136 lines
5.0 KiB
Python
136 lines
5.0 KiB
Python
import torch
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import numpy as np
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from torch_geometric.datasets import Planetoid
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from torch_geometric.utils import add_self_loops, remove_self_loops, \
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to_undirected
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from torch_geometric.data import Data
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from federatedscope.core.auxiliaries.splitter_builder import get_splitter
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from federatedscope.core.auxiliaries.transform_builder import get_transform
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INF = np.iinfo(np.int64).max
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def load_nodelevel_dataset(config=None):
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r"""
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:returns:
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data_dict
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:rtype:
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Dict: dict{'client_id': Data()}
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"""
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path = config.data.root
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name = config.data.type.lower()
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# TODO: remove splitter
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# Splitter
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splitter = get_splitter(config)
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# Transforms
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transforms_funcs, _, _ = get_transform(config, 'torch_geometric')
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# Dataset
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if name in ["cora", "citeseer", "pubmed"]:
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num_split = {
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'cora': [232, 542, INF],
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'citeseer': [332, 665, INF],
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'pubmed': [3943, 3943, INF],
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}
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dataset = Planetoid(path,
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name,
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split='random',
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num_train_per_class=num_split[name][0],
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num_val=num_split[name][1],
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num_test=num_split[name][2],
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**transforms_funcs)
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dataset = splitter(dataset[0])
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global_dataset = Planetoid(path,
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name,
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split='random',
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num_train_per_class=num_split[name][0],
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num_val=num_split[name][1],
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num_test=num_split[name][2],
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**transforms_funcs)
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elif name == "dblp_conf":
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from federatedscope.gfl.dataset.dblp_new import DBLPNew
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dataset = DBLPNew(path,
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FL=1,
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splits=config.data.splits,
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**transforms_funcs)
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global_dataset = DBLPNew(path,
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FL=0,
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splits=config.data.splits,
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**transforms_funcs)
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elif name == "dblp_org":
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from federatedscope.gfl.dataset.dblp_new import DBLPNew
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dataset = DBLPNew(path,
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FL=2,
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splits=config.data.splits,
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**transforms_funcs)
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global_dataset = DBLPNew(path,
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FL=0,
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splits=config.data.splits,
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**transforms_funcs)
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elif name.startswith("csbm"):
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from federatedscope.gfl.dataset.cSBM_dataset import \
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dataset_ContextualSBM
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dataset = dataset_ContextualSBM(
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root=path,
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name=name if len(name) > len("csbm") else None,
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theta=config.data.cSBM_phi,
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epsilon=3.25,
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n=2500,
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d=5,
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p=1000,
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train_percent=0.2)
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global_dataset = None
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else:
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raise ValueError(f'No dataset named: {name}!')
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dataset = [ds for ds in dataset]
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client_num = min(len(dataset), config.federate.client_num
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) if config.federate.client_num > 0 else len(dataset)
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config.merge_from_list(['federate.client_num', client_num])
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# get local dataset
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data_dict = dict()
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for client_idx in range(1, len(dataset) + 1):
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local_data = dataset[client_idx - 1]
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# To undirected and add self-loop
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local_data.edge_index = add_self_loops(
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to_undirected(remove_self_loops(local_data.edge_index)[0]),
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num_nodes=local_data.x.shape[0])[0]
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data_dict[client_idx] = {
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'data': local_data,
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'train': [local_data],
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'val': [local_data],
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'test': [local_data]
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}
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# Keep ML split consistent with local graphs
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if global_dataset is not None:
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global_graph = global_dataset[0]
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train_mask = torch.zeros_like(global_graph.train_mask)
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val_mask = torch.zeros_like(global_graph.val_mask)
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test_mask = torch.zeros_like(global_graph.test_mask)
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for client_sampler in data_dict.values():
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if isinstance(client_sampler, Data):
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client_subgraph = client_sampler
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else:
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client_subgraph = client_sampler['data']
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train_mask[client_subgraph.index_orig[
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client_subgraph.train_mask]] = True
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val_mask[client_subgraph.index_orig[
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client_subgraph.val_mask]] = True
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test_mask[client_subgraph.index_orig[
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client_subgraph.test_mask]] = True
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global_graph.train_mask = train_mask
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global_graph.val_mask = val_mask
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global_graph.test_mask = test_mask
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data_dict[0] = {
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'data': global_graph,
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'train': [global_graph],
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'val': [global_graph],
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'test': [global_graph]
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}
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return data_dict, config
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