outputGPU4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graph Neural Networks for SMall Molecule Regression\n",
    "_by David Holmberg (August 2023)_\n",
    "#### Dataset\n",
    "For this exercise we will use the same dataset of aqueous solubility of 1142 diverse chemical compounds.\n",
    "\n",
    "#### Modelling comparisons\n",
    "1. Compare the results of linear regression to those of Graph Neural Networks\n",
    "\n",
    "#### Aims\n",
    "* Introduce the concept of Graph Neural Networks\n",
    "* Introduce PyTorch code for GNNs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-09-12T21:18:17.508663Z"
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[23:18:16] Enabling RDKit 2019.09.1 jupyter extensions\n",
      "RDKit WARNING: [23:18:16] Enabling RDKit 2019.09.1 jupyter extensions\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "\n",
    "# Pytorch and Pytorch Geometric\n",
    "import torch as tch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "\n",
    "from torch_geometric.nn import GCNConv, GATConv, summary as gsummary, global_mean_pool, global_max_pool\n",
    "from torch_geometric.data import Data#, DataLoader\n",
    "from torch_geometric.loader import DataLoader\n",
    "from torch_geometric.datasets import MoleculeNet\n",
    "from torch_geometric.utils import dropout_adj\n",
    "\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit.Chem import Descriptors\n",
    "from rdkit.Chem import Lipinski\n",
    "\n",
    "\n",
    "# Helper libraries\n",
    "from torchsummary import summary as asummary\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import KFold\n",
    "import os\n",
    "\n",
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
    "device = tch.device(\"cuda\" if tch.cuda.is_available() else \"cpu\")\n",
    "# device = tch.device('cuda')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## functions\n",
    "Run these cells to have access to the necessary functions for the lab. Highly encouraged that you read through it and understand, though not necessary for the aims of this lab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def plot_history(train_losses, val_losses, model_name):\n",
    "    fig = plt.figure(figsize=(15, 5), facecolor='w')\n",
    "    ax = fig.add_subplot(121)\n",
    "    ax.plot(train_losses)\n",
    "    ax.plot(val_losses)\n",
    "    ax.set(title=model_name + ': Model loss', ylabel='Loss', xlabel='Epoch')\n",
    "    ax.legend(['Train', 'Test'], loc='upper right')\n",
    "    ax = fig.add_subplot(122)\n",
    "    ax.plot(np.log(train_losses))\n",
    "    ax.plot(np.log(val_losses))\n",
    "    ax.set(title=model_name + ': Log model loss', ylabel='Log loss', xlabel='Epoch')\n",
    "    ax.legend(['Train', 'Test'], loc='upper right')\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "#Set morgan to 3 or 4 and nBits to 1024\n",
    "def smiles_to_fingerprint(smiles):\n",
    "    mol = Chem.MolFromSmiles(smiles)\n",
    "    fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2, nBits=1024)\n",
    "    return list(fp.ToBitString())\n",
    "\n",
    "def smiles_to_mol(smiles):\n",
    "    mol = Chem.MolFromSmiles(smiles)\n",
    "    # if mol is not None:\n",
    "    #     mol = Chem.AddHs(mol)\n",
    "    return mol\n",
    "\n",
    "def read_smiles_data(path_data):\n",
    "    df = pd.read_csv(path_data, sep=',')\n",
    "    df['fingerprint'] = df['SMILES'].apply(smiles_to_fingerprint)\n",
    "    df['fingerprint'] = df['fingerprint'].apply(lambda x: [int(bit) for bit in x])\n",
    "    df['fingerprint'] = df['fingerprint'].apply(lambda x: np.array(x))\n",
    "    df['mol'] = df['SMILES'].apply(smiles_to_mol)\n",
    "    return df\n",
    "\n",
    "def is_hydrogen_donor(atomic_num, hybridization):\n",
    "    return int((atomic_num == 8 or atomic_num == 7) and (hybridization == 3 or hybridization == 2))\n",
    "\n",
    "def is_polar_bond(atom1_num, atom2_num, electronegativity):\n",
