solubility-feature-engineering.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparation of Dataset for solubility prediction\n",
    "\n",
    "## Datasets\n",
    "\n",
    "1. [ESOL: Estimating Aqueous Solubility Directly from Molecular Structure](https://pubs.acs.org/doi/10.1021/ci034243x)\n",
    "2. [AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds](https://www.nature.com/articles/s41597-019-0151-1)\n",
    "3. [Is Experimental Data Quality the Limiting Factor in Predicting the Aqueous Solubility of Driglike Molecules](https://pubs.acs.org/doi/full/10.1021/mp500103r)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Backgroud:\n",
    "\n",
    "Aqueous solubility is a key factor in drug discovery, since if a molecule is not soluble. it will typically be poorly bioavailable, making it difficult to perform <i>in-vivo</i> studies with it, and hence deliver to patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: rdkit in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (2022.9.3)\n",
      "Requirement already satisfied: pandas in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (1.5.2)\n",
      "Requirement already satisfied: numpy in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (1.22.1)\n",
      "Requirement already satisfied: Pillow in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from rdkit) (9.3.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from pandas) (2022.7)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "! pip install rdkit pandas numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import Descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_csv(path: str) -> pd.DataFrame:\n",
    "    return pd.read_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "esol = load_csv(path=\"data/esol.csv\")\n",
    "aqsol = load_csv(path=\"data/solubility-dataset-train.csv\")\n",
    "dsl = load_csv(path=\"data/dsl-100-unique.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1128 entries, 0 to 1127\n",
      "Data columns (total 10 columns):\n",
      " #   Column                                           Non-Null Count  Dtype  \n",
      "---  ------                                           --------------  -----  \n",
      " 0   Compound ID                                      1128 non-null   object \n",
      " 1   ESOL predicted log solubility in mols per litre  1128 non-null   float64\n",
      " 2   Minimum Degree                                   1128 non-null   int64  \n",
      " 3   Molecular Weight                                 1128 non-null   float64\n",
      " 4   Number of H-Bond Donors                          1128 non-null   int64  \n",
      " 5   Number of Rings                                  1128 non-null   int64  \n",
      " 6   Number of Rotatable Bonds                        1128 non-null   int64  \n",
      " 7   Polar Surface Area                               1128 non-null   float64\n",
      " 8   measured log solubility in mols per litre        1128 non-null   float64\n",
      " 9   smiles                                           1128 non-null   object \n",
      "dtypes: float64(4), int64(4), object(2)\n",
      "memory usage: 88.2+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(esol.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9982 entries, 0 to 9981\n",
      "Data columns (total 26 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   ID                   9982 non-null   object \n",
      " 1   Name                 9982 non-null   object \n",
      " 2   InChI                9982 non-null   object \n",
      " 3   InChIKey             9982 non-null   object \n",
      " 4   SMILES               9982 non-null   object \n",
      " 5   Solubility           9982 non-null   float64\n",
      " 6   SD                   9982 non-null   float64\n",
      " 7   Ocurrences           9982 non-null   int64  \n",
      " 8   Group                9982 non-null   object \n",
      " 9   MolWt                9982 non-null   float64\n",
      " 10  MolLogP              9982 non-null   float64\n",
      " 11  MolMR                9982 non-null   float64\n",
      " 12  HeavyAtomCount       9982 non-null   float64\n",
      " 13  NumHAcceptors        9982 non-null   float64\n",
      " 14  NumHDonors           9982 non-null   float64\n",
      " 15  NumHeteroatoms       9982 non-null   float64\n",
      " 16  NumRotatableBonds    9982 non-null   float64\n",
      " 17  NumValenceElectrons  9982 non-null   float64\n",
      " 18  NumAromaticRings     9982 non-null   float64\n",
      " 19  NumSaturatedRings    9982 non-null   float64\n",
      " 20  NumAliphaticRings    9982 non-null   float64\n",
      " 21  RingCount            9982 non-null   float64\n",
      " 22  TPSA                 9982 non-null   float64\n",
      " 23  LabuteASA            9982 non-null   float64\n",
      " 24  BalabanJ             9982 non-null   float64\n",
      " 25  BertzCT              9982 non-null   float64\n",
      "dtypes: float64(19), int64(1), object(6)\n",
      "memory usage: 2.0+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(aqsol.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 56 entries, 0 to 55\n",
      "Data columns (total 12 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Unnamed: 0                     56 non-null     int64  \n",
      " 1   Chemical name                  56 non-null     object \n",
      " 2   Crystal structure CSD refcode  56 non-null     object \n",
      " 3   LogS exp (mol/L)               56 non-null     float64\n",
      " 4   Reference                      56 non-null     int64  \n",
      " 5   Test                           56 non-null     bool   \n",
      " 6   SMILES                         56 non-null     object \n",
      " 7   Smiles Source                  56 non-null     object \n",
      " 8   Chemspider reference number    56 non-null     int64  \n",
      " 9   InChl                          56 non-null     object \n",
      " 10  cansmi                         56 non-null     object \n",
      " 11  is_unique                      56 non-null     bool   \n",
      "dtypes: bool(2), float64(1), int64(3), object(6)\n",
