ESOL_GNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Predicting Solubility of Organic Molecules from Molecular Structure</h1>\n",
    "\n",
    "In this project a model is built to predict the degree of <em>Water Solubility - <b>(logarithm of solubility in mols per litre)</b></em> by using the molecular structure as features for the Deep Learning Model. \n",
    "<br>\n",
    "To train and validate the model, we make use of the <a href=\"https://moleculenet.org/datasets-1\">ESOL Dataset</a> - which comprises of <em>1128</em> common organic molecules. In the dataset each molecule's structure is represented by its respective <b>SMILES</b> string. As water solubility is a <b><em>physical property</em></b> - not requiring assessment of any of its molecular chemical properties, hence information of the relative molecular conformers is not necessary i.e. 3D coordinates of molecular components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\palin\\Anaconda3\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "from rdkit import Chem\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from torch_geometric.nn import GCNConv, TopKPooling\n",
    "from torch_geometric.nn import global_mean_pool as gap\n",
    "from torch_geometric.nn import global_max_pool as gmp\n",
    "from torch_geometric.datasets import MoleculeNet\n",
    "from torch_geometric.data import DataLoader\n",
    "\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "import warnings\n",
    "\n",
    "sys.path.append(\"./gnns/\")\n",
    "from esolModel import esolModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Load & Inspect the ESOL Dataset</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ESOL(1128)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the 'ESOL' component of the dataset\n",
    "data = MoleculeNet(root='.', name=\"ESOL\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inspecting the dataset...\n",
      "Dataset Type: <class 'torch_geometric.datasets.molecule_net.MoleculeNet'>\n",
      "Number of features per molecule: 9 | Number of Classes: 734\n",
      "Number of Datapoints: <bound method InMemoryDataset.len of ESOL(1128)>\n"
     ]
    }
   ],
   "source": [
    "# inspect the dataset\n",
    "print(\"Inspecting the dataset...\\nDataset Type: {}\\nNumber of features per molecule: {} | Number of Classes: {}\\nNumber of Datapoints: {}\".format(type(data), data.num_features, data.num_classes, data.len))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inspecting a Molecule Graph data sample..\n",
      "First Molecule Graph Data in dataset: Data(x=[32, 9], edge_index=[2, 68], edge_attr=[68, 3], y=[1, 1], smiles='OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)C(O)C3O ')\n",
      "Number of Nodes in Molecule Graph: 32 | Number of Edges in Molecule Graph: 68\n",
      "\n",
      "Inspect the features in the data sample:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[8, 0, 2, 5, 1, 0, 4, 0, 0],\n",
       "        [6, 0, 4, 5, 2, 0, 4, 0, 0],\n",
       "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
       "        [8, 0, 2, 5, 0, 0, 4, 0, 1],\n",
       "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
       "        [8, 0, 2, 5, 0, 0, 4, 0, 0],\n",
       "        [6, 0, 4, 5, 2, 0, 4, 0, 0],\n",
       "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
       "        [8, 0, 2, 5, 0, 0, 4, 0, 1],\n",
       "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
       "        [8, 0, 2, 5, 0, 0, 4, 0, 0],\n",
       "        [6, 0, 4, 5, 1, 0, 4, 0, 0],\n",
       "        [6, 0, 2, 5, 0, 0, 2, 0, 0],\n",
       "        [7, 0, 1, 5, 0, 0, 2, 0, 0],\n",
       "        [6, 0, 3, 5, 0, 0, 3, 1, 1],\n",
       "        [6, 0, 3, 5, 1, 0, 3, 1, 1],\n",
       "        [6, 0, 3, 5, 1, 0, 3, 1, 1],\n",
       "        [6, 0, 3, 5, 1, 0, 3, 1, 1],\n",
       "        [6, 0, 3, 5, 1, 0, 3, 1, 1],\n",
       "        [6, 0, 3, 5, 1, 0, 3, 1, 1],\n",
       "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
       "        [8, 0, 2, 5, 1, 0, 4, 0, 0],\n",
       "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
       "        [8, 0, 2, 5, 1, 0, 4, 0, 0],\n",
       "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
       "        [8, 0, 2, 5, 1, 0, 4, 0, 0],\n",
       "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
       "        [8, 0, 2, 5, 1, 0, 4, 0, 0],\n",
       "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
       "        [8, 0, 2, 5, 1, 0, 4, 0, 0],\n",
       "        [6, 0, 4, 5, 1, 0, 4, 0, 1],\n",
       "        [8, 0, 2, 5, 1, 0, 4, 0, 0]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The target variable for the Graph Data sample:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[-0.7700]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The graph data has 32 nodes & each node is comprised of Node-features which consists of a tensor of 9 features for each node.\n"
     ]
    }
   ],
   "source": [
    "# inspecting a sample of the dataset\n",
    "print(\"Inspecting a Molecule Graph data sample..\\nFirst Molecule Graph Data in dataset: {}\\nNumber of Nodes in Molecule Graph: {} | Number of Edges in Molecule Graph: {}\".format(data[0], data[0].num_nodes, data[0].num_edges))\n",
    "print(\"\\nInspect the features in the data sample:\")\n",
    "display(data[0].x)\n",
    "print(\"\\nThe target variable for the Graph Data sample:\")\n",
    "display(data[0].y)\n",
    "# print(data[0].edge_index.t()) # Get the Edge Insformation i.e. tuple of nodes connected by an edge.\n",
    "print(\"The graph data has 32 nodes & each node is comprised of Node-features which consists of a tensor of 9 features for each node.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"border: 3px double gray\"></hr>\n",
    "Upon inspecting the dataset - The target variable comprises of a single real (<em>float</em>) value - which indicates this as a <b><em>graph-level prediction problem</em></b>.\n",
    "Each data-point in the dataset represents the molecular structure as a combination of <b>nodes</b> with associated features, i.e. <b>'node-features'</b>. Each node has corresponding <b>9 features</b> associated with it which encapsulates edge features associated with the respective node as well.\n",
    "<h4>For each <em>SMILE string</em> - there is only <u>one</u> target variable for the entire molecule (graph data)</h4>\n",
    "\n",
    "Inspect a sample of molecule SMILES and the corresponding molecular structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Molecule SMILES string:  OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)C(O)C3O \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x21fb8c20ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------\n",
      "Molecule SMILES string:  Cc1occc1C(=O)Nc2ccccc2\n"
     ]
    },
    {
     "data": {
      "image/png": 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VoaGhtDROp06dTp48ydWdw8LCaHqHl5dXfn4+V7fVNy5dugTAxcVFXcCVK1cAtGvXjk9VesvBgwcBeHt7Cy1EX0hNTaUlWoKCggzLSdn61G9cvXpj4cK1a9dWVla+/fbbV69e9fHx4erewcHBCQkJzs7OSUlJQ4cOTU9P5+rOegVd9xw/frzmgEZU1TRK2MLoK3h6esbHxzs4OERERMyZM6em5YkBILSP6wGVleTjj4mZGRGJ/jlxIi2r0SB++eUXbcIePHgwYsQIABYWFrt27WqwTr2HHordvXu3uoCpU6cCMMqfvRH07t0bwOnTp4UWol+cP3/e3t4ewKxZs4TWoi3N3kavXCFDhxKAiEQkOJgUFzf0Brt375ZIJKGhodoEKxSKt956i36ABQcHV1ZWNlyxnlJTNPP+/ft1BiiVSnrKKycnh2dtesijR49EIpGNjY0xvQe44uLFi9bW1vPmzdNxZ4I3mrGNVlWR0FBiZkYA0qULafgglLJp0yaa3DNnzpyysjJtnhIWFkYryI0ZM0afq+02iLNnzwLo2bOnuoC0tDQA3bp141OV3vLDDz8AmDRpktBC9BGFQmFpaSkSiQylXEtzXRu9cgXDh2PtWlRVITgYV640upvFhx9+GBkZaW9vv2/fPg8Pj7t379b7lODg4DNnzri4uCQnJw8dOvT8+fONe2m9ot6imdpX1WwOsBOxGjhz5oxcLh84cOAr5631luZno1VV2LwZ7u64eBGuroiPR1iYjl3V/P39z58/37dv3ytXrri7u2tTeHTIkCHp6ene3t4PHjwYO3bsjh07dBGgD7Cj9A2CfahowPA234QeDvNLZiZxc/t9JbS0lMN7FxcXv/baa2jIafqqqqqQkBD6HxEcHFxRUcGhHp757LPPRo0a9eTJkzofraiosLa2FolEjx8/5lmYHpKTkwOgZcuWhnv8sUnx9PQEEBUVJbQQbWk2NlpZSUJDiakpAUjXrk3USa1xhUf37NlDT0x5eno+evSoKYTxRklJSWRkZHBwcNeuXcPCwmjDdEJIcnIygH79+gkrT0/4/vvvAcyYMUNoIQJQVFS0evXq2NhYdQGGWK6ledjopUtk0CACELGY80Hon2lE4dGLFy927twZQLt27c6cOdOk8jhHqVSeO3du48aNHh4etZtWikQiBwcHWgN/w4YNAFauXCm0WL3g9ddfB/D1118LLUQAIiMjAYwaNUpdQExMDIARI0bwqUpHjN1G5XISEkIkEgKQbt3IqVP8vOytW7do42VbW9tDhw5p85SnT5/SvHQTExMt06eEJS8vLyIiYt68eS1btqyxTtq0cv369efOnZs+fToAOzu7s2fP5ufn79u3r2Zw2syhHUCvXbsmtBABWLVqFYCPPvpIXcDq1asBfPDBB3yq0hFDtlGFguTlaRpanj1LevcmADExISEhRC7nUVxjCo8qlcqapdJ58+bp4VFruVweFxcXEhIyZMiQ2jWcXF1dg4ODIyIiaq9jKJXKN954A4C1tXViYqJwqvWL69evA2jTpo1BV9hsNAMHDgSg4f1giOVaDNBGX7wg69e/9Ef61aYNWbyY3Lz5e0ztQWjfvuT8eaHE1pym177w6L59+2iF08GDB+tJpvqdO3fCwsICAwNr96S0srKSSqWhoaHp6enqnqhUKufPn0+dtBHHw4ySr7/+GsDcuXOFFiIABQUFYrHYwsJCrmZMU1hYKJFIDK5ci