notebooks/23_FreeSolv_ESOL_eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ESOL data\n",
    "\n",
    "1. There are different properties of molecules inside dataset like 'Minimum Degree', 'Molecular Weight', 'Number of H-Bond Donors', 'Number of Rings', 'Number of Rotatable Bonds', 'Polar Surface Area'\n",
    "2. All in all there are 1128 rows of Smiles+LogS and no duplicates, no Nans\n",
    "\n",
    "FreeSolv data\n",
    "\n",
    "1. There are 642 rows of Smiles+LogS and no duplicates, no Nans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "\n",
    "import sys\n",
    "\n",
    "\n",
    "DATA_PATH = \"../data/raw/\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ESOL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "FILENAME = 'ESOL.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "SMILES_COLUMN = 'smiles'\n",
    "VALUE_COLUMN_OLD = 'measured log solubility in mols per litre'\n",
    "VALUE_COLUMN = 'logS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(os.path.join(DATA_PATH, FILENAME))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['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', 'Polar Surface Area',\n",
       "       'measured log solubility in mols per litre', 'smiles'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "esol_data = pd.DataFrame(columns = [SMILES_COLUMN, VALUE_COLUMN])\n",
    "esol_data[SMILES_COLUMN] = data[SMILES_COLUMN]\n",
    "esol_data[VALUE_COLUMN] = data[VALUE_COLUMN_OLD]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_na(df, column):\n",
    "    n = df.shape[0]\n",
    "    num_of_na = df[column].isna().sum()\n",
    "    frac_of_na = int(100.0*(num_of_na / n))\n",
    "    print(\"# of NA values \" + column + \": \" + str(num_of_na) + \", \" + str(frac_of_na) + \"%\")\n",
    "    print(df[df[column].isna()].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>smiles</th>\n",
       "      <th>logS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)...</td>\n",
       "      <td>-0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cc1occc1C(=O)Nc2ccccc2</td>\n",
       "      <td>-3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CC(C)=CCCC(C)=CC(=O)</td>\n",
       "      <td>-2.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c1ccc2c(c1)ccc3c2ccc4c5ccccc5ccc43</td>\n",
       "      <td>-7.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c1ccsc1</td>\n",
       "      <td>-1.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              smiles  logS\n",
       "0  OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)... -0.77\n",
       "1                             Cc1occc1C(=O)Nc2ccccc2 -3.30\n",
       "2                               CC(C)=CCCC(C)=CC(=O) -2.06\n",
       "3                 c1ccc2c(c1)ccc3c2ccc4c5ccccc5ccc43 -7.87\n",
       "4                                            c1ccsc1 -1.33"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esol_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1128, 2)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esol_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of NA values smiles: 0, 0%\n",
      "Empty DataFrame\n",
      "Columns: [smiles, logS]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "check_na(esol_data,SMILES_COLUMN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of NA values logS: 0, 0%\n",
      "Empty DataFrame\n",
      "Columns: [smiles, logS]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "check_na(esol_data,VALUE_COLUMN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esol_data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='logS'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(esol_data[VALUE_COLUMN])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROCESSED_PATH = \"../data/1_filtering/esol_freesolv/esol.csv\"\n",
    "\n",
    "esol_data.to_csv(PROCESSED_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FreeSolv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "FILENAME = 'FreeSolv.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "SMILES_COLUMN = 'smiles'\n",
    "VALUE_COLUMN_OLD = 'expt'\n",
    "VALUE_COLUMN = 'Energy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(os.path.join(DATA_PATH, FILENAME))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['iupac', 'smiles', 'expt', 'calc'], dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "freesolv_data = pd.DataFrame(columns = [SMILES_COLUMN, VALUE_COLUMN])\n",
    "freesolv_data[SMILES_COLUMN] = data[SMILES_COLUMN]\n",
    "freesolv_data[VALUE_COLUMN] = data[VALUE_COLUMN_OLD]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_na(df, column):\n",
    "    n = df.shape[0]\n",
    "    num_of_na = df[column].isna().sum()\n",
    "    frac_of_na = int(100.0*(num_of_na / n))\n",
    "    print(\"# of NA values \" + column + \": \" + str(num_of_na) + \", \" + str(frac_of_na) + \"%\")\n",
    "    print(df[df[column].isna()].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>smiles</th>\n",
       "      <th>Energy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CN(C)C(=O)c1ccc(cc1)OC</td>\n",
       "      <td>-11.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CS(=O)(=O)Cl</td>\n",
       "      <td>-4.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CC(C)C=C</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CCc1cnccn1</td>\n",
       "      <td>-5.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CCCCCCCO</td>\n",
       "      <td>-4.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   smiles  Energy\n",
       "0  CN(C)C(=O)c1ccc(cc1)OC  -11.01\n",
       "1            CS(=O)(=O)Cl   -4.87\n",
       "2                CC(C)C=C    1.83\n",
       "3              CCc1cnccn1   -5.45\n",
       "4                CCCCCCCO   -4.21"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freesolv_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(642, 2)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freesolv_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of NA values smiles: 0, 0%\n",
      "Empty DataFrame\n",
      "Columns: [smiles, Energy]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "check_na(freesolv_data, SMILES_COLUMN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of NA values Energy: 0, 0%\n",
      "Empty DataFrame\n",
      "Columns: [smiles, Energy]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "check_na(freesolv_data, VALUE_COLUMN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freesolv_data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Energy'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(freesolv_data[VALUE_COLUMN])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROCESSED_PATH = \"../data/1_filtering/esol_freesolv/freesolv.csv\"\n",
    "\n",
    "freesolv_data.to_csv(PROCESSED_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}