dataset_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%matplotlib inline\n",
    "!pip install pandas seaborn matplotlib bokeh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import bokeh as bk\n",
    "from sklearn.decomposition import PCA\n",
    "import plotly.express as px\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Name</th>\n",
       "      <th>Class</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Charge</th>\n",
       "      <th>Hydropathy</th>\n",
       "      <th>HelixProb</th>\n",
       "      <th>SheetProb</th>\n",
       "      <th>ResidueWeight</th>\n",
       "      <th>pKa1</th>\n",
       "      <th>pKb2</th>\n",
       "      <th>pKx3</th>\n",
       "      <th>pl4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sym</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Ala</td>\n",
       "      <td>Alanine</td>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.23</td>\n",
       "      <td>71.08</td>\n",
       "      <td>2.34</td>\n",
       "      <td>9.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>Arg</td>\n",
       "      <td>Arginine</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Basic polar</td>\n",
       "      <td>Positive</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.25</td>\n",
       "      <td>156.19</td>\n",
       "      <td>2.17</td>\n",
       "      <td>9.04</td>\n",
       "      <td>12.48</td>\n",
       "      <td>10.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>Asn</td>\n",
       "      <td>Asparagine</td>\n",
       "      <td>Amide</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.22</td>\n",
       "      <td>114.11</td>\n",
       "      <td>2.02</td>\n",
       "      <td>8.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>Asp</td>\n",
       "      <td>Aspartic acid</td>\n",
       "      <td>Acid</td>\n",
       "      <td>Acidic polar</td>\n",
       "      <td>Negative</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.20</td>\n",
       "      <td>115.09</td>\n",
       "      <td>1.88</td>\n",
       "      <td>9.60</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>Cys</td>\n",
       "      <td>Cysteine</td>\n",
       "      <td>Sulfuric</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.41</td>\n",
       "      <td>103.15</td>\n",
       "      <td>1.96</td>\n",
       "      <td>10.28</td>\n",
       "      <td>8.18</td>\n",
       "      <td>5.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>Glu</td>\n",
       "      <td>Glutamic acid</td>\n",
       "      <td>Acid</td>\n",
       "      <td>Acidic polar</td>\n",
       "      <td>Negative</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.21</td>\n",
       "      <td>129.12</td>\n",
       "      <td>2.19</td>\n",
       "      <td>9.67</td>\n",
       "      <td>4.25</td>\n",
       "      <td>3.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>Gln</td>\n",
       "      <td>Glutamine</td>\n",
       "      <td>Amide</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.25</td>\n",
       "      <td>128.13</td>\n",
       "      <td>2.17</td>\n",
       "      <td>9.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>Gly</td>\n",
       "      <td>Glycine</td>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>57.05</td>\n",
       "      <td>2.34</td>\n",
       "      <td>9.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>His</td>\n",
       "      <td>Histidine</td>\n",
       "      <td>Basic aromatic</td>\n",
       "      <td>Basic polar</td>\n",
       "      <td>Positive</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.30</td>\n",
       "      <td>137.14</td>\n",
       "      <td>1.82</td>\n",
       "      <td>9.17</td>\n",
       "      <td>6.00</td>\n",
       "      <td>7.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O</th>\n",
       "      <td>Hyp</td>\n",
       "      <td>Hydroxyproline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.11</td>\n",
       "      <td>1.82</td>\n",
       "      <td>9.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>Ile</td>\n",
       "      <td>Isoleucine</td>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.45</td>\n",
       "      <td>113.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>9.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>Leu</td>\n",
       "      <td>Leucine</td>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.31</td>\n",
       "      <td>113.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>9.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K</th>\n",
       "      <td>Lys</td>\n",
       "      <td>Lysine</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Basic polar</td>\n",
       "      <td>Positive</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.27</td>\n",
       "      <td>128.18</td>\n",
       "      <td>2.18</td>\n",
       "      <td>8.95</td>\n",
