04_hemolysis_model_v02.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning applied to identify low hemolytic Antimicobrial Peptides\n",
    "\n",
    "In this notebook is described all the procedure used to train and test the hemolysis model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import joblib\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, LeaveOneOut, LeavePOut\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore', category=UserWarning, module='seaborn.axisgrid', message='The figure layout has changed to tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>%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>2.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PG-1.20d</td>\n",
       "      <td>RGGRLRYCRRRFCVSVGR</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>3.233927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PG-1.12A</td>\n",
       "      <td>RGGRLCYCRRRACVCVGR</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>15.821069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>35.460367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>PG-1.60</td>\n",
       "      <td>RGGRLCYCRRRFCVCVGF</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>46.397292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>PG-1.7</td>\n",
       "      <td>RRWCLCFCRRRFCVCVCR</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>78.280710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID            Sequence  %Hemolysis\n",
       "0    PG-1.37  HVRRLCYCRRRFCACVGS    2.600000\n",
       "1   PG-1.20d  RGGRLRYCRRRFCVSVGR    1.607281\n",
       "2    PG-1.62  RGGRLCYCRRRFCTCVGR    3.233927\n",
       "3   PG-1.12A  RGGRLCYCRRRACVCVGR   12.103021\n",
       "4   PG-1.16A  RGGRLCYCRRRFCVCAGR   15.821069\n",
       "..       ...                 ...         ...\n",
       "88   PG-1.32  HGWRLCYCRRRFCVCVGC   35.460367\n",
       "89   PG-1.60  RGGRLCYCRRRFCVCVGF   36.664085\n",
       "90   PG-1.25  RGWRLCYCRRRFCVCVGH   46.397292\n",
       "91    PG-1.7  RRWCLCFCRRRFCVCVCR   51.181276\n",
       "92   PG-1.24  RGWRLCYCRRRFCVCVGC   78.280710\n",
       "\n",
       "[93 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load meta data\n",
    "meta_data = pd.read_excel(\"data/Cumulative_Peptide_data_PG1_v01.2.xlsx\", usecols=['ID', 'Sequence', '%Hemolysis'])\n",
    "meta_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embedding Extraction\n",
    "\n",
    "The embeddings I used for the model were computed using the ESM-2 15B model and I used the mean of the last layer as the embedding.\n",
    "\n",
    "Here's how to exytract them using the pre-trained model from esm2.  \n",
    "\n",
    "Download the python file extract from [esm2-github](https://github.com/facebookresearch/esm), and make sure to have a conda enviromment seted up accordinly.\n",
    "\n",
    "Then run:  \n",
    "python extract.py esm2_t48_15B_UR50D data/Cumulative_Peptide_data_PG1.fasta data/embedding/esm2_15B_PG1 --repr_layers 48 --include mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>%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>6</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>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>-0.119524</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>1.607281</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>-0.132696</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>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>-0.101294</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>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>-0.120923</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>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>-0.093198</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>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>-0.117476</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>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>-0.079077</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>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>-0.080665</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>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>-0.059348</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>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>-0.113110</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 × 5123 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID            Sequence  %Hemolysis         0         1         2  \\\n",
       "0    PG-1.37  HVRRLCYCRRRFCACVGS    2.600000  0.056671 -0.074099 -0.040746   \n",
       "1   PG-1.20d  RGGRLRYCRRRFCVSVGR    1.607281  0.133009 -0.124348 -0.109747   \n",
       "2    PG-1.62  RGGRLCYCRRRFCTCVGR    3.233927  0.155696 -0.016959 -0.179328   \n",
       "3   PG-1.12A  RGGRLCYCRRRACVCVGR   12.103021  0.056310 -0.137829 -0.098538   \n",
       "4   PG-1.16A  RGGRLCYCRRRFCVCAGR   15.821069  0.122564 -0.024471 -0.127546   \n",
       "..       ...                 ...         ...       ...       ...       ...   \n",
       "88   PG-1.32  HGWRLCYCRRRFCVCVGC   35.460367  0.007245 -0.115889 -0.040796   \n",
       "89   PG-1.60  RGGRLCYCRRRFCVCVGF   36.664085  0.143025 -0.030791 -0.110694   \n",
       "90   PG-1.25  RGWRLCYCRRRFCVCVGH   46.397292  0.068697 -0.038422 -0.142241   \n",
       "91    PG-1.7  RRWCLCFCRRRFCVCVCR   51.181276  0.066839 -0.037917 -0.087912   \n",
       "92   PG-1.24  RGWRLCYCRRRFCVCVGC   78.280710  0.069902 -0.039020 -0.122685   \n",
       "\n",
       "           3         4         5         6  ...      5110      5111      5112  \\\n",
       "0   0.217794 -0.197767  0.223628 -0.119524  ... -0.130863 -0.107892  0.140624   \n",
       "1   0.222604 -0.261453  0.138052 -0.132696  ... -0.105108 -0.053451  0.092585   \n",
       "2   0.153639 -0.241203  0.130882 -0.101294  ... -0.160722 -0.136943  0.049061   \n",
