{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa9e8f7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "AVAILABLE_LIGAND_REPRESENTATION_TYPES = ['dft_nbd_model', 'ecfp', 'ohe', 'random']\n",
    "AVAILABLE_SUBSTRATE_REPRESENTATION_TYPES = ['dft_steric_fingerprint', 'smiles_steric_fingerprint', 'ecfp', 'ohe']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "38f9220b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from obelix_ml_pipeline.predictions_on_partially_seen_substrate import *\n",
    "import pickle\n",
    "from sklearn.metrics import balanced_accuracy_score, r2_score\n",
    "\n",
    "dict_cases = {'Case 1':{'a':'SM1','b':'SM2','p':'SM3'},\n",
    "              'Case 2':{'a':'SM1','b':'SM3','p':'SM2'},\n",
    "              'Case 3':{'a':'SM2','b':'SM3','p':'SM1'},\n",
    "              'Case 4':{'a':'SM1','b':'SM2','c':'SM7','d':'SM8','p':'SM3'},\n",
    "              'Case 5':{'a':'SM1','b':'SM3','c':'SM7','d':'SM8','p':'SM2'},\n",
    "              'Case 6':{'a':'SM2','b':'SM3','c':'SM7','d':'SM8','p':'SM1'},\n",
    "              'Case 7':{'a':'SM3','b':'SM8','p':'SM7'},\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "afccdee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.23.5'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy\n",
    "numpy.version.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "86073f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open(f\"dict_res_obj1.pkl\",\"rb\") as pickle_file:\n",
    "    dict_res_obj1 = pickle.load(pickle_file)\n",
    "    \n",
    "with open(f\"dict_res_obj2.pkl\",\"rb\") as pickle_file:\n",
    "    dict_res_obj2 = pickle.load(pickle_file)\n",
    "    \n",
    "with open(f\"dict_res_obj3.pkl\",\"rb\") as pickle_file:\n",
    "    dict_res_obj3 = pickle.load(pickle_file)\n",
    "    \n",
    "with open(f\"dict_res_obj4.pkl\",\"rb\") as pickle_file:\n",
    "    dict_res_obj4 = pickle.load(pickle_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6ba9e687",
   "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>Conversion</th>\n",
       "      <th>DDG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dft_nbd_model</th>\n",
       "      <td>{'dft_steric_fingerprint': {'Case 1': {'train'...</td>\n",
       "      <td>{'dft_steric_fingerprint': {'Case 1': {'train'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ecfp</th>\n",
       "      <td>{'dft_steric_fingerprint': {'Case 1': {'train'...</td>\n",
       "      <td>{'dft_steric_fingerprint': {'Case 1': {'train'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ohe</th>\n",
       "      <td>{'dft_steric_fingerprint': {'Case 1': {'train'...</td>\n",
       "      <td>{'dft_steric_fingerprint': {'Case 1': {'train'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random</th>\n",
       "      <td>{'dft_steric_fingerprint': {'Case 1': {'train'...</td>\n",
       "      <td>{'dft_steric_fingerprint': {'Case 1': {'train'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random_35</th>\n",
       "      <td>{'dft_steric_fingerprint': {'Case 1': {'train'...</td>\n",
       "      <td>{'dft_steric_fingerprint': {'Case 1': {'train'...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      Conversion  \\\n",
       "dft_nbd_model  {'dft_steric_fingerprint': {'Case 1': {'train'...   \n",
       "ecfp           {'dft_steric_fingerprint': {'Case 1': {'train'...   \n",
       "ohe            {'dft_steric_fingerprint': {'Case 1': {'train'...   \n",
       "random         {'dft_steric_fingerprint': {'Case 1': {'train'...   \n",
       "random_35      {'dft_steric_fingerprint': {'Case 1': {'train'...   \n",
       "\n",
       "                                                             DDG  \n",
       "dft_nbd_model  {'dft_steric_fingerprint': {'Case 1': {'train'...  \n",
       "ecfp           {'dft_steric_fingerprint': {'Case 1': {'train'...  \n",
       "ohe            {'dft_steric_fingerprint': {'Case 1': {'train'...  \n",
       "random         {'dft_steric_fingerprint': {'Case 1': {'train'...  \n",
       "random_35      {'dft_steric_fingerprint': {'Case 1': {'train'...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(dict_res_obj1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f7a36256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>case</th>\n",
       "      <th>target</th>\n",
       "      <th>ligand_descr</th>\n",
       "      <th>subs_descr</th>\n",
       "      <th>dataset</th>\n",
       "      <th>Case 1</th>\n",
       "      <th>Case 2</th>\n",
       "      <th>Case 3</th>\n",
       "      <th>Case 4</th>\n",
       "      <th>Case 5</th>\n",
       "      <th>Case 6</th>\n",
       "      <th>Case 7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>dft_steric_fingerprint</td>\n",
       "      <td>test</td>\n",
       "      <td>0.745233</td>\n",
       "      <td>0.774951</td>\n",
       "      <td>0.733397</td>\n",
       "      <td>0.731486</td>\n",
       "      <td>0.751583</td>\n",
       "      <td>0.732685</td>\n",
       "      <td>0.588542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>dft_steric_fingerprint</td>\n",
       "      <td>train</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997573</td>\n",
       "      <td>0.925609</td>\n",
       "      <td>0.916930</td>\n",
       "      <td>0.989170</td>\n",
       "      <td>0.935695</td>\n",
       "      <td>0.918482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>test</td>\n",
       "      <td>0.745233</td>\n",
       "      <td>0.664153</td>\n",
       "      <td>0.754269</td>\n",
       "      <td>0.739024</td>\n",
       "      <td>0.693565</td>\n",
       "      <td>0.762334</td>\n",
       "      <td>0.578125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>train</td>\n",
       "      <td>0.994924</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997817</td>\n",
       "      <td>0.979123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>ohe</td>\n",
       "      <td>test</td>\n",
       "      <td>0.850998</td>\n",
       "      <td>0.786463</td>\n",
       "      <td>0.741461</td>\n",
       "      <td>0.730710</td>\n",
       "      <td>0.740705</td>\n",
       "      <td>0.716556</td>\n",
       "      <td>0.656250</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random_35</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>train</td>\n",
       "      <td>0.926573</td>\n",
       "      <td>0.958778</td>\n",
       "      <td>0.913440</td>\n",
       "      <td>0.938290</td>\n",
       "      <td>0.903099</td>\n",
       "      <td>0.888831</td>\n",
       "      <td>0.306071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random_35</td>\n",
       "      <td>ohe</td>\n",
       "      <td>test</td>\n",
       "      <td>0.530622</td>\n",
       "      <td>0.628145</td>\n",
       "      <td>0.676836</td>\n",
       "      <td>0.546684</td>\n",
       "      <td>0.358485</td>\n",
       "      <td>0.491112</td>\n",
       "      <td>-0.227212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random_35</td>\n",
       "      <td>ohe</td>\n",
       "      <td>train</td>\n",
       "      <td>0.902544</td>\n",
       "      <td>0.936671</td>\n",
       "      <td>0.879699</td>\n",
       "      <td>0.544468</td>\n",
       "      <td>0.461657</td>\n",
       "      <td>0.486042</td>\n",
       "      <td>0.292458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random_35</td>\n",
       "      <td>smiles_steric_fingerprint</td>\n",
