diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..12e0edcb --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ + +.DS_Store +dig/.DS_Store +dig/xgraph/.DS_Store +dig/xgraph/TAGE/.DS_Store diff --git a/README.md b/README.md index fed58f35..4ec4a4c8 100644 --- a/README.md +++ b/README.md @@ -83,6 +83,8 @@ Please cite our paper if you find *DIG* useful in your work: ## Contact -If you have any questions, please submit a new issue or contact us: Meng Liu [mengliu@tamu.edu] or Shuiwang Ji [sji@tamu.edu]. +If you have any technical questions, please submit a new issue. + +If you have other questions, please contact us: Meng Liu [mengliu@tamu.edu] or Shuiwang Ji [sji@tamu.edu]. diff --git a/dig/ggraph/JT-VAE/sample.py b/dig/ggraph/JT-VAE/sample.py new file mode 100644 index 00000000..f6410147 --- /dev/null +++ b/dig/ggraph/JT-VAE/sample.py @@ -0,0 +1,36 @@ +import torch +import torch.nn as nn + +import math, random, sys +import argparse + +from jtnn_vae import JTNNVAE +from vocab import Vocab + +import rdkit + +lg = rdkit.RDLogger.logger() +lg.setLevel(rdkit.RDLogger.CRITICAL) + +parser = argparse.ArgumentParser() +parser.add_argument('--nsample', type=int, required=True) +parser.add_argument('--vocab', required=True) +parser.add_argument('--model', required=True) + +parser.add_argument('--hidden_size', type=int, default=450) +parser.add_argument('--latent_size', type=int, default=56) +parser.add_argument('--depthT', type=int, default=20) +parser.add_argument('--depthG', type=int, default=3) + +args = parser.parse_args() + +vocab = [x.strip("\r\n ") for x in open(args.vocab)] +vocab = Vocab(vocab) + +model = JTNNVAE(vocab, args.hidden_size, args.latent_size, args.depthT, args.depthG) +model.load_state_dict(torch.load(args.model)) +model = model.cuda() + +torch.manual_seed(0) +for i in range(args.nsample): + print(model.sample_prior()) diff --git a/dig/ggraph/JT-VAE/vae_train.py b/dig/ggraph/JT-VAE/vae_train.py new file mode 100644 index 00000000..e493c629 --- /dev/null +++ b/dig/ggraph/JT-VAE/vae_train.py @@ -0,0 +1,114 @@ +import torch +import torch.nn as nn +import torch.optim as optim +import torch.optim.lr_scheduler as lr_scheduler +from torch.utils.data import DataLoader +from torch.autograd import Variable + +import math, random, sys +import numpy as np +import argparse +from collections import deque +import pickle + +# from fast_jtnn import * +import rdkit + +from vocab import Vocab +from jtnn_vae import JTNNVAE +from datautils import MolTreeFolder, PairTreeFolder, MolTreeDataset + +from rdkit import RDLogger + + +lg = RDLogger.logger() +lg.setLevel(RDLogger.CRITICAL) + +parser = argparse.ArgumentParser() +parser.add_argument('--train', required=True) +parser.add_argument('--vocab', required=True) +parser.add_argument('--save_dir', required=True) +parser.add_argument('--load_epoch', type=int, default=0) + +parser.add_argument('--hidden_size', type=int, default=450) +parser.add_argument('--batch_size', type=int, default=32) +parser.add_argument('--latent_size', type=int, default=56) +parser.add_argument('--depthT', type=int, default=20) +parser.add_argument('--depthG', type=int, default=3) + +parser.add_argument('--lr', type=float, default=1e-3) +parser.add_argument('--clip_norm', type=float, default=50.0) +parser.add_argument('--beta', type=float, default=0.0) +parser.add_argument('--step_beta', type=float, default=0.002) +parser.add_argument('--max_beta', type=float, default=1.0) +parser.add_argument('--warmup', type=int, default=40000) + +parser.add_argument('--epoch', type=int, default=20) +parser.add_argument('--anneal_rate', type=float, default=0.9) +parser.add_argument('--kl_anneal_iter', type=int, default=2000) +parser.add_argument('--print_iter', type=int, default=50) +parser.add_argument('--save_iter', type=int, default=5000) + +args = parser.parse_args() +print(args) + +vocab = [x.strip("\r\n ") for x in open(args.vocab)] +vocab = Vocab(vocab) + +model = JTNNVAE(vocab, args.hidden_size, args.latent_size, args.depthT, args.depthG).cuda() +print(model) + +for param in model.parameters(): + if param.dim() == 1: + nn.init.constant_(param, 0) + else: + nn.init.xavier_normal_(param) + +if args.load_epoch > 0: + model.load_state_dict(torch.load(args.save_dir + "/model.iter-" + str(args.load_epoch))) + +print("Model #Params: %dK" % (sum([x.nelement() for x in model.parameters()]) / 1000,)) + +optimizer = optim.Adam(model.parameters(), lr=args.lr) +scheduler = lr_scheduler.ExponentialLR(optimizer, args.anneal_rate) +scheduler.step() + +param_norm = lambda m: math.sqrt(sum([p.norm().item() ** 2 for p in m.parameters()])) +grad_norm = lambda m: math.sqrt(sum([p.grad.norm().item() ** 2 for p in m.parameters() if p.grad is not None])) + +total_step = args.load_epoch +beta = args.beta +meters = np.zeros(4) + +for epoch in range(args.epoch): + loader = MolTreeFolder(args.train, vocab, args.batch_size, num_workers=4) + for batch in loader: + total_step += 1 + try: + model.zero_grad() + loss, kl_div, wacc, tacc, sacc = model(batch, beta) + loss.backward() + nn.utils.clip_grad_norm_(model.parameters(), args.clip_norm) + optimizer.step() + except Exception as e: + print(e) + continue + + meters = meters + np.array([kl_div, wacc * 100, tacc * 100, sacc * 100]) + + if total_step % args.print_iter == 0: + meters /= args.print_iter + print("[%d] Beta: %.3f, KL: %.2f, Word: %.2f, Topo: %.2f, Assm: %.2f, PNorm: %.2f, GNorm: %.2f" % (total_step, beta, meters[0], meters[1], meters[2], meters[3], para$ + sys.stdout.flush() + meters *= 0 + + if total_step % args.save_iter == 0: + torch.save(model.state_dict(), args.save_dir + "/model.iter-" + str(total_step)) + + if total_step % args.anneal_iter == 0: + scheduler.step() + print("learning rate: %.6f" % scheduler.get_lr()[0]) + + if total_step % args.kl_anneal_iter == 0 and total_step >= args.warmup: + beta = min(args.max_beta, beta + args.step_beta) + diff --git a/dig/ggraph/JT-VAE/vocab.py b/dig/ggraph/JT-VAE/vocab.py new file mode 100644 index 00000000..3124e99c --- /dev/null +++ b/dig/ggraph/JT-VAE/vocab.py @@ -0,0 +1,30 @@ +import rdkit +import rdkit.Chem as Chem +import copy + +def get_slots(smiles): + mol = Chem.MolFromSmiles(smiles) + return [(atom.GetSymbol(), atom.GetFormalCharge(), atom.GetTotalNumHs()) for atom in mol.GetAtoms()] + +class Vocab(object): + benzynes = ['C1=CC=CC=C1', 'C1=CC=NC=C1', 'C1=CC=NN=C1', 'C1=CN=CC=N1', 'C1=CN=CN=C1', 'C1=CN=NC=N1', 'C1=CN=NN=C1', 'C1=NC=NC=N1', 'C1=NN=CN=N1'] + penzynes = ['C1=C[NH]C=C1', 'C1=C[NH]C=N1', 'C1=C[NH]N=C1', 'C1=C[NH]N=N1', 'C1=COC=C1', 'C1=COC=N1', 'C1=CON=C1', 'C1=CSC=C1', 'C1=CSC=N1', 'C1=CSN=C1', 'C1=CSN=N1', 'C1=NN=C[NH]1', 'C1=NN=CO1', 'C1=NN=CS1', 'C1=N[NH]C=N1', 'C1=N[NH]N=C1', 'C1=N[NH]N=N1', 'C1=NN=N[NH]1', 'C1=NN=NS1', 'C1=NOC=N1', 'C1=NON=C1', 'C1=NSC=N1', 'C1=NSN=C1'] + + def __init__(self, smiles_list): + self.vocab = smiles_list + self.vmap = {x:i for i,x in enumerate(self.vocab)} + self.slots = [get_slots(smiles) for smiles in self.vocab] + Vocab.benzynes = [s for s in smiles_list if s.count('=') >= 2 and Chem.MolFromSmiles(s).GetNumAtoms() == 6] + ['C1=CCNCC1'] + Vocab.penzynes = [s for s in smiles_list if s.count('=') >= 2 and Chem.MolFromSmiles(s).GetNumAtoms() == 5] + ['C1=NCCN1','C1=NNCC1'] + + def get_index(self, smiles): + return self.vmap[smiles] + + def get_smiles(self, idx): + return self.vocab[idx] + + def get_slots(self, idx): + return copy.deepcopy(self.slots[idx]) + + def size(self): + return len(self.vocab) diff --git a/dig/sslgraph/sslgraph/contrastive/views_fn/feature.py b/dig/sslgraph/sslgraph/contrastive/views_fn/feature.py index 6172f7ed..cb8d912f 100644 --- a/dig/sslgraph/sslgraph/contrastive/views_fn/feature.py +++ b/dig/sslgraph/sslgraph/contrastive/views_fn/feature.py @@ -18,6 +18,7 @@ def node_attr_mask(mode='whole', mask_ratio=0.1, mask_mean=0.5, mask_std=0.5): def do_trans(data): node_num, feat_dim = data.x.size() x = data.x.detach().clone() + mask = torch.zeros(node_num) if mode == 'whole': mask_num = int(node_num * mask_ratio) diff --git a/dig/xgraph/.gitignore b/dig/xgraph/.gitignore index 551658db..d27f51b8 100644 --- a/dig/xgraph/.gitignore +++ b/dig/xgraph/.gitignore @@ -1,3 +1,8 @@ /datasets/* !/datasets/Readme.md -!/datasets/load_datasets.py \ No newline at end of file +!/datasets/load_datasets.py +.gitignore +.idea/ +checkpoint* +dig/xgraph/SubgraphX/*.zip +*.zip \ No newline at end of file diff --git a/dig/xgraph/PGExplainer/Configures.py b/dig/xgraph/PGExplainer/Configures.py index 1b526830..2f89c8be 100644 --- a/dig/xgraph/PGExplainer/Configures.py +++ b/dig/xgraph/PGExplainer/Configures.py @@ -6,7 +6,7 @@ class DataParser(Tap): dataset_name: str = 'bbbp' - dataset_dir: str = './datasets' + dataset_dir: str = '../datasets' random_split: bool = True data_split_ratio: List = [0.8, 0.1, 0.1] # the ratio of training, validation and testing set for random split seed: int = 1 diff --git a/dig/xgraph/PGExplainer/install.sh b/dig/xgraph/PGExplainer/install.sh index 37e334a2..77d394b2 100644 --- a/dig/xgraph/PGExplainer/install.sh +++ b/dig/xgraph/PGExplainer/install.sh @@ -1 +1,13 @@ -textwrap \ No newline at end of file +#!/bin/bash +conda create -y -n xgraph python=3.8 +source activate xgraph +conda install -y pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch +pip install scipy +CUDA="cu102" +pip install --no-index torch-scatter -f https://pytorch-geometric.com/whl/torch-1.6.0+${CUDA}.html +pip install --no-index torch-sparse -f https://pytorch-geometric.com/whl/torch-1.6.0+${CUDA}.html +pip install --no-index torch-cluster -f https://pytorch-geometric.com/whl/torch-1.6.0+${CUDA}.html +pip install --no-index torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.6.0+${CUDA}.html +pip install torch-geometric +pip install cilog typed-argument-parser==1.5.4 tqdm +conda install -y -c conda-forge rdkit diff --git a/dig/xgraph/PGExplainer/metrics.py b/dig/xgraph/PGExplainer/metrics.py index c58dd0c2..a86b5a8f 100644 --- a/dig/xgraph/PGExplainer/metrics.py +++ b/dig/xgraph/PGExplainer/metrics.py @@ -1,9 +1,10 @@ import torch import numpy as np from torch_geometric.data import Data, Batch +from typing import Optional -def calculate_selected_nodes(data, edge_mask, top_k): +def calculate_selected_nodes(data, edge_mask, top_k, node_idx=None): threshold = float(edge_mask.reshape(-1).sort(descending=True).values[min(top_k, edge_mask.shape[0]-1)]) hard_mask = (edge_mask > threshold).cpu() edge_idx_list = torch.where(hard_mask == 1)[0] @@ -12,25 +13,32 @@ def calculate_selected_nodes(data, edge_mask, top_k): for edge_idx in edge_idx_list: selected_nodes += [edge_index[0][edge_idx], edge_index[1][edge_idx]] selected_nodes = list(set(selected_nodes)) + if node_idx is not None: + selected_nodes.append(node_idx) return selected_nodes def top_k_fidelity(data: Data, edge_mask: np.array, top_k: int, - gnnNets: torch.nn.Module, label: int, target_id: int = -1): + gnnNets: torch.nn.Module, label: int, + target_id: int = -1, node_idx: Optional[int]=None, + undirected=True): """ return the fidelity score of the subgraph with top_k score edges """ + if undirected: + top_k = 2 * top_k all_nodes = np.arange(data.x.shape[0]).tolist() - selected_nodes = calculate_selected_nodes(data, edge_mask, top_k) - score = gnn_score(all_nodes, data, gnnNets, label, target_id, + selected_nodes = calculate_selected_nodes(data, edge_mask, top_k, node_idx) + score = gnn_score(all_nodes, data, gnnNets, label, target_id, node_idx=node_idx, subgraph_building_method='zero_filling') - unimportant_nodes = [node for node in all_nodes if node not in selected_nodes] - score_mask_important = gnn_score(unimportant_nodes, data, gnnNets, label, target_id, + score_mask_important = gnn_score(unimportant_nodes, data, gnnNets, label, target_id, node_idx=node_idx, subgraph_building_method='zero_filling') return score - score_mask_important -def top_k_sparsity(data: Data, edge_mask: np.array, top_k: int): +def top_k_sparsity(data: Data, edge_mask: np.array, top_k: int, undirected=True): """ return the size ratio of the subgraph with top_k score edges""" + if undirected: + top_k = 2 * top_k selected_nodes = calculate_selected_nodes(data, edge_mask, top_k) return 1 - len(selected_nodes) / data.x.shape[0] @@ -59,7 +67,7 @@ def graph_build_split(X, edge_index, node_mask: np.array): def gnn_score(coalition: list, data: Data, gnnNets, label: int, - target_id: int = -1, subgraph_building_method='zero_filling') -> torch.Tensor: + target_id: int = -1, node_idx=None, subgraph_building_method='zero_filling') -> torch.Tensor: """ the prob of subgraph with selected nodes for required label and target node """ num_nodes = data.num_nodes subgraph_build_func = get_graph_build_func(subgraph_building_method) @@ -71,9 +79,9 @@ def gnn_score(coalition: list, data: Data, gnnNets, label: int, logits, probs, _ = gnnNets(mask_data) # get the score of predicted class for graph or specific node idx + node_idx = 0 if node_idx is None else node_idx if target_id == -1: - score = probs[0, label].item() + score = probs[node_idx, label].item() else: - score = probs[0, target_id, label].item() + score = probs[node_idx, target_id, label].item() return score - diff --git a/dig/xgraph/PGExplainer/models/GAT.py b/dig/xgraph/PGExplainer/models/GAT.py index c8546fbb..e412ce1c 100644 --- a/dig/xgraph/PGExplainer/models/GAT.py +++ b/dig/xgraph/PGExplainer/models/GAT.py @@ -1,8 +1,11 @@ import torch import torch.nn as nn +from torch import Tensor import torch.nn.functional as F +from torch_sparse import SparseTensor from torch_geometric.nn.conv import GATConv from torch_geometric.nn.glob import global_mean_pool, global_add_pool, global_max_pool +from torch_geometric.typing import Adj, Size def get_readout_layers(readout): @@ -19,6 +22,54 @@ def get_readout_layers(readout): return ret_readout +class GATConv(GATConv): + def __init__(self, *args, **kwargs): + super(GATConv, self).__init__(*args, **kwargs) + + def propagate(self, edge_index: Adj, size: Size = None, **kwargs): + size = self.__check_input__(edge_index, size) + + # Run "fused" message and aggregation (if applicable). + if (isinstance(edge_index, SparseTensor) and self.fuse + and not self.__explain__): + coll_dict = self.__collect__(self.__fused_user_args__, edge_index, + size, kwargs) + + msg_aggr_kwargs = self.inspector.distribute( + 'message_and_aggregate', coll_dict) + out = self.message_and_aggregate(edge_index, **msg_aggr_kwargs) + + update_kwargs = self.inspector.distribute('update', coll_dict) + return self.update(out, **update_kwargs) + + # Otherwise, run both functions in separation. + elif isinstance(edge_index, Tensor) or not self.fuse: + coll_dict = self.__collect__(self.__user_args__, edge_index, size, + kwargs) + + msg_kwargs = self.inspector.distribute('message', coll_dict) + out = self.message(**msg_kwargs) + + # For `GNNExplainer`, we require a separate message and aggregate + # procedure since this allows us to inject the `edge_mask` into the + # message passing computation scheme. + if self.__explain__: + edge_mask = self.__edge_mask__ + # Some ops add self-loops to `edge_index`. We need to do the + # same for `edge_mask` (but do not train those). + if out.size(self.node_dim) != edge_mask.size(0): + loop = edge_mask.new_ones(size[0]) + edge_mask = torch.cat([edge_mask, loop], dim=0) + assert out.size(self.node_dim) == edge_mask.size(0) + out = out * edge_mask.view([-1] + [1] * (out.dim() - 1)) + + aggr_kwargs = self.inspector.distribute('aggregate', coll_dict) + out = self.aggregate(out, **aggr_kwargs) + + update_kwargs = self.inspector.distribute('update', coll_dict) + return self.update(out, **update_kwargs) + + # GAT class GATNet(nn.Module): def __init__(self, input_dim, output_dim, model_args): diff --git a/dig/xgraph/PGExplainer/models/GCN.py b/dig/xgraph/PGExplainer/models/GCN.py index 453d32d8..b02abc4a 100644 --- a/dig/xgraph/PGExplainer/models/GCN.py +++ b/dig/xgraph/PGExplainer/models/GCN.py @@ -1,8 +1,11 @@ import torch import torch.nn as nn +from torch import Tensor import torch.nn.functional as F +from torch_sparse import SparseTensor from torch_geometric.nn.conv import GCNConv from torch_geometric.nn.glob import global_mean_pool, global_add_pool, global_max_pool +from torch_geometric.typing import Adj, Size def get_readout_layers(readout): @@ -19,6 +22,54 @@ def get_readout_layers(readout): return ret_readout +class GCNConv(GCNConv): + def __init__(self, *args, **kwargs): + super(GCNConv, self).__init__(*args, **kwargs) + + def propagate(self, edge_index: Adj, size: Size = None, **kwargs): + size = self.__check_input__(edge_index, size) + + # Run "fused" message and aggregation (if applicable). + if (isinstance(edge_index, SparseTensor) and self.fuse + and not self.__explain__): + coll_dict = self.__collect__(self.__fused_user_args__, edge_index, + size, kwargs) + + msg_aggr_kwargs = self.inspector.distribute( + 'message_and_aggregate', coll_dict) + out = self.message_and_aggregate(edge_index, **msg_aggr_kwargs) + + update_kwargs = self.inspector.distribute('update', coll_dict) + return self.update(out, **update_kwargs) + + # Otherwise, run both functions in separation. + elif isinstance(edge_index, Tensor) or not self.fuse: + coll_dict = self.__collect__(self.__user_args__, edge_index, size, + kwargs) + + msg_kwargs = self.inspector.distribute('message', coll_dict) + out = self.message(**msg_kwargs) + + # For `GNNExplainer`, we require a separate message and aggregate + # procedure since this allows us to inject the `edge_mask` into the + # message passing computation scheme. + if self.__explain__: + edge_mask = self.__edge_mask__ + # Some ops add self-loops to `edge_index`. We need to do the + # same for `edge_mask` (but do not train those). + if out.size(self.node_dim) != edge_mask.size(0): + loop = edge_mask.new_ones(size[0]) + edge_mask = torch.cat([edge_mask, loop], dim=0) + assert out.size(self.node_dim) == edge_mask.size(0) + out = out * edge_mask.view([-1] + [1] * (out.dim() - 1)) + + aggr_kwargs = self.inspector.distribute('aggregate', coll_dict) + out = self.aggregate(out, **aggr_kwargs) + + update_kwargs = self.inspector.distribute('update', coll_dict) + return self.update(out, **update_kwargs) + + # GCN class GCNNet(nn.Module): def __init__(self, input_dim, output_dim, model_args): diff --git a/dig/xgraph/PGExplainer/models/GIN.py b/dig/xgraph/PGExplainer/models/GIN.py index 0d9638a2..e16f60e0 100644 --- a/dig/xgraph/PGExplainer/models/GIN.py +++ b/dig/xgraph/PGExplainer/models/GIN.py @@ -1,8 +1,11 @@ import torch import torch.nn as nn +from torch import Tensor import torch.nn.functional as F +from torch_sparse import SparseTensor from torch_geometric.nn.conv import GINConv from torch_geometric.nn.glob import global_mean_pool, global_add_pool, global_max_pool +from torch_geometric.typing import Adj, Size def get_readout_layers(readout): @@ -19,6 +22,54 @@ def get_readout_layers(readout): return ret_readout +class GINConv(GINConv): + def __init__(self, *args, **kwargs): + super(GINConv, self).__init__(*args, **kwargs) + + def propagate(self, edge_index: Adj, size: Size = None, **kwargs): + size = self.__check_input__(edge_index, size) + + # Run "fused" message and aggregation (if applicable). + if (isinstance(edge_index, SparseTensor) and self.fuse + and not self.__explain__): + coll_dict = self.__collect__(self.__fused_user_args__, edge_index, + size, kwargs) + + msg_aggr_kwargs = self.inspector.distribute( + 'message_and_aggregate', coll_dict) + out = self.message_and_aggregate(edge_index, **msg_aggr_kwargs) + + update_kwargs = self.inspector.distribute('update', coll_dict) + return self.update(out, **update_kwargs) + + # Otherwise, run both functions in separation. + elif isinstance(edge_index, Tensor) or not self.fuse: + coll_dict = self.__collect__(self.__user_args__, edge_index, size, + kwargs) + + msg_kwargs = self.inspector.distribute('message', coll_dict) + out = self.message(**msg_kwargs) + + # For `GNNExplainer`, we require a separate message and aggregate + # procedure since this allows us to inject the `edge_mask` into the + # message passing computation scheme. + if self.__explain__: + edge_mask = self.__edge_mask__ + # Some ops add self-loops to `edge_index`. We need to do the + # same for `edge_mask` (but do not train those). + if out.size(self.node_dim) != edge_mask.size(0): + loop = edge_mask.new_ones(size[0]) + edge_mask = torch.cat([edge_mask, loop], dim=0) + assert out.size(self.node_dim) == edge_mask.size(0) + out = out * edge_mask.view([-1] + [1] * (out.dim() - 1)) + + aggr_kwargs = self.inspector.distribute('aggregate', coll_dict) + out = self.aggregate(out, **aggr_kwargs) + + update_kwargs = self.inspector.distribute('update', coll_dict) + return self.update(out, **update_kwargs) + + # GIN class GINNet(nn.Module): def __init__(self, input_dim, output_dim, model_args): diff --git a/dig/xgraph/PGExplainer/pgexplainer.py b/dig/xgraph/PGExplainer/pgexplainer.py index da139ac9..af992b2d 100644 --- a/dig/xgraph/PGExplainer/pgexplainer.py +++ b/dig/xgraph/PGExplainer/pgexplainer.py @@ -1,8 +1,3 @@ -""" -Description: The implement of PGExplainer model - -""" - from typing import Optional from math import sqrt @@ -17,21 +12,12 @@ import networkx as nx from torch_geometric.nn import MessagePassing from torch_geometric.utils import to_networkx -from utils import k_hop_subgraph_with_default_whole_graph -from Configures import model_args, data_args, explainer_args +from PGExplainer.utils import k_hop_subgraph_with_default_whole_graph +from PGExplainer.Configures import model_args, data_args, explainer_args EPS = 1e-6 -def inv_sigmoid(t: torch.Tensor): - """ except the case t is 0 or 1 """ - if t.shape[0] != 0: - if t.min().item() == 0 or t.max().item() == 1: - t = 0.99 * t + 0.005 - ret = - torch.log(1 / t - 1) - return ret - - class PGExplainer(nn.Module): def __init__(self, model, epochs: int = 20, lr: float = 0.003, top_k: int = 6, num_hops: Optional[int] = None): @@ -164,8 +150,6 @@ def forward(self, inputs, training=None): sym_mask = (self.mask_sigmoid + self.mask_sigmoid.transpose(0, 1)) / 2 edge_mask = sym_mask[edge_index[0], edge_index[1]] - # inverse the weights before sigmoid in MessagePassing Module - edge_mask = inv_sigmoid(edge_mask) self.__clear_masks__() self.__set_masks__(x, edge_index, edge_mask) @@ -214,8 +198,6 @@ def train_GC_explanation_network(self, dataset): duration = 0.0 for epoch in range(self.epochs): loss = 0.0 - pred_list = [] - acc_list = [] tmp = float(self.t0 * np.power(self.t1 / self.t0, epoch / self.epochs)) self.elayers.train() optimizer.zero_grad() @@ -226,15 +208,10 @@ def train_GC_explanation_network(self, dataset): loss_tmp = self.