06_Activity_model_v02.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning applied to identify Active Antimicobrial Peptides\n",
    "\n",
    "In this notebook is described all the procedure used to train and test the Activity model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import SVC\n",
    "import pickle\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn import metrics\n",
    "import joblib\n",
    "from sklearn.metrics import precision_recall_curve, auc\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This function wil convert the undefined MIC values or MIC > 8 to 0.\n",
    "def convert_mic(mic_value):\n",
    "    if type(mic_value) == str and mic_value.startswith('>'):\n",
    "        return 0\n",
    "    elif float(mic_value) > 8:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "    \n",
    "convert_mic('>64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>ID</th>\n",
       "      <th>Sequence</th>\n",
       "      <th>MBC</th>\n",
       "      <th>%Hemolysis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PG-1.37</td>\n",
       "      <td>HVRRLCYCRRRFCACVGS</td>\n",
       "      <td>1</td>\n",
       "      <td>2.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PG-1.20d</td>\n",
       "      <td>RGGRLRYCRRRFCVSVGR</td>\n",
       "      <td>1</td>\n",
       "      <td>1.607281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PG-1.62</td>\n",
       "      <td>RGGRLCYCRRRFCTCVGR</td>\n",
       "      <td>1</td>\n",
       "      <td>3.233927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PG-1.12A</td>\n",
       "      <td>RGGRLCYCRRRACVCVGR</td>\n",
       "      <td>1</td>\n",
       "      <td>12.103021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PG-1.16A</td>\n",
       "      <td>RGGRLCYCRRRFCVCAGR</td>\n",
       "      <td>1</td>\n",
       "      <td>15.821069</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>88</th>\n",
       "      <td>PG-1.32</td>\n",
       "      <td>HGWRLCYCRRRFCVCVGC</td>\n",
       "      <td>0</td>\n",
       "      <td>35.460367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>PG-1.60</td>\n",
       "      <td>RGGRLCYCRRRFCVCVGF</td>\n",
       "      <td>0</td>\n",
       "      <td>36.664085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>PG-1.25</td>\n",
       "      <td>RGWRLCYCRRRFCVCVGH</td>\n",
       "      <td>0</td>\n",
       "      <td>46.397292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>PG-1.7</td>\n",
       "      <td>RRWCLCFCRRRFCVCVCR</td>\n",
       "      <td>0</td>\n",
       "      <td>51.181276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>PG-1.24</td>\n",
       "      <td>RGWRLCYCRRRFCVCVGC</td>\n",
       "      <td>0</td>\n",
       "      <td>78.280710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID            Sequence  MBC  %Hemolysis\n",
       "0    PG-1.37  HVRRLCYCRRRFCACVGS    1    2.600000\n",
       "1   PG-1.20d  RGGRLRYCRRRFCVSVGR    1    1.607281\n",
       "2    PG-1.62  RGGRLCYCRRRFCTCVGR    1    3.233927\n",
       "3   PG-1.12A  RGGRLCYCRRRACVCVGR    1   12.103021\n",
       "4   PG-1.16A  RGGRLCYCRRRFCVCAGR    1   15.821069\n",
       "..       ...                 ...  ...         ...\n",
       "88   PG-1.32  HGWRLCYCRRRFCVCVGC    0   35.460367\n",
       "89   PG-1.60  RGGRLCYCRRRFCVCVGF    0   36.664085\n",
       "90   PG-1.25  RGWRLCYCRRRFCVCVGH    0   46.397292\n",
       "91    PG-1.7  RRWCLCFCRRRFCVCVCR    0   51.181276\n",
       "92   PG-1.24  RGWRLCYCRRRFCVCVGC    0   78.280710\n",
       "\n",
       "[93 rows x 4 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load meta data\n",
    "meta_data = pd.read_excel('data/Cumulative_Peptide_data_PG1_v01.2.xlsx')\n",
    "meta_data['MBC'] = [convert_mic(x) for x in meta_data['MBC']]\n",
    "meta_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the new target selectivity\n",
    "sns.catplot(data=meta_data, x='MBC', kind='count')\n",
    "plt.xticks([0,1], ['Non_AMP', 'AMP'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Sequence</th>\n",
       "      <th>MBC</th>\n",
       "      <th>%Hemolysis</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>...</th>\n",
       "      <th>5110</th>\n",
       "      <th>5111</th>\n",
       "      <th>5112</th>\n",
       "      <th>5113</th>\n",
       "      <th>5114</th>\n",
       "      <th>5115</th>\n",
       "      <th>5116</th>\n",
       "      <th>5117</th>\n",
       "      <th>5118</th>\n",
       "      <th>5119</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PG-1.37</td>\n",
       "      <td>HVRRLCYCRRRFCACVGS</td>\n",
       "      <td>1</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.056671</td>\n",
       "      <td>-0.074099</td>\n",
       "      <td>-0.040746</td>\n",
       "      <td>0.217794</td>\n",
       "      <td>-0.197767</td>\n",
       "      <td>0.223628</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.130863</td>\n",
       "      <td>-0.107892</td>\n",
       "      <td>0.140624</td>\n",
       "      <td>-0.169443</td>\n",
       "      <td>0.165775</td>\n",
       "      <td>-0.120193</td>\n",
       "      <td>0.094954</td>\n",
       "      <td>-0.000617</td>\n",
       "      <td>-0.199790</td>\n",
       "      <td>0.001056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PG-1.20d</td>\n",
       "      <td>RGGRLRYCRRRFCVSVGR</td>\n",
       "      <td>1</td>\n",
       "      <td>1.607281</td>\n",
       "      <td>0.133009</td>\n",
       "      <td>-0.124348</td>\n",
       "      <td>-0.109747</td>\n",
       "      <td>0.222604</td>\n",
       "      <td>-0.261453</td>\n",
       "      <td>0.138052</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.105108</td>\n",
       "      <td>-0.053451</td>\n",
       "      <td>0.092585</td>\n",
       "      <td>-0.201894</td>\n",
       "      <td>0.094672</td>\n",
       "      <td>-0.104331</td>\n",
       "      <td>0.135752</td>\n",
       "      <td>0.036512</td>\n",
       "      <td>-0.230271</td>\n",
       "      <td>0.020399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PG-1.62</td>\n",
       "      <td>RGGRLCYCRRRFCTCVGR</td>\n",
       "      <td>1</td>\n",
       "      <td>3.233927</td>\n",
       "      <td>0.155696</td>\n",
       "      <td>-0.016959</td>\n",
       "      <td>-0.179328</td>\n",
       "      <td>0.153639</td>\n",
       "      <td>-0.241203</td>\n",
       "      <td>0.130882</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.160722</td>\n",
       "      <td>-0.136943</td>\n",
       "      <td>0.049061</td>\n",
       "      <td>-0.175572</td>\n",
       "      <td>0.109034</td>\n",
       "      <td>-0.239965</td>\n",
       "      <td>-0.050614</td>\n",
       "      <td>-0.096664</td>\n",
       "      <td>-0.249927</td>\n",
       "      <td>0.020688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PG-1.12A</td>\n",
       "      <td>RGGRLCYCRRRACVCVGR</td>\n",
       "      <td>1</td>\n",
       "      <td>12.103021</td>\n",
       "      <td>0.056310</td>\n",
       "      <td>-0.137829</td>\n",
       "      <td>-0.098538</td>\n",
       "      <td>0.162526</td>\n",
       "      <td>-0.252675</td>\n",
       "      <td>0.163847</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.124351</td>\n",
       "      <td>-0.181434</td>\n",
       "      <td>0.037783</td>\n",
       "      <td>-0.201551</td>\n",
       "      <td>0.092573</td>\n",
       "      <td>-0.168867</td>\n",
       "      <td>0.078524</td>\n",
       "      <td>-0.003103</td>\n",
       "      <td>-0.212419</td>\n",
       "      <td>0.049512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PG-1.16A</td>\n",
       "      <td>RGGRLCYCRRRFCVCAGR</td>\n",
       "      <td>1</td>\n",
       "      <td>15.821069</td>\n",
       "      <td>0.122564</td>\n",
       "      <td>-0.024471</td>\n",
       "      <td>-0.127546</td>\n",
       "      <td>0.165324</td>\n",
       "      <td>-0.258667</td>\n",
       "      <td>0.142153</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.155189</td>\n",
       "      <td>-0.155856</td>\n",
       "      <td>0.061196</td>\n",
       "      <td>-0.220575</td>\n",
       "      <td>0.092058</td>\n",
       "      <td>-0.220014</td>\n",
       "      <td>0.025014</td>\n",
       "      <td>-0.053914</td>\n",
       "      <td>-0.198191</td>\n",
       "      <td>0.036471</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>PG-1.32</td>\n",
       "      <td>HGWRLCYCRRRFCVCVGC</td>\n",
       "      <td>0</td>\n",
       "      <td>35.460367</td>\n",
       "      <td>0.007245</td>\n",
       "      <td>-0.115889</td>\n",
       "      <td>-0.040796</td>\n",
       "      <td>0.235570</td>\n",
       "      <td>-0.216340</td>\n",
       "      <td>0.226666</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.166121</td>\n",
       "      <td>-0.146094</td>\n",
       "      <td>0.080064</td>\n",
       "      <td>-0.143502</td>\n",
       "      <td>0.155125</td>\n",
       "      <td>-0.067742</td>\n",
       "      <td>0.087349</td>\n",
       "      <td>-0.027154</td>\n",
       "      <td>-0.214921</td>\n",
       "      <td>0.023557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>PG-1.60</td>\n",
       "      <td>RGGRLCYCRRRFCVCVGF</td>\n",
       "      <td>0</td>\n",
       "      <td>36.664085</td>\n",
       "      <td>0.143025</td>\n",
       "      <td>-0.030791</td>\n",
       "      <td>-0.110694</td>\n",
       "      <td>0.109217</td>\n",
       "      <td>-0.223111</td>\n",
       "      <td>0.105739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.119124</td>\n",
       "      <td>-0.189840</td>\n",
       "      <td>-0.030339</td>\n",
       "      <td>-0.218860</td>\n",
       "      <td>0.098898</td>\n",
       "      <td>-0.174355</td>\n",
       "      <td>0.023488</td>\n",
       "      <td>-0.059298</td>\n",
       "      <td>-0.252023</td>\n",
       "      <td>0.029996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>PG-1.25</td>\n",
       "      <td>RGWRLCYCRRRFCVCVGH</td>\n",
       "      <td>0</td>\n",
       "      <td>46.397292</td>\n",
       "      <td>0.068697</td>\n",
       "      <td>-0.038422</td>\n",
       "      <td>-0.142241</td>\n",
       "      <td>0.184863</td>\n",
       "      <td>-0.232976</td>\n",
       "      <td>0.162749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.180917</td>\n",
       "      <td>-0.172996</td>\n",
       "      <td>0.046710</td>\n",
       "      <td>-0.179037</td>\n",
       "      <td>0.106190</td>\n",
       "      <td>-0.103997</td>\n",
       "      <td>-0.029128</td>\n",
       "      <td>0.000693</td>\n",
       "      <td>-0.243295</td>\n",
       "      <td>0.019351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>PG-1.7</td>\n",
       "      <td>RRWCLCFCRRRFCVCVCR</td>\n",
       "      <td>0</td>\n",
       "      <td>51.181276</td>\n",
       "      <td>0.066839</td>\n",
       "      <td>-0.037917</td>\n",
       "      <td>-0.087912</td>\n",
       "      <td>0.137855</td>\n",
       "      <td>-0.218455</td>\n",
       "      <td>0.279858</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.167717</td>\n",
       "      <td>-0.251613</td>\n",
       "      <td>-0.086687</td>\n",
       "      <td>-0.231941</td>\n",
       "      <td>0.015133</td>\n",
       "      <td>-0.017062</td>\n",
       "      <td>0.048716</td>\n",
       "      <td>-0.028251</td>\n",
       "      <td>-0.203544</td>\n",
       "      <td>-0.114390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>PG-1.24</td>\n",
       "      <td>RGWRLCYCRRRFCVCVGC</td>\n",
       "      <td>0</td>\n",
       "      <td>78.280710</td>\n",
       "      <td>0.069902</td>\n",
       "      <td>-0.039020</td>\n",
       "      <td>-0.122685</td>\n",
       "      <td>0.183626</td>\n",
       "      <td>-0.228307</td>\n",
       "      <td>0.150014</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.200931</td>\n",
       "      <td>-0.165114</td>\n",
       "      <td>0.059687</td>\n",
       "      <td>-0.133821</td>\n",
       "      <td>0.111872</td>\n",
       "      <td>-0.142763</td>\n",
       "      <td>-0.034693</td>\n",
       "      <td>-0.022357</td>\n",
       "      <td>-0.249541</td>\n",
       "      <td>0.047750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 5124 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID            Sequence  MBC  %Hemolysis         0         1  \\\n",
       "0    PG-1.37  HVRRLCYCRRRFCACVGS    1    2.600000  0.056671 -0.074099   \n",
       "1   PG-1.20d  RGGRLRYCRRRFCVSVGR    1    1.607281  0.133009 -0.124348   \n",
       "2    PG-1.62  RGGRLCYCRRRFCTCVGR    1    3.233927  0.155696 -0.016959   \n",
       "3   PG-1.12A  RGGRLCYCRRRACVCVGR    1   12.103021  0.056310 -0.137829   \n",
       "4   PG-1.16A  RGGRLCYCRRRFCVCAGR    1   15.821069  0.122564 -0.024471   \n",
       "..       ...                 ...  ...         ...       ...       ...   \n",
       "88   PG-1.32  HGWRLCYCRRRFCVCVGC    0   35.460367  0.007245 -0.115889   \n",
       "89   PG-1.60  RGGRLCYCRRRFCVCVGF    0   36.664085  0.143025 -0.030791   \n",
       "90   PG-1.25  RGWRLCYCRRRFCVCVGH    0   46.397292  0.068697 -0.038422   \n",
       "91    PG-1.7  RRWCLCFCRRRFCVCVCR    0   51.181276  0.066839 -0.037917   \n",
       "92   PG-1.24  RGWRLCYCRRRFCVCVGC    0   78.280710  0.069902 -0.039020   \n",
       "\n",
       "           2         3         4         5  ...      5110      5111      5112  \\\n",
       "0  -0.040746  0.217794 -0.197767  0.223628  ... -0.130863 -0.107892  0.140624   \n",
       "1  -0.109747  0.222604 -0.261453  0.138052  ... -0.105108 -0.053451  0.092585   \n",
       "2  -0.179328  0.153639 -0.241203  0.130882  ... -0.160722 -0.136943  0.049061   \n",
       "3  -0.098538  0.162526 -0.252675  0.163847  ... -0.124351 -0.181434  0.037783   \n",
       "4  -0.127546  0.165324 -0.258667  0.142153  ... -0.155189 -0.155856  0.061196   \n",
       "..       ...       ...       ...       ...  ...       ...       ...       ...   \n",
       "88 -0.040796  0.235570 -0.216340  0.226666  ... -0.166121 -0.146094  0.080064   \n",
       "89 -0.110694  0.109217 -0.223111  0.105739  ... -0.119124 -0.189840 -0.030339   \n",