       "      <td>test</td>\n",
       "      <td>0.595736</td>\n",
       "      <td>0.624771</td>\n",
       "      <td>0.699934</td>\n",
       "      <td>0.638514</td>\n",
       "      <td>0.444195</td>\n",
       "      <td>0.678617</td>\n",
       "      <td>-0.216052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random_35</td>\n",
       "      <td>smiles_steric_fingerprint</td>\n",
       "      <td>train</td>\n",
       "      <td>0.956587</td>\n",
       "      <td>0.939978</td>\n",
       "      <td>0.946503</td>\n",
       "      <td>0.948392</td>\n",
       "      <td>0.946692</td>\n",
       "      <td>0.936451</td>\n",
       "      <td>0.330100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "case      target   ligand_descr                 subs_descr dataset    Case 1  \\\n",
       "0     Conversion  dft_nbd_model     dft_steric_fingerprint    test  0.745233   \n",
       "1     Conversion  dft_nbd_model     dft_steric_fingerprint   train  1.000000   \n",
       "2     Conversion  dft_nbd_model                       ecfp    test  0.745233   \n",
       "3     Conversion  dft_nbd_model                       ecfp   train  0.994924   \n",
       "4     Conversion  dft_nbd_model                        ohe    test  0.850998   \n",
       "..           ...            ...                        ...     ...       ...   \n",
       "75           DDG      random_35                       ecfp   train  0.926573   \n",
       "76           DDG      random_35                        ohe    test  0.530622   \n",
       "77           DDG      random_35                        ohe   train  0.902544   \n",
       "78           DDG      random_35  smiles_steric_fingerprint    test  0.595736   \n",
       "79           DDG      random_35  smiles_steric_fingerprint   train  0.956587   \n",
       "\n",
       "case    Case 2    Case 3    Case 4    Case 5    Case 6    Case 7  \n",
       "0     0.774951  0.733397  0.731486  0.751583  0.732685  0.588542  \n",
       "1     0.997573  0.925609  0.916930  0.989170  0.935695  0.918482  \n",
       "2     0.664153  0.754269  0.739024  0.693565  0.762334  0.578125  \n",
       "3     1.000000  0.997817  0.979123  1.000000  1.000000  1.000000  \n",
       "4     0.786463  0.741461  0.730710  0.740705  0.716556  0.656250  \n",
       "..         ...       ...       ...       ...       ...       ...  \n",
       "75    0.958778  0.913440  0.938290  0.903099  0.888831  0.306071  \n",
       "76    0.628145  0.676836  0.546684  0.358485  0.491112 -0.227212  \n",
       "77    0.936671  0.879699  0.544468  0.461657  0.486042  0.292458  \n",
       "78    0.624771  0.699934  0.638514  0.444195  0.678617 -0.216052  \n",
       "79    0.939978  0.946503  0.948392  0.946692  0.936451  0.330100  \n",
       "\n",
       "[80 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_df = {}\n",
    "dict_df['target'] = []\n",
    "dict_df['ligand_descr'] = []\n",
    "dict_df['subs_descr'] = []\n",
    "dict_df['case'] = []\n",
    "dict_df['dataset'] = []\n",
    "dict_df['score'] = []\n",
    "\n",
    "for k,v in dict_res_obj1.items():\n",
    "    for kk,vv in v.items():\n",
    "        for kkk,vvv in vv.items():\n",
    "            for kkkk,vvvv in vvv.items():\n",
    "                for kkkkk,vvvvv in vvvv.items():\n",
    "                    dict_df['target'].append(k)\n",
    "                    dict_df['ligand_descr'].append(kk)\n",
    "                    dict_df['subs_descr'].append(kkk)\n",
    "                    dict_df['case'].append(kkkk)\n",
    "                    dict_df['dataset'].append(kkkkk)\n",
    "                    dict_df['score'].append(vvvvv[0])\n",
    "obj1_df = pd.DataFrame(dict_df)\n",
    "obj1_df = obj1_df.pivot(index=['target','ligand_descr','subs_descr','dataset'], \n",
    "                        columns='case', values='score').reset_index()\n",
    "\n",
    "obj1_df.to_csv('obj1.csv')\n",
    "obj1_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "22908dff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>case</th>\n",
       "      <th>target</th>\n",
       "      <th>ligand_descr</th>\n",
       "      <th>subs_descr</th>\n",
       "      <th>dataset</th>\n",
       "      <th>Case 1</th>\n",
       "      <th>Case 2</th>\n",
       "      <th>Case 3</th>\n",
       "      <th>Case 4</th>\n",
       "      <th>Case 5</th>\n",
       "      <th>Case 6</th>\n",
       "      <th>Case 7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>dft_steric_fingerprint</td>\n",
       "      <td>test</td>\n",
       "      <td>[0.83, 0.86, 0.79]</td>\n",
       "      <td>[0.76, 0.85, 0.78]</td>\n",
       "      <td>[0.72, 0.67, 0.76]</td>\n",
       "      <td>[0.77, 0.81, 0.65]</td>\n",
       "      <td>[0.72, 0.72, 0.68]</td>\n",
       "      <td>[0.74, 0.73, 0.82]</td>\n",
       "      <td>[0.73, 0.61, 0.71]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>dft_steric_fingerprint</td>\n",
       "      <td>train</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>[0.97, 1.0, 0.96]</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>[0.97, 0.96, 0.95]</td>\n",
       "      <td>[0.96, 0.95, 1.0]</td>\n",
       "      <td>[0.95, 0.94, 0.94]</td>\n",
       "      <td>[0.92, 0.93, 0.95]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>test</td>\n",
       "      <td>[0.85, 0.82, 0.78]</td>\n",
       "      <td>[0.72, 0.86, 0.74]</td>\n",
       "      <td>[0.7, 0.68, 0.8]</td>\n",
       "      <td>[0.81, 0.79, 0.67]</td>\n",
       "      <td>[0.68, 0.76, 0.7]</td>\n",
       "      <td>[0.65, 0.69, 0.85]</td>\n",
       "      <td>[0.78, 0.68, 0.67]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>train</td>\n",
       "      <td>[0.99, 0.98, 1.0]</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>[0.97, 1.0, 1.0]</td>\n",
       "      <td>[0.96, 1.0, 0.97]</td>\n",
       "      <td>[1.0, 1.0, 0.96]</td>\n",
       "      <td>[1.0, 0.96, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>ohe</td>\n",
       "      <td>test</td>\n",
       "      <td>[0.83, 0.84, 0.78]</td>\n",
       "      <td>[0.77, 0.83, 0.78]</td>\n",
       "      <td>[0.7, 0.74, 0.75]</td>\n",
       "      <td>[0.72, 0.76, 0.67]</td>\n",
       "      <td>[0.77, 0.74, 0.72]</td>\n",
       "      <td>[0.68, 0.74, 0.76]</td>\n",
       "      <td>[0.67, 0.55, 0.62]</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random_35</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>train</td>\n",
       "      <td>[0.93, 0.93, 0.93]</td>\n",
       "      <td>[0.93, 0.93, 0.94]</td>\n",
       "      <td>[0.93, 0.94, 0.94]</td>\n",
       "      <td>[0.93, 0.91, 0.93]</td>\n",
       "      <td>[0.9, 0.92, 0.92]</td>\n",
       "      <td>[0.92, 0.92, 0.92]</td>\n",
       "      <td>[0.26, 0.24, 0.3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random_35</td>\n",
       "      <td>ohe</td>\n",
       "      <td>test</td>\n",
       "      <td>[0.57, 0.6, 0.64]</td>\n",
       "      <td>[0.63, 0.66, 0.64]</td>\n",
       "      <td>[0.78, 0.73, 0.73]</td>\n",
       "      <td>[0.52, 0.55, 0.55]</td>\n",
       "      <td>[0.4, 0.44, 0.43]</td>\n",
       "      <td>[0.6, 0.49, 0.57]</td>\n",
       "      <td>[-0.23, -0.2, -0.26]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random_35</td>\n",
       "      <td>ohe</td>\n",
       "      <td>train</td>\n",
       "      <td>[0.89, 0.88, 0.89]</td>\n",
