LightGBM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "123192a1-9f07-4f98-87b3-c96ac5513a53",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from lightgbm import LGBMRegressor\n",
    "import shap\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f9f0b158-4455-40ce-8f78-7f8dd43761ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings_df = pd.read_csv(\"./embeddings.csv\")\n",
    "metadata_df = pd.read_csv(\"./chains_evaluation.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4e7576ce-7f98-4f40-a747-642e05ef844d",
   "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>sequence_id</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>...</th>\n",
       "      <th>1014</th>\n",
       "      <th>1015</th>\n",
       "      <th>1016</th>\n",
       "      <th>1017</th>\n",
       "      <th>1018</th>\n",
       "      <th>1019</th>\n",
       "      <th>1020</th>\n",
       "      <th>1021</th>\n",
       "      <th>1022</th>\n",
       "      <th>1023</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5SSZ:A</td>\n",
       "      <td>0.041698</td>\n",
       "      <td>0.082317</td>\n",
       "      <td>0.017337</td>\n",
       "      <td>-0.011511</td>\n",
       "      <td>-0.035085</td>\n",
       "      <td>0.112324</td>\n",
       "      <td>-0.028229</td>\n",
       "      <td>-0.023020</td>\n",
       "      <td>-0.021959</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006125</td>\n",
       "      <td>-0.015080</td>\n",
       "      <td>-0.028736</td>\n",
       "      <td>-0.015365</td>\n",
       "      <td>0.071466</td>\n",
       "      <td>0.034531</td>\n",
       "      <td>-0.021253</td>\n",
       "      <td>-0.044281</td>\n",
       "      <td>-0.019104</td>\n",
       "      <td>0.006675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7FY1:A</td>\n",
       "      <td>0.047498</td>\n",
       "      <td>0.025369</td>\n",
       "      <td>0.009100</td>\n",
       "      <td>-0.032689</td>\n",
       "      <td>0.043294</td>\n",
       "      <td>-0.014423</td>\n",
       "      <td>-0.024067</td>\n",
       "      <td>-0.161998</td>\n",
       "      <td>0.039027</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.047904</td>\n",
       "      <td>0.030155</td>\n",
       "      <td>-0.008186</td>\n",
       "      <td>-0.003443</td>\n",
       "      <td>0.015064</td>\n",
       "      <td>-0.079059</td>\n",
       "      <td>-0.009469</td>\n",
       "      <td>-0.074569</td>\n",
       "      <td>0.049260</td>\n",
       "      <td>0.021386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7G1Q:A</td>\n",
       "      <td>0.070862</td>\n",
       "      <td>0.011154</td>\n",
       "      <td>-0.006863</td>\n",
       "      <td>-0.022787</td>\n",
       "      <td>0.028306</td>\n",
       "      <td>0.004775</td>\n",
       "      <td>-0.007784</td>\n",
       "      <td>-0.156496</td>\n",
       "      <td>0.027262</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.057456</td>\n",
       "      <td>0.012036</td>\n",
       "      <td>0.008975</td>\n",
       "      <td>-0.017833</td>\n",
       "      <td>0.000690</td>\n",
       "      <td>-0.106954</td>\n",
       "      <td>-0.049073</td>\n",
       "      <td>-0.081838</td>\n",
       "      <td>0.044579</td>\n",
       "      <td>0.043472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7G1X:A</td>\n",
       "      <td>0.029538</td>\n",
       "      <td>-0.015357</td>\n",
       "      <td>0.009438</td>\n",
       "      <td>-0.020062</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>0.016320</td>\n",
       "      <td>-0.025629</td>\n",
       "      <td>-0.113614</td>\n",
       "      <td>0.044924</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.047083</td>\n",
       "      <td>0.033137</td>\n",
       "      <td>-0.024161</td>\n",
       "      <td>-0.046833</td>\n",
       "      <td>0.028594</td>\n",
       "      <td>-0.063806</td>\n",
       "      <td>-0.023561</td>\n",
       "      <td>-0.093419</td>\n",
       "      <td>0.046764</td>\n",
       "      <td>0.015743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7H9J:A</td>\n",
       "      <td>0.080503</td>\n",
       "      <td>-0.010999</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.011473</td>\n",
       "      <td>0.044677</td>\n",
       "      <td>0.068076</td>\n",
       "      <td>-0.060599</td>\n",
       "      <td>-0.020509</td>\n",
       "      <td>-0.018876</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.041747</td>\n",
       "      <td>0.004042</td>\n",
       "      <td>-0.009992</td>\n",
       "      <td>-0.106962</td>\n",
       "      <td>0.006102</td>\n",
       "      <td>0.031285</td>\n",
       "      <td>-0.022343</td>\n",
       "      <td>-0.027299</td>\n",
       "      <td>-0.041440</td>\n",
       "      <td>-0.051504</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>1458</th>\n",
       "      <td>9FWC:A</td>\n",
       "      <td>0.016753</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.117039</td>\n",
       "      <td>0.009715</td>\n",
       "      <td>0.012918</td>\n",
       "      <td>-0.006572</td>\n",
       "      <td>-0.031365</td>\n",
       "      <td>-0.106783</td>\n",
       "      <td>-0.014808</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051225</td>\n",
       "      <td>-0.010409</td>\n",
       "      <td>0.063774</td>\n",
       "      <td>-0.085060</td>\n",
       "      <td>0.029770</td>\n",
       "      <td>-0.005850</td>\n",
       "      <td>0.010760</td>\n",
       "      <td>-0.053217</td>\n",
       "      <td>0.001542</td>\n",
       "      <td>-0.072770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>9FX5:A</td>\n",
       "      <td>-0.015200</td>\n",
       "      <td>0.094616</td>\n",
       "      <td>0.029028</td>\n",
       "      <td>-0.005583</td>\n",
       "      <td>0.007496</td>\n",
       "      <td>-0.010617</td>\n",
       "      <td>0.052864</td>\n",
       "      <td>-0.057743</td>\n",
       "      <td>0.017855</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008281</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.013113</td>\n",
       "      <td>-0.004962</td>\n",
       "      <td>0.028970</td>\n",
       "      <td>-0.014288</td>\n",
       "      <td>0.010970</td>\n",
       "      <td>0.069420</td>\n",
       "      <td>0.035354</td>\n",
       "      <td>-0.040685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>9G35:A</td>\n",
       "      <td>-0.009528</td>\n",
       "      <td>0.067932</td>\n",
       "      <td>-0.021398</td>\n",
       "      <td>0.032119</td>\n",
       "      <td>-0.025142</td>\n",
       "      <td>-0.010401</td>\n",
       "      <td>-0.075979</td>\n",
       "      <td>-0.037079</td>\n",
       "      <td>0.011097</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.100129</td>\n",
       "      <td>0.018395</td>\n",
       "      <td>-0.007278</td>\n",
       "      <td>-0.024896</td>\n",
       "      <td>0.012935</td>\n",
       "      <td>0.000325</td>\n",
       "      <td>-0.019377</td>\n",
       "      <td>0.029905</td>\n",
       "      <td>-0.043913</td>\n",
       "      <td>-0.001953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>9IID:A</td>\n",
       "      <td>0.021642</td>\n",
       "      <td>0.069980</td>\n",
       "      <td>0.014065</td>\n",
       "      <td>-0.065714</td>\n",
       "      <td>-0.132132</td>\n",
       "      <td>0.019948</td>\n",
       "      <td>-0.071130</td>\n",
       "      <td>-0.083702</td>\n",
       "      <td>0.021212</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.034138</td>\n",
       "      <td>-0.029562</td>\n",
       "      <td>-0.044261</td>\n",
       "      <td>-0.111832</td>\n",
       "      <td>0.026946</td>\n",
       "      <td>-0.016103</td>\n",
       "      <td>-0.055561</td>\n",
       "      <td>0.095822</td>\n",
       "      <td>0.022081</td>\n",
       "      <td>0.047651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>9IJP:B</td>\n",
       "      <td>0.026310</td>\n",
       "      <td>0.038509</td>\n",
       "      <td>0.033396</td>\n",
       "      <td>-0.007139</td>\n",
       "      <td>0.060688</td>\n",
       "      <td>0.005658</td>\n",
       "      <td>0.028074</td>\n",
       "      <td>-0.071596</td>\n",
       "      <td>0.001605</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.054311</td>\n",
       "      <td>-0.032958</td>\n",
       "      <td>-0.020931</td>\n",