       "90 -0.142241  0.184863 -0.232976  0.162749  ... -0.180917 -0.172996  0.046710   \n",
       "91 -0.087912  0.137855 -0.218455  0.279858  ... -0.167717 -0.251613 -0.086687   \n",
       "92 -0.122685  0.183626 -0.228307  0.150014  ... -0.200931 -0.165114  0.059687   \n",
       "\n",
       "        5113      5114      5115      5116      5117      5118      5119  \n",
       "0  -0.169443  0.165775 -0.120193  0.094954 -0.000617 -0.199790  0.001056  \n",
       "1  -0.201894  0.094672 -0.104331  0.135752  0.036512 -0.230271  0.020399  \n",
       "2  -0.175572  0.109034 -0.239965 -0.050614 -0.096664 -0.249927  0.020688  \n",
       "3  -0.201551  0.092573 -0.168867  0.078524 -0.003103 -0.212419  0.049512  \n",
       "4  -0.220575  0.092058 -0.220014  0.025014 -0.053914 -0.198191  0.036471  \n",
       "..       ...       ...       ...       ...       ...       ...       ...  \n",
       "88 -0.143502  0.155125 -0.067742  0.087349 -0.027154 -0.214921  0.023557  \n",
       "89 -0.218860  0.098898 -0.174355  0.023488 -0.059298 -0.252023  0.029996  \n",
       "90 -0.179037  0.106190 -0.103997 -0.029128  0.000693 -0.243295  0.019351  \n",
       "91 -0.231941  0.015133 -0.017062  0.048716 -0.028251 -0.203544 -0.114390  \n",
       "92 -0.133821  0.111872 -0.142763 -0.034693 -0.022357 -0.249541  0.047750  \n",
       "\n",
       "[93 rows x 5124 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # creates a empty dictionary, and updates it with the embedds\n",
    "dir_path = 'data/embedding/esm2_15B_PG1/'\n",
    "\n",
    "embeddings = {}\n",
    "for file in os.listdir(dir_path):\n",
    "    file_path = str(dir_path) + file \n",
    "    label = file.split('.pt')[0]\n",
    "    embeddings[label] = np.array(torch.load(file_path)['mean_representations'][48])\n",
    "\n",
    "# here I creating a dictionary with ID as key and semantic value as value \n",
    "data = meta_data.merge(pd.DataFrame.from_dict(embeddings, orient='index'), how='inner', left_on='ID', right_index=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    " # define target to evaluete the model\n",
    "target = data[\"MBC\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>9</th>\n",
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       "      <th>5110</th>\n",
       "      <th>5111</th>\n",
       "      <th>5112</th>\n",
       "      <th>5113</th>\n",
       "      <th>5114</th>\n",
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       "      <th>5116</th>\n",
       "      <th>5117</th>\n",
       "      <th>5118</th>\n",
       "      <th>5119</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.056671</td>\n",
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       "      <td>-0.130863</td>\n",
       "      <td>-0.107892</td>\n",
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       "      <td>-0.169443</td>\n",
       "      <td>0.165775</td>\n",
       "      <td>-0.120193</td>\n",
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       "      <td>-0.000617</td>\n",
       "      <td>-0.199790</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.133009</td>\n",
       "      <td>-0.124348</td>\n",
       "      <td>-0.109747</td>\n",
       "      <td>0.222604</td>\n",
       "      <td>-0.261453</td>\n",
       "      <td>0.138052</td>\n",
       "      <td>-0.132696</td>\n",
       "      <td>0.162016</td>\n",
       "      <td>-0.073308</td>\n",
       "      <td>0.382890</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.105108</td>\n",
       "      <td>-0.053451</td>\n",
       "      <td>0.092585</td>\n",
       "      <td>-0.201894</td>\n",
       "      <td>0.094672</td>\n",
       "      <td>-0.104331</td>\n",
       "      <td>0.135752</td>\n",
       "      <td>0.036512</td>\n",
       "      <td>-0.230271</td>\n",
       "      <td>0.020399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.155696</td>\n",
       "      <td>-0.016959</td>\n",
       "      <td>-0.179328</td>\n",
       "      <td>0.153639</td>\n",
       "      <td>-0.241203</td>\n",
       "      <td>0.130882</td>\n",
       "      <td>-0.101294</td>\n",
       "      <td>0.178330</td>\n",
       "      <td>-0.043959</td>\n",
       "      <td>0.144877</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.160722</td>\n",
       "      <td>-0.136943</td>\n",
       "      <td>0.049061</td>\n",
       "      <td>-0.175572</td>\n",
       "      <td>0.109034</td>\n",
       "      <td>-0.239965</td>\n",
       "      <td>-0.050614</td>\n",
       "      <td>-0.096664</td>\n",
       "      <td>-0.249927</td>\n",
       "      <td>0.020688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.056310</td>\n",
       "      <td>-0.137829</td>\n",
       "      <td>-0.098538</td>\n",
       "      <td>0.162526</td>\n",
       "      <td>-0.252675</td>\n",
       "      <td>0.163847</td>\n",
       "      <td>-0.120923</td>\n",
       "      <td>0.205509</td>\n",
       "      <td>-0.078092</td>\n",
       "      <td>0.267088</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.124351</td>\n",
       "      <td>-0.181434</td>\n",
       "      <td>0.037783</td>\n",
       "      <td>-0.201551</td>\n",
       "      <td>0.092573</td>\n",
       "      <td>-0.168867</td>\n",
       "      <td>0.078524</td>\n",
       "      <td>-0.003103</td>\n",
       "      <td>-0.212419</td>\n",
       "      <td>0.049512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.122564</td>\n",
       "      <td>-0.024471</td>\n",
       "      <td>-0.127546</td>\n",
       "      <td>0.165324</td>\n",
       "      <td>-0.258667</td>\n",
       "      <td>0.142153</td>\n",
       "      <td>-0.093198</td>\n",
       "      <td>0.185513</td>\n",
       "      <td>-0.060469</td>\n",
       "      <td>0.173319</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.155189</td>\n",
       "      <td>-0.155856</td>\n",
       "      <td>0.061196</td>\n",
       "      <td>-0.220575</td>\n",
       "      <td>0.092058</td>\n",
       "      <td>-0.220014</td>\n",
       "      <td>0.025014</td>\n",
       "      <td>-0.053914</td>\n",
       "      <td>-0.198191</td>\n",
       "      <td>0.036471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 5120 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0         1         2         3         4         5         6     \\\n",
       "0  0.056671 -0.074099 -0.040746  0.217794 -0.197767  0.223628 -0.119524   \n",
       "1  0.133009 -0.124348 -0.109747  0.222604 -0.261453  0.138052 -0.132696   \n",
       "2  0.155696 -0.016959 -0.179328  0.153639 -0.241203  0.130882 -0.101294   \n",
       "3  0.056310 -0.137829 -0.098538  0.162526 -0.252675  0.163847 -0.120923   \n",
       "4  0.122564 -0.024471 -0.127546  0.165324 -0.258667  0.142153 -0.093198   \n",
       "\n",
       "       7         8         9     ...      5110      5111      5112      5113  \\\n",
       "0  0.080779 -0.021853  0.285255  ... -0.130863 -0.107892  0.140624 -0.169443   \n",
       "1  0.162016 -0.073308  0.382890  ... -0.105108 -0.053451  0.092585 -0.201894   \n",
       "2  0.178330 -0.043959  0.144877  ... -0.160722 -0.136943  0.049061 -0.175572   \n",
       "3  0.205509 -0.078092  0.267088  ... -0.124351 -0.181434  0.037783 -0.201551   \n",
       "4  0.185513 -0.060469  0.173319  ... -0.155189 -0.155856  0.061196 -0.220575   \n",
       "\n",
       "       5114      5115      5116      5117      5118      5119  \n",
       "0  0.165775 -0.120193  0.094954 -0.000617 -0.199790  0.001056  \n",
       "1  0.094672 -0.104331  0.135752  0.036512 -0.230271  0.020399  \n",
       "2  0.109034 -0.239965 -0.050614 -0.096664 -0.249927  0.020688  \n",
       "3  0.092573 -0.168867  0.078524 -0.003103 -0.212419  0.049512  \n",
       "4  0.092058 -0.220014  0.025014 -0.053914 -0.198191  0.036471  \n",
       "\n",
       "[5 rows x 5120 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define target\n",
    "features = data.iloc[:, meta_data.shape[1]:]\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set shape: (74, 5120), Test set shape: (19, 5120)\n"
     ]
    }
   ],
   "source": [
    "# split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
    "\n",
    "print(f'Train set shape: {X_train.shape}, Test set shape: {X_test.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcas = [10, 15, 20, 25, 30, 35, 40, 70]\n",
    "\n",
    "results_final = pd.DataFrame()\n",
    "results_cv_final = pd.DataFrame()\n",
    "\n",
    "for num_pca in pcas:\n",
    "    # instantiate PCA\n",
    "    pca = PCA(num_pca)\n",
    "    pca.fit(X_train)\n",
    "    X_train_pca = pca.transform(X_train)\n",
    "    X_test_pca = pca.transform(X_test)\n",
    "\n",
    "    # store the results\n",
    "    results = pd.DataFrame()\n",
    "    results_cv = pd.DataFrame()\n",
    "\n",
    "    # create a list of classifiers to test\n",
    "    classifiers = [\n",
    "        LogisticRegression(),\n",
    "        GaussianNB(),\n",
    "        SVC(),\n",
    "    ]\n",
    "\n",
    "    # create a dictionary of hyperparameters to search over for each classifier\n",
    "    param_grids = [\n",
    "        {'penalty': ['l1', 'l2'], 'C': [0.01, 0.1, 1, 10], 'fit_intercept': [True, False], 'solver': ['liblinear', 'saga'],'max_iter': [100000]},\n",
    "        {'var_smoothing': [1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3,]},\n",
    "        {'kernel': ['linear', 'rbf'], 'C': [0.1, 1, 10], 'gamma': ['scale', 'auto', 1, 0.1, 0.01], 'max_iter': [-1], 'class_weight': [None, 'balanced']},\n",
    "    ]\n",
    "\n",
    "    # create a list of names for the classifiers for printing results\n",
    "    model_names = [\"LogisticRegression\", 'GaussianNB', \"SVM\", 'RandomForest']\n",
    "\n",
    "\n",
    "    # loop over the classifiers and hyperparameter grids\n",
    "    for name, clf, param_grid in zip(model_names, classifiers, param_grids):\n",
    "\n",
    "        # create a GridSearchCV object with 5-fold cross-validation\n",
    "        grid_search = GridSearchCV(clf, param_grid, cv=25, n_jobs=25)\n",
    "\n",
    "        # fit the grid search object to the training data\n",
    "        grid_search.fit(X_train_pca, y_train)\n",
    "\n",
    "        # k-fold cross-validation on the training set\n",
    "        accuracy_cv_scores = cross_val_score(grid_search, X_train_pca, y_train, cv=25, scoring='accuracy', n_jobs=25)\n",
    "        recall_cv_scores = cross_val_score(grid_search, X_train_pca, y_train, cv=25, scoring='recall', n_jobs=25)\n",
    "        precision_cv_scores = cross_val_score(grid_search, X_train_pca, y_train, cv=25, scoring='precision', n_jobs=25)\n",
    "        f1_cv_scores = cross_val_score(grid_search, X_train_pca, y_train, cv=25, scoring='f1', n_jobs=25)\n",
    "\n",
    "\n",
    "        # make predictions on the test data using the best model found by the grid search\n",
    "        train_y_pred = grid_search.best_estimator_.predict(X_train_pca)\n",
    "        test_y_pred = grid_search.best_estimator_.predict(X_test_pca)\n",
    "\n",
    "        # evaluate the performance of the best model on the train data using accuracy score\n",
    "        train_accuracy = metrics.accuracy_score(y_train, train_y_pred)\n",
    "        train_recall = metrics.recall_score(y_train, train_y_pred)\n",
    "        train_precision = metrics.precision_score(y_train, train_y_pred, zero_division=0)\n",
    "        train_f1_score = metrics.f1_score(y_train, train_y_pred)\n",
    "\n",
    "        # evaluate the performance of the best model on the test data using accuracy score\n",
    "        test_accuracy = metrics.accuracy_score(y_test, test_y_pred)\n",
    "        test_recall = metrics.recall_score(y_test, test_y_pred)\n",
    "        test_precision = metrics.precision_score(y_test, test_y_pred, zero_division=0)\n",
    "        test_f1_score = metrics.f1_score(y_test, test_y_pred)\n",
    "\n",
    "        print(name)\n",
    "        print(f'NUm PCA {num_pca}, Train Accuracy: {train_accuracy.round(2)}, Train Recall: {train_recall.round(2)}, Train Precision: {train_precision.round(2)}')\n",
    "        print(f'NUm PCA {num_pca}, Test Accuracy: {test_accuracy.round(2)}, Test Recall: {test_recall.round(2)}, Test Precision: {test_precision.round(2)}')\n",
    "        print()\n",
    "\n",
    "        # Saving results\n",
    "        res_dict = {}\n",
    "        res_dict[\"embedding\"] = 'esm2_15B'\n",
    "        res_dict[\"embed_size\"] = features.shape[1]\n",
    "        res_dict[\"model\"] = name\n",
    "        res_dict[\"NUm_pca\"] = num_pca\n",
    "\n",
    "        res_dict[\"Train_Accuracy\"] = train_accuracy.round(2)\n",
    "        res_dict[\"Train_recall\"] = train_recall.round(2)\n",
    "        res_dict[\"Train_precision\"] = train_precision.round(2)\n",
    "        res_dict[\"Train_f1\"] = train_f1_score.round(2)\n",
    "\n",
    "        res_dict[\"Test_Accuracy\"] = test_accuracy.round(2)\n",
    "        res_dict[\"Test_recall\"] = test_recall.round(2)\n",
    "        res_dict[\"Test_precision\"] = test_precision.round(2)\n",
    "        res_dict[\"Test_f1\"] = test_f1_score.round(2)\n",
    "        res_dict['best_params'] = str(grid_search.best_params_)\n",
    "        # updating data frame with results\n",
    "        results = pd.concat([results, pd.DataFrame(res_dict, index=[0])]).reset_index(drop=True)\n",
    "    \n",
    "\n",
    "        # save results from cross-validation\n",
    "        res_cv_dict = {\n",
    "            \"model\": [name for i in range(len(accuracy_cv_scores))],\n",
    "            \"Num_pca\": [num_pca for i in range(len(accuracy_cv_scores))],\n",
    "            \"Accuracy_CV_scores\": accuracy_cv_scores,\n",
    "            \"Recall_CV_scores\": recall_cv_scores,\n",
    "            \"Precision_CV_scores\": precision_cv_scores,\n",
    "            \"F1_CV_scores\": f1_cv_scores,\n",
    "        }\n",
    "        # updating data frame with results\n",
    "        results_cv = pd.concat([results_cv, pd.DataFrame(res_cv_dict)]).reset_index(drop=True)\n",
    "\n",
    "    results_final = pd.concat([results_final, results]).reset_index(drop=True)\n",
    "    results_cv_final = pd.concat([results_cv_final, results_cv]).reset_index(drop=True)\n",
    "\n",
