LMU_ANN_GNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Artificial neural networks for QSAR\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 as you previously explored during the QSAR lab last week. However, here we will only use the PhysChem descriptors.\n",
    "\n",
    "#### Modelling comparisons\n",
    "1. Compare the results of linear regression to those of a simple neural network with no hidden layers and no non-linear activation functions\n",
    "2. Compare the results of a a random forest regressor, a support vector regressor, and a neural network with two hidden layers (with non-linear activations) and dropout.\n",
    "\n",
    "#### Aims\n",
    "* to see the link between neural networks and linear regression\n",
    "* to learn the basics of how to define, compile, fit and evaluate neural networks via TensorFlow.\n",
    "\n",
    "#### Note\n",
    "We will be using the open-source machine learning framework TensorFlow (https://www.tensorflow.org) and Keras (https://keras.io) for our neural networks. TensorFlow was developed by the Google Brain team and is today one of the most widely used machine learning frameworks in research and industry and Keras was/is the most popular higher-level API that runs atop TensorFlow. However, last year TensorFlow 2 was released. In TensorFow 2 (which we will use for all our neural network work) Keras is now fully integrated. This means that we get all the benefits of TensorFlow with a much easier (Keras-type) way to define and train models than was previously possible with TensorFlow 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RDKit WARNING: [20:25:31] Enabling RDKit 2019.09.1 jupyter extensions\n",
      "[20:25:31] Enabling RDKit 2019.09.1 jupyter extensions\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   0  NVIDIA GeForce RTX 3090        Off | 00000000:C2:00.0 Off |                  N/A |\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",
    "from torch_geometric.nn import GCNConv, summary as gsummary, global_mean_pool\n",
    "from torch_geometric.data import Data, DataLoader\n",
    "\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\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",
    "import os\n",
    "\n",
    "import ANN_helper\n",
    "\n",
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
    "\n",
    "# tch.backends.cudnn.enabled = False\n",
    "# tch.cuda.is_available = lambda : False\n",
    "# device = tch.device('cpu')\n",
    "!nvidia-smi | grep GeForce\n",
    "device = tch.device(\"cuda\" if tch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and preprocess data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load and check shape of X and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\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>1,1,1,2-Tetrachloroethane</td>\n",
       "      <td>-2.18</td>\n",
       "      <td>-2.794</td>\n",
       "      <td>ClCC(Cl)(Cl)Cl</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 0x7f3eab68a280&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1,1,1-Trichloroethane</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>-2.232</td>\n",
       "      <td>CC(Cl)(Cl)Cl</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7f4023c30700&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1,1,2,2-Tetrachloroethane</td>\n",
       "      <td>-1.74</td>\n",
       "      <td>-2.549</td>\n",
       "      <td>ClC(Cl)C(Cl)Cl</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 0x7f4023c30280&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1,1,2-Trichloroethane</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-1.961</td>\n",
       "      <td>ClCC(Cl)Cl</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 0x7f4023c30100&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1,1,2-Trichlorotrifluoroethane</td>\n",
       "      <td>-3.04</td>\n",
       "      <td>-3.077</td>\n",
       "      <td>FC(F)(Cl)C(F)(Cl)Cl</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7f4023c30040&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Compound.ID  measured.log.solubility.mol.L.  \\\n",
       "0       1,1,1,2-Tetrachloroethane                           -2.18   \n",
       "1           1,1,1-Trichloroethane                           -2.00   \n",
       "2       1,1,2,2-Tetrachloroethane                           -1.74   \n",
