Yield Prediction using Molecular Fingerprints_Morgan_2048_Condensation_reactions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7a6f84cd-dd22-4825-8006-7d19f4d8e9b7",
   "metadata": {},
   "source": [
    "# Yield Prediction using Molecular Fingerprints_Morgan_Condensation_reactions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dea71387-823a-4d78-96b9-3b972cc2d9f0",
   "metadata": {},
   "source": [
    "Found this dataset from a recent publication by a D.M. Makarov et al. (Journal of Computational Science 74 (2023) 102173) (https://doi.org/10.1016/j.jocs.2023.102173)\n",
    "\n",
    "\"\"\"We considered pyrrole or dipyrromethane condensation reactions with various aldehydes, resulting in the production of boron(III) dipyrromethene or BODIPY (681 records). These reactions were retrieved from articles (see “Dataset reactions” and Scheme S1). Addi tionally, we used the reactions of the production of dipyrromethane (111 records) and porphyrins (457 records). All condensation reactions for dipyrromethanes and 213 reactions for porphyrins with various al dehydes were obtained in our laboratory. The remaining 244 reactions for the porphyrins synthesis were obtained from articles (see “Dataset reactions”). Our experimental dataset is based on a study of pyrrole condensation processes with aldehydes, using catalytic amounts of organic acids to produce ms-aryl- and ß-alkyl-substituted dipyrro methanes. \"\"\"\n",
    "\n",
    "The objective of this notebook is to introduce a method for predicting yield\n",
    "\n",
    "As a beginner, there may be numerous opportunities for improvement in this notebook. I was largely inspired by the work of D.M. Makarov et al and the STEPHEN LEE's notebook (BELKA: Molecule Representations for ML Tutorial) thanks to them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfe57040-508a-4bdb-81f0-3e87d2c8a90a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1a9c8899-646e-4095-b592-7c9a11b71df5",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f9beab50-bd54-4202-a7e5-dfe8e042df3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e574fdd2-28f2-493b-b3f7-c2d822a63a28",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_row', 211)\n",
    "pd.set_option('display.max_columns',211)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dd2413c3-c477-49b5-8ebb-cb5421d79481",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1249, 4)\n"
     ]
    },
    {
     "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>SMILES</th>\n",
       "      <th>yield</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Ind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CC(C)(C)c1ccc(OCCCCOc2ccc(C(C)(C)C)cc2C=O)c(C=...</td>\n",
       "      <td>0.05</td>\n",
       "      <td>20</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>O=Cc1ccccc1OCCOC(=O)C#CC(=O)OCCOc1ccccc1C=O.c1...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>40</td>\n",
       "      <td>909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c1cc[nH]c1.Cc1cc(C)c(C=O)c(C)c1.COC(=O)c1ccc(C...</td>\n",
       "      <td>2.00</td>\n",
       "      <td>20</td>\n",
       "      <td>747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CC(C)(C)c1ccc(OCCCCCOc2ccc(C(C)(C)C)cc2C=O)c(C...</td>\n",
       "      <td>2.10</td>\n",
       "      <td>20</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cc1ccc(-c2ccccc2C=O)cc1.Fc1c(F)c(F)c(C(c2ccc[n...</td>\n",
       "      <td>2.50</td>\n",
       "      <td>25</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              SMILES  yield  Temperature  Ind\n",
       "0  CC(C)(C)c1ccc(OCCCCOc2ccc(C(C)(C)C)cc2C=O)c(C=...   0.05           20   78\n",
       "1  O=Cc1ccccc1OCCOC(=O)C#CC(=O)OCCOc1ccccc1C=O.c1...   1.00           40  909\n",
       "2  c1cc[nH]c1.Cc1cc(C)c(C=O)c(C)c1.COC(=O)c1ccc(C...   2.00           20  747\n",
       "3  CC(C)(C)c1ccc(OCCCCCOc2ccc(C(C)(C)C)cc2C=O)c(C...   2.10           20   77\n",
       "4  Cc1ccc(-c2ccccc2C=O)cc1.Fc1c(F)c(F)c(C(c2ccc[n...   2.50           25  266"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Téléchargement du Dataset \n",
    "DataFrame = r\"C:\\Users\\loris\\Desktop\\IA_chemistry\\Dipyrromethanes_condensation_reactions-main\\Condensation_reactions - Copie.xlsx\"\n",
    "\n",
    "df = pd.read_excel(DataFrame)\n",
    "\n",
    "df= df.copy()\n",
    "\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b35da81-46ea-4231-82fe-c5266ef60cd1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "507eb84e-cdc3-4c9b-b7b0-59759f282be3",
   "metadata": {},
   "source": [
    "## Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2e30ffcf-58f9-458f-9b95-723e08307e9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SMILES         0\n",
       "yield          0\n",
       "Temperature    0\n",
       "Ind            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recherche des valeurs manquantes\n",
    "missing_values = df.isnull().sum()\n",
    "missing_values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "543e4c2b-71cc-4980-85b6-771867f1744b",
   "metadata": {},
   "source": [
    "## Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c603859a-9e8a-45b7-a125-d2e80cb1b010",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Création de la figure et des axes pour les subplots\n",
    "fig, axs = plt.subplots(2, 1, figsize=(10,8))\n",