       "      <td>10.53</td>\n",
       "      <td>9.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Met</td>\n",
       "      <td>Methionine</td>\n",
       "      <td>Sulfuric</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.32</td>\n",
       "      <td>131.20</td>\n",
       "      <td>2.28</td>\n",
       "      <td>9.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>Phe</td>\n",
       "      <td>Phenylalanine</td>\n",
       "      <td>Aromatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.38</td>\n",
       "      <td>147.18</td>\n",
       "      <td>1.83</td>\n",
       "      <td>9.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>Pro</td>\n",
       "      <td>Proline</td>\n",
       "      <td>Cyclic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.34</td>\n",
       "      <td>97.12</td>\n",
       "      <td>1.99</td>\n",
       "      <td>10.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U</th>\n",
       "      <td>Glp</td>\n",
       "      <td>Pyroglutamatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>Ser</td>\n",
       "      <td>Serine</td>\n",
       "      <td>Hydroxylic</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.28</td>\n",
       "      <td>87.08</td>\n",
       "      <td>2.21</td>\n",
       "      <td>9.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>Thr</td>\n",
       "      <td>Threonine</td>\n",
       "      <td>Hydroxylic</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.36</td>\n",
       "      <td>101.11</td>\n",
       "      <td>2.09</td>\n",
       "      <td>9.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>Trp</td>\n",
       "      <td>Tryptophan</td>\n",
       "      <td>Aromatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.42</td>\n",
       "      <td>186.22</td>\n",
       "      <td>2.83</td>\n",
       "      <td>9.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>Tyr</td>\n",
       "      <td>Tyrosine</td>\n",
       "      <td>Aromatic</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.41</td>\n",
       "      <td>163.18</td>\n",
       "      <td>2.20</td>\n",
       "      <td>9.11</td>\n",
       "      <td>10.07</td>\n",
       "      <td>5.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>Val</td>\n",
       "      <td>Valine</td>\n",
       "      <td>Aliphatic</td>\n",
       "      <td>Nonpolar</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.49</td>\n",
       "      <td>99.13</td>\n",
       "      <td>2.32</td>\n",
       "      <td>9.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0            Name           Class      Polarity    Charge  \\\n",
       "Sym                                                                      \n",
       "A          Ala         Alanine       Aliphatic      Nonpolar   Neutral   \n",
       "R          Arg        Arginine           Basic   Basic polar  Positive   \n",
       "N          Asn      Asparagine           Amide         Polar   Neutral   \n",
       "D          Asp   Aspartic acid            Acid  Acidic polar  Negative   \n",
       "C          Cys        Cysteine        Sulfuric      Nonpolar   Neutral   \n",
       "E          Glu   Glutamic acid            Acid  Acidic polar  Negative   \n",
       "Q          Gln       Glutamine           Amide         Polar   Neutral   \n",
       "G          Gly         Glycine       Aliphatic      Nonpolar   Neutral   \n",
       "H          His       Histidine  Basic aromatic   Basic polar  Positive   \n",
       "O          Hyp  Hydroxyproline             NaN           NaN       NaN   \n",
       "I          Ile      Isoleucine       Aliphatic      Nonpolar   Neutral   \n",
       "L          Leu         Leucine       Aliphatic      Nonpolar   Neutral   \n",
       "K          Lys          Lysine           Basic   Basic polar  Positive   \n",
       "M          Met      Methionine        Sulfuric      Nonpolar   Neutral   \n",
       "F          Phe   Phenylalanine        Aromatic      Nonpolar   Neutral   \n",
       "P          Pro         Proline          Cyclic      Nonpolar   Neutral   \n",
       "U          Glp  Pyroglutamatic             NaN           NaN       NaN   \n",
       "S          Ser          Serine      Hydroxylic         Polar   Neutral   \n",
       "T          Thr       Threonine      Hydroxylic         Polar   Neutral   \n",
       "W          Trp      Tryptophan        Aromatic      Nonpolar   Neutral   \n",
       "Y          Tyr        Tyrosine        Aromatic         Polar   Neutral   \n",
       "V          Val          Valine       Aliphatic      Nonpolar   Neutral   \n",
       "\n",
       "     Hydropathy  HelixProb  SheetProb  ResidueWeight  pKa1   pKb2   pKx3  \\\n",
       "Sym                                                                        \n",
       "A           1.8       0.42       0.23          71.08  2.34   9.69   0.00   \n",