       "      <td>[0.9, 0.89, 0.88]</td>\n",
       "      <td>[0.85, 0.86, 0.86]</td>\n",
       "      <td>[0.51, 0.52, 0.51]</td>\n",
       "      <td>[0.52, 0.54, 0.51]</td>\n",
       "      <td>[0.51, 0.5, 0.51]</td>\n",
       "      <td>[0.26, 0.26, 0.26]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random_35</td>\n",
       "      <td>smiles_steric_fingerprint</td>\n",
       "      <td>test</td>\n",
       "      <td>[0.64, 0.65, 0.71]</td>\n",
       "      <td>[0.63, 0.66, 0.65]</td>\n",
       "      <td>[0.83, 0.76, 0.79]</td>\n",
       "      <td>[0.64, 0.68, 0.71]</td>\n",
       "      <td>[0.55, 0.58, 0.59]</td>\n",
       "      <td>[0.82, 0.73, 0.74]</td>\n",
       "      <td>[-0.11, -0.11, -0.2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random_35</td>\n",
       "      <td>smiles_steric_fingerprint</td>\n",
       "      <td>train</td>\n",
       "      <td>[0.93, 0.95, 0.94]</td>\n",
       "      <td>[0.95, 0.95, 0.94]</td>\n",
       "      <td>[0.94, 0.94, 0.94]</td>\n",
       "      <td>[0.92, 0.94, 0.94]</td>\n",
       "      <td>[0.94, 0.94, 0.94]</td>\n",
       "      <td>[0.94, 0.94, 0.91]</td>\n",
       "      <td>[0.32, 0.3, 0.3]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "case      target   ligand_descr                 subs_descr dataset  \\\n",
       "0     Conversion  dft_nbd_model     dft_steric_fingerprint    test   \n",
       "1     Conversion  dft_nbd_model     dft_steric_fingerprint   train   \n",
       "2     Conversion  dft_nbd_model                       ecfp    test   \n",
       "3     Conversion  dft_nbd_model                       ecfp   train   \n",
       "4     Conversion  dft_nbd_model                        ohe    test   \n",
       "..           ...            ...                        ...     ...   \n",
       "75           DDG      random_35                       ecfp   train   \n",
       "76           DDG      random_35                        ohe    test   \n",
       "77           DDG      random_35                        ohe   train   \n",
       "78           DDG      random_35  smiles_steric_fingerprint    test   \n",
       "79           DDG      random_35  smiles_steric_fingerprint   train   \n",
       "\n",
       "case              Case 1              Case 2              Case 3  \\\n",
       "0     [0.83, 0.86, 0.79]  [0.76, 0.85, 0.78]  [0.72, 0.67, 0.76]   \n",
       "1        [1.0, 1.0, 1.0]   [0.97, 1.0, 0.96]     [1.0, 1.0, 1.0]   \n",
       "2     [0.85, 0.82, 0.78]  [0.72, 0.86, 0.74]    [0.7, 0.68, 0.8]   \n",
       "3      [0.99, 0.98, 1.0]     [1.0, 1.0, 1.0]     [1.0, 1.0, 1.0]   \n",
       "4     [0.83, 0.84, 0.78]  [0.77, 0.83, 0.78]   [0.7, 0.74, 0.75]   \n",
       "..                   ...                 ...                 ...   \n",
       "75    [0.93, 0.93, 0.93]  [0.93, 0.93, 0.94]  [0.93, 0.94, 0.94]   \n",
       "76     [0.57, 0.6, 0.64]  [0.63, 0.66, 0.64]  [0.78, 0.73, 0.73]   \n",
       "77    [0.89, 0.88, 0.89]   [0.9, 0.89, 0.88]  [0.85, 0.86, 0.86]   \n",
       "78    [0.64, 0.65, 0.71]  [0.63, 0.66, 0.65]  [0.83, 0.76, 0.79]   \n",
       "79    [0.93, 0.95, 0.94]  [0.95, 0.95, 0.94]  [0.94, 0.94, 0.94]   \n",
       "\n",
       "case              Case 4              Case 5              Case 6  \\\n",
       "0     [0.77, 0.81, 0.65]  [0.72, 0.72, 0.68]  [0.74, 0.73, 0.82]   \n",
       "1     [0.97, 0.96, 0.95]   [0.96, 0.95, 1.0]  [0.95, 0.94, 0.94]   \n",
       "2     [0.81, 0.79, 0.67]   [0.68, 0.76, 0.7]  [0.65, 0.69, 0.85]   \n",
       "3       [0.97, 1.0, 1.0]   [0.96, 1.0, 0.97]    [1.0, 1.0, 0.96]   \n",
       "4     [0.72, 0.76, 0.67]  [0.77, 0.74, 0.72]  [0.68, 0.74, 0.76]   \n",
       "..                   ...                 ...                 ...   \n",
       "75    [0.93, 0.91, 0.93]   [0.9, 0.92, 0.92]  [0.92, 0.92, 0.92]   \n",
       "76    [0.52, 0.55, 0.55]   [0.4, 0.44, 0.43]   [0.6, 0.49, 0.57]   \n",
       "77    [0.51, 0.52, 0.51]  [0.52, 0.54, 0.51]   [0.51, 0.5, 0.51]   \n",
       "78    [0.64, 0.68, 0.71]  [0.55, 0.58, 0.59]  [0.82, 0.73, 0.74]   \n",
       "79    [0.92, 0.94, 0.94]  [0.94, 0.94, 0.94]  [0.94, 0.94, 0.91]   \n",
       "\n",
       "case                Case 7  \n",
       "0       [0.73, 0.61, 0.71]  \n",
       "1       [0.92, 0.93, 0.95]  \n",
       "2       [0.78, 0.68, 0.67]  \n",
       "3         [1.0, 0.96, 1.0]  \n",
       "4       [0.67, 0.55, 0.62]  \n",
       "..                     ...  \n",
       "75       [0.26, 0.24, 0.3]  \n",
       "76    [-0.23, -0.2, -0.26]  \n",
       "77      [0.26, 0.26, 0.26]  \n",
       "78    [-0.11, -0.11, -0.2]  \n",
       "79        [0.32, 0.3, 0.3]  \n",
       "\n",
       "[80 rows x 11 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_df = {}\n",
    "dict_df['target'] = []\n",
    "dict_df['ligand_descr'] = []\n",
    "dict_df['subs_descr'] = []\n",
    "dict_df['case'] = []\n",
    "dict_df['dataset'] = []\n",
    "dict_df['score'] = []\n",
    "\n",
    "for k,v in dict_res_obj2.items():\n",
    "    for kk,vv in v.items():\n",
    "        for kkk,vvv in vv.items():\n",
    "            for kkkk,vvvv in vvv.items():\n",
    "                for kkkkk,vvvvv in vvvv.items():\n",
    "                    dict_df['target'].append(k)\n",
    "                    dict_df['ligand_descr'].append(kk)\n",
    "                    dict_df['subs_descr'].append(kkk)\n",
    "                    dict_df['case'].append(kkkk)\n",
    "                    dict_df['dataset'].append(kkkkk)\n",
    "                    dict_df['score'].append([round(i,2) for i in vvvvv[0]])\n",
    "\n",
    "obj2_df = pd.DataFrame(dict_df)\n",
    "obj2_df = obj2_df.pivot(index=['target','ligand_descr','subs_descr','dataset'], \n",
    "                        columns='case', values='score').reset_index()\n",
    "\n",
    "obj2_df.to_csv('obj2.csv')\n",
    "obj2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d72f3c14",
   "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>sm</th>\n",
       "      <th>target</th>\n",
       "      <th>ligand_descr</th>\n",
       "      <th>dataset</th>\n",
       "      <th>SM1</th>\n",
       "      <th>SM2</th>\n",
       "      <th>SM3</th>\n",
       "      <th>SM7</th>\n",
       "      <th>SM8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>test</td>\n",
       "      <td>[0.74, 0.72, 0.73]</td>\n",
       "      <td>[0.7, 0.72, 0.69]</td>\n",
       "      <td>[0.82, 0.8, 0.78]</td>\n",
       "      <td>[0.77, 0.81, 0.72]</td>\n",
       "      <td>[0.63, 0.72, 0.73]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>train</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>[0.93, 0.92, 0.99]</td>\n",
       "      <td>[0.88, 0.89, 0.9]</td>\n",
       "      <td>[0.98, 1.0, 0.97]</td>\n",
       "      <td>[0.99, 1.0, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>test</td>\n",
       "      <td>[0.67, 0.72, 0.63]</td>\n",
       "      <td>[0.7, 0.63, 0.7]</td>\n",
       "      <td>[0.77, 0.77, 0.7]</td>\n",
       "      <td>[0.62, 0.53, 0.73]</td>\n",
       "      <td>[0.64, 0.62, 0.69]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>train</td>\n",
       "      <td>[0.95, 0.84, 0.98]</td>\n",
       "      <td>[0.9, 0.88, 0.87]</td>\n",
       "      <td>[0.88, 0.93, 0.93]</td>\n",
       "      <td>[0.84, 0.84, 0.9]</td>\n",
       "      <td>[0.95, 0.86, 0.91]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>ohe</td>\n",
       "      <td>test</td>\n",