    "    en1 = electronegativity.get(atom1_num, None)\n",
    "    en2 = electronegativity.get(atom2_num, None)\n",
    "    if en1 is None or en2 is None:\n",
    "        return 0  # Unknown electronegativity, consider as non-polar\n",
    "    return int(abs(en1 - en2) > 0.4)\n",
    "\n",
    "def electroneg():\n",
    "    return {\n",
    "    1: 2.20,  # H\n",
    "    3: 0.98,  # Li\n",
    "    4: 1.57,  # Be\n",
    "    5: 2.04,  # B\n",
    "    6: 2.55,  # C\n",
    "    7: 3.04,  # N\n",
    "    8: 3.44,  # O\n",
    "    9: 3.98,  # F\n",
    "    11: 0.93, # Na\n",
    "    12: 1.31, # Mg\n",
    "    13: 1.61, # Al\n",
    "    14: 1.90, # Si\n",
    "    15: 2.19, # P\n",
    "    16: 2.58, # S\n",
    "    17: 3.16, # Cl\n",
    "    19: 0.82, # K\n",
    "    20: 1.00, # Ca\n",
    "    22: 1.54, # Ti\n",
    "    24: 1.66, # Cr\n",
    "    25: 1.55, # Mn\n",
    "    26: 1.83, # Fe\n",
    "    27: 1.88, # Co\n",
    "    28: 1.91, # Ni\n",
    "    29: 1.90, # Cu\n",
    "    30: 1.65, # Zn\n",
    "    35: 2.96, # Br\n",
    "    53: 2.66, # I\n",
    "}\n",
    "\n",
    "def weights_init(m):\n",
    "    if isinstance(m, (GCNConv, GATConv)):\n",
    "        nn.init.xavier_uniform_(m.weight.data)\n",
    "    elif isinstance(m, nn.Linear):\n",
    "        nn.init.xavier_uniform_(m.weight.data)\n",
    "\n",
    "def make_pyg(row):\n",
    "    # Create node features\n",
    "    mol = row['mol']\n",
    "    # pauling = electroneg()\n",
    "    atom_num = [atom.GetAtomicNum() for atom in mol.GetAtoms()]\n",
    "    atom_hyb = [int(atom.GetHybridization()) for atom in mol.GetAtoms()]\n",
    "    atom_deg = [atom.GetDegree() for atom in mol.GetAtoms()]\n",
    "    atom_arom = [int(atom.GetIsAromatic()) for atom in mol.GetAtoms()]  # Aromaticity\n",
    "    atom_hydrogens = [atom.GetTotalNumHs() for atom in mol.GetAtoms()]  # Number of hydrogens\n",
    "    # atom_h_donor = [is_hydrogen_donor(num, hyb) for num, hyb in zip(atom_num, atom_hyb)]\n",
    "    atom_charge = [atom.GetFormalCharge() for atom in mol.GetAtoms()]  # Formal charge\n",
    "    atom_chiral_tag = [int(atom.GetChiralTag()) for atom in mol.GetAtoms()]  # Chirality\n",
    "    atom_val = [atom.GetExplicitValence() for atom in mol.GetAtoms()]\n",
    "    #atom_mass = [atom.GetMass() for atom in mol.GetAtoms()]\n",
    "    #atom_pauling = [pauling.get(num, 0) for num in atom_num]\n",
    "    \n",
    "    x1 = tch.tensor(atom_num, dtype=tch.float).view(-1, 1)\n",
    "    x2 = tch.tensor(atom_hyb, dtype=tch.float).view(-1, 1)\n",
    "    x3 = tch.tensor(atom_deg, dtype=tch.float).view(-1, 1)\n",
    "    x4 = tch.tensor(atom_arom, dtype=tch.float).view(-1, 1)\n",
    "    x5 = tch.tensor(atom_hydrogens, dtype=tch.float).view(-1, 1)\n",
    "    x6 = tch.tensor(atom_charge, dtype=tch.float).view(-1, 1)\n",
    "    x7 = tch.tensor(atom_chiral_tag, dtype=tch.float).view(-1, 1)\n",
    "    x8 = tch.tensor(atom_val, dtype=tch.float).view(-1, 1)\n",
    "    # x9 = tch.tensor(atom_h_donor, dtype=tch.float).view(-1, 1)\n",
    "    #x10 = tch.tensor(atom_mass, dtype=tch.float).view(-1, 1)\n",
    "    #x11 = tch.tensor(atom_pauling, dtype=tch.float).view(-1, 1)\n",
    "    \n",
    "    y = tch.tensor(row['measured.log.solubility.mol.L.'], dtype=tch.float).view(-1, 1)\n",
    "    x = tch.cat([x1\n",
    "                 , x2\n",
    "                 , x3\n",
    "                 , x4\n",
    "                 , x5\n",
    "                 , x6\n",
    "                 , x7\n",
    "                 , x8\n",
    "                # , x9\n",
    "                # , x10\n",
    "                 #, x11\n",
    "                 ], dim=1)\n",
    "    \n",
    "    # Create edge features (connectivity)\n",
    "    edge_indices = []\n",
    "    edge_features = []\n",
    "    \n",
    "    for bond in mol.GetBonds():\n",
    "        i = bond.GetBeginAtomIdx()\n",
    "        j = bond.GetEndAtomIdx()\n",