       "R          -4.5       0.36       0.25         156.19  2.17   9.04  12.48   \n",
       "N          -3.5       0.21       0.22         114.11  2.02   8.80   0.00   \n",
       "D          -3.5       0.25       0.20         115.09  1.88   9.60   3.65   \n",
       "C           2.5       0.17       0.41         103.15  1.96  10.28   8.18   \n",
       "E          -3.5       0.42       0.21         129.12  2.19   9.67   4.25   \n",
       "Q          -3.5       0.36       0.25         128.13  2.17   9.13   0.00   \n",
       "G          -0.4       0.13       0.15          57.05  2.34   9.60   0.00   \n",
       "H          -3.2       0.27       0.30         137.14  1.82   9.17   6.00   \n",
       "O           0.0        NaN        NaN         113.11  1.82   9.65   0.00   \n",
       "I           4.5       0.30       0.45         113.16  2.36   9.60   0.00   \n",
       "L           3.8       0.39       0.31         113.16  2.36   9.60   0.00   \n",
       "K          -3.9       0.32       0.27         128.18  2.18   8.95  10.53   \n",
       "M           1.9       0.38       0.32         131.20  2.28   9.21   0.00   \n",
       "F           2.8       0.30       0.38         147.18  1.83   9.13   0.00   \n",
       "P          -1.6       0.13       0.34          97.12  1.99  10.60   0.00   \n",
       "U           0.0        NaN        NaN         121.09  0.00   0.00   0.00   \n",
       "S          -0.8       0.20       0.28          87.08  2.21   9.15   0.00   \n",
       "T          -0.7       0.21       0.36         101.11  2.09   9.10   0.00   \n",
       "W          -0.9       0.32       0.42         186.22  2.83   9.39   0.00   \n",
       "Y          -1.3       0.25       0.41         163.18  2.20   9.11  10.07   \n",
       "V           4.2       0.27       0.49          99.13  2.32   9.62   0.00   \n",
       "\n",
       "       pl4  \n",
       "Sym         \n",
       "A     6.00  \n",
       "R    10.76  \n",
       "N     5.41  \n",
       "D     2.77  \n",
       "C     5.07  \n",
       "E     3.22  \n",
       "Q     5.65  \n",
       "G     5.97  \n",
       "H     7.59  \n",
       "O     0.00  \n",
       "I     6.02  \n",
       "L     5.98  \n",
       "K     9.74  \n",
       "M     5.74  \n",
       "F     5.48  \n",
       "P     6.30  \n",
       "U     5.68  \n",
       "S     5.68  \n",
       "T     5.60  \n",
       "W     5.89  \n",
       "Y     5.66  \n",
       "V     5.96  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aminoacids = pd.read_csv(\"./data/aminoacids_properties.csv\")\n",
    "aminoacids.set_index(\"Sym\", inplace=True)\n",
    "aminoacids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sequence</th>\n",
       "      <th>secondary_structure</th>\n",
       "      <th>stabilityscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EHEE_rd2_0005.pdb_V26L</td>\n",
       "      <td>GSSTTRYRFTDEEEARRAAKEWARRGYQLHVTQNGTYWEVEVR</td>\n",
       "      <td>TTTTEEEEETTHHHHHHHHHHHHHHTTTEEEEEETTEEEEEET</td>\n",
       "      <td>1.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HHH_rd2_0134.pdb_R22D</td>\n",
       "      <td>SKDEAQREAERAIRSGNKEEADRILEEAGYSPEQAERIIRKLG</td>\n",
       "      <td>THHHHHHHHHHHHHTTTHHHHHHHHHHHTTTHHHHHHHHHHHT</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HHH_rd2_0134.pdb_Q6K</td>\n",
       "      <td>SKDEAKREAERAIRSGNKEEARRILEEAGYSPEQAERIIRKLG</td>\n",
       "      <td>THHHHHHHHHHHHHTTTHHHHHHHHHHHTTTHHHHHHHHHHHT</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EHEE_0882.pdb_E2I</td>\n",
       "      <td>GSSQITIEVEDEEEARRVAKELRKKGYEVKIERRGNKWHVHRT</td>\n",
       "      <td>TTTTEEEEETTHHHHHHHHHHHHHHTTTEEEEEETTEEEEEET</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EEHEE_rd3_1716.pdb_A26R</td>\n",
       "      <td>TEVHLGDIKLKYPNPEQAKKAAEKLRQKYNLTWTVIGDYVKIE</td>\n",
       "      <td>TEEEETTEEEETTTHHHHHHHHHHHHHHHTTTEEEETTEEEET</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8545</th>\n",
       "      <td>HHH_rd2_0134.pdb_Y30N</td>\n",
       "      <td>SKDEAQREAERAIRSGNKEEARRILEEAGNSPEQAERIIRKLG</td>\n",
       "      <td>THHHHHHHHHHHHHTTTHHHHHHHHHHHTTTHHHHHHHHHHHT</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8546</th>\n",
       "      <td>EHEE_rd2_0005.pdb_W35E</td>\n",
       "      <td>GSSTTRYRFTDEEEARRAAKEWARRGYQVHVTQNGTYEEVEVR</td>\n",
       "      <td>TTTTEEEEETTHHHHHHHHHHHHHHTTTEEEEEETTEEEEEET</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8547</th>\n",
       "      <td>HEEH_rd3_0872.pdb_A35T</td>\n",
       "      <td>TWQDLVKIAEKALEKGEPITINGITVTTKEQAKQTIEYLKKAY</td>\n",
       "      <td>THHHHHHHHHHHHHTTEEEETTEEEETTTHHHHHHHHHHHHHT</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8548</th>\n",
       "      <td>EEHEE_rd3_0037.pdb_E17S</td>\n",
       "      <td>TTIKVNGQEYTVPLSPSQAAKAAKKRWPDYEVQIHGNTVKVTR</td>\n",