__loss__(prob, ori_pred_dict[gid]) loss_tmp.backward() loss += loss_tmp.item() - pred_label = prob.argmax(-1).item() - pred_list.append(pred_label) - acc_list.append(pred_label == data.y) optimizer.step() duration += time.perf_counter() - tic - accs = torch.stack(acc_list, dim=0) - acc = np.array(accs).mean() - print(f'Epoch: {epoch} | Loss: {loss} | Acc : {acc}') + print(f'Epoch: {epoch} | Loss: {loss}') torch.save(self.elayers.cpu().state_dict(), self.ckpt_path) self.elayers.to(self.device) print(f"training time is {duration:.5}s") @@ -310,11 +287,8 @@ def train_NC_explanation_network(self, dataset): emb_dict[gid] = emb.data.cpu() # train the explanation network - torch.autograd.set_detect_anomaly(True) - for epoch in range(self.epochs): loss = 0.0 - acc_list = [] optimizer.zero_grad() tmp = float(self.t0 * np.power(self.t1 / self.t0, epoch / self.epochs)) self.elayers.train() @@ -324,12 +298,8 @@ def train_NC_explanation_network(self, dataset): loss_tmp.backward() loss += loss_tmp.item() - acc_list.append(pred[node_idx_dict[gid]].argmax().item() == data.y[gid]) - optimizer.step() - accs = torch.stack(acc_list, dim=0) - acc = np.array(accs).mean() - print(f'Epoch: {epoch} | Loss: {loss} | Acc : {acc}') + print(f'Epoch: {epoch} | Loss: {loss}') torch.save(self.elayers.cpu().state_dict(), self.ckpt_path) self.elayers.to(self.device) @@ -342,13 +312,5 @@ def get_node_prediction(self, node_idx: int, x: torch.Tensor, edge_index: torch. outputs = self.get_model_output(x, edge_index, edge_mask=None, **kwargs) return outputs[1][node_idx].argmax(dim=-1) - def explain_node(self, node_idx, x, edge_index, **kwargs): - data = Batch.from_data_list([Data(x=x, edge_index=edge_index)]) - data = data.to(self.device) - with torch.no_grad(): - _, prob, emb = self.get_model_output(data.x, data.edge_index) - _, edge_mask = self.forward((data.x, emb, data.edge_index, 1.0), training=False) - return edge_mask - def __repr__(self): return f'{self.__class__.__name__}()' diff --git a/dig/xgraph/PGExplainer/pipeline.py b/dig/xgraph/PGExplainer/pipeline.py index 0e0ca37b..c6b42d31 100644 --- a/dig/xgraph/PGExplainer/pipeline.py +++ b/dig/xgraph/PGExplainer/pipeline.py @@ -19,7 +19,11 @@ def pipeline_GC(top_k): data_indices = list(range(len(dataset))) pgexplainer_trainset = dataset else: - loader = get_dataloader(dataset, data_args, train_args) + loader = get_dataloader(dataset, + batch_size=train_args.batch_size, + random_split_flag=data_args.random_split, + data_split_ratio=data_args.data_split_ratio, + seed=data_args.seed) data_indices = loader['test'].dataset.indices pgexplainer_trainset = loader['train'].dataset @@ -122,7 +126,7 @@ def pipeline_NC(top_k): f"{model_args.model_name}_" f"pgexplainer") if not os.path.isdir(save_dir): - os.mkdir(save_dir) + os.makedirs(save_dir) pgexplainer = PGExplainer(gnnNets) @@ -168,8 +172,9 @@ def pipeline_NC(top_k): graph = to_networkx(sub_data) - fidelity_score = top_k_fidelity(sub_data, edge_mask, top_k, gnnNets, pred_label) - sparsity_score = top_k_sparsity(sub_data, edge_mask, top_k) + fidelity_score = top_k_fidelity(sub_data, edge_mask, top_k, gnnNets, pred_label, + node_idx=node_idx, undirected=True) + sparsity_score = top_k_sparsity(sub_data, edge_mask, top_k, undirected=True) fidelity_score_list.append(fidelity_score) sparsity_score_list.append(sparsity_score) @@ -195,7 +200,7 @@ def pipeline(top_k): if __name__ == '__main__': - top_k = 5 + top_k = 6 fidelity_scores, sparsity_scores = pipeline(top_k) print(f"fidelity score: {fidelity_scores.mean().item():.4f}, " f"sparsity score: {sparsity_scores.mean().item():.4f}") diff --git a/dig/xgraph/PGExplainer/scripts.sh b/dig/xgraph/PGExplainer/scripts.sh index d69c811a..b5779cdb 100644 --- a/dig/xgraph/PGExplainer/scripts.sh +++ b/dig/xgraph/PGExplainer/scripts.sh @@ -1,15 +1,12 @@ #!/bin/sh -#python pipeline.py --dataset_name bbbp --random_split True --reward_method mc_l_shapley +#python pipeline.py --dataset_name bbbp --random_split True #python pipeline.py --dataset_name mutag --model_name gin \ -# --c_puct 10.0 --reward_method mc_l_shapley #python pipeline.py --dataset_name BA_2Motifs --random_split True \ -# --latent_dim 20 20 20 --adj_normlize False --emb_normlize True \ -# --readout mean --c_puct 10.0 --min_atom 5 --reward_method mc_l_shapley +# --latent_dim 20 20 20 --adj_normlize False --emb_normlize True\ # #python pipeline.py --dataset_name Graph_SST2 --model_name gat \ -# --random_split False --reward_method mc_l_shapley +# --random_split False python pipeline.py --dataset_name BA_shapes --random_split True \ - --latent_dim 20 20 20 --concate True --adj_normlize False --emb_normlize True \ - --high2low True --min_atom 5 --reward_method nc_mc_l_shapley \ No newline at end of file + --latent_dim 20 20 20 --concate True --adj_normlize False --emb_normlize True \ No newline at end of file diff --git a/dig/xgraph/PGExplainer/utils.py b/dig/xgraph/PGExplainer/utils.py index 1cba6ebb..6405e445 100644 --- a/dig/xgraph/PGExplainer/utils.py +++ b/dig/xgraph/PGExplainer/utils.py @@ -248,8 +248,8 @@ def get_topk_edges_subgraph(self, edge_index, edge_mask, top_k, un_directed=Fals return nodelist, edgelist def plot_soft_edge_mask(self, graph, edge_mask, top_k, un_directed, figname, **kwargs): - edge_index = torch.tensor(list(graph.edges())).T - edge_mask = torch.tensor(edge_mask) + edge_index = torch.FloatTensor(list(graph.edges())).T + edge_mask = torch.FloatTensor(edge_mask) if self.dataset_name.lower() == 'BA_2motifs'.lower(): nodelist, edgelist = self.get_topk_edges_subgraph(edge_index, edge_mask, top_k, un_directed) self.plot_ba2motifs(graph, nodelist, edgelist, figname=figname) diff --git a/dig/xgraph/Readme.md b/dig/xgraph/Readme.md index 37ad44f1..9d5f0d60 100644 --- a/dig/xgraph/Readme.md +++ b/dig/xgraph/Readme.md @@ -23,6 +23,7 @@ The `xgraph` package implements seven existing algorithms for GNN explanation ta | DeepLIFT | [Paper](https://arxiv.org/abs/1704.02685)
[Code](https://github.com/divelab/DIG/tree/main/dig/xgraph/DeepLIFT) | DeepLIFT is a popular explanation models for image classifer. We extend it to apply for deep graph models. It decomposes the predictions to different nodes abd can be considered as an efficent approximations for Shapley values. | | Grad-CAM | [Paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Pope_Explainability_Methods_for_Graph_Convolutional_Neural_Networks_CVPR_2019_paper.pdf)
[Code](https://github.com/divelab/DIG/tree/main/dig/xgraph/GradCAM) | Grad-CAM is a popular explanation models for image classifer. It is extended to graph models to measure the importance of different nodes. The key idea is to combine the hidden feature maps and the gradients to indicate node importance. | | XGNN | [Paper](https://arxiv.org/abs/2006.02587)
[Code](https://github.com/divelab/DIG/tree/main/dig/xgraph/XGNN) | XGNN is a model-level explanation method for Graph Neural Networks. Instead of explaining specific predictions, it studies the general behavior of GNNs, such as what input graph patterns will maximize the prediction of a certai class. It employs graph generation algorithm to generate graphs to maximize a target prediction score. | +| TAGE | [Paper](https://arxiv.org/abs/2202.08335)
[Code](https://github.com/divelab/DIG/tree/main/dig/xgraph/TAGE) | Task-Agnostic GNN Explainer (TAGE) is proposed to fulfill the desire of explaining two-stage trained models and multitask models. The explainer is independent of downstream models and trained under self-supervision with no knowledge of downstream tasks. TAGE enables the explanation of GNN embedding models with unseen downstream tasks and allows efficient explanation of multitask models. | ## Package Usage diff --git a/dig/xgraph/SubgraphX/forgraph/mcts.py b/dig/xgraph/SubgraphX/forgraph/mcts.py index 5bde88d4..3f126b54 100644 --- a/dig/xgraph/SubgraphX/forgraph/mcts.py +++ b/dig/xgraph/SubgraphX/forgraph/mcts.py @@ -163,39 +163,3 @@ def reward_func(reward_args, value_func): else: raise NotImplementedError - -# def reward_func(reward_args, value_func, node_idx=-1): -# if reward_args.reward_method.lower() == 'gnn_score': -# return partial(gnn_score, -# value_func=value_func, -# subgraph_build_method=reward_args.subgraph_build_method) -# -# elif reward_args.reward_method.lower() == 'mc_shapley': -# return partial(mc_shapley, -# value_func=value_func, -# subgraph_build_method=reward_args.subgraph_build_method, -# sample_num=reward_args.sample_num) -# -# elif reward_args.reward_method.lower() == 'l_shapley': -# return partial(l_shapley, -# local_raduis=reward_args.local_raduis, -# value_func=value_func, -# subgraph_build_method=reward_args.subgraph_build_method) -# -# elif reward_args.reward_method.lower() == 'mc_l_shapley': -# return partial(mc_l_shapley, -# local_raduis=reward_args.local_raduis, -# value_func=value_func, -# subgraph_build_method=reward_args.subgraph_build_method, -# sample_num=reward_args.sample_num) -# -# elif reward_args.reward_method.lower() == 'nc_mc_l_shapley': -# return partial(NC_mc_l_shapley, -# node_idx=node_idx, -# local_raduis=reward_args.local_raduis, -# value_func=value_func, -# subgraph_build_method=reward_args.subgraph_build_method, -# sample_num=reward_args.sample_num) -# -# else: -# raise NotImplementedError diff --git a/dig/xgraph/SubgraphX/forgraph/subgraphx.py b/dig/xgraph/SubgraphX/forgraph/subgraphx.py index 711b2a27..71cd2fd3 100644 --- a/dig/xgraph/SubgraphX/forgraph/subgraphx.py +++ b/dig/xgraph/SubgraphX/forgraph/subgraphx.py @@ -11,7 +11,7 @@ def pipeline(max_nodes): - dataset = get_dataset(data_args) + dataset = get_dataset(data_args.dataset_dir, data_args.dataset_name) plotutils = PlotUtils(dataset_name=data_args.dataset_name) input_dim = dataset.num_node_features output_dim = dataset.num_classes @@ -19,7 +19,11 @@ def pipeline(max_nodes): if data_args.dataset_name == 'mutag': data_indices = list(range(len(dataset))) else: - loader = get_dataloader(dataset, data_args, train_args) + loader = get_dataloader(dataset, + batch_size=train_args.batch_size, + random_split_flag=data_args.random_split, + data_split_ratio=data_args.data_split_ratio, + seed=data_args.seed) data_indices = loader['test'].dataset.indices gnnNets = GnnNets(input_dim, output_dim, model_args) @@ -42,6 +46,7 @@ def pipeline(max_nodes): data = dataset[i] _, probs, _ = gnnNets(Batch.from_data_list([data.clone()])) prediction = probs.squeeze().argmax(-1).item() + original_score = probs.squeeze()[prediction] # get the reward func value_func = GnnNets_GC2value_func(gnnNets, target_class=prediction) @@ -66,7 +71,10 @@ def pipeline(max_nodes): # l sharply score graph_node_x = find_closest_node_result(results, max_nodes=max_nodes) - fidelity_score = gnn_score(graph_node_x.coalition, data, value_func, subgraph_building_method='zero_filling') + masked_node_list = [node for node in list(range(graph_node_x.data.x.shape[0])) + if node not in graph_node_x.coalition] + fidelity_score = original_score - gnn_score(masked_node_list, data, value_func, + subgraph_building_method='zero_filling') sparsity_score = 1 - len(graph_node_x.coalition) / graph_node_x.ori_graph.number_of_nodes() fidelity_score_list.append(fidelity_score) sparsity_score_list.append(sparsity_score) @@ -87,5 +95,5 @@ def pipeline(max_nodes): if __name__ == '__main__': fidelity_scores, sparsity_scores = pipeline(15) - print(f"{fidelity_scores.mean().item()}_{sparsity_scores.mean().item()}") + print(f"Fidelity: {fidelity_scores.mean().item()}, Sparsity: {sparsity_scores.mean().item()}") diff --git a/dig/xgraph/SubgraphX/fornode/subgraphx.py b/dig/xgraph/SubgraphX/fornode/subgraphx.py index 58446fb7..2c3a3889 100644 --- a/dig/xgraph/SubgraphX/fornode/subgraphx.py +++ b/dig/xgraph/SubgraphX/fornode/subgraphx.py @@ -12,7 +12,7 @@ def pipeline(subgraph_max_nodes): - dataset = get_dataset(data_args) + dataset = get_dataset(data_args.dataset_dir, data_args.dataset_name) input_dim = dataset.num_node_features output_dim = dataset.num_classes data = dataset[0] diff --git a/dig/xgraph/SubgraphX/models/train_gnns.py b/dig/xgraph/SubgraphX/models/train_gnns.py index 82015677..7c6c615a 100644 --- a/dig/xgraph/SubgraphX/models/train_gnns.py +++ b/dig/xgraph/SubgraphX/models/train_gnns.py @@ -12,7 +12,7 @@ def train_MUTAG(): # attention the multi-task here print('start loading data====================') - dataset = get_dataset(data_args) + dataset = get_dataset(data_args.dataset_dir， data_args.dataset_name) input_dim = dataset.num_node_features output_dim = int(dataset.num_classes) dataloader = get_dataloader(dataset, data_args, train_args) @@ -87,7 +87,7 @@ def train_MUTAG(): def train_GC(): # attention the multi-task here print('start loading data====================') - dataset = get_dataset(data_args) + dataset = get_dataset(data_args.dataset_dir， data_args.dataset_name) input_dim = dataset.num_node_features output_dim = int(dataset.num_classes) dataloader = get_dataloader(dataset, data_args, train_args) @@ -242,8 +242,7 @@ def predict_GC(test_dataloader, gnnNets): # train for node classification task def train_NC(): print('start loading data====================') - import pdb; pdb.set_trace() - dataset = get_dataset(data_args) + dataset = get_dataset(data_args.dataset_dir， data_args.dataset_name) input_dim = dataset.num_node_features output_dim = int(dataset.num_classes) diff --git a/dig/xgraph/SubgraphX/scripts.sh b/dig/xgraph/SubgraphX/scripts.sh index c30e6931..0770645d 100644 --- a/dig/xgraph/SubgraphX/scripts.sh +++ b/dig/xgraph/SubgraphX/scripts.sh @@ -3,13 +3,13 @@ #python -m forgraph.subgraphx --dataset_name mutag --model_name gin \ # --c_puct 10.0 --reward_method mc_l_shapley # -python -m forgraph.subgraphx --dataset_name BA_2Motifs --random_split True \ - --latent_dim 20 20 20 --adj_normlize False --emb_normlize True \ - --readout mean --c_puct 10.0 --min_atom 5 --reward_method mc_l_shapley +#python -m forgraph.subgraphx --dataset_name BA_2Motifs --random_split True \ +# --latent_dim 20 20 20 --adj_normlize False --emb_normlize True \ +# --readout mean --c_puct 10.0 --min_atom 5 --reward_method mc_l_shapley #python -m forgraph.subgraphx --dataset_name grt_sst2_BERT_Identity --model_name gat \ # --random_split False --reward_method mc_l_shapley -#python -m fornode.subgraphx --dataset_name BA_shapes --random_split True \ -# --latent_dim 20 20 20 --concate True --adj_normlize False --emb_normlize True \ -# --high2low True --min_atom 5 --reward_method nc_mc_l_shapley +python -m fornode.subgraphx --dataset_name BA_shapes --random_split True \ + --latent_dim 20 20 20 --concate True --adj_normlize False --emb_normlize True \ + --high2low True --min_atom 5 --reward_method nc_mc_l_shapley diff --git a/dig/xgraph/TAGE/README.md b/dig/xgraph/TAGE/README.md new file mode 100644 index 00000000..6941c5fd --- /dev/null +++ b/dig/xgraph/TAGE/README.md @@ -0,0 +1,41 @@ +# Task-Agnostic Graph Explanations + +This is the official implementation of the paper [*"Task-Agnostic Graph Explanations"*](https://arxiv.org/abs/2202.08335) appears in NeurIPS 2022. + +

+
+ task-agnostic +
+

+ +## Abstract + +Graph Neural Networks (GNNs) have emerged as powerful tools to encode graph-structured data. Due to their broad applications, there is an increasing need to develop tools to explain how GNNs make decisions given graph-structured data. Existing learning-based GNN explanation approaches are task-specific in training and hence suffer from crucial drawbacks. Specifically, they are incapable of producing explanations for a multitask prediction model with a single explainer. They are also unable to provide explanations in cases where the GNN is trained in a self-supervised manner, and the resulting representations are used in future downstream tasks. To address these limitations, we propose a Task-Agnostic GNN Explainer (TAGE) that is independent of downstream models and trained under self-supervision with no knowledge of downstream tasks. TAGE enables the explanation of GNN embedding models with unseen downstream tasks and allows efficient explanation of multitask models. Our extensive experiments show that TAGE can significantly speed up the explanation efficiency by using the same model to explain predictions for multiple downstream tasks while achieving explanation quality as good as or even better than current state-of-the-art GNN explanation approaches. + +## Environment Requirements +- jupyter +- pytorch +- pytorch-geometric +- rdkit +- dig + +You can follow the instructions of [dig](https://github.com/divelab/DIG) to install compatible version of pytorch and pytorch-geometric. + + +## Usage + +The public datasets used in this work are MoleculeNet and PPI. MoleculeNet can be downloaded from [here](https://github.com/snap-stanford/pretrain-gnns#dataset-download). You can move the `dataset` folder to the root directory of the repo. + +We have provided trained GNN models to be explained. The trained explainers are also provided. You can follow the examples in `gexplain_2stage_quant.ipynb` and `nexplain_2stage_quant.ipynb` to reproduce the results. The visualizations appear in the paper can be reproduced by running `gexplain_2stage_visual_[bace/hiv/sider].ipynb`. Results on the synthetic dataset, BAShapes, can be reproduced by running `syn_dataset.ipynb`. + +## Bibtex + +If you use this code, please cite the paper. +``` +@inproceedings{xie2022task, + title={Task-Agnostic Graph Explanations}, + author={Xie, Yaochen and Katariya, Sumeet and Tang, Xianfeng and Huang, Edward and Rao, Nikhil and Subbian, Karthik and Ji, Shuiwang}, + booktitle={The 36th Annual Conference on Neural Information Processing Systems}, + year={2022} +} +``` diff --git a/dig/xgraph/TAGE/bashaps.py b/dig/xgraph/TAGE/bashaps.py new file mode 100644 index 00000000..fdc6a223 --- /dev/null +++ b/dig/xgraph/TAGE/bashaps.py @@ -0,0 +1,81 @@ +from typing import Optional, Callable + +import torch +from torch_geometric.data import InMemoryDataset, Data +from torch_geometric.utils import barabasi_albert_graph + + +def house(): + edge_index = torch.tensor([[0, 0, 0, 1, 1, 1, 2, 2, 3, 3, 4, 4], + [1, 3, 4, 4, 2, 0, 1, 3, 2, 0, 0, 1]]) + label = torch.tensor([1, 1, 2, 2, 3]) + return edge_index, label + +class BAShapes(InMemoryDataset): + r"""The BA-Shapes dataset from the `"GNNExplainer: Generating Explanations + for Graph Neural Networks" `_ paper, + containing a Barabasi-Albert (BA) graph with 300 nodes and a set of 80 + "house"-structured graphs connected to it. + + Args: + connection_distribution (string, optional): Specifies how the houses + and the BA graph get connected. Valid inputs are :obj:`"random"` + (random BA graph nodes are selected for connection to the houses), + and :obj:`"uniform"` (uniformly distributed BA graph nodes are + selected for connection to the houses). (default: :obj:`"random"`) + transform (callable, optional): A function/transform that takes in an + :obj:`torch_geometric.data.Data` object and returns a transformed + version. The data object will be transformed before every access. + (default: :obj:`None`) + """ + def __init__(self, connection_distribution: str = "random", + transform: Optional[Callable] = None): + super().__init__('.', transform) + assert connection_distribution in ['random', 'uniform'] + + # Build the Barabasi-Albert graph: + num_nodes = 300 + edge_index = barabasi_albert_graph(num_nodes, num_edges=5) + edge_label = torch.zeros(edge_index.size(1), dtype=torch.int64) + node_label = torch.zeros(num_nodes, dtype=torch.int64) + + # Select nodes to connect shapes: + num_houses = 80 + if connection_distribution == 'random': + connecting_nodes = torch.randperm(num_nodes)[:num_houses] + else: + step = num_nodes // num_houses + connecting_nodes = torch.arange(0, num_nodes, step) + + # Connect houses to Barabasi-Albert graph: + edge_indices = [edge_index] + edge_labels = [edge_label] + node_labels = [node_label] + for i in range(num_houses): + house_edge_index, house_label = house() + + edge_indices.append(house_edge_index + num_nodes) + edge_indices.append( + torch.tensor([[int(connecting_nodes[i]), num_nodes], + [num_nodes, int(connecting_nodes[i])]])) + + edge_labels.append( + torch.ones(house_edge_index.size(1), dtype=torch.long)) + edge_labels.append(torch.zeros(2, dtype=torch.long)) + + node_labels.append(house_label) + + num_nodes += 5 + + edge_index = torch.cat(edge_indices, dim=1) + edge_label = torch.cat(edge_labels, dim=0) + node_label = torch.cat(node_labels, dim=0) + + x = torch.ones((num_nodes, 10), dtype=torch.float) + expl_mask = torch.zeros(num_nodes, dtype=torch.bool) + expl_mask[torch.arange(400, num_nodes, 5)] = True + + data = Data(x=x, edge_index=edge_index, y=node_label, + expl_mask=expl_mask, edge_label=edge_label) + + self.data, self.slices = self.collate([data]) \ No newline at end of file diff --git a/dig/xgraph/TAGE/ckpts_explainer/Readme.md b/dig/xgraph/TAGE/ckpts_explainer/Readme.md new file mode 100644 index 00000000..2fce9fab --- /dev/null +++ b/dig/xgraph/TAGE/ckpts_explainer/Readme.md @@ -0,0 +1 @@ +The saved explainer models can be downloaded at: https://drive.google.com/drive/folders/1z2z1nMVCqehpQsANT5cwQNQ5wsx1LGfr?usp=sharing \ No newline at end of file diff --git a/dig/xgraph/TAGE/ckpts_model/Readme.md b/dig/xgraph/TAGE/ckpts_model/Readme.md new file mode 100644 index 00000000..4f868310 --- /dev/null +++ b/dig/xgraph/TAGE/ckpts_model/Readme.md @@ -0,0 +1 @@ +The saved GNN models to be explained can be downloaded at: https://drive.google.com/drive/folders/1z2z1nMVCqehpQsANT5cwQNQ5wsx1LGfr?usp=sharing \ No newline at end of file diff --git a/dig/xgraph/TAGE/downstream.py b/dig/xgraph/TAGE/downstream.py new file mode 100644 index 00000000..9dc5f2db --- /dev/null +++ b/dig/xgraph/TAGE/downstream.py @@ -0,0 +1,147 @@ +import torch +import torch.nn.functional as F +import numpy as np +from torch import optim, nn +from tqdm import tqdm +from sklearn.metrics import roc_auc_score + +from torch_geometric.data import Data + +criterion = nn.BCEWithLogitsLoss(reduction = "none") +lr = 0.001 +weight_decay = 0 +epochs = 100 + + +class MLP(torch.nn.Module): + + def __init__(self, num_layer, emb_dim, hidden_dim, out_dim=1): + super(MLP, self).