    "#results_final.to_excel('results/results_classification_activity_v02.xlsx')\n",
    "#results_cv_final.to_excel('results/results_cv_classification_activity_v02.xlsx')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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></th>\n",
       "      <th>Accuracy_CV_scores</th>\n",
       "      <th>Recall_CV_scores</th>\n",
       "      <th>Precision_CV_scores</th>\n",
       "      <th>F1_CV_scores</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th>Num_pca</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <th>40</th>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.464000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <th>40</th>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.585333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <th>40</th>\n",
       "      <td>0.613333</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.493333</td>\n",
       "      <td>0.569333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Accuracy_CV_scores  Recall_CV_scores  \\\n",
       "model              Num_pca                                         \n",
       "GaussianNB         40                 0.620000              0.54   \n",
       "LogisticRegression 40                 0.620000              0.74   \n",
       "SVM                40                 0.613333              0.74   \n",
       "\n",
       "                            Precision_CV_scores  F1_CV_scores  \n",
       "model              Num_pca                                     \n",
       "GaussianNB         40                  0.460000      0.464000  \n",
       "LogisticRegression 40                  0.520000      0.585333  \n",
       "SVM                40                  0.493333      0.569333  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#results_cv = pd.read_excel('results/results_cv_classification_activity_v02.xlsx', index_col=0)\n",
    "results_cv.groupby(['model', 'Num_pca']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=results_cv, x='Num_pca', y='Accuracy_CV_scores', hue='model', showfliers=False)\n",
    "plt.title('Accuracy Cross-validation scores on training set')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=results_cv, x='Num_pca', y='Precision_CV_scores', hue='model', showfliers=False)\n",
    "plt.title('Precision Cross-validation scores on training set')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train x Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>embedding</th>\n",
       "      <th>embed_size</th>\n",
       "      <th>model</th>\n",
       "      <th>NUm_pca</th>\n",
       "      <th>Train_Accuracy</th>\n",
       "      <th>Train_recall</th>\n",
       "      <th>Train_precision</th>\n",
       "      <th>Train_f1</th>\n",
       "      <th>Test_Accuracy</th>\n",
       "      <th>Test_recall</th>\n",
       "      <th>Test_precision</th>\n",
       "      <th>Test_f1</th>\n",
       "      <th>best_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.73</td>\n",
       "      <td>{'C': 10, 'fit_intercept': True, 'max_iter': 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>10</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.70</td>\n",
       "      <td>{'var_smoothing': 1e-09}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>SVM</td>\n",
       "      <td>10</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.67</td>\n",
       "      <td>{'C': 10, 'class_weight': 'balanced', 'gamma':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>15</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>{'C': 1, 'fit_intercept': True, 'max_iter': 10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>15</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.87</td>\n",
       "      <td>{'var_smoothing': 1e-09}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>SVM</td>\n",
       "      <td>15</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.73</td>\n",
       "      <td>{'C': 10, 'class_weight': None, 'gamma': 0.1, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>20</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.73</td>\n",
       "      <td>{'C': 1, 'fit_intercept': True, 'max_iter': 10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>20</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>{'var_smoothing': 0.0001}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>SVM</td>\n",
       "      <td>20</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.70</td>\n",
       "      <td>{'C': 10, 'class_weight': None, 'gamma': 'auto...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>25</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>{'C': 0.1, 'fit_intercept': False, 'max_iter':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>25</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>{'var_smoothing': 0.001}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>SVM</td>\n",
       "      <td>25</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.73</td>\n",
       "      <td>{'C': 1, 'class_weight': None, 'gamma': 0.1, '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>30</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.78</td>\n",
       "      <td>{'C': 10, 'fit_intercept': True, 'max_iter': 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>30</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.77</td>\n",
       "      <td>{'var_smoothing': 0.001}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>SVM</td>\n",
       "      <td>30</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.73</td>\n",
       "      <td>{'C': 1, 'class_weight': None, 'gamma': 0.1, '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>35</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>{'C': 0.1, 'fit_intercept': False, 'max_iter':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>35</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.77</td>\n",
       "      <td>{'var_smoothing': 1e-09}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>SVM</td>\n",
       "      <td>35</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.73</td>\n",
       "      <td>{'C': 1, 'class_weight': None, 'gamma': 0.1, '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>40</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.50</td>\n",
       "      <td>{'C': 0.01, 'fit_intercept': False, 'max_iter'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>40</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.77</td>\n",
       "      <td>{'var_smoothing': 1e-09}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>SVM</td>\n",
       "      <td>40</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.73</td>\n",
       "      <td>{'C': 1, 'class_weight': None, 'gamma': 0.1, '...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   embedding  embed_size               model  NUm_pca  Train_Accuracy  \\\n",
       "0   esm2_15B        5120  LogisticRegression       10            0.80   \n",
       "1   esm2_15B        5120          GaussianNB       10            0.78   \n",
       "2   esm2_15B        5120                 SVM       10            0.95   \n",
       "3   esm2_15B        5120  LogisticRegression       15            0.78   \n",
       "4   esm2_15B        5120          GaussianNB       15            0.81   \n",
       "5   esm2_15B        5120                 SVM       15            0.97   \n",
       "6   esm2_15B        5120  LogisticRegression       20            0.81   \n",
       "7   esm2_15B        5120          GaussianNB       20            0.81   \n",
       "8   esm2_15B        5120                 SVM       20            0.92   \n",
       "9   esm2_15B        5120  LogisticRegression       25            0.76   \n",
       "10  esm2_15B        5120          GaussianNB       25            0.80   \n",
       "11  esm2_15B        5120                 SVM       25            0.88   \n",
       "12  esm2_15B        5120  LogisticRegression       30            0.92   \n",
       "13  esm2_15B        5120          GaussianNB       30            0.82   \n",
       "14  esm2_15B        5120                 SVM       30            0.88   \n",
       "15  esm2_15B        5120  LogisticRegression       35            0.74   \n",
       "16  esm2_15B        5120          GaussianNB       35            0.85   \n",
       "17  esm2_15B        5120                 SVM       35            0.89   \n",
       "18  esm2_15B        5120  LogisticRegression       40            0.68   \n",
       "19  esm2_15B        5120          GaussianNB       40            0.82   \n",
       "20  esm2_15B        5120                 SVM       40            0.89   \n",
       "\n",
       "    Train_recall  Train_precision  Train_f1  Test_Accuracy  Test_recall  \\\n",
       "0           0.82             0.75      0.78           0.68         0.67   \n",
       "1           0.82             0.73      0.77           0.68         0.58   \n",
       "2           1.00             0.89      0.94           0.63         0.58   \n",
       "3           0.82             0.73      0.77           0.58         0.58   \n",
       "4           0.82             0.77      0.79           0.84         0.83   \n",
       "5           1.00             0.94      0.97           0.68         0.67   \n",
       "6           0.82             0.77      0.79           0.68         0.67   \n",
       "7           0.82             0.77      0.79           0.68         0.75   \n",
       "8           0.94             0.89      0.91           0.63         0.67   \n",
       "9           0.85             0.68      0.76           0.68         0.75   \n",
       "10          0.85             0.74      0.79           0.68         0.75   \n",
       "11          0.94             0.82      0.87           0.68         0.67   \n",
       "12          0.94             0.89      0.91           0.74         0.75   \n",
       "13          0.94             0.74      0.83           0.68         0.83   \n",
       "14          0.94             0.82      0.87           0.68         0.67   \n",
       "15          0.88             0.66      0.75           0.68         0.75   \n",
       "16          0.94             0.78      0.85           0.68         0.83   \n",
       "17          0.94             0.84      0.89           0.68         0.67   \n",
       "18          0.73             0.62      0.67           0.47         0.42   \n",
       "19          0.88             0.76      0.82           0.68         0.83   \n",
       "20          0.94             0.84      0.89           0.68         0.67   \n",
       "\n",
       "    Test_precision  Test_f1                                        best_params  \n",
       "0             0.80     0.73  {'C': 10, 'fit_intercept': True, 'max_iter': 1...  \n",
       "1             0.88     0.70                           {'var_smoothing': 1e-09}  \n",
       "2             0.78     0.67  {'C': 10, 'class_weight': 'balanced', 'gamma':...  \n",
       "3             0.70     0.64  {'C': 1, 'fit_intercept': True, 'max_iter': 10...  \n",
       "4             0.91     0.87                           {'var_smoothing': 1e-09}  \n",
       "5             0.80     0.73  {'C': 10, 'class_weight': None, 'gamma': 0.1, ...  \n",
       "6             0.80     0.73  {'C': 1, 'fit_intercept': True, 'max_iter': 10...  \n",
       "7             0.75     0.75                          {'var_smoothing': 0.0001}  \n",
       "8             0.73     0.70  {'C': 10, 'class_weight': None, 'gamma': 'auto...  \n",
       "9             0.75     0.75  {'C': 0.1, 'fit_intercept': False, 'max_iter':...  \n",
       "10            0.75     0.75                           {'var_smoothing': 0.001}  \n",
       "11            0.80     0.73  {'C': 1, 'class_weight': None, 'gamma': 0.1, '...  \n",
       "12            0.82     0.78  {'C': 10, 'fit_intercept': True, 'max_iter': 1...  \n",
       "13            0.71     0.77                           {'var_smoothing': 0.001}  \n",
       "14            0.80     0.73  {'C': 1, 'class_weight': None, 'gamma': 0.1, '...  \n",
       "15            0.75     0.75  {'C': 0.1, 'fit_intercept': False, 'max_iter':...  \n",
       "16            0.71     0.77                           {'var_smoothing': 1e-09}  \n",
       "17            0.80     0.73  {'C': 1, 'class_weight': None, 'gamma': 0.1, '...  \n",
       "18            0.62     0.50  {'C': 0.01, 'fit_intercept': False, 'max_iter'...  \n",
       "19            0.71     0.77                           {'var_smoothing': 1e-09}  \n",
       "20            0.80     0.73  {'C': 1, 'class_weight': None, 'gamma': 0.1, '...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#results_final = pd.read_excel('results/results_classification_activity_v02.xlsx', index_col=0)\n",
    "results_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape the dataframe to long format\n",
    "melted_df = pd.melt(results_final, \n",
    "                    id_vars=['NUm_pca', 'model'], \n",
    "                    value_vars=['Test_Accuracy', 'Train_Accuracy'], \n",
    "                    var_name='Score Type', value_name='Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1378.62x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example using FacetGrid for a categorical breakdown\n",
    "g = sns.FacetGrid(melted_df, col=\"Score Type\", sharey=False, height=5, aspect=1.2, ylim=(0,1), col_order=['Train_Accuracy', 'Test_Accuracy'])\n",
    "g.map_dataframe(sns.barplot, x=\"NUm_pca\", y=\"Accuracy\", hue='model', palette='muted')\n",
    "g.set_axis_labels(\"Number of PCAs\", \"Accuracy\")\n",
    "g.set_titles(col_template=\"{col_name}\", row_template=\"{row_name}\")\n",
    "g.add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To determine if the model is overfitting, it's important to evaluate its performance on a validation set or using cross-validation.   \n",
    "If the model's performance on the validation set is significantly worse than its performance on the training set, then it may be overfitting.   \n",
    "In such cases, it may be necessary to adjust the hyperparameters of the model or to use a more complex kernel function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine Tunning Model with best hyperparameter found with Gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>embedding</th>\n",
