wehub-resource-sync
304 lines
9.6 KiB
Python
304 lines
9.6 KiB
Python
import time
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import warnings
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import easygraph as eg
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import numpy as np
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import torch
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import torch.nn as nn
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from easygraph.utils import alias_draw
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from easygraph.utils import alias_setup
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from sklearn import preprocessing
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# from easygraph.functions.graph_embedding import *
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from tqdm import tqdm
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warnings.filterwarnings("ignore")
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class LINE(nn.Module):
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"""Graph embedding via LINE.
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Parameters
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----------
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G : easygraph.Graph or easygraph.DiGraph
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dimension: int
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walk_length: int
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walk_num: int
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negative: int
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batch_size: int
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init_alpha: float
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order: int
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Returns
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-------
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embedding_vector : dict
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The embedding vector of each node
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Examples
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--------
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>>> model = LINE(
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... dimension=128,
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... walk_length=80,
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... walk_num=20,
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... negative=5,
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... batch_size=128,
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... init_alpha=0.025,
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... order=3 )
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>>> model.train()
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>>> emb = model(g, return_dict=True) # g: easygraph.Graph or easygraph.DiGraph
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References
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----------
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.. [1] Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015, May). Line: Large-scale information network embedding. In Proceedings of the 24th international conference on world wide web (pp. 1067-1077).
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https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp0228-Tang.pdf
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"""
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@staticmethod
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def add_args(parser):
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"""Add model-specific arguments to the parser."""
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parser.add_argument(
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"--walk-length",
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type=int,
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default=80,
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help="Length of walk per source. Default is 80.",
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)
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parser.add_argument(
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"--walk-num",
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type=int,
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default=20,
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help="Number of walks per source. Default is 20.",
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)
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parser.add_argument(
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"--negative",
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type=int,
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default=5,
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help="Number of negative node in sampling. Default is 5.",
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)
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parser.add_argument(
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"--batch-size",
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type=int,
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default=1000,
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help="Batch size in SGD training process. Default is 1000.",
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)
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parser.add_argument(
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"--alpha",
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type=float,
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default=0.025,
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help="Initial learning rate of SGD. Default is 0.025.",
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)
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parser.add_argument(
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"--order",
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type=int,
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default=3,
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help="Order of proximity in LINE. Default is 3 for 1+2.",
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)
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parser.add_argument("--hidden-size", type=int, default=128)
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@classmethod
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def build_model_from_args(cls, args):
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return cls(
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args.hidden_size,
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args.walk_length,
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args.walk_num,
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args.negative,
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args.batch_size,
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args.alpha,
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args.order,
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)
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def __init__(
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self,
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dimension=128,
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walk_length=80,
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walk_num=20,
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negative=5,
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batch_size=128,
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init_alpha=0.025,
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order=3,
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):
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super(LINE, self).__init__()
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self.dimension = dimension
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self.walk_length = walk_length
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self.walk_num = walk_num
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self.negative = negative
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self.batch_size = batch_size
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self.init_alpha = init_alpha
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self.order = order
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def forward(self, g, return_dict=True):
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# run LINE algorithm, 1-order, 2-order or 3(1-order + 2-order)
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self.G = g
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self.is_directed = g.is_directed()
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self.num_node = len(g.nodes)