    "        edge_indices.append((i, j))\n",
    "        bond_type = bond.GetBondTypeAsDouble()\n",
    "        is_conjugated = int(bond.GetIsConjugated())  # Conjugation\n",
    "        is_in_ring = int(bond.IsInRing())  # Ring membership\n",
    "        bond_stereo = int(bond.GetStereo())  # Stereo configuration\n",
    "        #bond_polarity = is_polar_bond(atom_num[i], atom_num[j], pauling)\n",
    "\n",
    "        edge_features.append([bond_type\n",
    "                              , is_conjugated\n",
    "                              , is_in_ring\n",
    "                              , bond_stereo\n",
    "                              #, bond_polarity\n",
    "                              ])\n",
    "    \n",
    "    edge_index = tch.tensor(edge_indices, dtype=tch.long).t().contiguous()\n",
    "    edge_attr = tch.tensor(edge_features, dtype=tch.float)\n",
    "    \n",
    "    data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y)\n",
    "\n",
    "    data.smiles = row['SMILES']\n",
    "    \n",
    "    return data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Functions for Neural Networks\n",
    "This cell defines the training methods for the neural networks you will use later in this notebook. WHile they are ready to use, you should read them carefully to understand what parameters you will need to use, and how."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T21:18:17.537894Z",
     "iopub.status.busy": "2023-09-12T21:18:17.537663Z",
     "iopub.status.idle": "2023-09-12T21:18:17.545768Z",
     "shell.execute_reply": "2023-09-12T21:18:17.545010Z"
    }
   },
   "outputs": [],
   "source": [
    "def fitGNN(gnn1_model, t_loader, v_loader, num_epochs, optimizer, criterion, scheduler):\n",
    "    train_losses = []\n",
    "    val_losses = []\n",
    "    pbar = tqdm(range(num_epochs), desc=\"Epochs\")\n",
    "    pbar.reset()\n",
    "    pbar_t = tqdm(total=len(t_loader), desc=\"Training Batch:\", leave=False)\n",
    "    pbar_v = tqdm(total=len(v_loader), desc=\"validation Batch:\", leave=False)\n",
    "    for epoch in range(num_epochs):\n",
    "        # Training Phase\n",
    "        gnn1_model.train()\n",
    "        train_loss_items = []\n",
    "        pbar_t.reset()\n",
    "        pbar_v.reset()\n",
    "        for batch in t_loader:\n",
    "            batch.to(device)\n",
    "            optimizer.zero_grad()\n",
    "            # Use Batch Data object in forward pass\n",
    "            outputs = gnn1_model(batch.x.float(), batch.edge_index, batch.batch)\n",
    "            loss = criterion(outputs, batch.y)\n",
    "\n",
    "            l1_lambda = 0.0001\n",
    "            l1_norm = sum(p.abs().sum() for p in gnn1_model.parameters())\n",
    "            loss += l1_lambda * l1_norm\n",
    "\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            train_loss_items.append(loss.item())\n",
    "            pbar_t.update()\n",
    "        avg_train_loss = sum(train_loss_items) / len(train_loss_items)\n",
    "        train_losses.append(avg_train_loss)\n",
    "        # Validation Phase (assuming you have a separate validation loader)\n",
    "        gnn1_model.eval()\n",
    "        val_loss_items = []\n",
    "        with tch.no_grad():\n",
    "            for val_batch in v_loader:\n",
    "                val_batch.to(device)\n",
    "                val_outputs = gnn1_model(val_batch.x.float(), val_batch.edge_index, val_batch.batch)\n",
    "                val_loss = criterion(val_outputs, val_batch.y)\n",
    "                val_loss_items.append(val_loss.item())\n",
    "                pbar_v.update()\n",
    "\n",
    "        avg_val_loss = sum(val_loss_items) / len(val_loss_items)\n",
    "        val_losses.append(avg_val_loss)\n",
    "        # print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {train_losses[-1]:.4f}, Val Loss: {avg_val_loss:.4f}')\n",
    "        pbar.update(1)\n",
