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inference
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inference
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "c2a48994",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-27T03:27:28.279431Z",
"iopub.status.busy": "2024-05-27T03:27:28.279176Z",
"iopub.status.idle": "2024-05-27T03:27:46.384963Z",
"shell.execute_reply": "2024-05-27T03:27:46.383728Z"
},
"papermill": {
"duration": 18.114161,
"end_time": "2024-05-27T03:27:46.387447",
"exception": false,
"start_time": "2024-05-27T03:27:28.273286",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r\n",
"\r\n",
"\r\n",
"E: Unable to locate package python-rdkit\r\n",
"Collecting rdkit\r\n",
" Downloading rdkit-2023.9.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.9 kB)\r\n",
"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from rdkit) (1.26.4)\r\n",
"Requirement already satisfied: Pillow in /opt/conda/lib/python3.10/site-packages (from rdkit) (9.5.0)\r\n",
"Downloading rdkit-2023.9.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (34.9 MB)\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m34.9/34.9 MB\u001b[0m \u001b[31m42.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[?25hInstalling collected packages: rdkit\r\n",
"Successfully installed rdkit-2023.9.6\r\n"
]
}
],
"source": [
"!apt-get install -y python-rdkit librdkit1 rdkit-data\n",
"!pip install rdkit"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "19e23fb1",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-27T03:27:46.403860Z",
"iopub.status.busy": "2024-05-27T03:27:46.402966Z",
"iopub.status.idle": "2024-05-27T03:28:01.327863Z",
"shell.execute_reply": "2024-05-27T03:28:01.326726Z"
},
"papermill": {
"duration": 14.935443,
"end_time": "2024-05-27T03:28:01.330259",
"exception": false,
"start_time": "2024-05-27T03:27:46.394816",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting ogb\r\n",
" Downloading ogb-1.3.6-py3-none-any.whl.metadata (6.2 kB)\r\n",
"Requirement already satisfied: torch>=1.6.0 in /opt/conda/lib/python3.10/site-packages (from ogb) (2.1.2)\r\n",
"Requirement already satisfied: numpy>=1.16.0 in /opt/conda/lib/python3.10/site-packages (from ogb) (1.26.4)\r\n",
"Requirement already satisfied: tqdm>=4.29.0 in /opt/conda/lib/python3.10/site-packages (from ogb) (4.66.1)\r\n",
"Requirement already satisfied: scikit-learn>=0.20.0 in /opt/conda/lib/python3.10/site-packages (from ogb) (1.2.2)\r\n",
"Requirement already satisfied: pandas>=0.24.0 in /opt/conda/lib/python3.10/site-packages (from ogb) (2.1.4)\r\n",
"Requirement already satisfied: six>=1.12.0 in /opt/conda/lib/python3.10/site-packages (from ogb) (1.16.0)\r\n",
"Requirement already satisfied: urllib3>=1.24.0 in /opt/conda/lib/python3.10/site-packages (from ogb) (1.26.18)\r\n",
"Collecting outdated>=0.2.0 (from ogb)\r\n",
" Downloading outdated-0.2.2-py2.py3-none-any.whl.metadata (4.7 kB)\r\n",
"Requirement already satisfied: setuptools>=44 in /opt/conda/lib/python3.10/site-packages (from outdated>=0.2.0->ogb) (69.0.3)\r\n",
"Collecting littleutils (from outdated>=0.2.0->ogb)\r\n",
" Downloading littleutils-0.2.2.tar.gz (6.6 kB)\r\n",
" Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
"\u001b[?25hRequirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from outdated>=0.2.0->ogb) (2.31.0)\r\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas>=0.24.0->ogb) (2.9.0.post0)\r\n",
"Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=0.24.0->ogb) (2023.3.post1)\r\n",
"Requirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=0.24.0->ogb) (2023.4)\r\n",
"Requirement already satisfied: scipy>=1.3.2 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=0.20.0->ogb) (1.11.4)\r\n",
"Requirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=0.20.0->ogb) (1.3.2)\r\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=0.20.0->ogb) (3.2.0)\r\n",
"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch>=1.6.0->ogb) (3.13.1)\r\n",
"Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch>=1.6.0->ogb) (4.9.0)\r\n",
"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.6.0->ogb) (1.12)\r\n",
"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.6.0->ogb) (3.2.1)\r\n",