      "memory usage: 4.6+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(dsl.info())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating Molecular Descriptors\n",
    "\n",
    "To predict log(S), the study of Delaney used four molecular descriptors:\n",
    "- cLogP (Octanol-Water partition coefficient)\n",
    "- MW (Molecular Weight)\n",
    "- RB (Number of rotatable bonds)\n",
    "- AP (Aromatic proportion = Number of aromatic atoms / total number of heavy atoms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_number_aromatic_atoms(molecule) -> int:\n",
    "    \n",
    "    aromatic_atoms = [molecule.GetAtomWithIdx(idx).GetIsAromatic() for idx in range(molecule.GetNumAtoms())]\n",
    "    count = []\n",
    "    for _, value in enumerate(aromatic_atoms):\n",
    "        if value == True:\n",
    "            count.append(value)\n",
    "    sum_count = sum(count)\n",
    "    \n",
    "    return sum_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_aromatic_descriptors(molecule) -> float:\n",
    "    aromatic_atoms: int = get_number_aromatic_atoms(molecule)\n",
    "    num_heavy_atoms: int = Descriptors.HeavyAtomCount(molecule)\n",
    "    \n",
    "    aromatic_proportion: float = aromatic_atoms / num_heavy_atoms\n",
    "    return aromatic_proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_dataset(molecules) -> pd.DataFrame:\n",
    "    smiles = []\n",
    "    rows = []\n",
    "\n",
    "    for molecule in molecules:\n",
    "        descriptor = Chem.MolFromSmiles(molecule)\n",
    "        smiles.append(descriptor)\n",
    "    \n",
    "    for smile in smiles:\n",
    "        mol_log_p: float = Descriptors.MolLogP(smile)\n",
    "        mol_weight: float = Descriptors.MolWt(smile)\n",
    "        num_rot_bonds: int = Descriptors.NumRotatableBonds(smile)\n",
    "        aromatic_proportion: float = calc_aromatic_descriptors(smile)\n",
    "        \n",
    "        row = [smile, mol_log_p, mol_weight, num_rot_bonds, aromatic_proportion]\n",
    "        rows.append(row)\n",
    "    \n",
    "    df = pd.DataFrame(data=rows, columns=[\"SMILES\", \"MolLogP\", \"MolWeight\", \"NumRotatableBonds\", \"AromaticProportion\"])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[21:54:42] WARNING: not removing hydrogen atom without neighbors\n",
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      "[21:54:44] WARNING: not removing hydrogen atom without neighbors\n"
     ]
    }
   ],
   "source": [
    "esol_parsed = generate_dataset(molecules=esol[\"smiles\"])\n",
    "aqsol_parsed = generate_dataset(molecules=aqsol[\"SMILES\"])\n",
    "dsl_parsed = generate_dataset(molecules=dsl[\"SMILES\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Compound ID', 'ESOL predicted log solubility in mols per litre',\n",
       "       'Minimum Degree', 'Molecular Weight', 'Number of H-Bond Donors',\n",
       "       'Number of Rings', 'Number of Rotatable Bonds',\n",
       "       'Polar Surface Area', 'measured log solubility in mols per litre',\n",
       "       'smiles'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esol.columns.values # y = measured log solubility in mols / L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'Solubility', 'SD',\n",
       "       'Ocurrences', 'Group', 'MolWt', 'MolLogP', 'MolMR',\n",
       "       'HeavyAtomCount', 'NumHAcceptors', 'NumHDonors', 'NumHeteroatoms',\n",
       "       'NumRotatableBonds', 'NumValenceElectrons', 'NumAromaticRings',\n",
       "       'NumSaturatedRings', 'NumAliphaticRings', 'RingCount', 'TPSA',\n",
       "       'LabuteASA', 'BalabanJ', 'BertzCT'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aqsol.columns.values # y = Solubility (reported as LogS (mol/L))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Unnamed: 0', 'Chemical name', 'Crystal structure CSD refcode',\n",
       "       'LogS exp (mol/L)', 'Reference', 'Test', 'SMILES', 'Smiles Source',\n",
       "       'Chemspider reference number', 'InChl', 'cansmi', 'is_unique'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsl.columns.values # y = LogS exp (mol/L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>MolLogP</th>\n",
       "      <th>MolWeight</th>\n",
       "      <th>NumRotatableBonds</th>\n",
       "      <th>AromaticProportion</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.514901</td>\n",
       "      <td>1.103667</td>\n",
       "      <td>0.572566</td>\n",
       "      <td>-0.435543</td>\n",
       "      <td>-0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.239799</td>\n",
       "      <td>-0.330717</td>\n",
       "      <td>-0.345172</td>\n",
       "      <td>1.368067</td>\n",
       "      <td>-3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.250914</td>\n",
       "      <td>-0.604978</td>\n",
       "      <td>0.021923</td>\n",
       "      <td>-1.055104</td>\n",
       "      <td>-2.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.260193</td>\n",
       "      <td>0.101092</td>\n",
       "      <td>-0.712267</td>\n",
       "      <td>2.249220</td>\n",
       "      <td>-7.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.082395</td>\n",
       "      <td>-0.986205</td>\n",
       "      <td>-0.712267</td>\n",
       "      <td>2.249220</td>\n",
       "      <td>-1.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MolLogP  MolWeight  NumRotatableBonds  AromaticProportion     y\n",
       "0 -1.514901   1.103667           0.572566           -0.435543 -0.77\n",
       "1  0.239799  -0.330717          -0.345172            1.368067 -3.30\n",
       "2  0.250914  -0.604978           0.021923           -1.055104 -2.06\n",
       "3  1.260193   0.101092          -0.712267            2.249220 -7.87\n",
       "4 -0.082395  -0.986205          -0.712267            2.249220 -1.33"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "tmp_train = pd.concat([esol_parsed, aqsol_parsed], axis=0)\n",
    "X_train = tmp_train[[\"MolLogP\", \"MolWeight\", \"NumRotatableBonds\", \"AromaticProportion\"]]\n",
    "\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "train_dataset = pd.DataFrame(columns=[\"MolLogP\", \"MolWeight\", \"NumRotatableBonds\", \"AromaticProportion\"], data=X_train)\n",
    "\n",
    "y, y_aqsol = list(esol[\"measured log solubility in mols per litre\"]), list(aqsol[\"Solubility\"])\n",