6HZ6PXrxMWFAMTamkyYQJYsITNmECcnAhBzc7J//8uwb755OQj94AOiUAiqmCQlJTk5OQHo1q2bhprwtcnMzHR1dQXQqlUrodynpKQkLi5u5cqVtFh97YHnypUr4+LiFNr9YpVK5cKFC6ntxsXFNbVs/WfGjBkAvv/+e6GFCEBERAQAqVSqLuDnn3+GAZZrMSgbLSoirq4EIDNmkNqdPCoqyPr1BCCmpiQtjRBClEqyYAFRP0rimUYUHn327Jmfnx/PS6XV1dXp6emhoaFSqZQOoimtWrUKDAwMCwt78OBBI26rUqmWLVsGwNLSksO6WYZIdXU1TSm/e/eu0FoE4J133gHwj3/8Q13Au+++C2DTpk18qtIdg7LRjRsJQIYNI3UW0VqxggBkzBjeZWlFIwqP1k6fmjt3rpYnTRtBfn5+REREcHBw27ZtX9ksCgkJSUlJ0T29UaVSLV++HIC5uXlkZCQnsg2RjIwMAJ06dRJaiDD07NkTwNmzZ9UF9OnTB0BqaiqfqnTHoGy0c2cCkCNH6n40P//lAfnbt/mV1QAaUXg0MjLSzs4OwKBBgzgcwlRVVaWkpGi5WcQJKpVq5cqVAMzMzI6o+080dr744gsAixcvFlqIADx8+JDmrqirxpKXlycSiaytrQ3uHIrh2Oj9+y8TP0tK1MaMGEEAEh7Oo6wGk5KSQivAd+zYkeZU1kt2djYtqubo6KjjpLjRm0WcoFKp/vKXv1An1TIPzMiYNGkSgB9++EFoIQKwe/duAP7+/uoC9u7dC2DixIl8quIEw7HR06cJQNq31xQzfz4ByMaNfGlqJI0oPFpcXEzTMGlRvgblypSWlnKyWcQV69atoz/I3r17+XxdwampJdi4JWZDh+40fvXVV+oClixZAuDzzz/nUxUnGI6NHj9OANKjh6aY5csJQFav5ktT42lE4dHaS6WzZs0q1XgWq/ZmES3Kx8lmEVd8+OGH1En37NkjoAyeOX36NIDevXsLLUQY6Kd4ZmamugCandLUU6KmwHBsNDmZAER9nx9CCFm40CBGozU0ovBodHQ0rQ3eq1evGzduvPIoD5tFXLF+/XqqLVy/F2E45OOPPwawfPlyoYUIwK1bt+iqlLp34L179wDY29sbYttHw7HR27dfpjRpmIF6exOA/Pe/PMrSlfT0dBcXFwAdOnQ4r90xgZs3b/br1w9AixYtjhw5om6zqEuXLnSzSMu0f/4JDQ2lTtpM2orQym8HDx4UWogAhIWFAQgMDFQXQGtFTp8+nU9VXGE4NqpSkVatCKC2OFNpKbGxIQC5fJlfZbry5MkTWoXT3Nxcy6zs4uJimsUtEoksLCxqrNPa2trf33/btm03a5/p0mM+//xz+lN88803QmtpWuRyuaWlpVgsNpSK7twye/ZsANu3b1cXQM8N//vf/+ZTFVcYjo0SQt59lwDktdfqfnTrVgIQwyzF1ojCoyqVKigoyMbGRsDNIk6gTmpqahkebsylSOPj4wG4ubkJLUQAVCoVzU7Jzs5WF0PLtWRlZfEpjCsMykZzckiLFgQgX3756kOJiS+HoocPC6GMGxpaeJRWhPriiy940NakfPXVvzw9fxWJiBGXjqP5Ce+//77QQgTg6tWrANq1a6cu4MaNGwCcnJwMtFyLQdkoIeTIEWJuTgDi4UG++IL89BP59lsycyYRiwlA1q4VWp+uXLhwoWPHjvQ9p7ms3IMHDyQSiYWFhaF0T9RMWBgRi4lIRP71L6GlNA0eHh4AoqOjhRYiAFu3bgUwb948dQHffPMNgDlz5vCpikMMzUYJIWlpZMwYIhL9XuGJ1hLdt09oZdxAC486Oztrzkn6xz/+AYPq5V0v33338tPwk0+ElsI1xcXFBlfRnUOmTZsGYOfOneoCaPed/xrU5nBtDNBGKY8ekWPHSHg4OXKEGF3526qqqnr3iHr16gUgJiaGH0n8sGPHSyc1nKQ1rTh27BiAkSNHCi1EAJRKJW0G8euvv9YZUFOu5c6dO/xK4wwTGCht22Ly5Jff5+djzx6MHo3OnYWUxB0mJibdu3fXEHD27Nns7Ow2bdr4+PjwpooHFi2CpSXmz8f69SgvR2io0II4wvBaXXLHpUuXCgsLu3bt2lnNn+fly5cLCgpcXFxo+r0hYhQNlv/2N8yfj8OHhdbBH+Hh4QAWLFhgYmKwH4RqmDMHP/wAExNs3oy1a4VWwxG0nXK9zaUrKyu//vprlUrFiyieqLeVNA2g+6UGilHYqJ8fAGjRHd44UCgU+/fvB0BT7YyPWbPw448wNcXmzVizRmg1OvP8+fPLly9bWFjQXSYNvPnmmytXrly4cGF1dTU/2niAjsQ1fITUG2AACL2qwAVPnxKxmFhYkCaryKlX7Nu3D4C7u7vQQpqWqKiXSRl//avQUhrLzZs3t23bRhumanOUPjU1ldYuCQoKqqqzqK6hUVlZaWNjIxKJHj+uOynYOMq1GIWNEkIGDyYAOXFCaB18MGHCBADbtm0TWkiTc+wYsbAgAHn7bWIoCYW0nlZISAitQEyhTrF58+Z6n37hwgW6ITNz5kxtCtboOSkpKQD69u2rLuDMmTMAevXqxacqzjEWG/3gAwKQVauE1tHkPHz4UCKRmJmZNZMzhTExL500OJjoTVmVOsjKyvpzPS1HR0daT+vf//63RCIBsGHDhnpvlZ6eTntWT5482RCPpdVmw4YNAFauXKku4JNPPgGwbNkyPlVxjrHY6KlTBCB9+gito8mh5TxmzpwptBD+OHGCWFoSgCxZol9O+uTJE1pPq127djXWWVNPKy4urvZwct++fXQ/MCQkpN47X7x40dHREcCkSZPUNdE0CMaOHQvgsPqzhePHjwegZYMyvcVYbLSqitjZEYDcuye0lKaF1naKiooSWgivJCW9POu7aJHATlpVVZWenr5+/fohQ4bQ2q8UZ2fnefPmRUREaDhUtn//fuqka9asqfeFLl26RLMpJ0yYYFjdhmsoKyszNzeXSCTqficKhcLS0lIkEhn61MpYbJQQMnUqAYhR9609f/48ACcnJyNYNWsoycnE1pYAZPbsulsaNik1zVdoXyyKpaVlTfMVLQ+DHzhwgDbjWq1FcfHr16/TurG+vr6G6KQnT54EMHToUHUBNNVp0KBBfKpqCozIRrdvJwBRX9DQCKBtiv9quFvXupGS8tJJg4L4cNLiYnLkCHnnHSKVzqyd3NK/f//Vq1efPHmycdPtqKgoc3NzAO+880695nvjxg26YjBmzJgSDV3I9JL/+7//0zz0pk0QjOD9bEQ2evcuAYiDAzHA6tnaUFFRQdfLNLRhMHpSU18W+Vq5kty+TeLiSGJiHWGVlWofqpesLBIaSqTSl31mAeLltbFmsyg3N1enH4AQQkh0dDStErt06dJ6nTQ7O5sWkRs1alRxcbHur84bw4cPB3D8+HF1AZ6ensaxQmVENkoI6daNAOTMGaF1NAkRERForgUra5OeTnr3JjdukL///aXN/Xl/4tkzAhB7e23vmZdHdu8mr79OnJx+L3djYkI8PcmmTeTChTLOO1scP36cOulbb71Vb2eXmzdvdujQAYCnp6ehFDcpLi42MTExNTVVN4guKSkxmnItxmWjtKXd+vVC62gSJk+eDGDLli1CCxEeajs1Ntq2LXnx4g8B2thoVRVJTyfr15MhQ17WQ6Ffzs5k3jwSEUGaugBhbGyspaUlgMWLF9frpL/++muXLl3oUuOzZ8+aVhkXREZG0hG0uoDo6GgAI0aM4FNVE2FcNhoZSQBiFP8xr5CXl2dqampqavrkyROhtegL1EbbtSMAee+9PzykwUbv3CFhYSQw8GVmB/2ytCRSKQkNJenpvOb5nzp1ivYvmDt3br3Hlu7du9e1a1cAgwcPLigo4Edho1m1ahWAjz76SF3A6tWrAaxbt45PVU2EcdloaSkxNycSCTGEj+sG8eWXXwKYNm2a0EL0CGqjmzYRZ2cikZCMjN8fesVGy8pIXBwJCSF9+vyhSq2rKwkOJpGRRMDUzOTkZFtbWwCzZ8+u10lzc3O7detG13b03EkHDhwIIFH9+vTgwYMByGQyHkU1FcZlo+S35qAREULr4JgBAwZoTmNuhlAb/ec/yY4dBCBDh/6+uVjbRgMCiKnp79bp6EhmzSI7dhD9OcNd+yh9valsjx8/psdMBw0apLfplgUFBWKx2MLCQl0yQ2FhoUQiMTc3N8RErj9jFBWeamOM1Z4yMjKuXLni6Og4adIkobXoIwsXYtgwpKfj22/reHyRtz0AAArPSURBVFQshkqFIUMQEoK4ODx+jJ9+wqJFaN+ed6Fq8PT0jI+Pb9myZUREBJ3dawh2dnZOSEjo27dvZmbmmDFjHj9+zJtO7SkqKpoxY8bUqVNrt62tTUJCQnV19ciRI+nqsMEjtI9zzaVLBCAdOgitg0tWrFgB4L1X1v+aPTWjUULI2bNELCZ2doR2Aqw9Gr17lxQWCqlTSzIyMrQ/Sp+fn9+/f38AvXr1evjwIT8KOeTdd98FsGnTJqGFcIPRjUYHDkTbtnjwANevCy2FGyorK3/66ScACxYsEFqL/jJiBBYvRlERVq169aEuXWBvL4SmBjJ48GCZTObo6BgdHT19+nSFQqEh2MnJKT4+fsCAAdnZ2d7e3g8fPuRNJyfUW8vZsDA6GxWJQMtoG8u8Pjo6+unTp/369XNzcxNai14TGorWrbF/P06dElpKY3Fzc5PJZK1atTp+/Pj06dPlcrmG4NatWyclJQ0dOvTmzZujRo3KycnhS6au5Ofn37hxw9ra2t3dXWgt3GB0NgpjWx6l/ULefPNNoYXoOy1b4tNPAeAvf4HhFo8fNGhQcnJy27ZtT5w4MXHixNLSUg3BDg4OcXFxw4YNy8nJ8fLy+vXXX3nTqQvx8fGEkDFjxtQuKmjQGKON+vgU2dsfkss1f5gbBE+fPo2JiTExMZk7d67QWgyARYvg4YHMTISHCy1FB3r37p2QkNCuXbtTp05NnjxZs5Pa29ufPHlyxIgR9+7d8/LyunPnDm86G40xdA35I8Zoo05Ovt27v5acnJycLLQUXdm7d29VVdXEiROdnZ2F1mIAiMXYvh0SycthqeHSq1evxMTE9u3bJycnT5w4saSkREOwnZ3diRMnPDw8cnNzvb29b9++zZvOxmFkC6MwThsFpD4+AGINf15f0wFUaCEGw6BBWL4chYVC69CZHj16pKSkdO7cOTU1deLEicXFxRqC7ezs4uLivL2979+/P3r06Ot6vL+am5t79+5de3v7QYMGCa2FM4zTRv38/GD4NpqVlZWZmdmyZUt/f3+htRgSH3+MWtXoDZguXbokJSV16dLl9OnT48aNe/78uYZga2vrY8eOjRs3Li8vb9y4cdeuXeNNZ4OQyWQAvL29aVcV48A4bXTkyJF2dnbXr1/Pzc0VWkvj2blzJ4A5c+bQ8pSMV3BwgKtrHclMLVrgq6/g6orOnQVQxS2dOnVKSkrq2rVrRkaGj4/Ps2fPNARbWVlFRUVJpdL8/Pxx48ZdvXqVN53aY3wLo4Dxpd//xtSpUwF8b7DF8Kuqquh6aFpamtBaGAJTc5RemwOgCoWCTl9at259+fJlfhTWS1VVVXJy8rp162gBgaysLKEVcYnR2uj27dsBBBpsMfyjR48C6NMMmvQ1mi1byPz55OzZuh9dtozMn288NWpqjtIPHDiw3ipfcrmcnhseMGCAlt1NmohHjx6Fh4cHBgba/zZrEIlEhw8fFlYV5xitjd69exeAvb19vVVz9JMZM2YA+Pzzz4UWor8EBBCA7N1b96MODsbW4TAvL69v374Aevfu/YgeelWPQqFYvHhxdnY2P9pqU1paGh0dvWLFih49etSe+Pbq1eu9996LiYkx9K7Rf8ZobZQQ0r17dwBnDLAY/rNnz2hLxfv37wutRX9pbjZKah2l79mzp74dpadd//z9/WuXI7G2tpZKpVu2bMnJyRFaYBNi0sRLr0Li5+d369at2NhYDw8PobU0jL1791ZUVEycOJG2jmAwKPQovVQqvXLlire3d0JCQntBC1U9e/YsISFBJpMdP378/v379KJYLB4yZIhUKpVKpcZ0VEkDRm6j27Zti42N3bBhg4aw5OTkO3fu2NjY2NraWlhY2NraWltbW1hY2NnZWVlZCbJLztJFGeqgR+n9/PwuXLgwatSoxMTEzvxmJFRXV2dmZspkMplMlpSUpFQq6XUnJ6exY8dKpdKAgADaF7r5YMw26u3tbW5ufuHChefPn9MSZHXy3Xff7d27V8N9LCwsLC0tLSwsHBwcXvlG3fVXvrG1tTUx0fZXfe3atYyMDDs7uylTpjTsB2Y0DxwcHE6ePOnn55eWlubl5ZWQkODq6trUL5qXl3fy5Mljx47FxcW9ePGCXjQxMfH09AwICJBKpYMHDxaJRE0tQz8xZhu1trYeOXJkYmKiTCYLCgpSFzZ69GgTE5Py8vKioqKKiorS0tKSkhKFQlFSUlJWVlZZWalQKGjVMl1K5LZo0cLc3NzW1nb69Om0I4g6/ve//wGYM2eOkVS0bWLu3EFaWh3XDbc6iTbQo/QTJkw4d+4cnd3TNk3cIpfLT58+TQeeGRkZNdddXV3pnN3Pz4/W7W/miAghQmtoQjZv3rx27dpFixbt2LGj0TeRy+UKhUIulxcWFqr7RnNAcXFx9W9/1gsXLty1a5e611IqlS4uLo8fPz579uyIESMarbk5MGUKoqLqibl3Dy4uvKgRgtLS0oCAgKSkpI4dOyYkJNDcUt25e/euTCaLioqSyWQ1ZU+tra09PDz8/f2nTp3K8zKC/mPkNpqZmenm5ta+ffv79+8LO+MoKipSKBRlZWXm5uYatgViYmImT57co0eP7OzsZjtF0hJqo6+9hv7963h082bI5UZuowDKysoCAgISExOdnZ3j4+NpbmkjULdZ5Obm1qw2ixqJ0KkCTYtKpaKr3YZyaoIuPnz66adCCzEAmmHCU52UlZWNHz8eQJs2ba5evar9E5VKZXp6emhoqFQqrb127+TkFBgYGBYWVm92KoNizGujAEQikY+Pz+7du2NjY2nqsj5TVFQUFRUlFovfeOMNobUwDAYrK6vIyMhp06bFxcWNHz9eJpP1r3N8/hs1m0Uymazwt1pYbLNIF4zcRgH4+flRG/3rX/8qtJZ6+PHHH+Vyua+vb8eOHYXWwjAkaFGSwMDAqKiosWPHnjx5cujQobUD2GZRk2L8Nurr6ysWi5OTk8vLy62srISWowmWLspoNObm5gcPHgwKCjp69Kivr29sbKy7uzvbLOIH47fRVq1aubm5ZWRkJCcnT5gwQWg5arl58+b58+dbtGgxbdo0obUwDBIzM7OIiIjAwMDIyEgvLy9bW9v8/Hz6kFgsdnd39/Pz8/PzGzFihPZZzAxtaBa/TT8/v4yMjMOHD3t4eNjZ2Qktp25oFtSsWbP0fMjM0GfMzMwOHjw4Z84cQsihQ4ea88kiPjHyhCdKSkqKr69v7cbfmo8e1Xs2ydHRkdtDoiqVqnPnzvfv309NTfX09OTwzkbMzz/j1i1MmYI6k3y2boVcjmXL0AxX/FQqVWlpaU5OzoABA4TW0ixoFjZaXV2dm5s7fPjwiooKzT1ttMfc3NzKysrOzs7CwsLa2rrmPL6NjY2FhUWLFi2srKwsLCzs7e1rLJgadM1TWrRoUdNHITY2dsKECd26dbt58ybbJGUwDItmMamXSCRdunR58uQJ/WdZWZlCoSgqKiovL6+oqKg5cfTixQuFQlFeXl5cXKxQKF45FVrzFIVC8eLFi4qKCvpcXYSZmpra2NjY2NhQc1+wYAHzUAbD4GgWo9EmQpfjoXK5/Pnz5xUVFTV32759+5QpU4Ste8ZgMBoBs1EhqaysLCsro4PfLl26sMN2DIYhwmyUwWAwdMI4GywzGAwGbzAbZTAYDJ1gNspgMBg6wWyUwWAwdOL/ASbcnQH1/AiQAAABPnpUWHRyZGtpdFBLTCByZGtpdCAyMDIwLjA5LjEAAHice79v7T0GIOABYkYGCOAHYgEgbmBkY0gAiTOzOWgAaWYWDgjNxOaQAaKZGZEYEBVsDFCVEJqJnQGsgAmhAItWnAxuoJsYmRKYmBOYWTKYWFgTWNkU2Ng1mNg4FDg4FTi5Mpi4uBO4eTKYeHgTePkymFgZM5j4OBNEgAwmZhZWNi5uHl4+TnGYxxj4TeS9D3Qe4HUAcW44+R/ojzW3B7HPLOk/YPrq6V4Qm/V004GUmOP7Qewna1QPrFl8Hsw+JslyYLVs3D4Qe1q23H5faVs7EDvzRuW+v90XwebcMXluJ7YnFqz++6lP9htuvwWr//vM1iHhx0Gw+HujXgfHiX1g9jzDmQ4esWvB5ux+mOtw/MZGsDm3kwQd/B/XgNliAGd0UouqMJUtAAABOHpUWHRNT0wgcmRraXQgMjAyMC4wOS4xAAB4nJ2TwU4EMQiG7/MUvMA2/aEFenY9GdfEg+/g3fePdDrbnYMm2qaZ/AT4AgzdqJ/368vnF83D120jVIIS5R9va40+OOe8RfhFEkx8z0yM0rrKKbyZnug3xPkeFBavI9egskYpqbj3Ci45AXymvP2Hkt167gWpVqtrtXCq7jYoysyrlKwFoyOGLk43J4X7yIUXW6VwKbnnRkdiWJpuZJTad6vzXPU8l9vfKUje1AelaNGljmJjmxoffxq1rFFi68B1UADxVYqNrYtcEV/sSPrraWNC4m2xo3jJhmNLmttSR7EevH+7EUqmEapMI1Td/d0IpdMTyqYRymdYGG16GiHfjVDAPSwUZgWhINMjhFlBKDwq0F0dYfWBfiV6vl23b28tzVVwhv68AAAA1npUWHRTTUlMRVMgcmRraXQgMjAyMC4wOS4xAAB4nB2PwW0FQQhDW8lxV5qPxsCAR6uc/j0pYgtIA7/4wHJ8so39vnEf7+P79/y59e7T877/8PU5XiZISw4VhZPjKqJGX2NKIjRRSGWGzxyvWarYjaYESN0lAz3SH6Zuew9IWM7KmuILK1cbGYFVTgh3UBt5+Iocl8qO1EKQktsel4tDPZsARjRJpqHemTGmjsu6Xz8zbrdOqbIRpdhMNqneiwx2TKii17lMpj5oVTM8qIbzWQeoho3z8w9ezj907OSQogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x21fcdd65670>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------\n",