       "      <td>[0.5, 0.5, 0.5]</td>\n",
       "      <td>[0.5, 0.5, 0.5]</td>\n",
       "      <td>[0.5, 0.5, 0.5]</td>\n",
       "      <td>[0.5, 0.5, 0.5]</td>\n",
       "      <td>[0.5, 0.5, 0.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>ohe</td>\n",
       "      <td>train</td>\n",
       "      <td>[0.5, 0.5, 0.5]</td>\n",
       "      <td>[0.5, 0.5, 0.5]</td>\n",
       "      <td>[0.5, 0.5, 0.5]</td>\n",
       "      <td>[0.5, 0.5, 0.5]</td>\n",
       "      <td>[0.5, 0.5, 0.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>random</td>\n",
       "      <td>test</td>\n",
       "      <td>[0.41, 0.54, 0.57]</td>\n",
       "      <td>[0.45, 0.36, 0.64]</td>\n",
       "      <td>[0.36, 0.61, 0.47]</td>\n",
       "      <td>[0.54, 0.58, 0.51]</td>\n",
       "      <td>[0.67, 0.46, 0.55]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>random</td>\n",
       "      <td>train</td>\n",
       "      <td>[1.0, 1.0, 0.99]</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>random_35</td>\n",
       "      <td>test</td>\n",
       "      <td>[0.67, 0.59, 0.52]</td>\n",
       "      <td>[0.48, 0.48, 0.37]</td>\n",
       "      <td>[0.64, 0.54, 0.49]</td>\n",
       "      <td>[0.46, 0.47, 0.58]</td>\n",
       "      <td>[0.44, 0.52, 0.57]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>random_35</td>\n",
       "      <td>train</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DDG</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>test</td>\n",
       "      <td>[0.12, 0.06, 0.08]</td>\n",
       "      <td>[0.18, 0.09, 0.14]</td>\n",
       "      <td>[0.12, 0.07, 0.14]</td>\n",
       "      <td>[0.22, 0.23, -0.19]</td>\n",
       "      <td>[-0.05, 0.13, -0.14]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DDG</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>train</td>\n",
       "      <td>[0.91, 0.63, 0.64]</td>\n",
       "      <td>[0.63, 0.57, 0.63]</td>\n",
       "      <td>[0.93, 0.92, 0.64]</td>\n",
       "      <td>[0.94, 0.72, 0.77]</td>\n",
       "      <td>[0.51, 0.88, 0.71]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DDG</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>test</td>\n",
       "      <td>[-0.0, -0.05, -0.03]</td>\n",
       "      <td>[-0.01, -0.07, -0.04]</td>\n",
       "      <td>[0.01, -0.02, -0.08]</td>\n",
       "      <td>[0.09, -0.08, 0.08]</td>\n",
       "      <td>[-0.06, -0.04, 0.01]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DDG</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>train</td>\n",
       "      <td>[0.06, 0.07, 0.12]</td>\n",
       "      <td>[0.06, 0.07, 0.06]</td>\n",
       "      <td>[0.14, 0.33, 0.29]</td>\n",
       "      <td>[0.4, 0.5, 0.52]</td>\n",
       "      <td>[0.08, 0.41, 0.08]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>DDG</td>\n",
       "      <td>ohe</td>\n",
       "      <td>test</td>\n",
       "      <td>[-0.02, -0.04, -0.02]</td>\n",
       "      <td>[-0.04, -0.05, -0.02]</td>\n",
       "      <td>[-0.0, -0.0, -0.03]</td>\n",
       "      <td>[-0.01, -0.01, -0.0]</td>\n",
       "      <td>[-0.05, -0.0, -0.01]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>DDG</td>\n",
       "      <td>ohe</td>\n",
       "      <td>train</td>\n",
       "      <td>[0.0, 0.0, 0.0]</td>\n",
       "      <td>[0.0, 0.0, 0.0]</td>\n",
       "      <td>[0.0, 0.0, 0.0]</td>\n",
       "      <td>[0.0, 0.0, 0.0]</td>\n",
       "      <td>[0.0, 0.0, 0.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random</td>\n",
       "      <td>test</td>\n",
       "      <td>[-0.03, -0.01, -0.11]</td>\n",
       "      <td>[-0.04, -0.08, 0.07]</td>\n",
       "      <td>[0.03, -0.06, -0.07]</td>\n",
       "      <td>[-0.05, -0.04, 0.03]</td>\n",
       "      <td>[-0.01, -0.08, -0.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random</td>\n",
       "      <td>train</td>\n",
       "      <td>[0.69, 0.7, 0.65]</td>\n",
       "      <td>[0.72, 0.55, 0.94]</td>\n",
       "      <td>[0.72, 0.96, 0.92]</td>\n",
       "      <td>[0.76, 0.55, 0.67]</td>\n",
       "      <td>[0.68, 0.67, 0.64]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random_35</td>\n",
       "      <td>test</td>\n",
       "      <td>[-0.12, -0.1, -0.01]</td>\n",
       "      <td>[-0.02, -0.11, -0.06]</td>\n",
       "      <td>[-0.12, -0.16, -0.17]</td>\n",
       "      <td>[-0.13, -0.12, -0.03]</td>\n",
       "      <td>[-0.16, -0.11, -0.21]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random_35</td>\n",
       "      <td>train</td>\n",
       "      <td>[0.68, 0.64, 0.61]</td>\n",
       "      <td>[0.67, 0.63, 0.66]</td>\n",
       "      <td>[0.72, 0.54, 0.55]</td>\n",
       "      <td>[0.55, 0.95, 0.51]</td>\n",
       "      <td>[0.48, 0.61, 0.48]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sm      target   ligand_descr dataset                    SM1  \\\n",
       "0   Conversion  dft_nbd_model    test     [0.74, 0.72, 0.73]   \n",
       "1   Conversion  dft_nbd_model   train        [1.0, 1.0, 1.0]   \n",
       "2   Conversion           ecfp    test     [0.67, 0.72, 0.63]   \n",
       "3   Conversion           ecfp   train     [0.95, 0.84, 0.98]   \n",
       "4   Conversion            ohe    test        [0.5, 0.5, 0.5]   \n",
       "5   Conversion            ohe   train        [0.5, 0.5, 0.5]   \n",
       "6   Conversion         random    test     [0.41, 0.54, 0.57]   \n",
       "7   Conversion         random   train       [1.0, 1.0, 0.99]   \n",
       "8   Conversion      random_35    test     [0.67, 0.59, 0.52]   \n",
       "9   Conversion      random_35   train        [1.0, 1.0, 1.0]   \n",
       "10         DDG  dft_nbd_model    test     [0.12, 0.06, 0.08]   \n",
       "11         DDG  dft_nbd_model   train     [0.91, 0.63, 0.64]   \n",
       "12         DDG           ecfp    test   [-0.0, -0.05, -0.03]   \n",
       "13         DDG           ecfp   train     [0.06, 0.07, 0.12]   \n",
       "14         DDG            ohe    test  [-0.02, -0.04, -0.02]   \n",
       "15         DDG            ohe   train        [0.0, 0.0, 0.0]   \n",
       "16         DDG         random    test  [-0.03, -0.01, -0.11]   \n",
       "17         DDG         random   train      [0.69, 0.7, 0.65]   \n",
       "18         DDG      random_35    test   [-0.12, -0.1, -0.01]   \n",
       "19         DDG      random_35   train     [0.68, 0.64, 0.61]   \n",
       "\n",
       "sm                    SM2                    SM3                    SM7  \\\n",
       "0       [0.7, 0.72, 0.69]      [0.82, 0.8, 0.78]     [0.77, 0.81, 0.72]   \n",
       "1      [0.93, 0.92, 0.99]      [0.88, 0.89, 0.9]      [0.98, 1.0, 0.97]   \n",
       "2        [0.7, 0.63, 0.7]      [0.77, 0.77, 0.7]     [0.62, 0.53, 0.73]   \n",
       "3       [0.9, 0.88, 0.87]     [0.88, 0.93, 0.93]      [0.84, 0.84, 0.9]   \n",
       "4         [0.5, 0.5, 0.5]        [0.5, 0.5, 0.5]        [0.5, 0.5, 0.5]   \n",
       "5         [0.5, 0.5, 0.5]        [0.5, 0.5, 0.5]        [0.5, 0.5, 0.5]   \n",
       "6      [0.45, 0.36, 0.64]     [0.36, 0.61, 0.47]     [0.54, 0.58, 0.51]   \n",
       "7         [1.0, 1.0, 1.0]        [1.0, 1.0, 1.0]        [1.0, 1.0, 1.0]   \n",
       "8      [0.48, 0.48, 0.37]     [0.64, 0.54, 0.49]     [0.46, 0.47, 0.58]   \n",
       "9         [1.0, 1.0, 1.0]        [1.0, 1.0, 1.0]        [1.0, 1.0, 1.0]   \n",
       "10     [0.18, 0.09, 0.14]     [0.12, 0.07, 0.14]    [0.22, 0.23, -0.19]   \n",