__init__() + self.num_layer = num_layer + self.layers = nn.ModuleList() + if num_layer > 1: + self.layers.append(nn.Linear(emb_dim, hidden_dim)) + for n in range(num_layer-1): + self.layers.append(nn.Linear(hidden_dim, hidden_dim)) + self.layers.append(nn.Linear(hidden_dim, out_dim)) + else: + self.layers.append(nn.Linear(emb_dim, out_dim)) + + def forward(self, emb): + out = self.layers[0](emb) + for layer in self.layers[1:]: + out = layer(F.relu(out)) + return out + + +class EndtoEnd(torch.nn.Module): + ''' + Class to wrap-up embedding model and downstream models into an end-to-end model. + Args: + embed_model, mlp_model: obj:`torch.nn.Module` objects. + wrapped_input: Boolean. Whether (GNN) embedding model taks input wrapped in obj:`Data` object + or node attributes and edge indices separately. + ''' + + def __init__(self, embed_model, mlp_model, wrapped_input=False): + super(EndtoEnd, self).__init__() + self.embed_model = embed_model + self.mlp_model = mlp_model + self.wrapped_input = wrapped_input + + def forward(self, x, edge_index, edge_attr=None, batch=None): + ''' + Forward propagation outputs the final prediction. + ''' + if self.wrapped_input: + if batch is None: + batch = torch.zeros_like(x[:,0], dtype=torch.long) + data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, batch=batch) + return self.forward_w(data) + else: + return self.forward_nw(x, edge_index, edge_attr, batch) + + def forward_w(self, data): + self.embed_model.eval() + with torch.no_grad(): + emb = self.embed_model(data) + out = self.mlp_model(emb) + return out + + def forward_nw(self, x, edge_index, edge_attr, batch): + self.embed_model.eval() + with torch.no_grad(): + emb = self.embed_model(x, edge_index, edge_attr, batch) + out = self.mlp_model(emb) + return out + + def get_emb(self, x, edge_index, edge_attr=None, batch=None): + ''' + Forward propagation outputs only node embeddings. + ''' + self.embed_model.eval() + if self.wrapped_input: + if batch is None: + batch = torch.zeros_like(x[:,0], dtype=torch.long) + data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, batch=batch) + emb = self.embed_model(data) + else: + emb = self.embed_model(x, edge_index, edge_attr, batch) + return emb + + + +def train_MLP(embed_model, mlp_model, device, loader, val_loader, save_to=None): + embed_model = embed_model.to(device) + mlp_model = mlp_model.to(device) + optimizer = optim.Adam(mlp_model.parameters(), lr=lr, weight_decay=weight_decay) + scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1) + best_roc = 0 + for _ in range(epochs): + print("====epoch " + str(_)) + embed_model.eval() + mlp_model.train() + for step, batch in enumerate(tqdm(loader, desc="Iteration")): + embeds = embed_model(batch.to(device)).detach() + pred = mlp_model(embeds) + y = batch.y.view(pred.shape).to(torch.float64) + + is_valid = y**2 > 0 + loss_mat = criterion(pred.double(), (y+1)/2) + loss_mat = torch.where(is_valid, loss_mat, torch.zeros(loss_mat.shape).to(loss_mat.device).to(loss_mat.dtype)) + + optimizer.zero_grad() + loss = torch.sum(loss_mat)/torch.sum(is_valid) + loss.backward() + + optimizer.step() + + mlp_model.eval() + y_true = [] + y_scores = [] + + print("====Evaluation") + for step, batch in enumerate(tqdm(val_loader, desc="Iteration")): + + with torch.no_grad(): + embeds = embed_model(batch.to(device)).detach() + pred = mlp_model(embeds) + + y_true.append(batch.y.view(pred.shape)) + y_scores.append(pred) + + y_true = torch.cat(y_true, dim = 0).cpu().numpy() + y_scores = torch.cat(y_scores, dim = 0).cpu().numpy() + + roc_list = [] + for i in range(y_true.shape[1]): + if np.sum(y_true[:,i] == 1) > 0 and np.sum(y_true[:,i] == -1) > 0: + is_valid = y_true[:,i]**2 > 0 + roc_list.append(roc_auc_score((y_true[is_valid,i] + 1)/2, y_scores[is_valid,i])) + + roc_score = sum(roc_list)/len(roc_list) + print(roc_score) + if roc_score > best_roc and save_to: + best_roc = roc_score + torch.save(mlp_model.state_dict(), save_to) + diff --git a/dig/xgraph/TAGE/embedding.py b/dig/xgraph/TAGE/embedding.py new file mode 100644 index 00000000..fd7e17d1 --- /dev/null +++ b/dig/xgraph/TAGE/embedding.py @@ -0,0 +1,372 @@ +import torch +from torch_geometric.nn import MessagePassing +from torch_geometric.utils import add_self_loops, degree, softmax +from torch_geometric.nn import global_add_pool, global_mean_pool, global_max_pool, GlobalAttention, Set2Set +import torch.nn.functional as F +from torch_scatter import scatter_add +from torch_geometric.nn.inits import glorot, zeros + +num_atom_type = 120 #including the extra mask tokens +num_chirality_tag = 3 + +num_bond_type = 6 #including aromatic and self-loop edge, and extra masked tokens +num_bond_direction = 3 + +class GINConv(MessagePassing): + """ + Extension of GIN aggregation to incorporate edge information by concatenation. + + Args: + emb_dim (int): dimensionality of embeddings for nodes and edges. + embed_input (bool): whether to embed input or not. + + See https://arxiv.org/abs/1810.00826 + """ + def __init__(self, emb_dim, aggr = "add"): + super(GINConv, self).__init__() + #multi-layer perceptron + self.mlp = torch.nn.Sequential(torch.nn.Linear(emb_dim, 2*emb_dim), torch.nn.ReLU(), torch.nn.Linear(2*emb_dim, emb_dim)) + self.edge_embedding1 = torch.nn.Embedding(num_bond_type, emb_dim) + self.edge_embedding2 = torch.nn.Embedding(num_bond_direction, emb_dim) + + torch.nn.init.xavier_uniform_(self.edge_embedding1.weight.data) + torch.nn.init.xavier_uniform_(self.edge_embedding2.weight.data) + self.aggr = aggr + + def forward(self, x, edge_index, edge_attr): + #add self loops in the edge space + edge_index = add_self_loops(edge_index, num_nodes = x.size(0)) + + #add features corresponding to self-loop edges. + self_loop_attr = torch.zeros(x.size(0), 2) + self_loop_attr[:,0] = 4 #bond type for self-loop edge + self_loop_attr = self_loop_attr.to(edge_attr.device).to(edge_attr.dtype) + edge_attr = torch.cat((edge_attr, self_loop_attr), dim = 0) + + edge_embeddings = self.edge_embedding1(edge_attr[:,0]) + self.edge_embedding2(edge_attr[:,1]) + + return self.propagate(edge_index[0], x=x, edge_attr=edge_embeddings) + + def message(self, x_j, edge_attr): + return x_j + edge_attr + + def update(self, aggr_out): + return self.mlp(aggr_out) + + +class GCNConv(MessagePassing): + + def __init__(self, emb_dim, aggr = "add"): + super(GCNConv, self).__init__() + + self.emb_dim = emb_dim + self.linear = torch.nn.Linear(emb_dim, emb_dim) + self.edge_embedding1 = torch.nn.Embedding(num_bond_type, emb_dim) + self.edge_embedding2 = torch.nn.Embedding(num_bond_direction, emb_dim) + + torch.nn.init.xavier_uniform_(self.edge_embedding1.weight.data) + torch.nn.init.xavier_uniform_(self.edge_embedding2.weight.data) + + self.aggr = aggr + + def norm(self, edge_index, num_nodes, dtype): + ### assuming that self-loops have been already added in edge_index + edge_weight = torch.ones((edge_index.size(1), ), dtype=dtype, + device=edge_index.device) + row, col = edge_index + deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes) + deg_inv_sqrt = deg.pow(-0.5) + deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0 + + return deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col] + + + def forward(self, x, edge_index, edge_attr): + #add self loops in the edge space + edge_index = add_self_loops(edge_index, num_nodes = x.size(0)) + + #add features corresponding to self-loop edges. + self_loop_attr = torch.zeros(x.size(0), 2) + self_loop_attr[:,0] = 4 #bond type for self-loop edge + self_loop_attr = self_loop_attr.to(edge_attr.device).to(edge_attr.dtype) + edge_attr = torch.cat((edge_attr, self_loop_attr), dim = 0) + + edge_embeddings = self.edge_embedding1(edge_attr[:,0]) + self.edge_embedding2(edge_attr[:,1]) + + norm = self.norm(edge_index, x.size(0), x.dtype) + + x = self.linear(x) + + return self.propagate(edge_index[0], x=x, edge_attr=edge_embeddings, norm = norm) + + def message(self, x_j, edge_attr, norm): + return norm.view(-1, 1) * (x_j + edge_attr) + + +class GATConv(MessagePassing): + def __init__(self, emb_dim, heads=2, negative_slope=0.2, aggr = "add"): + super(GATConv, self).__init__() + + self.aggr = aggr + + self.emb_dim = emb_dim + self.heads = heads + self.negative_slope = negative_slope + + self.weight_linear = torch.nn.Linear(emb_dim, heads * emb_dim) + self.att = torch.nn.Parameter(torch.Tensor(1, heads, 2 * emb_dim)) + + self.bias = torch.nn.Parameter(torch.Tensor(emb_dim)) + + self.edge_embedding1 = torch.nn.Embedding(num_bond_type, heads * emb_dim) + self.edge_embedding2 = torch.nn.Embedding(num_bond_direction, heads * emb_dim) + + torch.nn.init.xavier_uniform_(self.edge_embedding1.weight.data) + torch.nn.init.xavier_uniform_(self.edge_embedding2.weight.data) + + self.reset_parameters() + + def reset_parameters(self): + glorot(self.att) + zeros(self.bias) + + def forward(self, x, edge_index, edge_attr): + + #add self loops in the edge space + edge_index = add_self_loops(edge_index, num_nodes = x.size(0)) + + #add features corresponding to self-loop edges. + self_loop_attr = torch.zeros(x.size(0), 2) + self_loop_attr[:,0] = 4 #bond type for self-loop edge + self_loop_attr = self_loop_attr.to(edge_attr.device).to(edge_attr.dtype) + edge_attr = torch.cat((edge_attr, self_loop_attr), dim = 0) + + edge_embeddings = self.edge_embedding1(edge_attr[:,0]) + self.edge_embedding2(edge_attr[:,1]) + + x = self.weight_linear(x).view(-1, self.heads, self.emb_dim) + return self.propagate(edge_index[0], x=x, edge_attr=edge_embeddings) + + def message(self, edge_index, x_i, x_j, edge_attr): + edge_attr = edge_attr.view(-1, self.heads, self.emb_dim) + x_j += edge_attr + + alpha = (torch.cat([x_i, x_j], dim=-1) * self.att).sum(dim=-1) + + alpha = F.leaky_relu(alpha, self.negative_slope) + alpha = softmax(alpha, edge_index[0]) + + return x_j * alpha.view(-1, self.heads, 1) + + def update(self, aggr_out): + aggr_out = aggr_out.mean(dim=1) + aggr_out = aggr_out + self.bias + + return aggr_out + + +class GraphSAGEConv(MessagePassing): + def __init__(self, emb_dim, aggr = "mean"): + super(GraphSAGEConv, self).__init__() + + self.emb_dim = emb_dim + self.linear = torch.nn.Linear(emb_dim, emb_dim) + self.edge_embedding1 = torch.nn.Embedding(num_bond_type, emb_dim) + self.edge_embedding2 = torch.nn.Embedding(num_bond_direction, emb_dim) + + torch.nn.init.xavier_uniform_(self.edge_embedding1.weight.data) + torch.nn.init.xavier_uniform_(self.edge_embedding2.weight.data) + + self.aggr = aggr + + def forward(self, x, edge_index, edge_attr): + #add self loops in the edge space + edge_index = add_self_loops(edge_index, num_nodes = x.size(0)) + + #add features corresponding to self-loop edges. + self_loop_attr = torch.zeros(x.size(0), 2) + self_loop_attr[:,0] = 4 #bond type for self-loop edge + self_loop_attr = self_loop_attr.to(edge_attr.device).to(edge_attr.dtype) + edge_attr = torch.cat((edge_attr, self_loop_attr), dim = 0) + + edge_embeddings = self.edge_embedding1(edge_attr[:,0]) + self.edge_embedding2(edge_attr[:,1]) + + x = self.linear(x) + + return self.propagate(edge_index[0], x=x, edge_attr=edge_embeddings) + + def message(self, x_j, edge_attr): + return x_j + edge_attr + + def update(self, aggr_out): + return F.normalize(aggr_out, p = 2, dim = -1) + + + +class GNN(torch.nn.Module): + """ + Args: + num_layer (int): the number of GNN layers + emb_dim (int): dimensionality of embeddings + JK (str): last, concat, max or sum. + max_pool_layer (int): the layer from which we use max pool rather than add pool for neighbor aggregation + drop_ratio (float): dropout rate + gnn_type: gin, gcn, graphsage, gat + + Output: + node representations + + """ + def __init__(self, num_layer, emb_dim, JK = "last", drop_ratio = 0, gnn_type = "gin"): + super(GNN, self).__init__() + self.num_layer = num_layer + self.drop_ratio = drop_ratio + self.JK = JK + self.emb_dim = emb_dim + + if self.num_layer < 2: + raise ValueError("Number of GNN layers must be greater than 1.") + + self.x_embedding1 = torch.nn.Embedding(num_atom_type, emb_dim) + self.x_embedding2 = torch.nn.Embedding(num_chirality_tag, emb_dim) + + torch.nn.init.xavier_uniform_(self.x_embedding1.weight.data) + torch.nn.init.xavier_uniform_(self.x_embedding2.weight.data) + + ###List of MLPs + self.gnns = torch.nn.ModuleList() + for layer in range(num_layer): + if gnn_type == "gin": + self.gnns.append(GINConv(emb_dim, aggr = "add")) + elif gnn_type == "gcn": + self.gnns.append(GCNConv(emb_dim)) + elif gnn_type == "gat": + self.gnns.append(GATConv(emb_dim)) + elif gnn_type == "graphsage": + self.gnns.append(GraphSAGEConv(emb_dim)) + + ###List of batchnorms + self.batch_norms = torch.nn.ModuleList() + for layer in range(num_layer): + self.batch_norms.append(torch.nn.BatchNorm1d(emb_dim)) + + + def forward(self, data, emb=False): + x, edge_index, edge_attr, batch = data.x, data.edge_index, data.edge_attr, data.batch + + device = self.x_embedding2.weight.data.device + self.x_embedding1.weight.data = torch.cat([ + torch.zeros(1, self.emb_dim).to(device), self.x_embedding1.weight.data[1:]], 0) + x = self.x_embedding1(x[:,0]) + + h_list = [x] + for layer in range(self.num_layer): + h = self.gnns[layer](h_list[layer], edge_index, edge_attr) + h = self.batch_norms[layer](h) + #h = F.dropout(F.relu(h), self.drop_ratio, training = self.training) + if layer == self.num_layer - 1: + #remove relu for the last layer + h = F.dropout(h, self.drop_ratio, training = self.training) + else: + h = F.dropout(F.relu(h), self.drop_ratio, training = self.training) + h_list.append(h) + + ### Different implementations of Jk-concat + if self.JK == "concat": + node_representation = torch.cat(h_list, dim = 1) + elif self.JK == "last": + node_representation = h_list[-1] + elif self.JK == "max": + h_list = [h.unsqueeze_(0) for h in h_list] + node_representation = torch.max(torch.cat(h_list, dim = 0), dim = 0)[0] + elif self.JK == "sum": + h_list = [h.unsqueeze_(0) for h in h_list] + node_representation = torch.sum(torch.cat(h_list, dim = 0), dim = 0)[0] + + if emb: + return global_mean_pool(node_representation, batch), node_representation + + return global_mean_pool(node_representation, batch) + + +class GNN_graphpred(torch.nn.Module): + """ + Extension of GIN to incorporate edge information by concatenation. + + Args: + num_layer (int): the number of GNN layers + emb_dim (int): dimensionality of embeddings + num_tasks (int): number of tasks in multi-task learning scenario + drop_ratio (float): dropout rate + JK (str): last, concat, max or sum. + graph_pooling (str): sum, mean, max, attention, set2set + gnn_type: gin, gcn, graphsage, gat + + See https://arxiv.org/abs/1810.00826 + JK-net: https://arxiv.org/abs/1806.03536 + """ + def __init__(self, num_layer, emb_dim, num_tasks, JK = "last", drop_ratio = 0, graph_pooling = "mean", gnn_type = "gin"): + super(GNN_graphpred, self).__init__() + self.num_layer = num_layer + self.drop_ratio = drop_ratio + self.JK = JK + self.emb_dim = emb_dim + self.num_tasks = num_tasks + + if self.num_layer < 2: + raise ValueError("Number of GNN layers must be greater than 1.") + + self.gnn = GNN(num_layer, emb_dim, JK, drop_ratio, gnn_type = gnn_type) + + #Different kind of graph pooling + if graph_pooling == "sum": + self.pool = global_add_pool + elif graph_pooling == "mean": + self.pool = global_mean_pool + elif graph_pooling == "max": + self.pool = global_max_pool + elif graph_pooling == "attention": + if self.JK == "concat": + self.pool = GlobalAttention(gate_nn = torch.nn.Linear((self.num_layer + 1) * emb_dim, 1)) + else: + self.pool = GlobalAttention(gate_nn = torch.nn.Linear(emb_dim, 1)) + elif graph_pooling[:-1] == "set2set": + set2set_iter = int(graph_pooling[-1]) + if self.JK == "concat": + self.pool = Set2Set((self.num_layer + 1) * emb_dim, set2set_iter) + else: + self.pool = Set2Set(emb_dim, set2set_iter) + else: + raise ValueError("Invalid graph pooling type.") + + #For graph-level binary classification + if graph_pooling[:-1] == "set2set": + self.mult = 2 + else: + self.mult = 1 + + if self.JK == "concat": + self.graph_pred_linear = torch.nn.Linear(self.mult * (self.num_layer + 1) * self.emb_dim, self.num_tasks) + else: + self.graph_pred_linear = torch.nn.Linear(self.mult * self.emb_dim, self.num_tasks) + + def from_pretrained(self, model_file): + #self.gnn = GNN(self.num_layer, self.emb_dim, JK = self.JK, drop_ratio = self.drop_ratio) + self.gnn.load_state_dict(torch.load(model_file)) + + def forward(self, *argv): + if len(argv) == 4: + x, edge_index, edge_attr, batch = argv[0], argv[1], argv[2], argv[3] + elif len(argv) == 1: + data = argv[0] + x, edge_index, edge_attr, batch = data.x, data.edge_index, data.edge_attr, data.batch + else: + raise ValueError("unmatched number of arguments.") + + node_representation = self.gnn(x, edge_index, edge_attr) + + return self.graph_pred_linear(self.pool(node_representation, batch)) + + +if __name__ == "__main__": + pass + diff --git a/dig/xgraph/TAGE/gexplain_2stage_quant.ipynb b/dig/xgraph/TAGE/gexplain_2stage_quant.ipynb new file mode 100644 index 00000000..0af064b5 --- /dev/null +++ b/dig/xgraph/TAGE/gexplain_2stage_quant.ipynb @@ -0,0 +1,248 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "spiritual-search", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import pandas as pd\n", + "from torch_geometric.data import DataLoader, Batch, Data\n", + "from embedding import GNN\n", + "from downstream import MLP\n", + "from tagexplainer import TAGExplainer, MLPExplainer\n", + "from loader import MoleculeDataset\n", + "from splitters import scaffold_split\n", + "from dig.xgraph.evaluation import XCollector\n", + "\n", + "device = torch.device(\"cuda:2\" if torch.cuda.is_available() else torch.device(\"cpu\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "statutory-monitor", + "metadata": {}, + "outputs": [], + "source": [ + "pre_train_dataset = MoleculeDataset(\"dataset/zinc_standard_agent\", dataset='zinc_standard_agent')\n", + "train_loader = DataLoader(pre_train_dataset, 256, shuffle=True)\n", + "\n", + "embed_model = GNN(num_layer = 5, emb_dim = 600, JK = 'last', drop_ratio = 0, gnn_type = 'gin')\n", + "embed_model.load_state_dict(torch.load('ckpts_model/chem_pretrained_contextpred.pth', map_location='cpu'))\n", + "enc_explainer = TAGExplainer(embed_model, embed_dim=600, device=device, explain_graph=True, \n", + " grad_scale=0.2, coff_size=0.05, coff_ent=0.002, loss_type='JSE')" + ] + }, + { + "cell_type": "markdown", + "id": "closed-windows", + "metadata": {}, + "source": [ + "#### To train the explainer, uncomment the following cell." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "union-survivor", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7813/7813 [23:56<00:00, 5.44it/s, loss=0.0217, log=75.2979, 1.0000, -21.9580, 0.0079] \n" + ] + } + ], + "source": [ + "# enc_explainer.train_explainer_graph(train_loader, lr=0.0001, epochs=1)\n", + "# torch.save(enc_explainer.explainer.state_dict(), 'ckpts_explainer/explain_mol_twostage.pt')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "massive-straight", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_dict = torch.load('ckpts_explainer/explain_mol_twostage.pt')\n", + "enc_explainer.explainer.load_state_dict(state_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "raised-patrol", + "metadata": {}, + "outputs": [], + "source": [ + "def get_task(idx):\n", + " def transform(data):\n", + " return Data(x=data.x, edge_index=data.edge_index, edge_attr=data.edge_attr, y=data.y[idx:idx+1].long())\n", + " return transform\n", + "\n", + "def get_dataset(name, task=0):\n", + " task_transform = get_task(task)\n", + " dataset = MoleculeDataset(\"dataset/%s\"%name, dataset=name, transform=task_transform)\n", + " smiles_list = pd.read_csv('dataset/%s/processed/smiles.csv'%name, header=None)[0].tolist()\n", + " train_dataset, valid_dataset, test_dataset = scaffold_split(\n", + " dataset, smiles_list, null_value=0, frac_train=0.8, frac_valid=0.1, frac_test=0.1)\n", + " return train_dataset\n", + "\n", + "def get_results(task_name, top_k, pos=True):\n", + " train_dataset = get_dataset(task_name)\n", + " mlp_model = MLP(num_layer = 2, emb_dim =600, hidden_dim = 600)\n", + " mlp_model.load_state_dict(torch.load('ckpts_model/downstream_%s_contextpred.pth'%task_name, map_location='cpu'))\n", + " mlp_explainer = MLPExplainer(mlp_model, device)\n", + "\n", + " x_collector = XCollector()\n", + " dataloader = DataLoader(train_dataset, batch_size=1, shuffle=False, num_workers = 1)\n", + " for i, data in enumerate(dataloader):\n", + " if pos==(data.y < 0):\n", + " continue\n", + " if data.edge_index.shape[1]<=0:\n", + " continue\n", + "\n", + " print(f'explain graph {i}...', end='\\r')\n", + " walks, masks, related_preds = \\\n", + " enc_explainer(data.to(device), mlp_explainer, top_k=top_k, mask_mode='split')\n", + "\n", + " x_collector.collect_data(masks, related_preds)\n", + "\n", + " fid, fid_std = x_collector.fidelity\n", + " spa, spa_std = x_collector.sparsity\n", + "\n", + " print()\n", + " print(f'Fidelity: {fid:.4f} +/- {fid_std:.4f}\\n'\n", + " f'Sparsity: {spa:.4f} +/- {spa_std:.4f}')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "humanitarian-smoke", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "explain graph 965...\n", + "Fidelity: 0.3782 +/- 0.2934\n", + "Sparsity: 0.9026 +/- 0.0278\n" + ] + } + ], + "source": [ + "get_results('bace', top_k=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "normal-story", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "explain graph 32795...\n", + "Fidelity: 0.5952 +/- 0.3200\n", + "Sparsity: 0.8806 +/- 0.0582\n" + ] + } + ], + "source": [ + "get_results('hiv', top_k=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "dental-confidentiality", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "explain graph 1140...\n", + "Fidelity: 0.4067 +/- 0.3226\n", + "Sparsity: 0.8545 +/- 0.0751\n" + ] + } + ], + "source": [ + "get_results('sider', top_k=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "reported-filling", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "explain graph 1193...