    "\n",
    "# Tracé de l'histogramme sur le premier subplot\n",
    "sns.histplot(data=df, x='yield', bins=30, ax=axs[0])\n",
    "axs[0].set_xlabel('Rendement chimique (%)')\n",
    "axs[0].set_ylabel(\"Nombre d'occurences\")\n",
    "axs[0].set_title(\"Distribution des rendements chimiques\")\n",
    "\n",
    "# Tracé du boxplot sur le deuxième subplot\n",
    "sns.boxplot(df[\"yield\"], ax=axs[1])\n",
    "axs[1].set_ylabel(\"Rendement chimique (%)\")\n",
    "axs[1].set_title(\"Distribution des rendements chimiques\")\n",
    "\n",
    "# Ajustement de l'espacement entre les subplots\n",
    "plt.tight_layout()\n",
    "\n",
    "# Affichage des graphiques \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "853bcdec-68ac-41df-96c8-82229af2fe6c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06543bf8-dcf0-4773-952e-c904bffffc93",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c3020efc-9c99-44f8-a711-017629a79ca5",
   "metadata": {},
   "source": [
    "## Obtenir des Mols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4e9d50d7-5902-4fbb-a5df-836bdb180c8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7dcac50a-3254-42e5-99ac-24ffda470da7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change le '>>' par '.', pour obtenir des Mols\n",
    "def change_smile(df, columns_names):\n",
    "    df['SMILES_2'] = df[columns_names].replace('>>', '.', regex=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d4b269c3-12b1-4326-90db-41c3275270cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transforme le SMILE en Mols\n",
    "def Mols(df, columns_names):\n",
    "    df[\"mols\"] = pd.Series(df[columns_names]).apply(Chem.MolFromSmiles)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "87703df5-99b0-4a9b-917a-2520894ca3b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change le '>>' par '.', pour obtenir des Mols\n",
    "df = change_smile(df, \"SMILES\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4af635d8-427e-4c45-bd09-5d9514959f0f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[12:10:51] Explicit valence for atom # 26 H, 2, is greater than permitted\n",
      "[12:10:51] Explicit valence for atom # 26 H, 2, is greater than permitted\n",
      "[12:10:52] Explicit valence for atom # 0 O, 3, is greater than permitted\n",
      "[12:10:52] Explicit valence for atom # 10 O, 3, is greater than permitted\n"
     ]
    }
   ],
   "source": [
    "# Transforme le SMILE en Mols\n",
    "df = Mols(df, \"SMILES_2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b11f507-077f-4607-ae70-c02619f278ee",
   "metadata": {},
   "source": [
    "### Enlève les dernières valeurs manquantes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7b96fcc5-29e5-4e23-967e-f5d8c0a5948a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SMILES         0\n",
       "yield          0\n",
       "Temperature    0\n",
       "Ind            0\n",
       "SMILES_2       0\n",
       "mols           4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f6a3825e-ebf1-4491-8068-f79e3704357a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "80a998a9-2eb8-4705-aec3-fbfa2243a96c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SMILES         0\n",
       "yield          0\n",
       "Temperature    0\n",
       "Ind            0\n",
       "SMILES_2       0\n",
       "mols           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67752ade-74ea-45e4-9329-04a59c844e42",
   "metadata": {},
   "source": [
    "## 🗝️ Molecular Fingerprints"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46f89aa6-74e7-4043-96b1-3bc64d05df68",
   "metadata": {},
   "source": [
    "Molecular fingerprints are fixed-length numerical representations that encode molecular structures, usually as a binary vector. Each element (called a bit) of a fingerprint vector represents the presence of specific atomic or structural features within the chemical compound.\n",
    "\n",
    "Below is example code to generate two types of commonly used molecular fingerprints:\n",
    "\n",
    "MACCS Keys: a 166-bit fingerprint based on a predefined list of molecular substructures or patterns\n",
    "\n",
    "Morgan Fingerprints: a fixed-length bit fingerprint based on hashed topological features of atoms and their bond connectivities within a specified radius. Also referred to as Extended-Connectivity Fingerprints (ECFPs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "60399e62-ac47-42d4-bac8-070587f8236e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit.Chem import Descriptors\n",
    "from rdkit.ML.Descriptors import MoleculeDescriptors\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "454b9d6b-c035-4007-8bf5-8f7eaa0a8c3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "mols = df[\"mols\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ac7a5a03-352a-4064-a225-a8d4388785a4",
   "metadata": {},
   "outputs": [
    {
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       "      <th>90</th>\n",
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       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
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       "      <th>102</th>\n",
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       "      <th>1943</th>\n",
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       "      <th>1946</th>\n",