       "11     [0.63, 0.57, 0.63]     [0.93, 0.92, 0.64]     [0.94, 0.72, 0.77]   \n",
       "12  [-0.01, -0.07, -0.04]   [0.01, -0.02, -0.08]    [0.09, -0.08, 0.08]   \n",
       "13     [0.06, 0.07, 0.06]     [0.14, 0.33, 0.29]       [0.4, 0.5, 0.52]   \n",
       "14  [-0.04, -0.05, -0.02]    [-0.0, -0.0, -0.03]   [-0.01, -0.01, -0.0]   \n",
       "15        [0.0, 0.0, 0.0]        [0.0, 0.0, 0.0]        [0.0, 0.0, 0.0]   \n",
       "16   [-0.04, -0.08, 0.07]   [0.03, -0.06, -0.07]   [-0.05, -0.04, 0.03]   \n",
       "17     [0.72, 0.55, 0.94]     [0.72, 0.96, 0.92]     [0.76, 0.55, 0.67]   \n",
       "18  [-0.02, -0.11, -0.06]  [-0.12, -0.16, -0.17]  [-0.13, -0.12, -0.03]   \n",
       "19     [0.67, 0.63, 0.66]     [0.72, 0.54, 0.55]     [0.55, 0.95, 0.51]   \n",
       "\n",
       "sm                    SM8  \n",
       "0      [0.63, 0.72, 0.73]  \n",
       "1        [0.99, 1.0, 1.0]  \n",
       "2      [0.64, 0.62, 0.69]  \n",
       "3      [0.95, 0.86, 0.91]  \n",
       "4         [0.5, 0.5, 0.5]  \n",
       "5         [0.5, 0.5, 0.5]  \n",
       "6      [0.67, 0.46, 0.55]  \n",
       "7         [1.0, 1.0, 1.0]  \n",
       "8      [0.44, 0.52, 0.57]  \n",
       "9         [1.0, 1.0, 1.0]  \n",
       "10   [-0.05, 0.13, -0.14]  \n",
       "11     [0.51, 0.88, 0.71]  \n",
       "12   [-0.06, -0.04, 0.01]  \n",
       "13     [0.08, 0.41, 0.08]  \n",
       "14   [-0.05, -0.0, -0.01]  \n",
       "15        [0.0, 0.0, 0.0]  \n",
       "16   [-0.01, -0.08, -0.0]  \n",
       "17     [0.68, 0.67, 0.64]  \n",
       "18  [-0.16, -0.11, -0.21]  \n",
       "19     [0.48, 0.61, 0.48]  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_df = {}\n",
    "dict_df['target'] = []\n",
    "dict_df['ligand_descr'] = []\n",
    "dict_df['sm'] = []\n",
    "dict_df['dataset'] = []\n",
    "dict_df['score'] = []\n",
    "\n",
    "for k,v in dict_res_obj3.items():\n",
    "    for kk,vv in v.items():\n",
    "        for kkk,vvv in vv.items():\n",
    "            for kkkk,vvvv in vvv.items():\n",
    "                if kkkk in ['test','train']:\n",
    "                    dict_df['target'].append(k)\n",
    "                    dict_df['ligand_descr'].append(kk)\n",
    "                    dict_df['sm'].append(kkk)\n",
    "                    dict_df['dataset'].append(kkkk)\n",
    "                    dict_df['score'].append([round(i,2) for i in vvvv])  #[round(i,2) for i in vvvvv[0]]\n",
    "\n",
    "obj3_df = pd.DataFrame(dict_df)\n",
    "obj3_df = obj3_df.pivot(index=['target','ligand_descr','dataset'], \n",
    "                        columns='sm', values='score').reset_index()\n",
    "\n",
    "obj3_df.to_csv('obj3.csv')\n",
    "obj3_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eaea0aaa",
   "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>target</th>\n",
       "      <th>ligand_descr</th>\n",
       "      <th>sm</th>\n",
       "      <th>score</th>\n",
       "      <th>seed</th>\n",
       "      <th>fraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DDG</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>SM1</td>\n",
       "      <td>0.959551</td>\n",
       "      <td>398</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DDG</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>SM2</td>\n",
       "      <td>0.701214</td>\n",
       "      <td>672</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DDG</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>SM3</td>\n",
       "      <td>0.940780</td>\n",
       "      <td>830</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DDG</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>SM7</td>\n",
       "      <td>0.835100</td>\n",
       "      <td>661</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DDG</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>SM8</td>\n",
       "      <td>0.861272</td>\n",
       "      <td>147</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DDG</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>SM1</td>\n",
       "      <td>0.624172</td>\n",
       "      <td>418</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DDG</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>SM2</td>\n",
       "      <td>0.447072</td>\n",
       "      <td>410</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DDG</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>SM3</td>\n",
       "      <td>0.626293</td>\n",
       "      <td>240</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DDG</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>SM7</td>\n",
       "      <td>0.924063</td>\n",
       "      <td>853</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DDG</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>SM8</td>\n",
       "      <td>0.606066</td>\n",
       "      <td>924</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random_50</td>\n",
       "      <td>SM1</td>\n",
       "      <td>0.840070</td>\n",
       "      <td>697</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random_50</td>\n",
       "      <td>SM2</td>\n",
       "      <td>0.856407</td>\n",
       "      <td>211</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random_50</td>\n",
       "      <td>SM3</td>\n",
       "      <td>0.630472</td>\n",
       "      <td>594</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random_50</td>\n",
       "      <td>SM7</td>\n",
       "      <td>0.682130</td>\n",
       "      <td>320</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>DDG</td>\n",
       "      <td>random_50</td>\n",
       "      <td>SM8</td>\n",
       "      <td>0.627902</td>\n",
       "      <td>798</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>SM1</td>\n",
       "      <td>0.758492</td>\n",
       "      <td>658</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>SM2</td>\n",
       "      <td>0.760525</td>\n",
       "      <td>838</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>SM3</td>\n",
       "      <td>0.808966</td>\n",
       "      <td>470</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>SM7</td>\n",
       "      <td>0.801526</td>\n",
       "      <td>791</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>dft_nbd_model</td>\n",
       "      <td>SM8</td>\n",
       "      <td>0.752709</td>\n",
       "      <td>891</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>SM1</td>\n",
       "      <td>0.403239</td>\n",
       "      <td>577</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>SM2</td>\n",
       "      <td>0.466825</td>\n",
       "      <td>540</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>SM3</td>\n",
       "      <td>0.454114</td>\n",
       "      <td>758</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>SM7</td>\n",
       "      <td>0.500815</td>\n",
       "      <td>325</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>ecfp</td>\n",
       "      <td>SM8</td>\n",
       "      <td>0.401247</td>\n",
       "      <td>305</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>random_50</td>\n",
       "      <td>SM1</td>\n",
       "      <td>0.565415</td>\n",
       "      <td>384</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>random_50</td>\n",
       "      <td>SM2</td>\n",
       "      <td>0.576464</td>\n",
       "      <td>666</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>random_50</td>\n",
       "      <td>SM3</td>\n",