\n", + "Fidelity: 0.1878 +/- 0.1543\n", + "Sparsity: 0.7205 +/- 0.1162\n" + ] + } + ], + "source": [ + "get_results('bbbp', top_k=10, pos=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "confidential-carbon", + "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": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/dig/xgraph/TAGE/gexplain_2stage_visual_bace.ipynb b/dig/xgraph/TAGE/gexplain_2stage_visual_bace.ipynb new file mode 100644 index 00000000..fd5fdb86 --- /dev/null +++ b/dig/xgraph/TAGE/gexplain_2stage_visual_bace.ipynb @@ -0,0 +1,1324 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "active-labor", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import pandas as pd\n", + "from torch_geometric.data import DataLoader, Batch, Data\n", + "from embedding import GNN\n", + "from downstream import MLP\n", + "from tagexplainer import TAGExplainer, MLPExplainer\n", + "from loader import MoleculeDataset\n", + "from splitters import scaffold_split\n", + "from dig.xgraph.evaluation import XCollector\n", + "\n", + "from rdkit import Chem\n", + "from rdkit.Chem import rdDepictor\n", + "from rdkit.Chem.Draw import rdMolDraw2D\n", + "from IPython.display import SVG, display\n", + "\n", + "device = torch.device(\"cuda:2\" if torch.cuda.is_available() else torch.device(\"cpu\"))\n", + "\n", + "def get_task(idx):\n", + " def transform(data):\n", + " return Data(x=data.x, edge_index=data.edge_index, edge_attr=data.edge_attr, y=data.y[idx:idx+1].long())\n", + " return transform\n", + "\n", + "def get_dataset(name, task=0):\n", + " task_transform = get_task(task)\n", + " dataset = MoleculeDataset(\"dataset/%s\"%name, dataset=name, transform=task_transform)\n", + " smiles_list = pd.read_csv('dataset/%s/processed/smiles.csv'%name, header=None)[0].tolist()\n", + " return dataset, smiles_list" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "stable-enzyme", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "embed_model = GNN(num_layer = 5, emb_dim = 600, JK = 'last', drop_ratio = 0, gnn_type = 'gin')\n", + "embed_model.load_state_dict(torch.load('ckpts_model/chem_pretrained_contextpred.pth', map_location='cpu'))\n", + "enc_explainer = TAGExplainer(embed_model, embed_dim=600, device=device, explain_graph=True, \n", + " grad_scale=0.2, coff_size=0.05, coff_ent=0.002, loss_type='JSE')\n", + "\n", + "state_dict = torch.load('ckpts_explainer/explain_mol_twostage.pt')\n", + "enc_explainer.explainer.load_state_dict(state_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "thorough-aaron", + "metadata": {}, + "outputs": [], + "source": [ + "dataset, smiles_list = get_dataset('bace')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "framed-absence", + "metadata": {}, + "outputs": [], + "source": [ + "task_name = 'bace'\n", + "mlp_model = MLP(num_layer = 2, emb_dim =600, hidden_dim = 600)\n", + "mlp_model.load_state_dict(torch.load('ckpts_model/downstream_%s_contextpred.pth'%task_name, map_location='cpu'))\n", + "mlp_explainer = MLPExplainer(mlp_model, device)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "brown-canberra", + "metadata": {}, + "outputs": [], + "source": [ + "def visual_molecule(mid, cond=None):\n", + " smiles = smiles_list[mid]\n", + " mol = Chem.MolFromSmiles(smiles)\n", + " \n", + " data = Batch.from_data_list([dataset[mid]])\n", + " top_k = max(1, int(data.edge_index.shape[1]*0.1))\n", + " walks, masks, related_preds = \\\n", + " enc_explainer(data.to(device), mlp_explainer, top_k=top_k, mask_mode='split', cond_vec=cond)\n", + " print(related_preds[0]['origin']-related_preds[0]['maskout'])\n", + " highlights = torch.topk(masks[data.edge_index[0]>data.edge_index[1]], top_k)\n", + " \n", + " rdDepictor.Compute2DCoords(mol)\n", + " drawer = rdMolDraw2D.MolDraw2DSVG(280, 280)\n", + " drawer.SetFontSize(1)\n", + "\n", + " mol = rdMolDraw2D.PrepareMolForDrawing(mol)\n", + " drawer.DrawMolecule(mol, highlightAtoms=[], highlightBonds=highlights.indices.tolist())\n", + " \n", + " drawer.FinishDrawing()\n", + " svg = drawer.GetDrawingText().replace('svg:','')\n", + " \n", + " return svg" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "australian-premiere", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.701147697865963\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + 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"metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cond = torch.zeros([1, 600])\n", + "cond[0, 389] = 20\n", + "svg = visual_molecule(28, cond=cond)\n", + "display(SVG(svg))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "hollow-christianity", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.5022746920585632\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", 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], + "source": [ + "svg = visual_molecule(50)\n", + "display(SVG(svg))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "residential-oxide", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3705782890319824\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "F\n", + "N\n", + "N\n", + "N\n", + "H2N\n", + 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b/dig/xgraph/TAGE/gexplain_2stage_visual_hiv.ipynb new file mode 100644 index 00000000..4d6c8f93 --- /dev/null +++ b/dig/xgraph/TAGE/gexplain_2stage_visual_hiv.ipynb @@ -0,0 +1,1317 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 18, + "id": "circular-discharge", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import pandas as pd\n", + "from torch_geometric.data import DataLoader, Batch, Data\n", + "from embedding import GNN\n", + "from downstream import MLP\n", + "from conexplainer import ContExplainer, MLPExplainer\n", + "from loader import MoleculeDataset\n", + "from splitters import scaffold_split\n", + "from dig.xgraph.evaluation import XCollector\n", + "\n", + "from rdkit import Chem\n", + "from rdkit.Chem import rdDepictor\n", + "from rdkit.Chem.Draw import rdMolDraw2D\n", + "from IPython.display import SVG, display\n", + "\n", + "device = torch.device(\"cuda:3\" if torch.cuda.is_available() else torch.device(\"cpu\"))\n", + "\n", + "def get_task(idx):\n", + " def transform(data):\n", + " return Data(x=data.x, edge_index=data.edge_index, edge_attr=data.edge_attr, y=data.y[idx:idx+1].long())\n", + " return transform\n", + "\n", + "def get_dataset(name, task=0):\n", + " task_transform = get_task(task)\n", + " dataset = MoleculeDataset(\"dataset/%s\"%name, dataset=name, transform=task_transform)\n", + " smiles_list = pd.read_csv('dataset/%s/processed/smiles.csv'%name, header=None)[0].tolist()\n", + " return dataset, smiles_list" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "danish-airplane", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "embed_model = GNN(num_layer = 5, emb_dim = 600, JK = 'last', drop_ratio = 0, gnn_type = 'gin')\n", + "embed_model.load_state_dict(torch.load('ckpts_model/chem_pretrained_contextpred.pth', map_location='cpu'))\n", + "enc_explainer = ContExplainer(embed_model, embed_dim=600, device=device, explain_graph=True, \n", + " grad_scale=0.2, coff_size=0.05, coff_ent=0.002, loss_type='JSE')\n", + "\n", + "state_dict = torch.load('ckpts_explainer/explain_mol_twostage.pt')\n", + "enc_explainer.explainer.load_state_dict(state_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "orange-graph", + "metadata": {}, + "outputs": [], + "source": [ + "task_name = 'hiv'\n", + "dataset, smiles_list = get_dataset(task_name)\n", + "mlp_model = MLP(num_layer = 2, emb_dim =600, hidden_dim = 600)\n", + "mlp_model.load_state_dict(torch.load('ckpts_model/downstream_%s_contextpred.pth'%task_name, map_location='cpu'))\n", + "mlp_explainer = MLPExplainer(mlp_model, device)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "embedded-extraction", + "metadata": {}, + "outputs": [], + "source": [ + "def visual_molecule(mid, cond=None):\n", + " smiles = smiles_list[mid]\n", + " mol = Chem.MolFromSmiles(smiles)\n", + " \n", + " data = Batch.from_data_list([dataset[mid]])\n", + " top_k = max(1, int(data.edge_index.shape[1]*0.1))\n", + " walks, masks, related_preds = \\\n", + " enc_explainer(data.to(device), mlp_explainer, top_k=top_k, mask_mode='split', cond_vec=cond)\n", + " print(related_preds[0]['origin']-related_preds[0]['maskout'])\n", + " highlights = torch.topk(masks[data.edge_index[0]>data.edge_index[1]], top_k)\n", + " \n", + " rdDepictor.Compute2DCoords(mol)\n", + " drawer = rdMolDraw2D.MolDraw2DSVG(280, 280)\n", + " drawer.SetFontSize(1)\n", + "\n", + " mol = rdMolDraw2D.PrepareMolForDrawing(mol)\n", + " drawer.DrawMolecule(mol, highlightAtoms=[], highlightBonds=highlights.indices.tolist())\n", + " \n", + " drawer.FinishDrawing()\n", + " svg = drawer.GetDrawingText().replace('svg:','')\n", + " \n", + " return svg" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "cutting-knowing", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def display_id(data_id):\n", + " embed_model.eval()\n", + " data = Batch.from_data_list([dataset[data_id]])\n", + " topkd = embed_model(data.to(device)).topk(40)[1]\n", + "\n", + " for did in topkd[0]:\n", + " cond = torch.zeros([1, 600])\n", + " cond[0, did] = 20\n", + " svg = visual_molecule(data_id, cond=cond)\n", + " print(did)\n", + " display(SVG(svg))\n", + " \n", + "def spa_id(mid, cond=None):\n", + " smiles = smiles_list[mid]\n", + " mol = Chem.MolFromSmiles(smiles)\n", + " \n", + " data = Batch.from_data_list([dataset[mid]])\n", + " top_k = max(1, int(data.edge_index.shape[1]*0.1))\n", + " walks, masks, related_preds = \\\n", + " enc_explainer(data.to(device), mlp_explainer, top_k=top_k, mask_mode='split', cond_vec=cond)\n", + " return related_preds[0]['origin']-related_preds[0]['maskout']" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "adequate-universe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6444323062896729\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "O\n", + "OH\n", + "OH\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svg = visual_molecule(6)\n", + "SVG(svg)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "higher-stroke", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.014076590538024902\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "O\n", + "OH\n", + "OH\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cond = torch.zeros([1, 600])\n", + "cond[0, 78] = 20\n", + "svg = visual_molecule(6, cond=cond)\n", + "SVG(svg)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "banned-homeless", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.0994415283203125e-06\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "O\n", + "OH\n", + "OH\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cond = torch.zeros([1, 600])\n", + "cond[0, 312] = 20\n", + "svg = visual_molecule(6, cond=cond)\n", + "SVG(svg)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "adequate-classroom", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.2617366909980774\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "O\n", + "OH\n", + "OH\n", + "O\n", + "HO\n", + "HO\n", + "HO\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svg = visual_molecule(134)\n", + "SVG(svg)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "amino-cincinnati", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.026331841945648193\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "O\n", + "OH\n", + "OH\n", + "O\n", + "HO\n", + "HO\n", + "HO\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cond = torch.zeros([1, 600])\n", + "cond[0, 334] = 20\n", + "svg = visual_molecule(134, cond=cond)\n", + "SVG(svg)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "organic-amplifier", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + 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"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": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/dig/xgraph/TAGE/gexplain_2stage_visual_sider.ipynb b/dig/xgraph/TAGE/gexplain_2stage_visual_sider.ipynb new file mode 100644 index 00000000..aede00cf --- /dev/null +++ b/dig/xgraph/TAGE/gexplain_2stage_visual_sider.ipynb @@ -0,0 +1,3955 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "vertical-north", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import pandas as pd\n", + "from torch_geometric.data import DataLoader, Batch, Data\n", + "from embedding import GNN\n", + "from downstream import MLP\n", + "from conexplainer import ContExplainer, MLPExplainer\n", + "from loader import MoleculeDataset\n", + "from splitters import scaffold_split\n", + "from dig.xgraph.evaluation import XCollector\n", + "\n", + "from rdkit import Chem\n", + "from rdkit.Chem import rdDepictor\n", + "from rdkit.Chem.Draw import rdMolDraw2D\n", + "from IPython.display import SVG, display\n", + "\n", + "device = torch.device(\"cuda:3\" if torch.cuda.is_available() else torch.device(\"cpu\"))\n", + "\n", + "def get_task(idx):\n", + " def transform(data):\n", + " return Data(x=data.x, edge_index=data.edge_index, edge_attr=data.edge_attr, y=data.y[idx:idx+1].long())\n", + " return transform\n", + "\n", + "def get_dataset(name, task=0):\n", + " task_transform = get_task(task)\n", + " dataset = MoleculeDataset(\"dataset/%s\"%name, dataset=name, transform=task_transform)\n", + " smiles_list = pd.read_csv('dataset/%s/processed/smiles.csv'%name, header=None)[0].tolist()\n", + " return dataset, smiles_list" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "multiple-profit", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "embed_model = GNN(num_layer = 5, emb_dim = 600, JK = 'last', drop_ratio = 0, gnn_type = 'gin')\n", + "embed_model.load_state_dict(torch.load('ckpts_model/chem_pretrained_contextpred.pth', map_location='cpu'))\n", + "enc_explainer = ContExplainer(embed_model, embed_dim=600, device=device, explain_graph=True, \n", + " grad_scale=0.2, coff_size=0.05, coff_ent=0.002, loss_type='JSE')\n", + "\n", + "state_dict = torch.load('ckpts_explainer/explain_mol_twostage.pt')\n", + "enc_explainer.explainer.load_state_dict(state_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "respective-second", + "metadata": {}, + "outputs": [], + "source": [ + "task_name = 'sider'\n", + "dataset, smiles_list = get_dataset(task_name)\n", + "mlp_model = MLP(num_layer = 2, emb_dim =600, hidden_dim = 600)\n", + "mlp_model.load_state_dict(torch.load('ckpts_model/downstream_%s_contextpred.pth'%task_name, map_location='cpu'))\n", + "mlp_explainer = MLPExplainer(mlp_model, device)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "adverse-recognition", + "metadata": {}, + "outputs": [], + "source": [ + "def visual_molecule(mid, cond=None):\n", + " smiles = smiles_list[mid]\n", + " mol = Chem.MolFromSmiles(smiles)\n", + " \n", + " data = Batch.from_data_list([dataset[mid]])\n", + " top_k = max(1, int(data.edge_index.shape[1]*0.1))\n", + " walks, masks, related_preds = \\\n", + " enc_explainer(data.to(device), mlp_explainer, top_k=top_k, mask_mode='split', cond_vec=cond)\n", + " print(related_preds[0]['origin']-related_preds[0]['maskout'])\n", + " highlights = torch.topk(masks[data.edge_index[0]>data.edge_index[1]], top_k)\n", + " \n", + " rdDepictor.Compute2DCoords(mol)\n", + " drawer = rdMolDraw2D.MolDraw2DSVG(280, 280)\n", + " drawer.SetFontSize(1)\n", + "\n", + " mol = rdMolDraw2D.PrepareMolForDrawing(mol)\n", + " drawer.DrawMolecule(mol, highlightAtoms=[], highlightBonds=highlights.indices.tolist())\n", + " \n", + " drawer.FinishDrawing()\n", + " svg = drawer.GetDrawingText().replace('svg:','')\n", + " \n", + " return svg" + ] + }, + { + "cell_type": "code", + "execution_count": 295, + "id": "circular-scope", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.36984091997146606\n", + "tensor(312, device='cuda:3')\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + 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Batch.from_data_list([dataset[82]])\n", + "topkd = embed_model(data.to(device)).topk(40)[1]\n", + "\n", + "for did in topkd[0]:\n", + " cond = torch.zeros([1, 600])\n", + " cond[0, did] = 20\n", + " svg = visual_molecule(82, cond=cond)\n", + " print(did)\n", + " display(SVG(svg))" + ] + }, + { + "cell_type": "code", + "execution_count": 293, + "id": "accomplished-junction", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.46697887778282166\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "N\n", + "C\n", + "O\n", + "O\n", + "OH\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 293, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svg = visual_molecule(82)\n", + "SVG(svg)" + ] + }, + { + "cell_type": "code", + "execution_count": 297, + "id": "colored-implementation", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.18262434005737305\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "N\n", + "C\n", + "O\n", + "O\n", + "OH\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 297, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cond = torch.zeros([1, 600])\n", + "cond[0, 326] = 20\n", + "svg = visual_molecule(82, cond=cond)\n", + "SVG(svg)" + ] + }, + { + "cell_type": "code", + "execution_count": 296, + "id": "cathedral-jacksonville", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.2652842104434967\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "N\n", + "C\n", + "O\n", + "O\n", + "OH\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 296, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cond = torch.zeros([1, 600])\n", + "cond[0, 159] = 20\n", + "svg = visual_molecule(82, cond=cond)\n", + "SVG(svg)" + ] + }, + { + "cell_type": "code", + "execution_count": 288, + "id": "necessary-raise", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.640363484621048\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "O\n", + "N\n", + "N\n", + "N\n", + "O\n", + "NH2\n", + "OH\n", + "HO\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 288, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svg = visual_molecule(77)\n", + "SVG(svg)" + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "id": "meaningful-leisure", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.640363484621048\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "O\n", + "N\n", + "N\n", + "N\n", + "O\n", + "NH2\n", + "OH\n", + "HO\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 287, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cond = torch.zeros([1, 600])\n", + "cond[0, 183] = 20\n", + "svg = visual_molecule(77, cond=cond)\n", + "SVG(svg)" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "id": "established-puppy", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + 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torch.zeros([1, 600])\n", + "cond[0, 272] = 20\n", + "svg = visual_molecule(70, cond=cond)\n", + "SVG(svg)" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "id": "aggressive-texas", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.8071910397848114\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "N\n", + "S\n", + "Cl\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svg = visual_molecule(13)\n", + "SVG(svg)" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "id": "exterior-finish", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 78, 312, 447, 334, 272, 86, 402, 320, 55, 326, 560, 496, 205, 444,\n", + " 445, 389, 56, 151, 218, 434, 41, 172, 515, 168, 342, 378, 32, 413,\n", + " 575, 307, 90, 481, 262, 27, 349, 525, 141, 403, 505, 535]],\n", + " device='cuda:3')" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "embed_model.eval()\n", + "data = Batch.from_data_list([dataset[13]])\n", + "embed_model(data.to(device)).topk(40)[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "id": "ignored-letter", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3551807403564453\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + 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{}, + "output_type": "execute_result" + } + ], + "source": [ + "cond = torch.zeros([1, 600])\n", + "cond[0, 78] = 20\n", + "svg = visual_molecule(13, cond=cond)\n", + "SVG(svg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "distinct-flavor", + "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": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/dig/xgraph/TAGE/loader.py b/dig/xgraph/TAGE/loader.py new file mode 100644 index 00000000..a52ddd3f --- /dev/null +++ b/dig/xgraph/TAGE/loader.py @@ -0,0 +1,1329 @@ +from rdkit import Chem +import os +import torch +import pickle +import collections +import math +import pandas as pd +import numpy as np +import networkx as nx +from rdkit.Chem import Descriptors +from rdkit.Chem import AllChem +from rdkit import DataStructs +from rdkit.Chem.rdMolDescriptors import GetMorganFingerprintAsBitVect +from torch.utils import data +from torch_geometric.data import Data +from torch_geometric.data import InMemoryDataset +from torch_geometric.data import Batch +from itertools import repeat, product, chain + + +# allowable node and edge features +allowable_features = { + 'possible_atomic_num_list' : list(range(1, 119)), + 'possible_formal_charge_list' : [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5], + 'possible_chirality_list' : [ + Chem.rdchem.ChiralType.CHI_UNSPECIFIED, + Chem.rdchem.ChiralType.CHI_TETRAHEDRAL_CW, + Chem.rdchem.ChiralType.CHI_TETRAHEDRAL_CCW, + Chem.rdchem.ChiralType.CHI_OTHER + ], + 'possible_hybridization_list' : [ + Chem.rdchem.HybridizationType.S, + Chem.rdchem.HybridizationType.SP, Chem.rdchem.HybridizationType.SP2, + Chem.rdchem.HybridizationType.SP3, Chem.rdchem.HybridizationType.SP3D, + Chem.rdchem.HybridizationType.SP3D2, Chem.rdchem.HybridizationType.UNSPECIFIED + ], + 'possible_numH_list' : [0, 1, 2, 3, 4, 5, 6, 7, 8], + 'possible_implicit_valence_list' : [0, 1, 2, 3, 4, 5, 6], + 'possible_degree_list' : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], + 'possible_bonds' : [ + Chem.rdchem.BondType.SINGLE, + Chem.rdchem.BondType.DOUBLE, + Chem.rdchem.BondType.TRIPLE, + Chem.rdchem.BondType.AROMATIC + ], + 'possible_bond_dirs' : [ # only for double bond stereo information + Chem.rdchem.BondDir.NONE, + Chem.rdchem.BondDir.ENDUPRIGHT, + Chem.rdchem.BondDir.ENDDOWNRIGHT + ] +} + +def mol_to_graph_data_obj_simple(mol): + """ + Converts rdkit mol object to graph Data object required by the pytorch + geometric package. NB: Uses simplified atom and bond features, and represent + as indices + :param mol: rdkit mol object + :return: graph data object with the attributes: x, edge_index, edge_attr + """ + # atoms + num_atom_features = 2 # atom type, chirality tag + atom_features_list = [] + for atom in mol.GetAtoms(): + atom_feature = [allowable_features['possible_atomic_num_list'].index( + atom.GetAtomicNum())] + [allowable_features[ + 'possible_chirality_list'].index(atom.GetChiralTag())] + atom_features_list.append(atom_feature) + x = torch.tensor(np.array(atom_features_list), dtype=torch.long) + + # bonds + num_bond_features = 2 # bond type, bond direction + if len(mol.GetBonds()) > 0: # mol has bonds + edges_list = [] + edge_features_list = [] + for bond in mol.GetBonds(): + i = bond.GetBeginAtomIdx() + j = bond.GetEndAtomIdx() + edge_feature = [allowable_features['possible_bonds'].index( + bond.GetBondType())] + [allowable_features[ + 