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* added classes for peptides dataset
* loading peptides dataset
* parametrized down adj as kwargs from cli args in zinc, peptides, ogb datasets
* include down_adj tu
* Include AP & include_down_adj
* Added support for CIN++ models
* added support for cin++ experiments
* modified data/dataset/__init__.py
* modified data/data_loading.py for pept graphs
* added down_adj to sh files for cin++
* changed include_down_adj to bool
* if embed_dim is None:
embed_dim = hidden
* if embed_dim is None:
embed_dim = hidden
* remove dwn_msg from super(CINppCochainConv, self)
* init lower_nns before calling super
* remove override reset_params- Loading branch information
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| Original file line number | Diff line number | Diff line change |
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| #!/usr/bin/env python3 | ||
| # -*- coding: utf-8 -*- | ||
| """ | ||
| Created on Tue May 2 21:37:42 2023 | ||
| @author: renz | ||
| """ | ||
|
|
||
| import hashlib | ||
| import os.path as osp | ||
| import os | ||
| import pickle | ||
| import shutil | ||
|
|
||
| import pandas as pd | ||
| import torch | ||
| from ogb.utils import smiles2graph | ||
| from ogb.utils.torch_util import replace_numpy_with_torchtensor | ||
| from ogb.utils.url import decide_download | ||
| from torch_geometric.data import Data, download_url | ||
| from torch_geometric.data import InMemoryDataset | ||
| from data.utils import convert_graph_dataset_with_rings | ||
| from data.datasets import InMemoryComplexDataset | ||
| from tqdm import tqdm | ||
|
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||
|
|
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| class PeptidesFunctionalDataset(InMemoryComplexDataset): | ||
| """ | ||
| PyG dataset of 15,535 peptides represented as their molecular graph | ||
| (SMILES) with 10-way multi-task binary classification of their | ||
| functional classes. | ||
| The goal is use the molecular representation of peptides instead | ||
| of amino acid sequence representation ('peptide_seq' field in the file, | ||
| provided for possible baseline benchmarking but not used here) to test | ||
| GNNs' representation capability. | ||
| The 10 classes represent the following functional classes (in order): | ||
| ['antifungal', 'cell_cell_communication', 'anticancer', | ||
| 'drug_delivery_vehicle', 'antimicrobial', 'antiviral', | ||
| 'antihypertensive', 'antibacterial', 'antiparasitic', 'toxic'] | ||
| Args: | ||
| root (string): Root directory where the dataset should be saved. | ||
| smiles2graph (callable): A callable function that converts a SMILES | ||
| string into a graph object. We use the OGB featurization. | ||
| * The default smiles2graph requires rdkit to be installed * | ||
| """ | ||
| def __init__(self, root, max_ring_size, smiles2graph=smiles2graph, | ||
| transform=None, pre_transform=None, pre_filter=None, | ||
| include_down_adj=False, init_method='sum', n_jobs=2): | ||
| self.original_root = root | ||
| self.smiles2graph = smiles2graph | ||
| self.folder = osp.join(root, 'peptides-functional') | ||
|
|
||
| self.url = 'https://www.dropbox.com/s/ol2v01usvaxbsr8/peptide_multi_class_dataset.csv.gz?dl=1' | ||
| self.version = '701eb743e899f4d793f0e13c8fa5a1b4' # MD5 hash of the intended dataset file | ||
| self.url_stratified_split = 'https://www.dropbox.com/s/j4zcnx2eipuo0xz/splits_random_stratified_peptide.pickle?dl=1' | ||
| self.md5sum_stratified_split = '5a0114bdadc80b94fc7ae974f13ef061' | ||
|
|
||
| # Check version and update if necessary. | ||
| release_tag = osp.join(self.folder, self.version) | ||
| if osp.isdir(self.folder) and (not osp.exists(release_tag)): | ||
| print(f"{self.__class__.__name__} has been updated.") | ||
| if input("Will you update the dataset now? (y/N)\n").lower() == 'y': | ||
| shutil.rmtree(self.folder) | ||
|
|
||
| self.name = 'peptides_functional' | ||
| self._max_ring_size = max_ring_size | ||