       "      <td>TEEEETTEEEETTTHHHHHHHHHHHHHHHTTTEEEETTEEEET</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8549</th>\n",
       "      <td>EHEE_rd2_0005.pdb_A12T</td>\n",
       "      <td>GSSTTRYRFTDEEETRRAAKEWARRGYQVHVTQNGTYWEVEVR</td>\n",
       "      <td>TTTTEEEEETTHHHHHHHHHHHHHHTTTEEEEEETTEEEEEET</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8550 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         name                                     sequence  \\\n",
       "0      EHEE_rd2_0005.pdb_V26L  GSSTTRYRFTDEEEARRAAKEWARRGYQLHVTQNGTYWEVEVR   \n",
       "1       HHH_rd2_0134.pdb_R22D  SKDEAQREAERAIRSGNKEEADRILEEAGYSPEQAERIIRKLG   \n",
       "2        HHH_rd2_0134.pdb_Q6K  SKDEAKREAERAIRSGNKEEARRILEEAGYSPEQAERIIRKLG   \n",
       "3           EHEE_0882.pdb_E2I  GSSQITIEVEDEEEARRVAKELRKKGYEVKIERRGNKWHVHRT   \n",
       "4     EEHEE_rd3_1716.pdb_A26R  TEVHLGDIKLKYPNPEQAKKAAEKLRQKYNLTWTVIGDYVKIE   \n",
       "...                       ...                                          ...   \n",
       "8545    HHH_rd2_0134.pdb_Y30N  SKDEAQREAERAIRSGNKEEARRILEEAGNSPEQAERIIRKLG   \n",
       "8546   EHEE_rd2_0005.pdb_W35E  GSSTTRYRFTDEEEARRAAKEWARRGYQVHVTQNGTYEEVEVR   \n",
       "8547   HEEH_rd3_0872.pdb_A35T  TWQDLVKIAEKALEKGEPITINGITVTTKEQAKQTIEYLKKAY   \n",
       "8548  EEHEE_rd3_0037.pdb_E17S  TTIKVNGQEYTVPLSPSQAAKAAKKRWPDYEVQIHGNTVKVTR   \n",
       "8549   EHEE_rd2_0005.pdb_A12T  GSSTTRYRFTDEEETRRAAKEWARRGYQVHVTQNGTYWEVEVR   \n",
       "\n",
       "                              secondary_structure  stabilityscore  \n",
       "0     TTTTEEEEETTHHHHHHHHHHHHHHTTTEEEEEETTEEEEEET            1.72  \n",
       "1     THHHHHHHHHHHHHTTTHHHHHHHHHHHTTTHHHHHHHHHHHT            1.22  \n",
       "2     THHHHHHHHHHHHHTTTHHHHHHHHHHHTTTHHHHHHHHHHHT            1.20  \n",
       "3     TTTTEEEEETTHHHHHHHHHHHHHHTTTEEEEEETTEEEEEET            1.59  \n",
       "4     TEEEETTEEEETTTHHHHHHHHHHHHHHHTTTEEEETTEEEET            0.88  \n",
       "...                                           ...             ...  \n",
       "8545  THHHHHHHHHHHHHTTTHHHHHHHHHHHTTTHHHHHHHHHHHT            1.13  \n",
       "8546  TTTTEEEEETTHHHHHHHHHHHHHHTTTEEEEEETTEEEEEET            0.44  \n",
       "8547  THHHHHHHHHHHHHTTEEEETTEEEETTTHHHHHHHHHHHHHT            0.64  \n",
       "8548  TEEEETTEEEETTTHHHHHHHHHHHHHHHTTTEEEETTEEEET            0.68  \n",
       "8549  TTTTEEEEETTHHHHHHHHHHHHHHTTTEEEEEETTEEEEEET            1.68  \n",
       "\n",
       "[8550 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_train = pd.read_csv(\"./data/single_muts_train.csv\", index_col=0)\n",
    "single_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8550 entries, 0 to 8549\n",
      "Data columns (total 4 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   name                 8550 non-null   object \n",
      " 1   sequence             8550 non-null   object \n",
      " 2   secondary_structure  8550 non-null   object \n",
      " 3   stabilityscore       8550 non-null   float64\n",
      "dtypes: float64(1), object(3)\n",
      "memory usage: 334.0+ KB\n"
     ]
    }
   ],
   "source": [
    "single_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stabilityscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8550.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.054056</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.400587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.400000</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "       stabilityscore\n",
       "count     8550.000000\n",
       "mean         1.054056\n",
       "std          0.400587\n",
       "min         -0.270000\n",
       "25%          0.790000\n",
       "50%          1.040000\n",
       "75%          1.320000\n",
       "max          2.400000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8550.0\n",
       "mean       43.0\n",
       "std         0.0\n",
       "min        43.0\n",
       "25%        43.0\n",
       "50%        43.0\n",
       "75%        43.0\n",
       "max        43.0\n",
       "Name: sequence, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_len = single_train[\"sequence\"].apply(lambda x: len(str(x)))\n",
    "sequence_len.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8550.0\n",
       "mean       43.0\n",
       "std         0.0\n",
       "min        43.0\n",
       "25%        43.0\n",
       "50%        43.0\n",
       "75%        43.0\n",
       "max        43.0\n",
       "Name: secondary_structure, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "secondary_len = single_train[\"secondary_structure\"].apply(lambda x: len(str(x)))\n",
    "secondary_len.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'A',\n",
       "  'D',\n",
       "  'E',\n",
       "  'F',\n",
       "  'G',\n",
       "  'H',\n",
       "  'I',\n",
       "  'K',\n",
       "  'L',\n",
       "  'M',\n",
       "  'N',\n",