       "3           1,1,2-Trichloroethane                           -1.48   \n",
       "4  1,1,2-Trichlorotrifluoroethane                           -3.04   \n",
       "\n",
       "   ESOL.predicted.log.solubility.mol.L.               SMILES  \\\n",
       "0                                -2.794       ClCC(Cl)(Cl)Cl   \n",
       "1                                -2.232         CC(Cl)(Cl)Cl   \n",
       "2                                -2.549       ClC(Cl)C(Cl)Cl   \n",
       "3                                -1.961           ClCC(Cl)Cl   \n",
       "4                                -3.077  FC(F)(Cl)C(F)(Cl)Cl   \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, 1, 0, 0, 0, 0, 0, 0, ...   \n",
       "2  [0, 1, 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, 1, 0, 0, 0, 0, 0, 0, ...   \n",
       "\n",
       "                                                mol  \n",
       "0  <rdkit.Chem.rdchem.Mol object at 0x7f3eab68a280>  \n",
       "1  <rdkit.Chem.rdchem.Mol object at 0x7f4023c30700>  \n",
       "2  <rdkit.Chem.rdchem.Mol object at 0x7f4023c30280>  \n",
       "3  <rdkit.Chem.rdchem.Mol object at 0x7f4023c30100>  \n",
       "4  <rdkit.Chem.rdchem.Mol object at 0x7f4023c30040>  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = ANN_helper.read_smiles_data('data/solubility.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1126, 8)\n",
      "(1126,)\n"
     ]
    }
   ],
   "source": [
    "X = np.load('data/X_qsar.npy')\n",
    "y = np.load('data/y_qsar.npy')\n",
    "\n",
    "\n",
    "data_pyg = df.apply(ANN_helper.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",
    "##DEBUGG\n",
    "dbl = int(len(data_pyg) * 0.3)\n",
    "dbi = np.arange(dbl)\n",
    "data_pyg= data_pyg[dbi[:dbl]]\n",
    "data_pyg.reset_index(drop=True, inplace=True)\n",
    "##END DEBUGG\n",
    "print(X.shape)\n",
    "# print(Xold.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split into training and test sets and standardize the data\n",
    "Here we will just have a training and test set, so our results will not be quite as rigerous as those you got with cross-validation in the supervised machine learning lab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_train = int(len(y) * 0.7) # 70% of data for training and 30% for testing\n",
    "\n",
    "random.seed(1234)\n",
    "indices = np.arange(len(y))\n",
    "random.shuffle(indices)\n",
    "\n",
    "# X_train0 is our training data prior to standardization\n",
    "X_train0, X_test0 = X[indices[:n_train]], X[indices[n_train:]]\n",
    "y_train, y_test = y[indices[:n_train]], y[indices[n_train:]]\n",
    "\n",
    "# standardize X_train0 and X_test0 to give X_train and X_test\n",
    "scaler = StandardScaler().fit(X_train0)\n",
    "X_train = scaler.transform(X_train0)\n",
    "X_test = scaler.transform(X_test0)\n",
    "\n",
    "\n",
    "# Here we split the molecular data into a train and a test set\n",
    "n_train = int(len(data_pyg) * 0.7) # 70% of data for training and 30% for testing\n",
    "# random.seed(1234)\n",
    "indices = np.arange(n_train)\n",
    "# print(indices[-1])\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": [
    "## Linear Regression\n",
    "## Random Forest Regressor & Support Vector Regressor\n",
    "For comparative purposes, with the results we will explore later with a more involved neural network architectures than the one above, we will build a Linear Regression, Random Forest and Support Vector model. For these three machine learning algorithms we will just use the default hyper parameter settings, which are often a good place to start. This means that you will just have () after the model definition, as you did for the linear regression with LinearRegression(). To change the hyper parameters from the defaults one needs to specify them within the braces.\n",
    "\n",
    "The code cells for the random forest and support vector regressors have been left blank below. You should fill in these cells. You should define the models, fit them, make predictions from them, compute their MSEs and print out the results.\n",
    "\n",
    "* hint 1: look to the cell where we 'Load packages' to get the right model definition for the two machine learning methods\n",
    "* hint 2: look at the cell with Linear Regression. It should be similar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression: MSE = 0.938\n"