      "Molecule SMILES string:  CC(C)=CCCC(C)=CC(=O)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x21fcdd671c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------\n",
      "Molecule SMILES string:  c1ccc2c(c1)ccc3c2ccc4c5ccccc5ccc43\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x21fcdd67850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------\n",
      "Molecule SMILES string:  c1ccsc1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x21fcdd6c030>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------\n",
      "Molecule SMILES string:  c2ccc1scnc1c2 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x21fcdd6c530>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------\n",
      "Molecule SMILES string:  Clc1cc(Cl)c(c(Cl)c1)c2c(Cl)cccc2Cl\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x21fcdd6cb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------\n",
      "Molecule SMILES string:  CC12CCC3C(CCc4cc(O)ccc34)C2CCC1O\n"
     ]
    },
    {
     "data": {
      "image/png": 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XV9euXfFfWlpaikQivLayshIKhc1fW1tbCwSCxtc2NjbqAHXXar///ruXlxdCKDEx8ZVXXtGYPCIiYuXKlZ9++umSJUta85Wz0pEjR4KCgpydndPT0/GZ63rOnDkzYcIEOzu7zMxMfBKAmIgI+L//A39/+P57MDb+39/n5YGvL2RlwYUL8PrrkJYGHh4wbhzExdV/hjNnYMIEWLYMtm1rfQxtajBxCoVixowZAGBsbGzgkzWFhYXq401PT0/1DydmZGTk6ekZFBQUERFx/fp1mUxGt3ho3Ntvv433yCDO5h9SqRQAQkJCGk/+6NEjns1dqFSqfv36CYXCuLi4hh6Dj1pWrVplyGD1qVTo5ZeRUIg0jrLiw0x8K2k9H42KSX/TakUsFkdHR1tbW+/atWvy5MkHDhzAVVUf/v777zt1PH36tO5Hzc3N+/Tp06NHD09PT29v7/79+xvX/d0IAADr169ftGjRihUrAgICJBKJnnJyUUpKyqlTp5RK5ZYtW/DkEACUlJSoVKrG18XFxeifo/6G1kVFReoX0rhGCBUXF2t8jEgkwnd/a0S3bt2a/5VygkAg2L9/v0gk6tOnT0OP2bp1a79+/Xbu3NmzZ09HR0eNh/YCgcDa2hqvhUKh7o9bMzMhOxu8vUHj/6OAADAzg2vXQKHQ8esycPhNfV3h4eGrVq0SCAQ7d+7U1durunXz1q1bz58/r/tRS0vLXr16ef/Dw8ND/Y6yIUqlsk+fPg8fPty9ezfe55HCRowYER8fv2DBAnxrX4LqltQzZ87MnDnTwcEhIyPD0tKSbDAWGjx4cEZGRr2fi+YQiUT437NAKhUoFKD+txWLwcKidm1kBObmGtYSCeDzDJMnQ3U1BARAUBB8+aXmV/L2hqQk+PNPqKoCDw/o3Rvmz6//mORk+PxzLd/U86SMAsDu3bvxZYUrV67Ee3+1iFKpTEtLu3PnTkpKysOHDxMSEgoLC+s+wNraukePHuq6yXwj3xxxcXFvvPGGjY1NZmYmS97DEnfs2LGpU6fa2tqmpaXZ2dmRjvMvfn5+v/zyy7p16z766CPSWdjlxYsXbm5uRUVFffv2bdeuncZDe5VKVVJSwlyrIYkEqqtbmWDXLnBwgHffhQULoKHfvsOHw5UrcP8+SCTg4dHYs2lXRrn9pr6u0NBQa2vr2bNnh4eHV1ZWRkRENF7mFArFo0eP1Mebd+/elclkdR/Qvn17XDHxW3W8V5iWxo8fP2LEiMuXL4eHh4eHh2v/hFxXVla2YsUKAAgPD2dbDQWAbdu2DRw4cPv27e+9917Hjh1Jx2GR1atXFxUV+fv7X7x4saWfq1QqS0tLa/+jpgbKymrXCgWUl2tYV1dDRUXtWi4H/HPq6wt//AEAUFnZ4CvhzzIzq31fP2IE7N9f/zGXLmk4RG0pbU6sstDp06dxZ2D69On1TvzL5fLff/89JiYmNDTU19cXP6yu9u3bjx07NiwsLC4u7tmzZ3pKSLd4qGvZsmUA0K9fv5beUdVg3n77baAXTfzb7du38fdwamoqyRz37iEA5OfX4APatUNGRkgmo3OjLRYfH483qQsICLhw4cL27dsDAwM9PT3V0zOYSCTy9PQMDAzcvn37lStXcFPCMOgWD9jDhw/xnQJ+++030lka9McffxgbGwuFwsTERNJZWEGpVPr4+ADA6tWrCUepqUE2NsjYGBUWavjozZsIAA0ciBAdv2+VhIQEa2trExOTunVTLBbXHUJqaIdaA6BbPGDDhw8HgAULFpAO0oSlS5cCwLBhw0gHYQXcBnRxcSkrKyOdBaGlS/81Zq9WU4P8/REAOnwYIVpGW2vbtm0AYGNjM3/+/KioqMTERFbdSYZu8RAbGwsAtra2+fn5pLM0oaioyNbWFgDOnTtHOgthBQUF+BT2CZbca7K4GLm6IgC0aBHKyan9y9RUNHYsAkDDhtVu9E7LaOts3boVWLwlMHu3eDCI0tJSZ2dnADhw4ADpLM2yY8cOAPDw8ODZpH1LzZkzBwD8/f1JB6kjOxsNGFB70zYHB2RjU3tZ/VtvIfXxMi2jrYO3/Nq5cyfpIA3CWzy0zRsusr+zVI9cLsdj9p9//jnpLMSwpbPEpFKhS5fQqlUoKAgFB6NNm9CdO/96QEEB2roVaTxkycxEW7ci7Xbn4m0ZHTduHLD7ykv1Fg9t7YaLnOgsMX3zzTcA4ODgwIktw3WORZ0l9uFtGe3duzcA3Kn3S4llTp8+DQB2dnaGnBMgjiudJSZfX18AWL9+PekgBHz22Wcs6iyxDG/LKL6Al/33g8VbPKzkzu2jtcShzhLTzZs3BQKBiYkJJ/YL1yHWdZZYhp9lFF+aZm5uTjpI05KSkoRCoVQqfazd/tucwLnOEtPkyZMBYObMmaSDGBQbO0tsws8yeu/ePQDo2bMn6SDNMm3aNACYNm0a6SB6x7nOEpN6Gp/l54t0iL2dJdZoYlMijsrOzgYAvIk3+23ZssXU1DQ2Nvb27duks+hRSkrK7t27hULh3r17m9wNi7VcXV0XLVqkUqmWL19OOoshqFQq/PUuXbq0e/fupOOwFFe/mxv3+PFj4E4ZdXFxCQ0NRQjx+ydz8eLFCoVi3rx5uOHLXevWrbO1tb1y5cr58+dJZ9G7qKioW7duubi4rF27lnQW9uJnGeXW0SgArFmzxtHR8dq1a/jePvxz7Nix+Ph4W1vbTZs2kc6iLRsbmzVr1gDAsmXLampqSMfRo8LCwvXr1wPAzp07zdU7flIMtIyygoWFxbp16wDggw8+UOh/s24DY/lueK0QEhLStWvX1NTUQ4cOkc6iRytXrnzx4oW/v/9bb71FOgu7kT45qxf9+vUDgJs3b5IO0gLsuuGiTvGgs8T09ddfA6+n8Wlnqfn4WUbt7e0B4OnTp6SDtMypU6eAd9P4HL1mqTnwNP6GDRtIB9E99TVLhG9axxE8LKMVFRUAYGxszMXNk/AVPnz63sVf0cKFC0kH0b0bN27wdRqfXrPUIjwsoykpKQDg5uZGOkhr8Gwa/+jRowBga2v74sUL0ln0Ap80nDVrFukgukSvWWopHpZRPIYycuRI0kFaaerUqQAQGBhIOoi2eHDNUpN4OY1Pr1lqKR526rk1NMoUHh5uamp69OjRxMRE0lm08uGHH+bm5vbr12/mzJmks+iLq6vrwoULVSoVHkXggcTExEOHDkkkkj179pDOwhk8LKOcm3aqx8XFZfHixYjj0/j8uGapOdavX29raxsfH8+DaXx6zVLr8PD7m+tlFADWrl3r6Oh49epV7k7j42uW5s+fz/VrlppkY2OzevVq4MU0Pr1mqZVIn1XQvUGDBgHAtWvXSAfRyu7duwHA3d29urqadJYW431nqR65XN61a1cAiIqKIp2l9WhnqdV4WEY7dOgAANnZ2aSDaEWhUHh4eABAZGQk6Swt0xY6S0zHjx8HAAcHh9LSUtJZWol2llqNb2VULpcLhUKxWMyDW4999913AGBvb8+taXxeXrPUJJVKhafxw8LCSGdpDXrNkjb4VkYzMzMBoFOnTqSD6AaeXefQ3W94fM1Sk7g7jU+vWdIS38ropUuXAGDo0KGkg+gG56bxhw0bBjy9Zqk58DT+7Nmzm3xkdXV1YmJiYmJiZmZm1j8KCgoK/2HIa/DoNUtaEiCEDNvT0q9Dhw7Nnj17+vTpMTExpLPoxrRp02JjY4OCgr788kvSWZoQGxs7bdo0W1vb9PR0W1tb0nEI+OOPPzw9PRUKxe3bt/v27dvII3Nzc11cXJr5tDY2No2vBQKBtbU1c923b99Vq1Y1/uSFhYXu7u4vXrw4ceIE3cmplUjXcR3bsGED8Gu3iD///FMqlQoEgtu3b5PO0pi22Vliev/99wFg+PDhjT/s6dOn3t7e3t7eXbp06fyPdu3a2fxDJz/d48ePbzIw7Sxpj29Ho8HBwTExMQcPHpw1axbpLDqzcuXKTz/9dOjQoT///DPpLA1avnz59u3b+/fvf/PmTX7P2zeuqKioa9euhYWF58+fHzVqlK6eEy8QQsXFxY2vVSpVSUkJXjs7O+P5v4YkJiYOGDBALBbfv3+fztu3HtkqrnOvvfYaAFy6dIl0EF0qKirCA31nzpwhnUWzttxZYtq6dSsAeHh4sHxchHaWdIVvZbRTp04AkJGRQTqIju3atQtYPI3fxjtL9cjl8i5dugDAF198QTpLY2hnSVd4VUZramqMjIwEAkFlZSXpLDpWXV3t5uYGAHv37iWdpb62ds1Scxw7dgwAHB0dWTuNT69Z0iFeldEnT54AQIcOHUgH0YuTJ08CgJ2d3ePHj/FMDBsOImhnSSP2T+PTzpIO8arFdP369SFDhgwcODAhIYF0Fr1wd3fPzc3F2/vXI5FIzMzMGl8bGxubmpo2fy2VSk1MTBpff/311ydPnqSdJaabN28OGjRIKpU+evSo+bNNhkE7S7olJh1Al3iwt1MjTpw4kZWVJRKJrKyscMGqrq5Wl9Tq6urq6mrDp7KwsJBIJLt27aI1tJ5XX3110qRJJ0+e3Lhx44EDB0jH+R+6G57O8bCM4i4Tz1y6dCkoKEipVH788ccaB6rrlVT1Wi6Xy2SyxtdVVVWVlZWNrysrK6uqqpjra9eulZWVXblyZeDAgbr7cnli8+bNZ8+ejY6Ozs3Ntbe313g4b2JiIpVKmWtTU1NjY2O8NjMzk0gkzLW5ubmRkRFeW1hYiMVi5pqJ7oanczwso/w7Gr158+bEiRPlcnloaGhDF6VIJBL1T5chXb169bXXXtu8efOsWbOcnJwMH4DNunTp0r59e6VS+eOPP5LKYGlpKRKJ8Bq/j8nPzweAHTt2mJubk0rFM7wqo1y/fYhGDx48CAgIKC8vnzFjRkREBOk49Q0dOnTcuHFnz57dtGnTvn37SMdhl+jo6OzsbDs7u8OHDwuFwiYP7WUymVwux+uKigr1WZq66/LycoVCwVyXlZWpN40uLS1VKpXqtToPHuP38fEJDAycPHmy7r/gtopXLSZ3d/dHjx49fPjQ09OTdBbdyMrK8vPze/r06YQJE06cONHIOzWC0tPTe/XqpVKp7t+/36NHD9Jx2KKoqMjd3T0/Pz82Nvbdd98lFaNuSS0pKVGpVFKpFO/JS+kM6VEBnVGpVPh8ExvGgHQiJyfH1dUVAPz9/auqqkjHacyCBQsAYNy4caSDsAj+N/Hz8zPkXk0UEfw5Gn327Fn79u3t7OzwqR+uKygoGDJkSEpKyoABAy5dusTy01j5+fndunUrKSm5ePGiv78/6TjkJSUl+fj4CASCO3fu9O7dm3QcSr/4M6TCp/5SRUXF+PHjU1JSevXq9cMPP7C8hgKAvb39Bx98AAArVqxQqVSk4xCGEAoJCVEqlUuWLKE1tC3gWxnlwbRTdXX1pEmTEhISunTpcuHChXbt2pFO1CxLly7t2LHjvXv38LWhbdmhQ4du3Ljh5OS0fv160lkoQ+BbGeX60ahSqZw2bdpPP/3UoUOHixcvtm/fnnSi5pJKpR999BEArF69Wj2R2gYVFRXh+y3v2LHDysqKdBzKEGgZZRGE0Ny5c7/99ltbW9uLFy/i/hKHBAYGent75+bmsnAwy2DWrl2bn5/v5+c3ZcoU0lkoA+FPGeXB0Ojy5csPHTpkamoaFxfHxZktoVC4bds2ANiyZcuzZ89IxyEgKSkpKipKLBZHRkYKBALScSgD4U8Z5frR6MaNG3fs2CGRSL777rvGdyxns9dee23s2LFlZWX4DX6bQjtLbRfZeSsdsrS0BICioiLSQVojMjISAEQiEQ82f0xNTRWLxSKR6PfffyedxaDw/iNOTk7FxcWks1AGxZOjUZlM5urqamRkFBERUVBQQDpOyxw5ciQ0NFQgEOzfv58Ht2bs3r37nDlzlErlmjVrSGcxHNpZatNI13Gd+eabb/BXZGZmtmTJkuzsbNKJmuXMmTP4Es/t27eTzqIzz58/x28OeHZTrEbQa5baMv6UUYTQ9evXx4IlcEkAAAYYSURBVI4di0/tC4XCsWPHJiYmkg7VmPj4eLwxGmv3SG+1jz/+GABeeeUVpVJJOove3blzRyQS4V2QSWehCOBVGcWSk5ODgoLU+zD6+vrGxcWRDqXBrVu3LCwsAGDRokWks+ieTCbr2LEjAHz11Veks+iXSqXCe60uW7aMdBaKDB6WUezx48ehoaHq+2d4e3vHxMTU1NSQzlUrPT3dwcEBAAIDA/l6vHb48GEAcHZ2rqioIJ1Fj2hnieJtGcXy8/PDwsJsbW1xMe3atWtERATx+4Y+efIEH6mNHz+e5bcy14ZSqfT29gaATz75hHQWfSksLLS3tweA2NhY0lkoYnheRrHy8vL9+/d37doVF1MnJ6ewsDBSxw7Pnz93d3cHgGHDhhEv6Pp25coVALCwsHj27BnpLHpBO0sUaiNlFFMqlXFxcfj4CAAsLS1DQ0Nzc3MNmaG4uNjLywsA+vfvz9o7mOtWQEAAX8//0s4ShbWhMqqGG/q4mBobGwcFBaWnpxvgdSsqKvz8/ADAzc0tLy/PAK/IBngaXywWP3z4kHQWXaKdJUqtLZZR7M6dO0FBQfhuX3g66tatW/p7uerq6tGjRwPASy+9xJWZVl2ZP38+ALzxxhukg+gS7SxRam23jGIZGRmhoaHqu9rqaTpKqVS+8847AGBvb5+Wlqbz52e5vLw8nk3j084SVVdbL6PY06dPw8LCrK2tcTH18vLS4XSUSqWaO3cuAFhZWSUlJenkOTkH71Ti5eXFj+ku2lmi6qJl9H9KSkoiIiLUN03s3LlzRESETCbT8mnx3TVMTU2vXbumk5xcxKdpfNpZouqhZbS+qqqqmJgYNzc3XEwdHBzCwsIKCwtb92yffPIJABgZGf3www+6zck50dHRAODi4sLpaXzaWaKYaBnVDE9H+fj44GJqYWERGhqak5PToieJjo4WCARCofD48eN6yskh6mn8zZs3k87SerSzRDHRMtqEutNREokkKCgoNTW1OZ948uRJkUiEt7/Td0iuiI+P5/Q0Pu0sURrRMtosd+/erTcddePGjUYe/9NPPxkbGwNAeHi4wUJywpgxYwAgJCSEdJDWoJ0lSiNaRlsgKysrNDTUxMSk7nQU8yfqxo0beEuUVatWEcnJZtydxqedJaohtIy2WF5eXlhYmI2NDS6mffr0iYmJUe8wkpycjD8UHBxMj1k0mjdvHgBMmDCBdJAWoJ0lqhG0jLZSaWlpRESEs7MzLqaurq4RERHJyclOTk4AMGnSJPZsysc2XJzGp50lqhG0jGqlqqoqKiqqW7du6iv0AWDUqFFyuZx0NFbbtGkTh6bxaWeJapwAIQSUdlQq1blz5z755BN3d/eqqip8r3nSoVitsrLS3d39r7/+OnLkyLRp00jHacLChQs/++wzPz+/q1ev0rvPU0y0jOqSXC7HB6RUk6Kjo2fNmuXi4pKens7m3zpJSUk+Pj4CgeDOnTv07vOURjy5wTJL0BrafDNmzOjbt29OTs6ePXtIZ2kQQigkJESpVC5ZsoTWUKoh9GiUIubKlSvDhw+3sLDIyMhwdHQkHUeDgwcPzpkzx8nJKS0tjd59nmoIPRqliBk2bNiYMWPKysr++9//ks6iQVFR0erVqwFgx44dtIZSjaBHoxRJqamp+M1ycnKyh4cH6Tj/QjtLVDPRo1GKJA8Pj1mzZtXU1KxcubKoqEilUpFOVCspKSkqKkosFkdGRtIaSjWOHo1ShD179qxbt25CobC0tLTu31tbW6vrl/qasbprgUCg3mlbKBSq33fXXYtEIjzqDwBisdjCwoK5NjIyMjc3x2uJRGJmZoYQWrFixf3795ctW7Zt2zadfrkUD9EySpGXlJS0a9eus2fP4muESMcBAHj55ZcVCkVaWpq62lJUQ2gZpViqqKiIuUYIFRcX47VKpSopKWGulUql+sC27rqmpqasrAyvFQpFeXk5c11dXV1RUQEAr7/++syZM+3s7PTzxVG8QssoRVGUVmiLiaIoSiu0jFIURWmFllGKoiit0DJKURSlFVpGKYqitELLKEVRlFZoGaUoitIKLaMURVFaoWWUoihKK/8PLWNdW161v7cAAAGWelRYdHJka2l0UEtMIHJka2l0IDIwMjAuMDkuMQAAeJx7v2/tPQYg4AFiRgYIEAFicSBuYGRjSACJM7MxKABpFgiXiQmVZmZEpZHkHTRA4ixsDhkgGqgAKsDBABZgZESSQVOC00yoOAfEaYzcQGczMjEwMQP1MLCwMrCyMbCxM7BzMHBwZjBxciVwcStw8WQw8fAm8PJlMLHyM/ALMAgIMggKMQgJMwgxMvAzMvCxMPBxJIiwsDExs7DyM7KxsXPwsbCycXLx8PJxsArwMwoJisPChkGkYl3qAXfFq/Ygjv1EvQM6m+7bgdiTo37tT5pwBywu0PZyX1DYTTC7wa7bbtPDo2A1Og//77salrYfxD5+LNNuRQvvARD7heFd+w2K/GD2yz/yDkL2xWA1hT/rHQw3V4LZm9fNcPipMR9szvc1Jxz2OEwCs7OSGh1UVpwH2+V7QsnB5NElMHv9iUf2JT93gNVEffy/f9ujeLA5N1MdDzQV/QGz/5r1HDiatRrMDk9tO3AoVBKsfmHrygOCrNPA5ogBAJWRZ0w24IWXAAABbnpUWHRNT0wgcmRraXQgMjAyMC4wOS4xAAB4nJ1UO04FMQzs9xS5AFFsx3Fcs1QIkCi4Az33F87n+W0BxfMqWs0oyWT8SY40vs/z9fsn+YfncSQsCSml8udQ1fSFpZTDlj9R5t772Am5NYaBSrbZkp7TfxLXMVUwC0pbeys1jalAVmZdXgQJYyrmgBvsiFRiXmwHiqVxIuqNw15U+0J9oZgX7LTyjACxvMycdtgq1GpMBS8RKZegF8rWhW2rCAe91CwCtGsEJVjpZpWG3ffYWS4qH494KZVodx0iRbPLlXirVIl13ai0wI6DGsS8jNvoNepSW0yFckHoW08weKer3cEZkakQaVgFxuM390JVialwRl1vHWTwN+KxfrF2w/kfxBA5MVSdGGInhpoTQ+LEUHdiSJ1ogjIPMGJotPmaMQR4JeTLKIE7MAR8m7FDwR0YAndgCNyBIXAHhkaGnNzDHlJ3wpdIB7kJvKX08n4evxPBEUfLIRP2AAAA/3pUWHRTTUlMRVMgcmRraXQgMjAyMC4wOS4xAAB4nCWQza3DMAyDV3nHFnAMi/pHjh6gQ3iNDv+k9BIon02K9N6EvTcfOee8Pu9zpH831Refv+/r4qkR7oOmma4Y94XpcMNYU9hyWSGaqRpZlxxMTepQjRa1Lj113GvCKY1Kx2ESZVXXnVybGMGLYKqwRKvElcctcwnDGgCMBqVhbg2t0By3tTG1L0KtNvHMTBuVIcsj21Uygx6ii2rq1YiVo9qATfjJh0D6g4iAX4tML8+aoif8ymbVfZiLdsp6pQUKGX3ogBSSqklOjZgTD6GK4RWUJF0qxaWzNmrXI8fi8f7+AyKmUiAeQipxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x21fcdd73210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------\n",
      "Molecule SMILES string:  ClC4=C(Cl)C5(Cl)C3C1CC(C2OC12)C3C4(Cl)C5(Cl)Cl\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x21fcdd73940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------\n",
      "Molecule SMILES string:  COc5cc4OCC3Oc2c1CC(Oc1ccc2C(=O)C3c4cc5OC)C(C)=C \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x21fcdd74030>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Visualizing Molecules\n",
    "# Use RDKit to visualize molecular features of first 10 graph data samples\n",
    "\n",
    "for i in range(10):\n",
    "    molecule = Chem.MolFromSmiles(data[i]['smiles'])\n",
    "    print(\"Molecule SMILES string: \", data[i]['smiles'])\n",
    "    display(molecule)\n",
    "    print('---------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"border: 4px double orange;\"></hr>\n",
    "<h3>Modelling with Deep Graph Neural Nets</h3>\n",
    "\n",
    "Create a baseline Model to benchmark the performance, on top of which a refined GNN model can be built to obtain improved predictions on water solubility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "baseGCN(\n",
       "  (in_conv): GCNConv(9, 64)\n",
       "  (conv1): GCNConv(64, 64)\n",
       "  (conv2): GCNConv(64, 64)\n",
       "  (conv3): GCNConv(64, 64)\n",
       "  (out): Linear(in_features=128, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Implementing Baseline Graphical Neural network\n",
    "\n",
    "class baseGCN(torch.nn.Module):\n",
    "    def __init__(self, n_features, embedding_size):\n",
    "        super(baseGCN,self).__init__()\n",
    "        torch.manual_seed(95)\n",
    "        \n",
    "        # Initialise the Base Model Architecture\n",
    "        self.in_conv = GCNConv(n_features, embedding_size)\n",
    "        self.conv1 = GCNConv(embedding_size, embedding_size)\n",
    "        self.conv2 = GCNConv(embedding_size, embedding_size)\n",
    "        self.conv3 = GCNConv(embedding_size, embedding_size)\n",
    "        \n",
    "        # output layer of the Graph Neural Net\n",
    "        self.out = torch.nn.Linear(embedding_size*2, 1)\n",
    "        \n",
    "    def forward(self, x, edge_index, batch_index):\n",
    "        \n",
    "        hidden = self.in_conv(x, edge_index)\n",
    "        hidden = F.tanh(hidden)\n",
    "        \n",
    "        # Graph Message Passing\n",
    "        hidden = self.conv1(hidden, edge_index)\n",
    "        hidden = F.tanh(hidden)\n",
    "        \n",
    "        hidden = self.conv2(hidden, edge_index)\n",
    "        hidden = F.tanh(hidden)\n",
    "        \n",
    "        hidden = self.conv3(hidden, edge_index)\n",
    "        hidden = F.tanh(hidden)\n",
    "        \n",
    "        # Apply Pooling at Graph-level i.e. Global Pooling\n",
    "        # Pooling mechanism to represent the Graph of any number of nodes in to an array of fixed size\n",
    "        # Concatenate the Global Pooling of Mean (G.A.P) & Max (G.M.P) of these features into a single representation\n",
    "        hidden = torch.cat([gmp(hidden, batch_index), gap(hidden, batch_index)], dim=1)\n",
    "        \n",
    "        # Apply a final (linear) regressor\n",
    "        out = self.out(hidden)\n",
    "        \n",
    "        return out, hidden\n",
    "    \n",
    "emb_size = 64\n",
    "baseModel = baseGCN(data.num_features, emb_size)\n",
    "\n",
    "display(baseModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Model Parameters:  13249\n"
     ]
    }
   ],
   "source": [
    "# Summarize the instantiated Base Model\n",
    "print(\"Number of Model Parameters: \", sum(p.numel() for p in baseModel.parameters()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Compile the <em>baseline</em> model & define the Loss & Optimisation functions</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\palin\\Anaconda3\\lib\\site-packages\\torch_geometric\\deprecation.py:12: UserWarning: 'data.DataLoader' is deprecated, use 'loader.DataLoader' instead\n",
      "  warnings.warn(out)\n"
     ]
    }
   ],
   "source": [
    "# Use the GPU for Model training if available\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "baseModel = baseModel.to(device)\n",
    "\n",
    "# Define the Objective Function for Base Model compilation\n",
    "# MSE -> Objective / Loss Funciton\n",
    "loss_function = torch.nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(baseModel.parameters(), lr = 0.0005)\n",
    "\n",
    "# Use Data Loader to collate Data into batches\n",
    "data_size = len(data)\n",
    "batch_size = 64\n",
    "\n",
    "# Pass 75% of the data as Train to the Data Loader\n",
    "train_data_loader = DataLoader(data[:int(data_size * 0.75)], batch_size=batch_size, shuffle=True)\n",
    "\n",
    "# Pass 25% of the data as Test to the Data Loader\n",
    "test_data_loader = DataLoader(data[int(data_size * 0.75):], batch_size=batch_size, shuffle=True)\n",
    "\n",
    "def model_train(data):\n",
    "    \n",
    "    for batch_data in train_data_loader:\n",
    "        \n",
    "        batch_data=batch_data.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        prediction, embedding = baseModel(batch_data.x.float(), batch_data.edge_index, batch_data.batch)\n",
    "        loss_val = loss_function(prediction, batch_data.y)\n",
    "        loss_val.backward()\n",
    "        optimizer.step()\n",
    "    return loss_val, embedding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Train the Baseline Model</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\palin\\Anaconda3\\lib\\site-packages\\torch\\nn\\functional.py:1795: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed Epochs: 0 -> Loss: 3.5793051719665527\n",
      "Completed Epochs: 100 -> Loss: 2.4431710243225098\n",
      "Completed Epochs: 200 -> Loss: 1.113114833831787\n",
      "Completed Epochs: 300 -> Loss: 0.6751129627227783\n",
      "Completed Epochs: 400 -> Loss: 0.2598721981048584\n",