"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.6.0->ogb) (3.1.2)\r\n",
"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch>=1.6.0->ogb) (2024.2.0)\r\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.6.0->ogb) (2.1.3)\r\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->outdated>=0.2.0->ogb) (3.3.2)\r\n",
"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->outdated>=0.2.0->ogb) (3.6)\r\n",
"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->outdated>=0.2.0->ogb) (2024.2.2)\r\n",
"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.6.0->ogb) (1.3.0)\r\n",
"Downloading ogb-1.3.6-py3-none-any.whl (78 kB)\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.8/78.8 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[?25hDownloading outdated-0.2.2-py2.py3-none-any.whl (7.5 kB)\r\n",
"Building wheels for collected packages: littleutils\r\n",
" Building wheel for littleutils (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
"\u001b[?25h Created wheel for littleutils: filename=littleutils-0.2.2-py3-none-any.whl size=7026 sha256=a5ad07f1008a276c728cca4ca20b7a0a76173beec7dcae279da52f9340db3fcf\r\n",
" Stored in directory: /root/.cache/pip/wheels/3d/fe/b0/27a9892da57472e538c7452a721a9cf463cc03cf7379889266\r\n",
"Successfully built littleutils\r\n",
"Installing collected packages: littleutils, outdated, ogb\r\n",
"Successfully installed littleutils-0.2.2 ogb-1.3.6 outdated-0.2.2\r\n"
]
}
],
"source": [
"!pip install ogb"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0648a7c9",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-27T03:28:01.348790Z",
"iopub.status.busy": "2024-05-27T03:28:01.348480Z",
"iopub.status.idle": "2024-05-27T03:28:14.693028Z",
"shell.execute_reply": "2024-05-27T03:28:14.692047Z"
},
"papermill": {
"duration": 13.356578,
"end_time": "2024-05-27T03:28:14.695533",
"exception": false,
"start_time": "2024-05-27T03:28:01.338955",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting torch_geometric\r\n",
" Downloading torch_geometric-2.5.3-py3-none-any.whl.metadata (64 kB)\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.2/64.2 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[?25hRequirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from torch_geometric) (4.66.1)\r\n",
"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torch_geometric) (1.26.4)\r\n",
"Requirement already satisfied: scipy in /opt/conda/lib/python3.10/site-packages (from torch_geometric) (1.11.4)\r\n",
"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch_geometric) (2024.2.0)\r\n",
"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch_geometric) (3.1.2)\r\n",
"Requirement already satisfied: aiohttp in /opt/conda/lib/python3.10/site-packages (from torch_geometric) (3.9.1)\r\n",
"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torch_geometric) (2.31.0)\r\n",
"Requirement already satisfied: pyparsing in /opt/conda/lib/python3.10/site-packages (from torch_geometric) (3.1.1)\r\n",
"Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.10/site-packages (from torch_geometric) (1.2.2)\r\n",
"Requirement already satisfied: psutil>=5.8.0 in /opt/conda/lib/python3.10/site-packages (from torch_geometric) (5.9.3)\r\n",
"Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->torch_geometric) (23.2.0)\r\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.10/site-packages (from aiohttp->torch_geometric) (6.0.4)\r\n",
"Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->torch_geometric) (1.9.3)\r\n",
"Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from aiohttp->torch_geometric) (1.4.1)\r\n",
"Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.10/site-packages (from aiohttp->torch_geometric) (1.3.1)\r\n",
"Requirement already satisfied: async-timeout<5.0,>=4.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->torch_geometric) (4.0.3)\r\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch_geometric) (2.1.3)\r\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torch_geometric) (3.3.2)\r\n",
"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torch_geometric) (3.6)\r\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torch_geometric) (1.26.18)\r\n",
"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torch_geometric) (2024.2.2)\r\n",
"Requirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->torch_geometric) (1.3.2)\r\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->torch_geometric) (3.2.0)\r\n",
"Downloading torch_geometric-2.5.3-py3-none-any.whl (1.1 MB)\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m17.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[?25hInstalling collected packages: torch_geometric\r\n",
"Successfully installed torch_geometric-2.5.3\r\n"
]
}
],
"source": [
"!pip install torch_geometric"
]
},
{
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"source": [
"from ogb.lsc import PygPCQM4Mv2Dataset, PCQM4Mv2Evaluator\n",
"from ogb.graphproppred.mol_encoder import AtomEncoder,BondEncoder\n",
"import torch\n",
"import torch.nn.functional as F\n",
"from torch_geometric.nn import GINConv, GCNConv\n",
"from torch_geometric.nn.pool import global_add_pool\n",
"import torch.optim as optim\n",
"from torch.optim.lr_scheduler import StepLR\n",
"from torch_geometric.loader import DataLoader\n",
"import os\n",
"import time\n",
"import random\n",
"import numpy as np\n",
"from tqdm.auto import tqdm\n",
"from torch_geometric.datasets import PCQM4Mv2"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "abe9e6a8",
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"execution": {
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"tags": []
},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"torch.manual_seed(42)\n",
"torch.cuda.manual_seed(42)\n",
"random.seed(42)\n",
"device = torch.device('cuda')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "c72ce47e",
"metadata": {
"execution": {
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"source": [
"class GNN_graph(torch.nn.Module):\n",
" def __init__(self, num_layers=5, emb_dim=100, drop_ratio=0.5, gnn_type='GIN'):\n",
" super().__init__()\n",
" self.num_layers = num_layers\n",
" self.drop_ratio = drop_ratio\n",
" self.atom_encoder = AtomEncoder(emb_dim)\n",
" #self.bond_encoder = BondEncoder(emb_dim)\n",
" self.mlp = torch.nn.Sequential(torch.nn.Linear(emb_dim, emb_dim), torch.nn.BatchNorm1d(emb_dim), torch.nn.ReLU(), torch.nn.Linear(emb_dim, emb_dim))\n",
" \n",
" self.graph_pool = global_add_pool\n",
" self.linear_pred = torch.nn.Linear(emb_dim, 1)\n",
" \n",
" if self.num_layers<2:\n",
" raise ValueError(\"Number of layers must be more than 1\")\n",
" \n",
" self.convs = torch.nn.ModuleList()\n",
" self.norms = torch.nn.ModuleList()\n",
" \n",
" for i in range(num_layers):\n",
" if(gnn_type=='GIN'):\n",
" self.convs.append(GINConv(self.mlp))\n",
" elif(gnn_type=='GCN'):\n",
" self.convs.append(GCNConv(emb_dim, emb_dim, normalize=False))\n",
" else:\n",
" ValueError(\"Invalid GNN type called\")\n",
" \n",
" self.norms.append(torch.nn.BatchNorm1d(emb_dim))\n",
" \n",
" def forward(self, batched_data):\n",
" x, edge_index, edge_attr, batch = batched_data.x, batched_data.edge_index, batched_data.edge_attr, batched_data.batch\n",
" #edge_embedding = self.bond_encoder(edge_attr)\n",
" h_list = [self.atom_encoder(x)]\n",
" for layer in range(self.num_layers):\n",
"\n",
" h = self.convs[layer](h_list[layer], edge_index)\n",
" h = self.norms[layer](h)\n",
"\n",
" if layer == self.num_layers - 1:\n",
" #remove relu for the last layer\n",
" h = F.dropout(h, self.drop_ratio, training = self.training)\n",
" else:\n",
" h = F.dropout(F.relu(h), self.drop_ratio, training = self.training)\n",
"\n",
" h_list.append(h)\n",
" \n",
" \n",
" node_feat = h_list[-1]\n",
" graph_feat = self.graph_pool(node_feat, batch)\n",
" output = self.linear_pred(graph_feat)\n",
" \n",
" \n",
" return output"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "5d3eaedb",
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"execution": {
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"source": [
"def test(model, device, loader):\n",
" model.eval()\n",
" \n",
" y_pred = []\n",
"\n",
" for step, batch in enumerate(tqdm(loader, desc=\"Iteration\")):\n",
" batch = batch.to(device)\n",
"\n",
" with torch.no_grad():\n",
" pred = model(batch).view(-1,)\n",
"\n",
" y_pred.append(pred.detach().cpu())\n",
"\n",
" y_pred = torch.cat(y_pred, dim = 0)\n",
" \n",
" out = []\n",
