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import os
import copy
import math
import pickle
import random
import collections
import numpy as np
import networkx as nx
import torch
import torch.nn.functional as F
from torch_geometric.data import Data
TRAIN_MODE = "train"
VAL_MODE = "val"
TEST_MODE = "test"
# BROAD_TEST_MODE = "Extra_test_300"
GMN_DATA_TYPE = "gmn"
PYG_DATA_TYPE = "pyg"
GraphCollection = collections.namedtuple(
'GraphCollection',
['from_idx', 'to_idx', 'node_features', 'edge_features', 'graph_idx', 'num_graphs']
)
class SubgraphIsomorphismDataset:
def __init__(self, mode, dataset_name, dataset_size, batch_size, data_type, dataset_base_path, experiment, dataset_path_override=None, device=None):
# assert mode in [TRAIN_MODE, VAL_MODE, TEST_MODE, BROAD_TEST_MODE]
assert mode in [TRAIN_MODE, VAL_MODE, TEST_MODE]
self.mode = mode
self.dataset_name = dataset_name
self.dataset_size = dataset_size
self.max_node_set_size = {"small": 15, "large": 20}[dataset_size]
self.batch_size = batch_size
self.data_type = data_type
self.dataset_base_path = dataset_base_path
self.device = experiment.device if experiment else (device if device else 'cuda:0')
self.batch_setting = None
self.dataset_path_override = dataset_path_override
self.load_graphs(experiment=experiment)
self.preprocess_subgraphs_to_pyG_data()
self.build_adjacency_info()
self.max_edge_set_size = max(
max([graph.number_of_edges() for graph in self.query_graphs]),
max([graph.number_of_edges() for graph in self.corpus_graphs])
)
def load_graphs(self, experiment):
dataset_accessor = lambda file_name: os.path.join(
self.dataset_base_path, self.dataset_path_override or f"{self.dataset_size}_dataset",
"splits", self.mode, file_name
)
# Load query graphs
pair_count = f"{80 if self.dataset_size == 'small' else 240}k"
mode_prefix = "test" if "test" in self.mode else self.mode
query_graph_file = dataset_accessor(f"{mode_prefix}_{self.dataset_name}{pair_count}_query_subgraphs.pkl")
self.query_graphs = pickle.load(open(query_graph_file, 'rb'))
num_query_graphs = len(self.query_graphs)
if experiment:
experiment.log("loaded %s query graphs from %s", self.mode, query_graph_file)
# Load subgraph isomorphism relationships of query vs corpus graphs
relationships_file = query_graph_file.replace("query_subgraphs", "rel_nx_is_subgraph_iso")
self.relationships = pickle.load(open(relationships_file, 'rb'))
if experiment:
experiment.log("loaded %s relationships from %s", self.mode, relationships_file)
assert list(self.relationships.keys()) == list(range(num_query_graphs))
# Load corpus graphs
corpus_graph_file = os.path.join(
os.path.dirname(os.path.dirname(os.path.dirname(query_graph_file))), "corpus",
f"{self.dataset_name}{pair_count}_corpus_subgraphs.pkl"
)
self.corpus_graphs = pickle.load(open(corpus_graph_file, 'rb'))
if experiment:
experiment.log("loaded corpus graphs from %s", corpus_graph_file)
self.pos_pairs, self.neg_pairs = [], []
for query_idx in range(num_query_graphs):
for corpus_idx in self.relationships[query_idx]['pos']:
self.pos_pairs.append((query_idx, corpus_idx))
for corpus_idx in self.relationships[query_idx]['neg']:
self.neg_pairs.append((query_idx, corpus_idx))
def create_pyG_object(self, graph):
num_nodes = graph.number_of_nodes()
features = torch.ones(num_nodes, 1, dtype=torch.float, device=self.device)
edges = list(graph.edges)
doubled_edges = [[x, y] for (x, y) in edges] + [[y, x] for (x, y) in edges]
edge_index = torch.tensor(np.array(doubled_edges).T, dtype=torch.int64, device=self.device)
return Data(x = features, edge_index = edge_index), num_nodes
def preprocess_subgraphs_to_pyG_data(self):
self.query_graph_data, self.query_graph_sizes = zip(
*[self.create_pyG_object(query_graph) for query_graph in self.query_graphs]
)
self.corpus_graph_data, self.corpus_graph_sizes = zip(
*[self.create_pyG_object(corpus_graph) for corpus_graph in self.corpus_graphs]
)
def build_adjacency_info(self):
def adj_list_from_graph_list(graphs):
adj_list = []
for graph in graphs:
unpadded_adj = torch.tensor(nx.adjacency_matrix(graph).todense(), dtype=torch.float, device=self.device)
assert unpadded_adj.shape[0] == unpadded_adj.shape[1]
num_nodes = len(unpadded_adj)
padded_adj = F.pad(unpadded_adj, pad = (0, self.max_node_set_size - num_nodes, 0, self.max_node_set_size - num_nodes))
adj_list.append(padded_adj)