    "        pbar.set_postfix({\"Training Loss\": avg_train_loss, \"Validation Loss\": avg_val_loss})\n",
    "        scheduler.step()\n",
    "    return gnn1_model, train_losses, val_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T21:18:17.548663Z",
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     "shell.execute_reply": "2023-09-12T21:18:17.951562Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound.ID</th>\n",
       "      <th>measured.log.solubility.mol.L.</th>\n",
       "      <th>ESOL.predicted.log.solubility.mol.L.</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>fingerprint</th>\n",
       "      <th>mol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2,3,6-Trichlorophenol</td>\n",
       "      <td>-2.64</td>\n",
       "      <td>-3.572</td>\n",
       "      <td>Oc1c(Cl)ccc(Cl)c1Cl</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7fb9dbd6b040&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tetrachloroguaiacol</td>\n",
       "      <td>-4.02</td>\n",
       "      <td>-4.299</td>\n",
       "      <td>COc1c(O)c(Cl)c(Cl)c(Cl)c1Cl</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7fb9dbd04f40&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3-Methyl-3-heptanol</td>\n",
       "      <td>-1.60</td>\n",
       "      <td>-2.017</td>\n",
       "      <td>CCCCC(C)(O)CC</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7fb9dbd682e0&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cycloheximide</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>-1.532</td>\n",
       "      <td>CC1CC(C)C(=O)C(C1)C(O)CC2CC(=O)NC(=O)C2</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7fb9dbd62100&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Acridine</td>\n",
       "      <td>-3.67</td>\n",
       "      <td>-3.846</td>\n",
       "      <td>c3ccc2nc1ccccc1cc2c3</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7fb9dbd67fa0&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Compound.ID  measured.log.solubility.mol.L.  \\\n",
       "0  2,3,6-Trichlorophenol                           -2.64   \n",
       "1    tetrachloroguaiacol                           -4.02   \n",
       "2    3-Methyl-3-heptanol                           -1.60   \n",
       "3          cycloheximide                           -1.13   \n",
       "4               Acridine                           -3.67   \n",
       "\n",
       "   ESOL.predicted.log.solubility.mol.L.  \\\n",
       "0                                -3.572   \n",
       "1                                -4.299   \n",
       "2                                -2.017   \n",
       "3                                -1.532   \n",
       "4                                -3.846   \n",
       "\n",
       "                                     SMILES  \\\n",
       "0                       Oc1c(Cl)ccc(Cl)c1Cl   \n",
       "1              COc1c(O)c(Cl)c(Cl)c(Cl)c1Cl    \n",
       "2                             CCCCC(C)(O)CC   \n",
       "3  CC1CC(C)C(=O)C(C1)C(O)CC2CC(=O)NC(=O)C2    \n",
       "4                      c3ccc2nc1ccccc1cc2c3   \n",
       "\n",
       "                                         fingerprint  \\\n",
       "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "1  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "2  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "3  [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "4  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "\n",
       "                                                mol  \n",
       "0  <rdkit.Chem.rdchem.Mol object at 0x7fb9dbd6b040>  \n",
       "1  <rdkit.Chem.rdchem.Mol object at 0x7fb9dbd04f40>  \n",
       "2  <rdkit.Chem.rdchem.Mol object at 0x7fb9dbd682e0>  \n",
       "3  <rdkit.Chem.rdchem.Mol object at 0x7fb9dbd62100>  \n",
       "4  <rdkit.Chem.rdchem.Mol object at 0x7fb9dbd67fa0>  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = read_smiles_data('data/solubility.csv')\n",