       "      <th>embed_size</th>\n",
       "      <th>model</th>\n",
       "      <th>NUm_pca</th>\n",
       "      <th>Train_Accuracy</th>\n",
       "      <th>Train_recall</th>\n",
       "      <th>Train_precision</th>\n",
       "      <th>Train_f1</th>\n",
       "      <th>Test_Accuracy</th>\n",
       "      <th>Test_recall</th>\n",
       "      <th>Test_precision</th>\n",
       "      <th>Test_f1</th>\n",
       "      <th>best_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>15</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>{'C': 1, 'fit_intercept': True, 'max_iter': 10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>15</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.87</td>\n",
       "      <td>{'var_smoothing': 1e-09}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>esm2_15B</td>\n",
       "      <td>5120</td>\n",
       "      <td>SVM</td>\n",
       "      <td>15</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.73</td>\n",
       "      <td>{'C': 10, 'class_weight': None, 'gamma': 0.1, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  embedding  embed_size               model  NUm_pca  Train_Accuracy  \\\n",
       "3  esm2_15B        5120  LogisticRegression       15            0.78   \n",
       "4  esm2_15B        5120          GaussianNB       15            0.81   \n",
       "5  esm2_15B        5120                 SVM       15            0.97   \n",
       "\n",
       "   Train_recall  Train_precision  Train_f1  Test_Accuracy  Test_recall  \\\n",
       "3          0.82             0.73      0.77           0.58         0.58   \n",
       "4          0.82             0.77      0.79           0.84         0.83   \n",
       "5          1.00             0.94      0.97           0.68         0.67   \n",
       "\n",
       "   Test_precision  Test_f1                                        best_params  \n",
       "3            0.70     0.64  {'C': 1, 'fit_intercept': True, 'max_iter': 10...  \n",
       "4            0.91     0.87                           {'var_smoothing': 1e-09}  \n",
       "5            0.80     0.73  {'C': 10, 'class_weight': None, 'gamma': 0.1, ...  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_final.query('NUm_pca == 15')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set shape: (74, 5120), Test set shape: (19, 5120)\n"
     ]
    }
   ],
   "source": [
    "# split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
    "\n",
    "print(f'Train set shape: {X_train.shape}, Test set shape: {X_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pca_components = 15\n",
    "\n",
    "pca = PCA(num_pca_components)\n",
    "pca.fit(X_train)\n",
    "\n",
    "X_train_pca = pca.transform(X_train)\n",
    "X_test_pca = pca.transform(X_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"{'C': 1, 'fit_intercept': True, 'max_iter': 100000, 'penalty': 'l2', 'solver': 'liblinear'}\""
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_final.iloc[3, 12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2 score: 0.78\n",
      "Test R2 score: 0.58\n"
     ]
    }
   ],
   "source": [
    "logR= LogisticRegression(penalty='l2',\n",
    "                        C=1,\n",
    "                        fit_intercept=True,\n",
    "                        solver='liblinear',\n",
    "                        max_iter=10000)\n",
    "         \n",
    "\n",
    "# fit the model\n",
    "logR.fit(X_train_pca, y_train)\n",
    "\n",
    "# print Accuracy score\n",
    "print(f'Train R2 score: {logR.score(X_train_pca, y_train):.2f}')\n",
    "print(f'Test R2 score: {logR.score(X_test_pca, y_test):.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5cyYDBgw4qdd1tMLCQm6++WZGjhzJF1984Trepk0bBgwYQG5ubq0f82QYhsG0adN49NFH+ctf/gLAhx9+SGxsLF9++SVXX311tde1aNGi0vfPPfcc7dq1Y+jQoQB0796dzz//3PXzdu3a8eyzz3L99ddTXl6On58zVpx//vkcOHCAn3/+mfPOO68hXuJxKdyIiHijskKYnOD+5304HQJCTvlhZs+ezeOPP84bb7zBaaedxpo1axg7diwhISGMGTOG1157jfnz5/Ppp5/SqlUrUlNTSU1NBZytDzExMbz33ntceOGF+Pr61vg8S5Ys4YYbbqhy/KeffqKwsJBhw4aRmJjIoEGDeOWVVwgJqd1r+/bbb8nJyeGf//xntT+PjIys8do77riDjz766LiPf+jQoWqP79q1i8zMTIYNG+Y6FhERwYABA1i+fHmN4eZopaWlfPTRR0yYMAHLcQJrxS7dFcEGICAggN69e/PLL78o3IiIiABMmjSJl19+mcsvvxxwtnRs3LiRf/3rX4wZM4aUlBQ6dOjAmWeeicVioXXr1q5rK1ofIiMjK7UEHSs3N5e8vDwSEqqGwJkzZ3L11Vfj6+tL9+7dadu2LZ999hk33XRTrV5HxRiXzp071+o6gKeeeqrOXXOZmZkAxMbGVjoeGxvr+tmJfPnll+Tm5h73Nefk5PD0009z++23V/lZQkICe/bsOfmi65HCjYiIN/IPdraimPG8p6igoIAdO3Zw6623MnbsWNfx8vJyIiIiALjppps4//zz6dSpExdeeCEXX3xxrWc8FRUVARAYGFjpeG5uLvPmzWPJkiWuY9dffz0zZ86sdbgxDKNW5x8tJiaGmJiYOl9/qmbOnMmIESOqDX8ANpuNiy66iK5du1Y7+DsoKIjCQhO6RlG4ERHxThZLvXQPmaGiq+Wdd96pMs6looupT58+7Nq1i2+++YYffviBq666imHDhjF37tyTfp7mzZtjsVg4ePBgpeMff/wxxcXFlZ7bMAwcDgdbt26lY8eOhIeHA1SaJVQhNzfXFcI6duwIwObNmxk4cOBJ1wan1i1V0WKVlZVFfHy863hWVha9e/c+4XPv2bOHH374gXnz5lX78/z8fC688ELCwsL44osv8Pf3r3LOgQMHaNeu3QmfqyFotpSIiDQqsbGxJCQksHPnTtq3b1/p1qZNG9d54eHhjB49mnfeeYdPPvmEzz//nAMHDgDg7++P3W4/7vMEBATQtWtXNm7cWOn4zJkzue+++1i7dq3rtm7dOoYMGcKsWbMAaNasGdHR0axatarStTabje3bt7tCzQUXXEB0dDQvvPBCtTUcb0DxU089VamG6m41adOmDXFxcSxatKhSbb/99ttJhaz33nuPmJgYLrrooio/s9lsXHDBBQQEBDB//vwqLV8V1q9fz2mnnXbC52oIarkREZFG58knn+Tuu+8mIiKCCy+8kJKSElauXMnBgweZMGECU6dOJT4+ntNOOw0fHx8+++wz4uLiXAN0k5OTWbRoEYMHD8ZqtRIVFVXt8wwfPpwlS5a41qVZu3Ytq1evZvbs2VXGyVxzzTU89dRTPPPMM/j5+TFhwgQmT55MbGwsZ5xxBvv37+fpp5+mRYsWrrFCISEhvPvuu1x55ZVccskl3H333bRv356cnBw+/fRTUlJSmDNnTrW1nUq3lMVi4R//+AfPPPMMHTp0oE2bNjz22GMkJCRw6aWXus4777zzuOyyyxg/frzrmMPh4L333mPMmDGVBgnDkWBTWFjIRx99hM1mw2azAc6xThUta7t37yYtLa3SgGa3MpqYvLw8AzDy8vLq94FTVhjGpHDDmNazfh+3BgOe/cFo/eBXxp97c93yfCLSeBUVFRkbN240ioqKzC6lzt577z0jIiKi0rHZs2cbvXv3NgICAoyoqCjjrLPOMubNm2cYhmG8/fbbRu/evY2QkBAjPDzcOO+884zVq1e7rp0/f77Rvn17w8/Pz2jdunWNz7thwwYjKCjIyM11/i4dP3680bVr12rPzcjIMHx8fIz/+7//MwzDMMrLy43XXnvN6NGjhxEcHGy0bNnSGD16tLFr164q1/7+++/G5ZdfbrRo0cKwWq1G+/btjdtvv93Ytm1bLd6l2nE4HMZjjz1mxMbGGlar1TjvvPOMLVu2VDqndevWxqRJkyod+/bbbw2gyrmGYRg//fSTAVR7O/p1T5482Rg+fHitaz7ev+XafH5bDOMURjt5IJvNRkREhGvqWr1J/R1mDoOoZLhnXf09bg3OmLyITFsxX911Jt0TIxr8+USk8SouLmbXrl20adOmxi4CqdmVV15Jnz59mDhxotmleIXS0lI6dOjAxx9/XO3iiMdzvH/Ltfn81pgbERFp0l588UVCQ0PNLsNrpKSk8PDDD9c62NQnjbkREZEmLTk5mbvuusvsMrxGxeBvM6nlRqQW7A6Ddam5vL90F5szbWaXIyIi1VDLjcgJpOUWsWRbNv/blsPS7TnkFpYB0KdVJPPuNK/ZVeRYTWwIpXih+vo3rHAjcoyCknJ+27Wf/23N4Zdt2ezILqj0c39fC2V2g7yiMpMqlBMxDIMsWwm79xewO6eAXYe/tmoWzMMjuxx3nxxPVLGAWmFhIUFBQSZXI1J3paWlAMfdD+xkKNxIk+dwGKxPz+OXbTn8b2s2q1MOUmY/8teDjwV6J0UypEMLzuoYTUmZg2vf/c3EigWcASY7v4Td+wsrBZhdOQXs2V9IUVn1C7hdf0ZrWjf3zJV7a+Lr60tkZCT79u0DIDg42OsCnHg/h8NBdnY2wcHBVdbXqS2FG2mSSssdLNmezVfrMvhpyz4OFlZuhWkZFcRZHVtwVodoBraLJiLoyNLiv+3c7+5ym7TScge79xewfd+hSrc9+wsoKK15BVpfHwsto4Jo3TyENs2D+c/vqZSWOyh3eGfXTcVy+xUBR8QT+fj40KpVq1MO5wo30mSU2x38uvMA/12XzsINmZW6lUKtfgxq15whHaIZ0qEFrZvrL193KygpZ0f2kfCybd8hduw7xJ4DhdhrCCQ+FkiMCiK5eQhtokOcQSY6mOTmIbSMCibA78iciS/WpFFa7nDXy3E7i8VCfHw8MTExlJWpy1Q8U0BAAD4+pz7XSeFGvJrDYfD77gP89490vvkzk/0Fpa6fRYdaubhnPCO6x9GndRT+vpo86A7FZXa27zvExgwbmzPy2bYvnx37DpGeV1zjNaFWP9rHhLpu7VqE0iY6hKRmQVj9Tq1v3tv4+vqe8ngFEU+ncCNexzAM1qTm8tW6DL7+M50sW4nrZ1HB/ozoEc/FPeMZ0KY5vj5qnWkohmGQkVfM5kwbmzLy2ZRhY3NmPrtyCmpsiYkOtdI+JsQZYlqE0j4mjPYxocSGW9WSJiInTeFGvIJhGGxIt/HfP9L5+o8M9h4scv0sLNCP4d3iGNUrgUHtmquFpgEUl9nZknkkwFR8rWlGWVSwP13iw+kUF0an2DA6xDpbYyKDA9xcuZsU28AaBgpoIm6hcCMerbTcwYI/M5i1dBd/7M1zHQ8O8OX8rrFc3DOBszpGq+uiHpWWO9iSmc8fabn8kZrHH2l5bM3Kr7Y1xs/HQrsWoXSOD6NLfDid45xfY8K8sCXG4QDbXsjeCjlbIHsL5Gx1fi06AF1GweiPzK5SpElQuBGPdLCglI9XpPDh8t2ubqcAPx/O6xzDqF4JnNMphqAABZpTVW53sD37EH/szeOPvbn8uTePTRn5lNqrDsxtHhJAl/hwusSH0TkunM7xzi4lrwuW5aVwYOfhALPVGWBytkDONigrrPm6vSvdV6NIE6dwIx5lW1Y+s5buZt7qvZQcnvnSIszKjWe05toBrWgeajW5Qs9lGAZ7Dxaxas9BV5jZkG6rdr2YiCB/eraMoGfLCHokRtIrKYK48EDvao0pyT8cXLZVboU5uAsc5dVf4+MPzdtBdEdo0cn51TDgi9vdW7tIE6dwI42eYRj8vDWbWUt387+t2a7j3RLCufXMNlzcM6HSlF85OXaHwaYMGyt3H+D3PQdZuftApcHXFUICfOmeGEGvpEh6JDoDTatmXjJV3jCgIKdqN1LOVrCl1XxdQGjlAFNxPyqZEsOHPfsLKSy10yMxAt+sP933ekQEULiRRqyo1M68NXt5b+lutu87BDjHY17QNZZbBrehf5tm3vEB6yaFpeWsTc1l5e6D/L77AGtScjlUUrkFws/HQvfECHonRbpaZtpGh+Lj6bPKHA4S2Ucfnz1ErNkERbuPBJni3JqvC4k5HGA6QHQnaNHR+TU8gfyScnZkH15ccPchtq/IY0f2UlKOWpdn2ujeXBrnllcoIkdRuJFGJzOvmA+X7+bjFSmuTSpDrX5c1S+JmwYl06p5sMkVeoacQyWs3H3Q1TKzIS2vyuq8YVY/+rSO4vTkKPolN6NXy0jPHqtkL3N2I7nGwxxuhcnZzjcUQQCw7NiLLBDVunILTPThQBPcjOz8EtdaPNvXH2JH9l6279tMpq3mdXkq7D1YCAo3Im6ncCONRkFJOdN/2s67v+xyDVhNahbETYPacFW/loQF+p/gEZq24jI7K3YdYMl25x5ZmzPzq5wTFx7I6W2aOcNM62Z0igvz3LV+DANy90DaKti7yvk1Yy2UVx86SvFjpyOelh16Edqy25Eg07w9+AdRUFLO1qx8tmTms3ltPlsyt7I1K7/Swo/HahFmPbweT+Xb1O+28snK1AZ64SJyIgo3YjrDMJi/Lp3JCza5xnycnhzFrWe25fyusZ774dvAHA6DjRk2lmx37l7+++6DVbYX6BgbyunJzTg9uRn9kqNIjAzy3K68ooPOAJO22jnzKG0VFOZUPc8aXnk8zOGv/V/fSm6pg+8uOAsfC2zOzGfLH/lsztzAlsx8Ug8WYlSztqDFAq2aBbtCTLsWobQ7HGKO3nPs2GtExDwKN2Kq9Wl5PDF/Ayv3HAScHyKPXtSF87vGeu6HcANKzy1iybYcftmew7LtOVVaFeLCAzmzQzRDOkQzuH000Z46e6y8BDL/PNwqczjIHNhR9Twff4jrAYl9oWU/59dm7aCavWkcbAccjHj1l+OukNw5Lsy5uGBcGJ3jwugQE+bZXXUiTZDCjZjiQEEpL323hf+sSMEwIMjfl/HntufWM9sQ6K8Pkgol5XaW7djPz1uy+WVbNjuyCyr9PCTAlzPaNncFmnYtQj0vFDocznVj0lYeCTOZf4KjmtWNm7WFxH5Hwkxsd/APPKmniYsIxFZ8CLvDIDjAl46xztWRK0JMp7gwLSUg4iUUbsStyu0OPvp1D1O/34qt2DlT55JeCUwc2Zn4iCCTq2sc8ovLWLwlm283ZLJ4S3alGU0+FujZMpIhHaI5s300p7WK8rxp8IeyD3cvHQ4zaaugOK/qecHNnSGmIswk9oHgZnV+2vdv7s/mTBvtW4TRMirI82eAiUiNFG7EbZZtz+HJ/25kS5ZzoGuX+HCevKQb/dvU/QPLW2Tnl/DDpiy+25DJ0u37K60AHBNmZVjXWM7qEM3AttFEBHvQwOrSQshYVznM5KZUPc8vEOJ7HQ4xh29RyfU6eCUhMoiESAVokaZA4UYa3N6DhUxesIkFf2YCEBnsz/0XdOKa/q2a9GDhlP2FfLcxk283ZLJyz8FKg1nbRodwQbc4hneLpVfLSM9oZXDYndOuK8bIpK2ErI1gVF3hmOhOh8fI9HG2zMR2A18PCm0i0qgp3EiDKS6zM+PnHby1eAcl5Q58LHD9Ga2ZcH5H7939+TgMw2BTRj7fbnAGmmOnavdsGcHww4HGI8bO5GfC3t+PhJn0tVBadfo5obGHu5b6OANNwmkQGOH2ckWk6VC4kQaxM/sQYz9c6RoAe0bbZkwa1Y0u8eEmV+Z+WbZivliTxuer9rLt8ErLAL4+Fga0acbwbnGc3zW28XeZOByQvhq2LoQtC6G6bQX8g53hpaJrqWU/CE/U3GgRcSuFG6l3/9uazfiPV2MrLic23MrjF3djZI+4xt8SUY+Ky+x8tzGLuav2smRbNhUzj61+Pgzt2IILusVxXucYokIaeQtWST7s+Am2fgvbvoWC7KN+aHF2J1V0LSX2hRadwVe/VkTEXPotJPXGMAxmLtnF5AWbcBjQt3UUM67vS4uwpjG91jAMVqccZO6qvXz1Rwb5xUdmOZ2eHMVf+7RkZM94whv7SssHdzvDzNaFsHsJ2I9aSycgDNqfBx0vhA7nQ0i0aWWKiNRE4UbqRUm5nUe+WM/cVXsBuKpfS56+tDtWP+9fsyYtt4h5q/Yyb00au3KOrEOTGBnEX/skcnmfliRHh5hY4Qk47JC6whlmti6E7M2Vfx7VBjqNgI7DodUg8GvkrU0i0uQp3Mgp25dfzB3/XsXqlFx8LPDYxV25aVCy13dD7S8o5dp3fmX5zv2umU7BAb6M6B7PX/smckab5o13llNRLuxYdLi76Tvn1gYVLL7QaqAzzHS80LmBpJf/txQR76JwI6fkj7253P7hKjJtxUQE+fPGtacxpEMLs8tyi9zCMpbt2A/AwLbN+WvflozoHkeItZH+b5Wz/UjrTMpycBzpNiMw0tnN1PFCaHfuKS2WJyJitkb6W1g8wfx16Tzw2TpKyh20jwnlnRv70aYxd7/UkzYtQmgWEkBYoB9/7dOSy05LJKlZsNllVWUvc4aYivEz+7dX/nl0R2eY6XghJA3QQGAR8Rr6bSa15nAYvPTdFt5c7NzI8NzOMUy7unfjHyhbT2LCAln16LDG2e1WeAC2fe8MM9sXQclR2xr4+EPy4MODgS+A5u3Mq1NEpAEp3Eit5BeXce8na/lh0z4A7hjajgeGd2pyKw03mmBjGJC9BbZ+42yhSf0NjCNbNxDcHDoMd46faXcuBDa9dYa82T5bMRsybOzKLuCsji1oHxNqdkkijYLp4Wb69Om8+OKLZGZm0qtXL15//XX69+9f4/nTpk3jrbfeIiUlhejoaK644gqmTJlCYODJ7QwsdbdnfwG3fbCSbfsOYfXz4YUrevKX3olml9X0lJfAnqXOhfS2LoTcPZV/Htv9yGDgxL7g4/0z1rydw2Gwa38BG9NtbMywsSHdxsZ0GzmHSlznDGzbnP/cfoaJVYo0HqaGm08++YQJEyYwY8YMBgwYwLRp0xg+fDhbtmwhJiamyvkff/wxDz30ELNmzWLQoEFs3bqVm266CYvFwtSpU014BU3H0u053Dl7NXlFZcSGW3n7hn70Soo0u6ym41C2c1bT1m+ci+qVHlnpGF8rtDnrcKAZDpGtzKtTTllxmZ2tWfmuALMhPY/NmfkUllbdo8vHAs1DrWTnl5BXVGZCtSKNk6nhZurUqYwdO5abb74ZgBkzZvD1118za9YsHnrooSrnL1u2jMGDB3PttdcCkJyczDXXXMNvv/1W43OUlJRQUnLkrxubzVbPr8L7/d/aNCZ8ug67w6B3UiRv39CXmHC1lDU4h8MZaH6dDrt+AY7aWTM09kjrTJuhYFV3hCcqKbezOSOfP9Ly+HNvLn/szWPbvkPYHUaVcwP9fegcF063hHC6JoTTLSGCTrFhrNh9gDGzVphQvTRp9jLI2+tsOT64B3JTjtw/lAWD74HTbzWtPNPCTWlpKatWrWLixImuYz4+PgwbNozly5dXe82gQYP46KOPWLFiBf3792fnzp0sWLCAG264ocbnmTJlCk8++WS9199UrEvN5YG5f2B3GFx+WiKTL+9BoL+6ORpUWRGs+w8sfxP2bztyPL734dlNw533fXzMqlDqwAA2pdv4M80ZYv7Ym8fmTBtl9qpBpllIgDPExFcEmXDaRIc2ubFtYiKHHfIzqgaXivu2tMrj+461bk7TDDc5OTnY7XZiY2MrHY+NjWXz5s3VXnPttdeSk5PDmWeeiWEYlJeXc8cdd/Dwww/X+DwTJ05kwoQJru9tNhtJSUn18yK83D5bMbf/eyWl5Q6GdYnlpSt7Nd5F6bzBoX2w4h1YORMKnevnYA2HvmOg/+3qbvJAa1NzmbFvL3fg/P9p5Gu/VDknKtifHi0j6dUygh6JEfRoGUFceGDjGbQO2B0Gu/cXsDkjn82ZNpKigrnqdP0e9WiG4dwr7uAeZ1hxhZeKAJMKjhN0dfoFOn8vRbaGqNbO+4f2wfI33PMajlea2QXUxuLFi5k8eTJvvvkmAwYMYPv27dxzzz08/fTTPPbYY9VeY7VasVqbxt5G9amk3M4dH60iy1ZCh5hQXhmtYNNg9m1y/jL449Mj+zhFtIIz/g59bgBrmLn1SZ39sGkf6ZZ07rA6W27CrH50T4ygZ1IEPRMj6dkygpZRQY0qyOQVlbE5w8amDBubM/PZlGFjS1Y+xWWV/0o/u1MLdU83ZobhXHm8ulaXivvlRcd/DB8/iEhyhpao1s4Q4woyrSE0purq5Zu/btrhJjo6Gl9fX7Kysiodz8rKIi4urtprHnvsMW644QZuu+02AHr06EFBQQG33347jzzyCD5qpq8XhmHw6BfrWZ2SS3igH+/c2I+wJrKGjdsYBuz8CZa94dwGoUJiPxg0HjqP0qJ6Huz8rrH8si2H+IhAhjUrhU0QHWpl3X0XNJo/Eo5ujdl0VJhJy63+Ay/Q34dOceGsT8vD7jAoKqs6wFncrLTAudFtbkrlVpeK+yUnGmNqgfDEI60uR7fARLaG8ASPnW1p2m/PgIAA+vbty6JFi7j00ksBcDgcLFq0iPHjx1d7TWFhYZUA4+vrfOMNo2q/tdTNB8t289mqvfhY4I1r+zTuTR89TXkJ/PkZLJ8O+zY6j1l8oPPFMHA8tBpgbn1SL87rEst5XQ53uWf8AZvAz8finN5kgjK7g+37DrE+LY8N6TbWp+WxMcNW7QwscG762iU+jC7x4XSOC6dLfBitm4fg62Oh2+MLKajhOmkA5aXOoLJ/O+zfcfjrdjiw0znu5URCYo60tBwbYsJbeu1GuKb+aThhwgTGjBlDv3796N+/P9OmTaOgoMA1e+rGG28kMTGRKVOmADBq1CimTp3Kaaed5uqWeuyxxxg1apQr5MipWbY9h6e/3gTAwyO7cFbHprFPVIMrPAC/z4QVb0OBcwFE/EOc3U4D7oBmbcytT7xGxVTy9Wk21qfnsSEtj02Z+ZSWVx38WdEa0zU+7HCICadTXBgRQWqpdSuHA2x7jwovh78e2OFshTGOEyYDI48JL0fdj0iCgEa4NYwbmBpuRo8eTXZ2No8//jiZmZn07t2bhQsXugYZp6SkVGqpefTRR7FYLDz66KOkpaXRokULRo0axbPPPmvWS/AqqQcKufPj1a6ZUbeeqQ/cU5az3TmVe+1/jvRvhyXAgL9B35sgKNLM6sSLpBwoZMSrv7AtK5/yaqaSh1n96JYYTveECLonRtA9UTOw3MowoCDnSMtLRXjZv8PZClNeXPO1/iHO7VKat4Pm7Y/cmrXVJrc1ML1Tf/z48TV2Qy1evLjS935+fkyaNIlJkya5obKmpaCknLEfriS3sIxeLSOYfHmPRjXI0aMYhnMF4WVvOFcQrlifJq4nDLoLul0GvvrLWOpH0OGlGQ6VlLMpwznGomIqeffEiMNhJpykqGDTxvsUlJSTcqCQti1CsPp5eSt7cd7hlpcdh8PL9iOtMccbA+Pj7wwrx4aYZu0gLK7qwF05LtPDjZjP4TCY8OlaNmfm0yLMyr9u6Ke1bOrCXgYbvoTlr0PGuiPHO46AgeMg+Uz9gpJ61691FPed35Eyh0H3w4EmPsKcqeSGYZBpK2ZjunOA8sYMG5sy8tm9vwDDgEt6JfDaNae5va56V1YEB3Yd1fqy/Uigqeh2rpYFIpOOaX05HGYikjSJoB7pnRRe/3E7327IIsDXhxnX9yUuQtM7a6UoF1Z/AL/968gAP79A6H0tnHEnRHcwtTzxbj4+Fu46z/3/xsrsDtdeV5synFtFbMq0kVtY89oou3IK3FhhPSg8ANmbnYP/920+EmLyUqm0YvixQmMPh5d2h8PL4SATlQz++v3qDgo3Tdy3GzJ55YetADxzWXf6to4yuSIPcnA3/DoD1vz7yF5PITHOBff63QIhzU0tT6QhXfDK/6hmaA++Phbatwh1zbbqmhDOPlsJ9322rurJDcTuMPCxcPKtV6UFh0PMpsO3jc6v+Rk1X2ONgOhjWl8qxsEEhtfPC5E6U7hpwrZk5jPhk7UA3DQomav6acXRk5K6wrlI1ab/Hll+PKars+up+xX6y0y8WmxEIDuzC3AYEBboR9f4cFeI6RofTvuY0Crd2j9tOV5XTd0ZhsG+/BI2Z+azJdN2+Gs+2/YdIrl5MF/fPQR/36OWDykvdW5pcnSA2bfR+YdKTSJbOf//btEZojseaZEJbq5u5kZM4aaJOlhQytgPV1JQamdQu+Y8clEXs0tq3OzlsPkrZ6jZ+/uR4+3Oda5P0+5c/aKTJuHftw5ga2Y+HWJDSYx03+rK+cVlbM3KZ3NmPlsznV+3ZOVX2w3mg4PSfdvIW51NdOHOI0Fm/3ZwlFf/BKGxENPFGWQqvrbopBXCPZTCTRNUbncw/j+rSTlQSFKzIKZf26fyXzdyREk+rPkIfn3TufIngG8A9LwKzhgHsV3NrU/EzRIjg0iMDGqwx3c4DFIOFLKxYhzPcVdONki0HGBIZDb9QzLp7JNGYulurAe3Emgpg6+rucQa4QwvsV2PBJkWXdSN7GUUbpqgyQs2s3T7foIDfHnnxn5EhXjnCpWnJG+vc4Dwqg+gJM95LKgZnH6b8xYWe/zrReSEikrtbMnKrzS7anOGrdoVkJth44zQTAaGZdPdL42k8t1EHdqBb1k+FOG8VbBAkRGAb2xnAuK7V26RCU9QK2sToHDTxHy2MpVZS3cBMPWqXnSO08C3StLXOLdG2PDFkebr5u2d42l6Xt1kV/sUqQ8HC0t5a/GOw60yeezKKagyKDmEIvr7pXFWZDanWTNoa6TQvHAHAcX7oRw4eMyD+vhB8w6VAszwj3PYVtacn68+j6Rm+n+2KVK4aUJ2Zh/ikS/WA3DPeR24sHu8yRU1ImVFsPAhWPX+kWPJQ5zjaTpcANqUVeSU7T1YxPMLN7u+D6SEgcFpnBueRm/f3SSXbiE0fxcWDDiE8+ZicU6ldo2JORxmmrevsj9SCgtxoP2vmjKFmybkjZ+2U2p3MKRDNPeYsC5Go5W9FT67CfZtACzQ40pnS01Cb5MLE/EOfZKi6B5jpVX5ToaG7qWnzy6SirYQYtuGxeGA3GMuCIuvPLA3potzcG+ANvGVk6Nw00Sk7C/k/9amA3D/BZ1MW4a90Vk3B76aAGUFENICLn/bOfNJROquvNQ5Qyl9DaSvISJ9DV8d2ujs6i085tzQWEjoAwmnHb71htAYM6oWL6Jw00S8uXg7dofBWR1b0Csp0uxyzFdaAAv+CWs/cn6fPAT++q5zDxcROXn2cucCeIeDDOlrIGs92EurnhscfVSIOXwLV/e41D+FmyYgLbeIz1fvBeDuc9ubXE0jsG+TsxsqezNggbMfgrMeAB/tpyVyXA475GyrHGQy/zyy4/3RAiOrBpmIlpqpJG6hcNMEzFi8gzK7wcC2zemX3MzscsxjGM41axY84PxlHBrrbK1pc5bZlYk0Pg4HHNxVOchkrDuy1cjRrOEQ36tykIlKVpAR0yjceLksWzGfrEwF4K7zmnCrTckh+HoC/PGJ8/u258Dl70BoC3PrEmkMDANy91QOMunrjqzxdDT/kKpBpllbj55RWLGNw+6cAromhBMW6G92SXKKFG683L9+3klpuYN+raMY2LaJrsCZ+aezG2r/drD4wDmPwJkTPPqXscgpKc6DlN8g9dcjYabo2AVkcO5uH9ezcpCJ7uDRXbhldgc7sg+5Fg7clJHPxgwbBwqcY4Qu6hHP9Ov6mFylnCqFGy+Wc6iEj1fsAeCu8zq4bQ+YRsMwYNV78M1DYC+BsAS4Yia0HmR2ZSLuVbAfUpbDnqXOW+afRzZ9reAbALHdKweZFp3A13NbMfKKykjdkeMMMIfDzPZ9hyi1O2q8Zu/BY6dziSdSuPFi7/yyk+IyB71aRnBWh2izy3GvYhv89x7YMM/5ffvz4bJ/af8YaRryMw8HmWWweylkb6p6TrO20GoQtOzrDDIxXcHP6v5aG9DFry+p9niY1Y/O8WGuHc27xIez92AR4z5e7eYKpaEo3HipgwWl/Hv54Vabc5tYq036Wmc31MFdYPGFYZNg4F3qhhLvlZtyOMgscX49sKPqOS06Q+vBzpbL1oO9egp2UrMgtmYdct3vEnckxHRLCKdlVNXdzPcXlJhRqjQQhRsvNWvpLgpL7XSND+e8Lk1kQSzDgBXvwHePONfYiEiCK2ZBUn+zKxOpP4YBB3Y6W2Z2H26dyUs55iQLxPVwhpjkwdBqIIQ0ndbbObcPZFdOAR1iQwnX4OAmSeHGC+UVlfH+0t0A3HVu+6bRalOUC/PHw6b/Or/vNBL+Mh2Cm/DUd/EODgfkbDnSKrNnGRzKrHyOxdfZtdR6ECSfCUkDICjSlHIbg2YhATQLCTjxieK1FG680IfLdpNfUk7H2FCGd2sCK+7uXQVzb3I2zfv4w/lPwRl/1xob4pkcdueA3z3LjoybKTpQ+RzfAEjs52yVaT0IWvYHa6g59Yo0Qgo3XuZQSTkzl+4CYNw57b17DynDgF/fhO8ngaMMIlvDle9BYl+zKxM5efYy5zixiplMKb9Cia3yOX5Bzu7V5DOdYSaxH/gHmlKuiCdQuPEyH/26h9zCMtpGh3BxzwSzy2k4hQfgyzth6zfO77tcApe83qSb4sVDlBVD2qojYSZ1BZQdM/3YGg6tzjgy+De+N/ipm0XkZCnceJGiUjvv/rITgDvPaY+vt7bapPwGc28B215n8/zwyXD6beqGksatKBdmjYC0lVU3lQyKqjyTKa6HRy+U11SU2x3szCngUEk5vVpGeu/vXA+kcONFPl6RQs6hUpKaBfGX3l7YauNwwLLXYNFTYNghqg1c+T4k9Da7MpGa+Rz+NVteBCnLnPdDYg6Plzl8a9FZSxU0coWl5a7VjDem57Ex3cbmzHxKyp0LAk6/tg8X9fTe6fWeRuHGSxSX2fnXz861Le48uz3+vl72i7IgB764A7Z/7/y+2+Uw6lUIDDe3LpETiekCA+5wbm9QEWaat1NLYyOWc6iEDek2Nqbb2JCex8YMG7tyCjCMmq9Jy9XKxo2Jwo2X+GxlKvvyS0iICOSvfVqaXU792rPM2Q2VnwG+VhjxPPS9SR8O4hksFue/WfEI6/bm0e+ZH6r9WYswK90SwukaH063hAi6JoTz2qJtfLEmzc1Vyoko3HiB0nIHby12ttrccXY7Avy8pNXG4YAlL8NPk5374DTv4OyGiutudmUi4mXiwoNc9y0WaNM8hK4J4XRNcAaZLvFhxIRVnaGmv7EaJ4UbLzBv9V7S84ppEWblqn5JZpdTPw7tg3m3w86fnN/3vBouellreYhIg+iaEM6X4wZjdzjoHBdOiFUfj55M//U8nN1h8ObhVpu/ndWWQH8vmGGx82eYNxYOZTnX97joJeh9nf5EEpEG1Tsp0uwSpJ4o3Hi4/1ubTsqBQpqFBHDtgFZml3NqHHb4+QX4+XnAcM4gufJ954BMERGRk6Rw4+E+XuHc+fu2IW0IDvDg/5z5mfD5bbD7F+f3p10PI16EgGBz6xIREY/jwZ+GAlBc5iAiyJ8bByabXUrdbV/kHF9TmAP+IXDxVOh1tdlViYiIh1K48QK3DG5DqCcOfrOXw+LJ8MtUwICYbs5uqBYdza5MREQ8mAd+IsrRwqx+3DQ42ewyai8vzdkNVbFia9+b4cIp4B90/OtEREROQOHGw40ZlExEkL/ZZdTOoWx451w4lAkBYTBqGvS4wuyqRETESyjceKjL+iSyYtcBbhvSxuxSascw4OsJzmDTvD1c+6lzKXoREZF6onDjoR68sLPZJdTNhnmwab5zM8G/zlSwEZEmZV9+MRvSnHtWJTUL5i+9E80uySsp3Ij75GfB1/c57w+5X7t5i4jXMgyD9Lxi1qflsSEtj/XpNtan5bEvv8R1jsUCQzq0oFlIgImVeieFG3GPiu6oooMQ1wOG3Gd2RSIi9SYzr4T/rktnfXqeq2XmYGFZlfN8LNCuRSjbsw9hGFBUZj/p5ygoKSflQCFtW4Rg9fOC1egbkMKNuMefc2HzV87uqEvfAj/9pSIi3mPW0l2wtPIxPx8LHWPD6J4YTvfECNcGnMEBfnR69BtKyh01Pl7OoRI2pNvYmO4MShszbOzKKcAw4LLTEnlldO+GfUEeTuFGGl5+Jiy433l/6IPOlhsRES/QNT6ceaRh9fOhS3w43ROdu4h3T4igY1zoCVtYHA6DPfsLqgSZLFtJjdfsyimo75fhdRRupGEZBvz3H1CcC/G94Mx7za5IRKTe3DakLX/pnUhUsD9+vj61vv6CV/5XbdeUxQJtmofQNSGcrgnOwJSVV8w/P/+jPsr2ego30rDWzYGt34CPP1w6A3w9bE0eEZETaBFmrfU10aFW0nKLKCqzE+DrQ6e4MLrGh9MtMZxuCeF0jgsn5JiV53/YmFVfJZ+UgwWlBFt9PXJ8j8KNNBxbOix80Hn/nIkQ29XcekREGon3bj6djek2OsWF0T4mFP86tPrUlzK7g53ZBWzKsLEp08bmjHw2ZdjYl19CXHggP//zbI8LOAo30jAMA/57DxTnQUIfGHSP2RWJiDQaHWPD6Bgb5vbnzc4vYXNFgMm0sSkjn+378imzG9Wen2kr5kBBKfERnrU1jsKNNIy1s2Hbd+Ab4Jwd5at/aiIi7lJud7Arp4CNGc6ByhsznEEm51D1A5VDrX50jgujS3w4nePD6BwXztVvL68x9DR2+sSR+pe3FxZOdN4/5xGI8dDVlEVEPEBBSTmbM/MrBZnNGbZqp5pXDFSuCDBd4sPpHBdGy6ggLBZL5XOxAAo3Is7uqPl3Q4kNWp4Og+4yuyIREa9ysLCUNxdvdwaZdBu79jvXvzlWSIAvXeKds626xDtvHWNDCQ5owI/+oGbQejC0MPePWoUbqV+rP4Qdi8Av0Nkd5eNZg9BERBq7PfsLeWHhlkrHYsOtdD0cZLrGR9A1IZzWzYLx8bHU8CgNpPVAuHmBe5+zGgo3Un9yU+DbR5z3z30UojuYW4+IiBfp0zqKjrGhOAyOCjLOFpm6TEf3Zgo3Uj8MA/5vPJTmQ9IAOONOsysSEfEqzUIC+O7eoWaX4RHMm1gv3mXlLNj1M/gFwV/eVHeUiIiYRuFGTt3B3fDdY877wyZBdHtTyxERkaZN4UZOjcPh7I4qK4BWg6D/38yuSEREmjjTw8306dNJTk4mMDCQAQMGsGLFiuOen5uby7hx44iPj8dqtdKxY0cWLDB/ZHaTtXIm7P4F/IPh0ungY/o/KRERaeJMHVD8ySefMGHCBGbMmMGAAQOYNm0aw4cPZ8uWLcTExFQ5v7S0lPPPP5+YmBjmzp1LYmIie/bsITIy0v3FCxzYCd8/7rw/7Elo1tbcekRERDA53EydOpWxY8dy8803AzBjxgy+/vprZs2axUMPPVTl/FmzZnHgwAGWLVuGv79zd+nk5OTjPkdJSQklJUeWm7bZbPX3ApoyV3dUISQPgdNvM7siERERwMRuqdLSUlatWsWwYcOOFOPjw7Bhw1i+fHm118yfP5+BAwcybtw4YmNj6d69O5MnT8Zut9f4PFOmTCEiIsJ1S0pKqvfX0iSteBv2LAX/EPjLG+qOEhGRRsO0T6ScnBzsdjuxsbGVjsfGxpKZmVntNTt37mTu3LnY7XYWLFjAY489xssvv8wzzzxT4/NMnDiRvLw81y01NbVeX0eTtH8H/PCE8/4FT0NUspnViIiIVOJRi/g5HA5iYmJ4++238fX1pW/fvqSlpfHiiy8yadKkaq+xWq1YrVq5sd447PDlnVBeBG2GQr9bzK5IRESkEtPCTXR0NL6+vmRlZVU6npWVRVxcXLXXxMfH4+/vj6/vkQXiunTpQmZmJqWlpQQEBDRozQL8NgNSf4WAUGd3lMXN+5aIiIicgGndUgEBAfTt25dFixa5jjkcDhYtWsTAgQOrvWbw4MFs374dh+PINu5bt24lPj5ewcYdcrbBoqec94c/C5GtzK1HRESkGqaOAp0wYQLvvPMOH3zwAZs2beLvf/87BQUFrtlTN954IxMnTnSd//e//50DBw5wzz33sHXrVr7++msmT57MuHHjzHoJTYfDDl/+HcqLod250GeM2RWJiIhUy9QxN6NHjyY7O5vHH3+czMxMevfuzcKFC12DjFNSUvA5ahZOUlIS3377Lffeey89e/YkMTGRe+65hwcffNCsl9B0LH8D9v4O1nC45HV1R4mIiEtuYSkbM2xsyshnz/4C/tI7kb6to0yrx/QBxePHj2f8+PHV/mzx4sVVjg0cOJBff/21gauSSrK3wI/POu8PnwwRLc2tR0RETOFwGOw5UMimDBsb021synDe0vOKK523fd8hPh57hklV1jHc2O123n//fRYtWsS+ffsqjYEB+PHHH+ulOGkE7OXO7ih7CbQ/H0673uyKRETEjeatTiM9t4hNGTY2Z+ZTWFr92nJJzYKICPJnfZqN4rKa159zhzqFm3vuuYf333+fiy66iO7du2NRF4X3WvYapK0CawRc8pq6o0REmpgXv91S6Xurnw+d4sLoGh9Ol8O3zvFhhAf6892GTG7/9yqTKj2iTuFmzpw5fPrpp4wcObK+65HGJGsjLJ7ivD/ieQhPMLceERFxm7/2TWTxlmw6xoYdDjFhdEsIJ7l5CH6+jXtV+jqFm4CAANq3b1/ftUhjYi873B1VCh1HQK+rza5IRETcaMrlPc0uoc7qFL3uu+8+Xn31VQzDqO96pLFYOg0y1kJgJIyapu4oERHxGHVquVmyZAk//fQT33zzDd26dXPt0F1h3rx59VKcmCRzPSx+3nl/5IsQVv2K0SIiIo1RncJNZGQkl112WX3XIo2BvQy+vAMcZdD5YuhxpdkViYiI1Eqdws17771X33VIY/HLy5D5JwQ1g4tfUXeUiIh4nFNaxC87O5stW5xTxDp16kSLFi3qpSgxScYf8L8XnfcveglCY8ytR0REpA7qNKC4oKCAW265hfj4eM466yzOOussEhISuPXWWyksLKzvGsUdykuds6Mc5dDlEuh2udkViYiI1Emdws2ECRP4+eef+e9//0tubi65ubn83//9Hz///DP33Xdffdco7vC/FyFrPQQ3h4umqjtKREQ8Vp26pT7//HPmzp3L2Wef7To2cuRIgoKCuOqqq3jrrbfqqz5xh/Q1zrE2ABe9DKHqXhQREc9Vp5abwsJC187dR4uJiVG3lKcpL4Ev7wTD7uyK6qZZcCIi4tnqFG4GDhzIpEmTKC4+sgtoUVERTz75JAMHDqy34sQNfn4e9m2EkBYw8iWzqxERETlldeqWevXVVxk+fDgtW7akV69eAKxbt47AwEC+/fbbei1QGlDaKljyivP+xa9ASHNz6xEREakHdQo33bt3Z9u2bcyePZvNmzcDcM0113DdddcRFBRUrwVKAykrhi/+DobDuVBfl1FmVyQiIlIv6rzOTXBwMGPHjq3PWsSdFk+GnC0QGgsjXjC7GhERkXpz0uFm/vz5jBgxAn9/f+bPn3/ccy+55JJTLkwaUOrvsOx15/2Lp0FwM1PLERERqU8nHW4uvfRSMjMziYmJ4dJLL63xPIvFgt1ur4/apCGUFTkX6zMc0Osa6DzS7IpERETq1UmHG4fDUe198TA/PgP7t0FYPFw4xexqRERE6l2dpoJXJzc3t74eShpKyq+wfLrz/qhXISjK3HpEREQaQJ3CzfPPP88nn3zi+v7KK6+kWbNmJCYmsm7dunorTupRaaFzsT4M6H09dBxudkUiIiINok7hZsaMGSQlJQHw/fff88MPP7Bw4UJGjBjBAw88UK8FSj358Wk4sAPCE2H4s2ZXIyIi0mDqNBU8MzPTFW6++uorrrrqKi644AKSk5MZMGBAvRYo9WD3Uvj18H5fl7wGQZGmliMiItKQ6tRyExUVRWpqKgALFy5k2LBhABiGoZlSjU1pAfzf4e6oPjdC+2FmVyQiItKg6tRyc/nll3PttdfSoUMH9u/fz4gRIwBYs2YN7du3r9cC5RQteQUO7obwlnCBuqNERMT71SncvPLKKyQnJ5OamsoLL7xAaGgoABkZGdx55531WqCcom3fOb+e9xgEhptbi4iIiBvUKdz4+/tz//33Vzl+7733nnJBUo9KCyBzvfN+8hBzaxEREXETbb/gzdLXgGF3zpCKSDS7GhEREbfQ9gveLPU359eWp5tbh4iIiBtp+wVvlvq782tSf3PrEBERcaN6235BGhnDgL0rnPeTtPaQiIg0HXUKN3fffTevvfZaleNvvPEG//jHP061JqkPB3ZC4X7wtUJcT7OrERERcZs6hZvPP/+cwYMHVzk+aNAg5s6de8pFST1IPdxqk9Ab/AJMLUVERMSd6hRu9u/fT0RERJXj4eHh5OTknHJRUg8quqQ0mFhERJqYOoWb9u3bs3DhwirHv/nmG9q2bXvKRUk9cA0m1ngbERFpWuq0iN+ECRMYP3482dnZnHvuuQAsWrSIl19+mWnTptVnfVIXJfmwb4PzvmZKiYhIE1OncHPLLbdQUlLCs88+y9NPPw1AcnIyb731FjfeeGO9Fih1kLYKDAdEtIKwOLOrERERcas6hRuAv//97/z9738nOzuboKAg1/5S0gi4uqQ03kZERJqeOq9zU15ezg8//MC8efMwDAOA9PR0Dh06VG/FSR1pfRsREWnC6tRys2fPHi688EJSUlIoKSnh/PPPJywsjOeff56SkhJmzJhR33XKyXI4jkwD10wpERFpgurUcnPPPffQr18/Dh48SFBQkOv4ZZddxqJFi+qtOKmD/duhOBf8giCuh9nViIiIuF2dWm5++eUXli1bRkBA5cXhkpOTSUtLq5fCpI4quqQSTgNff3NrERERMUGdWm4cDke1O3/v3buXsLCwUy5KTkFFl5SmgIuISBNVp3BzwQUXVFrPxmKxcOjQISZNmsTIkSPrqzapC4UbERFp4urULfXSSy9x4YUX0rVrV4qLi7n22mvZtm0b0dHR/Oc//6nvGuVkFedB9mbn/ZYKNyIi4l5DOrTgl3+eg9WvzpOx60Wdwk1SUhLr1q3jk08+Yd26dRw6dIhbb72V6667rtIAY3GzvSsBA6KSIbSF2dWIiEgTExTgS1KzYLPLqH24KSsro3Pnznz11Vdcd911XHfddQ1Rl9TFXu0nJSIiUut2I39/f4qLixuiFjlVqb85v2p9GxERacLq1Ck2btw4nn/+ecrLy+u7HqkrhwP2rnLe12BiERFpwuo05ub3339n0aJFfPfdd/To0YOQkJBKP583b169FCe1kLMFSvLAPwRiupldjYiIiGnqFG4iIyP561//Wt+1yKmomAKe2Ad867wfqoiIiMer1aegw+HgxRdfZOvWrZSWlnLuuefyxBNPaIZUY6D1bURERIBajrl59tlnefjhhwkNDSUxMZHXXnuNcePGNVRtUhsV2y5ofRsREWniahVuPvzwQ958802+/fZbvvzyS/773/8ye/ZsHA5HQ9UnJ6PwAORsdd7XTCkREWniahVuUlJSKm2vMGzYMCwWC+np6fVemNTC3pXOr83bQ0hzc2sRERExWa3CTXl5OYGBgZWO+fv7U1ZWdkpFTJ8+neTkZAIDAxkwYAArVqw4qevmzJmDxWLh0ksvPaXn93jqkhIREXGp1YBiwzC46aabsFqtrmPFxcXccccdlaaD12Yq+CeffMKECROYMWMGAwYMYNq0aQwfPpwtW7YQExNT43W7d+/m/vvvZ8iQIbV5Cd7JNZhYXVIiIiK1arkZM2YMMTExREREuG7XX389CQkJlY7VxtSpUxk7diw333wzXbt2ZcaMGQQHBzNr1qwar7Hb7Vx33XU8+eSTtG3btlbP53Ucdkg7vHifWm5ERERq13Lz3nvv1euTl5aWsmrVKiZOnOg65uPjw7Bhw1i+fHmN1z311FPExMRw66238ssvvxz3OUpKSigpKXF9b7PZTr3wxmTfRig9BAFhENPF7GpERERMZ+qe5Dk5OdjtdmJjYysdj42NJTMzs9prlixZwsyZM3nnnXdO6jmmTJlSqVUpKSnplOtuVCq6pFr2BR9fc2sRERFpBEwNN7WVn5/PDTfcwDvvvEN0dPRJXTNx4kTy8vJct9TU1Aau0s0qdgJXl5SIiAhQx+0X6kt0dDS+vr5kZWVVOp6VlUVcXFyV83fs2MHu3bsZNWqU61jFGjt+fn5s2bKFdu3aVbrGarVWGgDtdVyDiQeYW4eIiEgjYWrLTUBAAH379mXRokWuYw6Hg0WLFjFw4MAq53fu3Jk///yTtWvXum6XXHIJ55xzDmvXrvW+LqcTKciBAzuc91v2NbcWERGRRsL0HRYnTJjAmDFj6NevH/3792fatGkUFBRw8803A3DjjTeSmJjIlClTCAwMpHv37pWuj4yMBKhyvEmo6JKK7gRBUebWIiIi0kiYHm5Gjx5NdnY2jz/+OJmZmfTu3ZuFCxe6BhmnpKTg4+NRQ4PcR+vbiIiIVGF6uAEYP34848ePr/ZnixcvPu6177//fv0X5CkqWm403kZERMRFTSKeyl6uxftERESqoXDjqbLWQ1khBEZAdEezqxEREWk0FG48VUWXVGI/0JgkERERF30qeiqtbyMiIlIthRtPlfqb86tmSomIiFSicOOJDu2D3D2AxdktJSIiIi4KN56ooksqpgsEhptbi4iISCOjcOOJXF1SmgIuIiJyLIUbT6SdwEVERGqkcONpykshfY3zvlpuREREqlC48TRZf0J5sXOjzObtza5GRESk0VG48TQVg4lb9geLxdxaREREGiGFG0+jncBFRESOS+HG02gwsYiIyHEp3HgSWzrkpYLFBxL7ml2NiIhIo6Rw40kquqRiu4E11NxaREREGimFG0+iLikREZETUrjxJK7BxAo3IiIiNVG48RTlJZCx1nm/pWZKiYiI1EThxlNkrAN7KQRHQ7O2ZlcjIiLSaCnceIqju6S0eJ+IiEiNFG48xd6KlYnVJSUiInI8CjeewDA0mFhEROQkKdx4gry9kJ8BFl9I6GN2NSIiIo2awo0nqOiSiusBAcHm1iIiItLIKdx4gtTDi/epS0pEROSEFG48gWswscKNiIjIiSjcNHZlRc41bkAtNyIiIidB4aaxS18LjnIIjYXIVmZXIyIi0ugp3DR2R69vo8X7RERETkjhprHT+jYiIiK1onDTmFVavG+AubWIiIh4CIWbxix3DxTsAx9/iO9tdjUiIiIeQeGmMatY3ya+J/gHmluLiIiIh1C4acxSf3N+1fo2IiIiJ03hpjHbq8HEIiIitaVw01iVFkDmeud9hRsREZGTpnDTWKWvAcMOYQkQ0dLsakRERDyGwk1jVTHeJul0c+sQERHxMAo3jZVrJ3CtbyMiIlIbCjeNkWFoJ3AREZE6UrhpjA7shML94BvgXONGRERETprCTWNUseVCwmngZzW3FhEREQ+jcNMYHb0TuIiIiNSKwk1j5BpMrPE2IiIitaVw09iU5MO+Dc77GkwsIiJSawo3jU3aKjAcENEKwuPNrkZERMTjKNw0Nq4uKY23ERERqQuFm8ZG69uIiIicEoWbxsThODINXC03IiIidaJw05js3w7FueAXBHFavE9ERKQuFG4ak71HLd7n629uLSIiIh5K4aYxUZeUiIjIKVO4aUxSNZhYRETkVCncNBbFeZC92XlfKxOLiIjUmcJNY7F3JWBAVDKExphdjYiIiMdqFOFm+vTpJCcnExgYyIABA1ixYkWN577zzjsMGTKEqKgooqKiGDZs2HHP9xh7Dy/epy4pERGRU2J6uPnkk0+YMGECkyZNYvXq1fTq1Yvhw4ezb9++as9fvHgx11xzDT/99BPLly8nKSmJCy64gLS0NDdXXs9Sf3N+VZeUiIjIKTE93EydOpWxY8dy880307VrV2bMmEFwcDCzZs2q9vzZs2dz55130rt3bzp37sy7776Lw+Fg0aJFbq68HjkcsHeV877CjYiIyCkxNdyUlpayatUqhg0b5jrm4+PDsGHDWL58+Uk9RmFhIWVlZTRr1qzan5eUlGCz2SrdGp2cLVCSB/4hENPN7GpEREQ8mqnhJicnB7vdTmxsbKXjsbGxZGZmntRjPPjggyQkJFQKSEebMmUKERERrltSUtIp113vKqaAJ/YBXz9zaxEREfFwpndLnYrnnnuOOXPm8MUXXxAYGFjtORMnTiQvL891S01NdXOVJ8G1vo0W7xMRETlVpjYTREdH4+vrS1ZWVqXjWVlZxMXFHffal156ieeee44ffviBnj1r3ofJarVitVrrpd4GU7HtQtIAc+sQERHxAqa23AQEBNC3b99Kg4ErBgcPHDiwxuteeOEFnn76aRYuXEi/fv3cUWrDKTwAOVud99VyIyIicspMH+AxYcIExowZQ79+/ejfvz/Tpk2joKCAm2++GYAbb7yRxMREpkyZAsDzzz/P448/zscff0xycrJrbE5oaCihoaGmvY46Szs8S6pZOwhpbm4tIiIiXsD0cDN69Giys7N5/PHHyczMpHfv3ixcuNA1yDglJQUfnyMNTG+99RalpaVcccUVlR5n0qRJPPHEE+4svX5ofRsREZF6ZXq4ARg/fjzjx4+v9meLFy+u9P3u3bsbviB3cu0ErnAjIiJSHzx6tpTHc9iPdEtp2wUREZF6oXBjpn2boPQQBIRBTBezqxEREfEKCjdmqhhvk9gHfHzNrUVERMRLKNyYqWIncK1vIyIiUm8UbsykwcQiIiL1TuHGLAU5cGCH835LD1+IUEREpBFRuDFLRZdUdEcIijK3FhERES+icGMWdUmJiIg0CIUbs1S03Gh9GxERkXqlcGMGe/mRxfvUciMiIlKvFG7MkLUeygrBGgHRncyuRkRExKso3JjB1SXVD3z0n0BERKQ+6ZPVDBpMLCIi0mAUbsxQse1Cy9PNrUNERMQLKdy426F9kLsHsGjxPhERkQagcONuFV1SMV0gMMLcWkRERLyQwo277T0cbtQlJSIi0iAUbtxNg4lFREQalMKNO5WXQvoa5/2kAebWIiIi4qUUbtwp608oL3ZulNm8vdnViIiIeCWFG3dKrVi873SwWMytRURExEsp3LiTa30bjbcRERFpKAo37lSx7YIGE4uIiDQYhRt3saVDXipYfCCxr9nViIiIeC2FG3dxLd7XDayh5tYiIiLixRRu3MXVJaXF+0RERBqSwo27uBbv0/o2IiIiDUnhxh3KSyBjrfO+tl0QERFpUAo37pCxDuylENwcmrU1uxoRERGvpnDjDhVdUi37a/E+ERGRBqZw4w57tVmmiIiIuyjcNDTD0E7gIiIibqRw09Dy9kJ+Blh8IeE0s6sRERHxego3Da2iSyquOwSEmFuLiIhIE6Bw09AqdgLX+jYiIiJuoXDT0PYeNVNKREREGpzCTUMqK3KucQPadkFERMRNFG4aUvpacJRDSAxEtja7GhERkSZB4aYhHb2+jRbvExERcQuFm4ak9W1ERETcTuGmoRy9eJ8GE4uIiLiNwk1Dyd0DBfvAxw8SeptdjYiISJOhcNNQKta3ie8F/kHm1iIiItKEKNw0FK1vIyIiYgqFm4aS+pvzq9a3ERERcSuFm4ZQWgCZ65331XIjIiLiVgo3DSF9DRh2CEuAiJZmVyMiItKkKNw0hKO7pLR4n4iIiFsp3DSEiplS6pISERFxO4Wb+mYYlbddEBEREbfyM7sAr2NLB0cZ+AY417gRERERt1LLTX1zlDm/xvcGP6uppYiIiDRFCjcNRV1SIiIiplC4aSgttXifiIiIGRRuGkrSALMrEBERaZIUbhpCRBKEx5tdhYiISJPUKMLN9OnTSU5OJjAwkAEDBrBixYrjnv/ZZ5/RuXNnAgMD6dGjBwsWLHBTpSdJXVIiIiKmMT3cfPLJJ0yYMIFJkyaxevVqevXqxfDhw9m3b1+15y9btoxrrrmGW2+9lTVr1nDppZdy6aWXsn79ejdXfhwaTCwiImIai2EYhpkFDBgwgNNPP5033ngDAIfDQVJSEnfddRcPPfRQlfNHjx5NQUEBX331levYGWecQe/evZkxY8YJn89msxEREUFeXh7h4eH190JSf4eZw5z3x/4IiX3r77FFRESauNp8fpvaclNaWsqqVasYNmyY65iPjw/Dhg1j+fLl1V6zfPnySucDDB8+vMbzS0pKsNlslW4Nyi8QYns07HOIiIhIjUwNNzk5OdjtdmJjYysdj42NJTMzs9prMjMza3X+lClTiIiIcN2SkpLqp/hjxXSG2O5w+m3gF9AwzyEiIiInZPqYm4Y2ceJE8vLyXLfU1NSGeSJrGPx9KQx/tmEeX0RERE6KqXtLRUdH4+vrS1ZWVqXjWVlZxMXFVXtNXFxcrc63Wq1YrdoGQUREpKkwteUmICCAvn37smjRItcxh8PBokWLGDhwYLXXDBw4sNL5AN9//32N54uIiEjTYvqu4BMmTGDMmDH069eP/v37M23aNAoKCrj55psBuPHGG0lMTGTKlCkA3HPPPQwdOpSXX36Ziy66iDlz5rBy5UrefvttM1+GiIiINBKmh5vRo0eTnZ3N448/TmZmJr1792bhwoWuQcMpKSn4+BxpYBo0aBAff/wxjz76KA8//DAdOnTgyy+/pHv37ma9BBEREWlETF/nxt0abJ0bERERaTAes86NiIiISH1TuBERERGvonAjIiIiXkXhRkRERLyKwo2IiIh4FYUbERER8SoKNyIiIuJVFG5ERETEqyjciIiIiFcxffsFd6tYkNlms5lciYiIiJysis/tk9lYocmFm/z8fACSkpJMrkRERERqKz8/n4iIiOOe0+T2lnI4HKSnpxMWFobFYqnXx7bZbCQlJZGamqp9qxqQ3mf30PvsHnqf3UfvtXs01PtsGAb5+fkkJCRU2lC7Ok2u5cbHx4eWLVs26HOEh4frfxw30PvsHnqf3UPvs/vovXaPhnifT9RiU0EDikVERMSrKNyIiIiIV1G4qUdWq5VJkyZhtVrNLsWr6X12D73P7qH32X30XrtHY3ifm9yAYhEREfFuarkRERERr6JwIyIiIl5F4UZERES8isKNiIiIeBWFm1qaPn06ycnJBAYGMmDAAFasWHHc8z/77DM6d+5MYGAgPXr0YMGCBW6q1LPV5n1+5513GDJkCFFRUURFRTFs2LAT/ncRp9r+e64wZ84cLBYLl156acMW6CVq+z7n5uYybtw44uPjsVqtdOzYUb87TkJt3+dp06bRqVMngoKCSEpK4t5776W4uNhN1Xqm//3vf4waNYqEhAQsFgtffvnlCa9ZvHgxffr0wWq10r59e95///0GrxNDTtqcOXOMgIAAY9asWcaGDRuMsWPHGpGRkUZWVla15y9dutTw9fU1XnjhBWPjxo3Go48+avj7+xt//vmnmyv3LLV9n6+99lpj+vTpxpo1a4xNmzYZN910kxEREWHs3bvXzZV7ltq+zxV27dplJCYmGkOGDDH+8pe/uKdYD1bb97mkpMTo16+fMXLkSGPJkiXGrl27jMWLFxtr1651c+Wepbbv8+zZsw2r1WrMnj3b2LVrl/Htt98a8fHxxr333uvmyj3LggULjEceecSYN2+eARhffPHFcc/fuXOnERwcbEyYMMHYuHGj8frrrxu+vr7GwoULG7ROhZta6N+/vzFu3DjX93a73UhISDCmTJlS7flXXXWVcdFFF1U6NmDAAONvf/tbg9bp6Wr7Ph+rvLzcCAsLMz744IOGKtEr1OV9Li8vNwYNGmS8++67xpgxYxRuTkJt3+e33nrLaNu2rVFaWuquEr1Cbd/ncePGGeeee26lYxMmTDAGDx7coHV6k5MJN//85z+Nbt26VTo2evRoY/jw4Q1YmWGoW+oklZaWsmrVKoYNG+Y65uPjw7Bhw1i+fHm11yxfvrzS+QDDhw+v8Xyp2/t8rMLCQsrKymjWrFlDlenx6vo+P/XUU8TExHDrrbe6o0yPV5f3ef78+QwcOJBx48YRGxtL9+7dmTx5Mna73V1le5y6vM+DBg1i1apVrq6rnTt3smDBAkaOHOmWmpsKsz4Hm9zGmXWVk5OD3W4nNja20vHY2Fg2b95c7TWZmZnVnp+ZmdlgdXq6urzPx3rwwQdJSEio8j+UHFGX93nJkiXMnDmTtWvXuqFC71CX93nnzp38+OOPXHfddSxYsIDt27dz5513UlZWxqRJk9xRtsepy/t87bXXkpOTw5lnnolhGJSXl3PHHXfw8MMPu6PkJqOmz0GbzUZRURFBQUEN8rxquRGv8txzzzFnzhy++OILAgMDzS7Ha+Tn53PDDTfwzjvvEB0dbXY5Xs3hcBATE8Pbb79N3759GT16NI888ggzZswwuzSvsnjxYiZPnsybb77J6tWrmTdvHl9//TVPP/202aVJPVDLzUmKjo7G19eXrKysSsezsrKIi4ur9pq4uLhanS91e58rvPTSSzz33HP88MMP9OzZsyHL9Hi1fZ937NjB7t27GTVqlOuYw+EAwM/Pjy1bttCuXbuGLdoD1eXfc3x8PP7+/vj6+rqOdenShczMTEpLSwkICGjQmj1RXd7nxx57jBtuuIHbbrsNgB49elBQUMDtt9/OI488go+P/vavDzV9DoaHhzdYqw2o5eakBQQE0LdvXxYtWuQ65nA4WLRoEQMHDqz2moEDB1Y6H+D777+v8Xyp2/sM8MILL/D000+zcOFC+vXr545SPVpt3+fOnTvz559/snbtWtftkksu4ZxzzmHt2rUkJSW5s3yPUZd/z4MHD2b79u2u8AiwdetW4uPjFWxqUJf3ubCwsEqAqQiUhrZcrDemfQ426HBlLzNnzhzDarUa77//vrFx40bj9ttvNyIjI43MzEzDMAzjhhtuMB566CHX+UuXLjX8/PyMl156ydi0aZMxadIkTQU/CbV9n5977jkjICDAmDt3rpGRkeG65efnm/USPEJt3+djabbUyant+5ySkmKEhYUZ48ePN7Zs2WJ89dVXRkxMjPHMM8+Y9RI8Qm3f50mTJhlhYWHGf/7zH2Pnzp3Gd999Z7Rr18646qqrzHoJHiE/P99Ys2aNsWbNGgMwpk6daqxZs8bYs2ePYRiG8dBDDxk33HCD6/yKqeAPPPCAsWnTJmP69OmaCt4Yvf7660arVq2MgIAAo3///savv/7q+tnQoUONMWPGVDr/008/NTp27GgEBAQY3bp1M77++ms3V+yZavM+t27d2gCq3CZNmuT+wj1Mbf89H03h5uTV9n1etmyZMWDAAMNqtRpt27Y1nn32WaO8vNzNVXue2rzPZWVlxhNPPGG0a9fOCAwMNJKSkow777zTOHjwoPsL9yA//fRTtb9vK97bMWPGGEOHDq1yTe/evY2AgACjbdu2xnvvvdfgdVoMQ+1vIiIi4j005kZERES8isKNiIiIeBWFGxEREfEqCjciIiLiVRRuRERExKso3IiIiIhXUbgRERERr6JwIyIiIl5F4UZEBLBYLHz55ZcA7N69G4vFwtq1a02tSUTqRuFGREx30003YbFYsFgs+Pv706ZNG/75z39SXFxsdmki4oH8zC5ARATgwgsv5L333qOsrIxVq1YxZswYLBYLzz//vNmliYiHUcuNiDQKVquVuLg4kpKSuPTSSxk2bBjff/89AA6HgylTptCmTRuCgoLo1asXc+fOrXT9hg0buPjiiwkPDycsLIwhQ4awY8cOAH7//XfOP/98oqOjiYiIYOjQoaxevdrtr1FE3EPhRkQanfXr17Ns2TICAgIAmDJlCh9++CEzZsxgw4YN3HvvvVx//fX8/PPPAKSlpXHWWWdhtVr58ccfWbVqFbfccgvl5eUA5OfnM2bMGJYsWcKvv/5Khw4dGDlyJPn5+aa9RhFpOOqWEpFG4auvviI0NJTy8nJKSkrw8fHhjTfeoKSkhMmTJ/PDDz8wcOBAANq2bcuSJUv417/+xdChQ5k+fToRERHMmTMHf39/ADp27Oh67HPPPbfSc7399ttERkby888/c/HFF7vvRYqIWyjciEijcM455/DWW29RUFDAK6+8gp+fH3/961/ZsGEDhYWFnH/++ZXOLy0t5bTTTgNg7dq1DBkyxBVsjpWVlcWjjz7K4sWL2bdvH3a7ncLCQlJSUhr8dYmI+ynciEijEBISQvv27QGYNWsWvXr1YubMmXTv3h2Ar7/+msTExErXWK1WAIKCgo772GPGjGH//v28+uqrtG7dGqvVysCBAyktLW2AVyIiZlO4EZFGx8fHh4cffpgJEyawdetWrFYrKSkpDB06tNrze/bsyQcffEBZWVm1rTdLly7lzTffZOTIkQCkpqaSk5PToK9BRMyjAcUi0ihdeeWV+Pr68q9//Yv777+fe++9lw8++IAdO3awevVqXn/9dT744AMAxo8fj81m4+qrr2blypVs27aNf//732zZsgWADh068O9//5tNmzbx22+/cd11152wtUdEPJdabkSkUfLz82P8+PG88MIL7Nq1ixYtWjBlyhR27txJZGQkffr04eGHHwagefPm/PjjjzzwwAMMHToUX19fevfuzeDBgwGYOXMmt99+O3369CEpKYnJkydz//33m/nyRKQBWQzDMMwuQkRERKS+qFtKREREvIrCjYiIiHgVhRsRERHxKgo3IiIi4lUUbkRERMSrKNyIiIiIV1G4EREREa+icCMiIiJeReFGREREvIrCjYiIiHgVhRsRERHxKv8PUm5N2gFuGzMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict probabilities on the training and testing sets\n",
    "y_train_proba = logR.predict_proba(X_train_pca)[:, 1]\n",
    "y_test_proba = logR.predict_proba(X_test_pca)[:, 1]\n",
    "\n",
    "# Compute precision-recall curves and AUCs for the training and testing sets\n",
    "train_precision, train_recall, _ = precision_recall_curve(y_train, y_train_proba)\n",
    "test_precision, test_recall, _ = precision_recall_curve(y_test, y_test_proba)\n",
    "train_auc = auc(train_recall, train_precision)\n",
    "test_auc = auc(test_recall, test_precision)\n",
    "\n",
    "# Plot the precision-recall curves and AUCs for the training and testing sets\n",
    "plt.plot(train_recall, train_precision, label=f'Train (AUC = {train_auc:.2f})')\n",
    "plt.plot(test_recall, test_precision, label=f'Test (AUC = {test_auc:.2f})')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#confusion_matrix, \n",
    "y_pred = logR.predict(X_test_pca)\n",
    "conf_m = metrics.confusion_matrix(y_test, y_pred)\n",
    "\n",
    "group_names = ['True Neg','False Pos','False Neg','True Pos']\n",
    "\n",
    "group_counts = [\"{0:0.0f}\".format(value) for value in conf_m.flatten()]\n",
    "\n",
    "group_percentages = [\"{0:.2%}\".format(value) for value in conf_m.flatten()/np.sum(conf_m)]\n",
    "\n",
    "labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in zip(group_names,group_counts,group_percentages)]\n",
    "\n",
    "labels = np.asarray(labels).reshape(2,2)\n",
    "\n",
    "\n",
    "labelsx = ['Non_hemolitic', 'Hemolitic']\n",
    "sns.heatmap(conf_m, annot=labels, fmt='', cmap=\"YlGnBu\", yticklabels=labelsx, xticklabels=labelsx)\n",
    "plt.title('Hemolitic Confusion Matrix', fontsize=16)\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian NB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"{'var_smoothing': 1e-09}\""
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_final.iloc[4, 12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2 score: 0.81\n",
      "Test R2 score: 0.84\n"
     ]
    }
   ],
   "source": [
    "gnb = GaussianNB(\n",
    "    var_smoothing= 1e-09,\n",
    "    )\n",
    "\n",
    "gnb.fit(X_train_pca, y_train)\n",
    "\n",
    "# print Accuracy score\n",
    "print(f'Train R2 score: {gnb.score(X_train_pca, y_train):.2f}')\n",
    "print(f'Test R2 score: {gnb.score(X_test_pca, y_test):.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict probabilities on the training and testing sets\n",
    "y_train_proba = gnb.predict_proba(X_train_pca)[:, 1]\n",
    "y_test_proba = gnb.predict_proba(X_test_pca)[:, 1]\n",
    "\n",
    "# Compute precision-recall curves and AUCs for the training and testing sets\n",
    "train_precision, train_recall, _ = precision_recall_curve(y_train, y_train_proba)\n",
    "test_precision, test_recall, _ = precision_recall_curve(y_test, y_test_proba)\n",
    "train_auc = auc(train_recall, train_precision)\n",
    "test_auc = auc(test_recall, test_precision)\n",
    "\n",
    "# Plot the precision-recall curves and AUCs for the training and testing sets\n",
    "plt.plot(train_recall, train_precision, label=f'Train (AUC = {train_auc:.2f})')\n",
    "plt.plot(test_recall, test_precision, label=f'Test (AUC = {test_auc:.2f})')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#confusion_matrix, \n",
    "y_pred = gnb.predict(X_test_pca)\n",
    "conf_m = metrics.confusion_matrix(y_test, y_pred)\n",
    "\n",
    "group_names = ['True Neg','False Pos','False Neg','True Pos']\n",
    "\n",
    "group_counts = [\"{0:0.0f}\".format(value) for value in conf_m.flatten()]\n",
    "\n",
    "group_percentages = [\"{0:.2%}\".format(value) for value in conf_m.flatten()/np.sum(conf_m)]\n",
    "\n",
    "labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in zip(group_names,group_counts,group_percentages)]\n",
    "\n",
    "labels = np.asarray(labels).reshape(2,2)\n",
    "\n",
    "\n",
    "labelsx = ['Non_hemolitic', 'Hemolitic']\n",
    "sns.heatmap(conf_m, annot=labels, fmt='', cmap=\"YlGnBu\", yticklabels=labelsx, xticklabels=labelsx)\n",
    "plt.title('Hemolitic Confusion Matrix', fontsize=16)\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the model and PCA weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/weights_pca15_v02_esm2_15B_activity.pkl']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the model\n",
    "joblib.dump(gnb, 'models/model_v02_GNB15_esm2_15B_activity_classification.pkl')\n",
    "\n",
    "# save pcs weights\n",
    "joblib.dump(pca, 'models/weights_pca15_v02_esm2_15B_activity.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the model\n",
    "model = joblib.load('models/model_v02_GNB15_esm2_15B_activity_classification.pkl')\n",
    "\n",
    "# Loading PCA weights\n",
    "pca = joblib.load('models/weights_pca15_v02_esm2_15B_activity.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <td>-0.116773</td>\n",
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       "      <td>-0.215111</td>\n",
       "      <td>-0.017543</td>\n",
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       "\n",
       "[5 rows x 5120 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load meta_data with mutations on PG1\n",
    "meta_data = pd.read_csv('results/v01.2/predictions_validation_set_v01.2.csv')\n",
    "\n",
    "####################### Load the embedding ######################\n",
    "dir_path = 'data/embedding/esm2_15B_PG1_validation/'\n",
    "embeddings = {}\n",
    "for file in os.listdir(dir_path):\n",
    "        file_path = str(dir_path) + file \n",
    "        label = file.split('.pt')[0]\n",
    "        embeddings[label] = np.array(torch.load(file_path)['mean_representations'][48])\n",
    "\n",
    "\n",
    "# here I creating a dictionary with ID as key and semantic value as value \n",
    "data = meta_data.merge(pd.DataFrame.from_dict(embeddings, orient='index'), how='inner', left_on='ID2', right_index=True)\n",
    "\n",
    "\n",
    "features = data.iloc[:,meta_data.shape[1]:]\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculates the probability of being active\n",
    "prob = model.predict_proba(pca.transform(features))\n",
    "pred = (prob[:, 1] * 100).round(2)\n",
    "\n",
    "sum(pred > 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ID2</th>\n",
       "      <th>Sequence</th>\n",
       "      <th>RGGRLCYCRRRFCVCVGR</th>\n",
       "      <th>Select</th>\n",
       "      <th>Charge</th>\n",
       "      <th>group</th>\n",
       "      <th>MIC MH</th>\n",
       "      <th>%hemo</th>\n",
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       "      <th>hemolysis_pred_v01.2</th>\n",
       "      <th>selectivity_pred_v01.2</th>\n",
       "      <th>hemolysis_pred_v02</th>\n",
       "      <th>selectivity_pred_v02</th>\n",
       "      <th>activity_pred_v02</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PG1_3mut_1739000</td>\n",
       "      <td>bsPG-1.1</td>\n",
       "      <td>RGGRLQYCRRRFCVARGR</td>\n",
       "      <td>-----Q--------AR--</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>6.742145</td>\n",
       "      <td>&lt;-0.25</td>\n",
       "      <td>16</td>\n",
       "      <td>0.428850</td>\n",
       "      <td>0.836425</td>\n",
       "      <td>-7.675692</td>\n",
       "      <td>-0.987362</td>\n",
       "      <td>-3.285721</td>\n",
       "      <td>0.307504</td>\n",
       "      <td>92.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PG1_3mut_1218602</td>\n",
       "      <td>bsPG-1.2</td>\n",
       "      <td>RGGRLQYCRRRGCVTVGR</td>\n",
       "      <td>-----Q-----G--T---</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>5.742155</td>\n",
       "      <td>&lt;-0.25</td>\n",
       "      <td>4</td>\n",
       "      <td>0.341131</td>\n",
       "      <td>0.134981</td>\n",
       "      <td>-14.298704</td>\n",
       "      <td>-1.215473</td>\n",
       "      <td>-6.084995</td>\n",
       "      <td>0.272666</td>\n",
       "      <td>88.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PG1_3mut_1896424</td>\n",
       "      <td>bsPG-1.3</td>\n",
       "      <td>RGGRLAYCRRRDCTCVGR</td>\n",
       "      <td>-----A-----D-T----</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>4.651497</td>\n",
       "      <td>CP</td>\n",
       "      <td>&gt;64</td>\n",
       "      <td>-0.165692</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.385390</td>\n",
       "      <td>-1.305665</td>\n",
       "      <td>-6.281200</td>\n",
       "      <td>0.345484</td>\n",
       "      <td>95.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PG1_3mut_1275003</td>\n",
       "      <td>bsPG-1.4</td>\n",
       "      <td>RGGRLDYCRRRFCVTTGR</td>\n",
       "      <td>-----D--------TT--</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>4.742406</td>\n",
       "      <td>&lt;-0.25</td>\n",
       "      <td>64</td>\n",
       "      <td>0.165692</td>\n",
       "      <td>1.025482</td>\n",
       "      <td>-19.078636</td>\n",
       "      <td>-0.686002</td>\n",
       "      <td>-1.958412</td>\n",
       "      <td>0.618075</td>\n",
       "      <td>90.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PG1_3mut_257802</td>\n",
       "      <td>bsPG-1.5</td>\n",
       "      <td>RGGRLQACRRRFCVTVGR</td>\n",
       "      <td>-----QA-------T---</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>5.744660</td>\n",
       "      <td>&lt;-0.25</td>\n",
       "      <td>8</td>\n",
       "      <td>0.721248</td>\n",
       "      <td>0.761174</td>\n",
       "      <td>-3.053714</td>\n",
       "      <td>-0.247956</td>\n",
       "      <td>-2.783794</td>\n",
       "      <td>0.578861</td>\n",
       "      <td>94.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PG1_3mut_1210489</td>\n",
       "      <td>bsPG-1.6</td>\n",
       "      <td>RGGRLRYCRRRFDVSVGR</td>\n",
       "      <td>-----R------D-S---</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>5.833305</td>\n",
       "      <td>&lt;-0.25</td>\n",
       "      <td>16</td>\n",
       "      <td>0.735867</td>\n",
       "      <td>1.070920</td>\n",
       "      <td>-13.542688</td>\n",
       "      <td>-0.695068</td>\n",
       "      <td>-2.916473</td>\n",
       "      <td>0.343804</td>\n",
       "      <td>90.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PG1_3mut_1536372</td>\n",