       "  'P',\n",
       "  'Q',\n",
       "  'R',\n",
       "  'S',\n",
       "  'T',\n",
       "  'V',\n",
       "  'W',\n",
       "  'Y'},\n",
       " 19)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_aminoacids = {\n",
    "    aminoacid\n",
    "    for sequence in single_train[\"sequence\"]\n",
    "    for aminoacid in sequence\n",
    "}\n",
    "sequence_aminoacids, len(sequence_aminoacids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_aminoacids.issubset(set(aminoacids.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "aminoacids = aminoacids.loc[list(sequence_aminoacids), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8546</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8547</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8548</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8549</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8550 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      A    D    E    F    G    H    I    K    L    M    N    P    Q    R    S  \\\n",
       "0     4  1.0  6.0  1.0  3.0  1.0  0.0  1.0  1.0  0.0  1.0  0.0  2.0  7.0  2.0   \n",
       "1     6  2.0  9.0  0.0  3.0  0.0  4.0  3.0  2.0  0.0  1.0  1.0  2.0  6.0  3.0   \n",
       "2     6  1.0  9.0  0.0  3.0  0.0  4.0  4.0  2.0  0.0  1.0  1.0  1.0  7.0  3.0   \n",
       "3     2  1.0  8.0  0.0  3.0  2.0  3.0  5.0  1.0  0.0  1.0  0.0  1.0  6.0  2.0   \n",
       "4     3  2.0  4.0  0.0  2.0  1.0  3.0  7.0  4.0  0.0  2.0  2.0  2.0  1.0  0.0   \n",
       "...  ..  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "8545  6  1.0  9.0  0.0  3.0  0.0  4.0  3.0  2.0  0.0  2.0  1.0  2.0  7.0  3.0   \n",
       "8546  4  1.0  7.0  1.0  3.0  1.0  0.0  1.0  0.0  0.0  1.0  0.0  2.0  7.0  2.0   \n",
       "8547  4  1.0  5.0  0.0  2.0  0.0  5.0  7.0  3.0  0.0  1.0  1.0  3.0  0.0  0.0   \n",
       "8548  4  1.0  2.0  0.0  2.0  1.0  2.0  5.0  1.0  0.0  2.0  3.0  3.0  2.0  2.0   \n",
       "8549  3  1.0  6.0  1.0  3.0  1.0  0.0  1.0  0.0  0.0  1.0  0.0  2.0  7.0  2.0   \n",
       "\n",
       "        T    V    W    Y  \n",
       "0     5.0  3.0  2.0  3.0  \n",
       "1     0.0  0.0  0.0  1.0  \n",
       "2     0.0  0.0  0.0  1.0  \n",
       "3     2.0  4.0  1.0  1.0  \n",
       "4     3.0  3.0  1.0  3.0  \n",
       "...   ...  ...  ...  ...  \n",
       "8545  0.0  0.0  0.0  0.0  \n",
       "8546  5.0  4.0  1.0  3.0  \n",
       "8547  6.0  2.0  1.0  2.0  \n",
       "8548  5.0  5.0  1.0  2.0  \n",
       "8549  6.0  4.0  2.0  3.0  \n",
       "\n",
       "[8550 rows x 19 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_freq = pd.DataFrame(\n",
    "    dict(Counter(sequence))\n",
    "    for sequence in single_train[\"sequence\"]\n",
    ").reindex(sorted(sequence_aminoacids), axis=1)\n",
    "sequence_freq = sequence_freq.fillna(0)\n",
    "sequence_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color = {\n",
    "    \"boxes\": sns.color_palette(\"tab10\")[0],\n",
    "    \"whiskers\": sns.color_palette(\"tab10\")[1],\n",
    "    \"medians\": sns.color_palette(\"tab10\")[3],\n",
    "    \"caps\": sns.color_palette(\"tab10\")[4],\n",
    "}\n",
    "\n",
    "#_ = sequence_freq.plot(subplots=True, figsize=(16, 4), kind='box', palette=color)\n",
    "palette=list(['#e6194B', '#3cb44b', '#ffe119', '#4363d8', '#f58231', '#911eb4', '#42d4f4', '#f032e6', \n",
    "             '#bfef45', '#fabed4', '#469990', '#dcbeff', '#9A6324', '#fffac8', '#800000', '#aaffc3', \n",
    "             '#808000', '#ffd8b1', '#000075', '#a9a9a9', '#ffffff', '#000000'])\n",
    "fig, ax = plt.subplots(figsize=(16, 4))\n",
    "_ = sns.boxplot(data=sequence_freq, ax=ax, palette=palette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(sequence_freq))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence_freq = sequence_freq.div(sequence_freq.sum(axis=1), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>E</th>\n",
       "      <th>H</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8545</th>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8546</th>\n",
       "      <td>12</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8547</th>\n",
       "      <td>9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8548</th>\n",
       "      <td>12</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8549</th>\n",
       "      <td>12</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8550 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       T     E   H\n",
       "0     12  17.0  14\n",
       "1      8   0.0  35\n",
       "2      8   0.0  35\n",
       "3     12  17.0  14\n",
       "4     12  16.0  15\n",
       "...   ..   ...  ..\n",
       "8545   8   0.0  35\n",
       "8546  12  17.0  14\n",
       "8547   9   8.0  26\n",
       "8548  12  16.0  15\n",
       "8549  12  17.0  14\n",
       "\n",