      "Completed Epochs: 500 -> Loss: 0.28296300768852234\n",
      "Completed Epochs: 600 -> Loss: 0.22908064723014832\n",
      "Completed Epochs: 700 -> Loss: 0.19578172266483307\n",
      "Completed Epochs: 800 -> Loss: 0.4741448163986206\n",
      "Completed Epochs: 900 -> Loss: 0.37046024203300476\n",
      "Completed Epochs: 1000 -> Loss: 0.21676291525363922\n",
      "Completed Epochs: 1100 -> Loss: 0.1741177886724472\n",
      "Completed Epochs: 1200 -> Loss: 0.5745912790298462\n",
      "Completed Epochs: 1300 -> Loss: 0.1163138821721077\n",
      "Completed Epochs: 1400 -> Loss: 0.12129074335098267\n",
      "Completed Epochs: 1500 -> Loss: 0.22089046239852905\n",
      "Completed Epochs: 1600 -> Loss: 0.10361021012067795\n",
      "Completed Epochs: 1700 -> Loss: 0.1024099662899971\n",
      "Completed Epochs: 1800 -> Loss: 0.07872648537158966\n",
      "Completed Epochs: 1900 -> Loss: 0.16129915416240692\n",
      "Completed Epochs: 2000 -> Loss: 0.27694663405418396\n",
      "Completed Epochs: 2100 -> Loss: 0.2022724449634552\n",
      "Completed Epochs: 2200 -> Loss: 0.10415931046009064\n",
      "Completed Epochs: 2300 -> Loss: 0.04590589553117752\n",
      "Completed Epochs: 2400 -> Loss: 0.11573132872581482\n"
     ]
    }
   ],
   "source": [
    "epochs = 2500\n",
    "hist_loss = []\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    loss, h = model_train(data)\n",
    "    hist_loss.append(loss)\n",
    "    if epoch%100 == 0:\n",
    "        print(\"Completed Epochs: {} -> Loss: {}\".format(epoch, loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the Trained Model\n",
    "torch.save(baseModel, \"./models/ESOL_baseModel.pth\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Inspect the Loss Curve over successive Train Epochs</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21fcdd82808>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lqW/RV6MuKG9wO09PO/f6YyW6ljl1zvvgTZ19Erqnduiu9Ppo0vp6e/rdEJF7xg5q7X8RwcAAjE8dCK6GUn1He5CA3oyrbzZpy7teIae0Dj9fnQrAfdNHdWNHM0PmuRpsOq7nRhUP3fO0/31p+rCsaymrnuYYi4YWExZ9fozNJhTWDB3Ud0w136F45eiBiOkVib1/mBvkEvmmpNoc3l/nXNC1/OVPbda97dLaZpTWOn857Mvu2NeC5XvxyAdp1p/L65pdjpPtqjklYeF6vL4zx+vt5Z7Y9s/ecKwEM5/bprv/90fJhfgouRCv7chxmtferrA7q4w9UajHM3RQL7hiBPKX3o7xPrZP/2b+ZD+XqGuyS+uwI/M81qQU6grt1IKOp8VUNTh30QPM429YBmvSo6C8HjOf24aH3j3oNK+jx4o5+Cq1boEvbj7VpYuJ+09fsK6bnFsOADhxprpT23A1zsiqr/PwwKoU3tZOPZ6hu+c5GhPXBw/OTsC7+/N1LR9pwK+hh95N1bXcR8mFWGHz7MbO9jpxVfM9VlyNO14zXxTdf7rcab5jjXrh2qPWeZbugBuPn4NSqlN3ilq6IXbl5hxX6xZWmNu8z1X7/uQeolBgwChzT0Tw9J2X614+lM+IF31+zO5nbyPX1TS1ImHhery2I9vtMnnl9R634djfu1a7TR2wHxVwn84mHEe+BHWEhy6DlmlBH16AKMBCKqjdmTpmoMvpwe577U/tXu4+X7TWHOwvbckC4NuXVN6Fert13X051NkEeGf4dCHSQ08Uy1mDp5x+fWcOEhau7/yOiQwkJIP6P4/Owe9u7mh/fsZNLduf42AE21dHz3qc39TakeRKub7k9z9rDtv9/LZN0woAnNKe12hZ211Qi5h7nlTUt+BcdZPL9vO6ZhNmP7/dLiR9Gc+6q7fKv7j5lI9rulda04RDXXjaPFFnhVQbtcX0sYMwfewga+3R8dR39KA++N7MMbhsVHDHsO6qS0cOsD701lvg7Msps77++GCRdcQ+T5ZsyMCSDRlO0601apsvukc+OGS3zPyXd2NwbDQq6lvQK1KQveQ2u/nphVU469B2bHkAcGfYDj5V32zC4x8fxjN3XYHRg/p0elv+csvyPahsaA3Yk3aIHIVkUFssuHwEUgsqnMbX2PfHuT2i3fLK0R1B7Y1tjfrN3ae7dHNJu1LYm13mNIiUowqtV0hrm4KprR2mdoWYXpH4OucCtmU4j1vd7HBB9KPkQjS1tuGh68a73YflfWxXwOYT57AtoxT9emdi+T0zOtqoHdaxDjlr8xlob1eI8NPIXpUNwevTrZTCibM1uGK06+Y+f++rvL4FQ/v1Dvi+yLOQDuo3758JAMi/YH+RrCeENAD8O7XYp/W6egfgij25OHHW/RfEb/51xGnapCc2AjA/z/HHK5Ndrmc7zOvft2Vbb8pxF9RKKXx4oEB7DURqQfuf9LO4fNTAjuYQh/f70ic3YfzQftj4+PXWae1KIcIm0jccK8HVCYMR3z+4IVRW24yh/aJ1f2bXHjqD//3kCN68byYWXDHC7XKr9uXhm1PiMTG+n89le/9AAZ784gS2/fYGTBrW3+ft+Ns/tmdj9qShmDkuLthF6TYh2UbtKEFHP+tg/0GGEk8hDXjuKuipFt7apvBekjl4be+ctFVR32K9c/H2V/chU7tVXill15yzZEMGirTueY4R19Ta7nQmYlusmqZW/PLDQ3jwnyluy6pHV260qW5oxYHccly9ZBs+6cQXclap+feRd8F9D57WtnYsXncS33tjv8/lA4A9WebmtNwyz72FfOXrgzSWbc3q8rGFmh4R1AAwJDba5fQXvz8VgLlG9tl/z+7OIoUlV2Nne1NcaQ7cxpY2XPXsVutAUidtwrZdKafHse3N1t9N0PbCclub0vbbiKqGFq9NPO685XAxVq+iigZMW7wF96w4AADY62N3R3csF21rvfTOybtQj999csRtYHp64k9XHT9TjYuf2IgdmXy0mx49Jqj/9YtrMKx/b8TaPGwAAL45JV57pTBzXBz+sGCK07qsbfvP1Uv0j9ZnkX+hAU9+cRyXPrnJ7TLtyvzH7Yrlxpd5y3a57Yr3z6/znaZVN7Zi+uKteOarE/g65wJSXAxi9UX6Gazc6zqQl27MdHtm4ElRpX3TlL9vgbd8KXnb7v/+Ox2fphVbn4fpKMJD18iuOqztc1tGqdO8NSmFSFi43uXQCH91efFb4ZVt2Thb1ej3chpFjwnqScP6I+WJ+TixeIHddMdbnx0/c6sfmoWvHrsO9866CNdOGKJrX9dNGtrl8lKH+95JxmqtScTio+RCu5+rGlrw2k7n8T4Ac5t6e7vCaYdT9L3ZHT1hXtiUiWZTG44WV+G2V/faLfdeUgF+vDIZP3gryTrtYH4FDuZX4PGP0/Hc+gy8uDkTCQvXY6PD6IbTntmCs1WNOJhf4TbQHTk+V7NTMahjYb3dIKMizH/+338zyeV8dzcbnatuQsLC9fjqiOcuo573rV0kdlHWT9PMTUGurrWscHEWk3uhHn/floVfvJ/mNK+nCOmLiXpYawXaz5YP8Z3TRuGOaaNww2Rzjfv5/7oSnx8uRlKu/a3VsdGRdkOcrn5oFm6YHM+bKALM8c7MXafK3Cxp5qqb4f3v2LdB3/rKXhRXNHp8dmXCwvW4d9ZYrEkpspv++s7TAID3kvKd1ikob8C9b5ubMX5+/QS0mNpxtqoRcbHRKKpoQGVDC6aOHoSBfXtBKWUd+8TKQ642tJhwuLAKcyYNxeHCSuuiLaZ2fJhcgO/OGI2+0fZ/xpbPuONma5paca66CZOHmy8MRthU01wNC9DRNdL8/6lztfgwucA69s6v1hzGpSP7677Q+Or2bJjaFX77rcmI1Dbu6kvFctHY1Kb3C8e8fKWb8XC6w5/WHsWs8YPx3RljArL9Hh/UjuMoWz4YCUNj8S2Hh+feMXUUlqzPwIW6jjf83lkXYaU2TCkAa7Dr339o38puFCYvtcR3bN4jd/ReFHMMaW+223RFTDpdbg1tR3v/MBfX/22n03TLDUbTF2/BrVeMwPP/NdU675EPDmFPVhme/PZlWLzuJAbEmP9kP0wuQGltM1LyKvD4vIshItYAtQa1su+WeP/KZBwprkbWc7fikQ/S7MY0P1RYieLKRtw1fbR1WkfXSIWD+RW420XNe/7Le6z9yZtNbahpNCG+f2+U1jZh9f4C/PZbk637f3mr+b4H22mfpBXjxbun2W3TEry2IX62qtHtRW7L31eLh376Ta1t+OuGDPxm/mTEOVzPqmlqxTNfnsRTd16GATHOwykfP1ONxtY2XJ0w2GafCq/vzMHdiWMxfEAM1qQUYU1KUcCCusc0fbgTFWl+08cNMX+ILYEd6aI7VFRkBA4+MR8A0KdXJE49twCLbrvUOv/IUzdbX+sdcnX1Q7N4Y0QPciDXuR3b9ovcXUgDwFNfun5GplLmmnxVQyvWpBTh/aR8NLSYUFjeYO15cbTY3KZbo10gLNN6xnyRfhY3LduNuS/twqp9eSiubECDzRnghEUbUKuN5X2k2NzGP/nPG7Ejs9SuP/j33kjC4x+no7apFU2tbZi3bBfSC837bFcKOaV1nn8xAH7xfpr1GsWitcfw2s4cLN+ejYSF65FTav+gC9v7sf6xPdvaJ988TwtqmxrO997Yj/+32vWAZiZtfAUF88BjJpszppzSWpTVNmPxupNYnVSA2Ut3oNqhH/yqfXn47FAxVmnvY3VDK8rrmjFryTakFVTg2//Yh7vfTEKzqc267cxztXhpSxZ+5XC3b6D0+Br1gJheWPmTRFyl9bnsr31jDuzj+tBFBNlLbgUAa3ewEQNicOWYgXYPLxg7uC+eufNyPPXlCfTpFYn0p76FRWuP47ND5va1tD/PR9b5Olw70dzuff814zBzXBx+7fA4qt/fMgUvbj6FmePikFZQicRxcbhi9EC8uz8fGYsXYN3Rs/j9p0dBoW9HpvOFM8A8IqGtv3xxAv/YkYPHbppknfafdPv2YFdnaYvXncTidSedpl/59BbdlYUrn96CH3/jIrv2fqXgcvxyi7d2n8aIgTHW5qm92WXWi4SvbjcPEmY7WuO56iZklnQE97KtWdiVVYa0gko8OneitW28vtnksYmxuLIBXx0psV5kLqttxh2v7cMvb5yIPywwPxFq/st70KdXpLVLaWNrG6Yt3oJvTo7Hew/NwscphdbeQ5beL9MWb7E2eS7f1jHI2ZQ/b8LVCXH45JHZ1u1561njLxKIK7qJiYkqNVXfcJ7drbWtHWtSCvGjWRchyg/joL69JxdzLxmGScP6oa1dYdHaY3hwTgIuHen69nXLB+/xeRfj8lEDMP/S4Vh7+AxmTxyC2Ut34I0fX4Vbrxxpd8pa09SKlXvzMCauD9akFOJwoeur9ACsf5A3/G2ntTcEAMwcF4fBsdFIGNIXT9x+Ga57YQeKK+2vkieOi7MbA9ub784Yjc8Pn9G9PAXPpl9fjwXL93pfMEAenTvR2s7vTe+oCJ+GG7AYN6QvfnJtAn6QOAZXPr3F7XKpf56PRJtnit476yJcf/FQ/PLDQ27XAYD9C2/C7KU7rD/fMW2U9cLqkSdvxsC+vj2NSkTSlFKJLufpCWoRWQDgFQCRAFYqpZZ6Wt7IQR1sb+w6jTFxfXDHtFE+rV/T1IoXNmZizqSh+PvWLIwa1Ae7s8rw3RmjkTAkFo/Pv9i6bFFFAz4/fAZt7Qo/vHosRtmMj/H6zhy8uPkUfnnjRPz/XeY/oFfumY7Y6Cgs2ZCBvAv1eO1HM/DYR65P7fKX3o6K+hZc9exW67SfXTce7+zLw6iBMfjyV9dh/dESnKlqxIo9uXh07kTMmTQUP3q7467FATFR1lN5d966fyZyy+rxwqZM69giFrddOQIbjvGhAWQcls4GvuhSUItIJIAsAN8CUAzgIIB7lVLO51gaBnX3aTa1obqhFcMGxHRqvYYWE5ZuzMQfF1yCXpERSMotxw0XD4WIoL7ZhGNnqnHNhCF4b38+2tqV9TbvK5/ejNomk92pdFpBJZRSSEwYjLwL9RgcG23XTGQ5OyitacKsv27HgstH4OfXj0diwmB8lFyIaWPNj1rr1zsK85btRl2zCR/87Bu4ZsJg61nPqXO1mDKiP97afRpbT57Ho3MnYe4lw7D+aAke/chzDShCgLfuT7S2cf7suvFoMbXj9wumYKpNjeubk+OxO8tz7xIibzKfXYCYXpHeF3TQ1aC+FsDTSqlbtJ//BABKqefdrcOg7rnKaptRUd+CKSMCM/ZDYXkDlm/LwtLvTUV0lPemKaUUXt2eg+/MGIW42GicLq3D0eJq9I+Jwtwpw+yu8Nc3myACu+5sB3LLMSE+FsP6m7/oKutbkHW+FuOGxOLLI2fww6svQv6FeuzLuWAdwfCRb07EibPVmDy8P35xwwT8ZFUK7rl6LO67ZhyiIiNw74oDSMotR/7S21FZ34LY3lFIL6rCyIExGDkwBquTCtAnOhIfJRfimNa++v7PZmH5tmykFVTi8lEDMKx/b+zU2nwfnJ2AhCF98eWRszhk0+z1wLXj8F5SAW69YgT++8aJWL4tGzsySzHvkmGYMqK/9UzJlctGDsDJkhoM7ReNeZcMx79SXfd0mTy8H85VN3k98wmUqAjx2uOnu1jOGL3Je/42n8Yb6mpQfx/AAqXUz7Wf7wfwDaXUYw7LPQzgYQC46KKLZhYUFDhtiygcNLSYUF7XgrGD+3ZpO/XNJkRFCnpH2dfOyuuaESHi1M2srV2hta3dqTa3J6sMx85U4zszRjsND2vpP62UwsH8SgyOjcaoQTGob25ze8duU2sb1h0twdUJcRgT1xfrj5Vg2piBqG0yoaWtHdPHDEJLWzvOVDVibFxfnC6rs3a5G9inF6KjIlDd2IpxQ2JRUd+CA7nluHbCEPTuFYHaJhMGx0bj5NkaXDy8n/VLtcXUjpqmVgzq0wtRkeb1S6obMXJgHxzMq8B8m662llH/DuZV4KZLh+HjlCJcO3EIhg+IwcA+vdBiasfB/Ar8+l/pWHH/TBRXNmLOpKH4d2oRRgyIwY1T4pF1vg5TxwxEcWUD4vvFoF9MFCLEfADYwuIAAASDSURBVEH4TFUjDuSW45bLRyC9qApfpp/Fzt/fiJKqJoyJ64PY3r710ehqUN8N4BaHoJ6llPqVu3VYoyYi6hxPQa2n20MxgLE2P48B4Pu9o0RE1Cl6gvoggItFZLyIRAO4B8CXgS0WERFZeG1MUUqZROQxAJth7p63Sinl+hYrIiLyO12t3kqpDQA2BLgsRETkQo8f64OIKNQxqImIDI5BTURkcAxqIiKDC8joeSJSBsDXWxOHAvDv0z6Nj8fc84Xb8QI85s4ap5RyOaJTQIK6K0Qk1d3dOT0Vj7nnC7fjBXjM/sSmDyIig2NQExEZnBGDekWwCxAEPOaeL9yOF+Ax+43h2qiJiMieEWvURERkg0FNRGRwhglqEVkgIqdEJEdEFga7PP4kIvkickxE0kUkVZs2WES2iki29n+cNl1E5FXt93BURK4Kbun1EZFVIlIqIsdtpnX6GEXkAW35bBF5IBjHopebY35aRM5o73W6iNxmM+9P2jGfEpFbbKaHzGdfRMaKyE4RyRCREyLyuDa9R77XHo63e99npVTQ/8E8fOppABMARAM4AuCyYJfLj8eXD2Cow7S/AViovV4I4AXt9W0ANgIQANcASA52+XUe4w0ArgJw3NdjBDAYQK72f5z2Oi7Yx9bJY34awO9cLHuZ9rnuDWC89nmPDLXPPoCRAK7SXveH+cHXl/XU99rD8Xbr+2yUGvUsADlKqVylVAuAjwHcFeQyBdpdAN7TXr8H4Ds201crswMABonIyGAUsDOUUnsAVDhM7uwx3gJgq1KqQilVCWArgAWBL71v3ByzO3cB+Fgp1ayUygOQA/PnPqQ++0qpEqXUIe11LYAMAKPRQ99rD8frTkDeZ6ME9WgAto9BLobnX0aoUQC2iEia9hBgABiulCoBzB8GAMO06T3pd9HZY+wpx/6Ydpq/ytIEgB54zCKSAGAGgGSEwXvtcLxAN77PRglqV89W70n9Bucopa4CcCuAR0XkBg/L9vTfBeD+GHvCsb8BYCKA6QBKACzTpveoYxaRfgA+A/BrpVSNp0VdTAu543ZxvN36PhslqHv0A3SVUme1/0sBfA7zadB5S5OG9n+ptnhP+l109hhD/tiVUueVUm1KqXYAb8P8XgM96JhFpBfMofWhUmqtNrnHvteujre732ejBHWPfYCuiMSKSH/LawA3AzgO8/FZrnQ/AOAL7fWXAH6iXS2/BkC15ZQyBHX2GDcDuFlE4rRTyZu1aSHD4XrCd2F+rwHzMd8jIr1FZDyAiwGkIMQ++yIiAN4BkKGUetlmVo98r90db7e/z8G+qmpztfQ2mK+ongbwRLDL48fjmgDzFd4jAE5Yjg3AEADbAWRr/w/WpguA17XfwzEAicE+Bp3HuQbmU8BWmGsPP/PlGAE8BPMFmBwAPw32cflwzO9rx3RU+0McabP8E9oxnwJwq830kPnsA7gO5lP2owDStX+39dT32sPxduv7zFvIiYgMzihNH0RE5AaDmojI4BjUREQGx6AmIjI4BjURkcExqImIDI5BTURkcP8HX2UPc+hIKiEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Baseline Model was trained over 2500 epochs\n",