       "      <td>bsPG-1.7</td>\n",
       "      <td>RGGRLCTARRRFCVRVGR</td>\n",
       "      <td>------TA------R---</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>6.744650</td>\n",
       "      <td>&lt;-0.25</td>\n",
       "      <td>2</td>\n",
       "      <td>0.906433</td>\n",
       "      <td>0.258366</td>\n",
       "      <td>-3.345207</td>\n",
       "      <td>-0.416047</td>\n",
       "      <td>-3.252567</td>\n",
       "      <td>0.452450</td>\n",
       "      <td>94.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PG1_3mut_1147726</td>\n",
       "      <td>bsPG-1.8</td>\n",
       "      <td>RGGRLTYCRRRFCTAVGR</td>\n",
       "      <td>-----T-------TA---</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>5.742155</td>\n",
       "      <td>&lt;-0.25</td>\n",
       "      <td>16</td>\n",
       "      <td>0.043860</td>\n",
       "      <td>-0.153815</td>\n",
       "      <td>-12.337255</td>\n",
       "      <td>-0.839417</td>\n",
       "      <td>-1.449774</td>\n",
       "      <td>0.491094</td>\n",
       "      <td>98.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PG1_3mut_1980555</td>\n",
       "      <td>bsPG-1.9</td>\n",
       "      <td>RGGRLTYCRRRDCVAVGR</td>\n",
       "      <td>-----T-----D--A---</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>4.742406</td>\n",
       "      <td>&lt;-0.25</td>\n",
       "      <td>32</td>\n",
       "      <td>0.116959</td>\n",
       "      <td>0.573184</td>\n",
       "      <td>-21.809423</td>\n",
       "      <td>-0.699560</td>\n",
       "      <td>-11.984425</td>\n",
       "      <td>0.122605</td>\n",
       "      <td>93.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PG1_3mut_1658870</td>\n",
       "      <td>bsPG-1.10</td>\n",
       "      <td>RGGRLAYCRRRFCVDTGR</td>\n",
       "      <td>-----A--------DT--</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>4.742406</td>\n",
       "      <td>&lt;-0.25</td>\n",
       "      <td>64</td>\n",
       "      <td>0.092593</td>\n",
       "      <td>0.772756</td>\n",
       "      <td>-9.999792</td>\n",
       "      <td>-0.342884</td>\n",
       "      <td>-4.005482</td>\n",
       "      <td>0.379449</td>\n",
       "      <td>93.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PG1_3mut_1618529</td>\n",
       "      <td>bsPG-1.11</td>\n",
       "      <td>RGGRLRYCRRRGTVCVGR</td>\n",
       "      <td>-----R-----GT-----</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>6.742145</td>\n",
       "      <td>CP</td>\n",
       "      <td>&gt;64</td>\n",
       "      <td>0.268031</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.660681</td>\n",
       "      <td>-0.654460</td>\n",
       "      <td>-1.349844</td>\n",
       "      <td>0.506019</td>\n",
       "      <td>83.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PG1_3mut_720432</td>\n",
       "      <td>bsPG-1.12</td>\n",
       "      <td>RGGRLQYCRRRFCARVGR</td>\n",
       "      <td>-----Q-------AR---</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>6.742145</td>\n",
       "      <td>&lt;-0.25</td>\n",
       "      <td>16</td>\n",
       "      <td>0.141326</td>\n",
       "      <td>0.354341</td>\n",
       "      <td>-2.912143</td>\n",
       "      <td>-0.348571</td>\n",
       "      <td>-3.223686</td>\n",
       "      <td>0.333009</td>\n",
       "      <td>90.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PG1_3mut_1247939</td>\n",
       "      <td>bsPG-1.13</td>\n",
       "      <td>RGGRLCYCRRRNCVTTGR</td>\n",
       "      <td>-----------N--TT--</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>5.651245</td>\n",
       "      <td>-0.25-0</td>\n",
       "      <td>&gt;64</td>\n",
       "      <td>-0.058480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.079775</td>\n",
       "      <td>-0.478759</td>\n",
       "      <td>-0.153374</td>\n",
       "      <td>0.732106</td>\n",
       "      <td>92.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PG1_3mut_1926344</td>\n",
       "      <td>bsPG-1.14</td>\n",
       "      <td>RGGRLCTARRRFCVHVGR</td>\n",
       "      <td>------TA------H---</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>5.835569</td>\n",
       "      <td>-0.25-0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.638402</td>\n",
       "      <td>1.009214</td>\n",
       "      <td>-1.718951</td>\n",
       "      <td>-0.199801</td>\n",
       "      <td>-2.703939</td>\n",
       "      <td>0.641877</td>\n",
       "      <td>87.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PG1_3mut_70973</td>\n",
       "      <td>bsPG-1.15</td>\n",
       "      <td>RGGRLCDCRRRFCNMVGR</td>\n",
       "      <td>------D------NM---</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>4.654002</td>\n",
       "      <td>-0.25-0</td>\n",
       "      <td>&gt;64</td>\n",
       "      <td>-0.097466</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.957634</td>\n",
       "      <td>0.108545</td>\n",
       "      <td>-5.610882</td>\n",
       "      <td>0.955437</td>\n",
       "      <td>68.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PG1_3mut_969074</td>\n",
       "      <td>bsPG-1.16</td>\n",
       "      <td>RGGRLQYCRRRFDTCVGR</td>\n",
       "      <td>-----Q------DT----</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>4.742406</td>\n",
       "      <td>-0.25-0</td>\n",
       "      <td>&gt;64</td>\n",
       "      <td>0.034113</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-15.366731</td>\n",
       "      <td>-1.027213</td>\n",
       "      <td>-1.885746</td>\n",
       "      <td>0.596881</td>\n",
       "      <td>91.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PG1_3mut_624778</td>\n",
       "      <td>bsPG-1.17</td>\n",
       "      <td>RGGRLCTCRRRFCASVGR</td>\n",
       "      <td>------T------AS---</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>5.653751</td>\n",
       "      <td>-0.25-0</td>\n",
       "      <td>32</td>\n",
       "      <td>0.487329</td>\n",
       "      <td>1.192973</td>\n",
       "      <td>0.506438</td>\n",
       "      <td>-0.073250</td>\n",
       "      <td>-0.959942</td>\n",
       "      <td>0.768069</td>\n",
       "      <td>98.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PG1_3mut_1114717</td>\n",
       "      <td>bsPG-1.18</td>\n",
       "      <td>RGGRLRYCRRRFRVSVGR</td>\n",
       "      <td>-----R------R-S---</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>7.833044</td>\n",
       "      <td>-0.25-0</td>\n",
       "      <td>16</td>\n",
       "      <td>1.525341</td>\n",
       "      <td>1.387487</td>\n",
       "      <td>0.554981</td>\n",
       "      <td>-0.397154</td>\n",
       "      <td>3.153166</td>\n",
       "      <td>0.380668</td>\n",
       "      <td>95.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PG1_3mut_902003</td>\n",
       "      <td>bsPG-1.19</td>\n",
       "      <td>RGGRLEYCRRRFCVNTGR</td>\n",
       "      <td>-----E--------NT--</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>4.742552</td>\n",
       "      <td>-0.25-0</td>\n",
       "      <td>&gt;64</td>\n",
       "      <td>1.281676</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.515178</td>\n",
       "      <td>-0.187527</td>\n",
       "      <td>-0.870571</td>\n",
       "      <td>0.748402</td>\n",
       "      <td>80.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>PG1_3mut_484549</td>\n",
       "      <td>bsPG-1.20</td>\n",
       "      <td>RGGRLTYCRRRFCADVGR</td>\n",
       "      <td>-----T-------AD---</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>4.742406</td>\n",
       "      <td>-0.25-0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.248538</td>\n",
       "      <td>1.201573</td>\n",
       "      <td>-15.554958</td>\n",
       "      <td>-0.281757</td>\n",
       "      <td>-5.237572</td>\n",
       "      <td>0.436773</td>\n",
       "      <td>95.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>PG1_3mut_789807</td>\n",
       "      <td>bsPG-1.21</td>\n",
       "      <td>RGGRLCYRRRRTAVCVGR</td>\n",
       "      <td>-------R---TA-----</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>6.742145</td>\n",
       "      <td>CP</td>\n",
       "      <td>8</td>\n",
       "      <td>0.404483</td>\n",
       "      <td>0.509991</td>\n",
       "      <td>-9.616057</td>\n",
       "      <td>-0.224147</td>\n",
       "      <td>-6.117950</td>\n",
       "      <td>0.475476</td>\n",
       "      <td>77.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PG1_3mut_1838343</td>\n",
       "      <td>bsPG-1.22</td>\n",
       "      <td>RGGRLCYCRRRFCTPVTR</td>\n",
       "      <td>-------------TP-T-</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>5.651245</td>\n",
       "      <td>-0.25-0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.389864</td>\n",
       "      <td>1.397093</td>\n",
       "      <td>-6.641514</td>\n",
       "      <td>-0.214472</td>\n",
       "      <td>-0.343727</td>\n",
       "      <td>1.191644</td>\n",
       "      <td>84.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PG1_3mut_945905</td>\n",
       "      <td>bsPG-1.23</td>\n",
       "      <td>RTGRLCYCRRRDCTCVGR</td>\n",
       "      <td>-T---------D-T----</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>4.560587</td>\n",
       "      <td>CP</td>\n",
       "      <td>32</td>\n",
       "      <td>0.053606</td>\n",
       "      <td>0.234365</td>\n",
       "      <td>-8.263932</td>\n",
       "      <td>-0.334165</td>\n",
       "      <td>-0.315247</td>\n",
       "      <td>0.952043</td>\n",
       "      <td>50.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PG1_3mut_26231</td>\n",
       "      <td>bsPG-1.24</td>\n",
       "      <td>RGGRLHYRRRRFCVRVGR</td>\n",
       "      <td>-----H-R------R---</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>7.923953</td>\n",
       "      <td>-0.25-0</td>\n",
       "      <td>8</td>\n",
       "      <td>9.722222</td>\n",
       "      <td>1.890856</td>\n",
       "      <td>-9.540926</td>\n",
       "      <td>0.028367</td>\n",
       "      <td>1.519218</td>\n",
       "      <td>0.625790</td>\n",
       "      <td>91.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PG1_3mut_485919</td>\n",
       "      <td>bsPG-1.25</td>\n",
       "      <td>RGGRPCYCRRRFCGTVGR</td>\n",
       "      <td>----P--------GT---</td>\n",
       "      <td>0.03</td>\n",
       "      <td>5.651245</td>\n",
       "      <td>0-0.5</td>\n",
       "      <td>&gt;64</td>\n",
       "      <td>-0.009747</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.994274</td>\n",
       "      <td>-0.381789</td>\n",
       "      <td>1.734261</td>\n",
       "      <td>0.858255</td>\n",
       "      <td>95.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PG1_3mut_437553</td>\n",
       "      <td>bsPG-1.26</td>\n",
       "      <td>RGGRLCDCRRRTCRCVGR</td>\n",
       "      <td>------D----T-R----</td>\n",
       "      <td>0.04</td>\n",
       "      <td>5.563083</td>\n",
       "      <td>CP</td>\n",
       "      <td>&gt;64</td>\n",
       "      <td>0.194932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.507852</td>\n",
       "      <td>-0.163775</td>\n",
       "      <td>-1.083811</td>\n",
       "      <td>0.883138</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>PG1_3mut_840730</td>\n",
       "      <td>bsPG-1.27</td>\n",
       "      <td>RGGRDIYCRRRFCVTVGR</td>\n",
       "      <td>----DI--------T---</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4.742406</td>\n",
       "      <td>0-0.5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.662768</td>\n",
       "      <td>0.724452</td>\n",
       "      <td>-9.883299</td>\n",
       "      <td>0.118627</td>\n",
       "      <td>0.797209</td>\n",
       "      <td>1.053053</td>\n",
       "      <td>7.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PG1_3mut_1102178</td>\n",
       "      <td>bsPG-1.28</td>\n",
       "      <td>RGGRLCYCRRRTCVDTGR</td>\n",
       "      <td>-----------T--DT--</td>\n",
       "      <td>0.17</td>\n",
       "      <td>4.651497</td>\n",
       "      <td>0-0.5</td>\n",
       "      <td>32</td>\n",
       "      <td>0.160819</td>\n",
       "      <td>0.711487</td>\n",
       "      <td>-11.357187</td>\n",
       "      <td>-0.072031</td>\n",
       "      <td>-0.809002</td>\n",
       "      <td>0.944423</td>\n",
       "      <td>88.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>PG1_3mut_939583</td>\n",
       "      <td>bsPG-1.29</td>\n",
       "      <td>RGGRLCYSRRRFCKTVGR</td>\n",
       "      <td>-------S-----KT---</td>\n",
       "      <td>0.33</td>\n",
       "      <td>6.741654</td>\n",
       "      <td>0-0.5</td>\n",
       "      <td>64</td>\n",
       "      <td>0.560429</td>\n",
       "      <td>1.554700</td>\n",
       "      <td>1.551211</td>\n",
       "      <td>0.101647</td>\n",
       "      <td>2.524612</td>\n",
       "      <td>0.736734</td>\n",
       "      <td>97.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>PG1_3mut_439660</td>\n",
       "      <td>bsPG-1.30</td>\n",
       "      <td>RGGRLAYCRRRFCVAVAR</td>\n",
       "      <td>-----A--------A-A-</td>\n",
       "      <td>0.36</td>\n",
       "      <td>5.742155</td>\n",
       "      <td>0-0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.076998</td>\n",
       "      <td>0.333245</td>\n",
       "      <td>-1.721362</td>\n",
       "      <td>0.664414</td>\n",
       "      <td>-4.533604</td>\n",
       "      <td>0.518300</td>\n",
       "      <td>96.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>PG1_3mut_341044</td>\n",
       "      <td>bsPG-1.31</td>\n",
       "      <td>RGGRLCYGRRRFCVNQGR</td>\n",
       "      <td>-------G------NQ--</td>\n",
       "      <td>0.37</td>\n",
       "      <td>5.742155</td>\n",
       "      <td>0-0.5</td>\n",
       "      <td>32</td>\n",
       "      <td>2.061404</td>\n",
       "      <td>1.819313</td>\n",
       "      <td>-1.774004</td>\n",
       "      <td>-0.034093</td>\n",
       "      <td>-1.188480</td>\n",
       "      <td>0.621463</td>\n",
       "      <td>93.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>PG1_3mut_1166571</td>\n",
       "      <td>bsPG-1.32</td>\n",