    "y.extend(y_aqsol)\n",
    "\n",
    "train_dataset[\"y\"] = y\n",
    "\n",
    "train_dataset.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot: title={'center': 'MolLogP'}>,\n",
       "        <AxesSubplot: title={'center': 'MolWeight'}>],\n",
       "       [<AxesSubplot: title={'center': 'NumRotatableBonds'}>,\n",
       "        <AxesSubplot: title={'center': 'AromaticProportion'}>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(columns=[\"MolLogP\", \"MolWeight\", \"NumRotatableBonds\", \"AromaticProportion\"], data=X_train).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>MolLogP</th>\n",
       "      <th>MolWeight</th>\n",
       "      <th>NumRotatableBonds</th>\n",
       "      <th>AromaticProportion</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.182146</td>\n",
       "      <td>0.038898</td>\n",
       "      <td>-0.345172</td>\n",
       "      <td>0.510102</td>\n",
       "      <td>-1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.580809</td>\n",
       "      <td>-0.695244</td>\n",
       "      <td>-0.712267</td>\n",
       "      <td>1.918788</td>\n",
       "      <td>-2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.227087</td>\n",
       "      <td>0.168101</td>\n",
       "      <td>0.205471</td>\n",
       "      <td>0.833081</td>\n",
       "      <td>-2.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.189564</td>\n",
       "      <td>-0.773573</td>\n",
       "      <td>-0.528719</td>\n",
       "      <td>1.147779</td>\n",
       "      <td>-1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.212810</td>\n",
       "      <td>-0.117469</td>\n",
       "      <td>0.021923</td>\n",
       "      <td>0.111128</td>\n",
       "      <td>-1.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MolLogP  MolWeight  NumRotatableBonds  AromaticProportion     y\n",
       "0 -1.182146   0.038898          -0.345172            0.510102 -1.73\n",
       "1 -0.580809  -0.695244          -0.712267            1.918788 -2.26\n",
       "2 -0.227087   0.168101           0.205471            0.833081 -2.95\n",
       "3 -0.189564  -0.773573          -0.528719            1.147779 -1.58\n",
       "4 -0.212810  -0.117469           0.021923            0.111128 -1.22"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = dsl_parsed[[\"MolLogP\", \"MolWeight\", \"NumRotatableBonds\", \"AromaticProportion\"]]\n",
    "X_test = scaler.transform(X_test)\n",
    "test_dataset = pd.DataFrame(columns=[\"MolLogP\", \"MolWeight\", \"NumRotatableBonds\", \"AromaticProportion\"], data=X_test)\n",
    "test_dataset[\"y\"] = dsl[\"LogS exp (mol/L)\"]\n",
    "\n",
    "test_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training dataset:  (11110, 5)\n",
      "Testing dataset:  (56, 5)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training dataset: \", train_dataset.shape)\n",
    "print(\"Testing dataset: \", test_dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot: title={'center': 'MolLogP'}>,\n",
       "        <AxesSubplot: title={'center': 'MolWeight'}>],\n",
       "       [<AxesSubplot: title={'center': 'NumRotatableBonds'}>,\n",
       "        <AxesSubplot: title={'center': 'AromaticProportion'}>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(columns=[\"MolLogP\", \"MolWeight\", \"NumRotatableBonds\", \"AromaticProportion\"], data=X_test).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MolLogP</th>\n",
       "      <th>MolWeight</th>\n",
       "      <th>NumRotatableBonds</th>\n",
       "      <th>AromaticProportion</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>-0.457474</td>\n",
       "      <td>-1.008896</td>\n",
       "      <td>-0.712267</td>\n",
       "      <td>2.249220</td>\n",
       "      <td>1.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>-0.391455</td>\n",
       "      <td>-0.157084</td>\n",
       "      <td>-0.345172</td>\n",
       "      <td>0.111128</td>\n",
       "      <td>-2.322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.545963</td>\n",
       "      <td>0.747723</td>\n",
       "      <td>0.756113</td>\n",
       "      <td>-0.193106</td>\n",
       "      <td>-6.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>-0.097044</td>\n",
       "      <td>-0.857366</td>\n",
       "      <td>-0.712267</td>\n",
       "      <td>1.423139</td>\n",
       "      <td>0.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0.005365</td>\n",
       "      <td>-0.737546</td>\n",
       "      <td>-0.712267</td>\n",
       "      <td>1.423139</td>\n",
       "      <td>-0.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11012</th>\n",
       "      <td>1.547690</td>\n",
       "      <td>0.563128</td>\n",
       "      <td>-0.528719</td>\n",
       "      <td>1.147779</td>\n",
       "      <td>-8.560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11073</th>\n",
       "      <td>-0.423344</td>\n",
       "      <td>0.762235</td>\n",
       "      <td>0.389018</td>\n",
       "      <td>-0.419657</td>\n",
       "      <td>-2.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11079</th>\n",
       "      <td>0.011501</td>\n",
       "      <td>0.543550</td>\n",
       "      <td>-0.161624</td>\n",
       "      <td>0.215790</td>\n",
       "      <td>-2.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11098</th>\n",
       "      <td>0.337995</td>\n",
       "      <td>0.359017</td>\n",
       "      <td>0.021923</td>\n",
       "      <td>0.321698</td>\n",
       "      <td>-2.790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11103</th>\n",
       "      <td>0.493868</td>\n",
       "      <td>0.773174</td>\n",
       "      <td>0.389018</td>\n",
       "      <td>1.069105</td>\n",
       "      <td>-6.140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>317 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MolLogP  MolWeight  NumRotatableBonds  AromaticProportion      y\n",
       "365   -0.457474  -1.008896          -0.712267            2.249220  1.100\n",
       "465   -0.391455  -0.157084          -0.345172            0.111128 -2.322\n",
       "500    0.545963   0.747723           0.756113           -0.193106 -6.340\n",
