04_hemolysis_model_v01.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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import joblib\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",
    "from sklearn.svm import SVR\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error, r2_score, mean_squared_error\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "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": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <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": 6,
     "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": 7,
   "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": 8,
   "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": 8,
     "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": 9,
   "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)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCAs before feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_pca_components = 2\n",
    "pca = PCA(num_pca_components)\n",
    "X_train_pca = pca.fit_transform(features)\n",
    "fig_dims = (6, 4)\n",
    "fig, ax = plt.subplots(figsize=fig_dims)\n",
    "sc = ax.scatter(X_train_pca[:,0], X_train_pca[:,1], c=target, marker='.')\n",
    "plt.title(f'PCA ESM2_15B')\n",
    "ax.set_xlabel('PCA first principal component')\n",
    "ax.set_ylabel('PCA second principal component')\n",
    "plt.colorbar(sc, label='Hemolysis')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting features by correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "115"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_correlated_columns(df, target, threshold):\n",
    "    # correlating train target  with train set\n",
    "    corr_matrix = df.corrwith(target).abs().sort_values()\n",
    "\n",
    "    # create a list of columns with high positive or negative correlation\n",
    "    correlated_columns = []\n",
    "    for column in corr_matrix.index:\n",
    "        if (corr_matrix[column] >= threshold) & (column not in correlated_columns):\n",
    "            correlated_columns.append(column)\n",
    "\n",
    "    return(correlated_columns)\n",
    "\n",
    "corr_columns = get_correlated_columns(X_train, y_train, 0.6)\n",
    "len(corr_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# selecting features\n",
    "XX_train = X_train[corr_columns]\n",
    "XX_test = X_test[corr_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCAs after feature selection "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_pca_components = 2\n",
    "pca = PCA(num_pca_components)\n",
    "X_train_pca = pca.fit_transform(XX_train)\n",
    "fig_dims = (6, 4)\n",
    "fig, ax = plt.subplots(figsize=fig_dims)\n",
    "sc = ax.scatter(X_train_pca[:,0], X_train_pca[:,1], c=y_train, marker='.')\n",
    "plt.title(f'PCA ESM2_15B')\n",
    "ax.set_xlabel('PCA first principal component')\n",
    "ax.set_ylabel('PCA second principal component')\n",
    "plt.colorbar(sc, label='Hemolysis')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running Regression Models for testing code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Lasso, Train R2:0.77, Test R2:0.73, best params {'alpha': 0.03, 'max_iter': 10000}\n",
      "R2 CV Scores: [0.2272147  0.82221172 0.30062215 0.744938   0.47289064 0.56639901\n",
      " 0.62593993 0.76854613 0.27646877 0.89952465]\n",
      "\n",
      "Model: SVR, Train R2:0.75, Test R2:0.73, best params {'C': 100, 'gamma': 'scale', 'kernel': 'rbf'}\n",
      "R2 CV Scores: [0.30424542 0.68582209 0.19833875 0.35682633 0.32414268 0.49690145\n",
      " 0.68257869 0.60070241 0.396763   0.51689326]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 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(), RandomForestRegressor()]\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, 100000]},\n",
    "    # SVR\n",
    "    {'C': [0.1, 1, 10, 100, 1000], 'kernel': ['linear', 'rbf', 'poly', 'sigmoid'], \n",
    "    'gamma': ['scale', 0.1, 0.01, 0.001, 0.0001]},\n",
    "    ## RandomForestRegressor\n",
    "    #{'n_estimators': [100, 200, 300, 500, 700], 'max_depth': [None, 2, 3, 4, 5, 10]}, \n",
    "    ]\n",
    "\n",
    "    # train scores\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='neg_mean_absolute_error', cv=10, n_jobs=10)\n",
    "    \n",
    "    # k-fold cross-validation on the training set\n",
    "    r2_cv_scores = cross_val_score(grid_search, XX_train, y_train, cv=10, scoring='r2', n_jobs=10)\n",
    "    mae_cv_scores = cross_val_score(grid_search, XX_train, y_train, cv=10, scoring='neg_mean_absolute_error', n_jobs=10)\n",
    "\n",
    "    # train in the whole training set\n",
    "    grid_search.fit(XX_train, y_train)\n",
    "\n",
    "    # make predictions\n",
    "    train_pred = grid_search.predict(XX_train)\n",
    "    test_y_pred = grid_search.predict(XX_test)\n",
    "    \n",
    "    # Model evaluation on test data\n",
    "    train_r2 = r2_score(y_train, train_pred)\n",
    "    train_mae = mean_absolute_error(y_train, train_pred)\n",
    "    train_mse = mean_squared_error(y_train, train_pred)\n",
    "    train_rmse = np.sqrt(train_mse)\n",
    "\n",
    "    # Model evaluation on test data\n",
    "    test_r2 = r2_score(y_test, test_y_pred)\n",
    "    test_mae = mean_absolute_error(y_test, test_y_pred)\n",
    "    test_mse = mean_squared_error(y_test, 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}, 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[\"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(10)],\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",
    "#results.to_excel('results/results_regression_hemolysis_v01.xlsx')\n",
    "#results_cv.to_excel('results/results_cv_regression_hemolysis_v01.xlsx')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>R2_CV_scores</th>\n",
       "      <th>Mae_CV_scores</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lasso</th>\n",
       "      <td>0.570476</td>\n",
       "      <td>-9.176693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVR</th>\n",
       "      <td>0.456321</td>\n",
       "      <td>-10.927249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       R2_CV_scores  Mae_CV_scores\n",
       "model                             \n",
       "Lasso      0.570476      -9.176693\n",