       "      <th>1947</th>\n",
       "      <th>1948</th>\n",
       "      <th>1949</th>\n",
       "      <th>1950</th>\n",
       "      <th>1951</th>\n",
       "      <th>1952</th>\n",
       "      <th>1953</th>\n",
       "      <th>1954</th>\n",
       "      <th>1955</th>\n",
       "      <th>1956</th>\n",
       "      <th>1957</th>\n",
       "      <th>1958</th>\n",
       "      <th>1959</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>1966</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>1970</th>\n",
       "      <th>1971</th>\n",
       "      <th>1972</th>\n",
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       "      <th>1974</th>\n",
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       "      <th>1976</th>\n",
       "      <th>1977</th>\n",
       "      <th>1978</th>\n",
       "      <th>1979</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
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       "      <th>2018</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 2048 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0     1     2     3     4     5     6     7     8     9     10    11    \\\n",
       "0     0     1     1     0     0     0     0     0     0     0     0     0   \n",
       "1     0     1     0     0     0     0     0     0     0     0     0     0   \n",
       "2     0     1     0     0     0     0     0     0     0     0     0     0   \n",
       "3     0     1     1     0     0     0     0     0     0     0     0     0   \n",
       "4     0     1     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   12    13    14    15    16    17    18    19    20    21    22    23    \\\n",
       "0     0     1     0     0     0     0     0     0     1     0     0     0   \n",
       "1     0     1     0     0     0     0     0     0     0     0     0     0   \n",
       "2     0     0     1     0     0     0     0     0     0     0     0     0   \n",
       "3     0     1     0     0     0     0     0     0     1     0     0     0   \n",
       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   24    25    26    27    28    29    30    31    32    33    34    35    \\\n",
       "0     0     1     0     0     0     0     0     0     0     1     0     0   \n",
       "1     0     1     0     0     0     0     0     0     0     0     0     0   \n",
       "2     0     1     0     0     0     0     0     0     0     0     0     0   \n",
       "3     0     1     0     0     0     0     0     0     0     1     0     0   \n",
       "4     0     1     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   36    37    38    39    40    41    42    43    44    45    46    47    \\\n",
       "0     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "1     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "2     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   48    49    50    51    52    53    54    55    56    57    58    59    \\\n",
       "0     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "1     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "2     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   60    61    62    63    64    65    66    67    68    69    70    71    \\\n",
       "0     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "1     0     0     0     0     0     0     0     0     0     0     0     1   \n",
       "2     1     0     0     0     0     0     0     0     0     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     0     1     0     0     0     0   \n",
       "\n",
       "   72    73    74    75    76    77    78    79    80    81    82    83    \\\n",
       "0     0     0     0     0     0     0     0     0     1     0     0     0   \n",
       "1     0     0     0     0     0     0     0     0     1     0     0     0   \n",
       "2     0     0     0     0     0     0     0     0     1     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     1     0     0     0   \n",
       "4     0     0     0     0     0     0     0     0     1     0     0     0   \n",
       "\n",
       "   84    85    86    87    88    89    90    91    92    93    94    95    \\\n",
       "0     0     0     0     1     0     0     0     0     0     0     1     0   \n",
       "1     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "2     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "3     0     0     0     1     0     0     0     0     0     0     1     0   \n",
       "4     0     0     0     0     0     0     0     0     1     0     0     0   \n",
       "\n",
       "   96    97    98    99    100   101   102   103   104   ...  1943  1944  \\\n",
       "0     0     1     0     0     0     0     0     0     0  ...     0     0   \n",
       "1     0     1     0     0     0     0     0     0     0  ...     0     0   \n",
       "2     0     0     0     0     0     0     0     0     0  ...     0     0   \n",
       "3     0     1     0     0     0     0     0     0     0  ...     0     0   \n",
       "4     0     1     0     0     0     0     0     0     0  ...     0     0   \n",
       "\n",
       "   1945  1946  1947  1948  1949  1950  1951  1952  1953  1954  1955  1956  \\\n",
       "0     0     0     1     0     0     0     0     0     0     0     0     0   \n",
       "1     0     0     1     0     0     0     0     0     0     0     0     0   \n",