       "      <td>0.514555</td>\n",
       "      <td>163</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>random_50</td>\n",
       "      <td>SM7</td>\n",
       "      <td>0.631158</td>\n",
       "      <td>577</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Conversion</td>\n",
       "      <td>random_50</td>\n",
       "      <td>SM8</td>\n",
       "      <td>0.707377</td>\n",
       "      <td>654</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        target   ligand_descr   sm     score  seed  fraction\n",
       "0          DDG  dft_nbd_model  SM1  0.959551   398       0.1\n",
       "1          DDG  dft_nbd_model  SM2  0.701214   672       0.1\n",
       "2          DDG  dft_nbd_model  SM3  0.940780   830       0.1\n",
       "3          DDG  dft_nbd_model  SM7  0.835100   661       0.1\n",
       "4          DDG  dft_nbd_model  SM8  0.861272   147       0.1\n",
       "5          DDG           ecfp  SM1  0.624172   418       0.2\n",
       "6          DDG           ecfp  SM2  0.447072   410       0.1\n",
       "7          DDG           ecfp  SM3  0.626293   240       0.1\n",
       "8          DDG           ecfp  SM7  0.924063   853       0.1\n",
       "9          DDG           ecfp  SM8  0.606066   924       0.3\n",
       "10         DDG      random_50  SM1  0.840070   697       0.1\n",
       "11         DDG      random_50  SM2  0.856407   211       0.1\n",
       "12         DDG      random_50  SM3  0.630472   594       0.1\n",
       "13         DDG      random_50  SM7  0.682130   320       0.2\n",
       "14         DDG      random_50  SM8  0.627902   798       0.1\n",
       "15  Conversion  dft_nbd_model  SM1  0.758492   658       0.5\n",
       "16  Conversion  dft_nbd_model  SM2  0.760525   838       0.6\n",
       "17  Conversion  dft_nbd_model  SM3  0.808966   470       0.7\n",
       "18  Conversion  dft_nbd_model  SM7  0.801526   791       0.7\n",
       "19  Conversion  dft_nbd_model  SM8  0.752709   891       0.4\n",
       "20  Conversion           ecfp  SM1  0.403239   577       0.3\n",
       "21  Conversion           ecfp  SM2  0.466825   540       0.2\n",
       "22  Conversion           ecfp  SM3  0.454114   758       0.6\n",
       "23  Conversion           ecfp  SM7  0.500815   325       0.5\n",
       "24  Conversion           ecfp  SM8  0.401247   305       0.5\n",
       "25  Conversion      random_50  SM1  0.565415   384       0.1\n",
       "26  Conversion      random_50  SM2  0.576464   666       0.1\n",
       "27  Conversion      random_50  SM3  0.514555   163       0.1\n",
       "28  Conversion      random_50  SM7  0.631158   577       0.1\n",
       "29  Conversion      random_50  SM8  0.707377   654       0.1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_df = {}\n",
    "dict_df['target'] = []\n",
    "dict_df['ligand_descr'] = []\n",
    "dict_df['sm'] = []\n",
    "dict_df['score'] = []\n",
    "dict_df['seed'] = []\n",
    "dict_df['fraction'] = []\n",
    "\n",
    "for k,v in dict_res_obj4.items():\n",
    "    for kk,vv in v.items():\n",
    "        for kkk,vvv in vv.items():\n",
    "            dict_df['target'].append(k)\n",
    "            dict_df['ligand_descr'].append(kk)\n",
    "            dict_df['sm'].append(kkk)\n",
    "            dict_df['score'].append(vvv[42]['true'])\n",
    "            dict_df['seed'].append(vvv[42]['true_seed'])\n",
    "            dict_df['fraction'].append(vvv[42]['true_fraction'])\n",
    "\n",
    "obj4_df = pd.DataFrame(dict_df)\n",
    "\n",
    "obj4_df.to_csv('obj4.csv')\n",
    "obj4_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25d9dad2",
   "metadata": {},
   "source": [
    "## ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef6921b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "positions = [0, 1, 3, 4, 6, 7, 9]\n",
    "plt.rcParams.update({'font.size': 11})\n",
    "\n",
    "AVAILABLE_LIGAND_REPRESENTATION_TYPES = ['dft_nbd_model', 'ecfp', 'ohe', 'random']\n",
    "AVAILABLE_SUBSTRATE_REPRESENTATION_TYPES = ['dft_steric_fingerprint', 'smiles_steric_fingerprint', 'ecfp', 'ohe']\n",
    "\n",
    "for target in ['Conversion', 'DDG']:\n",
    "    fig, axs = plt.subplots(len(AVAILABLE_LIGAND_REPRESENTATION_TYPES), len(AVAILABLE_SUBSTRATE_REPRESENTATION_TYPES)+1, figsize=(20,20))\n",
    "    for nl, L in enumerate(AVAILABLE_LIGAND_REPRESENTATION_TYPES):\n",
    "        for ns, S in enumerate(AVAILABLE_SUBSTRATE_REPRESENTATION_TYPES):\n",
    "            \n",
    "            plt.suptitle(f'{target}')\n",
    "\n",
    "            list_test = [dict_res_obj2[target][L][S][case]['test'] for case in dict_cases.keys()]\n",
    "            list_train = [dict_res_obj2[target][L][S][case]['train'] for case in dict_cases.keys()]\n",
    "            obj1_dot = [dict_res_obj1[target][L][S][case]['test'][0] for case in dict_cases.keys()]\n",
    "            \n",
    "            bp1 = axs[nl,ns].boxplot([c[0] for c in list_test], positions = positions, patch_artist=True, boxprops=dict(facecolor=\"C0\"))\n",
    "\n",
    "            for n,j in zip(positions, obj1_dot):\n",
    "                sc1 = axs[nl,ns].scatter(n, j, alpha=0.9, marker = 'o',  color = 'red', s = 60) # c=cm.prism(clevel)\n",
    "                \n",
    "            if target == 'Conversion':\n",
    "                axs[nl,ns].set_ylabel('Balanced accuracy')\n",
    "            else:\n",
    "                axs[nl,ns].set_ylabel('R2score')\n",
    "            if target == 'Conversion':\n",
    "                axs[nl,ns].set_ylim([0.48,1])\n",
    "            else:\n",
    "                axs[nl,ns].set_ylim([-0.15,1])\n",
    "            axs[nl,ns].set_xticks(positions, dict_cases.keys(), rotation=90, ha='right')\n",
    "            axs[nl,ns].set_title(f'{L.replace(\"_model\",\"\").replace(\"_with_solvation\",\"_solv\")}+{S.replace(\"_fingerprint\",\"_fps\")}')\n",
    "            axs[nl,ns].grid()\n",
    "                \n",
    "        obj3_box = [dict_res_obj3[target][L][sm]['test'] for sm in ['SM1','SM2','SM3','SM7','SM8']] #[:-1]\n",
    "        bp2 = axs[nl,len(AVAILABLE_SUBSTRATE_REPRESENTATION_TYPES)].boxplot(obj3_box, positions = [0, 1, 2, 3, 4], widths = 0.2, patch_artist=True, boxprops=dict(facecolor=\"C2\"))\n",
    "        axs[nl,len(AVAILABLE_SUBSTRATE_REPRESENTATION_TYPES)].set_xticks([0, 1, 2, 3, 4],['SM1','SM2','SM3','SM7','SM8'], rotation=90, ha='right')\n",
    "        axs[nl,len(AVAILABLE_SUBSTRATE_REPRESENTATION_TYPES)].set_title(f'{L.replace(\"_model\",\"\")}')\n",
    "        axs[nl,len(AVAILABLE_SUBSTRATE_REPRESENTATION_TYPES)].grid()\n",
    "        if target == 'Conversion':\n",
    "            axs[nl,len(AVAILABLE_SUBSTRATE_REPRESENTATION_TYPES)].set_ylim([0.48,1])\n",
    "        else:\n",
    "            axs[nl,len(AVAILABLE_SUBSTRATE_REPRESENTATION_TYPES)].set_ylim([-0.5,1])\n",
    "\n",
    "        if nl == 0:\n",
    "            #if target == 'Conversion':\n",
    "            axs[nl,len(AVAILABLE_SUBSTRATE_REPRESENTATION_TYPES)].legend((sc1, bp1[\"boxes\"][0], bp2[\"boxes\"][0]), ('out-of-domain', 'partially out-of-domain', 'in-domain'), loc='upper right')\n",
    "            #else:\n",
    "            #    axs[nl,len(AVAILABLE_SUBSTRATE_REPRESENTATION_TYPES)].legend((sc1, bp1[\"boxes\"][0], bp2[\"boxes\"][0], sc2, sc3), ('Obj1', 'Obj2', 'Obj3', 'Obj4','Obj4(yscrambling)'), loc='upper right')\n",
    "        \n",
    "    for ax in fig.get_axes():\n",