     ]
    }
   ],
   "source": [
    "#Linear Regression\n",
    "LR_model = LinearRegression()\n",
    "LR_model.fit(X_train, y_train)\n",
    "LR_pred = LR_model.predict(X_test)\n",
    "LR_mse = mean_squared_error(y_test, LR_pred)\n",
    "print('Linear Regression: MSE = ' + str(np.round(LR_mse, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Regressor: MSE = 0.451\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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Regressor: MSE = 0.488\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": [
    "## Artifical neural network as a linear regression\n",
    "If we define a neural network with no hidden layers and no non-linear activations we essentailly get the same results as we do with basic linear regression. The results below should help clarify that to you (there are some minor differences hovever, hence the MSE for the neural network will not be _exactly_ the same as the results above for linear regression, but they are neverthelss very close).\n",
    "\n",
    "<p>\n",
    "    <img src=\"figs/lin-reg.png\" alt=\"drawing\" style=\"width:1200px;\"/>\n",
    "    <center>Figure 1. Our neural network version of linear regression.</center>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define model\n",
    "You will need to convert the data into tensors and define the model to use Neural Networks. IN this particular case, you'll get some help - but keep note of how it's done, you *will* have to do it yourselves in the rest of the assignment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "In the cells below we define, compile, fit and evaluate a neural network model with:\n",
    "* two hiiden layers, each with 32 neurons and non-linear activations (relu)\n",
    "* a dropout layer at the end with a dropout rate of 0.2\n",
    "\n",
    "<p>\n",
    "    <img src=\"figs/relu-activation.png\" alt=\"drawing\" style=\"width:500px;\"/>\n",
    "    <center>Figure 2. relu activation.</center>\n",
    "</p>\n",
    "\n",
    "Dropout can help to avoid overfitting, much as L1 and L2 regularizations do (as you explored in the supervise machine learning lab). In the model loss plots (below) this stops the test loss from increasing as you train for more epochs.\n",
    "\n",
    "Some quotes from a paper co-authored by members of our group called \"Deep Learning in Image Cytometry: A Review\" (https://onlinelibrary.wiley.com/doi/full/10.1002/cyto.a.23701):\n",
    "\n",
    "\"_Overfitting occurs when the parameters of a model fit too closely to the input training data, without capturing the underlying distribution, and thus reducing the model’s ability to generalize to other datasets_\".\n",
    "\n",
    "DROPOUT: \"_A regularization technique that reduces the interdependent learning among the neurons to prevent overfitting. Some neurons are randomly “dropped,” or disconnected from other neurons, at every training iteration, removing their influence on the optimization of the other neurons. Dropout creates a sparse network composed of several networks—each trained with a subset of the neurons. This transformation into an ensemble of networks hugely decreases the possibility of overfitting, and can lead to better generalization and increased accuracy_\".\n",
    "\n",
    "<p>\n",
    "    <img src=\"figs/dropout.png\" alt=\"drawing\" style=\"width:1200px;\"/>\n",
    "    <center>Figure 3. Dropout.</center>\n",
    "</p>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Linear-1                   [-1, 32]             288\n",
      "              ReLU-2                   [-1, 32]               0\n",
      "           Dropout-3                   [-1, 32]               0\n",
      "            Linear-4                   [-1, 32]           1,056\n",
      "              ReLU-5                   [-1, 32]               0\n",
      "           Dropout-6                   [-1, 32]               0\n",
      "            Linear-7                    [-1, 1]              33\n",
      "================================================================\n",
      "Total params: 1,377\n",
      "Trainable params: 1,377\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 0.00\n",
      "Params size (MB): 0.01\n",
      "Estimated Total Size (MB): 0.01\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Initialize the ANN2 model\n",
    "\n",