    "df = df.sample(frac=1, random_state=42).reset_index(drop=True)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T21:18:17.956650Z",
     "iopub.status.busy": "2023-09-12T21:18:17.956335Z",
     "iopub.status.idle": "2023-09-12T21:18:17.961808Z",
     "shell.execute_reply": "2023-09-12T21:18:17.961183Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1024"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['fingerprint'].iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T21:18:17.964422Z",
     "iopub.status.busy": "2023-09-12T21:18:17.964137Z",
     "iopub.status.idle": "2023-09-12T21:18:19.493259Z",
     "shell.execute_reply": "2023-09-12T21:18:19.492511Z"
    }
   },
   "outputs": [],
   "source": [
    "# scaler = StandardScaler()\n",
    "# df['measured.log.solubility.mol.L.'] = scaler.fit_transform(df[['measured.log.solubility.mol.L.']])\n",
    "data_pyg = df.apply(make_pyg, axis=1)\n",
    "data_pyg = data_pyg[data_pyg.apply(lambda x: len(x.edge_index.shape) != 1)]\n",
    "data_pyg.reset_index(drop=True, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T21:18:19.496916Z",
     "iopub.status.busy": "2023-09-12T21:18:19.496722Z",
     "iopub.status.idle": "2023-09-12T21:18:19.627686Z",
     "shell.execute_reply": "2023-09-12T21:18:19.626967Z"
    }
   },
   "outputs": [],
   "source": [
    "n_df = int(len(df) * 0.7)\n",
    "df_train = df.iloc[:n_df]\n",
    "# df_train = df.sample(frac=0.8)\n",
    "df_test = df.drop(df_train.index)\n",
    "\n",
    "df_train.reset_index(drop=True)\n",
    "df_test.reset_index(drop=True)\n",
    "X_train, y_train = df_train['fingerprint'].tolist(), df_train['measured.log.solubility.mol.L.'].tolist()\n",
    "X_test, y_test = df_test['fingerprint'].tolist(), df_test['measured.log.solubility.mol.L.'].tolist()\n",
    "\n",
    "n_train = int(len(data_pyg) * 0.7) # 70% of data for training and 30% for testing\n",
    "indices = np.arange(n_train)\n",
    "data_train = data_pyg[indices[:n_train]]\n",
    "data_train.reset_index(drop=True, inplace=True)\n",
    "data_test = data_pyg[~data_pyg.isin(data_train)]\n",
    "data_test.reset_index(drop=True, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T21:18:19.630948Z",
     "iopub.status.busy": "2023-09-12T21:18:19.630741Z",
     "iopub.status.idle": "2023-09-12T21:18:24.536456Z",
     "shell.execute_reply": "2023-09-12T21:18:24.534989Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Regressor: MSE = 1.617\n"
     ]
    }
   ],
   "source": [
    "RF_model = RandomForestRegressor()\n",
    "RF_model.fit(X_train, y_train)\n",
    "RF_pred = RF_model.predict(X_test)\n",
    "RF_mse = mean_squared_error(y_test, RF_pred)\n",
    "print('Random Forest Regressor: MSE = ' + str(np.round(RF_mse, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Support Vector Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T21:18:24.541067Z",
     "iopub.status.busy": "2023-09-12T21:18:24.540606Z",
     "iopub.status.idle": "2023-09-12T21:18:24.791497Z",
     "shell.execute_reply": "2023-09-12T21:18:24.790407Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Regressor: MSE = 1.579\n"
     ]
    }
   ],
   "source": [
    "SV_model = SVR()\n",
    "SV_model.fit(X_train, y_train)\n",
    "SV_pred = SV_model.predict(X_test)\n",
    "SV_mse = mean_squared_error(y_test, SV_pred)\n",
    "print('Support Vector Regressor: MSE = ' + str(np.round(SV_mse, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GNNs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T21:18:24.798952Z",
     "iopub.status.busy": "2023-09-12T21:18:24.798643Z",
     "iopub.status.idle": "2023-09-12T21:18:24.807686Z",
     "shell.execute_reply": "2023-09-12T21:18:24.806608Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input Dimensions:  8\n"
     ]
    }
   ],
   "source": [
    "input_dim = data_train.iloc[5].x.size(1)\n",
    "print('Input Dimensions: ', input_dim)\n",
    "#Loss, Epochs, Batch-size\n",
    "num_epochs = 10000\n",
    "batch_size = 64\n",