       "2     0     0     0     0     1     0     0     0     1     0     0     0   \n",
       "3     0     0     1     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   1957  1958  1959  1960  1961  1962  1963  1964  1965  1966  1967  1968  \\\n",
       "0     0     0     0     0     0     0     1     0     0     1     0     0   \n",
       "1     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "2     0     0     0     0     0     1     0     0     0     0     0     0   \n",
       "3     0     0     0     0     0     0     1     0     0     1     0     0   \n",
       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   1969  1970  1971  1972  1973  1974  1975  1976  1977  1978  1979  1980  \\\n",
       "0     0     1     0     0     0     0     0     0     0     0     0     0   \n",
       "1     0     1     0     0     0     0     0     0     0     0     0     0   \n",
       "2     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "3     0     1     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     0     0     1     0     0     0   \n",
       "\n",
       "   1981  1982  1983  1984  1985  1986  1987  1988  1989  1990  1991  1992  \\\n",
       "0     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "1     0     0     0     0     0     0     0     0     0     0     1     0   \n",
       "2     0     0     0     1     0     0     0     0     0     0     1     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     1     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   1993  1994  1995  1996  1997  1998  1999  2000  2001  2002  2003  2004  \\\n",
       "0     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "1     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "2     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     1     0     0     0     0     0     0   \n",
       "\n",
       "   2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016  \\\n",
       "0     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "1     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "2     0     0     0     0     0     0     0     0     0     1     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   2017  2018  2019  2020  2021  2022  2023  2024  2025  2026  2027  2028  \\\n",
       "0     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "1     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "2     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   2029  2030  2031  2032  2033  2034  2035  2036  2037  2038  2039  2040  \\\n",
       "0     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "1     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "2     0     0     0     0     1     0     0     0     0     0     0     0   \n",
       "3     0     0     0     0     0     0     0     0     0     0     0     0   \n",
       "4     0     0     0     0     0     0     1     0     0     0     0     0   \n",
       "\n",
       "   2041  2042  2043  2044  2045  2046  2047  \n",
       "0     0     0     0     1     0     0     0  \n",
       "1     0     0     0     0     0     0     0  \n",
       "2     0     0     0     0     0     0     0  \n",
       "3     0     0     0     1     0     0     0  \n",
       "4     0     0     0     0     0     0     0  \n",
       "\n",
       "[5 rows x 2048 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate Morgan Fingerprints from RDKit Mol objects as a dataframe\n",
    "mfpt_list = [AllChem.GetMorganFingerprintAsBitVect(mol, radius=3, nBits=2048) for mol in mols]\n",
    "\n",
    "mfpt_df = pd.DataFrame([list(fpt) for fpt in mfpt_list])\n",
    "\n",
    "mfpt_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71b422de-0ed3-430b-aec0-7813adee50eb",
   "metadata": {},
   "source": [
    "Note that different values can be specified for radius and nBits when generating Morgan Fingerprints.\n",
    "\n",
    "    radius: number of bond steps to include from each atom when calculating the fingerprint. A radius of 1 means that only the immediate neighbors are considered; a radius of 2 includes neighbors up to two bonds away, etc. \n",
    "\n",
    "    nBits: total number of bits in the fingerprint (i.e. resolution). Larger nBits values increases feature dimensionality and typically increases feature sparsity.\n",
    "    \n",
    "Bit Collision refers to the scenario where different substructural features are mapped to the same bit position. This is undesirable as it reduces the fingerprint's ability to differentiate molecules. Increasing nBits increases the available feature space and thus lowers the chance of bit collisions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cf4b35e8-1af0-4afc-bb5b-ce0852f237e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1245, 2048)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mfpt_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ecf75f11-d9e8-4173-99ee-f2542a441df9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1245, 6)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1f1b16cf-9261-4833-a7e5-d13cea871114",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = pd.concat([df, mfpt_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "89418beb-d754-407d-b214-5c65ea03b1fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.isna().sum().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7f024523-c53e-4812-ab31-ea9b076db78f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "71890497-0f1f-4848-8cd8-a8e449abdcf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.isna().sum().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0faa782a-3bf2-4434-8acc-e062ac3ba3b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1241, 2054)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "06b668d5-b2bf-4720-ac33-cc028b9eee76",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>SMILES</th>\n",