    "        ax.label_outer()\n",
    "        \n",
    "        \n",
    "    plt.tight_layout()\n",
    "    #plt.savefig(f'./imgs/{target}_all_boxplots.png',dpi = 600, bbox_inches='tight')\n",
    "    #plt.savefig(f'./imgs/{target}_all_boxplots.svg',bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8789b525",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "def do_grouped_boxplots(data_dict, obj1_res, S, dict_cases, target, data_set):\n",
    "    # Convert dictionary to DataFrame\n",
    "    data = []\n",
    "    for key, values in data_dict.items():\n",
    "        for i, value in enumerate(values):\n",
    "            group = f\"Case {i + 1}\"\n",
    "            for v in value:\n",
    "                data.append([key, group, v])\n",
    "                \n",
    "    if target == 'Conversion':\n",
    "        scr = 'BalancedAccuracy'\n",
    "    else:\n",
    "        scr = 'R2score'\n",
    "    df = pd.DataFrame(data, columns=['Ligand_descriptors', 'Case', scr])\n",
    "\n",
    "    # Draw boxplot\n",
    "    plt.figure(figsize=(8, 5))\n",
    "\n",
    "    colors = sns.cubehelix_palette(n_colors=len(data_dict), start=2, rot=0, dark=0.2, light=0.8)\n",
    "    ax = sns.boxplot(data=df, x='Case', y=scr, hue='Ligand_descriptors', palette=colors)\n",
    "    \n",
    "    new_legend_labels = ['dft-nbd', 'ecfp', 'ohe', 'random'] # Update with your desired labels\n",
    "    handles, _ = ax.get_legend_handles_labels()\n",
    "    ax.legend(handles, new_legend_labels)  # You can change the title too\n",
    "\n",
    "    \n",
    "    new_labels = list(dict_cases.keys())\n",
    "    ax.set_xticklabels(new_labels)\n",
    "    \n",
    "    positions = []\n",
    "    for keys_count in range(len(df['Case'].unique())):\n",
    "        line_position = keys_count + 0.5\n",
    "        plt.axvline(x=line_position, color='gray', linestyle='--')\n",
    "        positions = positions + list(np.arange(keys_count-0.30,keys_count+0.4,(0.5+0.30)/len(obj1_res.keys())))\n",
    "            \n",
    "    #positions = [-0.3, -0.10, 0.10, 0.3]    # Plot a circle above each box\n",
    "    #positions = [-0.35,-0.24, -0.15, -0, 0.1, 0.24, 0.34,0.45\n",
    "    ncases = len(df['Case'].unique())\n",
    "    positions_updated = []\n",
    "    for r in range(len(new_legend_labels)):\n",
    "        positions_updated= positions_updated+positions[r::len(new_legend_labels)]\n",
    "    \n",
    "    cnt = 0\n",
    "    for i, key in enumerate(obj1_res.keys()):\n",
    "        for j, group in enumerate(df['Case'].unique()):\n",
    "            x_coord = positions_updated[cnt]\n",
    "            plt.scatter(x_coord, obj1_res[key][j], marker='o', color='red', s=30, alpha = 0.7)\n",
    "            cnt+=1\n",
    "\n",
    "    plt.title(f\"Out-of-Domain: Target={target} - Substrate={S} - {data_set} set\")\n",
    "    if target == 'Conversion':\n",
    "        plt.ylim([0.4,1])\n",
    "    else:\n",
    "        plt.ylim([-0.4,1])\n",
    "    plt.xlim([-0.5,6.5])\n",
    "    #plt.savefig(f'./imgs/{target}_{data_set}_obj1_2_substrateOHE.png',dpi = 600, bbox_inches='tight')\n",
    "    #plt.savefig(f'./imgs/{target}_{data_set}_obj1_2_substrateOHE.svg', bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dd6cfda9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "positions = [0, 1, 3, 4, 6, 7, 9]\n",
    "plt.rcParams.update({'font.size': 11})\n",
    "S = 'ohe'\n",
    "\n",
    "AVAILABLE_LIGAND_REPRESENTATION_TYPES = ['dft_nbd_model', 'ecfp', 'ohe', 'random']\n",
    "\n",
    "for target in ['Conversion', 'DDG']:\n",
    "    obj1_res = dict()\n",
    "    obj2_res = dict()\n",
    "    for nl, L in enumerate(AVAILABLE_LIGAND_REPRESENTATION_TYPES):\n",
    "        list_test = [dict_res_obj2[target][L][S][case]['test'] for case in dict_cases.keys()]\n",
    "        obj1_dot = [dict_res_obj1[target][L][S][case]['test'][0] for case in dict_cases.keys()]\n",
    "        \n",
    "        obj1_res[L] = obj1_dot\n",
    "        obj2_res[L] = [i[0] for i in list_test]\n",
    "    do_grouped_boxplots(obj2_res, obj1_res, S, dict_cases, target,'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "14ebbad7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "positions = [0, 1, 3, 4, 6, 7, 9]\n",
    "plt.rcParams.update({'font.size': 11})\n",
    "S = 'ohe'\n",
    "\n",
    "for target in ['Conversion', 'DDG']:\n",
    "    obj1_res = dict()\n",
    "    obj2_res = dict()\n",
    "    for nl, L in enumerate(AVAILABLE_LIGAND_REPRESENTATION_TYPES):\n",
    "        list_test = [dict_res_obj2[target][L][S][case]['train'] for case in dict_cases.keys()]\n",
    "        obj1_dot = [dict_res_obj1[target][L][S][case]['train'][0] for case in dict_cases.keys()]\n",
    "        \n",
    "        obj1_res[L] = obj1_dot\n",
    "        obj2_res[L] = [i[0] for i in list_test]\n",
    "    do_grouped_boxplots(obj2_res, obj1_res, S, dict_cases, target, 'train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "34596906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib.patches import Rectangle\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams.update({'font.size': 11})\n",
    "\n",
    "def do_grouped_boxplots2(data_dict, target,list_thr, data_set, new_legend_labels):\n",
    "    # Convert dictionary to DataFrame\n",
    "    data = []\n",
    "    for key, values in data_dict.items():\n",
    "        for i, value in enumerate(values):\n",
    "            group = f\"Case {i + 1}\"\n",
    "            for v in value:\n",
    "                data.append([key, group, v])\n",
    "    \n",
    "    if target == 'Conversion':\n",
    "        scr = 'BalancedAccuracy'\n",
    "    else:\n",
    "        scr = 'R2score'\n",
    "        \n",
    "    df = pd.DataFrame(data, columns=['Ligand_descr', 'Case', scr])\n",
    "    # Draw boxplot\n",
    "    plt.figure(figsize=(8, 5))\n",
    "\n",
    "    colors = sns.cubehelix_palette(n_colors=len(data_dict), start=2, rot=0, dark=0.2, light=0.8)\n",
    "    ax = sns.boxplot(data=df, x='Case', y=scr, hue='Ligand_descr', palette=colors)\n",
    "    #####\n",
    "    #handles, labels = ax.get_legend_handles_labels()\n",
    "    #ax.legend(handles=handles, labels=labels, loc='upper left', bbox_to_anchor=(1, 1))\n",
    "    \n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    ax.legend(handles=handles, labels=new_legend_labels)  # You can change the title too\n",
    "\n",
    "    #######\n",
    "    if list_thr:\n",
    "        new_labels = [f'{i}\\nthr={str(j)[:4]}' for i,j in zip(['SM1','SM2','SM3','SM4','SM5'], list_thr[:-1]+[0.99])]\n",
    "    else:\n",
    "        new_labels = ['SM1','SM2','SM3','SM4','SM5']\n",
    "    ax.set_xticklabels(new_labels)\n",
    "    \n",
    "\n",
    "    for keys_count in range(len(new_labels)-1):\n",
    "        line_position = keys_count + 0.5\n",
    "        plt.axvline(x=line_position, color='gray', linestyle='--')\n",
    "        \n",
    "\n",
    "    #plt.title(f\"Substrate={S}\")\n",
    "    if target =='Conversion':\n",
    "        plt.ylim([0.4,0.9])\n",
    "    else:\n",
    "        plt.ylim([-0.3,0.4])\n",
    "    plt.title(f\"In-domain: Target={target} - {data_set} set\")\n",
    "    #plt.savefig(f'./imgs/{target}_{data_set}_obj3.svg', bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "AVAILABLE_LIGAND_REPRESENTATION_TYPES = ['dft_nbd_model', 'ecfp', 'random']\n",