'possible_bond_dirs'].index( + bond.GetBondDir())] + edges_list.append((i, j)) + edge_features_list.append(edge_feature) + edges_list.append((j, i)) + edge_features_list.append(edge_feature) + + # data.edge_index: Graph connectivity in COO format with shape [2, num_edges] + edge_index = torch.tensor(np.array(edges_list).T, dtype=torch.long) + + # data.edge_attr: Edge feature matrix with shape [num_edges, num_edge_features] + edge_attr = torch.tensor(np.array(edge_features_list), + dtype=torch.long) + else: # mol has no bonds + edge_index = torch.empty((2, 0), dtype=torch.long) + edge_attr = torch.empty((0, num_bond_features), dtype=torch.long) + + data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr) + + return data + +def graph_data_obj_to_mol_simple(data_x, data_edge_index, data_edge_attr): + """ + Convert pytorch geometric data obj to rdkit mol object. NB: Uses simplified + atom and bond features, and represent as indices. + :param: data_x: + :param: data_edge_index: + :param: data_edge_attr + :return: + """ + mol = Chem.RWMol() + + # atoms + atom_features = data_x.cpu().numpy() + num_atoms = atom_features.shape[0] + for i in range(num_atoms): + atomic_num_idx, chirality_tag_idx = atom_features[i] + atomic_num = allowable_features['possible_atomic_num_list'][atomic_num_idx] + chirality_tag = allowable_features['possible_chirality_list'][chirality_tag_idx] + atom = Chem.Atom(atomic_num) + atom.SetChiralTag(chirality_tag) + mol.AddAtom(atom) + + # bonds + edge_index = data_edge_index.cpu().numpy() + edge_attr = data_edge_attr.cpu().numpy() + num_bonds = edge_index.shape[1] + for j in range(0, num_bonds, 2): + begin_idx = int(edge_index[0, j]) + end_idx = int(edge_index[1, j]) + bond_type_idx, bond_dir_idx = edge_attr[j] + bond_type = allowable_features['possible_bonds'][bond_type_idx] + bond_dir = allowable_features['possible_bond_dirs'][bond_dir_idx] + mol.AddBond(begin_idx, end_idx, bond_type) + # set bond direction + new_bond = mol.GetBondBetweenAtoms(begin_idx, end_idx) + new_bond.SetBondDir(bond_dir) + + # Chem.SanitizeMol(mol) # fails for COC1=CC2=C(NC(=N2)[S@@](=O)CC2=NC=C( + # C)C(OC)=C2C)C=C1, when aromatic bond is possible + # when we do not have aromatic bonds + # Chem.SanitizeMol(mol, sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + + return mol + +def graph_data_obj_to_nx_simple(data): + """ + Converts graph Data object required by the pytorch geometric package to + network x data object. NB: Uses simplified atom and bond features, + and represent as indices. NB: possible issues with recapitulating relative + stereochemistry since the edges in the nx object are unordered. + :param data: pytorch geometric Data object + :return: network x object + """ + G = nx.Graph() + + # atoms + atom_features = data.x.cpu().numpy() + num_atoms = atom_features.shape[0] + for i in range(num_atoms): + atomic_num_idx, chirality_tag_idx = atom_features[i] + G.add_node(i, atom_num_idx=atomic_num_idx, chirality_tag_idx=chirality_tag_idx) + pass + + # bonds + edge_index = data.edge_index.cpu().numpy() + edge_attr = data.edge_attr.cpu().numpy() + num_bonds = edge_index.shape[1] + for j in range(0, num_bonds, 2): + begin_idx = int(edge_index[0, j]) + end_idx = int(edge_index[1, j]) + bond_type_idx, bond_dir_idx = edge_attr[j] + if not G.has_edge(begin_idx, end_idx): + G.add_edge(begin_idx, end_idx, bond_type_idx=bond_type_idx, + bond_dir_idx=bond_dir_idx) + + return G + +def nx_to_graph_data_obj_simple(G): + """ + Converts nx graph to pytorch geometric Data object. Assume node indices + are numbered from 0 to num_nodes - 1. NB: Uses simplified atom and bond + features, and represent as indices. NB: possible issues with + recapitulating relative stereochemistry since the edges in the nx + object are unordered. + :param G: nx graph obj + :return: pytorch geometric Data object + """ + # atoms + num_atom_features = 2 # atom type, chirality tag + atom_features_list = [] + for _, node in G.nodes(data=True): + atom_feature = [node['atom_num_idx'], node['chirality_tag_idx']] + atom_features_list.append(atom_feature) + x = torch.tensor(np.array(atom_features_list), dtype=torch.long) + + # bonds + num_bond_features = 2 # bond type, bond direction + if len(G.edges()) > 0: # mol has bonds + edges_list = [] + edge_features_list = [] + for i, j, edge in G.edges(data=True): + edge_feature = [edge['bond_type_idx'], edge['bond_dir_idx']] + edges_list.append((i, j)) + edge_features_list.append(edge_feature) + edges_list.append((j, i)) + edge_features_list.append(edge_feature) + + # data.edge_index: Graph connectivity in COO format with shape [2, num_edges] + edge_index = torch.tensor(np.array(edges_list).T, dtype=torch.long) + + # data.edge_attr: Edge feature matrix with shape [num_edges, num_edge_features] + edge_attr = torch.tensor(np.array(edge_features_list), + dtype=torch.long) + else: # mol has no bonds + edge_index = torch.empty((2, 0), dtype=torch.long) + edge_attr = torch.empty((0, num_bond_features), dtype=torch.long) + + data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr) + + return data + +def get_gasteiger_partial_charges(mol, n_iter=12): + """ + Calculates list of gasteiger partial charges for each atom in mol object. + :param mol: rdkit mol object + :param n_iter: number of iterations. Default 12 + :return: list of computed partial charges for each atom. + """ + Chem.rdPartialCharges.ComputeGasteigerCharges(mol, nIter=n_iter, + throwOnParamFailure=True) + partial_charges = [float(a.GetProp('_GasteigerCharge')) for a in + mol.GetAtoms()] + return partial_charges + +def create_standardized_mol_id(smiles): + """ + + :param smiles: + :return: inchi + """ + if check_smiles_validity(smiles): + # remove stereochemistry + smiles = AllChem.MolToSmiles(AllChem.MolFromSmiles(smiles), + isomericSmiles=False) + mol = AllChem.MolFromSmiles(smiles) + if mol != None: # to catch weird issue with O=C1O[al]2oc(=O)c3ccc(cn3)c3ccccc3c3cccc(c3)c3ccccc3c3cc(C(F)(F)F)c(cc3o2)-c2ccccc2-c2cccc(c2)-c2ccccc2-c2cccnc21 + if '.' in smiles: # if multiple species, pick largest molecule + mol_species_list = split_rdkit_mol_obj(mol) + largest_mol = get_largest_mol(mol_species_list) + inchi = AllChem.MolToInchi(largest_mol) + else: + inchi = AllChem.MolToInchi(mol) + return inchi + else: + return + else: + return + +class MoleculeDataset(InMemoryDataset): + def __init__(self, + root, + #data = None, + #slices = None, + transform=None, + pre_transform=None, + pre_filter=None, + dataset='zinc250k', + empty=False): + """ + Adapted from qm9.py. Disabled the download functionality + :param root: directory of the dataset, containing a raw and processed + dir. The raw dir should contain the file containing the smiles, and the + processed dir can either empty or a previously processed file + :param dataset: name of the dataset. Currently only implemented for + zinc250k, chembl_with_labels, tox21, hiv, bace, bbbp, clintox, esol, + freesolv, lipophilicity, muv, pcba, sider, toxcast + :param empty: if True, then will not load any data obj. For + initializing empty dataset + """ + self.dataset = dataset + self.root = root + + super(MoleculeDataset, self).__init__(root, transform, pre_transform, + pre_filter) + self.transform, self.pre_transform, self.pre_filter = transform, pre_transform, pre_filter + + if not empty: + self.data, self.slices = torch.load(self.processed_paths[0]) + + + def get(self, idx): + data = Data() + for key in self.data.keys: + item, slices = self.data[key], self.slices[key] + s = list(repeat(slice(None), item.dim())) + s[data.cat_dim(key, item)] = slice(slices[idx], + slices[idx + 1]) + data[key] = item[s] + return data + + + @property + def raw_file_names(self): + file_name_list = os.listdir(self.raw_dir) + # assert len(file_name_list) == 1 # currently assume we have a + # # single raw file + return file_name_list + + @property + def processed_file_names(self): + return 'geometric_data_processed.pt' + + def download(self): + raise NotImplementedError('Must indicate valid location of raw data. ' + 'No download allowed') + + def process(self): + data_smiles_list = [] + data_list = [] + + if self.dataset == 'zinc_standard_agent': + input_path = self.raw_paths[0] + input_df = pd.read_csv(input_path, sep=',', compression='gzip', + dtype='str') + smiles_list = list(input_df['smiles']) + zinc_id_list = list(input_df['zinc_id']) + for i in range(len(smiles_list)): + print(i) + s = smiles_list[i] + # each example contains a single species + try: + rdkit_mol = AllChem.MolFromSmiles(s) + if rdkit_mol != None: # ignore invalid mol objects + # # convert aromatic bonds to double bonds + # Chem.SanitizeMol(rdkit_mol, + # sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + data = mol_to_graph_data_obj_simple(rdkit_mol) + # manually add mol id + id = int(zinc_id_list[i].split('ZINC')[1].lstrip('0')) + data.id = torch.tensor( + [id]) # id here is zinc id value, stripped of + # leading zeros + data_list.append(data) + data_smiles_list.append(smiles_list[i]) + except: + continue + + elif self.dataset == 'chembl_filtered': + ### get downstream test molecules. + from splitters import scaffold_split + + ### + downstream_dir = [ + 'dataset/bace', + 'dataset/bbbp', + 'dataset/clintox', + 'dataset/esol', + 'dataset/freesolv', + 'dataset/hiv', + 'dataset/lipophilicity', + 'dataset/muv', + # 'dataset/pcba/processed/smiles.csv', + 'dataset/sider', + 'dataset/tox21', + 'dataset/toxcast' + ] + + downstream_inchi_set = set() + for d_path in downstream_dir: + print(d_path) + dataset_name = d_path.split('/')[1] + downstream_dataset = MoleculeDataset(d_path, dataset=dataset_name) + downstream_smiles = pd.read_csv(os.path.join(d_path, + 'processed', 'smiles.csv'), + header=None)[0].tolist() + + assert len(downstream_dataset) == len(downstream_smiles) + + _, _, _, (train_smiles, valid_smiles, test_smiles) = scaffold_split(downstream_dataset, downstream_smiles, task_idx=None, null_value=0, + frac_train=0.8,frac_valid=0.1, frac_test=0.1, + return_smiles=True) + + ### remove both test and validation molecules + remove_smiles = test_smiles + valid_smiles + + downstream_inchis = [] + for smiles in remove_smiles: + species_list = smiles.split('.') + for s in species_list: # record inchi for all species, not just + # largest (by default in create_standardized_mol_id if input has + # multiple species) + inchi = create_standardized_mol_id(s) + downstream_inchis.append(inchi) + downstream_inchi_set.update(downstream_inchis) + + smiles_list, rdkit_mol_objs, folds, labels = \ + _load_chembl_with_labels_dataset(os.path.join(self.root, 'raw')) + + print('processing') + for i in range(len(rdkit_mol_objs)): + print(i) + rdkit_mol = rdkit_mol_objs[i] + if rdkit_mol != None: + # # convert aromatic bonds to double bonds + # Chem.SanitizeMol(rdkit_mol, + # sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + mw = Descriptors.MolWt(rdkit_mol) + if 50 <= mw <= 900: + inchi = create_standardized_mol_id(smiles_list[i]) + if inchi != None and inchi not in downstream_inchi_set: + data = mol_to_graph_data_obj_simple(rdkit_mol) + # manually add mol id + data.id = torch.tensor( + [i]) # id here is the index of the mol in + # the dataset + data.y = torch.tensor(labels[i, :]) + # fold information + if i in folds[0]: + data.fold = torch.tensor([0]) + elif i in folds[1]: + data.fold = torch.tensor([1]) + else: + data.fold = torch.tensor([2]) + data_list.append(data) + data_smiles_list.append(smiles_list[i]) + + elif self.dataset == 'tox21': + smiles_list, rdkit_mol_objs, labels = \ + _load_tox21_dataset(self.raw_paths[0]) + for i in range(len(smiles_list)): + print(i) + rdkit_mol = rdkit_mol_objs[i] + ## convert aromatic bonds to double bonds + #Chem.SanitizeMol(rdkit_mol, + #sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + data = mol_to_graph_data_obj_simple(rdkit_mol) + # manually add mol id + data.id = torch.tensor( + [i]) # id here is the index of the mol in + # the dataset + data.y = torch.tensor(labels[i, :]) + data_list.append(data) + data_smiles_list.append(smiles_list[i]) + + elif self.dataset == 'hiv': + smiles_list, rdkit_mol_objs, labels = \ + _load_hiv_dataset(self.raw_paths[0]) + for i in range(len(smiles_list)): + print(i) + rdkit_mol = rdkit_mol_objs[i] + # # convert aromatic bonds to double bonds + # Chem.SanitizeMol(rdkit_mol, + # sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + data = mol_to_graph_data_obj_simple(rdkit_mol) + # manually add mol id + data.id = torch.tensor( + [i]) # id here is the index of the mol in + # the dataset + data.y = torch.tensor([labels[i]]) + data_list.append(data) + data_smiles_list.append(smiles_list[i]) + + elif self.dataset == 'bace': + smiles_list, rdkit_mol_objs, folds, labels = \ + _load_bace_dataset(self.raw_paths[0]) + for i in range(len(smiles_list)): + print(i) + rdkit_mol = rdkit_mol_objs[i] + # # convert aromatic bonds to double bonds + # Chem.SanitizeMol(rdkit_mol, + # sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + data = mol_to_graph_data_obj_simple(rdkit_mol) + # manually add mol id + data.id = torch.tensor( + [i]) # id here is the index of the mol in + # the dataset + data.y = torch.tensor([labels[i]]) + data.fold = torch.tensor([folds[i]]) + data_list.append(data) + data_smiles_list.append(smiles_list[i]) + + elif self.dataset == 'bbbp': + smiles_list, rdkit_mol_objs, labels = \ + _load_bbbp_dataset(self.raw_paths[0]) + for i in range(len(smiles_list)): + print(i) + rdkit_mol = rdkit_mol_objs[i] + if rdkit_mol != None: + # # convert aromatic bonds to double bonds + # Chem.SanitizeMol(rdkit_mol, + # sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + data = mol_to_graph_data_obj_simple(rdkit_mol) + # manually add mol id + data.id = torch.tensor( + [i]) # id here is the index of the mol in + # the dataset + data.y = torch.tensor([labels[i]]) + data_list.append(data) + data_smiles_list.append(smiles_list[i]) + + elif self.dataset == 'clintox': + smiles_list, rdkit_mol_objs, labels = \ + _load_clintox_dataset(self.raw_paths[0]) + for i in range(len(smiles_list)): + print(i) + rdkit_mol = rdkit_mol_objs[i] + if rdkit_mol != None: + # # convert aromatic bonds to double bonds + # Chem.SanitizeMol(rdkit_mol, + # sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + data = mol_to_graph_data_obj_simple(rdkit_mol) + # manually add mol id + data.id = torch.tensor( + [i]) # id here is the index of the mol in + # the dataset + data.y = torch.tensor(labels[i, :]) + data_list.append(data) + data_smiles_list.append(smiles_list[i]) + + elif self.dataset == 'esol': + smiles_list, rdkit_mol_objs, labels = \ + _load_esol_dataset(self.raw_paths[0]) + for i in range(len(smiles_list)): + print(i) + rdkit_mol = rdkit_mol_objs[i] + # # convert aromatic bonds to double bonds + # Chem.SanitizeMol(rdkit_mol, + # sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + data = mol_to_graph_data_obj_simple(rdkit_mol) + # manually add mol id + data.id = torch.tensor( + [i]) # id here is the index of the mol in + # the dataset + data.y = torch.tensor([labels[i]]) + data_list.append(data) + data_smiles_list.append(smiles_list[i]) + + elif self.dataset == 'freesolv': + smiles_list, rdkit_mol_objs, labels = \ + _load_freesolv_dataset(self.raw_paths[0]) + for i in range(len(smiles_list)): + print(i) + rdkit_mol = rdkit_mol_objs[i] + # # convert aromatic bonds to double bonds + # Chem.SanitizeMol(rdkit_mol, + # sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + data = mol_to_graph_data_obj_simple(rdkit_mol) + # manually add mol id + data.id = torch.tensor( + [i]) # id here is the index of the mol in + # the dataset + data.y = torch.tensor([labels[i]]) + data_list.append(data) + data_smiles_list.append(smiles_list[i]) + + elif self.dataset == 'lipophilicity': + smiles_list, rdkit_mol_objs, labels = \ + _load_lipophilicity_dataset(self.raw_paths[0]) + for i in range(len(smiles_list)): + print(i) + rdkit_mol = rdkit_mol_objs[i] + # # convert aromatic bonds to double bonds + # Chem.SanitizeMol(rdkit_mol, + # sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + data = mol_to_graph_data_obj_simple(rdkit_mol) + # manually add mol id + data.id = torch.tensor( + [i]) # id here is the index of the mol in + # the dataset + data.y = torch.tensor([labels[i]]) + data_list.append(data) + data_smiles_list.append(smiles_list[i]) + + elif self.dataset == 'muv': + smiles_list, rdkit_mol_objs, labels = \ + _load_muv_dataset(self.raw_paths[0]) + for i in range(len(smiles_list)): + print(i) + rdkit_mol = rdkit_mol_objs[i] + # # convert aromatic bonds to double bonds + # Chem.SanitizeMol(rdkit_mol, + # sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + data = mol_to_graph_data_obj_simple(rdkit_mol) + # manually add mol id + data.id = torch.tensor( + [i]) # id here is the index of the mol in + # the dataset + data.y = torch.tensor(labels[i, :]) + data_list.append(data) + data_smiles_list.append(smiles_list[i]) + + elif self.dataset == 'pcba': + smiles_list, rdkit_mol_objs, labels = \ + _load_pcba_dataset(self.raw_paths[0]) + for i in range(len(smiles_list)): + print(i) + rdkit_mol = rdkit_mol_objs[i] + # # convert aromatic bonds to double bonds + # Chem.SanitizeMol(rdkit_mol, + # sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + data = mol_to_graph_data_obj_simple(rdkit_mol) + # manually add mol id + data.id = torch.tensor( + [i]) # id here is the index of the mol in + # the dataset + data.y = torch.tensor(labels[i, :]) + data_list.append(data) + data_smiles_list.append(smiles_list[i]) + + elif self.dataset == 'pcba_pretrain': + smiles_list, rdkit_mol_objs, labels = \ + _load_pcba_dataset(self.raw_paths[0]) + downstream_inchi = set(pd.read_csv(os.path.join(self.root, + 'downstream_mol_inchi_may_24_2019'), + sep=',', header=None)[0]) + for i in range(len(smiles_list)): + print(i) + if '.' not in smiles_list[i]: # remove examples with + # multiples species + rdkit_mol = rdkit_mol_objs[i] + mw = Descriptors.MolWt(rdkit_mol) + if 50 <= mw <= 900: + inchi = create_standardized_mol_id(smiles_list[i]) + if inchi != None and inchi not in downstream_inchi: + # # convert aromatic bonds to double bonds + # Chem.SanitizeMol(rdkit_mol, + # sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + data = mol_to_graph_data_obj_simple(rdkit_mol) + # manually add mol id + data.id = torch.tensor( + [i]) # id here is the index of the mol in + # the dataset + data.y = torch.tensor(labels[i, :]) + data_list.append(data) + data_smiles_list.append(smiles_list[i]) + + # elif self.dataset == '' + + elif self.dataset == 'sider': + smiles_list, rdkit_mol_objs, labels = \ + _load_sider_dataset(self.raw_paths[0]) + for i in range(len(smiles_list)): + print(i) + rdkit_mol = rdkit_mol_objs[i] + # # convert aromatic bonds to double bonds + # Chem.SanitizeMol(rdkit_mol, + # sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + data = mol_to_graph_data_obj_simple(rdkit_mol) + # manually add mol id + data.id = torch.tensor( + [i]) # id here is the index of the mol in + # the dataset + data.y = torch.tensor(labels[i, :]) + data_list.append(data) + data_smiles_list.append(smiles_list[i]) + + elif self.dataset == 'toxcast': + smiles_list, rdkit_mol_objs, labels = \ + _load_toxcast_dataset(self.raw_paths[0]) + for i in range(len(smiles_list)): + print(i) + rdkit_mol = rdkit_mol_objs[i] + if rdkit_mol != None: + # # convert aromatic bonds to double bonds + # Chem.SanitizeMol(rdkit_mol, + # sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + data = mol_to_graph_data_obj_simple(rdkit_mol) + # manually add mol id + data.id = torch.tensor( + [i]) # id here is the index of the mol in + # the dataset + data.y = torch.tensor(labels[i, :]) + data_list.append(data) + data_smiles_list.append(smiles_list[i]) + + elif self.dataset == 'ptc_mr': + input_path = self.raw_paths[0] + input_df = pd.read_csv(input_path, sep=',', header=None, names=['id', 'label', 'smiles']) + smiles_list = input_df['smiles'] + labels = input_df['label'].values + for i in range(len(smiles_list)): + print(i) + s = smiles_list[i] + rdkit_mol = AllChem.MolFromSmiles(s) + if rdkit_mol != None: # ignore invalid mol objects + # # convert aromatic bonds to double bonds + # Chem.SanitizeMol(rdkit_mol, + # sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + data = mol_to_graph_data_obj_simple(rdkit_mol) + # manually add mol id + data.id = torch.tensor( + [i]) + data.y = torch.tensor([labels[i]]) + data_list.append(data) + data_smiles_list.append(smiles_list[i]) + + elif self.dataset == 'mutag': + smiles_path = os.path.join(self.root, 'raw', 'mutag_188_data.can') + # smiles_path = 'dataset/mutag/raw/mutag_188_data.can' + labels_path = os.path.join(self.root, 'raw', 'mutag_188_target.txt') + # labels_path = 'dataset/mutag/raw/mutag_188_target.txt' + smiles_list = pd.read_csv(smiles_path, sep=' ', header=None)[0] + labels = pd.read_csv(labels_path, header=None)[0].values + for i in range(len(smiles_list)): + print(i) + s = smiles_list[i] + rdkit_mol = AllChem.MolFromSmiles(s) + if rdkit_mol != None: # ignore invalid mol objects + # # convert aromatic bonds to double bonds + # Chem.SanitizeMol(rdkit_mol, + # sanitizeOps=Chem.SanitizeFlags.SANITIZE_KEKULIZE) + data = mol_to_graph_data_obj_simple(rdkit_mol) + # manually add mol id + data.id = torch.tensor( + [i]) + data.y = torch.tensor([labels[i]]) + data_list.append(data) + data_smiles_list.append(smiles_list[i]) + + + else: + raise ValueError('Invalid dataset name') + + if self.pre_filter is not None: + data_list = [data for data in data_list if self.pre_filter(data)] + + if self.pre_transform is not None: + data_list = [self.pre_transform(data) for data in data_list] + + # write data_smiles_list in processed paths + data_smiles_series = pd.Series(data_smiles_list) + data_smiles_series.to_csv(os.path.join(self.processed_dir, + 'smiles.csv'), index=False, + header=False) + + data, slices = self.collate(data_list) + torch.save((data, slices), self.processed_paths[0]) + +# NB: only properly tested when dataset_1 is chembl_with_labels and dataset_2 +# is pcba_pretrain +def merge_dataset_objs(dataset_1, dataset_2): + """ + Naively merge 2 molecule dataset objects, and ignore identities of + molecules. Assumes both datasets have multiple y labels, and will pad + accordingly. ie if dataset_1 has obj_1 with y dim 1310 and dataset_2 has + obj_2 with y dim 128, then the resulting obj_1 and obj_2 will have dim + 1438, where obj_1 have the last 128 cols with 0, and obj_2 have + the first 1310 cols with 0. + :return: pytorch geometric dataset obj, with the x, edge_attr, edge_index, + new y attributes only + """ + d_1_y_dim = dataset_1[0].y.size()[0] + d_2_y_dim = dataset_2[0].y.size()[0] + + data_list = [] + # keep only x, edge_attr, edge_index, padded_y then append + for d in dataset_1: + old_y = d.y + new_y = torch.cat([old_y, torch.zeros(d_2_y_dim, dtype=torch.long)]) + data_list.append(Data(x=d.x, edge_index=d.edge_index, + edge_attr=d.edge_attr, y=new_y)) + + for d in dataset_2: + old_y = d.y + new_y = torch.cat([torch.zeros(d_1_y_dim, dtype=torch.long), old_y.long()]) + data_list.append(Data(x=d.x, edge_index=d.edge_index, + edge_attr=d.edge_attr, y=new_y)) + + # create 'empty' dataset obj. Just randomly pick a dataset and root path + # that has already been processed + new_dataset = MoleculeDataset(root='dataset/chembl_with_labels', + dataset='chembl_with_labels', empty=True) + # collate manually + new_dataset.data, new_dataset.slices = new_dataset.collate(data_list) + + return new_dataset + +def create_circular_fingerprint(mol, radius, size, chirality): + """ + + :param mol: + :param radius: + :param size: + :param chirality: + :return: np array of morgan fingerprint + """ + fp = GetMorganFingerprintAsBitVect(mol, radius, + nBits=size, useChirality=chirality) + return np.array(fp) + +class MoleculeFingerprintDataset(data.Dataset): + def __init__(self, root, dataset, radius, size, chirality=True): + """ + Create dataset object containing list of dicts, where each dict + contains the circular fingerprint of the molecule, label, id, + and possibly precomputed fold information + :param root: directory of the dataset, containing a raw and + processed_fp dir. The raw dir should contain the file containing the + smiles, and the processed_fp dir can either be empty or a + previously processed file + :param dataset: name of dataset. Currently only implemented for + tox21, hiv, chembl_with_labels + :param radius: radius of the circular fingerprints + :param size: size of the folded fingerprint vector + :param chirality: if True, fingerprint includes chirality information + """ + self.dataset = dataset + self.root = root + self.radius = radius + self.size = size + self.chirality = chirality + + self._load() + + def _process(self): + data_smiles_list = [] + data_list = [] + if self.dataset == 'chembl_with_labels': + smiles_list, rdkit_mol_objs, folds, labels = \ + _load_chembl_with_labels_dataset(os.path.join(self.root, 'raw')) + print('processing') + for i in range(len(rdkit_mol_objs)): + print(i) + rdkit_mol = rdkit_mol_objs[i] + if rdkit_mol != None: + # # convert aromatic bonds to double bonds + fp_arr = create_circular_fingerprint(rdkit_mol, + self.radius, + self.size, self.chirality) + fp_arr = torch.tensor(fp_arr) + # manually add mol id + id = torch.tensor([i]) # id here is the index of the mol in + # the dataset + y = torch.tensor(labels[i, :]) + # fold information + if i in folds[0]: + fold = torch.tensor([0]) + elif i in folds[1]: + fold = torch.tensor([1]) + else: + fold = torch.tensor([2]) + data_list.append({'fp_arr': fp_arr, 'id': id, 'y': y, + 'fold': fold}) + data_smiles_list.append(smiles_list[i]) + elif self.dataset == 'tox21': + smiles_list, rdkit_mol_objs, labels = \ + _load_tox21_dataset(os.path.join(self.root, 'raw/tox21.csv')) + print('processing') + for i in range(len(smiles_list)): + print(i) + rdkit_mol = rdkit_mol_objs[i] + ## convert aromatic bonds to double bonds + fp_arr = create_circular_fingerprint(rdkit_mol, + self.radius, + self.size, + self.chirality) + fp_arr = torch.tensor(fp_arr) + + # manually add mol id + id = torch.tensor([i]) # id here is the index of the mol in + # the dataset + y = torch.tensor(labels[i, :]) + data_list.append({'fp_arr': fp_arr, 'id': id, 'y': y}) + data_smiles_list.append(smiles_list[i]) + elif self.dataset == 'hiv': + smiles_list, rdkit_mol_objs, labels = \ + _load_hiv_dataset(os.path.join(self.root, 'raw/HIV.csv')) + print('processing') + for i in range(len(smiles_list)): + print(i) + rdkit_mol = rdkit_mol_objs[i] + # # convert aromatic bonds to double bonds + fp_arr = create_circular_fingerprint(rdkit_mol, + self.radius, + self.size, + self.chirality) + fp_arr = torch.tensor(fp_arr) + + # manually add mol id + id = torch.tensor([i]) # id here is the index of the mol in + # the dataset + y = torch.tensor([labels[i]]) + data_list.append({'fp_arr': fp_arr, 'id': id, 'y': y}) + data_smiles_list.append(smiles_list[i]) + else: + raise ValueError('Invalid dataset name') + + # save processed data objects and smiles + processed_dir = os.path.join(self.root, 'processed_fp') + data_smiles_series = pd.Series(data_smiles_list) + data_smiles_series.to_csv(os.path.join(processed_dir, 'smiles.csv'), + index=False, + header=False) + with open(os.path.join(processed_dir, + 'fingerprint_data_processed.pkl'), + 'wb') as f: + pickle.dump(data_list, f) + + def _load(self): + processed_dir = os.path.join(self.root, 'processed_fp') + # check if saved file exist. If so, then load from save + file_name_list = os.listdir(processed_dir) + if 'fingerprint_data_processed.pkl' in file_name_list: + with open(os.path.join(processed_dir, + 'fingerprint_data_processed.pkl'), + 'rb') as f: + self.data_list = pickle.load(f) + # if no saved file exist, then perform processing steps, save then + # reload + else: + self._process() + self._load() + + def __len__(self): + return len(self.data_list) + + def __getitem__(self, index): + ## if iterable class is passed, return dataset objection + if hasattr(index, "__iter__"): + dataset = MoleculeFingerprintDataset(self.root, self.dataset, self.radius, self.size, chirality=self.chirality) + dataset.data_list = [self.data_list[i] for i in index] + return dataset + else: + return self.data_list[index] + + +def _load_tox21_dataset(input_path): + """ + + :param input_path: + :return: list of smiles, list of rdkit mol obj, np.array containing the + labels + """ + input_df = pd.read_csv(input_path, sep=',') + smiles_list = input_df['smiles'] + rdkit_mol_objs_list = [AllChem.MolFromSmiles(s) for s in smiles_list] + tasks = ['NR-AR', 'NR-AR-LBD', 'NR-AhR', 'NR-Aromatase', 'NR-ER', 'NR-ER-LBD', + 'NR-PPAR-gamma', 'SR-ARE', 'SR-ATAD5', 'SR-HSE', 'SR-MMP', 'SR-p53'] + labels = input_df[tasks] + # convert 0 to -1 + labels = labels.replace(0, -1) + # convert nan to 0 + labels = labels.fillna(0) + assert len(smiles_list) == len(rdkit_mol_objs_list) + assert len(smiles_list) == len(labels) + return smiles_list, rdkit_mol_objs_list, labels.values + +def _load_hiv_dataset(input_path): + """ + :param input_path: + :return: list of smiles, list of rdkit mol obj, np.array containing the + labels + """ + input_df = pd.read_csv(input_path, sep=',') + smiles_list = input_df['smiles'] + rdkit_mol_objs_list = [AllChem.MolFromSmiles(s) for s in smiles_list] + labels = input_df['HIV_active'] + # convert 0 to -1 + labels = labels.replace(0, -1) + # there are no nans + assert len(smiles_list) == len(rdkit_mol_objs_list) + assert len(smiles_list) == len(labels) + return smiles_list, rdkit_mol_objs_list, labels.values + +def _load_bace_dataset(input_path): + """ + + :param input_path: + :return: list of smiles, list of rdkit mol obj, np.array + containing indices for each of the 3 folds, np.array containing the + labels + """ + input_df = pd.read_csv(input_path, sep=',') + smiles_list = input_df['mol'] + rdkit_mol_objs_list = [AllChem.MolFromSmiles(s) for s in smiles_list] + labels = input_df['Class'] + # convert 0 to -1 + labels = labels.replace(0, -1) + # there are no nans + folds = input_df['Model'] + folds = folds.replace('Train', 0) # 0 -> train + folds = folds.replace('Valid', 1) # 1 -> valid + folds = folds.replace('Test', 2) # 2 -> test + assert len(smiles_list) == len(rdkit_mol_objs_list) + assert len(smiles_list) == len(labels) + assert len(smiles_list) == len(folds) + return smiles_list, rdkit_mol_objs_list, folds.values, labels.values + +def _load_bbbp_dataset(input_path): + """ + + :param input_path: + :return: list of smiles, list of rdkit mol obj, np.array containing the + labels + """ + input_df = pd.read_csv(input_path, sep=',') + smiles_list = input_df['smiles'] + rdkit_mol_objs_list = [AllChem.MolFromSmiles(s) for s in smiles_list] + + preprocessed_rdkit_mol_objs_list = [m if m != None else None for m in + rdkit_mol_objs_list] + preprocessed_smiles_list = [AllChem.MolToSmiles(m) if m != None else + None for m in preprocessed_rdkit_mol_objs_list] + labels = input_df['p_np'] + # convert 0 to -1 + labels = labels.replace(0, -1) + # there are no nans + assert len(smiles_list) == len(preprocessed_rdkit_mol_objs_list) + assert len(smiles_list) == len(preprocessed_smiles_list) + assert len(smiles_list) == len(labels) + return preprocessed_smiles_list, preprocessed_rdkit_mol_objs_list, \ + labels.values + +def _load_clintox_dataset(input_path): + """ + + :param input_path: + :return: list of smiles, list of rdkit mol obj, np.array containing the + labels + """ + input_df = pd.read_csv(input_path, sep=',') + smiles_list = input_df['smiles'] + rdkit_mol_objs_list = [AllChem.MolFromSmiles(s) for s in smiles_list] + + preprocessed_rdkit_mol_objs_list = [m if m != None else None for m in + rdkit_mol_objs_list] + preprocessed_smiles_list = [AllChem.MolToSmiles(m) if m != None else + None for m in preprocessed_rdkit_mol_objs_list] + tasks = ['FDA_APPROVED', 'CT_TOX'] + labels = input_df[tasks] + # convert 0 to -1 + labels = labels.replace(0, -1) + # there are no nans + assert len(smiles_list) == len(preprocessed_rdkit_mol_objs_list) + assert len(smiles_list) == len(preprocessed_smiles_list) + assert len(smiles_list) == len(labels) + return preprocessed_smiles_list, preprocessed_rdkit_mol_objs_list, \ + labels.values +# input_path = 'dataset/clintox/raw/clintox.csv' +# smiles_list, rdkit_mol_objs_list, labels = _load_clintox_dataset(input_path) + +def _load_esol_dataset(input_path): + """ + + :param input_path: + :return: list of smiles, list of rdkit mol obj, np.array containing the + labels (regression task) + """ + # NB: some examples have multiple species + input_df = pd.read_csv(input_path, sep=',') + smiles_list = input_df['smiles'] + rdkit_mol_objs_list = [AllChem.MolFromSmiles(s) for s in smiles_list] + labels = input_df['measured log solubility in mols per litre'] + assert len(smiles_list) == len(rdkit_mol_objs_list) + assert len(smiles_list) == len(labels) + return smiles_list, rdkit_mol_objs_list, labels.values +# input_path = 'dataset/esol/raw/delaney-processed.csv' +# smiles_list, rdkit_mol_objs_list, labels = _load_esol_dataset(input_path) + +def _load_freesolv_dataset(input_path): + """ + + :param input_path: + :return: list of smiles, list of rdkit mol obj, np.array containing the + labels (regression task) + """ + input_df = pd.read_csv(input_path, sep=',') + smiles_list = input_df['smiles'] + rdkit_mol_objs_list = [AllChem.MolFromSmiles(s) for s in smiles_list] + labels = input_df['expt'] + assert len(smiles_list) == len(rdkit_mol_objs_list) + assert len(smiles_list) == len(labels) + return smiles_list, rdkit_mol_objs_list, labels.values + + +def _load_lipophilicity_dataset(input_path): + """ + + :param input_path: + :return: list of smiles, list of rdkit mol obj, np.array containing the + labels (regression task) + """ + input_df = pd.read_csv(input_path, sep=',') + smiles_list = input_df['smiles'] + rdkit_mol_objs_list = [AllChem.MolFromSmiles(s) for s in smiles_list] + labels = input_df['exp'] + assert len(smiles_list) == len(rdkit_mol_objs_list) + assert len(smiles_list) == len(labels) + return smiles_list, rdkit_mol_objs_list, labels.values + + +def _load_muv_dataset(input_path): + """ + + :param input_path: + :return: list of smiles, list of rdkit mol obj, np.array containing the + labels + """ + input_df = pd.read_csv(input_path, sep=',') + smiles_list = input_df['smiles'] + rdkit_mol_objs_list = [AllChem.MolFromSmiles(s) for s in smiles_list] + tasks = ['MUV-466', 'MUV-548', 'MUV-600', 'MUV-644', 'MUV-652', 'MUV-689', + 'MUV-692', 'MUV-712', 'MUV-713', 'MUV-733', 'MUV-737', 'MUV-810', + 'MUV-832', 'MUV-846', 'MUV-852', 'MUV-858', 'MUV-859'] + labels = input_df[tasks] + # convert 0 to -1 + labels = labels.replace(0, -1) + # convert nan to 0 + labels = labels.fillna(0) + assert len(smiles_list) == len(rdkit_mol_objs_list) + assert len(smiles_list) == len(labels) + return smiles_list, rdkit_mol_objs_list, labels.values + +def _load_sider_dataset(input_path): + """ + + :param input_path: + :return: list of smiles, list of rdkit mol obj, np.array containing the + labels + """ + input_df = pd.read_csv(input_path, sep=',') + smiles_list = input_df['smiles'] + rdkit_mol_objs_list = [AllChem.MolFromSmiles(s) for s in smiles_list] + tasks = ['Hepatobiliary disorders', + 'Metabolism and nutrition disorders', 'Product issues', 'Eye disorders', + 'Investigations', 'Musculoskeletal and connective tissue disorders', + 'Gastrointestinal disorders', 'Social circumstances', + 'Immune system disorders', 'Reproductive system and breast disorders', + 'Neoplasms benign, malignant and unspecified (incl cysts and polyps)', + 'General disorders and administration site conditions', + 'Endocrine disorders', 'Surgical and medical procedures', + 'Vascular disorders', 'Blood and lymphatic system disorders', + 'Skin and subcutaneous tissue disorders', + 'Congenital, familial and genetic disorders', + 'Infections and infestations', + 'Respiratory, thoracic and mediastinal disorders', + 'Psychiatric disorders', 'Renal and urinary disorders', + 'Pregnancy, puerperium and perinatal conditions', + 'Ear and labyrinth disorders', 'Cardiac disorders', + 'Nervous system disorders', + 'Injury, poisoning and procedural complications'] + labels = input_df[tasks] + # convert 0 to -1 + labels = labels.replace(0, -1) + assert len(smiles_list) == len(rdkit_mol_objs_list) + assert len(smiles_list) == len(labels) + return smiles_list, rdkit_mol_objs_list, labels.value + +def _load_toxcast_dataset(input_path): + """ + + :param input_path: + :return: list of smiles, list of rdkit mol obj, np.array containing the + labels + """ + # NB: some examples have multiple species, some example smiles are invalid + input_df = pd.read_csv(input_path, sep=',') + smiles_list = input_df['smiles'] + rdkit_mol_objs_list = [AllChem.MolFromSmiles(s) for s in smiles_list] + # Some smiles could not be successfully converted + # to rdkit mol object so them to None + preprocessed_rdkit_mol_objs_list = [m if m != None else None for m in + rdkit_mol_objs_list] + preprocessed_smiles_list = [AllChem.MolToSmiles(m) if m != None else + None for m in preprocessed_rdkit_mol_objs_list] + tasks = list(input_df.columns)[1:] + labels = input_df[tasks] + # convert 0 to -1 + labels = labels.replace(0, -1) + # convert nan to 0 + labels = labels.fillna(0) + assert len(smiles_list) == len(preprocessed_rdkit_mol_objs_list) + assert len(smiles_list) == len(preprocessed_smiles_list) + assert len(smiles_list) == len(labels) + return preprocessed_smiles_list, preprocessed_rdkit_mol_objs_list, \ + labels.values + +def _load_chembl_with_labels_dataset(root_path): + """ + Data from 'Large-scale comparison of machine learning methods for drug target prediction on ChEMBL' + :param root_path: path to the folder containing the reduced chembl dataset + :return: list of smiles, preprocessed rdkit mol obj list, list of np.array + containing indices for each of the 3 folds, np.array containing the labels + """ + # adapted from https://github.com/ml-jku/lsc/blob/master/pythonCode/lstm/loadData.py + # first need to download the files and unzip: + # wget http://bioinf.jku.at/research/lsc/chembl20/dataPythonReduced.zip + # unzip and rename to chembl_with_labels + # wget http://bioinf.jku.at/research/lsc/chembl20/dataPythonReduced/chembl20Smiles.pckl + # into the dataPythonReduced directory + # wget http://bioinf.jku.at/research/lsc/chembl20/dataPythonReduced/chembl20LSTM.pckl + + # 1. load folds and labels + f=open(os.path.join(root_path, 'folds0.pckl'), 'rb') + folds=pickle.load(f) + f.close() + + f=open(os.path.join(root_path, 'labelsHard.pckl'), 'rb') + targetMat=pickle.load(f) + sampleAnnInd=pickle.load(f) + targetAnnInd=pickle.load(f) + f.close() + + targetMat=targetMat + targetMat=targetMat.copy().tocsr() + targetMat.sort_indices() + targetAnnInd=targetAnnInd + targetAnnInd=targetAnnInd-targetAnnInd.min() + + folds=[np.intersect1d(fold, sampleAnnInd.index.values).tolist() for fold in folds] + targetMatTransposed=targetMat[sampleAnnInd[list(chain(*folds))]].T.tocsr() + targetMatTransposed.sort_indices() + # # num positive examples in each of the 1310 targets + trainPosOverall=np.array([np.sum(targetMatTransposed[x].data > 0.5) for x in range(targetMatTransposed.shape[0])]) + # # num negative examples in each of the 1310 targets + trainNegOverall=np.array([np.sum(targetMatTransposed[x].data < -0.5) for x in range(targetMatTransposed.shape[0])]) + # dense array containing the labels for the 456331 molecules and 1310 targets + denseOutputData=targetMat.A # possible values are {-1, 0, 1} + + # 2. load structures + f=open(os.path.join(root_path, 'chembl20LSTM.pckl'), 'rb') + rdkitArr=pickle.load(f) + f.close() + + assert len(rdkitArr) == denseOutputData.shape[0] + assert len(rdkitArr) == len(folds[0]) + len(folds[1]) + len(folds[2]) + + preprocessed_rdkitArr = [] + print('preprocessing') + for i in range(len(rdkitArr)): + print(i) + m = rdkitArr[i] + if m == None: + preprocessed_rdkitArr.append(None) + else: + mol_species_list = split_rdkit_mol_obj(m) + if len(mol_species_list) == 0: + preprocessed_rdkitArr.append(None) + else: + largest_mol = get_largest_mol(mol_species_list) + if len(largest_mol.GetAtoms()) <= 2: + preprocessed_rdkitArr.append(None) + else: + preprocessed_rdkitArr.append(largest_mol) + + assert len(preprocessed_rdkitArr) == denseOutputData.shape[0] + + smiles_list = [AllChem.MolToSmiles(m) if m != None else None for m in + preprocessed_rdkitArr] # bc some empty mol in the + # rdkitArr zzz... + + assert len(preprocessed_rdkitArr) == len(smiles_list) + + return smiles_list, preprocessed_rdkitArr, folds, denseOutputData +# root_path = 'dataset/chembl_with_labels' + +def check_smiles_validity(smiles): + try: + m = Chem.MolFromSmiles(smiles) + if m: + return True + else: + return False + except: + return False + +def split_rdkit_mol_obj(mol): + """ + Split rdkit mol object containing multiple species or one species into a + list of mol objects or a list containing a single object respectively + :param mol: + :return: + """ + smiles = AllChem.MolToSmiles(mol, isomericSmiles=True) + smiles_list = smiles.split('.') + mol_species_list = [] + for s in smiles_list: + if check_smiles_validity(s): + mol_species_list.append(AllChem.MolFromSmiles(s)) + return mol_species_list + +def get_largest_mol(mol_list): + """ + Given a list of rdkit mol objects, returns mol object containing the + largest num of atoms. If multiple containing largest num of atoms, + picks the first one + :param mol_list: + :return: + """ + num_atoms_list = [len(m.GetAtoms()) for m in mol_list] + largest_mol_idx = num_atoms_list.index(max(num_atoms_list)) + return mol_list[largest_mol_idx] + +def create_all_datasets(): + #### create dataset + downstream_dir = [ + 'bace', + 'bbbp', + 'clintox', + 'esol', + 'freesolv', + 'hiv', + 'lipophilicity', + 'muv', + 'sider', + 'tox21', + 'toxcast' + ] + + for dataset_name in downstream_dir: + print(dataset_name) + root = "dataset/" + dataset_name + os.makedirs(root + "/processed", exist_ok=True) + dataset = MoleculeDataset(root, dataset=dataset_name) + print(dataset) + + + dataset = MoleculeDataset(root = "dataset/chembl_filtered", dataset="chembl_filtered") + print(dataset) + dataset = MoleculeDataset(root = "dataset/zinc_standard_agent", dataset="zinc_standard_agent") + print(dataset) + + +# test MoleculeDataset object +if __name__ == "__main__": + + create_all_datasets() + diff --git a/dig/xgraph/TAGE/nexplain_2stage_quant.ipynb b/dig/xgraph/TAGE/nexplain_2stage_quant.ipynb new file mode 100644 index 00000000..77a7b260 --- /dev/null +++ b/dig/xgraph/TAGE/nexplain_2stage_quant.ipynb @@ -0,0 +1,319 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "phantom-newman", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import pandas as pd\n", + "\n", + "from torch_geometric.data import DataLoader, Data\n", + "from torch_geometric.datasets import PPI\n", + "from torch_geometric.utils import remove_isolated_nodes\n", + "\n", + "from dig.sslgraph.utils import Encoder\n", + "from dig.sslgraph.dataset import get_node_dataset\n", + "\n", + "from downstream import MLP, EndtoEnd, train_MLP\n", + "from dig.xgraph.evaluation import XCollector\n", + "\n", + "device = torch.device(\"cuda:1\" if torch.cuda.is_available() else torch.device(\"cpu\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "hungry-stuart", + "metadata": {}, + "outputs": [], + "source": [ + "def get_task(idx):\n", + " def transform(data):\n", + " return Data(x=data.x, edge_index=data.edge_index, y=data.y[:, idx])\n", + " return transform\n", + "\n", + "def get_task_rm_iso(idx):\n", + " def transform(data):\n", + " edge_index, _, mask = remove_isolated_nodes(data.edge_index, num_nodes=data.x.shape[0])\n", + " return Data(x=data.x[mask], edge_index=edge_index, y=data.y[mask, idx])\n", + " return transform\n", + " \n", + "ppi = PPI('node_dataset/ppi/', transform=get_task_rm_iso(0))\n", + "loader = DataLoader(ppi, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cosmetic-conclusion", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encoder = Encoder(feat_dim=ppi[0].x.shape[1], hidden_dim=600, \n", + " n_layers=2, gnn='gcn', node_level=True, graph_level=False)\n", + "encoder.load_state_dict(torch.load('ckpts_model/ppi_pretrain_grace600_h2.pth', map_location='cpu'))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "saving-engineering", + "metadata": {}, + "outputs": [], + "source": [ + "from tagexplainer import TAGExplainer, MLPExplainer\n", + "enc_explainer = TAGExplainer(encoder, embed_dim=600, device=device, explain_graph=False, \n", + " grad_scale=0.1, coff_size=0.05, coff_ent=0.002, loss_type='JSE')" + ] + }, + { + "cell_type": "markdown", + "id": "surprising-daily", + "metadata": {}, + "source": [ + "#### To train the explainer, uncomment the following cell." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "quality-kernel", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 390/390 [00:31<00:00, 12.25it/s, loss=-.24, log=0.4901, 0.9028, 0.1317, 0.1679] \n", + "100%|██████████| 345/345 [00:27<00:00, 12.40it/s, loss=-.262, log=0.4368, 0.8754, -1.0137, 0.1058] \n", + "100%|██████████| 566/566 [01:02<00:00, 9.04it/s, loss=-.359, log=2.0513, 0.9053, -1.1522, 0.1367] \n", + "100%|██████████| 585/585 [01:10<00:00, 8.27it/s, loss=-.465, log=5.0124, 0.9107, -1.9695, 0.1184] \n", + "100%|██████████| 395/395 [00:33<00:00, 11.84it/s, loss=0.133, log=3.8440, 0.9185, -2.5551, 0.1190] \n", + "100%|██████████| 256/256 [00:17<00:00, 14.82it/s, loss=-.32, log=3.0553, 0.8555, -1.9011, 0.1751] \n", + "100%|██████████| 456/456 [00:41<00:00, 10.87it/s, loss=-.127, log=3.7548, 0.8240, -2.5014, 0.0741] \n", + "100%|██████████| 622/622 [01:17<00:00, 8.05it/s, loss=-.397, log=10.1655, 0.9261, -4.8972, 0.0603] \n", + "100%|██████████| 148/148 [00:08<00:00, 18.12it/s, loss=-.394, log=10.5567, 0.9312, -1.6987, 0.2227]\n", + "100%|██████████| 828/828 [02:19<00:00, 5.94it/s, loss=-.27, log=13.6232, 0.9427, -9.1332, 0.0388] \n", + "100%|██████████| 601/601 [01:09<00:00, 8.67it/s, loss=-.405, log=6.4849, 0.8588, -6.5346, 0.0813] \n", + "100%|██████████| 470/470 [00:45<00:00, 10.34it/s, loss=-.355, log=9.2056, 0.9030, -5.9569, 0.0418] \n", + "100%|██████████| 455/455 [00:44<00:00, 10.18it/s, loss=-.0371, log=8.9175, 0.9016, -5.8061, 0.0773] \n", + "100%|██████████| 870/870 [02:19<00:00, 6.22it/s, loss=-.411, log=18.2061, 0.9869, -8.4921, 0.0640] \n", + "100%|██████████| 699/699 [01:40<00:00, 6.96it/s, loss=-.128, log=17.9049, 0.9764, -13.0799, 0.0216] \n", + "100%|██████████| 582/582 [01:03<00:00, 9.14it/s, loss=-.392, log=12.9812, 0.9487, -9.8769, 0.0544] \n", + "100%|██████████| 663/663 [01:28<00:00, 7.47it/s, loss=-.835, log=6.7467, 0.8425, -13.8651, 0.0107] \n", + "100%|██████████| 704/704 [01:41<00:00, 6.97it/s, loss=-.188, log=18.0522, 0.9812, -15.0068, 0.0208] \n", + "100%|██████████| 791/791 [01:54<00:00, 6.89it/s, loss=-.458, log=16.1347, 0.9659, -11.5103, 0.0328] \n", + "100%|██████████| 756/756 [01:50<00:00, 6.83it/s, loss=-.424, log=33.1915, 0.9987, -12.8400, 0.0121] \n" + ] + } + ], + "source": [ + "# enc_explainer.train_explainer_node(loader, batch_size=4, lr=5e-6, epochs=1)\n", + "# torch.save(enc_explainer.state_dict(), 'ckpts_explainer/explain_ppi_grace.pt')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "aerial-wound", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_dict = torch.load('ckpts_explainer/explain_ppi_grace.pt')\n", + "enc_explainer.load_state_dict(state_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "proper-prime", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def get_results(task_id, top_k):\n", + " ppi = PPI('node_dataset/ppi/', transform=get_task_rm_iso(task_id))\n", + " loader = DataLoader(ppi, 1)\n", + "\n", + " mlp_model = MLP(num_layer = 2, emb_dim = 600, hidden_dim = 600, out_dim = 2)\n", + " mlp_model.load_state_dict(torch.load('ckpts_model/downstream_ppi%d_grace600.pth'%task_id, map_location='cpu'))\n", + " mlp_explainer = MLPExplainer(mlp_model, device)\n", + "\n", + " x_collector = XCollector()\n", + " for i, data in enumerate(loader):\n", + " for j, node_idx in enumerate(torch.where(data.y)[0]):\n", + " data.to(device)\n", + " walks, masks, related_preds = \\\n", + " enc_explainer(data, mlp_explainer, node_idx=node_idx, top_k=top_k)\n", + " fidelity = related_preds[0]['origin'] - related_preds[0]['maskout']\n", + "\n", + " print(f'explain graph {i} node {node_idx}'+' fidelity %.4f'%fidelity, end='\\r')\n", + " x_collector.collect_data(masks, related_preds)\n", + "\n", + " fid, fid_std = x_collector.fidelity\n", + " spa, spa_std = x_collector.sparsity\n", + "\n", + " print()\n", + " print(f'Fidelity: {fid:.4f} ±{fid_std:.4f}\\n'\n", + " f'Sparsity: {spa:.4f} ±{spa_std:.4f}')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "filled-visitor", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "explain graph 19 node 3020 fidelity 0.05188\n", + "Fidelity: 0.2694 ±0.3878\n", + "Sparsity: 0.8545 ±0.1814\n" + ] + } + ], + "source": [ + "get_results(task_id=0, top_k=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "comfortable-athletics", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "explain graph 19 node 3020 fidelity -0.0007\n", + "Fidelity: 0.3038 ±0.4385\n", + "Sparsity: 0.8671 ±0.1770\n" + ] + } + ], + "source": [ + "get_results(task_id=1, top_k=120)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "worldwide-vanilla", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "explain graph 19 node 3015 fidelity 0.00002\n", + "Fidelity: 0.5042 ±0.4782\n", + "Sparsity: 0.8444 ±0.2278\n" + ] + } + ], + "source": [ + "get_results(task_id=2, top_k=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "respective-michael", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "explain graph 19 node 3015 fidelity 0.30530\n", + "Fidelity: 0.2763 ±0.4332\n", + "Sparsity: 0.8541 ±0.2171\n" + ] + } + ], + "source": [ + "get_results(task_id=3, top_k=400)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "major-czech", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "explain graph 19 node 3015 fidelity 0.01282\n", + "Fidelity: 0.3234 ±0.4460\n", + "Sparsity: 0.8547 ±0.2490\n" + ] + } + ], + "source": [ + "get_results(task_id=4, top_k=400)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "enhanced-career", + "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": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/dig/xgraph/TAGE/pipeline.jpg b/dig/xgraph/TAGE/pipeline.jpg new file mode 100644 index 00000000..6ab3a279 Binary files /dev/null and b/dig/xgraph/TAGE/pipeline.jpg differ diff --git a/dig/xgraph/TAGE/splitters.py b/dig/xgraph/TAGE/splitters.py new file mode 100644 index 00000000..ac29be7d --- /dev/null +++ b/dig/xgraph/TAGE/splitters.py @@ -0,0 +1,352 @@ +import torch +import random +import numpy as np +from itertools import compress +from rdkit.Chem.Scaffolds import MurckoScaffold +from collections import defaultdict +from sklearn.model_selection import StratifiedKFold + +# splitter function + +def generate_scaffold(smiles, include_chirality=False): + """ + Obtain Bemis-Murcko scaffold from smiles + :param smiles: + :param include_chirality: + :return: smiles of scaffold + """ + scaffold = MurckoScaffold.MurckoScaffoldSmiles( + smiles=smiles, includeChirality=include_chirality) + return scaffold + +# # test generate_scaffold +# s = 'Cc1cc(Oc2nccc(CCC)c2)ccc1' +# scaffold = generate_scaffold(s) +# assert scaffold == 'c1ccc(Oc2ccccn2)cc1' + +def scaffold_split(dataset, smiles_list, task_idx=None, null_value=0, + frac_train=0.8, frac_valid=0.1, frac_test=0.1, + return_smiles=False): + """ + Adapted from https://github.com/deepchem/deepchem/blob/master/deepchem/splits/splitters.py + Split dataset by Bemis-Murcko scaffolds + This function can also ignore examples containing null values for a + selected task when splitting. Deterministic split + :param dataset: pytorch geometric dataset obj + :param smiles_list: list of smiles corresponding to the dataset obj + :param task_idx: column idx of the data.y tensor. Will filter out + examples with null value in specified task column of the data.y tensor + prior to splitting. If None, then no filtering + :param null_value: float that specifies null value in data.y to filter if + task_idx is provided + :param frac_train: + :param frac_valid: + :param frac_test: + :param return_smiles: + :return: train, valid, test slices of the input dataset obj. If + return_smiles = True, also returns ([train_smiles_list], + [valid_smiles_list], [test_smiles_list]) + """ + np.testing.assert_almost_equal(frac_train + frac_valid + frac_test, 1.0) + + if task_idx != None: + # filter based on null values in task_idx + # get task array + y_task = np.array([data.y[task_idx].item() for data in dataset]) + # boolean array that correspond to non null values + non_null = y_task != null_value + smiles_list = list(compress(enumerate(smiles_list), non_null)) + else: + non_null = np.ones(len(dataset)) == 1 + smiles_list = list(compress(enumerate(smiles_list), non_null)) + + # create dict of the form {scaffold_i: [idx1, idx....]} + all_scaffolds = {} + for i, smiles in smiles_list: + scaffold = generate_scaffold(smiles, include_chirality=True) + if scaffold not in all_scaffolds: + all_scaffolds[scaffold] = [i] + else: + all_scaffolds[scaffold].append(i) + + # sort from largest to smallest sets + all_scaffolds = {key: sorted(value) for key, value in all_scaffolds.items()} + all_scaffold_sets = [ + scaffold_set for (scaffold, scaffold_set) in sorted( + all_scaffolds.items(), key=lambda x: (len(x[1]), x[1][0]), reverse=True) + ] + + # get train, valid test indices + train_cutoff = frac_train * len(smiles_list) + valid_cutoff = (frac_train + frac_valid) * len(smiles_list) + train_idx, valid_idx, test_idx = [], [], [] + for scaffold_set in all_scaffold_sets: + if len(train_idx) + len(scaffold_set) > train_cutoff: + if len(train_idx) + len(valid_idx) + len(scaffold_set) > valid_cutoff: + test_idx.extend(scaffold_set) + else: + valid_idx.extend(scaffold_set) + else: + train_idx.extend(scaffold_set) + + assert len(set(train_idx).intersection(set(valid_idx))) == 0 + assert len(set(test_idx).intersection(set(valid_idx))) == 0 + + train_dataset = dataset[torch.tensor(train_idx)] + valid_dataset = dataset[torch.tensor(valid_idx)] + test_dataset = dataset[torch.tensor(test_idx)] + + if not return_smiles: + return train_dataset, valid_dataset, test_dataset + else: + train_smiles = [smiles_list[i][1] for i in train_idx] + valid_smiles = [smiles_list[i][1] for i in valid_idx] + test_smiles = [smiles_list[i][1] for i in test_idx] + return train_dataset, valid_dataset, test_dataset, (train_smiles, + valid_smiles, + test_smiles) + +def random_scaffold_split(dataset, smiles_list, task_idx=None, null_value=0, + frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=0): + """ + Adapted from https://github.com/pfnet-research/chainer-chemistry/blob/master/chainer_chemistry/dataset/splitters/scaffold_splitter.py + Split dataset by Bemis-Murcko scaffolds + This function can also ignore examples containing null values for a + selected task when splitting. Deterministic split + :param dataset: pytorch geometric dataset obj + :param smiles_list: list of smiles corresponding to the dataset obj + :param task_idx: column idx of the data.y tensor. Will filter out + examples with null value in specified task column of the data.y tensor + prior to splitting. If None, then no filtering + :param null_value: float that specifies null value in data.y to filter if + task_idx is provided + :param frac_train: + :param frac_valid: + :param frac_test: + :param seed; + :return: train, valid, test slices of the input dataset obj + """ + + np.testing.assert_almost_equal(frac_train + frac_valid + frac_test, 1.0) + + if task_idx != None: + # filter based on null values in task_idx + # get task array + y_task = np.array([data.y[task_idx].item() for data in dataset]) + # boolean array that correspond to non null values + non_null = y_task != null_value + smiles_list = list(compress(enumerate(smiles_list), non_null)) + else: + non_null = np.ones(len(dataset)) == 1 + smiles_list = list(compress(enumerate(smiles_list), non_null)) + + rng = np.random.RandomState(seed) + + scaffolds = defaultdict(list) + for ind, smiles in smiles_list: + scaffold = generate_scaffold(smiles, include_chirality=True) + scaffolds[scaffold].append(ind) + + scaffold_sets = rng.permutation(list(scaffolds.values())) + + n_total_valid = int(np.floor(frac_valid * len(dataset))) + n_total_test = int(np.floor(frac_test * len(dataset))) + + train_idx = [] + valid_idx = [] + test_idx = [] + + for scaffold_set in scaffold_sets: + if len(valid_idx) + len(scaffold_set) <= n_total_valid: + valid_idx.extend(scaffold_set) + elif len(test_idx) + len(scaffold_set) <= n_total_test: + test_idx.extend(scaffold_set) + else: + train_idx.extend(scaffold_set) + + train_dataset = dataset[torch.tensor(train_idx)] + valid_dataset = dataset[torch.tensor(valid_idx)] + test_dataset = dataset[torch.tensor(test_idx)] + + return train_dataset, valid_dataset, test_dataset + +def random_split(dataset, task_idx=None, null_value=0, + frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=0, + smiles_list=None): + """ + + :param dataset: + :param task_idx: + :param null_value: + :param frac_train: + :param frac_valid: + :param frac_test: + :param seed: + :param smiles_list: list of smiles corresponding to the dataset obj, or None + :return: train, valid, test slices of the input dataset obj. If + smiles_list != None, also returns ([train_smiles_list], + [valid_smiles_list], [test_smiles_list]) + """ + np.testing.assert_almost_equal(frac_train + frac_valid + frac_test, 1.0) + + if task_idx != None: + # filter based on null values in task_idx + # get task array + y_task = np.array([data.y[task_idx].item() for data in dataset]) + non_null = y_task != null_value # boolean array that correspond to non null values + idx_array = np.where(non_null)[0] + dataset = dataset[torch.tensor(idx_array)] # examples containing non + # null labels in the specified task_idx + else: + pass + + num_mols = len(dataset) + random.seed(seed) + all_idx = list(range(num_mols)) + random.shuffle(all_idx) + + train_idx = all_idx[:int(frac_train * num_mols)] + valid_idx = all_idx[int(frac_train * num_mols):int(frac_valid * num_mols) + + int(frac_train * num_mols)] + test_idx = all_idx[int(frac_valid * num_mols) + int(frac_train * num_mols):] + + assert len(set(train_idx).intersection(set(valid_idx))) == 0 + assert len(set(valid_idx).intersection(set(test_idx))) == 0 + assert len(train_idx) + len(valid_idx) + len(test_idx) == num_mols + + train_dataset = dataset[torch.tensor(train_idx)] + valid_dataset = dataset[torch.tensor(valid_idx)] + test_dataset = dataset[torch.tensor(test_idx)] + + if not smiles_list: + return train_dataset, valid_dataset, test_dataset + else: + train_smiles = [smiles_list[i] for i in train_idx] + valid_smiles = [smiles_list[i] for i in valid_idx] + test_smiles = [smiles_list[i] for i in test_idx] + return train_dataset, valid_dataset, test_dataset, (train_smiles, + valid_smiles, + test_smiles) + + +def cv_random_split(dataset, fold_idx = 0, + frac_train=0.9, frac_valid=0.1, seed=0, + smiles_list=None): + """ + + :param dataset: + :param task_idx: + :param null_value: + :param frac_train: + :param frac_valid: + :param frac_test: + :param seed: + :param smiles_list: list of smiles corresponding to the dataset obj, or None + :return: train, valid, test slices of the input dataset obj. If + smiles_list != None, also returns ([train_smiles_list], + [valid_smiles_list], [test_smiles_list]) + """ + + np.testing.assert_almost_equal(frac_train + frac_valid, 1.0) + + skf = StratifiedKFold(n_splits=10, shuffle = True, random_state = seed) + + labels = [data.y.item() for data in dataset] + + idx_list = [] + + for idx in skf.split(np.zeros(len(labels)), labels): + idx_list.append(idx) + train_idx, val_idx = idx_list[fold_idx] + + train_dataset = dataset[torch.tensor(train_idx)] + valid_dataset = dataset[torch.tensor(val_idx)] + + return train_dataset, valid_dataset + + +if __name__ == "__main__": + from loader import MoleculeDataset + from rdkit import Chem + import pandas as pd + + # # test scaffold_split + dataset = MoleculeDataset('dataset/tox21', dataset='tox21') + smiles_list = pd.read_csv('dataset/tox21/processed/smiles.csv', header=None)[0].tolist() + + train_dataset, valid_dataset, test_dataset = scaffold_split(dataset, smiles_list, task_idx=None, null_value=0, frac_train=0.8,frac_valid=0.1, frac_test=0.1) + # train_dataset, valid_dataset, test_dataset = random_scaffold_split(dataset, smiles_list, task_idx=None, null_value=0, frac_train=0.8,frac_valid=0.1, frac_test=0.1, seed = 0) + unique_ids = set(train_dataset.data.id.tolist() + + valid_dataset.data.id.tolist() + + test_dataset.data.id.tolist()) + assert len(unique_ids) == len(dataset) # check that we did not have any + # missing or overlapping examples + + # test scaffold_split with smiles returned + dataset = MoleculeDataset('dataset/bbbp', dataset='bbbp') + smiles_list = pd.read_csv('dataset/bbbp/processed/smiles.csv', header=None)[ + 0].tolist() + train_dataset, valid_dataset, test_dataset, (train_smiles, valid_smiles, + test_smiles) = \ + scaffold_split(dataset, smiles_list, task_idx=None, null_value=0, + frac_train=0.8,frac_valid=0.1, frac_test=0.1, + return_smiles=True) + assert len(train_dataset) == len(train_smiles) + for i in range(len(train_dataset)): + data_obj_n_atoms = train_dataset[i].x.size()[0] + smiles_n_atoms = len(list(Chem.MolFromSmiles(train_smiles[ + i]).GetAtoms())) + assert data_obj_n_atoms == smiles_n_atoms + assert len(valid_dataset) == len(valid_smiles) + for i in range(len(valid_dataset)): + data_obj_n_atoms = valid_dataset[i].x.size()[0] + smiles_n_atoms = len(list(Chem.MolFromSmiles(valid_smiles[ + i]).GetAtoms())) + assert data_obj_n_atoms == smiles_n_atoms + assert len(test_dataset) == len(test_smiles) + for i in range(len(test_dataset)): + data_obj_n_atoms = test_dataset[i].x.size()[0] + smiles_n_atoms = len(list(Chem.MolFromSmiles(test_smiles[ + i]).GetAtoms())) + assert data_obj_n_atoms == smiles_n_atoms + + # test random_split + from loader import MoleculeDataset + + dataset = MoleculeDataset('dataset/tox21', dataset='tox21') + train_dataset, valid_dataset, test_dataset = random_split(dataset, task_idx=None, null_value=0, frac_train=0.8,frac_valid=0.1, frac_test=0.1) + unique_ids = set(train_dataset.data.id.tolist() + + valid_dataset.data.id.tolist() + + test_dataset.data.id.tolist()) + assert len(unique_ids) == len(dataset) # check that we did not have any + # missing or overlapping examples + + # test random_split with smiles returned + dataset = MoleculeDataset('dataset/bbbp', dataset='bbbp') + smiles_list = pd.read_csv('dataset/bbbp/processed/smiles.csv', header=None)[ + 0].tolist() + train_dataset, valid_dataset, test_dataset, (train_smiles, valid_smiles, + test_smiles) = \ + random_split(dataset, task_idx=None, null_value=0, + frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=42, + smiles_list=smiles_list) + assert len(train_dataset) == len(train_smiles) + for i in range(len(train_dataset)): + data_obj_n_atoms = train_dataset[i].x.size()[0] + smiles_n_atoms = len(list(Chem.MolFromSmiles(train_smiles[ + i]).GetAtoms())) + assert data_obj_n_atoms == smiles_n_atoms + assert len(valid_dataset) == len(valid_smiles) + for i in range(len(valid_dataset)): + data_obj_n_atoms = valid_dataset[i].x.size()[0] + smiles_n_atoms = len(list(Chem.MolFromSmiles(valid_smiles[ + i]).GetAtoms())) + assert data_obj_n_atoms == smiles_n_atoms + assert len(test_dataset) == len(test_smiles) + for i in range(len(test_dataset)): + data_obj_n_atoms = test_dataset[i].x.size()[0] + smiles_n_atoms = len(list(Chem.MolFromSmiles(test_smiles[ + i]).GetAtoms())) + assert data_obj_n_atoms == smiles_n_atoms + + diff --git a/dig/xgraph/TAGE/syn_dataset.ipynb b/dig/xgraph/TAGE/syn_dataset.ipynb new file mode 100644 index 00000000..7a3b431b --- /dev/null +++ b/dig/xgraph/TAGE/syn_dataset.ipynb @@ -0,0 +1,312 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "photographic-saturn", + "metadata": {}, + "outputs": [], + "source": [ + "import os.path as osp\n", + "import os\n", + "import torch\n", + "from torch_geometric.data import DataLoader, Batch, Data\n", + "from torch_geometric.data import Data, InMemoryDataset, download_url, extract_zip\n", + "from torch_geometric.utils import sort_edge_index\n", + "from dig.xgraph.dataset import SynGraphDataset\n", + "from dig.xgraph.models import *\n", + "from dig.sslgraph.utils import Encoder\n", + "\n", + "from bashaps import BAShapes\n", + "from downstream import MLP, EndtoEnd, train_MLP\n", + "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "adequate-holly", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = BAShapes()\n", + "loader = DataLoader(dataset, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "focal-american", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encoder = Encoder(feat_dim=dataset[0].x.shape[1], hidden_dim=300, \n", + " n_layers=3, gnn='gcn', node_level=True, graph_level=False)\n", + "encoder.load_state_dict(torch.load('ckpts_model/encoder_bashape.pth', map_location='cpu'))\n", + "mlp_model = MLP(num_layer = 2, emb_dim = 300, hidden_dim = 300, out_dim = 4)\n", + "mlp_model.load_state_dict(torch.load('ckpts_model/downstream_bashape.pth'))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "north-sponsorship", + "metadata": {}, + "outputs": [], + "source": [ + "from tagexplainer import TAGExplainer, MLPExplainer\n", + "enc_explainer = TAGExplainer(encoder, embed_dim=300, device=device, explain_graph=False, \n", + " grad_scale=0.1, coff_size=0.05, coff_ent=0.002, loss_type='JSE')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "premium-expert", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 44/44 [00:09<00:00, 4.40it/s, loss=14.7, log=0.1884, 0.6985, -0.1304, 0.3375] \n", + "100%|██████████| 44/44 [00:09<00:00, 4.42it/s, loss=1.93, log=0.8269, 0.8682, 0.4042, 0.4382] \n", + "100%|██████████| 44/44 [00:09<00:00, 4.42it/s, loss=13.2, log=-0.2221, 0.8668, -15.6600, 0.0008]\n", + "100%|██████████| 44/44 [00:09<00:00, 4.41it/s, loss=0.797, log=12.0697, 0.9993, -12.0452, 0.0004]\n", + "100%|██████████| 44/44 [00:09<00:00, 4.42it/s, loss=0.198, log=-0.3255, 0.7022, -20.9112, 0.0000] \n" + ] + } + ], + "source": [ + "# enc_explainer.train_explainer_node(loader, batch_size=16, lr=5e-5, epochs=5)\n", + "# torch.save(enc_explainer.state_dict(), 'ckpts_explainer/explain_ba-shape.pt')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "peaceful-treat", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "explain graph 0 node 699 fidelity 0.99881\n", + "Fidelity: 0.7502 ±0.4290\n", + "Sparsity: 0.7638 ±0.1584\n" + ] + } + ], + "source": [ + "state_dict = torch.load('ckpts_explainer/explain_ba-shape.pt')\n", + "enc_explainer.load_state_dict(state_dict)\n", + "\n", + "from dig.xgraph.evaluation import XCollector\n", + "loader = DataLoader(dataset, 1)\n", + "mlp_explainer = MLPExplainer(mlp_model, device)\n", + "\n", + "x_collector = XCollector()\n", + "node_indices = torch.where(dataset[0].y != 0)[0]\n", + "subgraphs = []\n", + "mask_lst = []\n", + "for i, data in enumerate(loader):\n", + " for j, node_idx in enumerate(node_indices):\n", + " data.to(device)\n", + " data.edge_attr = data.edge_label\n", + " subgraph, masks, related_preds = \\\n", + " enc_explainer(data, mlp_explainer, node_idx=node_idx, top_k=5)\n", + " fidelity = related_preds[0]['origin'] - related_preds[0]['maskout']\n", + " subgraphs.append(subgraph)\n", + " mask_lst.append(masks)\n", + "\n", + " print(f'explain