       "      <th>yield</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Ind</th>\n",
       "      <th>SMILES_2</th>\n",
       "      <th>mols</th>\n",
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       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
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       "      <th>10</th>\n",
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       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
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       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>56</th>\n",
       "      <th>57</th>\n",
       "      <th>58</th>\n",
       "      <th>59</th>\n",
       "      <th>60</th>\n",
       "      <th>61</th>\n",
       "      <th>62</th>\n",
       "      <th>63</th>\n",
       "      <th>64</th>\n",
       "      <th>65</th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
       "      <th>71</th>\n",
       "      <th>72</th>\n",
       "      <th>73</th>\n",
       "      <th>74</th>\n",
       "      <th>75</th>\n",
       "      <th>76</th>\n",
       "      <th>77</th>\n",
       "      <th>78</th>\n",
       "      <th>79</th>\n",
       "      <th>80</th>\n",
       "      <th>81</th>\n",
       "      <th>82</th>\n",
       "      <th>83</th>\n",
       "      <th>84</th>\n",
       "      <th>85</th>\n",
       "      <th>86</th>\n",
       "      <th>87</th>\n",
       "      <th>88</th>\n",
       "      <th>89</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>...</th>\n",
       "      <th>1943</th>\n",
       "      <th>1944</th>\n",
       "      <th>1945</th>\n",
       "      <th>1946</th>\n",
       "      <th>1947</th>\n",
       "      <th>1948</th>\n",
       "      <th>1949</th>\n",
       "      <th>1950</th>\n",
       "      <th>1951</th>\n",
       "      <th>1952</th>\n",
       "      <th>1953</th>\n",
       "      <th>1954</th>\n",
       "      <th>1955</th>\n",
       "      <th>1956</th>\n",
       "      <th>1957</th>\n",
       "      <th>1958</th>\n",
       "      <th>1959</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>1966</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>1970</th>\n",
       "      <th>1971</th>\n",
       "      <th>1972</th>\n",
       "      <th>1973</th>\n",
       "      <th>1974</th>\n",
       "      <th>1975</th>\n",
       "      <th>1976</th>\n",
       "      <th>1977</th>\n",
       "      <th>1978</th>\n",
       "      <th>1979</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2023</th>\n",
       "      <th>2024</th>\n",
       "      <th>2025</th>\n",
       "      <th>2026</th>\n",
       "      <th>2027</th>\n",
       "      <th>2028</th>\n",
       "      <th>2029</th>\n",
       "      <th>2030</th>\n",
       "      <th>2031</th>\n",
       "      <th>2032</th>\n",
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       "      <th>2035</th>\n",
       "      <th>2036</th>\n",
       "      <th>2037</th>\n",
       "      <th>2038</th>\n",
       "      <th>2039</th>\n",
       "      <th>2040</th>\n",
       "      <th>2041</th>\n",
       "      <th>2042</th>\n",
       "      <th>2043</th>\n",
       "      <th>2044</th>\n",
       "      <th>2045</th>\n",
       "      <th>2046</th>\n",
       "      <th>2047</th>\n",
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       "      <td>909.0</td>\n",
       "      <td>O=Cc1ccccc1OCCOC(=O)C#CC(=O)OCCOc1ccccc1C=O.c1...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c1cc[nH]c1.Cc1cc(C)c(C=O)c(C)c1.COC(=O)c1ccc(C...</td>\n",
       "      <td>2.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>747.0</td>\n",
       "      <td>c1cc[nH]c1.Cc1cc(C)c(C=O)c(C)c1.COC(=O)c1ccc(C...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CC(C)(C)c1ccc(OCCCCCOc2ccc(C(C)(C)C)cc2C=O)c(C...</td>\n",
       "      <td>2.10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>CC(C)(C)c1ccc(OCCCCCOc2ccc(C(C)(C)C)cc2C=O)c(C...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x0000028CF1F...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cc1ccc(-c2ccccc2C=O)cc1.Fc1c(F)c(F)c(C(c2ccc[n...</td>\n",
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       "      <td>Cc1ccc(-c2ccccc2C=O)cc1.Fc1c(F)c(F)c(C(c2ccc[n...</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 2054 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              SMILES  yield  Temperature  \\\n",
       "0  CC(C)(C)c1ccc(OCCCCOc2ccc(C(C)(C)C)cc2C=O)c(C=...   0.05         20.0   \n",
       "1  O=Cc1ccccc1OCCOC(=O)C#CC(=O)OCCOc1ccccc1C=O.c1...   1.00         40.0   \n",
       "2  c1cc[nH]c1.Cc1cc(C)c(C=O)c(C)c1.COC(=O)c1ccc(C...   2.00         20.0   \n",
       "3  CC(C)(C)c1ccc(OCCCCCOc2ccc(C(C)(C)C)cc2C=O)c(C...   2.10         20.0   \n",