    "sched_size = int(num_epochs//5)\n",
    "weight_decay = 1e-4\n",
    "gamma = 0.81\n",
    "criterion = nn.MSELoss()\n",
    "#Data Loaders to handle the graphs we made earlier\n",
    "t_loader = DataLoader(data_train, batch_size=batch_size, shuffle=True)\n",
    "v_loader = DataLoader(data_test, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T21:18:24.813175Z",
     "iopub.status.busy": "2023-09-12T21:18:24.812422Z",
     "iopub.status.idle": "2023-09-12T21:18:24.820534Z",
     "shell.execute_reply": "2023-09-12T21:18:24.819716Z"
    }
   },
   "outputs": [],
   "source": [
    "class GNN1(nn.Module):\n",
    "    def __init__(self, input_dim):\n",
    "        super(GNN1, self).__init__()\n",
    "        self.conv1 = GCNConv(input_dim, 32)\n",
    "        self.conv2 = GCNConv(32, 64)\n",
    "        self.conv3 = GCNConv(64, 16)\n",
    "        self.fc3 = nn.Linear(16, 1)  # Output layer with 1 node\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dropout = nn.Dropout(p=0.5)\n",
    "\n",
    "    def forward(self, x, edge_index, batch):\n",
    "        x = self.relu(self.conv1(x, edge_index))\n",
    "        x = self.dropout(x)\n",
    "        x = self.relu(self.conv2(x, edge_index))\n",
    "        x = self.dropout(x)\n",
    "        x = self.relu(self.conv3(x, edge_index))\n",
    "        x = self.dropout(x)\n",
    "        x = global_mean_pool(x, batch)\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T21:18:24.823598Z",
     "iopub.status.busy": "2023-09-12T21:18:24.823016Z",
     "iopub.status.idle": "2023-09-12T21:39:56.254935Z",
     "shell.execute_reply": "2023-09-12T21:39:56.254013Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6362561cc8f94b4e889d643be13c7343",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epochs:   0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "26f2f86694fd405dbebedeab29479cfa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training Batch::   0%|          | 0/13 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "080ec0701eb1480bad60da797e7f8208",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "validation Batch::   0%|          | 0/6 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gnn1_model = GNN1(input_dim)\n",
    "gnn1_model = gnn1_model.to(device)\n",
    "# Adam adjusts learning rate as needed\n",
    "optimizer = optim.AdamW(gnn1_model.parameters(), lr=0.0001,weight_decay=weight_decay)\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=sched_size, gamma=gamma)\n",
    "#Traing the Model\n",
    "gnn1_model, train_losses, val_losses = fitGNN(gnn1_model, t_loader, v_loader, num_epochs, optimizer, criterion, scheduler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T21:39:56.258970Z",
     "iopub.status.busy": "2023-09-12T21:39:56.258574Z",
     "iopub.status.idle": "2023-09-12T21:39:56.607707Z",
     "shell.execute_reply": "2023-09-12T21:39:56.606853Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GNN1 Regression: MSE = 1.924\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gnn1_model.eval()\n",
    "all_preds = []\n",
    "all_labels = []\n",
    "with tch.no_grad():\n",
    "    for v_batch in v_loader:\n",
    "        v_batch.to(device)\n",
    "        test_outputs = gnn1_model(v_batch.x.float(), v_batch.edge_index, v_batch.batch)\n",
    "        all_preds.extend(test_outputs.tolist())\n",
    "        all_labels.extend(v_batch.y.tolist())\n",
    "all_preds_tensor = tch.tensor(all_preds)\n",
    "all_labels_tensor = tch.tensor(all_labels)\n",
    "gnn1_mse = mean_squared_error(all_labels_tensor, all_preds_tensor)\n",
    "\n",
    "print(f'GNN1 Regression: MSE = {gnn1_mse:.3f}')\n",
    "plot_history(train_losses, val_losses, 'GNN1 Regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T21:39:56.612185Z",
     "iopub.status.busy": "2023-09-12T21:39:56.611270Z",
     "iopub.status.idle": "2023-09-12T21:39:56.621656Z",
     "shell.execute_reply": "2023-09-12T21:39:56.620927Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "class GNN2(nn.Module):\n",