       "612   -0.097044  -0.857366          -0.712267            1.423139  0.380\n",
       "886    0.005365  -0.737546          -0.712267            1.423139 -0.700\n",
       "...         ...        ...                ...                 ...    ...\n",
       "11012  1.547690   0.563128          -0.528719            1.147779 -8.560\n",
       "11073 -0.423344   0.762235           0.389018           -0.419657 -2.940\n",
       "11079  0.011501   0.543550          -0.161624            0.215790 -2.900\n",
       "11098  0.337995   0.359017           0.021923            0.321698 -2.790\n",
       "11103  0.493868   0.773174           0.389018            1.069105 -6.140\n",
       "\n",
       "[317 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset[train_dataset.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (1.2.0)\n",
      "Requirement already satisfied: lightgbm in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (3.3.3)\n",
      "Requirement already satisfied: xgboost in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (1.7.2)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from scikit-learn) (1.22.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from scikit-learn) (3.1.0)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from scikit-learn) (1.7.3)\n",
      "Requirement already satisfied: wheel in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from lightgbm) (0.38.4)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "! pip install scikit-learn lightgbm xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training dataset statistics: linear-regression \n",
      "\n",
      "Coefficients:  [-1.35620427 -0.73790469  0.41853347 -0.22668507]\n",
      "Intercept:  -2.90617323936847\n",
      "RMSE: 1.716\n",
      "MAE: 1.254\n",
      "Coefficient of Correlation: 0.464\n",
      "===============================================\n",
      "\n",
      "Training dataset statistics: random-forest \n",
      "\n",
      "RMSE: 0.476\n",
      "MAE: 0.318\n",
      "Coefficient of Correlation: 0.959\n",
      "===============================================\n",
      "\n",
      "Training dataset statistics: lightgbm-regressor \n",
      "\n",
      "RMSE: 1.101\n",
      "MAE: 0.782\n",
      "Coefficient of Correlation: 0.779\n",
      "===============================================\n",
      "\n",
      "Training dataset statistics: xgboost-regressor \n",
      "\n",
      "RMSE: 0.877\n",
      "MAE: 0.627\n",
      "Coefficient of Correlation: 0.860\n",
      "===============================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from xgboost.sklearn import XGBRegressor\n",
    "from lightgbm.sklearn import LGBMRegressor\n",
    "\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_dataset, y = train_dataset[[\"MolLogP\", \"MolWeight\", \"NumRotatableBonds\", \"AromaticProportion\"]], train_dataset[\"y\"]\n",
    "\n",
    "\n",
    "models = {\n",
    "    \"linear-regression\": LinearRegression().fit(X_dataset, y),\n",
    "    \"random-forest\": RandomForestRegressor().fit(X_dataset, y),\n",
    "    \"lightgbm-regressor\": LGBMRegressor().fit(X_dataset, y),\n",
    "    \"xgboost-regressor\": XGBRegressor().fit(X_dataset, y)\n",
    "}\n",
    "\n",
    "for key, model in models.items():\n",
    "    print(f\"Training dataset statistics: {key} \\n\")\n",
    "    \n",
    "    pred_train = model.predict(X_dataset)\n",
    "    \n",
    "    if key == \"linear-regression\":\n",
    "        print(\"Coefficients: \", model.coef_)\n",
    "        print(\"Intercept: \", model.intercept_)\n",
    "        \n",
    "    print(f\"RMSE: {math.sqrt(mean_squared_error(y, pred_train)):.3f}\")\n",
    "    print(f\"MAE: {mean_absolute_error(y, pred_train):.3f}\")\n",
    "    print(f\"Coefficient of Correlation: {r2_score(y, pred_train):.3f}\")\n",
    "    \n",
    "    print(\"===============================================\\n\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training dataset statistics: linear-regression \n",
      "\n",
      "RMSE: 1.122\n",
      "MAE: 0.883\n",
      "Coefficient of Correlation: 0.543\n",
      "===============================================\n",
      "\n",
      "Training dataset statistics: random-forest \n",
      "\n",
      "RMSE: 0.626\n",
      "MAE: 0.396\n",
      "Coefficient of Correlation: 0.858\n",
      "===============================================\n",
      "\n",
      "Training dataset statistics: lightgbm-regressor \n",
      "\n",
      "RMSE: 0.927\n",
      "MAE: 0.721\n",
      "Coefficient of Correlation: 0.688\n",
      "===============================================\n",
      "\n",
      "Training dataset statistics: xgboost-regressor \n",
      "\n",
      "RMSE: 0.938\n",
      "MAE: 0.712\n",
      "Coefficient of Correlation: 0.680\n",
      "===============================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_test, y_test = test_dataset[[\"MolLogP\", \"MolWeight\", \"NumRotatableBonds\", \"AromaticProportion\"]], test_dataset[\"y\"]\n",
    "\n",
    "for key, model in models.items():\n",
    "    print(f\"Training dataset statistics: {key} \\n\")\n",
    "    \n",
    "    pred_train = model.predict(X_test)\n",
    "        \n",
    "    print(f\"RMSE: {math.sqrt(mean_squared_error(y_test, pred_train)):.3f}\")\n",
    "    print(f\"MAE: {mean_absolute_error(y_test, pred_train):.3f}\")\n",
    "    print(f\"Coefficient of Correlation: {r2_score(y_test, pred_train):.3f}\")\n",
    "    \n",
    "    print(\"===============================================\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline model: Linear regression \n",
      "\n",
      "RMSE: 1.122\n",
      "MAE: 0.883\n",
      "Coefficient of Correlation: 0.543\n"
     ]
    }
   ],
   "source": [
    "print(\"Baseline model: Linear regression \\n\")\n",
    "pred_train = models[\"linear-regression\"].predict(X_test)\n",
    "        \n",
    "print(f\"RMSE: {math.sqrt(mean_squared_error(y_test, pred_train)):.3f}\")\n",
    "print(f\"MAE: {mean_absolute_error(y_test, pred_train):.3f}\")\n",
    "print(f\"Coefficient of Correlation: {r2_score(y_test, pred_train):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model: Random Forest Regression \n",