       "      <td>0.022073</td>\n",
       "      <td>-0.034295</td>\n",
       "      <td>-0.077946</td>\n",
       "      <td>-0.051034</td>\n",
       "      <td>-0.034202</td>\n",
       "      <td>0.017765</td>\n",
       "      <td>0.044527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1463 rows × 1025 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sequence_id         0         1         2         3         4         5  \\\n",
       "0         5SSZ:A  0.041698  0.082317  0.017337 -0.011511 -0.035085  0.112324   \n",
       "1         7FY1:A  0.047498  0.025369  0.009100 -0.032689  0.043294 -0.014423   \n",
       "2         7G1Q:A  0.070862  0.011154 -0.006863 -0.022787  0.028306  0.004775   \n",
       "3         7G1X:A  0.029538 -0.015357  0.009438 -0.020062  0.003761  0.016320   \n",
       "4         7H9J:A  0.080503 -0.010999  0.064930  0.011473  0.044677  0.068076   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "1458      9FWC:A  0.016753  0.000013  0.117039  0.009715  0.012918 -0.006572   \n",
       "1459      9FX5:A -0.015200  0.094616  0.029028 -0.005583  0.007496 -0.010617   \n",
       "1460      9G35:A -0.009528  0.067932 -0.021398  0.032119 -0.025142 -0.010401   \n",
       "1461      9IID:A  0.021642  0.069980  0.014065 -0.065714 -0.132132  0.019948   \n",
       "1462      9IJP:B  0.026310  0.038509  0.033396 -0.007139  0.060688  0.005658   \n",
       "\n",
       "             6         7         8  ...      1014      1015      1016  \\\n",
       "0    -0.028229 -0.023020 -0.021959  ... -0.006125 -0.015080 -0.028736   \n",
       "1    -0.024067 -0.161998  0.039027  ... -0.047904  0.030155 -0.008186   \n",
       "2    -0.007784 -0.156496  0.027262  ... -0.057456  0.012036  0.008975   \n",
       "3    -0.025629 -0.113614  0.044924  ... -0.047083  0.033137 -0.024161   \n",
       "4    -0.060599 -0.020509 -0.018876  ... -0.041747  0.004042 -0.009992   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "1458 -0.031365 -0.106783 -0.014808  ... -0.051225 -0.010409  0.063774   \n",
       "1459  0.052864 -0.057743  0.017855  ...  0.008281 -0.000009 -0.013113   \n",
       "1460 -0.075979 -0.037079  0.011097  ... -0.100129  0.018395 -0.007278   \n",
       "1461 -0.071130 -0.083702  0.021212  ... -0.034138 -0.029562 -0.044261   \n",
       "1462  0.028074 -0.071596  0.001605  ... -0.054311 -0.032958 -0.020931   \n",
       "\n",
       "          1017      1018      1019      1020      1021      1022      1023  \n",
       "0    -0.015365  0.071466  0.034531 -0.021253 -0.044281 -0.019104  0.006675  \n",
       "1    -0.003443  0.015064 -0.079059 -0.009469 -0.074569  0.049260  0.021386  \n",
       "2    -0.017833  0.000690 -0.106954 -0.049073 -0.081838  0.044579  0.043472  \n",
       "3    -0.046833  0.028594 -0.063806 -0.023561 -0.093419  0.046764  0.015743  \n",
       "4    -0.106962  0.006102  0.031285 -0.022343 -0.027299 -0.041440 -0.051504  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "1458 -0.085060  0.029770 -0.005850  0.010760 -0.053217  0.001542 -0.072770  \n",
       "1459 -0.004962  0.028970 -0.014288  0.010970  0.069420  0.035354 -0.040685  \n",
       "1460 -0.024896  0.012935  0.000325 -0.019377  0.029905 -0.043913 -0.001953  \n",
       "1461 -0.111832  0.026946 -0.016103 -0.055561  0.095822  0.022081  0.047651  \n",
       "1462  0.022073 -0.034295 -0.077946 -0.051034 -0.034202  0.017765  0.044527  \n",
       "\n",
       "[1463 rows x 1025 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d0a13a42-0bf4-41bb-9113-64198c5404f2",
   "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>pdb_id</th>\n",
       "      <th>label</th>\n",
       "      <th>chain_id</th>\n",
       "      <th>AF_average_pLDDT</th>\n",
       "      <th>OF_average_pLDDT</th>\n",
       "      <th>EF_average_pLDDT</th>\n",
       "      <th>AF_TM_score</th>\n",
       "      <th>OF_TM_score</th>\n",
       "      <th>EF_TM_score</th>\n",
       "      <th>AF_vs_OF_TM_score</th>\n",
       "      <th>...</th>\n",
       "      <th>EF_vs_AF_TM_score</th>\n",
       "      <th>AF_RMSD</th>\n",
       "      <th>OF_RMSD</th>\n",
       "      <th>EF_RMSD</th>\n",
       "      <th>AF_vs_OF_RMSD</th>\n",
       "      <th>OF_vs_EF_RMSD</th>\n",
       "      <th>EF_vs_AF_RMSD</th>\n",
       "      <th>sequence</th>\n",
       "      <th>sequence_length</th>\n",
       "      <th>method_of_acquisition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8P0E</td>\n",
       "      <td>monomer</td>\n",
       "      <td>8P0E:A</td>\n",
       "      <td>93.108947</td>\n",
       "      <td>93.535368</td>\n",
       "      <td>89.263158</td>\n",
       "      <td>0.966687</td>\n",
       "      <td>0.967459</td>\n",
       "      <td>0.982825</td>\n",
       "      <td>0.995942</td>\n",
       "      <td>...</td>\n",
       "      <td>0.978701</td>\n",
       "      <td>1.757680</td>\n",
       "      <td>1.738276</td>\n",
       "      <td>0.703625</td>\n",
       "      <td>0.332187</td>\n",
       "      <td>1.373544</td>\n",
       "      <td>1.427521</td>\n",
       "      <td>MADSDIVESYARAAGPVHLRVRDIMDPPPGCKVVVNAANEGLLAGS...</td>\n",
       "      <td>190</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8PX8</td>\n",
       "      <td>monomer</td>\n",
       "      <td>8PX8:A</td>\n",
       "      <td>95.914087</td>\n",
       "      <td>95.609217</td>\n",
       "      <td>95.147826</td>\n",
       "      <td>0.995011</td>\n",
       "      <td>0.995740</td>\n",
       "      <td>0.989534</td>\n",
       "      <td>0.995857</td>\n",
       "      <td>...</td>\n",
       "      <td>0.987997</td>\n",
       "      <td>0.275844</td>\n",
       "      <td>0.253193</td>\n",
       "      <td>0.706370</td>\n",
       "      <td>0.252605</td>\n",
       "      <td>0.671283</td>\n",
       "      <td>0.694650</td>\n",
       "      <td>GSMGKLSEQLKHCNGILKELLSKKHAAYAWPFYKPVDASALGLHDY...</td>\n",
       "      <td>115</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8B2E</td>\n",
       "      <td>monomer</td>\n",
       "      <td>8B2E:A</td>\n",
       "      <td>97.636503</td>\n",
       "      <td>97.136084</td>\n",
       "      <td>94.762238</td>\n",
       "      <td>0.991946</td>\n",
       "      <td>0.979414</td>\n",
       "      <td>0.990990</td>\n",
       "      <td>0.982193</td>\n",
       "      <td>...</td>\n",
       "      <td>0.996032</td>\n",
       "      <td>0.400699</td>\n",
       "      <td>0.668645</td>\n",
       "      <td>0.425292</td>\n",
       "      <td>0.612311</td>\n",
       "      <td>0.497249</td>\n",
       "      <td>0.279309</td>\n",
       "      <td>LVLPGLDALQTRNALAIIAEAKKENVGPHGCQAAITTGLTESSLRI...</td>\n",
       "      <td>143</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8HOE</td>\n",
       "      <td>monomer</td>\n",
       "      <td>8HOE:A</td>\n",
       "      <td>93.577196</td>\n",
       "      <td>79.554392</td>\n",
       "      <td>89.613757</td>\n",
       "      <td>0.988360</td>\n",
       "      <td>0.863854</td>\n",
       "      <td>0.976008</td>\n",
       "      <td>0.873645</td>\n",
       "      <td>...</td>\n",
       "      <td>0.993624</td>\n",
       "      <td>0.564870</td>\n",
       "      <td>2.354328</td>\n",
       "      <td>0.834659</td>\n",
       "      <td>2.298557</td>\n",
       "      <td>2.257695</td>\n",
       "      <td>0.398065</td>\n",
       "      <td>MGSSHHHHHHAFHDVPSLGQKVGAGSQKDVFHSRQDPRQCICLFRP...</td>\n",
       "      <td>189</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8TCE</td>\n",
       "      <td>monomer</td>\n",
       "      <td>8TCE:A</td>\n",
       "      <td>90.252128</td>\n",
       "      <td>89.159681</td>\n",
       "      <td>86.180851</td>\n",
       "      <td>0.938542</td>\n",
       "      <td>0.920785</td>\n",
       "      <td>0.920808</td>\n",
       "      <td>0.948216</td>\n",
       "      <td>...</td>\n",
       "      <td>0.947691</td>\n",
       "      <td>1.805852</td>\n",
       "      <td>2.571247</td>\n",
       "      <td>2.617893</td>\n",
       "      <td>1.681326</td>\n",
       "      <td>0.509903</td>\n",
       "      <td>1.830767</td>\n",
       "      <td>APTENSTGVQDCYRGDGQSYRGTLSTTITGRTCQSWSSMTPHWHRR...</td>\n",
       "      <td>94</td>\n",
       "      <td>X-RAY DIFFRACTION</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>1458</th>\n",
       "      <td>8G9J</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>8G9J:A</td>\n",