       "3   0.162526 -0.252675  0.163847 -0.120923  ... -0.124351 -0.181434  0.037783   \n",
       "4   0.165324 -0.258667  0.142153 -0.093198  ... -0.155189 -0.155856  0.061196   \n",
       "..       ...       ...       ...       ...  ...       ...       ...       ...   \n",
       "88  0.235570 -0.216340  0.226666 -0.117476  ... -0.166121 -0.146094  0.080064   \n",
       "89  0.109217 -0.223111  0.105739 -0.079077  ... -0.119124 -0.189840 -0.030339   \n",
       "90  0.184863 -0.232976  0.162749 -0.080665  ... -0.180917 -0.172996  0.046710   \n",
       "91  0.137855 -0.218455  0.279858 -0.059348  ... -0.167717 -0.251613 -0.086687   \n",
       "92  0.183626 -0.228307  0.150014 -0.113110  ... -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 5123 columns]"
      ]
     },
     "execution_count": 3,
     "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",
    "\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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot target distribution\n",
    "sns.histplot(data=data, x='%Hemolysis', bins=10, kde=True, stat='percent')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</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>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>-0.119524</td>\n",
       "      <td>0.080779</td>\n",
       "      <td>-0.021853</td>\n",
       "      <td>0.285255</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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define target and features\n",
    "data[\"%Hemolysis\"] = data[\"%Hemolysis\"].round(2)\n",
    "target = data[\"%Hemolysis\"]\n",
    "\n",
    "# define features\n",
    "features = data.iloc[:, meta_data.shape[1]:]\n",
    "features.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split data into train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((74, 5120), (19, 5120))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running Regression Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Lasso, PCA: 10, Train R2:0.59, Test R2:0.59, best params {'alpha': 0.6, 'max_iter': 10000}\n",
      "R2 CV Scores: [-0.15814408  0.60409492  0.29318961  0.58352323  0.46534703  0.48172941\n",
      "  0.46931503  0.49111384  0.26937267  0.75681203]\n",
      "\n",
      "Model: SVR, PCA: 10, Train R2:0.54, Test R2:0.57, best params {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}\n",
      "R2 CV Scores: [0.12480423 0.61630911 0.26060188 0.53038574 0.25496127 0.44002933\n",
      " 0.52813763 0.55390998 0.30062041 0.65869058]\n",
      "\n",
      "Model: Lasso, PCA: 15, Train R2:0.66, Test R2:0.66, best params {'alpha': 0.49, 'max_iter': 10000}\n",
      "R2 CV Scores: [-0.27601828  0.70921396  0.08354272  0.70744764  0.39847456  0.52972385\n",
      "  0.62828816  0.59447635  0.38212682  0.82752245]\n",
      "\n",
      "Model: SVR, PCA: 15, Train R2:0.59, Test R2:0.62, best params {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}\n",
      "R2 CV Scores: [ 0.17412521  0.64142947 -0.00160259  0.5845198   0.1748533   0.45210751\n",
      "  0.66776399  0.68011699  0.399502    0.72905705]\n",
      "\n",
      "Model: Lasso, PCA: 20, Train R2:0.65, Test R2:0.65, best params {'alpha': 0.5800000000000001, 'max_iter': 10000}\n",
      "R2 CV Scores: [0.24903327 0.7093263  0.08444038 0.70413853 0.34718736 0.51137056\n",
      " 0.47004478 0.58971617 0.3852711  0.80970907]\n",
      "\n",
      "Model: SVR, PCA: 20, Train R2:0.59, Test R2:0.61, best params {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}\n",
      "R2 CV Scores: [0.15767827 0.65286872 0.15925373 0.57359662 0.0874525  0.43677741\n",
      " 0.62412034 0.58333044 0.36607012 0.69378634]\n",
      "\n",
      "Model: Lasso, PCA: 25, Train R2:0.70, Test R2:0.69, best params {'alpha': 0.44, 'max_iter': 10000}\n",
      "R2 CV Scores: [0.36046884 0.72772774 0.15876029 0.69400141 0.4433713  0.52760808\n",
      " 0.50886072 0.62196285 0.27412809 0.85334853]\n",
      "\n",
      "Model: SVR, PCA: 25, Train R2:0.61, Test R2:0.62, best params {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}\n",
      "R2 CV Scores: [0.26499359 0.6571681  0.1612882  0.5706204  0.0966384  0.45088312\n",
      " 0.63102331 0.58342185 0.34941166 0.65077294]\n",
      "\n",
      "Model: Lasso, PCA: 30, Train R2:0.69, Test R2:0.68, best params {'alpha': 0.5, 'max_iter': 10000}\n",
      "R2 CV Scores: [0.35282977 0.72774656 0.16103045 0.69168008 0.43893532 0.52427811\n",
      " 0.50852795 0.62004806 0.27974498 0.84312211]\n",
      "\n",
      "Model: SVR, PCA: 30, Train R2:0.62, Test R2:0.63, best params {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}\n",
      "R2 CV Scores: [0.25536501 0.66503485 0.18589548 0.54242591 0.16332665 0.46076949\n",
      " 0.61462778 0.5900369  0.3638289  0.62897349]\n",
      "\n",
      "Model: Lasso, PCA: 35, Train R2:0.69, Test R2:0.68, best params {'alpha': 0.5, 'max_iter': 10000}\n",
      "R2 CV Scores: [0.35286341 0.72679619 0.162161   0.69161499 0.4363687  0.52086411\n",
      " 0.50722643 0.6183632  0.27748246 0.84313982]\n",
      "\n",
      "Model: SVR, PCA: 35, Train R2:0.62, Test R2:0.62, best params {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}\n",
      "R2 CV Scores: [0.28566175 0.68161959 0.17169455 0.54093189 0.13827398 0.44902181\n",
      " 0.62694432 0.58835513 0.38785373 0.62757171]\n",
      "\n",
      "Model: Lasso, PCA: 40, Train R2:0.69, Test R2:0.68, best params {'alpha': 0.5, 'max_iter': 10000}\n",
      "R2 CV Scores: [0.35287307 0.72583115 0.16215599 0.69164125 0.43687481 0.5243157\n",
      " 0.50719409 0.61837006 0.2819323  0.840923  ]\n",
      "\n",
      "Model: SVR, PCA: 40, Train R2:0.63, Test R2:0.62, best params {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}\n",
      "R2 CV Scores: [0.28413775 0.69247989 0.18614846 0.56288685 0.1561785  0.45442736\n",