    "hist_losses = [float(loss.cpu().detach().numpy()) for loss in hist_loss[:]]\n",
    "loss_index = [i for i in range(len(hist_losses))]\n",
    "\n",
    "plt = sns.lineplot(loss_index, hist_losses)\n",
    "plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Evaluate the Model over a Validation Dataset</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtaining The test predictions - Load the Stored baseline model\n",
    "model_save_path = \"./models/ESOL_baseModel.pth\"\n",
    "base_model= torch.load( model_save_path )\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "base_model = base_model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Obtain relevant predictions on the validation dataset</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\palin\\Anaconda3\\lib\\site-packages\\torch\\nn\\functional.py:1795: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\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>y_truth</th>\n",
       "      <th>y_predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-3.59</td>\n",
       "      <td>-5.357649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.17</td>\n",
       "      <td>-1.843094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.854907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.10</td>\n",
       "      <td>-3.829373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.21</td>\n",
       "      <td>-2.132792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>-3.15</td>\n",
       "      <td>-2.974726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>-5.24</td>\n",
       "      <td>-5.256596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>-1.01</td>\n",
       "      <td>-1.509138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.150229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>-2.34</td>\n",
       "      <td>-2.290824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    y_truth  y_predict\n",
       "0     -3.59  -5.357649\n",
       "1     -0.17  -1.843094\n",
       "2     -0.85  -0.854907\n",
       "3     -3.10  -3.829373\n",
       "4     -2.21  -2.132792\n",
       "..      ...        ...\n",
       "59    -3.15  -2.974726\n",
       "60    -5.24  -5.256596\n",
       "61    -1.01  -1.509138\n",
       "62     0.51   0.150229\n",
       "63    -2.34  -2.290824\n",
       "\n",
       "[64 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_batch_data = next(iter(test_data_loader))\n",
    "\n",
    "with torch.no_grad():\n",
    "    test_batch_data = test_batch_data.to(device)\n",
    "    prediction, embedding = base_model(test_batch_data.x.float(), test_batch_data.edge_index, test_batch_data.batch)\n",
    "    df = pd.DataFrame()\n",
    "    df['y_truth'] = test_batch_data.y.tolist()\n",
    "    df['y_predict'] = prediction.tolist()\n",
    "\n",
    "df['y_truth'] = df['y_truth'].apply(lambda row: row[0])\n",
    "df['y_predict'] = df['y_predict'].apply(lambda row: row[0])\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"border:3px double blue;\"></hr>\n",
    "<h3>Inspect Model Performance on Test Data</h3>\n",
    "\n",
    "Since this is a <b>Regression Problem</b> with a single target variable, a model which gives predicted value for solubility as close to the <em>target variable</em> as possible is the one which is most desireable. So if we use <b><em>('Ground-Truth', 'Prediction')</em></b> as absicca - ordinate pair to define a data-point & plot it on a cartesian plane i.e. <em>X = \"Ground-Truth\" | Y = \"Prediction\"</em> - an ideal model's prediction would return a line <u>close to</u> (<em>but not exactly</em>) <b>X ≈ Y</b> i.e. <b><em>\"Prediction\" ≈ \"Ground-Truth\"</em></b>.\n",
    "\n",
    "<b>NOTE: </b>Identity Line, <em><b>X = Y</b></em> is shown in <span style=\"color: red\">red</span>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d5de83db48>"
      ]
     },
     "execution_count": 28,
     "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": [
    "%matplotlib inline\n",
    "\n",
    "plot=sns.scatterplot(data=df, x=\"y_truth\", y=\"y_predict\")\n",
    "sns.lineplot([-12, 2], [-12, 2], color=\"r\", dashes=[(2, 2)])\n",
    "plot.set(xlim=(-10, 1.5))\n",
    "plot.set(ylim=(-10, 1.5))\n",
    "\n",
    "plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Inspect Baseline-Model Performance on the entire dataset</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def all_data_eval(data, model, device=torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\") ):\n",
    "    all_data = DataLoader(data, batch_size=batch_size, shuffle=True)\n",
    "    # all_data_iter = next(iter(all_data))\n",
    "    df = {'y_truth': [], 'y_predict': []}\n",
    "    with torch.no_grad():\n",
    "        for all_data_batch in all_data:\n",
    "            all_data_batch = all_data_batch.to(device)\n",
    "            prediction, embedding = model(all_data_batch.x.float(), all_data_batch.edge_index, all_data_batch.batch)\n",
    "\n",
    "            df['y_truth']+= all_data_batch.y.tolist()\n",
    "            df['y_predict']+= prediction.tolist()\n",
    "    \n",
    "    df = pd.DataFrame(df)\n",
    "    df['y_truth'] = df['y_truth'].apply(lambda row: row[0])\n",
    "    df['y_predict'] = df['y_predict'].apply(lambda row: row[0])\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\palin\\Anaconda3\\lib\\site-packages\\torch_geometric\\deprecation.py:12: UserWarning: 'data.DataLoader' is deprecated, use 'loader.DataLoader' instead\n",
      "  warnings.warn(out)\n",
      "C:\\Users\\palin\\Anaconda3\\lib\\site-packages\\torch\\nn\\functional.py:1795: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\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>y_truth</th>\n",
       "      <th>y_predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.120</td>\n",
       "      <td>1.167301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.730</td>\n",
       "      <td>-3.542468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.740</td>\n",
       "      <td>0.827182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.230</td>\n",
       "      <td>-1.288209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-3.140</td>\n",
       "      <td>-3.078417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>-2.658</td>\n",
       "      <td>-2.424474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>0.540</td>\n",
       "      <td>0.564313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>-2.560</td>\n",
       "      <td>-2.635663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>-6.620</td>\n",
       "      <td>-6.722585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>-3.400</td>\n",
       "      <td>-3.212191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1128 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      y_truth  y_predict\n",
       "0       1.120   1.167301\n",
       "1      -3.730  -3.542468\n",
       "2       0.740   0.827182\n",
       "3      -1.230  -1.288209\n",
       "4      -3.140  -3.078417\n",
       "...       ...        ...\n",
       "1123   -2.658  -2.424474\n",
       "1124    0.540   0.564313\n",
       "1125   -2.560  -2.635663\n",
       "1126   -6.620  -6.722585\n",
       "1127   -3.400  -3.212191\n",
       "\n",
       "[1128 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_pred_df = all_data_eval(data, base_model)\n",
    "base_pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE of the Base Model predictions over the entire dataset: 0.21219844920930298 | Mean Absolute Error: 0.24474308388439794\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d5edd8c248>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "print(\"MSE of the Base Model predictions over the entire dataset: {} | Mean Absolute Error: {}\".format(\n",