       "      <td>90.586771</td>\n",
       "      <td>92.542063</td>\n",
       "      <td>89.071749</td>\n",
       "      <td>0.637726</td>\n",
       "      <td>0.984365</td>\n",
       "      <td>0.984592</td>\n",
       "      <td>0.625623</td>\n",
       "      <td>...</td>\n",
       "      <td>0.623380</td>\n",
       "      <td>6.550639</td>\n",
       "      <td>0.678841</td>\n",
       "      <td>0.674661</td>\n",
       "      <td>6.686531</td>\n",
       "      <td>0.287093</td>\n",
       "      <td>6.716066</td>\n",
       "      <td>HHHHHHSGSGENLYFQGSGSEEIVEEAETALKALLEEAEKGGKEDA...</td>\n",
       "      <td>223</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>8OYV</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>8OYV:A</td>\n",
       "      <td>86.604205</td>\n",
       "      <td>88.043795</td>\n",
       "      <td>82.605128</td>\n",
       "      <td>0.981296</td>\n",
       "      <td>0.979309</td>\n",
       "      <td>0.980517</td>\n",
       "      <td>0.992664</td>\n",
       "      <td>...</td>\n",
       "      <td>0.989139</td>\n",
       "      <td>0.725727</td>\n",
       "      <td>0.761958</td>\n",
       "      <td>0.734274</td>\n",
       "      <td>0.453600</td>\n",
       "      <td>0.643009</td>\n",
       "      <td>0.556589</td>\n",
       "      <td>MNEEKREELLEEAKRLLEESLKLLKQAYNTPIEIDLPISGGVKAIL...</td>\n",
       "      <td>195</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>8TNO</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>8TNO:A</td>\n",
       "      <td>90.392837</td>\n",
       "      <td>90.149504</td>\n",
       "      <td>87.758865</td>\n",
       "      <td>0.966437</td>\n",
       "      <td>0.966444</td>\n",
       "      <td>0.958189</td>\n",
       "      <td>0.986057</td>\n",
       "      <td>...</td>\n",
       "      <td>0.968848</td>\n",
       "      <td>1.621622</td>\n",
       "      <td>1.482032</td>\n",
       "      <td>1.525321</td>\n",
       "      <td>0.932028</td>\n",
       "      <td>1.400783</td>\n",
       "      <td>1.206631</td>\n",
       "      <td>MGSVEEVKRIMDLARQKISDAMDELNMDATLKQSVDESMKRAEQRA...</td>\n",
       "      <td>282</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>8FJE</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>8FJE:A</td>\n",
       "      <td>93.426345</td>\n",
       "      <td>93.104138</td>\n",
       "      <td>88.586207</td>\n",
       "      <td>0.970529</td>\n",
       "      <td>0.965841</td>\n",
       "      <td>0.972681</td>\n",
       "      <td>0.995234</td>\n",
       "      <td>...</td>\n",
       "      <td>0.993875</td>\n",
       "      <td>0.845441</td>\n",
       "      <td>0.909988</td>\n",
       "      <td>0.804459</td>\n",
       "      <td>0.302041</td>\n",
       "      <td>0.403145</td>\n",
       "      <td>0.341156</td>\n",
       "      <td>SGSPTPLETLPLEELERRALKIYLRRHGSVPEEEIETMPLEELERK...</td>\n",
       "      <td>145</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>8F4X</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>8F4X:1</td>\n",
       "      <td>96.517612</td>\n",
       "      <td>94.300149</td>\n",
       "      <td>92.283582</td>\n",
       "      <td>0.971265</td>\n",
       "      <td>0.979005</td>\n",
       "      <td>0.981622</td>\n",
       "      <td>0.969641</td>\n",
       "      <td>...</td>\n",
       "      <td>0.981169</td>\n",
       "      <td>0.489375</td>\n",
       "      <td>0.418138</td>\n",
       "      <td>0.390787</td>\n",
       "      <td>0.508402</td>\n",
       "      <td>0.235132</td>\n",
       "      <td>0.397330</td>\n",
       "      <td>MEEERRRHLAAAEARFLLELGRPDEVLRLLERLLEEGDPALFAALR...</td>\n",
       "      <td>67</td>\n",
       "      <td>ELECTRON MICROSCOPY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1463 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pdb_id      label chain_id  AF_average_pLDDT  OF_average_pLDDT  \\\n",
       "0      8P0E    monomer   8P0E:A         93.108947         93.535368   \n",
       "1      8PX8    monomer   8PX8:A         95.914087         95.609217   \n",
       "2      8B2E    monomer   8B2E:A         97.636503         97.136084   \n",
       "3      8HOE    monomer   8HOE:A         93.577196         79.554392   \n",
       "4      8TCE    monomer   8TCE:A         90.252128         89.159681   \n",
       "...     ...        ...      ...               ...               ...   \n",
       "1458   8G9J  synthetic   8G9J:A         90.586771         92.542063   \n",
       "1459   8OYV  synthetic   8OYV:A         86.604205         88.043795   \n",
       "1460   8TNO  synthetic   8TNO:A         90.392837         90.149504   \n",
       "1461   8FJE  synthetic   8FJE:A         93.426345         93.104138   \n",
       "1462   8F4X  synthetic   8F4X:1         96.517612         94.300149   \n",
       "\n",
       "      EF_average_pLDDT  AF_TM_score  OF_TM_score  EF_TM_score  \\\n",
       "0            89.263158     0.966687     0.967459     0.982825   \n",
       "1            95.147826     0.995011     0.995740     0.989534   \n",
       "2            94.762238     0.991946     0.979414     0.990990   \n",
       "3            89.613757     0.988360     0.863854     0.976008   \n",
       "4            86.180851     0.938542     0.920785     0.920808   \n",
       "...                ...          ...          ...          ...   \n",
       "1458         89.071749     0.637726     0.984365     0.984592   \n",
       "1459         82.605128     0.981296     0.979309     0.980517   \n",
       "1460         87.758865     0.966437     0.966444     0.958189   \n",
       "1461         88.586207     0.970529     0.965841     0.972681   \n",
       "1462         92.283582     0.971265     0.979005     0.981622   \n",
       "\n",
       "      AF_vs_OF_TM_score  ...  EF_vs_AF_TM_score   AF_RMSD   OF_RMSD   EF_RMSD  \\\n",
       "0              0.995942  ...           0.978701  1.757680  1.738276  0.703625   \n",
       "1              0.995857  ...           0.987997  0.275844  0.253193  0.706370   \n",
       "2              0.982193  ...           0.996032  0.400699  0.668645  0.425292   \n",
       "3              0.873645  ...           0.993624  0.564870  2.354328  0.834659   \n",
       "4              0.948216  ...           0.947691  1.805852  2.571247  2.617893   \n",
       "...                 ...  ...                ...       ...       ...       ...   \n",
       "1458           0.625623  ...           0.623380  6.550639  0.678841  0.674661   \n",
       "1459           0.992664  ...           0.989139  0.725727  0.761958  0.734274   \n",
       "1460           0.986057  ...           0.968848  1.621622  1.482032  1.525321   \n",
       "1461           0.995234  ...           0.993875  0.845441  0.909988  0.804459   \n",
       "1462           0.969641  ...           0.981169  0.489375  0.418138  0.390787   \n",
       "\n",
       "      AF_vs_OF_RMSD  OF_vs_EF_RMSD  EF_vs_AF_RMSD  \\\n",
       "0          0.332187       1.373544       1.427521   \n",
       "1          0.252605       0.671283       0.694650   \n",
       "2          0.612311       0.497249       0.279309   \n",
       "3          2.298557       2.257695       0.398065   \n",
       "4          1.681326       0.509903       1.830767   \n",
       "...             ...            ...            ...   \n",
       "1458       6.686531       0.287093       6.716066   \n",
       "1459       0.453600       0.643009       0.556589   \n",
       "1460       0.932028       1.400783       1.206631   \n",
       "1461       0.302041       0.403145       0.341156   \n",
       "1462       0.508402       0.235132       0.397330   \n",
       "\n",
       "                                               sequence sequence_length  \\\n",
       "0     MADSDIVESYARAAGPVHLRVRDIMDPPPGCKVVVNAANEGLLAGS...             190   \n",
       "1     GSMGKLSEQLKHCNGILKELLSKKHAAYAWPFYKPVDASALGLHDY...             115   \n",
       "2     LVLPGLDALQTRNALAIIAEAKKENVGPHGCQAAITTGLTESSLRI...             143   \n",
       "3     MGSSHHHHHHAFHDVPSLGQKVGAGSQKDVFHSRQDPRQCICLFRP...             189   \n",
       "4     APTENSTGVQDCYRGDGQSYRGTLSTTITGRTCQSWSSMTPHWHRR...              94   \n",
       "...                                                 ...             ...   \n",
       "1458  HHHHHHSGSGENLYFQGSGSEEIVEEAETALKALLEEAEKGGKEDA...             223   \n",
       "1459  MNEEKREELLEEAKRLLEESLKLLKQAYNTPIEIDLPISGGVKAIL...             195   \n",
