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import os.path as osp
from pyvis.network import Network
import torch
import numpy as np
from src.tools.graph import k_hop_subgraph
from src.tools.node import Node, register_node
from torch_geometric.utils import to_undirected, is_undirected
color_types = ['#97c2fc', 'lightgreen', 'lightpink', 'lightpurple']
class SemiStructureKB:
def __init__(self, node_info, edge_index,
node_type_dict=None,
edge_type_dict=None,
node_types=None, edge_types=None,
indirected=True, **kwargs):
"""
A abstract dataset for semistructure data
Args:
node_info (Dict[dict]): A meta dictionary, where each key is node ID and each value is a dictionary
containing information about the corresponding node.
The dictionary can be in arbitrary structure (e.g., hierarchical).
node_types (torch.LongTensor): node types
node_type_dict (torch.LongTensor): A meta dictionary, where each key is node ID (if node_types is None) or node type
(if node_types is not None) and each value dictionary contains information about
the node of the node type.
edge_index (torch.LongTensor): edge index in the pyg format.
edge_types (torch.LongTensor): edge types
edge_type_dict (List[dict]): A meta dictionary, where each key is edge ID (if edge_types is None) or edge type
(if edge_types is not None) and each value dictionary contains information about
the edge of the edge type.
"""
self.node_info = node_info
self.edge_index = edge_index
self.edge_type_dict = edge_type_dict
self.node_type_dict = node_type_dict
self.node_types = node_types
self.edge_types = edge_types
if indirected and not is_undirected(self.edge_index):
self.edge_index, self.edge_types = to_undirected(self.edge_index, self.edge_types,
num_nodes=self.num_nodes(), reduce='mean')
self.edge_types = self.edge_types.long()
if hasattr(self, 'candidate_types'):
self.candidate_ids = self.get_candidate_ids()
else:
self.candidate_ids = [i for i in range(len(self.node_info))]
self.num_candidates = len(self.candidate_ids)
self._build_sparse_adj()
def __len__(self) -> int:
return len(self.node_info)
def __getitem__(self, idx):
idx = int(idx)
node = Node()
register_node(node, self.node_info[idx])
return node
def get_doc_info(self, idx,
add_rel=False, compact=False) -> str:
'''
Return a text document containing information about the node.
Args:
idx (int): node index
add_rel (bool): whether to add relational information explicitly
compact (bool): whether to compact the text
'''
raise NotImplementedError
def _build_sparse_adj(self):
'''
Build the sparse adjacency matrix.
'''
self.sparse_adj = torch.sparse.FloatTensor(self.edge_index,
torch.ones(self.edge_index.shape[1]),
torch.Size([self.num_nodes(), self.num_nodes()]))
self.sparse_adj_by_type = {}
for edge_type in self.rel_type_lst():
edge_idx = torch.arange(self.num_edges())[self.edge_types == self.edge_type2id(edge_type)]
self.sparse_adj_by_type[edge_type] = torch.sparse.FloatTensor(self.edge_index[:, edge_idx],
torch.ones(edge_idx.shape[0]),
torch.Size([self.num_nodes(), self.num_nodes()]))
def get_rel_info(self, idx, rel_type=None) -> str:
'''
Return a text document containing information about the node.
Args:
idx (int): node index
add_rel (bool): whether to add relational information explicitly
compact (bool): whether to compact the text
'''
raise NotImplementedError
def get_candidate_ids(self) -> list:
'''
Get the candidate IDs.
'''
assert hasattr(self, 'candidate_types')
candidate_ids = np.concatenate([self.get_node_ids_by_type(candidate_type) for candidate_type in self.candidate_types]).tolist()
candidate_ids.sort()
return candidate_ids
def num_nodes(self, node_type_id=None):
if node_type_id is None:
return len(self.node_types)
else:
return sum(self.node_types == node_type_id)
def num_edges(self, node_type_id=None):
if node_type_id is None:
return len(self.edge_types)
else:
return sum(self.edge_types == node_type_id)
def rel_type_lst(self):
return list(self.edge_type_dict.values())
def node_type_lst(self):
return list(self.node_type_dict.values())
def node_attr_dict(self):
raise NotImplementedError
def is_rel_type(self, edge_type: str):
return edge_type in self.rel_type_lst()
def edge_type2id(self, edge_type: str) -> int:
'''
Get the edge type ID given the edge type.
'''
try:
idx = list(self.edge_type_dict.values()).index(edge_type)
except:
raise ValueError(f"Edge type {edge_type} not found")
return list(self.edge_type_dict.keys())[idx]
def node_type2id(self, node_type: str) -> int:
'''
Get the node type ID given the node type.
'''
try:
idx = list(self.node_type_dict.values()).index(node_type)
except:
raise ValueError(f"Node type {node_type} not found")
return list(self.node_type_dict.keys())[idx]
def get_node_type_by_id(self, node_id: int) -> str:
'''
Get the node type given the node ID.
'''
return self.node_type_dict[self.node_types[node_id].item()]
def get_edge_type_by_id(self, edge_id: int) -> str:
'''
Get the edge type given the edge ID.
'''
return self.edge_type_dict[self.edge_types[edge_id].item()]
def get_node_ids_by_type(self, node_type: str) -> list:
'''
Get the node IDs given the node type.
'''
return torch.arange(self.num_nodes())[self.node_types == self.node_type2id(node_type)].tolist()
def get_node_ids_by_value(self, node_type, key, value) -> list:
'''
Get the node IDs given the node type and the value of a specific attribute.
'''
ids = self.get_node_ids_by_type(node_type)
indices = []
for idx in ids:
if hasattr(self[idx], key) and getattr(self[idx], key) == value:
indices.append(idx)
return indices
def get_edge_ids_by_type(self, edge_type: str) -> list:
'''
Get the edge IDs given the edge type.