      "\n",
      "RMSE: 0.626\n",
      "MAE: 0.396\n",
      "Coefficient of Correlation: 0.858\n"
     ]
    }
   ],
   "source": [
    "print(\"Best model: Random Forest Regression \\n\")\n",
    "pred_train = models[\"random-forest\"].predict(X_test)\n",
    "        \n",
    "print(f\"RMSE: {math.sqrt(mean_squared_error(y_test, pred_train)):.3f}\")\n",
    "print(f\"MAE: {mean_absolute_error(y_test, pred_train):.3f}\")\n",
    "print(f\"Coefficient of Correlation: {r2_score(y_test, pred_train):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset.to_csv(\"data/processed/esol-aqsol-training-set-standard.csv\", index=False)\n",
    "test_dataset.to_csv(\"data/processed/dsl-100-test-set-standard.csv\", index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use optuna for hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: optuna in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (3.0.5)\n",
      "Requirement already satisfied: scipy<1.9.0,>=1.7.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from optuna) (1.7.3)\n",
      "Requirement already satisfied: colorlog in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from optuna) (6.7.0)\n",
      "Requirement already satisfied: numpy in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from optuna) (1.22.1)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from optuna) (22.0)\n",
      "Requirement already satisfied: PyYAML in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from optuna) (6.0)\n",
      "Requirement already satisfied: sqlalchemy>=1.3.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from optuna) (1.4.45)\n",
      "Requirement already satisfied: alembic>=1.5.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from optuna) (1.9.1)\n",
      "Requirement already satisfied: tqdm in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from optuna) (4.64.1)\n",
      "Requirement already satisfied: cmaes>=0.8.2 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from optuna) (0.9.0)\n",
      "Requirement already satisfied: importlib-metadata<5.0.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from optuna) (4.13.0)\n",
      "Requirement already satisfied: cliff in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from optuna) (4.1.0)\n",
      "Requirement already satisfied: Mako in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from alembic>=1.5.0->optuna) (1.2.4)\n",
      "Requirement already satisfied: zipp>=0.5 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from importlib-metadata<5.0.0->optuna) (3.11.0)\n",
      "Requirement already satisfied: greenlet!=0.4.17 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from sqlalchemy>=1.3.0->optuna) (2.0.1)\n",
      "Requirement already satisfied: autopage>=0.4.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from cliff->optuna) (0.5.1)\n",
      "Requirement already satisfied: stevedore>=2.0.1 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from cliff->optuna) (4.1.1)\n",
      "Requirement already satisfied: PrettyTable>=0.7.2 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from cliff->optuna) (3.5.0)\n",
      "Requirement already satisfied: cmd2>=1.0.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from cliff->optuna) (2.4.2)\n",
      "Requirement already satisfied: colorama in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from colorlog->optuna) (0.4.6)\n",
      "Requirement already satisfied: pyperclip>=1.6 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from cmd2>=1.0.0->cliff->optuna) (1.8.2)\n",
      "Requirement already satisfied: wcwidth>=0.1.7 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from cmd2>=1.0.0->cliff->optuna) (0.2.5)\n",
      "Requirement already satisfied: attrs>=16.3.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from cmd2>=1.0.0->cliff->optuna) (22.2.0)\n",
      "Requirement already satisfied: pyreadline3 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from cmd2>=1.0.0->cliff->optuna) (3.4.1)\n",
      "Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from stevedore>=2.0.1->cliff->optuna) (5.11.0)\n",
      "Requirement already satisfied: MarkupSafe>=0.9.2 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from Mako->alembic>=1.5.0->optuna) (2.1.1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "! pip install optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\user\\OneDrive\\Desktop\\GitHub\\small-molecules\\env\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "\u001b[32m[I 2023-01-03 21:55:34,968]\u001b[0m A new study created in memory with name: experiment-2023-01-03 21:55:34.968794\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:55:52,038]\u001b[0m Trial 3 finished with value: -1.2614151065145034 and parameters: {'max_depth': 83, 'n_estimators': 74, 'criterion': 'squared_error', 'max_features': 'log2'}. Best is trial 3 with value: -1.2614151065145034.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:55:54,669]\u001b[0m Trial 2 finished with value: -1.2582327516542509 and parameters: {'max_depth': 20, 'n_estimators': 92, 'criterion': 'friedman_mse', 'max_features': 'sqrt'}. Best is trial 2 with value: -1.2582327516542509.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:56:13,877]\u001b[0m Trial 5 finished with value: -1.2603397213991776 and parameters: {'max_depth': 92, 'n_estimators': 89, 'criterion': 'squared_error', 'max_features': 'log2'}. Best is trial 2 with value: -1.2582327516542509.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:56:15,556]\u001b[0m Trial 6 finished with value: -1.280185272992155 and parameters: {'max_depth': 9, 'n_estimators': 18, 'criterion': 'friedman_mse', 'max_features': 'log2'}. Best is trial 2 with value: -1.2582327516542509.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:56:20,906]\u001b[0m Trial 4 finished with value: -1.272620523347289 and parameters: {'max_depth': 40, 'n_estimators': 84, 'criterion': 'friedman_mse', 'max_features': None}. Best is trial 2 with value: -1.2582327516542509.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:56:28,892]\u001b[0m Trial 7 finished with value: -1.2613411996942259 and parameters: {'max_depth': 70, 'n_estimators': 75, 'criterion': 'friedman_mse', 'max_features': 'log2'}. Best is trial 2 with value: -1.2582327516542509.