      " 0.62464141 0.61779512 0.40238353 0.62516084]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pcas = [10, 15, 20, 25, 30, 35, 40]\n",
    "\n",
    "results_final = pd.DataFrame()\n",
    "results_cv_final = pd.DataFrame()\n",
    "\n",
    "# Instantiate LeavePOut cross-validator \n",
    "loo = LeaveOneOut()  \n",
    "\n",
    "for num_pca in pcas:\n",
    "\n",
    "    pca = PCA(num_pca)\n",
    "    pca.fit(X_train)\n",
    "\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 models to test\n",
    "    models = [Lasso(), SVR()]\n",
    "\n",
    "    # Create a dictionary of hyperparameters for each model\n",
    "    param_grid = [\n",
    "        # Lasso\n",
    "        {'alpha': np.arange(0.01, 1, 0.01),'max_iter': [10000]},\n",
    "        # SVR\n",
    "        {'C': [0.1, 1, 10], 'kernel': ['linear'], 'gamma': ['scale', 0.1, 0.01, 0.001]},\n",
    "        ]\n",
    "    \n",
    "    \n",
    "    # Iterate over the models and fit and predict using grid search\n",
    "    for model, param_grid in zip(models, param_grid):\n",
    "\n",
    "        # search best hyperparameter \n",
    "        grid_search = GridSearchCV(model, param_grid, scoring='r2', cv=10, n_jobs=10)\n",
    "\n",
    "        # train in the whole training set\n",
    "        grid_search.fit(X_train_pca, y_train)\n",
    "\n",
    "        # k-fold cross-validation on the training set\n",
    "        r2_cv_scores = cross_val_score(grid_search, X_train_pca, y_train, cv=10, scoring='r2', n_jobs=10)\n",
    "        mae_cv_scores = cross_val_score(grid_search, X_train_pca, y_train, cv=10, scoring='neg_mean_absolute_error', n_jobs=10)\n",
    "\n",
    "        # make predictions\n",
    "        train_pred = grid_search.predict(X_train_pca)\n",
    "        y_pred = grid_search.predict(X_test_pca)\n",
    "\n",
    "        # Model evaluation on test data\n",
    "        train_r2 = metrics.r2_score(y_train, train_pred)\n",
    "        train_mae = metrics.mean_absolute_error(y_train, train_pred)\n",
    "        train_mse = metrics.mean_squared_error(y_train, train_pred)\n",
    "        train_rmse = np.sqrt(train_mse)\n",
    "\n",
    "        # Model evaluation on test data\n",
    "        test_r2 = metrics.r2_score(y_test, y_pred)\n",
    "        test_mae = metrics.mean_absolute_error(y_test, y_pred)\n",
    "        test_mse = metrics.mean_squared_error(y_test, y_pred)\n",
    "        test_rmse = np.sqrt(test_mse)\n",
    "\n",
    "        model_name = grid_search.best_estimator_.__class__.__name__\n",
    "        embed_name = dir_path.split('/')[-2]\n",
    "        best_params = str(grid_search.best_params_)\n",
    "\n",
    "        # print best scores\n",
    "        print(f\"Model: {model_name}, PCA: {num_pca}, Train R2:{train_r2:.2f}, Test R2:{test_r2:.2f}, best params {best_params}\")\n",
    "        print(\"R2 CV Scores:\", r2_cv_scores)\n",
    "        print()\n",
    "\n",
    "        # Saving results\n",
    "        res_dict = {}\n",
    "        res_dict[\"Embedding\"] = embed_name\n",
    "        res_dict[\"model\"] = model_name\n",
    "        res_dict[\"num_pca\"] = num_pca\n",
    "        res_dict[\"Train_R2_score\"] = train_r2.round(2)\n",
    "        res_dict[\"Train_MAE_score\"] = train_mae.round(2)\n",
    "        res_dict[\"Train_RMSE_score\"] = train_rmse.round(2)\n",
    "\n",
    "        # test scores\n",
    "        res_dict[\"Test_R2_score\"] = test_r2.round(2)\n",
    "        res_dict[\"Test_MAE_score\"] = test_mae.round(2)\n",
    "        res_dict[\"Test_RMSE_score\"] = test_rmse.round(2)\n",
    "        res_dict[\"Test_Best_params\"] = best_params\n",
    "\n",
    "        # updating data frame with results\n",
    "        results = pd.concat([results, pd.DataFrame(res_dict, index=[0])]).reset_index(drop=True)\n",
    "\n",
    "        #save results from cross-validation\n",
    "        res_cv_dict = {\n",
    "            \"model\": [model_name for i in range(len(mae_cv_scores))],\n",
    "            \"num_pca\": [num_pca for i in range(len(mae_cv_scores))],\n",
    "            \"r2_CV_scores\": r2_cv_scores,\n",
    "            \"Mae_CV_scores\": mae_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",
    "    \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_regression_hemolysis_v02.xlsx')\n",
    "#results_cv_final.to_excel('results/results_cv_regression_hemolysis_v02.xlsx')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MAE across the the validation sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#results_cv_final = pd.read_excel('results/results_cv_regression_hemolysis_v02.xlsx', index_col=0)\n",
    "sns.boxplot(data=results_cv_final, x='num_pca', y='r2_CV_scores', hue='model' , showfliers=False)\n",
    "plt.title('Cross-validation R2 scores')\n",
    "plt.ylim(-1, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#results_cv_final = pd.read_excel('results/results_cv_regression_hemolysis_v02.xlsx', index_col=0)\n",
    "sns.boxplot(data=results_cv_final, x='num_pca', y='Mae_CV_scores', hue='model' , showfliers=False)\n",
    "plt.title('Cross-validation MAE scores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embedding</th>\n",
       "      <th>model</th>\n",
       "      <th>num_pca</th>\n",
       "      <th>Train_R2_score</th>\n",
       "      <th>Train_MAE_score</th>\n",
       "      <th>Train_RMSE_score</th>\n",
       "      <th>Test_R2_score</th>\n",
       "      <th>Test_MAE_score</th>\n",
       "      <th>Test_RMSE_score</th>\n",
       "      <th>Test_Best_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>esm2_15B_PG1</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>10</td>\n",
       "      <td>0.59</td>\n",
       "      <td>9.93</td>\n",
       "      <td>12.70</td>\n",
       "      <td>0.59</td>\n",
