (3309)  \n",
       "361  HLA-A3101 (3309)  \n",
       "362  HLA-A3101 (3309)  \n",
       "363  HLA-A3101 (3309)  \n",
       "364  HLA-A3101 (3309)  \n",
       "365  HLA-A3101 (3309)  \n",
       "366  HLA-A3101 (3309)  \n",
       "367  HLA-A3101 (3309)  \n",
       "368  HLA-A3101 (3309)  \n",
       "369  HLA-A3101 (3309)  \n",
       "370  HLA-A3101 (3309)  \n",
       "371  HLA-A3101 (3309)  \n",
       "372  HLA-A3101 (3309)  \n",
       "373  HLA-A3101 (3309)  \n",
       "374  HLA-A3101 (3309)  \n",
       "375  HLA-A3101 (3309)  \n",
       "376  HLA-A3101 (3309)  \n",
       "378  HLA-A3101 (3309)  \n",
       "331  HLA-A3101 (3309)  \n",
       "\n",
       "[150 rows x 19 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_df.ix[cv_df.allele == \"HLA-A3101\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13055ac10>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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08dbhFl7f3cCaJZNrKEVdyY/Q1tnP9+/dwebqU8wqy+H/ffY8hbdIHPJ6PNxx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\nwEVEXOr/A8ESZHpezo/BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d420550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.distplot(cv_df[cv_df.allele == \"HLA-A3101\"].test_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x12e2b9790>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kSmAGM/oNzz8WJC0DEyprHSQtg9NWdwNGZSrWaavnRxtkHcStU0X45M4qBLWe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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e6ff5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sub_cv_df = cv_df.ix[cv_df.allele != \"HLA-A2501\"]\n",
    "sub_cv_df = cv_df\n",
    "grid = seaborn.FacetGrid(\n",
    "    sub_cv_df,\n",
    "    col=\"label\",\n",
    "    col_wrap=5,\n",
    "    hue=\"impute\",\n",
    "    #col_order=[x[1] for x in sorted([(row.allele_size, row.label) for (_, row) in sub_cv_df.iterrows()])]\n",
    ")\n",
    "grid.map(pyplot.scatter, \"embedding_output_dim\", \"test_auc\", alpha=.5)\n",
    "#grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11a0a2890>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W/wTwf83sgtJjoZ//tk3DUG0OH3ffPcNTIlExmqEAfIpia38z0ElxkrmPunvB\nzK4GPkbx4+Rn3f3bkRRdYmbLgG+4+2ozO5viF6N1azazucA9wFKKIx+ucPdXIq5/OfAl4CjwK+Aa\ndz8Ux/rN7E+Afwc8XaqrAFwP3E7Mz3+N2jdSbPgk4dzPpThS5xSKoxE/X3otgX5fY1r/cxSzsiXn\nX3PviIhkSBy/SBURkRZR6IuIZIhCX0QkQxT6IiIZotAXEckQhb6ISIYo9EVEMkShLyKSIf8ftCo+\nvhQUE0sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119dfe190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.plot(cv_df.allele_size, cv_df.test_tau)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119dfeed0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YiyC4wa2MD7t1wQSW9V+OZ34LA95BzDoV1WlL6DJkkhG002XMwWyYfvq+c8tm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\nBEEQhKyIohAEQRCy8v8Byf9pzydI97UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118b77190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.swarmplot (data=cv_df, x=\"allele_size\", y=\"test_tau\", hue=\"embedding_output_dim\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 486,  782, 3309,  355,  368,  823, 2974, 1661,  237,  287,  502,\n",
       "       1295,  203, 2324,  621, 1700, 1985, 1165, 3188,  483,  519])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_df.allele_size.unique()"
   ]
  }
 ],
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