       "1460  MGSVEEVKRIMDLARQKISDAMDELNMDATLKQSVDESMKRAEQRA...             282   \n",
       "1461  SGSPTPLETLPLEELERRALKIYLRRHGSVPEEEIETMPLEELERK...             145   \n",
       "1462  MEEERRRHLAAAEARFLLELGRPDEVLRLLERLLEEGDPALFAALR...              67   \n",
       "\n",
       "      method_of_acquisition  \n",
       "0         X-RAY DIFFRACTION  \n",
       "1         X-RAY DIFFRACTION  \n",
       "2         X-RAY DIFFRACTION  \n",
       "3         X-RAY DIFFRACTION  \n",
       "4         X-RAY DIFFRACTION  \n",
       "...                     ...  \n",
       "1458      X-RAY DIFFRACTION  \n",
       "1459      X-RAY DIFFRACTION  \n",
       "1460      X-RAY DIFFRACTION  \n",
       "1461      X-RAY DIFFRACTION  \n",
       "1462    ELECTRON MICROSCOPY  \n",
       "\n",
       "[1463 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8303d16d-fdd6-430d-9396-93fa62e9614c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "method_of_acquisition\n",
       "X-RAY DIFFRACTION           1184\n",
       "ELECTRON MICROSCOPY          144\n",
       "SOLUTION NMR                 127\n",
       "SOLID-STATE NMR                4\n",
       "NEUTRON DIFFRACTION            2\n",
       "ELECTRON CRYSTALLOGRAPHY       2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_df.method_of_acquisition.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01f84bf2-b305-4bbc-997f-cb269b3ad37f",
   "metadata": {},
   "source": [
    "## Merge data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2665f69d-44ce-4984-83bd-6e6f71d937eb",
   "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>pdb_id</th>\n",
       "      <th>label</th>\n",
       "      <th>chain_id</th>\n",
       "      <th>AF_average_pLDDT</th>\n",
       "      <th>OF_average_pLDDT</th>\n",
       "      <th>EF_average_pLDDT</th>\n",
       "      <th>AF_TM_score</th>\n",
       "      <th>OF_TM_score</th>\n",
       "      <th>EF_TM_score</th>\n",
       "      <th>AF_vs_OF_TM_score</th>\n",
       "      <th>...</th>\n",
       "      <th>1014</th>\n",
       "      <th>1015</th>\n",
       "      <th>1016</th>\n",
       "      <th>1017</th>\n",
       "      <th>1018</th>\n",
       "      <th>1019</th>\n",
       "      <th>1020</th>\n",
       "      <th>1021</th>\n",
       "      <th>1022</th>\n",
       "      <th>1023</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8P0E</td>\n",
       "      <td>monomer</td>\n",
       "      <td>8P0E:A</td>\n",
       "      <td>93.108947</td>\n",
       "      <td>93.535368</td>\n",
       "      <td>89.263158</td>\n",
       "      <td>0.966687</td>\n",
       "      <td>0.967459</td>\n",
       "      <td>0.982825</td>\n",
       "      <td>0.995942</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018924</td>\n",
       "      <td>-0.055101</td>\n",
       "      <td>-0.010038</td>\n",
       "      <td>-0.079929</td>\n",
       "      <td>-0.040604</td>\n",
       "      <td>0.023142</td>\n",
       "      <td>-0.028256</td>\n",
       "      <td>0.006632</td>\n",
       "      <td>0.010835</td>\n",
       "      <td>-0.043973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8PX8</td>\n",
       "      <td>monomer</td>\n",
       "      <td>8PX8:A</td>\n",
       "      <td>95.914087</td>\n",
       "      <td>95.609217</td>\n",
       "      <td>95.147826</td>\n",
       "      <td>0.995011</td>\n",
       "      <td>0.995740</td>\n",
       "      <td>0.989534</td>\n",
       "      <td>0.995857</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003862</td>\n",
       "      <td>-0.036268</td>\n",
       "      <td>-0.056777</td>\n",
       "      <td>-0.021434</td>\n",
       "      <td>0.075105</td>\n",
       "      <td>0.007325</td>\n",
       "      <td>0.011608</td>\n",
       "      <td>-0.015764</td>\n",
       "      <td>-0.016603</td>\n",
       "      <td>0.018275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8B2E</td>\n",
       "      <td>monomer</td>\n",
       "      <td>8B2E:A</td>\n",
       "      <td>97.636503</td>\n",
       "      <td>97.136084</td>\n",
       "      <td>94.762238</td>\n",
       "      <td>0.991946</td>\n",
       "      <td>0.979414</td>\n",
       "      <td>0.990990</td>\n",
       "      <td>0.982193</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.120371</td>\n",
       "      <td>-0.012060</td>\n",
       "      <td>-0.037939</td>\n",
       "      <td>-0.037256</td>\n",
       "      <td>-0.024697</td>\n",
       "      <td>0.034764</td>\n",
       "      <td>-0.069274</td>\n",
       "      <td>-0.034956</td>\n",
       "      <td>-0.067968</td>\n",
       "      <td>-0.019934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8HOE</td>\n",
       "      <td>monomer</td>\n",
       "      <td>8HOE:A</td>\n",
       "      <td>93.577196</td>\n",
       "      <td>79.554392</td>\n",
       "      <td>89.613757</td>\n",
       "      <td>0.988360</td>\n",
       "      <td>0.863854</td>\n",
       "      <td>0.976008</td>\n",
       "      <td>0.873645</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004794</td>\n",
       "      <td>-0.005468</td>\n",
       "      <td>-0.011793</td>\n",
       "      <td>-0.045643</td>\n",
       "      <td>-0.003415</td>\n",
       "      <td>0.048465</td>\n",
       "      <td>-0.037667</td>\n",
       "      <td>-0.041685</td>\n",
       "      <td>0.048311</td>\n",
       "      <td>0.030363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8TCE</td>\n",
       "      <td>monomer</td>\n",
       "      <td>8TCE:A</td>\n",
       "      <td>90.252128</td>\n",
       "      <td>89.159681</td>\n",
       "      <td>86.180851</td>\n",
       "      <td>0.938542</td>\n",
       "      <td>0.920785</td>\n",
       "      <td>0.920808</td>\n",
       "      <td>0.948216</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045217</td>\n",
       "      <td>-0.015651</td>\n",
       "      <td>-0.013003</td>\n",
       "      <td>-0.089254</td>\n",
       "      <td>0.042249</td>\n",
       "      <td>0.031570</td>\n",
       "      <td>0.010612</td>\n",
       "      <td>-0.017051</td>\n",
       "      <td>0.041658</td>\n",
       "      <td>-0.012780</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>1458</th>\n",
       "      <td>8G9J</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>8G9J:A</td>\n",
       "      <td>90.586771</td>\n",
       "      <td>92.542063</td>\n",
       "      <td>89.071749</td>\n",
       "      <td>0.637726</td>\n",
       "      <td>0.984365</td>\n",
       "      <td>0.984592</td>\n",
       "      <td>0.625623</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.098112</td>\n",
       "      <td>0.114060</td>\n",
       "      <td>0.092363</td>\n",
       "      <td>-0.111112</td>\n",
       "      <td>-0.112436</td>\n",
       "      <td>0.047038</td>\n",
       "      <td>-0.000830</td>\n",
       "      <td>-0.036431</td>\n",
       "      <td>0.046928</td>\n",
       "      <td>0.026370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>8OYV</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>8OYV:A</td>\n",
       "      <td>86.604205</td>\n",
       "      <td>88.043795</td>\n",
       "      <td>82.605128</td>\n",
       "      <td>0.981296</td>\n",
       "      <td>0.979309</td>\n",
       "      <td>0.980517</td>\n",
       "      <td>0.992664</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045095</td>\n",
       "      <td>0.218035</td>\n",
       "      <td>0.082360</td>\n",
       "      <td>-0.071727</td>\n",
       "      <td>0.033196</td>\n",
       "      <td>-0.031118</td>\n",
       "      <td>-0.008822</td>\n",
       "      <td>0.015488</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>-0.032048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>8TNO</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>8TNO:A</td>\n",
       "      <td>90.392837</td>\n",
       "      <td>90.149504</td>\n",
       "      <td>87.758865</td>\n",
       "      <td>0.966437</td>\n",
       "      <td>0.966444</td>\n",
       "      <td>0.958189</td>\n",
       "      <td>0.986057</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001655</td>\n",
       "      <td>-0.053243</td>\n",
       "      <td>0.000991</td>\n",
       "      <td>-0.050635</td>\n",
       "      <td>-0.062184</td>\n",
       "      <td>0.015350</td>\n",
       "      <td>-0.003545</td>\n",
       "      <td>0.025028</td>\n",
       "      <td>-0.022152</td>\n",