       "      <td>12.51</td>\n",
       "      <td>15.36</td>\n",
       "      <td>{'alpha': 0.6, 'max_iter': 10000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>esm2_15B_PG1</td>\n",
       "      <td>SVR</td>\n",
       "      <td>10</td>\n",
       "      <td>0.54</td>\n",
       "      <td>9.84</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0.57</td>\n",
       "      <td>12.72</td>\n",
       "      <td>15.73</td>\n",
       "      <td>{'C': 1, 'gamma': 'scale', 'kernel': 'linear'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>esm2_15B_PG1</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>15</td>\n",
       "      <td>0.66</td>\n",
       "      <td>9.07</td>\n",
       "      <td>11.53</td>\n",
       "      <td>0.66</td>\n",
       "      <td>11.82</td>\n",
       "      <td>14.07</td>\n",
       "      <td>{'alpha': 0.49, 'max_iter': 10000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>esm2_15B_PG1</td>\n",
       "      <td>SVR</td>\n",
       "      <td>15</td>\n",
       "      <td>0.59</td>\n",
       "      <td>9.17</td>\n",
       "      <td>12.71</td>\n",
       "      <td>0.62</td>\n",
       "      <td>11.87</td>\n",
       "      <td>14.80</td>\n",
       "      <td>{'C': 1, 'gamma': 'scale', 'kernel': 'linear'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>esm2_15B_PG1</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>20</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.19</td>\n",
       "      <td>11.68</td>\n",
       "      <td>0.65</td>\n",
       "      <td>11.92</td>\n",
       "      <td>14.22</td>\n",
       "      <td>{'alpha': 0.5800000000000001, 'max_iter': 10000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>esm2_15B_PG1</td>\n",
       "      <td>SVR</td>\n",
       "      <td>20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>9.10</td>\n",
       "      <td>12.69</td>\n",
       "      <td>0.61</td>\n",
       "      <td>12.22</td>\n",
       "      <td>15.06</td>\n",
       "      <td>{'C': 1, 'gamma': 'scale', 'kernel': 'linear'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>esm2_15B_PG1</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>25</td>\n",
       "      <td>0.70</td>\n",
       "      <td>8.73</td>\n",
       "      <td>10.92</td>\n",
       "      <td>0.69</td>\n",
       "      <td>11.08</td>\n",
       "      <td>13.36</td>\n",
       "      <td>{'alpha': 0.44, 'max_iter': 10000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>esm2_15B_PG1</td>\n",
       "      <td>SVR</td>\n",
       "      <td>25</td>\n",
       "      <td>0.61</td>\n",
       "      <td>8.87</td>\n",
       "      <td>12.42</td>\n",
       "      <td>0.62</td>\n",
       "      <td>12.02</td>\n",
       "      <td>14.86</td>\n",
       "      <td>{'C': 1, 'gamma': 'scale', 'kernel': 'linear'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>esm2_15B_PG1</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>30</td>\n",
       "      <td>0.69</td>\n",
       "      <td>8.83</td>\n",
       "      <td>11.05</td>\n",
       "      <td>0.68</td>\n",
       "      <td>11.17</td>\n",
       "      <td>13.51</td>\n",
       "      <td>{'alpha': 0.5, 'max_iter': 10000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>esm2_15B_PG1</td>\n",
       "      <td>SVR</td>\n",
       "      <td>30</td>\n",
       "      <td>0.62</td>\n",
       "      <td>8.72</td>\n",
       "      <td>12.28</td>\n",
       "      <td>0.63</td>\n",
       "      <td>11.92</td>\n",
       "      <td>14.62</td>\n",
       "      <td>{'C': 1, 'gamma': 'scale', 'kernel': 'linear'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>esm2_15B_PG1</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>35</td>\n",
       "      <td>0.69</td>\n",
       "      <td>8.83</td>\n",
       "      <td>11.05</td>\n",
       "      <td>0.68</td>\n",
       "      <td>11.17</td>\n",
       "      <td>13.51</td>\n",
       "      <td>{'alpha': 0.5, 'max_iter': 10000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>esm2_15B_PG1</td>\n",
       "      <td>SVR</td>\n",
       "      <td>35</td>\n",
       "      <td>0.62</td>\n",
       "      <td>8.59</td>\n",
       "      <td>12.22</td>\n",
       "      <td>0.62</td>\n",
       "      <td>11.88</td>\n",
       "      <td>14.79</td>\n",
       "      <td>{'C': 1, 'gamma': 'scale', 'kernel': 'linear'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>esm2_15B_PG1</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>40</td>\n",
       "      <td>0.69</td>\n",
       "      <td>8.83</td>\n",
       "      <td>11.05</td>\n",
       "      <td>0.68</td>\n",
       "      <td>11.17</td>\n",
       "      <td>13.51</td>\n",
       "      <td>{'alpha': 0.5, 'max_iter': 10000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>esm2_15B_PG1</td>\n",
       "      <td>SVR</td>\n",
       "      <td>40</td>\n",
       "      <td>0.63</td>\n",
       "      <td>8.34</td>\n",
       "      <td>11.97</td>\n",
       "      <td>0.62</td>\n",
       "      <td>11.80</td>\n",
       "      <td>14.84</td>\n",
       "      <td>{'C': 1, 'gamma': 'scale', 'kernel': 'linear'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Embedding  model  num_pca  Train_R2_score  Train_MAE_score  \\\n",
       "0   esm2_15B_PG1  Lasso       10            0.59             9.93   \n",
       "1   esm2_15B_PG1    SVR       10            0.54             9.84   \n",
       "2   esm2_15B_PG1  Lasso       15            0.66             9.07   \n",
       "3   esm2_15B_PG1    SVR       15            0.59             9.17   \n",
       "4   esm2_15B_PG1  Lasso       20            0.65             9.19   \n",
       "5   esm2_15B_PG1    SVR       20            0.59             9.10   \n",
       "6   esm2_15B_PG1  Lasso       25            0.70             8.73   \n",