return adj_list
self.query_adj_list = adj_list_from_graph_list(self.query_graphs)
self.corpus_adj_list = adj_list_from_graph_list(self.corpus_graphs)
def _pack_batch(self, graphs):
from_idx = []
to_idx = []
graph_idx = []
all_graphs = [individual_graph for graph_tuple in graphs for individual_graph in graph_tuple]
total_nodes, total_edges = 0, 0
for idx, graph in enumerate(all_graphs):
num_nodes = graph.number_of_nodes()
num_edges = graph.number_of_edges()
edges = np.array(graph.edges(), dtype=np.int32)
from_idx.append(edges[:, 0] + total_nodes)
to_idx.append(edges[:, 1] + total_nodes)
graph_idx.append(np.ones(num_nodes, dtype=np.int32) * idx)
total_nodes += num_nodes
total_edges += num_edges
return GraphCollection(
from_idx = torch.tensor(np.concatenate(from_idx, axis=0), dtype=torch.int64, device=self.device),
to_idx = torch.tensor(np.concatenate(to_idx, axis=0), dtype=torch.int64, device=self.device),
graph_idx = torch.tensor(np.concatenate(graph_idx, axis=0), dtype=torch.int64, device=self.device),
num_graphs = len(all_graphs),
node_features = torch.ones(total_nodes, 1, dtype=torch.float, device=self.device),
edge_features = torch.ones(total_edges, 1, dtype=torch.float, device=self.device)
)
def create_stratified_batches(self):
self.batch_setting = 'stratified'
random.shuffle(self.pos_pairs), random.shuffle(self.neg_pairs)
pos_to_neg_ratio = len(self.pos_pairs) / len(self.neg_pairs)
num_pos_per_batch = math.ceil(pos_to_neg_ratio/(1 + pos_to_neg_ratio) * self.batch_size)
num_neg_per_batch = self.batch_size - num_pos_per_batch
batches_pos, batches_neg = [], []
labels_pos, labels_neg = [], []
for idx in range(0, len(self.pos_pairs), num_pos_per_batch):
elements_remaining = len(self.pos_pairs) - idx
elements_chosen = min(num_pos_per_batch, elements_remaining)
batches_pos.append(self.pos_pairs[idx : idx + elements_chosen])
labels_pos.append([1.0] * elements_chosen)
for idx in range(0, len(self.neg_pairs), num_neg_per_batch):
elements_remaining = len(self.neg_pairs) - idx
elements_chosen = min(num_neg_per_batch, elements_remaining)
batches_neg.append(self.neg_pairs[idx : idx + elements_chosen])
labels_neg.append([0.0] * elements_chosen)
self.num_batches = min(len(batches_pos), len(batches_neg))
self.batches = [pos + neg for (pos, neg) in zip(batches_pos[:self.num_batches], batches_neg[:self.num_batches])]
self.labels = [pos + neg for (pos, neg) in zip(labels_pos[:self.num_batches], labels_neg[:self.num_batches])]
return self.num_batches
def create_custom_batches(self, pair_list):
self.batch_setting = 'custom'
self.batches = []
for idx in range(0, len(pair_list), self.batch_size):
self.batches.append(pair_list[idx : idx + self.batch_size])
self.num_batches = len(self.batches)
return self.num_batches
def fetch_batch_by_id(self, idx):
assert idx < self.num_batches
batch = self.batches[idx]
query_graph_idxs, corpus_graph_idxs = zip(*batch)
if self.data_type == GMN_DATA_TYPE:
query_graphs = [self.query_graphs[idx] for idx in query_graph_idxs]
corpus_graphs = [self.corpus_graphs[idx] for idx in corpus_graph_idxs]
all_graphs = self._pack_batch(zip(query_graphs, corpus_graphs))
elif self.data_type == PYG_DATA_TYPE:
query_graphs = [self.query_graph_data[idx] for idx in query_graph_idxs]
corpus_graphs = [self.corpus_graph_data[idx] for idx in corpus_graph_idxs]
all_graphs = list(zip(query_graphs, corpus_graphs))
query_graph_sizes = [self.query_graph_sizes[idx] for idx in query_graph_idxs]
corpus_graph_sizes = [self.corpus_graph_sizes[idx] for idx in corpus_graph_idxs]
all_graph_sizes = list(zip(query_graph_sizes, corpus_graph_sizes))
query_graph_adjs = [self.query_adj_list[idx] for idx in query_graph_idxs]
corpus_graph_adjs = [self.corpus_adj_list[idx] for idx in corpus_graph_idxs]
all_graph_adjs = list(zip(query_graph_adjs, corpus_graph_adjs))
if self.batch_setting == 'stratified':
target = torch.tensor(np.array(self.labels[idx]), dtype=torch.float, device=self.device)
return all_graphs, all_graph_sizes, target, all_graph_adjs
elif self.batch_setting == 'custom':
return all_graphs, all_graph_sizes, None, all_graph_adjs
else:
raise NotImplementedError
def get_datasets(dataset_config, experiment, data_type, modes=['train', 'val']):
return {
mode: SubgraphIsomorphismDataset(
mode = mode, experiment = experiment,
data_type = data_type, **dataset_config
) for mode in modes
} |