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:56:30,540]\u001b[0m Trial 8 finished with value: -1.2616320140865518 and parameters: {'max_depth': 54, 'n_estimators': 57, 'criterion': 'squared_error', 'max_features': 'sqrt'}. Best is trial 2 with value: -1.2582327516542509.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:56:43,233]\u001b[0m Trial 9 finished with value: -1.2624152528975052 and parameters: {'max_depth': 79, 'n_estimators': 80, 'criterion': 'friedman_mse', 'max_features': 'log2'}. Best is trial 2 with value: -1.2582327516542509.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:57:32,150]\u001b[0m Trial 0 finished with value: -1.2930294316902742 and parameters: {'max_depth': 100, 'n_estimators': 23, 'criterion': 'absolute_error', 'max_features': None}. Best is trial 2 with value: -1.2582327516542509.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:58:02,774]\u001b[0m Trial 11 finished with value: -1.2771992310270197 and parameters: {'max_depth': 87, 'n_estimators': 27, 'criterion': 'absolute_error', 'max_features': 'log2'}. Best is trial 2 with value: -1.2582327516542509.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:58:03,854]\u001b[0m Trial 13 finished with value: -1.7709989069854544 and parameters: {'max_depth': 1, 'n_estimators': 48, 'criterion': 'friedman_mse', 'max_features': 'sqrt'}. Best is trial 2 with value: -1.2582327516542509.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:58:05,160]\u001b[0m Trial 12 finished with value: -1.2769841505635706 and parameters: {'max_depth': 56, 'n_estimators': 97, 'criterion': 'friedman_mse', 'max_features': None}. Best is trial 2 with value: -1.2582327516542509.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:58:21,381]\u001b[0m Trial 14 finished with value: -1.257574090352105 and parameters: {'max_depth': 25, 'n_estimators': 100, 'criterion': 'squared_error', 'max_features': 'sqrt'}. Best is trial 14 with value: -1.257574090352105.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:58:22,338]\u001b[0m Trial 15 finished with value: -1.25735465455144 and parameters: {'max_depth': 24, 'n_estimators': 100, 'criterion': 'squared_error', 'max_features': 'sqrt'}. Best is trial 15 with value: -1.25735465455144.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:58:37,082]\u001b[0m Trial 16 finished with value: -1.2563482257643193 and parameters: {'max_depth': 27, 'n_estimators': 99, 'criterion': 'squared_error', 'max_features': 'sqrt'}. Best is trial 16 with value: -1.2563482257643193.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:58:38,272]\u001b[0m Trial 17 finished with value: -1.2560152147823094 and parameters: {'max_depth': 33, 'n_estimators': 100, 'criterion': 'squared_error', 'max_features': 'sqrt'}. Best is trial 17 with value: -1.2560152147823094.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:58:47,431]\u001b[0m Trial 18 finished with value: -1.259578191708912 and parameters: {'max_depth': 36, 'n_estimators': 62, 'criterion': 'squared_error', 'max_features': 'sqrt'}. Best is trial 17 with value: -1.2560152147823094.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:58:49,381]\u001b[0m Trial 19 finished with value: -1.263288444995538 and parameters: {'max_depth': 43, 'n_estimators': 63, 'criterion': 'squared_error', 'max_features': 'sqrt'}. Best is trial 17 with value: -1.2560152147823094.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:58:54,592]\u001b[0m Trial 20 finished with value: -1.2645925579778994 and parameters: {'max_depth': 40, 'n_estimators': 38, 'criterion': 'squared_error', 'max_features': 'sqrt'}. Best is trial 17 with value: -1.2560152147823094.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 21:58:56,974]\u001b[0m Trial 21 finished with value: -1.2661353970613891 and parameters: {'max_depth': 63, 'n_estimators': 44, 'criterion': 'squared_error', 'max_features': 'sqrt'}. Best is trial 17 with value: -1.2560152147823094.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 22:00:24,632]\u001b[0m Trial 10 finished with value: -1.2559802220178482 and parameters: {'max_depth': 86, 'n_estimators': 90, 'criterion': 'absolute_error', 'max_features': 'log2'}. Best is trial 10 with value: -1.2559802220178482.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 22:00:41,087]\u001b[0m Trial 1 finished with value: -1.2732649984267927 and parameters: {'max_depth': 23, 'n_estimators': 66, 'criterion': 'absolute_error', 'max_features': None}. Best is trial 10 with value: -1.2559802220178482.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 22:01:35,669]\u001b[0m Trial 22 finished with value: -1.257612164586691 and parameters: {'max_depth': 64, 'n_estimators': 68, 'criterion': 'absolute_error', 'max_features': 'sqrt'}. Best is trial 10 with value: -1.2559802220178482.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 22:01:45,026]\u001b[0m Trial 23 finished with value: -1.2571881912377645 and parameters: {'max_depth': 15, 'n_estimators': 74, 'criterion': 'absolute_error', 'max_features': 'sqrt'}. Best is trial 10 with value: -1.2559802220178482.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 22:04:00,355]\u001b[0m Trial 24 finished with value: -1.2564567292128237 and parameters: {'max_depth': 30, 'n_estimators': 90, 'criterion': 'absolute_error', 'max_features': 'sqrt'}. Best is trial 10 with value: -1.2559802220178482.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 22:04:13,018]\u001b[0m Trial 25 finished with value: -1.2597492345341732 and parameters: {'max_depth': 32, 'n_estimators': 89, 'criterion': 'absolute_error', 'max_features': 'sqrt'}. Best is trial 10 with value: -1.2559802220178482.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 22:04:13,938]\u001b[0m Trial 28 finished with value: -1.2617573095859518 and parameters: {'max_depth': 48, 'n_estimators': 85, 'criterion': 'squared_error', 'max_features': 'log2'}. Best is trial 10 with value: -1.2559802220178482.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 22:04:25,443]\u001b[0m Trial 29 finished with value: -1.2607495272403413 and parameters: {'max_depth': 47, 'n_estimators': 81, 'criterion': 'squared_error', 'max_features': 'log2'}. Best is trial 10 with value: -1.2559802220178482.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 22:05:02,322]\u001b[0m Trial 26 finished with value: -1.2564616902867276 and parameters: {'max_depth': 33, 'n_estimators': 89, 'criterion': 'absolute_error', 'max_features': 'log2'}. Best is trial 10 with value: -1.2559802220178482.\u001b[0m\n",