       "[8550 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure_freq = pd.DataFrame(\n",
    "    dict(Counter(structure))\n",
    "    for structure in single_train[\"secondary_structure\"]\n",
    ")\n",
    "structure_freq = structure_freq.fillna(0)\n",
    "structure_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = structure_freq.plot(subplots=True, figsize=(4, 4), kind='box', color=color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "structure_freq = structure_freq.div(structure_freq.sum(axis=1), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "structure_type = single_train[\"name\"].apply(lambda x: x.split(\"_\")[0])\n",
    "#structure_type.value_counts().plot.bar()\n",
    "structure_type = pd.DataFrame(structure_type.value_counts())\n",
    "_ = sns.barplot(x=structure_type.index, y=\"name\", data=structure_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class_Acid</th>\n",
       "      <th>Class_Aliphatic</th>\n",
       "      <th>Class_Amide</th>\n",
       "      <th>Class_Aromatic</th>\n",
       "      <th>Class_Basic</th>\n",
       "      <th>Class_Basic aromatic</th>\n",
       "      <th>Class_Cyclic</th>\n",
       "      <th>Class_Hydroxylic</th>\n",
       "      <th>Class_Sulfuric</th>\n",
       "      <th>Polarity_Acidic polar</th>\n",
       "      <th>...</th>\n",
       "      <th>Charge_Neutral</th>\n",
       "      <th>Charge_Positive</th>\n",
       "      <th>Hydropathy</th>\n",
       "      <th>HelixProb</th>\n",
       "      <th>SheetProb</th>\n",
       "      <th>ResidueWeight</th>\n",
       "      <th>pKa1</th>\n",
       "      <th>pKb2</th>\n",
       "      <th>pKx3</th>\n",
       "      <th>pl4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sym</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.28</td>\n",
       "      <td>87.08</td>\n",
       "      <td>2.21</td>\n",
       "      <td>9.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.22</td>\n",
       "      <td>114.11</td>\n",
       "      <td>2.02</td>\n",
       "      <td>8.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.27</td>\n",
       "      <td>128.18</td>\n",
       "      <td>2.18</td>\n",
       "      <td>8.95</td>\n",
       "      <td>10.53</td>\n",
       "      <td>9.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.21</td>\n",
       "      <td>129.12</td>\n",
       "      <td>2.19</td>\n",
       "      <td>9.67</td>\n",
       "      <td>4.25</td>\n",
       "      <td>3.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.45</td>\n",
       "      <td>113.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>9.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.41</td>\n",
       "      <td>163.18</td>\n",
       "      <td>2.20</td>\n",
       "      <td>9.11</td>\n",
       "      <td>10.07</td>\n",
       "      <td>5.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.38</td>\n",
       "      <td>147.18</td>\n",
       "      <td>1.83</td>\n",
       "      <td>9.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>57.05</td>\n",
       "      <td>2.34</td>\n",
       "      <td>9.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.25</td>\n",
       "      <td>156.19</td>\n",
       "      <td>2.17</td>\n",
       "      <td>9.04</td>\n",
       "      <td>12.48</td>\n",
       "      <td>10.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.31</td>\n",
       "      <td>113.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>9.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.34</td>\n",
       "      <td>97.12</td>\n",
       "      <td>1.99</td>\n",
       "      <td>10.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.49</td>\n",
       "      <td>99.13</td>\n",
       "      <td>2.32</td>\n",
       "      <td>9.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.36</td>\n",
       "      <td>101.11</td>\n",
       "      <td>2.09</td>\n",
       "      <td>9.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.42</td>\n",
       "      <td>186.22</td>\n",
       "      <td>2.83</td>\n",
       "      <td>9.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.23</td>\n",
       "      <td>71.08</td>\n",
       "      <td>2.34</td>\n",
       "      <td>9.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.30</td>\n",
       "      <td>137.14</td>\n",
       "      <td>1.82</td>\n",
       "      <td>9.17</td>\n",
       "      <td>6.00</td>\n",
       "      <td>7.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.25</td>\n",
       "      <td>128.13</td>\n",
       "      <td>2.17</td>\n",
       "      <td>9.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.32</td>\n",
       "      <td>131.20</td>\n",
       "      <td>2.28</td>\n",
       "      <td>9.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.20</td>\n",
       "      <td>115.09</td>\n",
       "      <td>1.88</td>\n",