'''
return torch.arange(self.num_edges())[self.edge_types == self.edge_type2id(edge_type)].tolist()
def sample_paths(self, node_types: list, edge_types: list, start_node_id=None, size=1) -> list:
'''
Sample paths give the node types and edge types.
Use "*" to indicate any edge type.
'''
assert len(node_types) == len(edge_types) + 1
for i in range(len(edge_types)):
if edge_types[i] == "*":
continue
_tuple = (node_types[i], edge_types[i], node_types[i+1])
assert _tuple in self.get_tuples(), f"{_tuple} invalid"
paths = []
while len(paths) < size:
p = []
for i in range(len(node_types)):
if i == 0:
node_idx = start_node_id if not start_node_id is None else \
np.random.choice(self.get_node_ids_by_type(node_types[i]))
else:
# neighbor_nodes = self.get_neighbor_nodes(node_idx, edge_types[i-1], direction='in-and-out')
neighbor_nodes = self.get_neighbor_nodes(node_idx, edge_types[i-1])
neighbor_nodes = torch.LongTensor(neighbor_nodes)
node_type_id = list(self.node_type_dict.keys())[list(self.node_type_dict.values()).index(node_types[1])]
neighbor_nodes = neighbor_nodes[self.node_types[neighbor_nodes] == node_type_id]
neighbor_nodes = neighbor_nodes.tolist()
if len(neighbor_nodes) == 0:
if i == 1 and not start_node_id is None:
return []
else:
break
node_idx = np.random.choice(neighbor_nodes)
p.append(node_idx)
if len(p) == len(node_types):
paths.append(p)
return paths
def get_all_paths(self, start_node_id: int,
node_types: list, edge_types: list,
max_num=None, direction='in-and-out') -> list:
'''
Sample paths give the node types and edge types.
Use "*" to indicate any edge type.
'''
assert len(node_types) == len(edge_types) + 1
paths = []
# neighbor_nodes = self.get_neighbor_nodes(start_node_id, edge_types[0], direction=direction)
neighbor_nodes = self.get_neighbor_nodes(start_node_id, edge_types[0])
neighbor_nodes = torch.LongTensor(neighbor_nodes)
node_type_id = list(self.node_type_dict.keys())[list(self.node_type_dict.values()).index(node_types[1])]
neighbor_nodes = neighbor_nodes[self.node_types[neighbor_nodes] == node_type_id]
neighbor_nodes = neighbor_nodes.tolist()
if len(neighbor_nodes) == 0:
# print(f'{start_node_id} => No neighbor nodes | len(node_types)={len(node_types)}')
return []
elif len(node_types) == 2:
return [[start_node_id, node_idx] for node_idx in neighbor_nodes]
else:
# print(f'Iterating over # {len(neighbor_nodes)} neighbors')
for iter_start_node_id in neighbor_nodes:
subpaths = self.get_all_paths(iter_start_node_id, node_types[1:], edge_types[1:])
if len(subpaths) == 0:
continue
for subpath in subpaths:
paths.append([start_node_id] + subpath)
# print((iter_start_node_id, node_types[1:], edge_types[1:]), '==> subpaths #', len(subpaths), ' | Total #', len(paths))
if not max_num is None and len(paths) > max_num:
print('max_num reached')
return []
# print('--------------Finished iterating--------------')
return paths
def get_tuples(self) -> list:
'''
Get all possible tuples of node types and edge types.
'''
col, row = self.edge_index.tolist()
edge_types = self.edge_types.tolist()
col_types, row_types = self.node_types[col].tolist(), self.node_types[row].tolist()
tuples_by_id = set([(n_i, e, n_j) for n_i, e, n_j in zip(col_types, edge_types, row_types)])
tuples = []
for n_i, e, n_j in tuples_by_id:
tuples.append((self.node_type_dict[n_i], self.edge_type_dict[e], self.node_type_dict[n_j]))
tuples = list(set(tuples))
tuples.sort()
return tuples
def get_neighbor_nodes(self, idx, edge_type: str = "*") -> list:
'''
Get the neighbor nodes given the node ID and the edge type.
Args:
idx (int): node index
edge_type (str): edge type, use "*" to indicate any edge type.
'''
if edge_type == "*":
neighbor_nodes = self.sparse_adj[idx].coalesce().indices().view(-1).tolist()
else:
neighbor_nodes = self.sparse_adj_by_type[edge_type][idx].coalesce().indices().view(-1).tolist()
return neighbor_nodes
def k_hop_neighbor(self, node_idx, num_hops, **kwargs):
subset, edge_index, _, edge_mask = k_hop_subgraph(node_idx,
num_hops,
self.edge_index,
num_nodes=self.num_nodes(),
flow='bidirectional',
**kwargs)
node_types = self.node_types[subset]
edge_types = self.edge_types[edge_mask]
return subset, edge_index, node_types, edge_types
def visualize(self, path='.'):
net = Network()
for idx in range(self.num_nodes()):
try:
net.add_node(idx, label=getattr(self[idx],
self.node_type_dict[self.node_types[idx].item()])[:1],
color=color_types[self.node_types[idx].item()]
)
except:
net.add_node(idx,
label=getattr(self[idx], 'title')[:1],
color=color_types[self.node_types[idx].item()]
)
for idx in range(self.num_edges()):
net.add_edge(self.edge_index[0][idx].item(),
self.edge_index[1][idx].item(),
color=color_types[self.edge_types[idx].item()])
net.toggle_physics(True)
net.show(osp.join(path, 'nodes.html'), notebook=False) |