       "4  Cc1ccc(-c2ccccc2C=O)cc1.Fc1c(F)c(F)c(C(c2ccc[n...   2.50         25.0   \n",
       "\n",
       "     Ind                                           SMILES_2  \\\n",
       "0   78.0  CC(C)(C)c1ccc(OCCCCOc2ccc(C(C)(C)C)cc2C=O)c(C=...   \n",
       "1  909.0  O=Cc1ccccc1OCCOC(=O)C#CC(=O)OCCOc1ccccc1C=O.c1...   \n",
       "2  747.0  c1cc[nH]c1.Cc1cc(C)c(C=O)c(C)c1.COC(=O)c1ccc(C...   \n",
       "3   77.0  CC(C)(C)c1ccc(OCCCCCOc2ccc(C(C)(C)C)cc2C=O)c(C...   \n",
       "4  266.0  Cc1ccc(-c2ccccc2C=O)cc1.Fc1c(F)c(F)c(C(c2ccc[n...   \n",
       "\n",
       "                                                mols    0    1    2    3    4  \\\n",
       "0  <rdkit.Chem.rdchem.Mol object at 0x0000028CF1F...  0.0  1.0  1.0  0.0  0.0   \n",
       "1  <rdkit.Chem.rdchem.Mol object at 0x0000028CF1F...  0.0  1.0  0.0  0.0  0.0   \n",
       "2  <rdkit.Chem.rdchem.Mol object at 0x0000028CF1F...  0.0  1.0  0.0  0.0  0.0   \n",
       "3  <rdkit.Chem.rdchem.Mol object at 0x0000028CF1F...  0.0  1.0  1.0  0.0  0.0   \n",
       "4  <rdkit.Chem.rdchem.Mol object at 0x0000028CF1F...  0.0  1.0  0.0  0.0  0.0   \n",
       "\n",
       "     5    6    7    8    9   10   11   12   13   14   15   16   17   18   19  \\\n",
       "0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "    20   21   22   23   24   25   26   27   28   29   30   31   32   33   34  \\\n",
       "0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0   \n",
       "1  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0   \n",
       "4  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "    35   36   37   38   39   40   41   42   43   44   45   46   47   48   49  \\\n",
       "0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "    50   51   52   53   54   55   56   57   58   59   60   61   62   63   64  \\\n",
       "0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       "3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "    65   66   67   68   69   70   71   72   73   74   75   76   77   78   79  \\\n",
       "0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "    80   81   82   83   84   85   86   87   88   89   90   91   92   93   94  \\\n",
       "0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0   \n",
       "1  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0   \n",
       "4  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       "\n",
       "    95   96   97   98  ...  1943  1944  1945  1946  1947  1948  1949  1950  \\\n",
       "0  0.0  0.0  1.0  0.0  ...   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   \n",
       "1  0.0  0.0  1.0  0.0  ...   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   \n",
       "2  0.0  0.0  0.0  0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0   1.0   0.0   \n",
       "3  0.0  0.0  1.0  0.0  ...   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   \n",
       "4  0.0  0.0  1.0  0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1951  1952  1953  1954  1955  1956  1957  1958  1959  1960  1961  1962  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1963  1964  1965  1966  1967  1968  1969  1970  1971  1972  1973  1974  \\\n",
       "0   1.0   0.0   0.0   1.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   1.0   0.0   0.0   1.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1975  1976  1977  1978  1979  1980  1981  1982  1983  1984  1985  1986  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0   \n",
       "\n",
       "   1987  1988  1989  1990  1991  1992  1993  1994  1995  1996  1997  1998  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0   \n",
       "\n",
       "   1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   2011  2012  2013  2014  2015  2016  2017  2018  2019  2020  2021  2022  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   2023  2024  2025  2026  2027  2028  2029  2030  2031  2032  2033  2034  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   2035  2036  2037  2038  2039  2040  2041  2042  2043  2044  2045  2046  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0   \n",
       "4   1.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   2047  \n",
       "0   0.0  \n",
       "1   0.0  \n",
       "2   0.0  \n",
       "3   0.0  \n",
       "4   0.0  \n",
       "\n",
       "[5 rows x 2054 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2d56eb2-5ae7-4c57-8cf9-9f7e17098fb8",
   "metadata": {},
   "source": [
    "## Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "04f4dc7f-f31d-4282-bbac-e2a8c05c07df",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import cross_val_score, KFold, train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
    "from statistics import mean, stdev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbfc3e3e-6b22-4f42-a24d-ca5c395ddc57",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "be00eb42-37ad-400f-948f-96038ea43781",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df_1.drop([\"SMILES\", \"SMILES_2\", \"yield\", \"Ind\", \"mols\"], axis=1)\n",
    "y=df_1['yield']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "61acc5d9-d272-4613-b82b-38558408c950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1241, 2049)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c4154e86-bcd8-4f69-b7de-8710af61380f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1241,)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39121f55-5895-48b9-af3a-294cc1342fae",