       "      <td>9.60</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Class_Acid  Class_Aliphatic  Class_Amide  Class_Aromatic  Class_Basic  \\\n",
       "Sym                                                                          \n",
       "S             0                0            0               0            0   \n",
       "N             0                0            1               0            0   \n",
       "K             0                0            0               0            1   \n",
       "E             1                0            0               0            0   \n",
       "I             0                1            0               0            0   \n",
       "Y             0                0            0               1            0   \n",
       "F             0                0            0               1            0   \n",
       "G             0                1            0               0            0   \n",
       "R             0                0            0               0            1   \n",
       "L             0                1            0               0            0   \n",
       "P             0                0            0               0            0   \n",
       "V             0                1            0               0            0   \n",
       "T             0                0            0               0            0   \n",
       "W             0                0            0               1            0   \n",
       "A             0                1            0               0            0   \n",
       "H             0                0            0               0            0   \n",
       "Q             0                0            1               0            0   \n",
       "M             0                0            0               0            0   \n",
       "D             1                0            0               0            0   \n",
       "\n",
       "     Class_Basic aromatic  Class_Cyclic  Class_Hydroxylic  Class_Sulfuric  \\\n",
       "Sym                                                                         \n",
       "S                       0             0                 1               0   \n",
       "N                       0             0                 0               0   \n",
       "K                       0             0                 0               0   \n",
       "E                       0             0                 0               0   \n",
       "I                       0             0                 0               0   \n",
       "Y                       0             0                 0               0   \n",
       "F                       0             0                 0               0   \n",
       "G                       0             0                 0               0   \n",
       "R                       0             0                 0               0   \n",
       "L                       0             0                 0               0   \n",
       "P                       0             1                 0               0   \n",
       "V                       0             0                 0               0   \n",
       "T                       0             0                 1               0   \n",
       "W                       0             0                 0               0   \n",
       "A                       0             0                 0               0   \n",
       "H                       1             0                 0               0   \n",
       "Q                       0             0                 0               0   \n",
       "M                       0             0                 0               1   \n",
       "D                       0             0                 0               0   \n",
       "\n",
       "     Polarity_Acidic polar  ...  Charge_Neutral  Charge_Positive  Hydropathy  \\\n",
       "Sym                         ...                                                \n",
       "S                        0  ...               1                0        -0.8   \n",
       "N                        0  ...               1                0        -3.5   \n",
       "K                        0  ...               0                1        -3.9   \n",
       "E                        1  ...               0                0        -3.5   \n",
       "I                        0  ...               1                0         4.5   \n",
       "Y                        0  ...               1                0        -1.3   \n",
       "F                        0  ...               1                0         2.8   \n",
       "G                        0  ...               1                0        -0.4   \n",
       "R                        0  ...               0                1        -4.5   \n",
       "L                        0  ...               1                0         3.8   \n",