graph {i} node {node_idx}'+' fidelity %.4f'%fidelity, end='\\r')\n", + " x_collector.collect_data(masks, related_preds)\n", + "\n", + "fid, fid_std = x_collector.fidelity\n", + "spa, spa_std = x_collector.sparsity\n", + "\n", + "print()\n", + "print(f'Fidelity: {fid:.4f} ±{fid_std:.4f}\\n'\n", + " f'Sparsity: {spa:.4f} ±{spa_std:.4f}')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "northern-dakota", + "metadata": {}, + "outputs": [], + "source": [ + "from torch_geometric.utils import to_networkx\n", + "from sklearn.metrics import roc_auc_score\n", + "import matplotlib.pyplot as plt\n", + "import networkx as nx\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "coupled-london", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9999547958585285" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "auc_scores = []\n", + "for idx in range(len(node_indices)):\n", + " subg = subgraphs[idx].cpu()\n", + " masks = mask_lst[idx].cpu()\n", + " gt = subgraphs[idx].edge_attr.cpu()\n", + " # direct to undirect\n", + " _, masks = sort_edge_index(subg.edge_index[:,subg.edge_index[0]subg.edge_index[1]]\n", + " colors = (masks >= masks.topk(6).values[-1]).int().tolist()\n", + " G = to_networkx(subg, to_undirected=True)\n", + " nx.draw(G, node_color = subg.y.tolist(), edge_color=np.array(['black', 'red'])[np.array(colors)])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "commercial-victory", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(25,20))\n", + "for i in range(20):\n", + " plt.subplot(4, 5, i+1)\n", + " visual(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "diagnostic-colorado", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(25,20))\n", + "for i in range(20,40):\n", + " plt.subplot(4, 5, i-19)\n", + " visual(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "familiar-revelation", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(25,20))\n", + "for i in range(40,60):\n", + " plt.subplot(4, 5, i-39)\n", + " visual(i)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "administrative-interpretation", + "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": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/dig/xgraph/TAGE/tagexplainer.py b/dig/xgraph/TAGE/tagexplainer.py new file mode 100644 index 00000000..e391f4fb --- /dev/null +++ b/dig/xgraph/TAGE/tagexplainer.py @@ -0,0 +1,546 @@ +from typing import Optional +from tqdm import tqdm +import gc +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor +from torch.utils.data import DataLoader as tDataLoader +from torch.utils.data import TensorDataset +from torch.optim import Adam +from torch_geometric.data import Data, Batch +from torch_geometric.nn.conv import MessagePassing +from torch_geometric import utils + +from rdkit import Chem +import matplotlib.pyplot as plt +import networkx as nx +from textwrap import wrap + +from dig.sslgraph.method.contrastive.objectives import JSE_loss, NCE_loss + + +class Explainer(nn.Module): + ''' The parametric explainer takes node embeddings and condition vector as inputs, + and predicts edge importance scores. Constructed as a 2-layer MLP. + Args: + embed_dim: Integer. Dimension of node embeddings. + graph_level: Boolean. Whether to explain a graph-level prediction task or + node-level prediction task. + hidden_dim: Integer. Hidden dimension of the MLP in the explainer. + ''' + + def __init__(self, embed_dim: int, graph_level: bool, hidden_dim: int = 600): + + super(Explainer, self).__init__() + + self.embed_dims = embed_dim * (2 if graph_level else 3) + self.cond_dims = embed_dim + + self.emb_linear1 = nn.Sequential(nn.Linear(self.embed_dims, hidden_dim), nn.ReLU()) + self.emb_linear2 = nn.Linear(hidden_dim, 1) + + self.cond_proj = nn.Sequential(nn.Linear(self.cond_dims, self.embed_dims), nn.ReLU()) + + def forward(self, embed, cond): + ''' + Args: + embeds: Tensor of shape [n_edges, 2*embed_dim] or [n_edges, 3*embed_dim*]. + cond: Tensor of shape [1, embed_dim]. Condition vector. + ''' + cond = self.cond_proj(cond) + out = embed * cond + out = self.emb_linear1(out) + out = self.emb_linear2(out) + return out + + +class KHopSampler(MessagePassing): + ''' A real-time sampler that samples k-hop ego networks surrounding the given seed node. + Used in node-level explanations for efficient computing. + Args: + k: Integer. Number of hops to sample. + ''' + def __init__(self, k): + super(KHopSampler, self).__init__(aggr='max', flow='source_to_target', node_dim=0) + self.k = k + + def forward(self, edge_index, num_nodes, node_idx=None): + ''' + Args: + edge_index: Tensor. Edge indices of the full graph. + num_nodes: Integer. Total number of nodes in the full graph. + node_idx: Integer. Index of the selected center node. If :obj:`None`, return + ego networks of every node. + Returns: + Boolean tensor of shape [num_nodes]. Indicating whether each node is selected + in the ego network. + ''' + if node_idx is None: + S = torch.eye(num_nodes).to(edge_index.device) + else: + S = torch.zeros(num_nodes).to(edge_index.device) + S = S.scatter_(0, node_idx.to(edge_index.device), 1.0) + + edge_index = utils.to_undirected(edge_index, num_nodes=len(S)) + edge_index, _ = utils.add_self_loops(edge_index, num_nodes=len(S)) + for it in range(self.k): + S = self.propagate(edge_index, x=S) + return S.bool() + + +class MLPExplainer(torch.nn.Module): + ''' Downstream MLP explainer based on gradient of output w.r.t. input embedding. + Args: + mlp_model: :obj:`torch.nn.Module` The downstream model to be explained. + device: Torch CUDA device. + ''' + + def __init__(self, mlp_model, device): + super(MLPExplainer, self).__init__() + self.model = mlp_model.to(device) + self.device = device + + def forward(self, embeds, mode='explain'): + '''Returns probability by forward propagation or gradients by backward propagation + based on the mode specified. + ''' + embeds = embeds.detach().to(self.device) + self.model.eval() + if mode == 'explain': + return self.get_grads(embeds) + elif mode == 'pred': + return self.get_probs(embeds) + else: + raise NotImplementedError + + def get_probs(self, embeds): + logits = self.model(embeds) + if logits.shape[1] == 1: + probs = torch.sigmoid(logits) + probs = torch.cat([1-probs, probs], 1) + else: + probs = F.softmax(logits, dim=-1) + return probs + + def get_grads(self, embeds): + optimizer = torch.optim.SGD([embeds.requires_grad_()], lr=0.01) + optimizer.zero_grad() + logits = self.model(embeds) + max_logits, _ = logits.max(dim=-1) + max_logits.sum().backward() + grads = embeds.grad + grads = grads/torch.abs(grads).mean() + return F.relu(grads) + +class TAGExplainer(nn.Module): + ''' The TAGExplainer that performs 2-stage explanations. Includes training and inference. + Args: + model: :obj:`torch.nn.Module`. the GNN embedding model to be explained. + embed_dim: Integer. Dimension of node embeddings. + device: Torch CUDA device. + explain_graph: Boolean. Whether to explain a graph-level prediction task or + node-level prediction task. + coff_size, coff_ent: Hyper-parameters for mask regularizations. + grad_scale: Float. The scale parameter for generating random condition vectors. + loss_type: String from "NCE" or "JSE". Type of the contrastive loss. + ''' + def __init__(self, model, embed_dim: int, device, explain_graph: bool = True, + coff_size: float = 0.01, coff_ent: float = 5e-4, grad_scale: float = 0.25, + loss_type = 'NCE', t0: float = 5.0, t1: float = 1.0, num_hops: Optional[int] = None): + + super(TAGExplainer, self).__init__() + self.device = device + self.embed_dim = embed_dim + self.explain_graph = explain_graph + self.model = model.to(device) + self.explainer = Explainer(embed_dim, explain_graph).to(device) + + # objective parameters for PGExplainer + self.grad_scale = grad_scale + self.coff_size = coff_size + self.coff_ent = coff_ent + self.t0 = t0 + self.t1 = t1 + self.loss_type = loss_type + + self._set_hops(num_hops) + self.sampler = KHopSampler(self.num_hops) + self.S = None + + + def _set_hops(self, num_hops: int): + if num_hops is None: + self.num_hops = sum( + [isinstance(m, MessagePassing) for m in self.model.modules()]) + else: + self.num_hops = num_hops + + + def __set_masks__(self, edge_mask: Tensor): + """ Set the edge weights before message passing + Args: + edge_mask (:obj:`torch.Tensor`): Edge weight matrix before message passing + (default: :obj:`None`) + """ + edge_mask = edge_mask.to(self.device) + for module in self.model.modules(): + if isinstance(module, MessagePassing): + module.__explain__ = True + module.__edge_mask__ = edge_mask + + + def __clear_masks__(self): + """ clear the edge weights to None, and set the explain flag to :obj:`False` """ + for module in self.model.modules(): + if isinstance(module, MessagePassing): + module.__explain__ = False + module.__edge_mask__ = None + + + def __loss__(self, embed: Tensor, pruned_embed: Tensor, + condition: Tensor, edge_mask: Tensor, **kwargs): + ''' + embed: Tensor of shape [n_sample, embed_dim] + pruned_embed: Tensor of shape [n_sample, embed_dim] + condition: Tensor of shape [1, embed_dim] + ''' + max_items = kwargs.get('max_items') + if self.loss_type=='NCE': + contrast_loss = NCE_loss([condition*embed, condition*pruned_embed]) + elif max_items and len(embed) > max_items: + contrast_loss = self.__batched_JSE__(condition*embed, condition*pruned_embed, max_items) + else: + contrast_loss = JSE_loss([condition*embed, condition*pruned_embed]) + + size_loss = self.coff_size * torch.mean(edge_mask) + edge_mask = edge_mask * 0.99 + 0.005 + mask_ent = - edge_mask * torch.log(edge_mask) - (1 - edge_mask) * torch.log(1 - edge_mask) + mask_ent = self.coff_ent * torch.mean(mask_ent) + + loss = contrast_loss + size_loss + mask_ent + return loss + + + def __batched_JSE__(self, cond_embed, cond_pruned_embed, batch_size): + loss = 0 + for i, (z1, z2) in enumerate(tDataLoader( + TensorDataset(cond_embed, cond_pruned_embed), batch_size)): + if len(z1)<=1: + i -= 1 + break + loss += JSE_loss([z1, z2]) + return loss/(i+1.0) + + def __rand_cond__(self, n_sample, max_val=None): + lap = torch.distributions.laplace.Laplace(loc=0, scale=self.grad_scale) + cond = F.relu(lap.sample([n_sample, self.embed_dim])).to(self.device) + if max_val is not None: + cond = torch.clip(cond, max=max_val) + return cond + + def get_subgraph(self, node_idx: int, data: Data): + + x, edge_index, edge_attr, y, batch = data.x, data.edge_index, data.edge_attr, data.y, data.batch + num_nodes, num_edges = x.size(0), edge_index.size(1) + col, row = edge_index + + node_mask = self.sampler(edge_index, num_nodes, node_idx) + edge_mask = node_mask[row] & node_mask[col] + subset = torch.nonzero(node_mask).view(-1) + edge_index, edge_attr = utils.subgraph(node_mask, edge_index, edge_attr, + relabel_nodes=True, num_nodes=num_nodes) + + x = x[subset] + y = y[subset] if y is not None else None + batch = batch[subset] if batch is not None else None + + data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, batch=batch) + return data, subset + + + def concrete_sample(self, log_alpha: Tensor, beta: float = 1.0, training: bool = True): + """ Sample from the instantiation of concrete distribution when training """ + if training: + random_noise = torch.rand(log_alpha.shape) + random_noise = torch.log(random_noise) - torch.log(1.0 - random_noise) + gate_inputs = (random_noise.to(log_alpha.device) + log_alpha) / beta + gate_inputs = gate_inputs.sigmoid() + else: + gate_inputs = log_alpha + + return gate_inputs + + + def explain(self, data: Data, embed: Tensor, condition: Tensor, + tmp: float = 1.0, training: bool = False, **kwargs): + """ + explain the GNN behavior for graph with explanation network + Args: + x (:obj:`torch.Tensor`): Node feature matrix with shape + :obj:`[num_nodes, dim_node_feature]` + edge_index (:obj:`torch.Tensor`): Graph connectivity in COO format + with shape :obj:`[2, num_edges]` + embed (:obj:`torch.Tensor`): Node embedding matrix with shape :obj:`[num_nodes, dim_embedding]` + tmp (:obj`float`): The temperature parameter fed to the sample procedure + training (:obj:`bool`): Whether in training procedure or not + Returns: + probs (:obj:`torch.Tensor`): The classification probability for graph with edge mask + edge_mask (:obj:`torch.Tensor`): The probability mask for graph edges + """ + + nodesize = embed.shape[0] + feature_dim = embed.shape[1] + col, row = data.edge_index + f1 = embed[col] + f2 = embed[row] + if self.explain_graph: + f12self = torch.cat([f1, f2], dim=-1) + else: + node_idx = kwargs.get('node_idx') + self_embed = embed[node_idx].repeat(f1.shape[0], 1) + f12self = torch.cat([f1, f2, self_embed], dim=-1) + + # using the node embedding to calculate the edge weight + h = self.explainer(f12self.to(self.device), condition.to(self.device)) + + mask_val = h.reshape(-1) + values = self.concrete_sample(mask_val, beta=tmp, training=training) + try: + out_log = '%.4f, %.4f, %.4f, %.4f'%( + h.max().item(), values.max().item(), h.min().item(), values.min().item()) + except: + out_log = '' + mask_sparse = torch.sparse_coo_tensor( + data.edge_index, values, (nodesize, nodesize) + ) + mask_sigmoid = mask_sparse.to_dense() + + # set the symmetric edge weights + sym_mask = (mask_sigmoid + mask_sigmoid.transpose(0, 1)) / 2 + edge_mask = sym_mask[col, row] + + # inverse the weights before sigmoid in MessagePassing Module + inv_sigmoid = lambda x: torch.log(x/(1-x)) + self.__clear_masks__() + self.__set_masks__(inv_sigmoid(edge_mask)) + + # the model prediction with edge mask + embed = self.model(data) + + self.__clear_masks__() + return embed, edge_mask, out_log + + + def train_explainer_graph(self, loader, lr=0.001, epochs=10): + """ training the explanation network by gradient descent(GD) using Adam optimizer """ + optimizer = Adam(self.explainer.parameters(), lr=lr) + for epoch in range(epochs): + tmp = float(self.t0 * np.power(self.t1 / self.t0, epoch / epochs)) + self.model.eval() + self.explainer.train() + pbar = tqdm(loader) + for data in pbar: + optimizer.zero_grad() + data = data.to(self.device) + embed, node_embed = self.model(data, emb=True) + cond = self.__rand_cond__(1) + pruned_embed, mask, log = self.explain(data, embed=node_embed, + condition=cond, tmp=tmp, training=True) + loss = self.__loss__(embed, pruned_embed, cond, mask) + pbar.set_postfix({'loss': loss.item(), 'log': log}) + loss.backward() + optimizer.step() + + + def train_large_explainer_node(self, loader, batch_size=2, lr=0.001, epochs=10, max_items=2000): + """ training the explanation network by gradient descent(GD) using Adam optimizer """ + optimizer = Adam(self.explainer.parameters(), lr=lr, weight_decay=0.01) + # train the mask generator + for epoch in range(epochs): + self.model.eval() + self.explainer.train() + for dt_idx, data in enumerate(loader): + loss = 0.0 + optimizer.zero_grad() + tmp = float(self.t0 * np.power(self.t1 / self.t0, epoch / epochs)) + data.to(self.device) + + with torch.no_grad(): + try: + data = Batch.from_data_list([data]) + except: + pass + try: + mask = data.train_mask + except: + mask = torch.ones_like(data.batch).bool() + + node_batches = torch.utils.data.DataLoader(torch.where(mask)[0].tolist(), + batch_size=batch_size, shuffle=True) + pbar = tqdm(node_batches) + for node_batch in pbar: + cond = self.__rand_cond__(1) + pruned_embeds, embeds = [], [] + for node_idx in node_batch: + subgraph, subset = self.get_subgraph(node_idx=node_idx, data=data) + if subgraph.edge_index.shape[0]>10000 or subgraph.x.shape[0]>3000 or subgraph.x.shape[0]<2: + continue + new_node_idx = int(torch.where(subset == node_idx)[0]) + with torch.no_grad(): + subg_embeds = self.model(subgraph) + pruned_embed, mask, log = self.explain(subgraph, subg_embeds.to(self.device), + condition=cond, tmp=tmp, training=True, node_idx=new_node_idx) + embeds.append(subg_embeds.cpu())#[new_node_idx:new_node_idx+1]) + pruned_embeds.append(pruned_embed.cpu())#[new_node_idx:new_node_idx+1]) + embeds = torch.cat(embeds, 0).to(self.device) + if len(embeds) <= 1: + continue + pruned_embeds = torch.cat(pruned_embeds, 0).to(self.device) + loss = self.__loss__(embeds, pruned_embeds, cond, mask)#, max_items=2000) + if torch.isnan(loss): + continue + loss.backward() + torch.nn.utils.clip_grad_norm_(self.explainer.parameters(), 2.0) + optimizer.step() + pbar.set_postfix({'loss': loss.item(), 'log': log}) + + + def train_explainer_node(self, loader, batch_size=128, lr=0.001, epochs=10): + """ training the explanation network by gradient descent(GD) using Adam optimizer """ + optimizer = Adam(self.explainer.parameters(), lr=lr) + # train the mask generator + for epoch in range(epochs): + self.model.eval() + self.explainer.train() + for dt_idx, data in enumerate(loader): + loss = 0.0 + optimizer.zero_grad() + tmp = float(self.t0 * np.power(self.t1 / self.t0, epoch / epochs)) + with torch.no_grad(): + self.model.cpu() + all_embeds = self.model(data) + self.model.to(self.device) + data.to(self.device) + try: + mask = data.train_mask + except: + mask = torch.ones_like(data.batch).bool() + + node_batches = torch.utils.data.DataLoader(torch.where(mask)[0].tolist(), + batch_size=batch_size, shuffle=True) + pbar = tqdm(node_batches) + for node_batch in pbar: + cond = self.__rand_cond__(1) + embeds = all_embeds[node_batch].to(self.device) + pruned_embeds = [] + masks = [] + for node_idx in node_batch: + subgraph, subset = self.get_subgraph(node_idx=node_idx, data=data) + new_node_idx = int(torch.where(subset == node_idx)[0]) + pruned_embed, mask, log = self.explain(subgraph, all_embeds[subset].to(self.device), + condition=cond, tmp=tmp, training=True, node_idx=new_node_idx) + pruned_embeds.append(pruned_embed.cpu()[new_node_idx:new_node_idx+1]) + masks.append(mask) + pruned_embeds = torch.cat(pruned_embeds, 0).to(self.device) + masks = torch.cat(masks, 0) + if len(pruned_embeds)<=1: + continue + loss = self.__loss__(embeds, pruned_embeds, cond, masks) + loss.backward() + optimizer.step() + pbar.set_postfix({'loss': loss.item(), 'log': log}) + + + def __edge_mask_to_node__(self, data, edge_mask, top_k): + threshold = float(edge_mask.reshape(-1).sort(descending=True).values[min(top_k, edge_mask.shape[0]-1)]) + hard_mask = (edge_mask > threshold).cpu() + edge_idx_list = torch.where(hard_mask == 1)[0] + + selected_nodes = [] + edge_index = data.edge_index.cpu().numpy() + for edge_idx in edge_idx_list: + selected_nodes += [edge_index[0][edge_idx], edge_index[1][edge_idx]] + selected_nodes = list(set(selected_nodes)) + maskout_nodes = [node for node in range(data.x.shape[0]) if node not in selected_nodes] + + node_mask = torch.zeros(data.num_nodes).type(torch.float32).to(self.device) + node_mask[maskout_nodes] = 1.0 + return node_mask + + + def forward(self, data: Data, mlp_explainer: nn.Module, **kwargs): + """ explain the GNN behavior for graph and calculate the metric values. + The interface for the :class:`dig.evaluation.XCollector`. + + Args: + x (:obj:`torch.Tensor`): Node feature matrix with shape + :obj:`[num_nodes, dim_node_feature]` + edge_index (:obj:`torch.Tensor`): Graph connectivity in COO format + with shape :obj:`[2, num_edges]` + kwargs(:obj:`Dict`): + The additional parameters + - top_k (:obj:`int`): The number of edges in the final explanation results + - y (:obj:`torch.Tensor`): The ground-truth labels + + :rtype: (:obj:`None`, List[torch.Tensor], List[Dict]) + """ + top_k = kwargs.get('top_k') if kwargs.get('top_k') is not None else 10 + node_idx = kwargs.get('node_idx') + cond_vec = kwargs.get('cond_vec') + self.model.eval() + mlp_explainer = mlp_explainer.to(self.device).eval() + data = data.to(self.device) + + self.__clear_masks__() + if node_idx is not None: + node_embed = self.model(data) + embed = node_embed[node_idx:node_idx+1] + elif self.explain_graph: + embed, node_embed = self.model(data, emb=True) + else: + assert node_idx is not None, "please input the node_idx" + probs = mlp_explainer(embed, mode='pred') + grads = mlp_explainer(embed, mode='explain') if cond_vec is None else cond_vec + probs = probs.squeeze() + + if self.explain_graph: + subgraph = None + target_class = torch.argmax(probs) if data.y is None else max(data.y.long(), 0) # sometimes labels are +1/-1 + _, edge_mask, log = self.explain(data, embed=node_embed, condition=grads, tmp=1.0, training=False) + node_mask = self.__edge_mask_to_node__(data, edge_mask, top_k) + masked_data = mask_fn(data, node_mask) + masked_embed = self.model(masked_data) + masked_prob = mlp_explainer(masked_embed, mode='pred') + masked_prob = masked_prob[:, target_class] + sparsity_score = sum(node_mask) / data.num_nodes + else: + target_class = torch.argmax(probs) if data.y is None else max(data.y[node_idx].long(), 0) # sometimes labels are +1/-1 + subgraph, subset = self.get_subgraph(node_idx=node_idx, data=data) + new_node_idx = torch.where(subset == node_idx)[0] + _, edge_mask, log = self.explain(subgraph, node_embed[subset], condition=grads, + tmp=1.0, training=False, node_idx=new_node_idx) + node_mask = self.__edge_mask_to_node__(subgraph, edge_mask, top_k) + masked_embed = self.model(mask_fn(subgraph, node_mask)) + masked_prob = mlp_explainer(masked_embed, mode='pred')[new_node_idx, target_class.long()] + sparsity_score = sum(node_mask) / subgraph.num_nodes + + # return variables + pred_mask = edge_mask.detach().cpu() + related_preds = [{ + 'maskout': masked_prob.item(), + 'origin': probs[target_class].item(), + 'sparsity': sparsity_score}] + return subgraph, pred_mask, related_preds + + +def mask_fn(data: Data, node_mask: np.array): + """ subgraph building through spliting the selected nodes from the original graph """ + row, col = data.edge_index + edge_mask = (node_mask[row] == 1) & (node_mask[col] == 1) + ret_edge_index = data.edge_index[:, edge_mask] + ret_edge_attr = None if data.edge_attr is None else data.edge_attr[edge_mask] + data = Data(x=data.x, edge_index=ret_edge_index, + edge_attr=ret_edge_attr, batch=data.batch) + return data diff --git a/dig/xgraph/XGNN/Readme.md b/dig/xgraph/XGNN/Readme.md index d8e7876b..319ad321 100644 --- a/dig/xgraph/XGNN/Readme.md +++ b/dig/xgraph/XGNN/Readme.md @@ -37,7 +37,7 @@ Place the checkpoint of the GNNs to be explained in the checkpoint folder. Also, In "gnn.py" we provide an example showing the training of GNNs, and then the trained GNNs become the model to be explained. -Our data and checkpoint are available upon request. +Our data and checkpoint are available (https://drive.google.com/drive/u/2/folders/1To5IQa-3H_m48OwhJzEhIwz1swnHcOoz). ## The XGNN Algorithm