   "metadata": {},
   "source": [
    "### Model_random_forest_regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1026cc02-b81a-4fec-a6d5-94179eb6d406",
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_forest_regression(X, y):\n",
    "    rmse_scores = []\n",
    "    r2_scores = []\n",
    "    for i in range(10):\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=i)\n",
    "        model = RandomForestRegressor(n_estimators=120, max_depth=25)\n",
    "        model.fit(X_train, y_train)\n",
    "        y_pred = model.predict(X_test)\n",
    "        rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "        rmse_scores.append(rmse)\n",
    "        r2 = r2_score(y_test, y_pred)\n",
    "        r2_scores.append(r2)\n",
    "        #print(f\"Iteration {i}: RMSE = {rmse:.3f}, R^2  = {r2:.3f}\")\n",
    "    # Retourner la moyenne et l'écart-type des scores\n",
    "    return mean(rmse_scores), stdev(rmse_scores), mean(r2_scores), stdev(r2_scores)\n",
    "    \n",
    "    print(\"\\nAverage performance of test set at 10 split\")\n",
    "    print(\"Metric\\tavg\\tstdev\")\n",
    "    print(\"R^2\\t%.2f\\t%.2f\" % (mean(r2_scores), stdev(r2_scores)))\n",
    "    print(\"RMSE\\t%.2f\\t%.2f\" % (mean(rmse_scores), stdev(rmse_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3e534363-e3db-4503-b04e-44428b67cb78",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from statistics import mean, stdev\n",
    "\n",
    "def statistics_for_the_training_set_with_5CV_RFR(X, y):\n",
    "    r2_scores = []\n",
    "    rmse_scores = []\n",
    "    \n",
    "    for i in range(10):\n",
    "        X_tr, X_t, y_tr, y_t = train_test_split(X, y, test_size=0.2, random_state=i)\n",
    "        X_tr.reset_index(drop=True, inplace=True)\n",
    "        y_tr.reset_index(drop=True, inplace=True)\n",
    "        \n",
    "        cv = KFold(n_splits=5, shuffle=True, random_state=1)\n",
    "        fold_r2_scores = []\n",
    "        fold_rmse_scores = []\n",
    "        \n",
    "        for train_index, test_index in cv.split(X_tr):\n",
    "            X_train, X_test = X_tr.loc[train_index].values, X_tr.loc[test_index].values\n",
    "            y_train, y_test = y_tr.loc[train_index].values, y_tr.loc[test_index].values\n",
    "            \n",
    "            model = RandomForestRegressor(n_estimators=120, max_depth=25)\n",
    "            model.fit(X_train, y_train)\n",
    "            y_pred = model.predict(X_test)\n",
    "            \n",
    "            fold_rmse_scores.append(np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "            fold_r2_scores.append(r2_score(y_test, y_pred))\n",
    "        \n",
    "        # Moyennes et écarts-types des scores pour chaque pli\n",
    "        rmse_scores.append(mean(fold_rmse_scores))\n",
    "        r2_scores.append(mean(fold_r2_scores))\n",
    "    \n",
    "    return mean(rmse_scores), stdev(rmse_scores), mean(r2_scores), stdev(r2_scores)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8cffae9-90a9-41a7-913f-080326ea2a62",
   "metadata": {},
   "source": [
    "### Model CatBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f9e132e5-108a-4550-8e53-3033e632299f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importer CatBoost après l'installation\n",
    "import catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "509d4741-1ed8-4abf-9f68-1614e77e3389",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def catboost_regression(X, y):\n",
    "    rmse_scores = []\n",
    "    r2_scores = []\n",
    "    for i in range(10):\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=i)\n",
    "        model = catboost.CatBoostRegressor(learning_rate=0.02, l2_leaf_reg=9.5, depth=7, silent=True)\n",
    "        model.fit(X_train, y_train)\n",
    "        y_pred = model.predict(X_test)\n",
    "        rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "        rmse_scores.append(rmse)\n",
    "        r2 = r2_score(y_test, y_pred)\n",
    "        r2_scores.append(r2)\n",
    "        #print(f\"Iteration {i}: RMSE = {rmse:.3f}, R2 = {r2:.3f}\")\n",
    "    \n",
    "    # Retourner la moyenne et l'écart-type des scores\n",
    "    return mean(rmse_scores), stdev(rmse_scores), mean(r2_scores), stdev(r2_scores)\n",
    "    \n",
    "    print(\"\\nAverage performance of test set at 10 split\")\n",
    "    print(\"Metric\\tavg\\tstdev\")\n",
    "    print(\"R^2\\t%.2f\\t%.2f\" % (mean(r2_scores), stdev(r2_scores)))\n",
    "    print(\"RMSE\\t%.2f\\t%.2f\" % (mean(rmse_scores), stdev(rmse_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d63b032f-f4f4-4e0b-a3e0-8630447c9ff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from statistics import mean, stdev\n",
    "\n",
    "def statistics_for_the_training_set_with_5CV_catBoost(X, y):\n",
    "    r2_scores = []\n",
    "    rmse_scores = []\n",
    "    \n",
    "    for i in range(10):\n",
    "        X_tr, X_t, y_tr, y_t = train_test_split(X, y, test_size=0.2, random_state=i)\n",
    "        X_tr.reset_index(drop=True, inplace=True)\n",
    "        y_tr.reset_index(drop=True, inplace=True)\n",
    "        \n",
    "        cv = KFold(n_splits=5, shuffle=True, random_state=1)\n",
    "        fold_r2_scores = []\n",