| self._use_edge_features = True | ||
| self._n_jobs = n_jobs | ||
| super(PeptidesFunctionalDataset, self).__init__(root, transform, pre_transform, pre_filter, | ||
| max_dim=2, init_method=init_method, include_down_adj=include_down_adj, | ||
| cellular=True, num_classes=1) | ||
|
|
||
| self.data, self.slices, idx, self.num_tasks = self.load_dataset() | ||
| self.train_ids = idx['train'] | ||
| self.val_ids = idx['val'] | ||
| self.test_ids = idx['test'] | ||
|
|
||
| self.num_node_type = 9 | ||
| self.num_edge_type = 3 | ||
|
|
||
| @property | ||
| def raw_file_names(self): | ||
| return 'peptide_multi_class_dataset.csv.gz' | ||
|
|
||
| @property | ||
| def processed_file_names(self): | ||
| return [f'{self.name}_complex.pt', f'{self.name}_idx.pt', f'{self.name}_tasks.pt'] | ||
|
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||
|
|
||
| @property | ||
| def processed_dir(self): | ||
| """Overwrite to change name based on edge and simple feats""" | ||
| directory = super(PeptidesFunctionalDataset, self).processed_dir | ||
| suffix1 = f"_{self._max_ring_size}rings" if self._cellular else "" | ||
| suffix2 = "-E" if self._use_edge_features else "" | ||
| return directory + suffix1 + suffix2 | ||
|
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||
|
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||
| def _md5sum(self, path): | ||
| hash_md5 = hashlib.md5() | ||
| with open(path, 'rb') as f: | ||
| buffer = f.read() | ||
| hash_md5.update(buffer) | ||
| return hash_md5.hexdigest() | ||
|
|
||
| def download(self): | ||
| if decide_download(self.url): | ||
| path = download_url(self.url, self.raw_dir) | ||
| # Save to disk the MD5 hash of the downloaded file. | ||
| hash = self._md5sum(path) | ||
| if hash != self.version: | ||
| raise ValueError("Unexpected MD5 hash of the downloaded file") | ||
| open(osp.join(self.root, hash), 'w').close() | ||
| # Download train/val/test splits. | ||
| path_split1 = download_url(self.url_stratified_split, self.root) | ||
| assert self._md5sum(path_split1) == self.md5sum_stratified_split | ||
| old_df_name = osp.join(self.raw_dir, | ||
| 'peptide_multi_class_dataset.csv.gz?dl=1') | ||
| new_df_name = osp.join(self.raw_dir, | ||
| 'peptide_multi_class_dataset.csv.gz') | ||
|
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||
|
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| old_split_file = osp.join(self.root, | ||
| "splits_random_stratified_peptide.pickle?dl=1") | ||
| new_split_file = osp.join(self.root, | ||
| "splits_random_stratified_peptide.pickle") | ||
| os.rename(old_df_name, new_df_name) | ||
| os.rename(old_split_file, new_split_file) | ||
|
|
||
| else: | ||
| print('Stop download.') | ||
| exit(-1) | ||
|
|
||
| def load_dataset(self): | ||
| """Load the dataset from here and process it if it doesn't exist""" | ||
| print("Loading dataset from disk...") | ||
| data, slices = torch.load(self.processed_paths[0]) | ||
| idx = torch.load(self.processed_paths[1]) | ||
| tasks = torch.load(self.processed_paths[2]) | ||
| return data, slices, idx, tasks | ||
|
|
||
| def process(self): | ||
| data_df = pd.read_csv(osp.join(self.raw_dir, | ||
| 'peptide_multi_class_dataset.csv.gz')) | ||
| smiles_list = data_df['smiles'] | ||
|
|
||
| print('Converting SMILES strings into graphs...') | ||
| data_list = [] | ||
| for i in tqdm(range(len(smiles_list))): | ||
| data = Data() | ||
|
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||
| smiles = smiles_list[i] | ||
| graph = self.smiles2graph(smiles) | ||
|
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||
| assert (len(graph['edge_feat']) == graph['edge_index'].shape[1]) | ||
| assert (len(graph['node_feat']) == graph['num_nodes']) | ||
|
|
||
| data.__num_nodes__ = int(graph['num_nodes']) | ||
| data.edge_index = torch.from_numpy(graph['edge_index']).to( | ||
| torch.int64) | ||
| data.edge_attr = torch.from_numpy(graph['edge_feat']).to( | ||
| torch.int64) | ||