    "    def __init__(self, input_dim):\n",
    "        super(GNN2, self).__init__()\n",
    "        self.conv1 = GATConv(input_dim, 32, heads=1, concat=True)  # Two attention heads\n",
    "        self.conv2 = GATConv(32, 32, heads=2, concat=True)  # Two attention heads\n",
    "        self.conv3 = GATConv(64, 8, heads=2, concat=True)  # Two attention heads\n",
    "        self.fc3 = nn.Linear(16, 1)  # Output layer with 1 node\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dropout = nn.Dropout(p=0.3)\n",
    "\n",
    "    def forward(self, x, edge_index, batch):\n",
    "        x = self.relu(self.conv1(x, edge_index))\n",
    "        x = self.dropout(x)\n",
    "        x = self.relu(self.conv2(x, edge_index))\n",
    "        x = self.dropout(x)\n",
    "        x = self.relu(self.conv3(x, edge_index))\n",
    "        x = self.dropout(x)\n",
    "        x = global_mean_pool(x, batch)\n",
    "        x = self.fc3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T21:39:56.624578Z",
     "iopub.status.busy": "2023-09-12T21:39:56.624006Z",
     "iopub.status.idle": "2023-09-12T22:12:19.700224Z",
     "shell.execute_reply": "2023-09-12T22:12:19.699220Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "570d1f3b09954598a0dc492a18a2c658",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epochs:   0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "adddd3bfd3eb43fbbec9b789e38d684c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training Batch::   0%|          | 0/13 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "86be52a2cba441209d53270e3375e603",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "validation Batch::   0%|          | 0/6 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "gnn2_model = GNN2(input_dim).to(device)\n",
    "optimizer = optim.AdamW(gnn2_model.parameters()\n",
    "                       , lr=0.001\n",
    "                       #, weight_decay= weight_decay\n",
    "                       )  # Adjust learning rate as needed\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=sched_size, gamma=gamma)\n",
    "gnn2_model, train_losses, val_losses = fitGNN(gnn2_model, t_loader, v_loader, num_epochs, optimizer, criterion, scheduler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T22:12:19.705304Z",
     "iopub.status.busy": "2023-09-12T22:12:19.704330Z",
     "iopub.status.idle": "2023-09-12T22:12:20.048960Z",
     "shell.execute_reply": "2023-09-12T22:12:20.048152Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GNN2 Regression: MSE = 1.304\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gnn2_model.eval()\n",
    "all_preds = []\n",
    "all_labels = []\n",
    "with tch.no_grad():\n",
    "    for v_batch in v_loader:\n",
    "        v_batch.to(device)\n",
    "        test_outputs = gnn2_model(v_batch.x, v_batch.edge_index, v_batch.batch)\n",
    "        all_preds.extend(test_outputs.tolist())\n",
    "        all_labels.extend(v_batch.y.tolist())\n",
    "all_preds_tensor = tch.tensor(all_preds)\n",
    "all_labels_tensor = tch.tensor(all_labels)\n",
    "gnn2_mse = mean_squared_error(all_labels_tensor, all_preds_tensor)\n",
    "\n",
    "print(f'GNN2 Regression: MSE = {gnn2_mse:.3f}')\n",
    "plot_history(train_losses, val_losses, 'GNN2 Regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T22:12:20.051165Z",
     "iopub.status.busy": "2023-09-12T22:12:20.051003Z",
     "iopub.status.idle": "2023-09-12T22:12:20.059181Z",
     "shell.execute_reply": "2023-09-12T22:12:20.058322Z"
    }
   },
   "outputs": [],
   "source": [
    "class GNN3(nn.Module):\n",
    "    def __init__(self, input_dim):\n",
    "        super(GNN3, self).__init__()\n",
    "        self.conv1 = GATConv(input_dim, 32, heads=1, concat=True)  # Single attention head\n",
    "        self.conv2 = GATConv(32, 32, heads=2, concat=True)  # Single attention head\n",
    "        self.conv3 = GATConv(64, 64, heads=2, concat=True)\n",
