data/molecules/prepare_molecules.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook for preparing and saving MOLECULAR graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import pickle\n",
    "import time\n",
    "import os\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download ZINC dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading..\n",
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
      "  0     0    0     0    0     0      0      0 --:--:--  0:00:01 --:--:--     0\n",
      "100  366M  100  366M    0     0  4351k      0  0:01:26  0:01:26 --:--:-- 4197k\n",
      "Archive:  molecules.zip\n",
      " extracting: ../molecules/atom_dict.pickle  \n",
      " extracting: ../molecules/bond_dict.pickle  \n",
      " extracting: ../molecules/test.pickle  \n",
      " extracting: ../molecules/train.pickle  \n",
      " extracting: ../molecules/val.pickle  \n"
     ]
    }
   ],
   "source": [
    "if not os.path.isfile('molecules.zip'):\n",
    "    print('downloading..')\n",
    "    !curl https://www.dropbox.com/s/feo9qle74kg48gy/molecules.zip?dl=1 -o molecules.zip -J -L -k\n",
    "    !unzip molecules.zip -d ../\n",
    "    # !tar -xvf molecules.zip -C ../\n",
    "else:\n",
    "    print('File already downloaded')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert to DGL format and save with pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/vijay/graphdeeplearning/benchmarking-gnns\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.chdir('../../') # go to root folder of the project\n",
    "print(os.getcwd())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from data.molecules import MoleculeDatasetDGL \n",
    "\n",
    "from data.data import LoadData\n",
    "from torch.utils.data import DataLoader\n",
    "from data.molecules import MoleculeDataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing 10000 graphs for the TRAIN set...\n",
      "preparing 1000 graphs for the VAL set...\n",
      "preparing 1000 graphs for the TEST set...\n",
      "Time taken: 84.0698s\n"
     ]
    }
   ],
   "source": [
    "DATASET_NAME = 'ZINC'\n",
    "dataset = MoleculeDatasetDGL(DATASET_NAME) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min/max : 9 37\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min/max : 10 36\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min/max : 11 37\n"
     ]
    }
   ],
   "source": [
    "def plot_histo_graphs(dataset, title):\n",
    "    # histogram of graph sizes\n",
    "    graph_sizes = []\n",
    "    for graph in dataset:\n",
    "        graph_sizes.append(graph[0].number_of_nodes())\n",
    "    plt.figure(1)\n",
    "    plt.hist(graph_sizes, bins=20)\n",
    "    plt.title(title)\n",
    "    plt.show()\n",
    "    graph_sizes = torch.Tensor(graph_sizes)\n",
    "    print('min/max :',graph_sizes.min().long().item(),graph_sizes.max().long().item())\n",
    "    \n",
    "plot_histo_graphs(dataset.train,'trainset')\n",
    "plot_histo_graphs(dataset.val,'valset')\n",
    "plot_histo_graphs(dataset.test,'testset')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n",
      "1000\n",
      "1000\n",
      "(DGLGraph(num_nodes=29, num_edges=64,\n",
      "         ndata_schemes={'feat': Scheme(shape=(), dtype=torch.int64)}\n",
      "         edata_schemes={'feat': Scheme(shape=(), dtype=torch.int64)}), tensor([0.8350]))\n",
      "(DGLGraph(num_nodes=35, num_edges=78,\n",
      "         ndata_schemes={'feat': Scheme(shape=(), dtype=torch.int64)}\n",
      "         edata_schemes={'feat': Scheme(shape=(), dtype=torch.int64)}), tensor([0.6299]))\n",
      "(DGLGraph(num_nodes=16, num_edges=34,\n",
      "         ndata_schemes={'feat': Scheme(shape=(), dtype=torch.int64)}\n",
      "         edata_schemes={'feat': Scheme(shape=(), dtype=torch.int64)}), tensor([1.9973]))\n"
     ]
    }
   ],
   "source": [
    "print(len(dataset.train))\n",
    "print(len(dataset.val))\n",
    "print(len(dataset.test))\n",
    "\n",
    "print(dataset.train[0])\n",
    "print(dataset.val[0])\n",
    "print(dataset.test[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_atom_type = 28\n",
    "num_bond_type = 4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time (sec): 6.659376859664917\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "with open('data/molecules/ZINC.pkl','wb') as f:\n",
    "        pickle.dump([dataset.train,dataset.val,dataset.test,num_atom_type,num_bond_type],f)\n",
    "print('Time (sec):',time.time() - start)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test load function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I] Loading dataset ZINC...\n",
      "train, test, val sizes : 10000 1000 1000\n",
      "[I] Finished loading.\n",
      "[I] Data load time: 8.5329s\n"
     ]
    }
   ],
   "source": [
    "DATASET_NAME = 'ZINC'\n",
    "dataset = LoadData(DATASET_NAME)\n",
    "trainset, valset, testset = dataset.train, dataset.val, dataset.test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'data.molecules.MoleculeDataset'>\n"
     ]
    }
   ],
   "source": [
    "batch_size = 10\n",
    "collate = MoleculeDataset.collate\n",
    "print(MoleculeDataset)\n",
    "train_loader = DataLoader(trainset, batch_size=batch_size, shuffle=True, collate_fn=collate)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}