| data.x = torch.from_numpy(graph['node_feat']).to(torch.int64) | ||
| data.y = torch.Tensor([eval(data_df['labels'].iloc[i])]) | ||
|
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||
| data_list.append(data) | ||
|
|
||
| if self.pre_transform is not None: | ||
| data_list = [self.pre_transform(data) for data in data_list] | ||
|
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| split_idx = self.get_idx_split() | ||
|
|
||
| # NB: the init method would basically have no effect if | ||
| # we use edge features and do not initialize rings. | ||
| print(f"Converting the {self.name} dataset to a cell complex...") | ||
| complexes, _, _ = convert_graph_dataset_with_rings( | ||
| data_list, | ||
| max_ring_size=self._max_ring_size, | ||
| include_down_adj=self.include_down_adj, | ||
| init_method=self._init_method, | ||
| init_edges=self._use_edge_features, | ||
| init_rings=False, | ||
| n_jobs=self._n_jobs) | ||
|
|
||
| print(f'Saving processed dataset in {self.processed_paths[0]}...') | ||
| torch.save(self.collate(complexes, self.max_dim), self.processed_paths[0]) | ||
|
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||
| print(f'Saving idx in {self.processed_paths[1]}...') | ||
| torch.save(split_idx, self.processed_paths[1]) | ||
|
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||
| print(f'Saving num_tasks in {self.processed_paths[2]}...') | ||
| torch.save(10, self.processed_paths[2]) | ||
|
|
||
| def get_idx_split(self): | ||
| """ Get dataset splits. | ||
| Returns: | ||
| Dict with 'train', 'val', 'test', splits indices. | ||
| """ | ||
| split_file = osp.join(self.root, | ||
| "splits_random_stratified_peptide.pickle") | ||
| with open(split_file, 'rb') as f: | ||
| splits = pickle.load(f) | ||
| split_dict = replace_numpy_with_torchtensor(splits) | ||
| split_dict['valid'] = split_dict['val'] | ||
| return split_dict | ||
|
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||
|
|
||
| def load_pep_f_graph_dataset(root): | ||
| raw_dir = osp.join(root, 'raw') | ||
| data_df = pd.read_csv(osp.join(raw_dir, | ||
| 'peptide_multi_class_dataset.csv.gz')) | ||
| smiles_list = data_df['smiles'] | ||
| target_names = ['Inertia_mass_a', 'Inertia_mass_b', 'Inertia_mass_c', | ||
| 'Inertia_valence_a', 'Inertia_valence_b', | ||
| 'Inertia_valence_c', 'length_a', 'length_b', 'length_c', | ||
| 'Spherocity', 'Plane_best_fit'] | ||
| # Normalize to zero mean and unit standard deviation. | ||
| data_df.loc[:, target_names] = data_df.loc[:, target_names].apply( | ||
| lambda x: (x - x.mean()) / x.std(), axis=0) | ||
|
|
||
| print('Converting SMILES strings into graphs...') | ||
| data_list = [] | ||
| for i in tqdm(range(len(smiles_list))): | ||
| data = Data() | ||
|
|
||
| smiles = smiles_list[i] | ||
| y = data_df.iloc[i][target_names] | ||
| graph = smiles2graph(smiles) | ||
|
|
||
| assert (len(graph['edge_feat']) == graph['edge_index'].shape[1]) | ||
| assert (len(graph['node_feat']) == graph['num_nodes']) | ||
|
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| data.__num_nodes__ = int(graph['num_nodes']) | ||
| data.edge_index = torch.from_numpy(graph['edge_index']).to( | ||
| torch.int64) | ||
| data.edge_attr = torch.from_numpy(graph['edge_feat']).to( | ||
| torch.int64) | ||
| data.x = torch.from_numpy(graph['node_feat']).to(torch.int64) | ||
| data.y = torch.Tensor([y]) | ||
|
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| data_list.append(data) | ||
|
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| dataset = InMemoryDataset.collate(data_list) | ||
|
|
||
| #get split file | ||
| split_file = osp.join(root, | ||
| "splits_random_stratified_peptide.pickle") | ||
| with open(split_file, 'rb') as f: | ||
| splits = pickle.load(f) | ||
| split_dict = replace_numpy_with_torchtensor(splits) | ||
| split_dict['valid'] = split_dict['val'] | ||
|
|
||
| return dataset, split_dict['train'], split_dict['valid'], split_dict['test'] |
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