       "7   esm2_15B_PG1    SVR       25            0.61             8.87   \n",
       "8   esm2_15B_PG1  Lasso       30            0.69             8.83   \n",
       "9   esm2_15B_PG1    SVR       30            0.62             8.72   \n",
       "10  esm2_15B_PG1  Lasso       35            0.69             8.83   \n",
       "11  esm2_15B_PG1    SVR       35            0.62             8.59   \n",
       "12  esm2_15B_PG1  Lasso       40            0.69             8.83   \n",
       "13  esm2_15B_PG1    SVR       40            0.63             8.34   \n",
       "\n",
       "    Train_RMSE_score  Test_R2_score  Test_MAE_score  Test_RMSE_score  \\\n",
       "0              12.70           0.59           12.51            15.36   \n",
       "1              13.40           0.57           12.72            15.73   \n",
       "2              11.53           0.66           11.82            14.07   \n",
       "3              12.71           0.62           11.87            14.80   \n",
       "4              11.68           0.65           11.92            14.22   \n",
       "5              12.69           0.61           12.22            15.06   \n",
       "6              10.92           0.69           11.08            13.36   \n",
       "7              12.42           0.62           12.02            14.86   \n",
       "8              11.05           0.68           11.17            13.51   \n",
       "9              12.28           0.63           11.92            14.62   \n",
       "10             11.05           0.68           11.17            13.51   \n",
       "11             12.22           0.62           11.88            14.79   \n",
       "12             11.05           0.68           11.17            13.51   \n",
       "13             11.97           0.62           11.80            14.84   \n",
       "\n",
       "                                    Test_Best_params  \n",
       "0                  {'alpha': 0.6, 'max_iter': 10000}  \n",
       "1     {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}  \n",
       "2                 {'alpha': 0.49, 'max_iter': 10000}  \n",
       "3     {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}  \n",
       "4   {'alpha': 0.5800000000000001, 'max_iter': 10000}  \n",
       "5     {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}  \n",
       "6                 {'alpha': 0.44, 'max_iter': 10000}  \n",
       "7     {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}  \n",
       "8                  {'alpha': 0.5, 'max_iter': 10000}  \n",
       "9     {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}  \n",
       "10                 {'alpha': 0.5, 'max_iter': 10000}  \n",
       "11    {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}  \n",
       "12                 {'alpha': 0.5, 'max_iter': 10000}  \n",
       "13    {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#results_final = pd.read_excel('results/results_regression_hemolysis_v02.xlsx', index_col=0)\n",
    "results_final"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train x Test Model performances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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_R2_score', 'Train_R2_score'], \n",
    "                    var_name='Score Type', value_name='R2 Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1289.12x500 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_R2_score', 'Test_R2_score'])\n",
    "g.map_dataframe(sns.barplot, x=\"num_pca\", y=\"R2 Score\", hue='model', palette='muted')\n",
    "g.set_axis_labels(\"Number of PCAs\", \"R2 Score\")\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 test set or using cross-validation.  \n",
    "If the model's performance on the test set is significantly worse than its performance on the training set, then it may be overfitting.  \n",
    "\n",
    "**Cross validation results**  \n",
    "The cross validation results shown a small variance using 30 PCAs of its Mean Absolute Error (MAE - values close the zero are better).\n",
    "\n",
    "**Train x Test**  \n",
    "As observed the results from test set aren't drastically different from the train set, indicating a good generalization of the model."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine Tunning Model with best hyperparameter found with Gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embedding</th>\n",
       "      <th>model</th>\n",
       "      <th>num_pca</th>\n",
       "      <th>Train_R2_score</th>\n",
       "      <th>Train_MAE_score</th>\n",
       "      <th>Train_RMSE_score</th>\n",
       "      <th>Test_R2_score</th>\n",
       "      <th>Test_MAE_score</th>\n",
       "      <th>Test_RMSE_score</th>\n",
       "      <th>Test_Best_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>esm2_15B_PG1</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>25</td>\n",
       "      <td>0.70</td>\n",
       "      <td>8.73</td>\n",
       "      <td>10.92</td>\n",
       "      <td>0.69</td>\n",
       "      <td>11.08</td>\n",
       "      <td>13.36</td>\n",
       "      <td>{'alpha': 0.44, 'max_iter': 10000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>esm2_15B_PG1</td>\n",
       "      <td>SVR</td>\n",
       "      <td>25</td>\n",
       "      <td>0.61</td>\n",
       "      <td>8.87</td>\n",
       "      <td>12.42</td>\n",
       "      <td>0.62</td>\n",
       "      <td>12.02</td>\n",
       "      <td>14.86</td>\n",
       "      <td>{'C': 1, 'gamma': 'scale', 'kernel': 'linear'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Embedding  model  num_pca  Train_R2_score  Train_MAE_score  \\\n",
       "6  esm2_15B_PG1  Lasso       25            0.70             8.73   \n",
       "7  esm2_15B_PG1    SVR       25            0.61             8.87   \n",
       "\n",
       "   Train_RMSE_score  Test_R2_score  Test_MAE_score  Test_RMSE_score  \\\n",