    "                                                                  mean_squared_error(base_pred_df.y_truth, base_pred_df.y_predict),\n",
    "                                                                  mean_absolute_error(base_pred_df.y_truth, base_pred_df.y_predict)\n",
    "                                                                                                  )\n",
    "     )\n",
    "\n",
    "plot=sns.scatterplot(data=base_pred_df, x=\"y_truth\", y=\"y_predict\")\n",
    "sns.lineplot([-12, 2], [-12, 2], color=\"r\")\n",
    "plot.set(xlim=(-12, 2))\n",
    "plot.set(ylim=(-12, 2))\n",
    "\n",
    "plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Baseline Model Performance Observation -</h4>\n",
    "\n",
    "The Baseline Model perform surprisingly well with an <b>MSE</b> of <em>0.212</em>. While inspecting the plot with each datapoints' absicca & ordinate values given as<em>x = \"Ground-Truth\" | y = \"Prediction\")</em>, it can be observed that the plot could be more condensed to a straigth line <em>(X=Y)</em> with a couple of <em>straggler</em> data-points which can be better predicted with a more refined model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Building a refined Model & Evaluting its Performance</h3>\n",
    "\n",
    "Before instantiating the refined model, we need to figure out a <b>Learning-Rate Scheduler</b> which decays the learning-rate over successive epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2052b5737c8>"
      ]
     },
     "execution_count": 6,
     "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": [
    "%matplotlib inline\n",
    "\n",
    "a = []\n",
    "# intial value of the Learning-rate\n",
    "initLR = 0.001\n",
    "\n",
    "for epo in range(5000):\n",
    "    a.append( initLR*(np.e**(-epo/20000)) )\n",
    "\n",
    "# plot the learning rate value over successive epochs as it decays over time\n",
    "pd.DataFrame({'a' : a}).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "esolModel(\n",
       "  (in_conv): GCNConv(9, 64)\n",
       "  (conv1): GATv2Conv(64, 64, heads=4)\n",
       "  (gnorm): GraphNorm(64)\n",
       "  (conv2): GCNConv(64, 64)\n",
       "  (gnorm2): GraphNorm(64)\n",
       "  (conv3): GCNConv(64, 64)\n",
       "  (conv4): GCNConv(64, 64)\n",
       "  (out): Linear(in_features=128, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Model Parameters:  47233\n"
     ]
    }
   ],
   "source": [
    "emb_size = 64\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = esolModel(data.num_features, emb_size)\n",
    "model = model.to(device)\n",
    "\n",
    "display(model)\n",
    "# Summarize the instantiated Model\n",
    "print(\"Number of Model Parameters: \", sum(m.numel() for m in model.parameters()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The <em>refined model</em> has more than <b>twice</b> the number of learnable parameters as the <b><em>Base Model</em></b>.\n",
    "\n",
    "<hr style=\"border: 3px double blue;\"></hr>\n",
    "\n",
    "<h3>Compile & Train the Model</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\palin\\Anaconda3\\lib\\site-packages\\torch_geometric\\deprecation.py:12: UserWarning: 'data.DataLoader' is deprecated, use 'loader.DataLoader' instead\n",
      "  warnings.warn(out)\n"
     ]
    }
   ],
   "source": [
    "# Use the GPU for Model training if available\n",
    "# MSE -> Objective / Loss Funciton\n",
    "loss_function = torch.nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 0.0005)\n",
    "num_epochs = 5000\n",
    "decay_const = 4.5*num_epochs\n",
    "\n",
    "lr_schedule = lambda epoch: (np.e)**(-epoch/decay_const)\n",
    "scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_schedule)\n",
    "\n",
    "# Use Data Loader to collate Data into batches\n",
    "data_size = len(data)\n",
    "batch_size = 64\n",
    "\n",
    "# Pass 75% of the data as Train to the Data Loader\n",
    "train_data_loader = DataLoader(data[:int(data_size * 0.75)], batch_size=batch_size, shuffle=True)\n",
    "\n",
    "# Pass 25% of the data as Test to the Data Loader\n",
    "test_data_loader = DataLoader(data[int(data_size * 0.75):], batch_size=batch_size, shuffle=True)\n",
    "\n",
    "def model_train(data):\n",
    "    \n",
    "    for batch_data in train_data_loader:\n",
    "        \n",
    "        batch_data=batch_data.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        prediction, embedding = model(batch_data.x.float(), batch_data.edge_index, batch_data.batch)\n",
    "        loss_val = loss_function(prediction, batch_data.y)\n",
    "        loss_val.backward()\n",
    "        \n",
    "        optimizer.step()\n",
    "        \n",
    "    return loss_val, embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\palin\\Anaconda3\\lib\\site-packages\\torch\\nn\\functional.py:1795: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed Epochs: 0 -> Loss: 4.633730411529541\n",
      "Completed Epochs: 100 -> Loss: 1.6038485765457153\n",
      "Completed Epochs: 200 -> Loss: 0.5125581622123718\n",
      "Completed Epochs: 300 -> Loss: 0.2636226713657379\n",
      "Completed Epochs: 400 -> Loss: 0.14102110266685486\n",
      "Completed Epochs: 500 -> Loss: 0.13448098301887512\n",
      "Completed Epochs: 600 -> Loss: 0.047903046011924744\n",
      "Completed Epochs: 700 -> Loss: 0.040809184312820435\n",
      "Completed Epochs: 800 -> Loss: 0.03769299015402794\n",
      "Completed Epochs: 900 -> Loss: 0.21341626346111298\n",
      "Completed Epochs: 1000 -> Loss: 0.037583693861961365\n",
      "Completed Epochs: 1100 -> Loss: 0.07403724640607834\n",
      "Completed Epochs: 1200 -> Loss: 0.1523086279630661\n",
      "Completed Epochs: 1300 -> Loss: 0.013843780383467674\n",
      "Completed Epochs: 1400 -> Loss: 0.06211886554956436\n",
      "Completed Epochs: 1500 -> Loss: 0.02931373193860054\n",
      "Completed Epochs: 1600 -> Loss: 0.05154039338231087\n",
      "Completed Epochs: 1700 -> Loss: 0.026229839771986008\n",
      "Completed Epochs: 1800 -> Loss: 0.007462234701961279\n",
      "Completed Epochs: 1900 -> Loss: 0.013662640936672688\n",
      "Completed Epochs: 2000 -> Loss: 0.03378399461507797\n",
      "Completed Epochs: 2100 -> Loss: 0.028834465891122818\n",
      "Completed Epochs: 2200 -> Loss: 0.009236267767846584\n",
      "Completed Epochs: 2300 -> Loss: 0.03165874257683754\n",
      "Completed Epochs: 2400 -> Loss: 0.020648382604122162\n",
      "Completed Epochs: 2500 -> Loss: 0.010165050625801086\n",
      "Completed Epochs: 2600 -> Loss: 0.023495828732848167\n",
      "Completed Epochs: 2700 -> Loss: 0.08172400295734406\n",
      "Completed Epochs: 2800 -> Loss: 0.020279088988900185\n",
      "Completed Epochs: 2900 -> Loss: 0.014304076321423054\n",
      "Completed Epochs: 3000 -> Loss: 0.01657840423285961\n",
      "Completed Epochs: 3100 -> Loss: 0.05347267538309097\n",
      "Completed Epochs: 3200 -> Loss: 0.015669647604227066\n",
      "Completed Epochs: 3300 -> Loss: 0.004649443086236715\n",
      "Completed Epochs: 3400 -> Loss: 0.013344609178602695\n",
      "Completed Epochs: 3500 -> Loss: 0.012006676755845547\n",
      "Completed Epochs: 3600 -> Loss: 0.00853284914046526\n",
      "Completed Epochs: 3700 -> Loss: 0.015296872705221176\n",
      "Completed Epochs: 3800 -> Loss: 0.01239884551614523\n",
      "Completed Epochs: 3900 -> Loss: 0.008272439241409302\n",
      "Completed Epochs: 4000 -> Loss: 0.005832189228385687\n",
      "Completed Epochs: 4100 -> Loss: 0.01196039654314518\n",
      "Completed Epochs: 4200 -> Loss: 0.004111026413738728\n",
      "Completed Epochs: 4300 -> Loss: 0.005470163654536009\n",
      "Completed Epochs: 4400 -> Loss: 0.01676117070019245\n",
      "Completed Epochs: 4500 -> Loss: 0.005434074439108372\n",
      "Completed Epochs: 4600 -> Loss: 0.0038777149748057127\n",
      "Completed Epochs: 4700 -> Loss: 0.043210212141275406\n",
      "Completed Epochs: 4800 -> Loss: 0.009169518947601318\n",
      "Completed Epochs: 4900 -> Loss: 0.005182074382901192\n"
     ]
    }
   ],
   "source": [
    "hist_loss = []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    loss, _ = model_train(data)\n",
    "    hist_loss.append(loss)\n",
    "    if epoch%100 == 0:\n",
    "        print(\"Completed Epochs: {} -> Loss: {}\".format(epoch, loss))\n",
    "    scheduler.step()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Inspect the Loss over successive train-epochs</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20533d2e188>"