       "      <td>-0.063211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>8FJE</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>8FJE:A</td>\n",
       "      <td>93.426345</td>\n",
       "      <td>93.104138</td>\n",
       "      <td>88.586207</td>\n",
       "      <td>0.970529</td>\n",
       "      <td>0.965841</td>\n",
       "      <td>0.972681</td>\n",
       "      <td>0.995234</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045988</td>\n",
       "      <td>0.127117</td>\n",
       "      <td>0.057083</td>\n",
       "      <td>-0.137953</td>\n",
       "      <td>0.075811</td>\n",
       "      <td>0.125753</td>\n",
       "      <td>-0.053419</td>\n",
       "      <td>-0.075770</td>\n",
       "      <td>-0.004380</td>\n",
       "      <td>0.033179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>8F4X</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>8F4X:1</td>\n",
       "      <td>96.517612</td>\n",
       "      <td>94.300149</td>\n",
       "      <td>92.283582</td>\n",
       "      <td>0.971265</td>\n",
       "      <td>0.979005</td>\n",
       "      <td>0.981622</td>\n",
       "      <td>0.969641</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.083261</td>\n",
       "      <td>0.023626</td>\n",
       "      <td>0.038690</td>\n",
       "      <td>-0.038298</td>\n",
       "      <td>-0.112825</td>\n",
       "      <td>0.029836</td>\n",
       "      <td>0.063231</td>\n",
       "      <td>0.114722</td>\n",
       "      <td>0.024375</td>\n",
       "      <td>-0.070563</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1463 rows × 1048 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pdb_id      label chain_id  AF_average_pLDDT  OF_average_pLDDT  \\\n",
       "0      8P0E    monomer   8P0E:A         93.108947         93.535368   \n",
       "1      8PX8    monomer   8PX8:A         95.914087         95.609217   \n",
       "2      8B2E    monomer   8B2E:A         97.636503         97.136084   \n",
       "3      8HOE    monomer   8HOE:A         93.577196         79.554392   \n",
       "4      8TCE    monomer   8TCE:A         90.252128         89.159681   \n",
       "...     ...        ...      ...               ...               ...   \n",
       "1458   8G9J  synthetic   8G9J:A         90.586771         92.542063   \n",
       "1459   8OYV  synthetic   8OYV:A         86.604205         88.043795   \n",
       "1460   8TNO  synthetic   8TNO:A         90.392837         90.149504   \n",
       "1461   8FJE  synthetic   8FJE:A         93.426345         93.104138   \n",
       "1462   8F4X  synthetic   8F4X:1         96.517612         94.300149   \n",
       "\n",
       "      EF_average_pLDDT  AF_TM_score  OF_TM_score  EF_TM_score  \\\n",
       "0            89.263158     0.966687     0.967459     0.982825   \n",
       "1            95.147826     0.995011     0.995740     0.989534   \n",
       "2            94.762238     0.991946     0.979414     0.990990   \n",
       "3            89.613757     0.988360     0.863854     0.976008   \n",
       "4            86.180851     0.938542     0.920785     0.920808   \n",
       "...                ...          ...          ...          ...   \n",
       "1458         89.071749     0.637726     0.984365     0.984592   \n",
       "1459         82.605128     0.981296     0.979309     0.980517   \n",
       "1460         87.758865     0.966437     0.966444     0.958189   \n",
       "1461         88.586207     0.970529     0.965841     0.972681   \n",
       "1462         92.283582     0.971265     0.979005     0.981622   \n",
       "\n",
       "      AF_vs_OF_TM_score  ...      1014      1015      1016      1017  \\\n",
       "0              0.995942  ... -0.018924 -0.055101 -0.010038 -0.079929   \n",
       "1              0.995857  ... -0.003862 -0.036268 -0.056777 -0.021434   \n",
       "2              0.982193  ... -0.120371 -0.012060 -0.037939 -0.037256   \n",
       "3              0.873645  ...  0.004794 -0.005468 -0.011793 -0.045643   \n",
       "4              0.948216  ... -0.045217 -0.015651 -0.013003 -0.089254   \n",
       "...                 ...  ...       ...       ...       ...       ...   \n",
       "1458           0.625623  ... -0.098112  0.114060  0.092363 -0.111112   \n",
       "1459           0.992664  ... -0.045095  0.218035  0.082360 -0.071727   \n",
       "1460           0.986057  ...  0.001655 -0.053243  0.000991 -0.050635   \n",
       "1461           0.995234  ... -0.045988  0.127117  0.057083 -0.137953   \n",
       "1462           0.969641  ... -0.083261  0.023626  0.038690 -0.038298   \n",
       "\n",
       "          1018      1019      1020      1021      1022      1023  \n",
       "0    -0.040604  0.023142 -0.028256  0.006632  0.010835 -0.043973  \n",
       "1     0.075105  0.007325  0.011608 -0.015764 -0.016603  0.018275  \n",
       "2    -0.024697  0.034764 -0.069274 -0.034956 -0.067968 -0.019934  \n",
       "3    -0.003415  0.048465 -0.037667 -0.041685  0.048311  0.030363  \n",
       "4     0.042249  0.031570  0.010612 -0.017051  0.041658 -0.012780  \n",
       "...        ...       ...       ...       ...       ...       ...  \n",
       "1458 -0.112436  0.047038 -0.000830 -0.036431  0.046928  0.026370  \n",
       "1459  0.033196 -0.031118 -0.008822  0.015488  0.019231 -0.032048  \n",
       "1460 -0.062184  0.015350 -0.003545  0.025028 -0.022152 -0.063211  \n",
       "1461  0.075811  0.125753 -0.053419 -0.075770 -0.004380  0.033179  \n",
       "1462 -0.112825  0.029836  0.063231  0.114722  0.024375 -0.070563  \n",
       "\n",
       "[1463 rows x 1048 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_embeddings = embeddings_df.drop('sequence_id', axis=1)\n",
    "\n",
    "#metadata_df = metadata_df.copy()\n",
    "metadata_df['chain_id'] = metadata_df['chain_id'].astype(str)\n",
    "embeddings_df['chain_id'] = embeddings_df['sequence_id'].astype(str)\n",
    "\n",
    "metadata_df[\"xray\"] = metadata_df[\"method_of_acquisition\"] == \"X-RAY DIFFRACTION\"\n",
    "metadata_df[\"EF_minus_AF_TM_score\"] = metadata_df[\"EF_TM_score\"] - metadata_df[\"AF_TM_score\"]\n",
    "\n",
    "merged_data = pd.merge(\n",
    "            metadata_df,\n",
    "            embeddings_df, \n",
    "            left_on='chain_id',\n",
    "            right_on='chain_id',\n",
    "            how='inner'\n",
    "        )\n",
    "\n",
    "merged_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c26d67f7-cc71-46aa-a7d8-ba710f8d91dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pdb_id', 'label', 'chain_id', 'AF_average_pLDDT', 'OF_average_pLDDT',\n",
       "       'EF_average_pLDDT', 'AF_TM_score', 'OF_TM_score', 'EF_TM_score',\n",
       "       'AF_vs_OF_TM_score', 'OF_vs_EF_TM_score', 'EF_vs_AF_TM_score',\n",
       "       'AF_RMSD', 'OF_RMSD', 'EF_RMSD', 'AF_vs_OF_RMSD', 'OF_vs_EF_RMSD',\n",
       "       'EF_vs_AF_RMSD', 'sequence', 'sequence_length', 'method_of_acquisition',\n",
       "       'xray', 'EF_minus_AF_TM_score', 'sequence_id', '0', '1', '2', '3', '4',\n",
       "       '5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data.columns[:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b201c2a6-8b32-4be9-bde8-6ed940e5763b",
   "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>1020</th>\n",
       "      <th>1021</th>\n",
       "      <th>1022</th>\n",
       "      <th>1023</th>\n",
       "      <th>label_complex</th>\n",
       "      <th>label_monomer</th>\n",
       "      <th>label_synthetic</th>\n",
       "      <th>sequence_length</th>\n",
       "      <th>xray</th>\n",
       "      <th>EF_average_pLDDT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.074701</td>\n",
       "      <td>-0.031634</td>\n",
       "      <td>0.010414</td>\n",
       "      <td>-0.010877</td>\n",
       "      <td>-0.031846</td>\n",
       "      <td>0.053747</td>\n",
       "      <td>-0.032714</td>\n",
       "      <td>-0.046688</td>\n",
       "      <td>-0.061814</td>\n",
       "      <td>-0.001304</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.028256</td>\n",
       "      <td>0.006632</td>\n",
       "      <td>0.010835</td>\n",
       "      <td>-0.043973</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>True</td>\n",
       "      <td>89.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.074191</td>\n",
       "      <td>0.045541</td>\n",
       "      <td>-0.006785</td>\n",
       "      <td>-0.029171</td>\n",
       "      <td>-0.012676</td>\n",
       "      <td>0.016806</td>\n",