       "SVR        0.456321     -10.927249"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#results_cv = pd.read_excel('results/results_cv_regression_hemolysis_new.xlsx', index_col=0)\n",
    "results_cv.groupby('model').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=results_cv, y='model', x='R2_CV_scores',showfliers=False)\n",
    "plt.title('R2 Cross-validation scores on training set')\n",
    "plt.xlim(right=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=results_cv, y='model', x='Mae_CV_scores',showfliers=False)\n",
    "plt.title('MAE Cross-validation scores on training set')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replace 'column1' and 'column2' with the names of the columns you want to plot\n",
    "column1 = 'Train_R2_score'\n",
    "column2 = 'Test_R2_score'\n",
    "\n",
    "# Create a bar plot with two bars per group\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "y = np.arange(len(results))\n",
    "bar_height = 0.35\n",
    "opacity = 0.8\n",
    "\n",
    "# +/- 0.01 is to create a small space between bars\n",
    "rects1 = ax.barh(y + bar_height/2 + 0.01, results[column1], bar_height, \n",
    "                 alpha=opacity, color='b', label=column1)\n",
    "rects2 = ax.barh(y - bar_height/2 - 0.01, results[column2], bar_height,\n",
    "                 alpha=opacity, color='g', label=column2)\n",
    "\n",
    "\n",
    "# Add labels and legend\n",
    "ax.set_ylabel('Model')\n",
    "ax.set_xlabel('R2 score')\n",
    "ax.set_title('Grouped Bar Plot')\n",
    "ax.set_yticks(y)\n",
    "ax.set_yticklabels(results['model'])\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To determine if the model is overfitting, it's important to evaluate its performance on a validation set or using cross-validation.   \n",
    "If the model's performance on the validation set is significantly worse than its performance on the training set, then it may be overfitting.   \n",
    "In such cases, it may be necessary to adjust the hyperparameters of the model or to use a less complex kernel function.\n",
    "\n",
    "\n",
    "**Train R-squared (R2: ~0.7):**  \n",
    "This is a measure of how well the SVR model fits the training data. An R2 score of 0.75 indicates that your model explainsapproximately 75% of the variability in the training data. This is a reasonably good fit, suggesting that the model has learned some of the underlying patterns in the training data.\n",
    "\n",
    "**Test R-squared (R2: ~0.7):**  \n",
    "The R2 score on the test data is also high (0.72), which is a positive sign. It suggests that the model's performance is consistent between the training and test datasets, which indicates that the model is generalizing well to unseen data.\n",
    " \n",
    "\n",
    "**R2 and MAE CV Scores:**    \n",
    "A consistent cross-validation (CV) scores indicates that the model performs well on different subsets of the data.\n",
    "The range of our model's CV scores suggests that your model's performance can vary slightly depending on the data split due the small size of the data, but the overall trend is positive.\n",
    "\n",
    "In summary, the Lasso model with a apha=0.04 appears to be performing well and avoiding overfitting. The train and test R2 scores are close, indicating good generalization."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine Tunning Model with best hyperparameter found with Gridsearch"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.iloc[0, 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2 score: 0.77\n",
      "Test R2 score: 0.73\n"
     ]
    }
   ],
   "source": [
    "lasso = Lasso(\n",
    "    alpha=0.03,\n",
    "    max_iter=10000    \n",
    ")\n",
    "\n",
    "# fined tuned model performance\n",
    "lasso.fit(XX_train, y_train)\n",
    "\n",
    "\n",
    "# print R2 score\n",
    "print(f'Train R2 score: {lasso.score(XX_train, y_train):.2f}')\n",
    "print(f'Test R2 score: {lasso.score(XX_test, y_test):.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "train_pred = lasso.predict(XX_train)\n",
    "y_pred = lasso.predict(XX_test)\n",
    "\n",
    "plt.scatter(train_pred, y_train, label=\"Train samples\", c='#d95f02', s=10)\n",
    "plt.scatter(y_pred, y_test, label=\"Test samples\", c='#7570b3', s=10)\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(XX_train, y_train).round(2)}', fontsize = 9)\n",
    "plt.text(0, 75, f'Test R$^2$: {lasso.score(XX_test, y_test).round(2)}', fontsize = 9)\n",
    "plt.legend()\n",
    "plt.savefig('results/lasso_regression_hemolysis.png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"{'C': 100, 'gamma': 'scale', 'kernel': 'rbf'}\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.iloc[1, 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2 score: 0.75\n",
      "Test R2 score: 0.73\n"
     ]
    }
   ],
   "source": [
    "# train support vector machine regressor\n",
    "svr = SVR(\n",
    "    C=100, \n",
    "    gamma='scale',\n",
    "    kernel='rbf',\n",
    "    )\n",
    "\n",
    "# fined tuned SVR performance\n",
    "svr.fit(XX_train, y_train)\n",
    "\n",
    "# print R2 score\n",
    "print(f'Train R2 score: {svr.score(XX_train, y_train):.2f}')\n",
    "print(f'Test R2 score: {svr.score(XX_test, y_test):.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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 = svr.predict(XX_train)\n",
    "y_pred = svr.predict(XX_test)\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$^2$: {svr.score(XX_train, y_train).round(2)}', fontsize = 9)\n",
    "plt.text(0, 75, f'Test R$^2$: {svr.score(XX_test, y_test).round(2)}', fontsize = 9)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the lasso madel and the correlated columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "\n",
    "joblib.dump(lasso, 'models/model_v01.2_Lasso_esm2_15B_Hemolysis_regression_corr06.pkl')\n",
    "\n",
    "# save the list to a file\n",
    "with open('models/correlated_columns_v01.2_esm2_15B_hemolysis_corr06.pkl', 'wb') as f:\n",
    "    pickle.dump(corr_columns, f)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ML",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "f1569419c614e8688ff5ac73ded8c36a7b1a231c09910336a299b563fe1d2720"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}