      ]
     },
     "execution_count": 11,
     "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": [
    "%matplotlib inline\n",
    "\n",
    "# Inspect the model Loss\n",
    "hist_losses = [float(loss.cpu().detach().numpy()) for loss in hist_loss]\n",
    "loss_index = [i for i in range(len(hist_losses))]\n",
    "\n",
    "plt = sns.lineplot(loss_index, hist_losses)\n",
    "plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the Model\n",
    "#torch.save(model, \"./models/ESOL_betaModel.pth\")\n",
    "torch.save(model, \"./models/ESOL_betaModel_v2.0.pth\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Obtain & Inspect predictions over the Test Dataset</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\palin\\Anaconda3\\lib\\site-packages\\torch\\nn\\functional.py:1795: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\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>y_truth</th>\n",
       "      <th>y_predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-6.680</td>\n",
       "      <td>-7.194107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.246</td>\n",
       "      <td>-3.050872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.445</td>\n",
       "      <td>-5.349409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-7.820</td>\n",
       "      <td>-8.340064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-5.230</td>\n",
       "      <td>-3.740345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>-1.790</td>\n",
       "      <td>-1.623713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>-0.670</td>\n",
       "      <td>-0.948405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>-4.799</td>\n",
       "      <td>-4.055529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>-3.500</td>\n",
       "      <td>-2.897092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>-2.460</td>\n",
       "      <td>-3.023239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    y_truth  y_predict\n",
       "0    -6.680  -7.194107\n",
       "1    -3.246  -3.050872\n",
       "2    -4.445  -5.349409\n",
       "3    -7.820  -8.340064\n",
       "4    -5.230  -3.740345\n",
       "..      ...        ...\n",
       "59   -1.790  -1.623713\n",
       "60   -0.670  -0.948405\n",
       "61   -4.799  -4.055529\n",
       "62   -3.500  -2.897092\n",
       "63   -2.460  -3.023239\n",
       "\n",
       "[64 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = torch.load(\"./models/ESOL_betaModel_v2.0.pth\")\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = model.to(device)\n",
    "\n",
    "test_batch_data = next(iter(test_data_loader))\n",
    "\n",
    "with torch.no_grad():\n",
    "    test_batch_data = test_batch_data.to(device)\n",
    "    prediction, embedding = model(test_batch_data.x.float(), test_batch_data.edge_index, test_batch_data.batch)\n",
    "    df = pd.DataFrame()\n",
    "    df['y_truth'] = test_batch_data.y.tolist()\n",
    "    df['y_predict'] = prediction.tolist()\n",
    "\n",
    "df['y_truth'] = df['y_truth'].apply(lambda row: row[0])\n",
    "df['y_predict'] = df['y_predict'].apply(lambda row: row[0])\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range of the Predicted variable -> Min: -8.872827529907227 | Max: 1.0390598773956299\n",
      "Range of the Target variable -> Min: -7.820000171661377 | Max: 1.1440000534057617\n"
     ]
    }
   ],
   "source": [
    "print(\"Range of the Predicted variable -> Min: {} | Max: {}\".format(df.y_predict.min(), df.y_predict.max()))\n",
    "print(\"Range of the Target variable -> Min: {} | Max: {}\".format(df.y_truth.min(), df.y_truth.max()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2747f2bf588>"
      ]
     },
     "execution_count": 74,
     "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": [
    "%matplotlib inline\n",
    "\n",
    "plot=sns.scatterplot(data=df, x=\"y_truth\", y=\"y_predict\")\n",
    "\n",
    "plot.set(xlim=(-10, 2))\n",
    "plot.set(ylim=(-10, 2))\n",
    "\n",
    "plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Inspect Model Predictions for the Entire Dataset</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\palin\\Anaconda3\\lib\\site-packages\\torch_geometric\\deprecation.py:12: UserWarning: 'data.DataLoader' is deprecated, use 'loader.DataLoader' instead\n",
      "  warnings.warn(out)\n",
      "C:\\Users\\palin\\Anaconda3\\lib\\site-packages\\torch\\nn\\functional.py:1795: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range of the Predicted variable -> Min: -11.675378799438477 | Max: 1.5713399648666382\n",
      "Range of the Target variable -> Min: -11.600000381469727 | Max: 1.5800000429153442\n"
     ]
    },
    {
     "data": {
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       "        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>y_truth</th>\n",
       "      <th>y_predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.000</td>\n",
       "      <td>-0.972344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.850</td>\n",
       "      <td>-1.810759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.490</td>\n",
       "      <td>-0.591831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-4.862</td>\n",
       "      <td>-4.854700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-3.270</td>\n",
       "      <td>-3.194841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>-3.610</td>\n",
       "      <td>-3.606540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>-0.807</td>\n",
       "      <td>-0.849019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>1.100</td>\n",
       "      <td>1.319252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>-1.090</td>\n",
       "      <td>-1.025608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>-3.550</td>\n",
       "      <td>-3.460228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1128 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      y_truth  y_predict\n",
       "0      -1.000  -0.972344\n",
       "1      -1.850  -1.810759\n",
       "2      -0.490  -0.591831\n",
       "3      -4.862  -4.854700\n",
       "4      -3.270  -3.194841\n",
       "...       ...        ...\n",
       "1123   -3.610  -3.606540\n",
       "1124   -0.807  -0.849019\n",
       "1125    1.100   1.319252\n",
       "1126   -1.090  -1.025608\n",
       "1127   -3.550  -3.460228\n",
       "\n",
       "[1128 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_df = all_data_eval(data, model)\n",
    "\n",
    "print(\"Range of the Predicted variable -> Min: {} | Max: {}\".format(pred_df.y_predict.min(), pred_df.y_predict.max()))\n",
    "print(\"Range of the Target variable -> Min: {} | Max: {}\".format(pred_df.y_truth.min(), pred_df.y_truth.max()))\n",
    "\n",
    "display(pred_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2053ace7108>"
      ]
     },
     "execution_count": 16,
     "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": [
    "%matplotlib inline\n",
    "\n",
    "# inspect the data plots with absicca as ground-truth values & ordinate as predicted values\n",
    "plot=sns.scatterplot(data=pred_df, x=\"y_truth\", y=\"y_predict\")\n",
    "sns.lineplot([-12, 2], [-12, 2], color=\"r\")\n",
    "plot.set(xlim=(-12, 2))\n",
    "plot.set(ylim=(-12, 2))\n",
    "\n",
    "plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE of the model predictions over the entire dataset: 0.1838070614214383 | Mean Absolute Error: 0.20784129944121374\n"
     ]
    }
   ],
   "source": [
    "print(\"MSE of the model predictions over the entire dataset: {} | Mean Absolute Error: {}\".format(\n",
    "                                                                  mean_squared_error(pred_df.y_truth, pred_df.y_predict),\n",
    "                                                                  mean_absolute_error(pred_df.y_truth, pred_df.y_predict)\n",
    "                                                                                                  )\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style='border: 4px double blue'></hr>\n",
    "<h3>Observation- Refined Model Performance</h3>\n",
    "\n",
    "The Model performs better than the baseline with <b>MSE</b> of <em>0.183</em>. The plot with each datapoints' absicca & ordinate values given as<em>x = \"Ground-Truth\" | y = \"Prediction\")</em>, is also more tightly packed and closer to the <b>Identity Line</b> <em style=\"color: red;\">(X=Y)</em> which indicates better performance than the base-line model."
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}