    "        fold_rmse_scores = []\n",
    "        \n",
    "        for train_index, test_index in cv.split(X_tr):\n",
    "            X_train, X_test = X_tr.loc[train_index].values, X_tr.loc[test_index].values\n",
    "            y_train, y_test = y_tr.loc[train_index].values, y_tr.loc[test_index].values\n",
    "            \n",
    "            model = catboost.CatBoostRegressor(learning_rate=0.02, l2_leaf_reg=9.5, depth=7, silent=True)\n",
    "            model.fit(X_train, y_train)\n",
    "            y_pred = model.predict(X_test)\n",
    "            \n",
    "            fold_rmse_scores.append(np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "            fold_r2_scores.append(r2_score(y_test, y_pred))\n",
    "        \n",
    "        # Moyennes et écarts-types des scores pour chaque pli\n",
    "        rmse_scores.append(mean(fold_rmse_scores))\n",
    "        r2_scores.append(mean(fold_r2_scores))\n",
    "    \n",
    "    return mean(rmse_scores), stdev(rmse_scores), mean(r2_scores), stdev(r2_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "362e226f-331c-4561-9422-dd7607799528",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e64c4d90-e08b-4fbc-a9c5-fa8d4fbf44fd",
   "metadata": {},
   "source": [
    "## Result Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3b0ed2e9-b5e8-4ac3-bd92-d319cacfb321",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import time\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "\n",
    "def run_and_compare_models(X, y):\n",
    "    models = {\n",
    "        \"Random_Forest\": random_forest_regression,\n",
    "        \"Random_Forest_Kfold\": statistics_for_the_training_set_with_5CV_RFR,\n",
    "        \"CatBoost\": catboost_regression,\n",
    "        \"CatBoostKflod\": statistics_for_the_training_set_with_5CV_catBoost\n",
    "    }\n",
    "    \n",
    "    results = []\n",
    "    \n",
    "    for model_name, model_func in tqdm(models.items(), desc=\"Running models\"):\n",
    "        start_time = time.time()\n",
    "        rmse_avg, rmse_std, r2_avg, r2_std = model_func(X, y)\n",
    "        end_time = time.time()  # Arrêter le chronomètre\n",
    "        elapsed_time = end_time - start_time\n",
    "        results.append({\n",
    "            \"Model\": model_name,\n",
    "            \"RMSE Avg\": rmse_avg,\n",
    "            \"RMSE Std\": rmse_std,\n",
    "            \"R^2 Avg\": r2_avg,\n",
    "            \"R^2 Std\": r2_std,\n",
    "            \"Time (s)\": elapsed_time  # Ajouter le temps écoulé à chaque entrée\n",
    "        })\n",
    "        \n",
    "    results_df = pd.DataFrame(results)\n",
    "    return results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "48a6d435-7098-4c0d-b1f9-3ee7dd7b8bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X.columns = X.columns.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "89a0bd67-b60f-4324-a81b-ad35d479345d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Running models: 100%|██████████| 4/4 [14:59<00:00, 224.91s/it]\n"
     ]
    }
   ],
   "source": [
    "results_df_ = run_and_compare_models(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "893b88c4-e13a-42e0-95f3-59ebc41efab1",
   "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>Model</th>\n",
       "      <th>RMSE Avg</th>\n",
       "      <th>RMSE Std</th>\n",
       "      <th>R^2 Avg</th>\n",
       "      <th>R^2 Std</th>\n",
       "      <th>Time (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random_Forest</td>\n",
       "      <td>13.223052</td>\n",
       "      <td>0.736384</td>\n",
       "      <td>0.588146</td>\n",
       "      <td>0.050911</td>\n",
       "      <td>68.658887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random_Forest_Kfold</td>\n",
       "      <td>13.582734</td>\n",
       "      <td>0.298427</td>\n",
       "      <td>0.579330</td>\n",
       "      <td>0.019495</td>\n",
       "      <td>267.485465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CatBoost</td>\n",
       "      <td>13.575072</td>\n",
       "      <td>0.794473</td>\n",
       "      <td>0.565656</td>\n",
       "      <td>0.057480</td>\n",
       "      <td>92.542989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CatBoostKflod</td>\n",
       "      <td>13.688731</td>\n",
       "      <td>0.235878</td>\n",
       "      <td>0.573253</td>\n",
       "      <td>0.015757</td>\n",
       "      <td>470.963685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model   RMSE Avg  RMSE Std   R^2 Avg   R^2 Std    Time (s)\n",
       "0        Random_Forest  13.223052  0.736384  0.588146  0.050911   68.658887\n",
       "1  Random_Forest_Kfold  13.582734  0.298427  0.579330  0.019495  267.485465\n",
       "2             CatBoost  13.575072  0.794473  0.565656  0.057480   92.542989\n",
       "3        CatBoostKflod  13.688731  0.235878  0.573253  0.015757  470.963685"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "1c8ff121-25f2-4781-afb5-37f89a996e26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sauvegarde du DataFrame dans un fichier CSV\n",
    "results_df_.to_csv(\"Resultat_Df_MorganFingerprint.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fec7c20f-723d-45c3-ad74-10bf5f878381",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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