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pcqm4mv2.py
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pcqm4mv2.py
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from typing import Optional, Union, Dict
import os
import os.path as osp
import shutil
from ogb.utils import smiles2graph
from ogb.utils.url import decide_download, download_url, extract_zip
import pandas as pd
import numpy as np
from tqdm.auto import tqdm
import torch
class PCQM4Mv2Dataset(object):
def __init__(self, root = 'dataset', smiles2graph = smiles2graph, only_smiles=False):
'''
Library-agnostic PCQM4Mv2 dataset object
- root (str): the dataset folder will be located at root/pcqm4m_kddcup2021
- smiles2graph (callable): A callable function that converts a SMILES string into a graph object
* The default smiles2graph requires rdkit to be installed
- only_smiles (bool): If this is true, we directly return the SMILES string in our __get_item__, without converting it into a graph.
'''
self.original_root = root
self.smiles2graph = smiles2graph
self.only_smiles = only_smiles
self.folder = osp.join(root, 'pcqm4m-v2')
self.version = 1
# Old url hosted at Stanford
# md5sum: 65b742bafca5670be4497499db7d361b
# self.url = f'http://ogb-data.stanford.edu/data/lsc/pcqm4m-v2.zip'
# New url hosted by DGL team at AWS--much faster to download
self.url = 'https://dgl-data.s3-accelerate.amazonaws.com/dataset/OGB-LSC/pcqm4m-v2.zip'
# check version and update if necessary
if osp.isdir(self.folder) and (not osp.exists(osp.join(self.folder, f'RELEASE_v{self.version}.txt'))):
print('PCQM4Mv2 dataset has been updated.')
if input('Will you update the dataset now? (y/N)\n').lower() == 'y':
shutil.rmtree(self.folder)
super(PCQM4Mv2Dataset, self).__init__()
# Prepare everything.
# download if there is no raw file
# preprocess if there is no processed file
# load data if processed file is found.
if self.only_smiles:
self.prepare_smiles()
else:
self.prepare_graph()
def download(self):
if decide_download(self.url):
path = download_url(self.url, self.original_root)
extract_zip(path, self.original_root)
os.unlink(path)
else:
print('Stop download.')
exit(-1)
def prepare_smiles(self):
raw_dir = osp.join(self.folder, 'raw')
if not osp.exists(osp.join(raw_dir, 'data.csv.gz')):
# if the raw file does not exist, then download it.
self.download()
data_df = pd.read_csv(osp.join(raw_dir, 'data.csv.gz'))
smiles_list = data_df['smiles'].values
homolumogap_list = data_df['homolumogap'].values
self.graphs = list(smiles_list)
self.labels = homolumogap_list
def prepare_graph(self):
processed_dir = osp.join(self.folder, 'processed')
raw_dir = osp.join(self.folder, 'raw')
pre_processed_file_path = osp.join(processed_dir, 'data_processed')
if osp.exists(pre_processed_file_path):
# if pre-processed file already exists
loaded_dict = torch.load(pre_processed_file_path, 'rb')
self.graphs, self.labels = loaded_dict['graphs'], loaded_dict['labels']
else:
# if pre-processed file does not exist
if not osp.exists(osp.join(raw_dir, 'data.csv.gz')):
# if the raw file does not exist, then download it.
self.download()
data_df = pd.read_csv(osp.join(raw_dir, 'data.csv.gz'))
smiles_list = data_df['smiles']
homolumogap_list = data_df['homolumogap']
print('Converting SMILES strings into graphs...')
self.graphs = []
self.labels = []
for i in tqdm(range(len(smiles_list))):
smiles = smiles_list[i]
homolumogap = homolumogap_list[i]
graph = self.smiles2graph(smiles)
assert(len(graph['edge_feat']) == graph['edge_index'].shape[1])
assert(len(graph['node_feat']) == graph['num_nodes'])
self.graphs.append(graph)
self.labels.append(homolumogap)
self.labels = np.array(self.labels)
print(self.labels)
# double-check prediction target
split_dict = self.get_idx_split()
assert(all([not np.isnan(self.labels[i]) for i in split_dict['train']]))
assert(all([not np.isnan(self.labels[i]) for i in split_dict['valid']]))
assert(all([np.isnan(self.labels[i]) for i in split_dict['test-dev']]))
assert(all([np.isnan(self.labels[i]) for i in split_dict['test-challenge']]))
print('Saving...')