" for pred in y_pred:\n",
" out.append(pred)\n",
"\n",
" return out"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "68a2b13c",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"import pandas as pd\n",
"from ogb.utils import smiles2graph\n",
"from torch_geometric.data import Data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "6902e593",
"metadata": {
"execution": {
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},
"outputs": [],
"source": [
"class OnTheFlyPCQMDataset(object):\n",
" def __init__(self, smiles_list, smiles2graph=smiles2graph):\n",
" super(OnTheFlyPCQMDataset, self).__init__()\n",
" self.smiles_list = smiles_list \n",
" self.smiles2graph = smiles2graph\n",
"\n",
" def __getitem__(self, idx):\n",
" '''Get datapoint with index'''\n",
" data = Data()\n",
" smiles, y = self.smiles_list[idx]\n",
" graph = self.smiles2graph(smiles)\n",
"\n",
" data.__num_nodes__ = int(graph['num_nodes'])\n",
" data.edge_index = torch.from_numpy(graph['edge_index']).to(torch.int64)\n",
" data.edge_attr = torch.from_numpy(graph['edge_feat']).to(torch.int64)\n",
" data.x = torch.from_numpy(graph['node_feat']).to(torch.int64)\n",
" \n",
" return data\n",
" \n",
" def __len__(self):\n",
" '''Length of the dataset\n",
" Returns\n",
" -------\n",
" int\n",
" Length of Dataset\n",
" '''\n",
" return len(self.smiles_list)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "533a9c0f",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"df = pd.read_csv(\"/kaggle/input/btp-data/Btp - Sheet1.csv\")\n",
"test_data = df.values.tolist()\n",
"onthefly_data = OnTheFlyPCQMDataset(test_data)\n",
"loader = DataLoader(onthefly_data, batch_size=10, shuffle=False)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "4ab78c71",
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{
"data": {
"text/plain": [
"GNN_graph(\n",
" (atom_encoder): AtomEncoder(\n",
" (atom_embedding_list): ModuleList(\n",
" (0): Embedding(119, 200)\n",
" (1): Embedding(5, 200)\n",
" (2-3): 2 x Embedding(12, 200)\n",
" (4): Embedding(10, 200)\n",
" (5-6): 2 x Embedding(6, 200)\n",
" (7-8): 2 x Embedding(2, 200)\n",
" )\n",
" )\n",
" (mlp): Sequential(\n",
" (0): Linear(in_features=200, out_features=200, bias=True)\n",
" (1): BatchNorm1d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" (3): Linear(in_features=200, out_features=200, bias=True)\n",
" )\n",
" (linear_pred): Linear(in_features=200, out_features=1, bias=True)\n",
" (convs): ModuleList(\n",
" (0-4): 5 x GINConv(nn=Sequential(\n",
" (0): Linear(in_features=200, out_features=200, bias=True)\n",
" (1): BatchNorm1d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" (3): Linear(in_features=200, out_features=200, bias=True)\n",
" ))\n",
" )\n",
" (norms): ModuleList(\n",
" (0-4): 5 x BatchNorm1d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
")"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gcn = GNN_graph(num_layers=5, emb_dim=200, drop_ratio=0.5, gnn_type='GCN')\n",
"gcn_checkpoint = torch.load('/kaggle/input/checkpoint-gcn/checkpoint (1).pt')\n",
"gcn.load_state_dict(gcn_checkpoint['model_state_dict'])\n",
"gcn.to(device)\n",
"\n",
"gin = GNN_graph(num_layers=5, emb_dim=200, drop_ratio=0.5, gnn_type='GIN')\n",
"gin_checkpoint = torch.load('/kaggle/input/checkpoint-gin/checkpoint (2).pt')\n",
"gin.load_state_dict(gin_checkpoint['model_state_dict'])\n",
"gin.to(device)"
]
},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicting on test data...\n"
]
},
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"Iteration: 0%| | 0/1 [00:00<?, ?it/s]"
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"source": [
"print(\"Predicting on test data...\")\n",
"gin_pred = test(gin, device, loader)\n",
"gcn_pred = test(gcn, device, loader)"
]
},
{
"cell_type": "code",
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"source": [
"df_gin = pd.DataFrame(gin_pred)\n",
"df_gin.to_csv('gin_predictions.csv', index = False)"
]
},
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"cell_type": "code",
"execution_count": 14,
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"df_gcn = pd.DataFrame(gcn_pred)\n",
"df_gcn.to_csv('gcn_predictions.csv', index = False)"
]
}
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