dnn_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0be1b3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import csv\n",
    "from tqdm import tqdm\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "53dc620f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device:  cpu\n",
      "Torch version: 1.12.1+cu102\n"
     ]
    }
   ],
   "source": [
    "# select cpu\n",
    "torch.cuda.is_available = lambda : False\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(\"Using device: \", device)\n",
    "if device.type == \"cuba\":\n",
    "    print(torch.cuba.get_device_name(0))\n",
    "    \n",
    "# version\n",
    "print(f\"Torch version: {torch.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2bb59d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data\n",
    "def get_smiles_solibity(filename):\n",
    "    \n",
    "    with open(filename, 'r') as f:\n",
    "        reader = csv.reader(f)\n",
    "        next(reader)\n",
    "        \n",
    "        smiles = []\n",
    "        sol = []\n",
    "        for row in reader:\n",
    "            smiles.append(row[3])\n",
    "            sol.append(float(row[4]))\n",
    "        return smiles, sol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "12dddd17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# molecular fingerprints calc\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "\n",
    "def fingerprints(smiles, radius=2, nbits=2048):\n",
    "    fps = []\n",
    "    mols = [Chem.MolFromSmiles(x) for x in smiles]\n",
    "    for m in mols:\n",
    "        fp = np.array(AllChem.GetMorganFingerprintAsBitVect(m, radius=radius, nBits=nbits))\n",
    "        fps.append(fp)\n",
    "    return fps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "355ffd8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = get_smiles_solibity('../../data/AqSolDB_v1.0_min.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8daf403e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n",
      "[22:50:36] WARNING: not removing hydrogen atom without neighbors\n"
     ]
    }
   ],
   "source": [
    "fprints = fingerprints(data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "470d28ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# features\n",
    "X = np.array(fprints)\n",
    "y = np.array(data[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a6544b87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, test_size=0.25) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9615b6b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardize features\n",
    "X_train_norm = (X_train - np.mean(X_train)) / np.std(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a99060fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create tensors from arrays\n",
    "X_train_norm = torch.from_numpy(X_train_norm).float()\n",
    "y_train = torch.from_numpy(y_train).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8fd4b3a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pytorch dataset\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "train_ds = TensorDataset(X_train_norm, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "896f69f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataloader\n",
    "torch.manual_seed(1)\n",
    "batch_size = 8\n",
    "train_dl = DataLoader(train_ds, batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2392e703",
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters\n",
    "input_size = X_train_norm.shape[1]\n",
    "hidden_size = 100\n",
    "output_size = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e49db3f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model bulding\n",
    "import torch.nn as nn\n",
    "\n",
    "model = nn.Sequential(\n",
    "    nn.Linear(input_size, hidden_size),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(hidden_size, hidden_size),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(hidden_size, output_size),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f70f2c88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=2048, out_features=100, bias=True)\n",
       "  (1): ReLU()\n",
       "  (2): Linear(in_features=100, out_features=100, bias=True)\n",
       "  (3): ReLU()\n",
       "  (4): Linear(in_features=100, out_features=3, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "12c0672f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loss function\n",
    "loss_fn = nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "346141c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# optimization\n",
    "learning_rate = 0.001\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "55ec2780",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 Loss 0.1460\n",
      "Epoch 20 Loss 0.1094\n",
      "Epoch 40 Loss 0.0987\n",
      "Epoch 60 Loss 0.0891\n",
      "Epoch 80 Loss 0.0875\n",
      "Epoch 100 Loss 0.0825\n",
      "Epoch 120 Loss 0.0819\n",
      "Epoch 140 Loss 0.0790\n",
      "Epoch 160 Loss 0.0779\n",
      "Epoch 180 Loss 0.0775\n"
     ]
    }
   ],
   "source": [
    "# training the model\n",
    "torch.manual_seed(1)\n",
    "num_epochs = 200\n",
    "log_epochs = 20\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    loss_hist_train = 0\n",
    "    for x_batch, y_batch in train_dl:\n",
    "        pred = model(x_batch)[:,0]\n",
    "        loss = loss_fn(pred, y_batch)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        optimizer.zero_grad()\n",
    "        loss_hist_train += loss.item()\n",
    "    if epoch % log_epochs ==0:\n",
    "        print(f\"Epoch {epoch} Loss \"\n",
    "              f\"{loss_hist_train / len(train_dl):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c83864af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 1.7611\n",
      "Test MAE: 0.9486\n"
     ]
    }
   ],
   "source": [
    "# model evaluation of test dataset\n",
    "X_test_norm = (X_test - np.mean(X_train)) / np.std(X_train)\n",
    "X_test_norm = torch.from_numpy(X_test_norm).float()\n",
    "y_test = torch.from_numpy(y_test).float()\n",
    "\n",
    "with torch.no_grad():\n",
    "    pred_test = model(X_test_norm)[:,0]\n",
    "    pred_train = model(X_train_norm)[:,0] ## for plotting\n",
    "    loss = loss_fn(pred_test, y_test)\n",
    "    print(f\"Test MSE: {(loss.item()):.4f}\")\n",
    "    print(f\"Test MAE: {nn.L1Loss()(pred_test, y_test).item():.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ef33d8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcdefaults()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "076ee252",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Linear Regression Prediction\")\n",
    "plt.xlabel(\"Real LogS\")\n",
    "plt.ylabel(\"Predicted LogS\")\n",
    "plt.grid()\n",
    "plt.rcParams[\"figure.figsize\"] = [6,6]\n",
    "\n",
    "plt.scatter(y_train, pred_train, \n",
    "            color=\"blue\", alpha=0.8, label=\"train\")\n",
    "plt.scatter(y_test, pred_test, \n",
    "            color=\"lightgreen\", alpha=0.8, label=\"test\")\n",
    "\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c278ef0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50f506a5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76d08699",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40f4b6b8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94ecc7e7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}