      "\u001b[32m[I 2023-01-03 22:05:14,860]\u001b[0m Trial 27 finished with value: -1.2544549609281652 and parameters: {'max_depth': 49, 'n_estimators': 90, 'criterion': 'absolute_error', 'max_features': 'log2'}. Best is trial 27 with value: -1.2544549609281652.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "import optuna\n",
    "from functools import partial\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def objective(trial, name, X_train, y_train):\n",
    "    max_depth = trial.suggest_int('max_depth', 1, 100)\n",
    "    n_estimators = trial.suggest_int('n_estimators', 10, 100)\n",
    "    criterion = trial.suggest_categorical(\"criterion\", [\"squared_error\", \"absolute_error\", \"friedman_mse\"])\n",
    "    max_features = trial.suggest_categorical(\"max_features\", [\"sqrt\", \"log2\", None])\n",
    "    \n",
    "    random_regressor = RandomForestRegressor(\n",
    "        n_estimators=n_estimators,\n",
    "        max_depth=max_depth,\n",
    "        criterion=criterion,\n",
    "        max_features=max_features\n",
    "    )\n",
    "    \n",
    "    score = cross_val_score(random_regressor, X_train, y_train, cv=5, scoring=\"neg_root_mean_squared_error\")\n",
    "    return score.mean()\n",
    "\n",
    "current_time = datetime.datetime.now()\n",
    "\n",
    "opt_function = partial(objective, name=\"rf\", X_train=X_dataset, y_train=y)\n",
    "\n",
    "study = optuna.create_study(direction=\"maximize\", study_name=f\"experiment-{current_time}\")\n",
    "study.optimize(opt_function, n_trials=30, n_jobs=-1)                                                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.495\n",
      "MAE: 0.330\n",
      "Coefficient of Correlation: 0.955\n"
     ]
    }
   ],
   "source": [
    "params = study.best_params\n",
    "\n",
    "random_forest = RandomForestRegressor(**params).fit(X_dataset, y)\n",
    "\n",
    "pred_train = random_forest.predict(X_dataset)\n",
    "        \n",
    "print(f\"RMSE: {math.sqrt(mean_squared_error(y, pred_train)):.3f}\")\n",
    "print(f\"MAE: {mean_absolute_error(y, pred_train):.3f}\")\n",
    "print(f\"Coefficient of Correlation: {r2_score(y, pred_train):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.617\n",
      "MAE: 0.404\n",
      "Coefficient of Correlation: 0.862\n"
     ]
    }
   ],
   "source": [
    "pred_test = random_forest.predict(X_test)\n",
    "        \n",
    "print(f\"RMSE: {math.sqrt(mean_squared_error(y_test, pred_test)):.3f}\")\n",
    "print(f\"MAE: {mean_absolute_error(y_test, pred_test):.3f}\")\n",
    "print(f\"Coefficient of Correlation: {r2_score(y_test, pred_test):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(5, 4))\n",
    "plt.scatter(y_test, pred_test)\n",
    "plt.xlabel(\"Calculate log(S) mol/L\")\n",
    "plt.ylabel(\"Predicted log(S) mol/L\")\n",
    "plt.title(\"Random Forest Regressor preditions of Aqueous solubility\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Predicted log(S) mol/L')]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "ax = sns.regplot(x=y_test, y=pred_test)\n",
    "ax.set(xlabel = \"Calculated log(S) mol/L\")\n",
    "ax.set(ylabel = \"Predicted log(S) mol/L\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: yellowbrick in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (1.5)\n",
      "Requirement already satisfied: scikit-learn>=1.0.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from yellowbrick) (1.2.0)\n",
      "Requirement already satisfied: numpy>=1.16.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from yellowbrick) (1.22.1)\n",
      "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.2 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from yellowbrick) (3.6.2)\n",
      "Requirement already satisfied: scipy>=1.0.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from yellowbrick) (1.7.3)\n",
      "Requirement already satisfied: cycler>=0.10.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from yellowbrick) (0.11.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (1.4.4)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (4.38.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (1.0.6)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (22.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (3.0.9)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (9.3.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (2.8.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from scikit-learn>=1.0.0->yellowbrick) (3.1.0)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from scikit-learn>=1.0.0->yellowbrick) (1.2.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.0.0,>=2.0.2->yellowbrick) (1.16.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\user\\onedrive\\desktop\\github\\small-molecules\\env\\lib\\site-packages)\n",
      "c:\\Users\\user\\OneDrive\\Desktop\\GitHub\\small-molecules\\env\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PredictionError(alpha=0.5,\n",
       "                ax=&lt;AxesSubplot: title={&#x27;center&#x27;: &#x27;Prediction Error for RandomForestRegressor&#x27;}, xlabel=&#x27;$y$&#x27;, ylabel=&#x27;$\\\\hat{y}$&#x27;&gt;,\n",
       "                estimator=RandomForestRegressor(criterion=&#x27;absolute_error&#x27;,\n",
       "                                                max_depth=49,\n",
       "                                                max_features=&#x27;log2&#x27;,\n",
       "                                                n_estimators=90))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PredictionError</label><div class=\"sk-toggleable__content\"><pre>PredictionError(alpha=0.5,\n",
       "                ax=&lt;AxesSubplot: title={&#x27;center&#x27;: &#x27;Prediction Error for RandomForestRegressor&#x27;}, xlabel=&#x27;$y$&#x27;, ylabel=&#x27;$\\\\hat{y}$&#x27;&gt;,\n",
       "                estimator=RandomForestRegressor(criterion=&#x27;absolute_error&#x27;,\n",