       "6             10.92           0.69           11.08            13.36   \n",
       "7             12.42           0.62           12.02            14.86   \n",
       "\n",
       "                                 Test_Best_params  \n",
       "6              {'alpha': 0.44, 'max_iter': 10000}  \n",
       "7  {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_final.query('num_pca == 25')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pca_components = 25\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": [
    "### Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2 score: 0.70\n",
      "Test R2 score: 0.69\n"
     ]
    }
   ],
   "source": [
    "lasso = Lasso(\n",
    "    alpha=0.44,\n",
    "    max_iter=10000    \n",
    ")\n",
    "\n",
    "# fined tuned SVR performance\n",
    "lasso.fit(X_train_pca, y_train)\n",
    "\n",
    "# print R2 score\n",
    "print(f'Train R2 score: {lasso.score(X_train_pca, y_train):.2f}')\n",
    "print(f'Test R2 score: {lasso.score(X_test_pca, y_test):.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_pred = lasso.predict(X_train_pca)\n",
    "y_pred = lasso.predict(X_test_pca)\n",
    "\n",
    "plt.scatter(train_pred, y_train, label=\"Train samples\", c='#d95f02')\n",
    "plt.scatter(y_pred, y_test, label=\"Test samples\", c='#7570b3')\n",
    "plt.title(f\"Model: Lasso Regressor\")\n",
    "plt.xlabel(\"Predicted Hemolysis (%)\")\n",
    "plt.ylabel(\"True Hemolysis (%)\")\n",
    "plt.plot([-1, 100], [-1, 100], c='k', zorder=0) \n",
    "plt.text(0, 80, f'Train R$^2$: {lasso.score(X_train_pca, y_train).round(2)}', fontsize = 9)\n",
    "plt.text(0, 75, f'Test R$^2$: {lasso.score(X_test_pca, y_test).round(2)}', fontsize = 9)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2 score: 0.61\n",
      "Test R2 score: 0.62\n"
     ]
    }
   ],
   "source": [
    "# train support vector machine regressor\n",
    "svmR = SVR(\n",
    "    C=1, \n",
    "    gamma='scale',\n",
    "    kernel='linear',\n",
    "    )\n",
    "\n",
    "# fined tuned SVR performance\n",
    "svmR.fit(X_train_pca, y_train)\n",
    "\n",
    "# print R2 score\n",
    "print(f'Train R2 score: {svmR.score(X_train_pca, y_train):.2f}')\n",
    "print(f'Test R2 score: {svmR.score(X_test_pca, y_test):.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_pred = svmR.predict(X_train_pca)\n",
    "y_pred = svmR.predict(X_test_pca)\n",
    "\n",
    "plt.scatter(train_pred, y_train, label=\"Train samples\", c='#d95f02')\n",
    "plt.scatter(y_pred, y_test, label=\"Test samples\", c='#7570b3')\n",
    "plt.title(f\"Model: SVM Regressor\")\n",
    "plt.xlabel(\"Predicted Hemolysis (%)\")\n",
    "plt.ylabel(\"True Hemolysis (%)\")\n",
    "plt.plot([-1, 100], [-1, 100], c='k', zorder=0) \n",
    "plt.text(0, 80, f'Train R$^2$: {svmR.score(X_train_pca, y_train).round(2)}', fontsize = 9)\n",
    "plt.text(0, 75, f'Test R$^2$: {svmR.score(X_test_pca, y_test).round(2)}', fontsize = 9)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving model and PCA weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/weights_pca30_esm2_15B_hemolysis.pkl']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(lasso, 'models/model_v02_Lasso30_esm2_15B_hemolysis_regression.pkl')\n",
    "joblib.dump(pca, 'models/weights_pca30_esm2_15B_hemolysis.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the model\n",
    "model = joblib.load('models/model_v02_Lasso_esm2_15B_hemolysis_regression.pkl')\n",
    "\n",
    "# Loading PCA weights\n",
    "pca = joblib.load('models/weights_pca30_esm2_15B_hemolysis.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "      <td>-0.195330</td>\n",
       "      <td>-0.116773</td>\n",
       "      <td>0.137988</td>\n",
       "      <td>-0.164647</td>\n",
       "      <td>0.119364</td>\n",
       "      <td>-0.178338</td>\n",
       "      <td>0.031593</td>\n",
       "      <td>0.029154</td>\n",
       "      <td>-0.215111</td>\n",
       "      <td>-0.017543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.156776</td>\n",
       "      <td>-0.105166</td>\n",
       "      <td>-0.063266</td>\n",
       "      <td>0.204592</td>\n",
       "      <td>-0.202521</td>\n",
       "      <td>0.076685</td>\n",
       "      <td>-0.140390</td>\n",
       "      <td>0.251750</td>\n",
       "      <td>-0.082083</td>\n",
       "      <td>0.299491</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.100395</td>\n",
       "      <td>-0.043842</td>\n",
       "      <td>0.144827</td>\n",
       "      <td>-0.125664</td>\n",
       "      <td>0.134358</td>\n",
       "      <td>-0.157067</td>\n",
       "      <td>0.040382</td>\n",
       "      <td>0.030294</td>\n",
       "      <td>-0.248444</td>\n",
       "      <td>0.020361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.148965</td>\n",
       "      <td>-0.141826</td>\n",
       "      <td>-0.064849</td>\n",
       "      <td>0.198645</td>\n",
       "      <td>-0.237333</td>\n",
       "      <td>0.096351</td>\n",
       "      <td>-0.192772</td>\n",
       "      <td>0.201036</td>\n",
       "      <td>-0.055550</td>\n",
       "      <td>0.323271</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108473</td>\n",
       "      <td>-0.066790</td>\n",
       "      <td>0.079047</td>\n",
       "      <td>-0.211575</td>\n",
       "      <td>0.114173</td>\n",
       "      <td>-0.089037</td>\n",
       "      <td>0.146931</td>\n",
       "      <td>0.012782</td>\n",
       "      <td>-0.173229</td>\n",
       "      <td>0.016016</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  0.109975 -0.134590 -0.111443  0.218644 -0.237748  0.131493 -0.119502   \n",
       "1  0.129701 -0.184889 -0.115536  0.161651 -0.224050  0.112427 -0.157890   \n",
       "2  0.099468 -0.114953 -0.117187  0.183187 -0.209792  0.091260 -0.130392   \n",