       "      <td>-0.043396</td>\n",
       "      <td>-0.060700</td>\n",
       "      <td>-0.024839</td>\n",
       "      <td>0.008174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011608</td>\n",
       "      <td>-0.015764</td>\n",
       "      <td>-0.016603</td>\n",
       "      <td>0.018275</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>True</td>\n",
       "      <td>95.147826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.060690</td>\n",
       "      <td>-0.028485</td>\n",
       "      <td>0.020705</td>\n",
       "      <td>-0.057476</td>\n",
       "      <td>-0.053076</td>\n",
       "      <td>-0.009133</td>\n",
       "      <td>-0.034624</td>\n",
       "      <td>-0.027073</td>\n",
       "      <td>-0.031988</td>\n",
       "      <td>-0.059633</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069274</td>\n",
       "      <td>-0.034956</td>\n",
       "      <td>-0.067968</td>\n",
       "      <td>-0.019934</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>143</td>\n",
       "      <td>True</td>\n",
       "      <td>94.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.028040</td>\n",
       "      <td>-0.033594</td>\n",
       "      <td>-0.044930</td>\n",
       "      <td>0.010464</td>\n",
       "      <td>-0.069993</td>\n",
       "      <td>-0.040113</td>\n",
       "      <td>-0.053979</td>\n",
       "      <td>-0.072420</td>\n",
       "      <td>0.007218</td>\n",
       "      <td>-0.006695</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.037667</td>\n",
       "      <td>-0.041685</td>\n",
       "      <td>0.048311</td>\n",
       "      <td>0.030363</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>189</td>\n",
       "      <td>True</td>\n",
       "      <td>89.613757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.034272</td>\n",
       "      <td>0.141000</td>\n",
       "      <td>0.034812</td>\n",
       "      <td>-0.102808</td>\n",
       "      <td>-0.045333</td>\n",
       "      <td>0.007360</td>\n",
       "      <td>0.026951</td>\n",
       "      <td>-0.143174</td>\n",
       "      <td>-0.016837</td>\n",
       "      <td>-0.074851</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010612</td>\n",
       "      <td>-0.017051</td>\n",
       "      <td>0.041658</td>\n",
       "      <td>-0.012780</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>True</td>\n",
       "      <td>86.180851</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>1458</th>\n",
       "      <td>0.121245</td>\n",
       "      <td>0.100528</td>\n",
       "      <td>0.090678</td>\n",
       "      <td>-0.184978</td>\n",
       "      <td>0.016666</td>\n",
       "      <td>-0.183547</td>\n",
       "      <td>-0.051702</td>\n",
       "      <td>-0.189545</td>\n",
       "      <td>-0.041149</td>\n",
       "      <td>0.116309</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000830</td>\n",
       "      <td>-0.036431</td>\n",
       "      <td>0.046928</td>\n",
       "      <td>0.026370</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>223</td>\n",
       "      <td>True</td>\n",
       "      <td>89.071749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>0.035015</td>\n",
       "      <td>-0.040276</td>\n",
       "      <td>0.084468</td>\n",
       "      <td>-0.068787</td>\n",
       "      <td>0.002935</td>\n",
       "      <td>-0.129411</td>\n",
       "      <td>-0.005936</td>\n",
       "      <td>-0.083939</td>\n",
       "      <td>0.165884</td>\n",
       "      <td>0.089207</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008822</td>\n",
       "      <td>0.015488</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>-0.032048</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>195</td>\n",
       "      <td>True</td>\n",
       "      <td>82.605128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>0.044024</td>\n",
       "      <td>0.000223</td>\n",
       "      <td>0.085759</td>\n",
       "      <td>-0.043875</td>\n",
       "      <td>0.020273</td>\n",
       "      <td>-0.011902</td>\n",
       "      <td>-0.077834</td>\n",
       "      <td>-0.057456</td>\n",
       "      <td>0.018621</td>\n",
       "      <td>0.023004</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003545</td>\n",
       "      <td>0.025028</td>\n",
       "      <td>-0.022152</td>\n",
       "      <td>-0.063211</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>282</td>\n",
       "      <td>True</td>\n",
       "      <td>87.758865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>0.060813</td>\n",
       "      <td>0.127557</td>\n",
       "      <td>0.079519</td>\n",
       "      <td>-0.160550</td>\n",
       "      <td>0.047703</td>\n",
       "      <td>-0.103272</td>\n",
       "      <td>0.024130</td>\n",
       "      <td>-0.188346</td>\n",
       "      <td>-0.054896</td>\n",
       "      <td>0.114020</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053419</td>\n",
       "      <td>-0.075770</td>\n",
       "      <td>-0.004380</td>\n",
       "      <td>0.033179</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>True</td>\n",
       "      <td>88.586207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>0.068246</td>\n",
       "      <td>-0.053673</td>\n",
       "      <td>0.100254</td>\n",
       "      <td>0.054667</td>\n",
       "      <td>0.069273</td>\n",
       "      <td>-0.039153</td>\n",
       "      <td>0.027054</td>\n",
       "      <td>-0.099557</td>\n",
       "      <td>0.037540</td>\n",
       "      <td>0.080714</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063231</td>\n",
       "      <td>0.114722</td>\n",
       "      <td>0.024375</td>\n",
       "      <td>-0.070563</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>False</td>\n",
       "      <td>92.283582</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1463 rows × 1030 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1         2         3         4         5         6  \\\n",
       "0     0.074701 -0.031634  0.010414 -0.010877 -0.031846  0.053747 -0.032714   \n",
       "1     0.074191  0.045541 -0.006785 -0.029171 -0.012676  0.016806 -0.043396   \n",
       "2     0.060690 -0.028485  0.020705 -0.057476 -0.053076 -0.009133 -0.034624   \n",
       "3    -0.028040 -0.033594 -0.044930  0.010464 -0.069993 -0.040113 -0.053979   \n",
       "4     0.034272  0.141000  0.034812 -0.102808 -0.045333  0.007360  0.026951   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "1458  0.121245  0.100528  0.090678 -0.184978  0.016666 -0.183547 -0.051702   \n",
       "1459  0.035015 -0.040276  0.084468 -0.068787  0.002935 -0.129411 -0.005936   \n",
       "1460  0.044024  0.000223  0.085759 -0.043875  0.020273 -0.011902 -0.077834   \n",
       "1461  0.060813  0.127557  0.079519 -0.160550  0.047703 -0.103272  0.024130   \n",
       "1462  0.068246 -0.053673  0.100254  0.054667  0.069273 -0.039153  0.027054   \n",
       "\n",
       "             7         8         9  ...      1020      1021      1022  \\\n",
       "0    -0.046688 -0.061814 -0.001304  ... -0.028256  0.006632  0.010835   \n",
       "1    -0.060700 -0.024839  0.008174  ...  0.011608 -0.015764 -0.016603   \n",
       "2    -0.027073 -0.031988 -0.059633  ... -0.069274 -0.034956 -0.067968   \n",
       "3    -0.072420  0.007218 -0.006695  ... -0.037667 -0.041685  0.048311   \n",
       "4    -0.143174 -0.016837 -0.074851  ...  0.010612 -0.017051  0.041658   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "1458 -0.189545 -0.041149  0.116309  ... -0.000830 -0.036431  0.046928   \n",
       "1459 -0.083939  0.165884  0.089207  ... -0.008822  0.015488  0.019231   \n",
       "1460 -0.057456  0.018621  0.023004  ... -0.003545  0.025028 -0.022152   \n",
       "1461 -0.188346 -0.054896  0.114020  ... -0.053419 -0.075770 -0.004380   \n",
       "1462 -0.099557  0.037540  0.080714  ...  0.063231  0.114722  0.024375   \n",
       "\n",
       "          1023  label_complex  label_monomer  label_synthetic  \\\n",
       "0    -0.043973              0              1                0   \n",
       "1     0.018275              0              1                0   \n",
       "2    -0.019934              0              1                0   \n",
       "3     0.030363              0              1                0   \n",
       "4    -0.012780              0              1                0   \n",
       "...        ...            ...            ...              ...   \n",