       "      <td>RGGRSCHCRRRFCVIVGR</td>\n",
       "      <td>----S-H-------I---</td>\n",
       "      <td>0.37</td>\n",
       "      <td>5.744660</td>\n",
       "      <td>0-0.5</td>\n",
       "      <td>4</td>\n",
       "      <td>15.696881</td>\n",
       "      <td>1.797873</td>\n",
       "      <td>-2.584238</td>\n",
       "      <td>0.779326</td>\n",
       "      <td>1.995186</td>\n",
       "      <td>0.961354</td>\n",
       "      <td>67.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>PG1_3mut_1080117</td>\n",
       "      <td>bsPG-1.33</td>\n",
       "      <td>RGRRACYCRRRFCVHVGR</td>\n",
       "      <td>--R-A---------H---</td>\n",
       "      <td>0.38</td>\n",
       "      <td>6.742145</td>\n",
       "      <td>0-0.5</td>\n",
       "      <td>4</td>\n",
       "      <td>3.664717</td>\n",
       "      <td>1.166100</td>\n",
       "      <td>-0.897124</td>\n",
       "      <td>0.549621</td>\n",
       "      <td>4.109623</td>\n",
       "      <td>0.974183</td>\n",
       "      <td>84.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>PG1_3mut_261082</td>\n",
       "      <td>bsPG-1.34</td>\n",
       "      <td>RGGRLSYCRRRFCVAGGR</td>\n",
       "      <td>-----S--------AG--</td>\n",
       "      <td>0.40</td>\n",
       "      <td>5.742155</td>\n",
       "      <td>0-0.5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.321637</td>\n",
       "      <td>0.711487</td>\n",
       "      <td>0.575238</td>\n",
       "      <td>0.464198</td>\n",
       "      <td>0.367903</td>\n",
       "      <td>0.685053</td>\n",
       "      <td>96.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>PG1_3mut_881885</td>\n",
       "      <td>bsPG-1.35</td>\n",
       "      <td>RGQRLCYCRRRFNVCTGR</td>\n",
       "      <td>--Q---------N--T--</td>\n",
       "      <td>0.41</td>\n",
       "      <td>5.651245</td>\n",
       "      <td>CP</td>\n",
       "      <td>32</td>\n",
       "      <td>0.399610</td>\n",
       "      <td>1.106786</td>\n",
       "      <td>-2.725072</td>\n",
       "      <td>-0.036225</td>\n",
       "      <td>6.032917</td>\n",
       "      <td>1.161217</td>\n",
       "      <td>64.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>PG1_3mut_702701</td>\n",
       "      <td>bsPG-1.36</td>\n",
       "      <td>RGGRLCYCRRRFCAVNGR</td>\n",
       "      <td>-------------AVN--</td>\n",
       "      <td>0.45</td>\n",
       "      <td>5.651245</td>\n",
       "      <td>0-0.5</td>\n",
       "      <td>64</td>\n",
       "      <td>0.116959</td>\n",
       "      <td>0.874214</td>\n",
       "      <td>-6.823680</td>\n",
       "      <td>0.701898</td>\n",
       "      <td>-2.326366</td>\n",
       "      <td>0.824783</td>\n",
       "      <td>85.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID        ID2            Sequence  RGGRLCYCRRRFCVCVGR  \\\n",
       "0   PG1_3mut_1739000   bsPG-1.1  RGGRLQYCRRRFCVARGR  -----Q--------AR--   \n",
       "1   PG1_3mut_1218602   bsPG-1.2  RGGRLQYCRRRGCVTVGR  -----Q-----G--T---   \n",
       "2   PG1_3mut_1896424   bsPG-1.3  RGGRLAYCRRRDCTCVGR  -----A-----D-T----   \n",
       "3   PG1_3mut_1275003   bsPG-1.4  RGGRLDYCRRRFCVTTGR  -----D--------TT--   \n",
       "4    PG1_3mut_257802   bsPG-1.5  RGGRLQACRRRFCVTVGR  -----QA-------T---   \n",
       "5   PG1_3mut_1210489   bsPG-1.6  RGGRLRYCRRRFDVSVGR  -----R------D-S---   \n",
       "6   PG1_3mut_1536372   bsPG-1.7  RGGRLCTARRRFCVRVGR  ------TA------R---   \n",
       "7   PG1_3mut_1147726   bsPG-1.8  RGGRLTYCRRRFCTAVGR  -----T-------TA---   \n",
       "8   PG1_3mut_1980555   bsPG-1.9  RGGRLTYCRRRDCVAVGR  -----T-----D--A---   \n",
       "9   PG1_3mut_1658870  bsPG-1.10  RGGRLAYCRRRFCVDTGR  -----A--------DT--   \n",
       "10  PG1_3mut_1618529  bsPG-1.11  RGGRLRYCRRRGTVCVGR  -----R-----GT-----   \n",
       "11   PG1_3mut_720432  bsPG-1.12  RGGRLQYCRRRFCARVGR  -----Q-------AR---   \n",
       "12  PG1_3mut_1247939  bsPG-1.13  RGGRLCYCRRRNCVTTGR  -----------N--TT--   \n",
       "13  PG1_3mut_1926344  bsPG-1.14  RGGRLCTARRRFCVHVGR  ------TA------H---   \n",
       "14    PG1_3mut_70973  bsPG-1.15  RGGRLCDCRRRFCNMVGR  ------D------NM---   \n",
       "15   PG1_3mut_969074  bsPG-1.16  RGGRLQYCRRRFDTCVGR  -----Q------DT----   \n",
       "16   PG1_3mut_624778  bsPG-1.17  RGGRLCTCRRRFCASVGR  ------T------AS---   \n",
       "17  PG1_3mut_1114717  bsPG-1.18  RGGRLRYCRRRFRVSVGR  -----R------R-S---   \n",
       "18   PG1_3mut_902003  bsPG-1.19  RGGRLEYCRRRFCVNTGR  -----E--------NT--   \n",
       "19   PG1_3mut_484549  bsPG-1.20  RGGRLTYCRRRFCADVGR  -----T-------AD---   \n",
       "20   PG1_3mut_789807  bsPG-1.21  RGGRLCYRRRRTAVCVGR  -------R---TA-----   \n",
       "21  PG1_3mut_1838343  bsPG-1.22  RGGRLCYCRRRFCTPVTR  -------------TP-T-   \n",
       "22   PG1_3mut_945905  bsPG-1.23  RTGRLCYCRRRDCTCVGR  -T---------D-T----   \n",
       "23    PG1_3mut_26231  bsPG-1.24  RGGRLHYRRRRFCVRVGR  -----H-R------R---   \n",
       "24   PG1_3mut_485919  bsPG-1.25  RGGRPCYCRRRFCGTVGR  ----P--------GT---   \n",
       "25   PG1_3mut_437553  bsPG-1.26  RGGRLCDCRRRTCRCVGR  ------D----T-R----   \n",
       "26   PG1_3mut_840730  bsPG-1.27  RGGRDIYCRRRFCVTVGR  ----DI--------T---   \n",
       "27  PG1_3mut_1102178  bsPG-1.28  RGGRLCYCRRRTCVDTGR  -----------T--DT--   \n",
       "28   PG1_3mut_939583  bsPG-1.29  RGGRLCYSRRRFCKTVGR  -------S-----KT---   \n",
       "29   PG1_3mut_439660  bsPG-1.30  RGGRLAYCRRRFCVAVAR  -----A--------A-A-   \n",
       "30   PG1_3mut_341044  bsPG-1.31  RGGRLCYGRRRFCVNQGR  -------G------NQ--   \n",
       "31  PG1_3mut_1166571  bsPG-1.32  RGGRSCHCRRRFCVIVGR  ----S-H-------I---   \n",
       "32  PG1_3mut_1080117  bsPG-1.33  RGRRACYCRRRFCVHVGR  --R-A---------H---   \n",
       "33   PG1_3mut_261082  bsPG-1.34  RGGRLSYCRRRFCVAGGR  -----S--------AG--   \n",
       "34   PG1_3mut_881885  bsPG-1.35  RGQRLCYCRRRFNVCTGR  --Q---------N--T--   \n",
       "35   PG1_3mut_702701  bsPG-1.36  RGGRLCYCRRRFCAVNGR  -------------AVN--   \n",
       "\n",
       "    Select    Charge    group MIC MH      %hemo  L10Select  \\\n",
       "0    -0.82  6.742145   <-0.25     16   0.428850   0.836425   \n",
       "1    -0.76  5.742155   <-0.25      4   0.341131   0.134981   \n",
       "2    -0.65  4.651497       CP    >64  -0.165692        NaN   \n",
       "3    -0.56  4.742406   <-0.25     64   0.165692   1.025482   \n",
       "4    -0.44  5.744660   <-0.25      8   0.721248   0.761174   \n",
       "5    -0.43  5.833305   <-0.25     16   0.735867   1.070920   \n",
       "6    -0.43  6.744650   <-0.25      2   0.906433   0.258366   \n",
       "7    -0.40  5.742155   <-0.25     16   0.043860  -0.153815   \n",
       "8    -0.29  4.742406   <-0.25     32   0.116959   0.573184   \n",
       "9    -0.28  4.742406   <-0.25     64   0.092593   0.772756   \n",
       "10   -0.28  6.742145       CP    >64   0.268031        NaN   \n",
       "11   -0.27  6.742145   <-0.25     16   0.141326   0.354341   \n",
       "12   -0.25  5.651245  -0.25-0    >64  -0.058480        NaN   \n",
       "13   -0.22  5.835569  -0.25-0     16   0.638402   1.009214   \n",
       "14   -0.15  4.654002  -0.25-0    >64  -0.097466        NaN   \n",
       "15   -0.14  4.742406  -0.25-0    >64   0.034113        NaN   \n",
       "16   -0.14  5.653751  -0.25-0     32   0.487329   1.192973   \n",
       "17   -0.13  7.833044  -0.25-0     16   1.525341   1.387487   \n",
       "18   -0.13  4.742552  -0.25-0    >64   1.281676        NaN   \n",
       "19   -0.10  4.742406  -0.25-0     64   0.248538   1.201573   \n",
       "20   -0.10  6.742145       CP      8   0.404483   0.509991   \n",
       "21   -0.03  5.651245  -0.25-0     64   0.389864   1.397093   \n",
       "22   -0.02  4.560587       CP     32   0.053606   0.234365   \n",
       "23   -0.01  7.923953  -0.25-0      8   9.722222   1.890856   \n",
       "24    0.03  5.651245    0-0.5    >64  -0.009747        NaN   \n",
       "25    0.04  5.563083       CP    >64   0.194932        NaN   \n",
       "26    0.10  4.742406    0-0.5      8   0.662768   0.724452   \n",
       "27    0.17  4.651497    0-0.5     32   0.160819   0.711487   \n",
       "28    0.33  6.741654    0-0.5     64   0.560429   1.554700   \n",
       "29    0.36  5.742155    0-0.5      2   1.076998   0.333245   \n",
       "30    0.37  5.742155    0-0.5     32   2.061404   1.819313   \n",
       "31    0.37  5.744660    0-0.5      4  15.696881   1.797873   \n",
       "32    0.38  6.742145    0-0.5      4   3.664717   1.166100   \n",
       "33    0.40  5.742155    0-0.5     16   0.321637   0.711487   \n",
       "34    0.41  5.651245       CP     32   0.399610   1.106786   \n",
       "35    0.45  5.651245    0-0.5     64   0.116959   0.874214   \n",
       "\n",
       "    hemolysis_pred_v01.2  selectivity_pred_v01.2  hemolysis_pred_v02  \\\n",
       "0              -7.675692               -0.987362           -3.285721   \n",
       "1             -14.298704               -1.215473           -6.084995   \n",
       "2             -14.385390               -1.305665           -6.281200   \n",
       "3             -19.078636               -0.686002           -1.958412   \n",
       "4              -3.053714               -0.247956           -2.783794   \n",
       "5             -13.542688               -0.695068           -2.916473   \n",
       "6              -3.345207               -0.416047           -3.252567   \n",
       "7             -12.337255               -0.839417           -1.449774   \n",
       "8             -21.809423               -0.699560          -11.984425   \n",
       "9              -9.999792               -0.342884           -4.005482   \n",
       "10             -5.660681               -0.654460           -1.349844   \n",
       "11             -2.912143               -0.348571           -3.223686   \n",
       "12            -14.079775               -0.478759           -0.153374   \n",
       "13             -1.718951               -0.199801           -2.703939   \n",
       "14             -8.957634                0.108545           -5.610882   \n",
       "15            -15.366731               -1.027213           -1.885746   \n",
       "16              0.506438               -0.073250           -0.959942   \n",
       "17              0.554981               -0.397154            3.153166   \n",
       "18            -10.515178               -0.187527           -0.870571   \n",
       "19            -15.554958               -0.281757           -5.237572   \n",
       "20             -9.616057               -0.224147           -6.117950   \n",
       "21             -6.641514               -0.214472           -0.343727   \n",
       "22             -8.263932               -0.334165           -0.315247   \n",
       "23             -9.540926                0.028367            1.519218   \n",
       "24             -0.994274               -0.381789            1.734261   \n",
       "25             -6.507852               -0.163775           -1.083811   \n",
       "26             -9.883299                0.118627            0.797209   \n",
       "27            -11.357187               -0.072031           -0.809002   \n",
       "28              1.551211                0.101647            2.524612   \n",
       "29             -1.721362                0.664414           -4.533604   \n",
       "30             -1.774004               -0.034093           -1.188480   \n",
       "31             -2.584238                0.779326            1.995186   \n",
       "32             -0.897124                0.549621            4.109623   \n",
       "33              0.575238                0.464198            0.367903   \n",
       "34             -2.725072               -0.036225            6.032917   \n",
       "35             -6.823680                0.701898           -2.326366   \n",
       "\n",
       "    selectivity_pred_v02  activity_pred_v02  \n",
       "0               0.307504              92.34  \n",
       "1               0.272666              88.34  \n",
       "2               0.345484              95.37  \n",
       "3               0.618075              90.87  \n",
       "4               0.578861              94.94  \n",
       "5               0.343804              90.10  \n",
       "6               0.452450              94.73  \n",
       "7               0.491094              98.64  \n",
       "8               0.122605              93.60  \n",
       "9               0.379449              93.70  \n",
       "10              0.506019              83.26  \n",
       "11              0.333009              90.30  \n",
       "12              0.732106              92.81  \n",
       "13              0.641877              87.66  \n",
       "14              0.955437              68.43  \n",
       "15              0.596881              91.49  \n",
       "16              0.768069              98.31  \n",
       "17              0.380668              95.43  \n",
       "18              0.748402              80.77  \n",
       "19              0.436773              95.17  \n",
       "20              0.475476              77.76  \n",
       "21              1.191644              84.77  \n",
       "22              0.952043              50.85  \n",
       "23              0.625790              91.49  \n",
       "24              0.858255              95.69  \n",
       "25              0.883138               0.49  \n",
       "26              1.053053               7.18  \n",
       "27              0.944423              88.73  \n",
       "28              0.736734              97.39  \n",
       "29              0.518300              96.13  \n",
       "30              0.621463              93.17  \n",
       "31              0.961354              67.97  \n",
       "32              0.974183              84.35  \n",
       "33              0.685053              96.83  \n",
       "34              1.161217              64.24  \n",
       "35              0.824783              85.33  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_data['activity_pred_v02'] = pred\n",
    "meta_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_data.to_csv('results/v01.2/predictions_validation_set_v01.2.csv', index=False)"
   ]
  }
 ],
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