       "3  0.156776 -0.105166 -0.063266  0.204592 -0.202521  0.076685 -0.140390   \n",
       "4  0.148965 -0.141826 -0.064849  0.198645 -0.237333  0.096351 -0.192772   \n",
       "\n",
       "       7         8         9     ...      5110      5111      5112      5113  \\\n",
       "0  0.234007 -0.042554  0.338132  ... -0.136047 -0.087660  0.107472 -0.270478   \n",
       "1  0.269912 -0.049523  0.307800  ... -0.106790 -0.129787  0.076798 -0.208119   \n",
       "2  0.256235 -0.053187  0.229403  ... -0.195330 -0.116773  0.137988 -0.164647   \n",
       "3  0.251750 -0.082083  0.299491  ... -0.100395 -0.043842  0.144827 -0.125664   \n",
       "4  0.201036 -0.055550  0.323271  ... -0.108473 -0.066790  0.079047 -0.211575   \n",
       "\n",
       "       5114      5115      5116      5117      5118      5119  \n",
       "0  0.122341 -0.089307  0.137297  0.075365 -0.198461 -0.035045  \n",
       "1  0.098149 -0.133004  0.089372  0.093026 -0.170869 -0.013929  \n",
       "2  0.119364 -0.178338  0.031593  0.029154 -0.215111 -0.017543  \n",
       "3  0.134358 -0.157067  0.040382  0.030294 -0.248444  0.020361  \n",
       "4  0.114173 -0.089037  0.146931  0.012782 -0.173229  0.016016  \n",
       "\n",
       "[5 rows x 5120 columns]"
      ]
     },
     "execution_count": 35,
     "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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculates the probability of being active\n",
    "pred = model.predict(pca.transform(features))\n",
    "\n",
    "sum(pred <=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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>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",
       "      <th>L10Select</th>\n",
       "      <th>hemolysis_pred_v01.2</th>\n",
       "      <th>selectivity_pred_v01.2</th>\n",
       "      <th>activity_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>100.0</td>\n",
       "      <td>-4.054558</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>100.0</td>\n",
       "      <td>-6.763184</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>100.0</td>\n",
       "      <td>-8.310135</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>100.0</td>\n",
       "      <td>-2.867607</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>100.0</td>\n",
       "      <td>-3.139629</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>100.0</td>\n",
       "      <td>-2.812325</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>100.0</td>\n",
       "      <td>-3.347273</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>100.0</td>\n",
       "      <td>-1.988224</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>100.0</td>\n",
       "      <td>-13.069695</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>100.0</td>\n",
       "      <td>-5.106495</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>100.0</td>\n",
       "      <td>-1.498447</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>100.0</td>\n",
       "      <td>-4.230751</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>100.0</td>\n",
       "      <td>-1.894341</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>100.0</td>\n",
       "      <td>-2.905787</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>100.0</td>\n",
       "      <td>-7.735706</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>100.0</td>\n",
       "      <td>-2.670889</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>100.0</td>\n",
       "      <td>-2.594505</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>100.0</td>\n",
       "      <td>3.938261</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>100.0</td>\n",
       "      <td>-1.731125</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>100.0</td>\n",
       "      <td>-5.932812</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>100.0</td>\n",
       "      <td>-7.072010</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>100.0</td>\n",
       "      <td>-2.631250</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>100.0</td>\n",
       "      <td>-2.657715</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>100.0</td>\n",
       "      <td>2.111897</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>100.0</td>\n",
       "      <td>-0.263622</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>100.0</td>\n",
       "      <td>-3.961687</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>100.0</td>\n",
       "      <td>0.154079</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>100.0</td>\n",
       "      <td>-2.748653</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>100.0</td>\n",
       "      <td>2.327160</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>100.0</td>\n",
       "      <td>-5.520584</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>100.0</td>\n",
       "      <td>-1.552879</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>100.0</td>\n",
       "      <td>0.003508</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>100.0</td>\n",
       "      <td>2.614080</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>100.0</td>\n",
       "      <td>-0.155737</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>100.0</td>\n",
       "      <td>4.564720</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>100.0</td>\n",
       "      <td>-4.328007</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  activity_pred_v01.2  \\\n",
       "0              -7.675692               -0.987362                100.0   \n",
       "1             -14.298704               -1.215473                100.0   \n",
       "2             -14.385390               -1.305665                100.0   \n",
       "3             -19.078636               -0.686002                100.0   \n",
       "4              -3.053714               -0.247956                100.0   \n",