       "1458  0.026370              0              0                1   \n",
       "1459 -0.032048              0              0                1   \n",
       "1460 -0.063211              0              0                1   \n",
       "1461  0.033179              0              0                1   \n",
       "1462 -0.070563              0              0                1   \n",
       "\n",
       "      sequence_length   xray  EF_average_pLDDT  \n",
       "0                 190   True         89.263158  \n",
       "1                 115   True         95.147826  \n",
       "2                 143   True         94.762238  \n",
       "3                 189   True         89.613757  \n",
       "4                  94   True         86.180851  \n",
       "...               ...    ...               ...  \n",
       "1458              223   True         89.071749  \n",
       "1459              195   True         82.605128  \n",
       "1460              282   True         87.758865  \n",
       "1461              145   True         88.586207  \n",
       "1462               67  False         92.283582  \n",
       "\n",
       "[1463 rows x 1030 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "additional_features = [\"sequence_length\", \"xray\", \"EF_average_pLDDT\"] # \"AF_average_pLDDT\"\n",
    "target_variable = \"EF_minus_AF_TM_score\"\n",
    "\n",
    "category1 = pd.get_dummies(merged_data['label'], prefix='label', dtype=int)\n",
    "\n",
    "merged_data = pd.concat([merged_data, category1], axis=1)\n",
    "\n",
    "X = merged_data[X_embeddings.columns.tolist() + category1.columns.tolist() + additional_features]\n",
    "y = merged_data[target_variable]\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8d72bab-ccbc-4ad8-9a4a-de51cddb3aa3",
   "metadata": {},
   "source": [
    "## LightGBM training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eb00b622-6d75-4218-bb51-441975a61b9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_state = 42\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y, test_size=0.3, random_state=random_state\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf59f37c-1e27-409e-bb62-2900779c9a7c",
   "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>1020</th>\n",
       "      <th>1021</th>\n",
       "      <th>1022</th>\n",
       "      <th>1023</th>\n",
       "      <th>label_complex</th>\n",
       "      <th>label_monomer</th>\n",
       "      <th>label_synthetic</th>\n",
       "      <th>sequence_length</th>\n",
       "      <th>xray</th>\n",
       "      <th>EF_average_pLDDT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.074701</td>\n",
       "      <td>-0.031634</td>\n",
       "      <td>0.010414</td>\n",
       "      <td>-0.010877</td>\n",
       "      <td>-0.031846</td>\n",
       "      <td>0.053747</td>\n",
       "      <td>-0.032714</td>\n",
       "      <td>-0.046688</td>\n",
       "      <td>-0.061814</td>\n",
       "      <td>-0.001304</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.028256</td>\n",
       "      <td>0.006632</td>\n",
       "      <td>0.010835</td>\n",
       "      <td>-0.043973</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>True</td>\n",
       "      <td>89.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.074191</td>\n",
       "      <td>0.045541</td>\n",
       "      <td>-0.006785</td>\n",
       "      <td>-0.029171</td>\n",
       "      <td>-0.012676</td>\n",
       "      <td>0.016806</td>\n",
       "      <td>-0.043396</td>\n",
       "      <td>-0.060700</td>\n",
       "      <td>-0.024839</td>\n",
       "      <td>0.008174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011608</td>\n",
       "      <td>-0.015764</td>\n",
       "      <td>-0.016603</td>\n",
       "      <td>0.018275</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>True</td>\n",
       "      <td>95.147826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.060690</td>\n",
       "      <td>-0.028485</td>\n",
       "      <td>0.020705</td>\n",
       "      <td>-0.057476</td>\n",
       "      <td>-0.053076</td>\n",
       "      <td>-0.009133</td>\n",
       "      <td>-0.034624</td>\n",
       "      <td>-0.027073</td>\n",
       "      <td>-0.031988</td>\n",
       "      <td>-0.059633</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069274</td>\n",
       "      <td>-0.034956</td>\n",
       "      <td>-0.067968</td>\n",
       "      <td>-0.019934</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>143</td>\n",
       "      <td>True</td>\n",
       "      <td>94.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.028040</td>\n",
       "      <td>-0.033594</td>\n",
       "      <td>-0.044930</td>\n",
       "      <td>0.010464</td>\n",
       "      <td>-0.069993</td>\n",
       "      <td>-0.040113</td>\n",
       "      <td>-0.053979</td>\n",
       "      <td>-0.072420</td>\n",
       "      <td>0.007218</td>\n",
       "      <td>-0.006695</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.037667</td>\n",
       "      <td>-0.041685</td>\n",
       "      <td>0.048311</td>\n",
       "      <td>0.030363</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>189</td>\n",
       "      <td>True</td>\n",
       "      <td>89.613757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.034272</td>\n",
       "      <td>0.141000</td>\n",
       "      <td>0.034812</td>\n",
       "      <td>-0.102808</td>\n",
       "      <td>-0.045333</td>\n",
       "      <td>0.007360</td>\n",
       "      <td>0.026951</td>\n",
       "      <td>-0.143174</td>\n",
       "      <td>-0.016837</td>\n",
       "      <td>-0.074851</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010612</td>\n",
       "      <td>-0.017051</td>\n",
       "      <td>0.041658</td>\n",
       "      <td>-0.012780</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>True</td>\n",
       "      <td>86.180851</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>1458</th>\n",
       "      <td>0.121245</td>\n",
       "      <td>0.100528</td>\n",
       "      <td>0.090678</td>\n",
       "      <td>-0.184978</td>\n",
       "      <td>0.016666</td>\n",
       "      <td>-0.183547</td>\n",
       "      <td>-0.051702</td>\n",
       "      <td>-0.189545</td>\n",
       "      <td>-0.041149</td>\n",
       "      <td>0.116309</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000830</td>\n",
       "      <td>-0.036431</td>\n",
       "      <td>0.046928</td>\n",
       "      <td>0.026370</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>223</td>\n",
       "      <td>True</td>\n",
       "      <td>89.071749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>0.035015</td>\n",
       "      <td>-0.040276</td>\n",
       "      <td>0.084468</td>\n",
       "      <td>-0.068787</td>\n",
       "      <td>0.002935</td>\n",
       "      <td>-0.129411</td>\n",
       "      <td>-0.005936</td>\n",
       "      <td>-0.083939</td>\n",
       "      <td>0.165884</td>\n",
       "      <td>0.089207</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008822</td>\n",
       "      <td>0.015488</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>-0.032048</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>195</td>\n",
       "      <td>True</td>\n",
       "      <td>82.605128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>0.044024</td>\n",
       "      <td>0.000223</td>\n",
       "      <td>0.085759</td>\n",
       "      <td>-0.043875</td>\n",
       "      <td>0.020273</td>\n",
       "      <td>-0.011902</td>\n",
       "      <td>-0.077834</td>\n",
       "      <td>-0.057456</td>\n",
       "      <td>0.018621</td>\n",
       "      <td>0.023004</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003545</td>\n",
       "      <td>0.025028</td>\n",
       "      <td>-0.022152</td>\n",
       "      <td>-0.063211</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>282</td>\n",
       "      <td>True</td>\n",
       "      <td>87.758865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>0.060813</td>\n",
       "      <td>0.127557</td>\n",
       "      <td>0.079519</td>\n",
       "      <td>-0.160550</td>\n",
       "      <td>0.047703</td>\n",
       "      <td>-0.103272</td>\n",
       "      <td>0.024130</td>\n",
       "      <td>-0.188346</td>\n",
       "      <td>-0.054896</td>\n",
       "      <td>0.114020</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053419</td>\n",
       "      <td>-0.075770</td>\n",