    "X_train_tensor = tch.tensor(X_train, dtype=tch.float32).to(device)\n",
    "y_train_tensor = tch.tensor(y_train, dtype=tch.float32).to(device).unsqueeze(1)  # Add dimension for regression\n",
    "X_test_tensor = tch.tensor(X_test, dtype=tch.float32).to(device)\n",
    "y_test_tensor = tch.tensor(y_test, dtype=tch.float32).to(device).unsqueeze(1)\n",
    "\n",
    "input_dim = X_train.shape[1]\n",
    "ann_model = ANN_helper.ANN(input_dim)\n",
    "ann_model.to(device)\n",
    "asummary(ann_model, input_size=(input_dim,))\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(ann_model.parameters(), lr=0.001)  # Adjust learning rate as needed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "660af3386f9540a7881655bcdc8327f9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epochs:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c143c30b678a49c4b757098f12419917",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epochs:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_epochs = 100\n",
    "train_losses = []\n",
    "val_losses = []\n",
    "\n",
    "pbar = tqdm(range(num_epochs), desc=\"Epochs\")\n",
    "\n",
    "\n",
    "ann2_model, train_losses, val_losses = ANN_helper.annFit(ann_model, X_train_tensor, y_train_tensor, X_test_tensor, y_test_tensor, num_epochs, optimizer, criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANN Regression: MSE = 1.588\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "ann2_model.eval()\n",
    "with tch.no_grad():\n",
    "    y_pred = ann2_model(X_test_tensor)\n",
    "    y_pred_np = y_pred.cpu().detach().numpy()\n",
    "    y_test_np = y_test_tensor.cpu().detach().numpy()\n",
    "    ann_mse = mean_squared_error(y_test_np, y_pred_np)\n",
    "    print('ANN Regression: MSE = {:.3f}'.format(ann_mse))\n",
    "\n",
    "# Plot training history\n",
    "ANN_helper.plot_history(train_losses, val_losses, 'ANN2 Regression')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing GNNs\n",
    "So, now you've tested regression on molecular descriptors with ANNs. ANother option that is gaining traction in the research world is using Graph Neural Networks or, as they can also be called, Graph Convolutional Networks. You will be using an extension library called Pytorch.Geometric for this. Training and Evaluation looks the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/torch_geometric/deprecation.py:22: UserWarning: 'data.DataLoader' is deprecated, use 'loader.DataLoader' instead\n",
      "  warnings.warn(out)\n"
     ]
    }
   ],
   "source": [
    "# Initialize the GNN1 model\n",
    "input_dim = data_train.iloc[5].x.size(1)\n",
    "gnn_model = ANN_helper.GNN(input_dim)\n",
    "#Loss and optimizer\n",
    "num_epochs = 100\n",
    "batch_size = 8\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(gnn_model.parameters(), lr=0.001)  # Adjust learning rate as needed\n",
    "t_loader = DataLoader(data_train, batch_size=batch_size, shuffle=True)\n",
    "v_loader = DataLoader(data_test, batch_size=batch_size, shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fe97c5efdbcc4be780eeeb936eaa5d77",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epochs:   0%|          | 0/15 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "baed7ae9c4ab445ca3f88ea7748496f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training Batch::   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ada5909a385b4584a7953230d5b9a631",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "validation Batch::   0%|          | 0/13 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gnn_model, train_losses, val_losses = ANN_helper.fitGNN(gnn_model, t_loader, v_loader, num_epochs, batch_size, optimizer, criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GNN1 Regression: MSE = 2.197\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gnn_model.eval()\n",
    "all_preds = []\n",
    "all_labels = []\n",
    "with tch.no_grad():\n",
    "    for v_batch in v_loader:\n",
    "        test_outputs = gnn_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",
    "gnn1_mse = mean_squared_error(all_labels_tensor, all_preds_tensor)\n",
    "\n",
    "print(f'GNN1 Regression: MSE = {gnn1_mse:.3f}')\n",
    "ANN_helper.plot_history(train_losses, val_losses, 'GNN1 Regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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