       "P                        0  ...               1                0        -1.6   \n",
       "V                        0  ...               1                0         4.2   \n",
       "T                        0  ...               1                0        -0.7   \n",
       "W                        0  ...               1                0        -0.9   \n",
       "A                        0  ...               1                0         1.8   \n",
       "H                        0  ...               0                1        -3.2   \n",
       "Q                        0  ...               1                0        -3.5   \n",
       "M                        0  ...               1                0         1.9   \n",
       "D                        1  ...               0                0        -3.5   \n",
       "\n",
       "     HelixProb  SheetProb  ResidueWeight  pKa1   pKb2   pKx3    pl4  \n",
       "Sym                                                                  \n",
       "S         0.20       0.28          87.08  2.21   9.15   0.00   5.68  \n",
       "N         0.21       0.22         114.11  2.02   8.80   0.00   5.41  \n",
       "K         0.32       0.27         128.18  2.18   8.95  10.53   9.74  \n",
       "E         0.42       0.21         129.12  2.19   9.67   4.25   3.22  \n",
       "I         0.30       0.45         113.16  2.36   9.60   0.00   6.02  \n",
       "Y         0.25       0.41         163.18  2.20   9.11  10.07   5.66  \n",
       "F         0.30       0.38         147.18  1.83   9.13   0.00   5.48  \n",
       "G         0.13       0.15          57.05  2.34   9.60   0.00   5.97  \n",
       "R         0.36       0.25         156.19  2.17   9.04  12.48  10.76  \n",
       "L         0.39       0.31         113.16  2.36   9.60   0.00   5.98  \n",
       "P         0.13       0.34          97.12  1.99  10.60   0.00   6.30  \n",
       "V         0.27       0.49          99.13  2.32   9.62   0.00   5.96  \n",
       "T         0.21       0.36         101.11  2.09   9.10   0.00   5.60  \n",
       "W         0.32       0.42         186.22  2.83   9.39   0.00   5.89  \n",
       "A         0.42       0.23          71.08  2.34   9.69   0.00   6.00  \n",
       "H         0.27       0.30         137.14  1.82   9.17   6.00   7.59  \n",
       "Q         0.36       0.25         128.13  2.17   9.13   0.00   5.65  \n",
       "M         0.38       0.32         131.20  2.28   9.21   0.00   5.74  \n",
       "D         0.25       0.20         115.09  1.88   9.60   3.65   2.77  \n",
       "\n",
       "[19 rows x 24 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoding = [\"Class\", \"Polarity\", \"Charge\"]\n",
    "aminoacids = pd.concat([\n",
    "    pd.get_dummies(aminoacids[e], prefix=e) for e in encoding\n",
    "] + [aminoacids.iloc[:, 2:]], axis=1)\n",
    "aminoacids.drop(encoding, axis=1, inplace=True)\n",
    "aminoacids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# min-max normalization \n",
    "aminoacids = (aminoacids - aminoacids.min()) / (aminoacids.max() - aminoacids.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sequence_features(sequence: str):\n",
    "    return np.vstack([\n",
    "        aminoacids.T[s].values\n",
    "        for s in sequence\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence_feat = np.array([\n",
    "    get_sequence_features(s)\n",
    "    for s in single_train[\"sequence\"]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(figsize=(8, 12))\n",
    "_ = sns.heatmap(sequence_feat[0], ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "structures = {\n",
    "    \"T\": np.array([1, 0, 0]),\n",
    "    \"H\": np.array([0, 1, 0]),\n",
    "    \"E\": np.array([0, 0, 1]),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_structure_features(structure: str):\n",
    "    return np.vstack([\n",
    "        structures[s]\n",
    "        for s in structure\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "structure_feat = np.array([\n",
    "    get_structure_features(s)\n",
    "    for s in single_train[\"secondary_structure\"]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(figsize=(2, 8))\n",
    "_ = sns.heatmap(structure_feat[0], ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez_compressed(\"./data/data.npz\", **{\n",
    "    \"sequence_freq\": sequence_freq,\n",
    "    \"structure_freq\": structure_freq,\n",
    "    \"sequence_feat\": sequence_feat,\n",
    "    \"structure_feat\": structure_feat,\n",
    "    \"score\": single_train[\"stabilityscore\"]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca = PCA(n_components=2)\n",
    "components = pd.DataFrame(pca.fit_transform(aminoacids), columns=[\"PC1\", \"PC2\"])\n",
    "_ = sns.scatterplot(x=\"PC1\", y=\"PC2\", data=components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}