    "new_legend_labels = ['dft-nbd', 'ecfp', 'random']\n",
    "#AVAILABLE_LIGAND_REPRESENTATION_TYPES = ['dft_nbd_model', 'ecfp', 'ohe']\n",
    "\n",
    "for target in ['Conversion', 'DDG']:\n",
    "    obj3_res = dict()\n",
    "    for nl, L in enumerate(AVAILABLE_LIGAND_REPRESENTATION_TYPES):\n",
    "        list_test = [dict_res_obj3[target][L][sm]['test'] for sm in ['SM1','SM2','SM3','SM7','SM8']]\n",
    "        if target == 'Conversion':\n",
    "            list_thr = [dict_res_obj3[target][L][sm]['thr'] for sm in ['SM1','SM2','SM3','SM7','SM8']]\n",
    "        obj3_res[L] = [i for i in list_test]\n",
    "    if target == 'Conversion':\n",
    "        do_grouped_boxplots2(obj3_res, target, list_thr, 'test', new_legend_labels)\n",
    "    else:\n",
    "        do_grouped_boxplots2(obj3_res, target, None, 'test', new_legend_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b00f3f54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "positions = [0, 1, 3, 4, 6, 7, 9]\n",
    "plt.rcParams.update({'font.size': 11})\n",
    "\n",
    "def do_grouped_boxplots2(data_dict, target,data_set):\n",
    "    # Convert dictionary to DataFrame\n",
    "    data = []\n",
    "    for key, values in data_dict.items():\n",
    "        for i, value in enumerate(values):\n",
    "            group = f\"Case {i + 1}\"\n",
    "            for v in value:\n",
    "                data.append([key, group, v])\n",
    "    df = pd.DataFrame(data, columns=['Ligand_descr', 'Case', 'Score'])\n",
    "\n",
    "    # Draw boxplot\n",
    "    plt.figure(figsize=(14, 6))\n",
    "\n",
    "    colors = sns.cubehelix_palette(n_colors=len(data_dict), start=2, rot=0, dark=0.2, light=0.8)\n",
    "    ax = sns.boxplot(data=df, x='Case', y='Score', hue='Ligand_descr', palette=colors)\n",
    "    \n",
    "    new_labels = ['SM1','SM2','SM3','SM4','SM5']\n",
    "    ax.set_xticklabels(new_labels)\n",
    "    \n",
    "\n",
    "    for keys_count in range(len(new_labels)-1):\n",
    "        line_position = keys_count + 0.5\n",
    "        plt.axvline(x=line_position, color='gray', linestyle='--')\n",
    "        \n",
    "\n",
    "    #plt.title(f\"Substrate={S}\")\n",
    "    if target =='Conversion':\n",
    "        plt.ylim([0.5,1])\n",
    "    else:\n",
    "        plt.ylim([0,1])\n",
    "    plt.title(f\"Target={target} - {data_set} set\")\n",
    "    #plt.savefig(f'./imgs/{target}_{data_set}_obj3.png',dpi = 600, bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "#AVAILABLE_LIGAND_REPRESENTATION_TYPES = ['dft_nbd_model', 'ecfp', 'ohe', 'random']\n",
    "AVAILABLE_LIGAND_REPRESENTATION_TYPES = ['dft_nbd_model', 'ecfp', 'random']\n",
    "\n",
    "for target in ['Conversion', 'DDG']:\n",
    "    obj3_res = dict()\n",
    "    for nl, L in enumerate(AVAILABLE_LIGAND_REPRESENTATION_TYPES):\n",
    "        list_test = [dict_res_obj3[target][L][sm]['train'] for sm in ['SM1','SM2','SM3','SM7','SM8']]\n",
    "        obj3_res[L] = [i for i in list_test]\n",
    "    do_grouped_boxplots2(obj3_res, target, 'train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b375b018",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\8\\ipykernel_80980\\2559009608.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  axs[idx].set_xticklabels(['', 'SM1', 'SM2', 'SM3', 'SM4', 'SM5'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib.patches import Rectangle\n",
    "import seaborn as sns\n",
    "\n",
    "positions = [0, 1, 3, 4, 6, 7, 9]\n",
    "plt.rcParams.update({'font.size': 11})\n",
    "\n",
    "def do_grouped_boxplots2(L, data_set, new_legend_labels):\n",
    "    # Convert dictionary to DataFrame\n",
    "    scr = 'R2score'\n",
    "    \n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "\n",
    "    scatter_handles = []\n",
    "    legend_labels = []\n",
    "    \n",
    "    for idx, target in enumerate(['Conversion', 'DDG']):\n",
    "        list_test = [dict_res_obj4[target]['dft_nbd_model'][sm][42]['true'] for sm in ['SM1', 'SM2', 'SM3', 'SM7', 'SM8']]\n",
    "        list_test_frac = [dict_res_obj4[target]['dft_nbd_model'][sm][42]['true_fraction'] for sm in\n",
    "                          ['SM1', 'SM2', 'SM3', 'SM7', 'SM8']]\n",
    "        list_test_rand = [dict_res_obj4[target]['random_50'][sm][42]['true'] for sm in\n",
    "                          ['SM1', 'SM2', 'SM3', 'SM7', 'SM8']]\n",
    "        list_test_rand_frac = [dict_res_obj4[target]['random_50'][sm][42]['true_fraction'] for sm in\n",
    "                               ['SM1', 'SM2', 'SM3', 'SM7', 'SM8']]\n",
    "        list_test_ecfp = [dict_res_obj4[target]['ecfp'][sm][42]['true'] for sm in\n",
    "                          ['SM1', 'SM2', 'SM3', 'SM7', 'SM8']]\n",
    "        list_test_ecfp_frac = [dict_res_obj4[target]['ecfp'][sm][42]['true_fraction'] for sm in\n",
    "                               ['SM1', 'SM2', 'SM3', 'SM7', 'SM8']]\n",
    "\n",
    "        for i, key in enumerate(list_test):\n",
    "            x_coord = i - 0.25\n",
    "            scatter_dft = axs[idx].scatter(i, list_test[i], marker='x', color='red', s=50, alpha=0.7)\n",
    "            axs[idx].text(i + 0.03, list_test[i], str(list_test_frac[i])[:3], fontsize=8)\n",
    "        \n",
    "        legend_labels.append('dft-nbd')\n",
    "        scatter_handles.append(scatter_dft)\n",
    "        \n",
    "        for i, key in enumerate(list_test_rand):\n",
    "            x_coord = i + 0.25\n",
    "            scatter_rand = axs[idx].scatter(i, list_test_rand[i], marker='o', color='blue', s=50, alpha=0.7)\n",
    "            axs[idx].text(i + 0.03, list_test_rand[i], str(list_test_rand_frac[i])[:3], fontsize=8)\n",
    "        \n",
    "        legend_labels.append('random')\n",
    "        scatter_handles.append(scatter_rand)\n",
    "\n",
    "        # if target=='DDG':\n",
    "        list_test_ecfp = [dict_res_obj4[target]['ecfp'][sm][42]['true'] for sm in\n",
    "                          ['SM1', 'SM2', 'SM3', 'SM7', 'SM8']]\n",
    "        list_test_ecfp_frac = [dict_res_obj4[target]['ecfp'][sm][42]['true_fraction'] for sm in\n",
    "                               ['SM1', 'SM2', 'SM3', 'SM7', 'SM8']]\n",
    "        for i, key in enumerate(list_test_ecfp):\n",
    "            x_coord = i\n",
    "            scatter_ecfp = axs[idx].scatter(i, list_test_ecfp[i], marker='*', color='magenta', s=50, alpha=0.7)\n",
    "            axs[idx].text(i + 0.03, list_test_ecfp[i], str(list_test_ecfp_frac[i])[:3], fontsize=8)\n",
    "        \n",
    "        legend_labels.append('ecfp')\n",
    "        scatter_handles.append(scatter_ecfp)\n",
    "\n",
    "        axs[idx].set_ylabel(scr)\n",
    "        axs[idx].set_ylim([0.3, 1])\n",
    "        axs[idx].set_xticklabels(['', 'SM1', 'SM2', 'SM3', 'SM4', 'SM5'])\n",
    "        axs[idx].set_title(f\"Target={target} - {data_set} set\")\n",
    "\n",
    "    # Create a legend with 'x', 'o', and '*' as markers\n",
    "    axs[1].legend(scatter_handles, legend_labels[:3], markerscale=1, fontsize=8)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "AVAILABLE_LIGAND_REPRESENTATION_TYPES = ['dft_nbd_model', 'ecfp', 'random_50']\n",
    "new_legend_labels = ['dft-nbd', 'ecfp', 'random']\n",
    "\n",
    "do_grouped_boxplots2(L, 'test', new_legend_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ec27283",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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