    "        self.conv4 = GATConv(128, 32, heads=2, concat=True)  # Single attention head\n",
    "        self.conv5 = GATConv(64, 8, heads=2, concat=True)  # Single attention head  # Single attention head\n",
    "        self.fc3 = nn.Linear(16, 1)  # Output layer with 1 node\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dropout = nn.Dropout(p=0.35)\n",
    "\n",
    "    def forward(self, x, edge_index, batch):\n",
    "        x = self.relu(self.conv1(x, edge_index))\n",
    "        x = self.dropout(x)\n",
    "        x = self.relu(self.conv2(x, edge_index))\n",
    "        x = self.dropout(x)\n",
    "        x = self.relu(self.conv3(x, edge_index))\n",
    "        x = self.dropout(x)\n",
    "        x = self.relu(self.conv4(x, edge_index))\n",
    "        x = self.dropout(x)\n",
    "        x = self.relu(self.conv5(x, edge_index))\n",
    "        x = self.dropout(x)\n",
    "        x = global_mean_pool(x, batch)\n",
    "        x = self.fc3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T22:12:20.061395Z",
     "iopub.status.busy": "2023-09-12T22:12:20.061015Z",
     "iopub.status.idle": "2023-09-12T22:56:40.711956Z",
     "shell.execute_reply": "2023-09-12T22:56:40.710522Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "752e2c275a8a48a1a406a9cac4669bd4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epochs:   0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8188650dcef54019b8948eac9c54cf72",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training Batch::   0%|          | 0/13 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cc65daff97194d8c98715243affc88f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "validation Batch::   0%|          | 0/6 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the GNN1 model\n",
    "gnn3_model = GNN3(input_dim).to(device)\n",
    "optimizer = optim.AdamW(gnn3_model.parameters()\n",
    "                       , lr=0.001\n",
    "                       , weight_decay= weight_decay*15\n",
    "                       )  # Adjust learning rate as needed\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=sched_size, gamma=gamma)\n",
    "gnn3_model, train_losses, val_losses = fitGNN(gnn3_model, t_loader, v_loader, num_epochs, optimizer, criterion, scheduler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T22:56:40.717782Z",
     "iopub.status.busy": "2023-09-12T22:56:40.716627Z",
     "iopub.status.idle": "2023-09-12T22:56:41.059992Z",
     "shell.execute_reply": "2023-09-12T22:56:41.059213Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GNN3 Regression: MSE = 1.284\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gnn3_model.eval()\n",
    "all_preds = []\n",
    "all_labels = []\n",
    "with tch.no_grad():\n",
    "    for v_batch in v_loader:\n",
    "        v_batch.to(device)\n",
    "        test_outputs = gnn3_model(v_batch.x, v_batch.edge_index, v_batch.batch)\n",
    "        all_preds.extend(test_outputs.tolist())\n",
    "        all_labels.extend(v_batch.y.tolist())\n",
    "all_preds_tensor = tch.tensor(all_preds)\n",
    "all_labels_tensor = tch.tensor(all_labels)\n",
    "gnn3_mse = mean_squared_error(all_labels_tensor, all_preds_tensor)\n",
    "\n",
    "print(f'GNN3 Regression: MSE = {gnn3_mse:.3f}')\n",
    "plot_history(train_losses, val_losses, 'GNN3 Regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-12T22:56:41.063112Z",
     "iopub.status.busy": "2023-09-12T22:56:41.062947Z",
     "iopub.status.idle": "2023-09-12T22:56:41.196569Z",
     "shell.execute_reply": "2023-09-12T22:56:41.195646Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Results')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar([\"SVM\", \"Random Forest\", \"GNN1\", \"GNN2\", \"GNN3\"], [SV_mse, RF_mse, gnn1_mse, gnn2_mse, gnn3_mse])\n",
    "plt.ylabel(\"MSE\")\n",
    "plt.title(\"Results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
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