       "      <td>-0.004380</td>\n",
       "      <td>0.033179</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>True</td>\n",
       "      <td>88.586207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>0.068246</td>\n",
       "      <td>-0.053673</td>\n",
       "      <td>0.100254</td>\n",
       "      <td>0.054667</td>\n",
       "      <td>0.069273</td>\n",
       "      <td>-0.039153</td>\n",
       "      <td>0.027054</td>\n",
       "      <td>-0.099557</td>\n",
       "      <td>0.037540</td>\n",
       "      <td>0.080714</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063231</td>\n",
       "      <td>0.114722</td>\n",
       "      <td>0.024375</td>\n",
       "      <td>-0.070563</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>False</td>\n",
       "      <td>92.283582</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1463 rows × 1030 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1         2         3         4         5         6  \\\n",
       "0     0.074701 -0.031634  0.010414 -0.010877 -0.031846  0.053747 -0.032714   \n",
       "1     0.074191  0.045541 -0.006785 -0.029171 -0.012676  0.016806 -0.043396   \n",
       "2     0.060690 -0.028485  0.020705 -0.057476 -0.053076 -0.009133 -0.034624   \n",
       "3    -0.028040 -0.033594 -0.044930  0.010464 -0.069993 -0.040113 -0.053979   \n",
       "4     0.034272  0.141000  0.034812 -0.102808 -0.045333  0.007360  0.026951   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "1458  0.121245  0.100528  0.090678 -0.184978  0.016666 -0.183547 -0.051702   \n",
       "1459  0.035015 -0.040276  0.084468 -0.068787  0.002935 -0.129411 -0.005936   \n",
       "1460  0.044024  0.000223  0.085759 -0.043875  0.020273 -0.011902 -0.077834   \n",
       "1461  0.060813  0.127557  0.079519 -0.160550  0.047703 -0.103272  0.024130   \n",
       "1462  0.068246 -0.053673  0.100254  0.054667  0.069273 -0.039153  0.027054   \n",
       "\n",
       "             7         8         9  ...      1020      1021      1022  \\\n",
       "0    -0.046688 -0.061814 -0.001304  ... -0.028256  0.006632  0.010835   \n",
       "1    -0.060700 -0.024839  0.008174  ...  0.011608 -0.015764 -0.016603   \n",
       "2    -0.027073 -0.031988 -0.059633  ... -0.069274 -0.034956 -0.067968   \n",
       "3    -0.072420  0.007218 -0.006695  ... -0.037667 -0.041685  0.048311   \n",
       "4    -0.143174 -0.016837 -0.074851  ...  0.010612 -0.017051  0.041658   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "1458 -0.189545 -0.041149  0.116309  ... -0.000830 -0.036431  0.046928   \n",
       "1459 -0.083939  0.165884  0.089207  ... -0.008822  0.015488  0.019231   \n",
       "1460 -0.057456  0.018621  0.023004  ... -0.003545  0.025028 -0.022152   \n",
       "1461 -0.188346 -0.054896  0.114020  ... -0.053419 -0.075770 -0.004380   \n",
       "1462 -0.099557  0.037540  0.080714  ...  0.063231  0.114722  0.024375   \n",
       "\n",
       "          1023  label_complex  label_monomer  label_synthetic  \\\n",
       "0    -0.043973              0              1                0   \n",
       "1     0.018275              0              1                0   \n",
       "2    -0.019934              0              1                0   \n",
       "3     0.030363              0              1                0   \n",
       "4    -0.012780              0              1                0   \n",
       "...        ...            ...            ...              ...   \n",
       "1458  0.026370              0              0                1   \n",
       "1459 -0.032048              0              0                1   \n",
       "1460 -0.063211              0              0                1   \n",
       "1461  0.033179              0              0                1   \n",
       "1462 -0.070563              0              0                1   \n",
       "\n",
       "      sequence_length   xray  EF_average_pLDDT  \n",
       "0                 190   True         89.263158  \n",
       "1                 115   True         95.147826  \n",
       "2                 143   True         94.762238  \n",
       "3                 189   True         89.613757  \n",
       "4                  94   True         86.180851  \n",
       "...               ...    ...               ...  \n",
       "1458              223   True         89.071749  \n",
       "1459              195   True         82.605128  \n",
       "1460              282   True         87.758865  \n",
       "1461              145   True         88.586207  \n",
       "1462               67  False         92.283582  \n",
       "\n",
       "[1463 rows x 1030 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ff7e923-e4af-4cab-8a40-2abbe2c2e5ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047315 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 261626\n",
      "[LightGBM] [Info] Number of data points in the train set: 1024, number of used features: 1030\n",
      "[LightGBM] [Info] Start training from score -0.046400\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(n_jobs=-1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LGBMRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LGBMRegressor(n_jobs=-1, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LGBMRegressor(n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LGBMRegressor(\n",
    "        n_estimators=100,\n",
    "        random_state=random_state,\n",
    "        n_jobs=-1\n",
    "    )\n",
    "\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "05a9f439-e9ca-4795-be3e-8efc77bc11fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d1149386-ec8d-48bc-bfc7-ca3f953eb4f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.00555684222581692), 0.7388259359410871)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "mse, r2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e0ff4d2-7cc2-4b7d-974d-bce519f3beae",
   "metadata": {},
   "source": [
    "## SHAP Explainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "52361445-f2de-4938-b208-c523fba75cf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "explainer = shap.TreeExplainer(model)\n",
    "shap_values = explainer.shap_values(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cced78f2-4644-427d-b508-2510d4b4c420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x950 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "shap.summary_plot(shap_values, X, feature_names=X.columns, show=False)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6388d74e-cedb-44f9-8254-43bd57ea25a1",
   "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>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>EF_average_pLDDT</td>\n",
       "      <td>0.057356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>986</td>\n",
       "      <td>0.004813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>848</td>\n",
       "      <td>0.004034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>64</td>\n",
       "      <td>0.002945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>807</td>\n",
       "      <td>0.002855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature  importance\n",
       "1029  EF_average_pLDDT    0.057356\n",
       "986                986    0.004813\n",
       "848                848    0.004034\n",
       "64                  64    0.002945\n",
       "807                807    0.002855"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance = pd.DataFrame({\n",
    "        'feature': X.columns,\n",
    "        'importance': np.abs(shap_values).mean(0)\n",
    "    })\n",
    "feature_importance = feature_importance.sort_values('importance', ascending=False)\n",
    "\n",
    "feature_importance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "27a790ca-012b-4031-a681-e42e3b6625bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Predicted')\n",
    "plt.title('Actual vs Predicted')\n",
    "plt.savefig('predictions.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7cd3f3a9-3b3d-4711-be54-0f1577c25222",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(-0.7547903082886976),\n",
       " np.float64(0.41938159879269366),\n",
       " np.float64(-0.046761415500238074),\n",
       " np.float64(0.1407055943421939))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.min(), y.max(), y.mean(), y.std()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "bioembeddings",
   "language": "python",
   "name": "bioembeddings"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}