       "5             -13.542688               -0.695068                100.0   \n",
       "6              -3.345207               -0.416047                100.0   \n",
       "7             -12.337255               -0.839417                100.0   \n",
       "8             -21.809423               -0.699560                100.0   \n",
       "9              -9.999792               -0.342884                100.0   \n",
       "10             -5.660681               -0.654460                100.0   \n",
       "11             -2.912143               -0.348571                100.0   \n",
       "12            -14.079775               -0.478759                100.0   \n",
       "13             -1.718951               -0.199801                100.0   \n",
       "14             -8.957634                0.108545                100.0   \n",
       "15            -15.366731               -1.027213                100.0   \n",
       "16              0.506438               -0.073250                100.0   \n",
       "17              0.554981               -0.397154                100.0   \n",
       "18            -10.515178               -0.187527                100.0   \n",
       "19            -15.554958               -0.281757                100.0   \n",
       "20             -9.616057               -0.224147                100.0   \n",
       "21             -6.641514               -0.214472                100.0   \n",
       "22             -8.263932               -0.334165                100.0   \n",
       "23             -9.540926                0.028367                100.0   \n",
       "24             -0.994274               -0.381789                100.0   \n",
       "25             -6.507852               -0.163775                100.0   \n",
       "26             -9.883299                0.118627                100.0   \n",
       "27            -11.357187               -0.072031                100.0   \n",
       "28              1.551211                0.101647                100.0   \n",
       "29             -1.721362                0.664414                100.0   \n",
       "30             -1.774004               -0.034093                100.0   \n",
       "31             -2.584238                0.779326                100.0   \n",
       "32             -0.897124                0.549621                100.0   \n",
       "33              0.575238                0.464198                100.0   \n",
       "34             -2.725072               -0.036225                100.0   \n",
       "35             -6.823680                0.701898                100.0   \n",
       "\n",
       "    hemolysis_pred_v02  selectivity_pred_v02  activity_pred_v02  \n",
       "0            -4.054558              0.307504              92.34  \n",
       "1            -6.763184              0.272666              88.34  \n",
       "2            -8.310135              0.345484              95.37  \n",
       "3            -2.867607              0.618075              90.87  \n",
       "4            -3.139629              0.578861              94.94  \n",
       "5            -2.812325              0.343804              90.10  \n",
       "6            -3.347273              0.452450              94.73  \n",
       "7            -1.988224              0.491094              98.64  \n",
       "8           -13.069695              0.122605              93.60  \n",
       "9            -5.106495              0.379449              93.70  \n",
       "10           -1.498447              0.506019              83.26  \n",
       "11           -4.230751              0.333009              90.30  \n",
       "12           -1.894341              0.732106              92.81  \n",
       "13           -2.905787              0.641877              87.66  \n",
       "14           -7.735706              0.955437              68.43  \n",
       "15           -2.670889              0.596881              91.49  \n",
       "16           -2.594505              0.768069              98.31  \n",
       "17            3.938261              0.380668              95.43  \n",
       "18           -1.731125              0.748402              80.77  \n",
       "19           -5.932812              0.436773              95.17  \n",
       "20           -7.072010              0.475476              77.76  \n",
       "21           -2.631250              1.191644              84.77  \n",
       "22           -2.657715              0.952043              50.85  \n",
       "23            2.111897              0.625790              91.49  \n",
       "24           -0.263622              0.858255              95.69  \n",
       "25           -3.961687              0.883138               0.49  \n",
       "26            0.154079              1.053053               7.18  \n",
       "27           -2.748653              0.944423              88.73  \n",
       "28            2.327160              0.736734              97.39  \n",
       "29           -5.520584              0.518300              96.13  \n",
       "30           -1.552879              0.621463              93.17  \n",
       "31            0.003508              0.961354              67.97  \n",
       "32            2.614080              0.974183              84.35  \n",
       "33           -0.155737              0.685053              96.83  \n",
       "34            4.564720              1.161217              64.24  \n",
       "35           -4.328007              0.824783              85.33  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_data['hemolysis_pred_v02'] = pred\n",
    "meta_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_data.to_csv('results/v01.2/predictions_validation_set_v01.2.csv', index=False)"
   ]
  }
 ],
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