       "                                                max_depth=49,\n",
       "                                                max_features=&#x27;log2&#x27;,\n",
       "                                                n_estimators=90))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(criterion=&#x27;absolute_error&#x27;, max_depth=49,\n",
       "                      max_features=&#x27;log2&#x27;, n_estimators=90)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(criterion=&#x27;absolute_error&#x27;, max_depth=49,\n",
       "                      max_features=&#x27;log2&#x27;, n_estimators=90)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "PredictionError(alpha=0.5,\n",
       "                ax=<AxesSubplot: title={'center': 'Prediction Error for RandomForestRegressor'}, xlabel='$y$', ylabel='$\\\\hat{y}$'>,\n",
       "                estimator=RandomForestRegressor(criterion='absolute_error',\n",
       "                                                max_depth=49,\n",
       "                                                max_features='log2',\n",
       "                                                n_estimators=90))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "! pip install yellowbrick\n",
    "\n",
    "from yellowbrick.regressor import prediction_error, ResidualsPlot\n",
    "\n",
    "prediction_error(random_forest, X_dataset, y, X_test, y_test, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\user\\OneDrive\\Desktop\\GitHub\\small-molecules\\env\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Residuals for RandomForestRegressor Model'}, xlabel='Predicted Value', ylabel='Residuals'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer = ResidualsPlot(random_forest)\n",
    "visualizer.fit(X_dataset, y)\n",
    "visualizer.score(X_test, y_test)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "kfolds = KFold(shuffle=True, random_state=42)\n",
    "\n",
    "# for _, test_index in kfolds.split(pd.DataFrame(X_test)):\n",
    "#     random_forest.predict(X_test[test_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "\n",
    "with open(file=\"model/regressors/solubility-rf-sklearn.model\", mode=\"wb\") as file:\n",
    "    joblib.dump(value=random_forest, filename=file)\n",
    "\n",
    "with open(file=\"model/regressors/solubility-standard-scaler-sklearn.model\", mode=\"wb\") as file:\n",
    "    joblib.dump(value=scaler, filename=file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\user\\OneDrive\\Desktop\\GitHub\\small-molecules\\env\\lib\\site-packages\\torch\\nn\\modules\\linear.py:96: UserWarning: Failed to initialize NumPy: module compiled against API version 0x10 but this version of numpy is 0xf (Triggered internally at ..\\torch\\csrc\\utils\\tensor_numpy.cpp:77.)\n",
      "  self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs))\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class Network(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Network, self).__init__()\n",
    "        self.layer_one = torch.nn.Linear(in_features=4, out_features=1)\n",
    "        # self.layer_two = torch.nn.Linear(in_features=2, out_features=1)\n",
    "        self.out = torch.nn.Linear(in_features=1, out_features=1)\n",
    "    \n",
    "    def forward(self, features) -> float:\n",
    "        output = F.leaky_relu(self.layer_one(features))\n",
    "        # output = F.leaky_relu(self.layer_two(output))\n",
    "        output = self.out(output)\n",
    "        \n",
    "        return output\n",
    "\n",
    "prediction_network = Network()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      "tensor([[ 0.0899, -0.3353,  0.3183,  0.3267]], requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.1201], requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[0.7627]], requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.4228], requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "for _, parameter in enumerate(list(prediction_network.parameters()), start = 1):\n",
    "    print(parameter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\user\\OneDrive\\Desktop\\GitHub\\small-molecules\\env\\lib\\site-packages\\torch\\nn\\modules\\loss.py:536: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n"
     ]
    }
   ],
   "source": [
    "X_dataset_tensor, y_tensor = torch.tensor(X_dataset.to_numpy(), dtype=torch.float32), torch.tensor(y.to_numpy(), dtype=torch.float32)\n",
    "X_test_tensor, y_test_tensor = torch.tensor(X_test.to_numpy(), dtype=torch.float32), torch.tensor(y_test.to_numpy(), dtype=torch.float32)\n",
    "\n",
    "optimizer = torch.optim.Adam(prediction_network.parameters(), lr=0.001)\n",
    "criterion = torch.nn.MSELoss()\n",
    "\n",
    "for x, target in zip(X_dataset_tensor, y_tensor):\n",
    "    optimizer.zero_grad()\n",
    "    output = prediction_network(x)\n",
    "    loss = criterion(output, target)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss: 1.2801777124404907\n"
     ]
    }
   ],
   "source": [
    "total_loss: float = 0.0\n",
    "\n",
    "for x, target in zip(X_test_tensor, y_test_tensor):\n",
    "    output = prediction_network(x)\n",
    "    loss = criterion(output, target)\n",
    "    total_loss = total_loss + loss\n",
    "\n",
    "print(f\"Average loss: {total_loss/len(X_test_tensor)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "Numpy is not available",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[41], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m mean_loss \u001b[39m=\u001b[39m total_loss\u001b[39m/\u001b[39m\u001b[39mlen\u001b[39m(X_test_tensor)\n\u001b[1;32m----> 3\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mTesting RMSE: \u001b[39m\u001b[39m{\u001b[39;00mtorch\u001b[39m.\u001b[39;49msqrt(mean_loss)\u001b[39m.\u001b[39;49mdetach()\u001b[39m.\u001b[39;49mnumpy()\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",
      "\u001b[1;31mRuntimeError\u001b[0m: Numpy is not available"
     ]
    }
   ],
   "source": [
    "mean_loss = total_loss/len(X_test_tensor)\n",
    "\n",
    "print(f\"Testing RMSE: {torch.sqrt(mean_loss).detach().numpy()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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