torch.save({'graphs': self.graphs, 'labels': self.labels}, pre_processed_file_path, pickle_protocol=4)
def get_idx_split(self):
split_dict = torch.load(osp.join(self.folder, 'split_dict.pt'))
return split_dict
def __getitem__(self, idx):
'''Get datapoint with index'''
if isinstance(idx, (int, np.integer)):
return self.graphs[idx], self.labels[idx]
raise IndexError(
'Only integer is valid index (got {}).'.format(type(idx).__name__))
def __len__(self):
'''Length of the dataset
Returns
-------
int
Length of Dataset
'''
return len(self.graphs)
def __repr__(self): # pragma: no cover
return '{}({})'.format(self.__class__.__name__, len(self))
class PCQM4Mv2Evaluator:
def __init__(self):
'''
Evaluator for the PCQM4Mv2 dataset
Metric is Mean Absolute Error
'''
pass
def eval(self, input_dict):
'''
y_true: numpy.ndarray or torch.Tensor of shape (num_graphs,)
y_pred: numpy.ndarray or torch.Tensor of shape (num_graphs,)
y_true and y_pred need to be of the same type (either numpy.ndarray or torch.Tensor)
'''
assert('y_pred' in input_dict)
assert('y_true' in input_dict)
y_pred, y_true = input_dict['y_pred'], input_dict['y_true']
assert((isinstance(y_true, np.ndarray) and isinstance(y_pred, np.ndarray))
or
(isinstance(y_true, torch.Tensor) and isinstance(y_pred, torch.Tensor)))
assert(y_true.shape == y_pred.shape)
assert(len(y_true.shape) == 1)
if isinstance(y_true, torch.Tensor):
return {'mae': torch.mean(torch.abs(y_pred - y_true)).cpu().item()}
else:
return {'mae': float(np.mean(np.absolute(y_pred - y_true)))}
def save_test_submission(self, input_dict: Dict, dir_path: str, mode: str):
'''
save test submission file at dir_path
'''
assert('y_pred' in input_dict)
assert mode in ['test-dev', 'test-challenge']
y_pred = input_dict['y_pred']
if mode == 'test-dev':
filename = osp.join(dir_path, 'y_pred_pcqm4m-v2_test-dev')
assert(y_pred.shape == (147037,))
elif mode == 'test-challenge':
filename = osp.join(dir_path, 'y_pred_pcqm4m-v2_test-challenge')
assert(y_pred.shape == (147432,))
assert(isinstance(filename, str))
assert(isinstance(y_pred, np.ndarray) or isinstance(y_pred, torch.Tensor))
if not osp.exists(dir_path):
os.makedirs(dir_path)
if isinstance(y_pred, torch.Tensor):
y_pred = y_pred.numpy()
y_pred = y_pred.astype(np.float32)
np.savez_compressed(filename, y_pred = y_pred)
if __name__ == '__main__':
dataset = PCQM4Mv2Dataset(only_smiles=True)
print(dataset)
print(dataset[1234])
split_dict = dataset.get_idx_split()
print(split_dict['train'].shape)
print(split_dict['valid'].shape)
print('-----------------')
print(split_dict['test-dev'].shape)
print(split_dict['test-challenge'].shape)
evaluator = PCQM4Mv2Evaluator()
y_true = torch.randn(100)
y_pred = torch.randn(100)
result = evaluator.eval({'y_true': y_true, 'y_pred': y_pred})
print(result)
print(len(split_dict['test-dev']))
print(len(split_dict['test-challenge']))
y_pred = torch.randn(len(split_dict['test-dev']))
evaluator.save_test_submission({'y_pred': y_pred}, 'results',mode = 'test-dev')
y_pred = torch.randn(len(split_dict['test-challenge']))
evaluator.save_test_submission({'y_pred': y_pred}, 'results',mode = 'test-challenge')