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self.num_edge = g.number_of_edges()
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self.num_sampling_edge = self.walk_length * self.walk_num * self.num_node
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node2id = dict([(node, vid) for vid, node in enumerate(g.nodes)])
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self.edges = [[node2id[e[0]], node2id[e[1]]] for e in self.G.edges]
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self.edges_prob = np.asarray([1.0 for e in g.edges])
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self.edges_prob /= np.sum(self.edges_prob)
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self.edges_table, self.edges_prob = alias_setup(self.edges_prob)
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degree_weight = np.asarray([0] * self.num_node)
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degree_weight = np.array(list(g.degree(node2id[u] for u in g.nodes).values()))
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# for u,v in g.edges:
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# degree_weight[node2id[u]] += 1.0
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# if not self.is_directed:
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# degree_weight[node2id[v]] += 1.0
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self.node_prob = np.power(degree_weight, 0.75)
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self.node_prob /= np.sum(self.node_prob)
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self.node_table, self.node_prob = alias_setup(self.node_prob)
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if self.order == 3:
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self.dimension = int(self.dimension / 2)
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if self.order == 1 or self.order == 3:
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print("train line with 1-order")
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print(type(self.dimension))
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self.emb_vertex = (
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np.random.random((self.num_node, self.dimension)) - 0.5
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) / self.dimension
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self._train_line(order=1)
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embedding1 = preprocessing.normalize(self.emb_vertex, "l2")
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if self.order == 2 or self.order == 3:
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print("train line with 2-order")
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self.emb_vertex = (
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np.random.random((self.num_node, self.dimension)) - 0.5
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) / self.dimension
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self.emb_context = self.emb_vertex
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self._train_line(order=2)
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embedding2 = preprocessing.normalize(self.emb_vertex, "l2")
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if self.order == 1:
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embeddings = embedding1
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elif self.order == 2:
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embeddings = embedding2
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else:
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print("concatenate two embedding...")
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embeddings = np.hstack((embedding1, embedding2))
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if return_dict:
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features_matrix = dict()
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for vid, node in enumerate(g.nodes):
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features_matrix[node] = embeddings[vid]
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else:
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features_matrix = np.zeros((len(g.nodes), embeddings.shape[1]))
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nx_nodes = list(g.nodes)
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features_matrix[nx_nodes] = embeddings[np.arange(len(g.nodes))]
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return features_matrix
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def _update(self, vec_u, vec_v, vec_error, label):
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# update vetex embedding and vec_error
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f = 1 / (1 + np.exp(-np.sum(vec_u * vec_v, axis=1)))
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g = (self.alpha * (label - f)).reshape((len(label), 1))
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vec_error += g * vec_v
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vec_v += g * vec_u
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def _train_line(self, order):
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# train Line model with order
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self.alpha = self.init_alpha
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batch_size = self.batch_size
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t0 = time.time()
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num_batch = int(self.num_sampling_edge / batch_size)
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epoch_iter = tqdm(range(num_batch))
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for b in epoch_iter:
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if b % 100 == 0:
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epoch_iter.set_description(
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# f"Progress: {b * 1.0 / num_batch * 100:.4f}, alpha: {self.alpha:.6f}, time: {time.time() - t0:.4f}"
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)
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self.alpha = self.init_alpha * max((1 - b * 1.0 / num_batch), 0.0001)
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u, v = [0] * batch_size, [0] * batch_size
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for i in range(batch_size):
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edge_id = alias_draw(self.edges_table, self.edges_prob)
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u[i], v[i] = self.edges[edge_id]
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if not self.is_directed and np.random.rand() > 0.5:
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v[i], u[i] = self.edges[edge_id]
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vec_error = np.zeros((batch_size, self.dimension))
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label, target = np.asarray([1 for i in range(batch_size)]), np.asarray(v)
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for j in range(1 + self.negative):
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if j != 0:
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label = np.asarray([0 for i in range(batch_size)])
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for i in range(batch_size):
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target[i] = alias_draw(self.node_table, self.node_prob)
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if order == 1:
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self._update(
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self.emb_vertex[u], self.emb_vertex[target], vec_error, label
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)
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else:
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self._update(
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self.emb_vertex[u], self.emb_context[target], vec_error, label
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)
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self.emb_vertex[u] += vec_error
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if __name__ == "__main__":
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dataset = eg.CiteseerGraphDataset(
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force_reload=True
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) # Download CiteseerGraphDataset contained in EasyGraph
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num_classes = dataset.num_classes
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g = dataset[0]
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labels = g.ndata["label"]
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edge_list = []
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for i in g.edges:
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edge_list.append((i[0], i[1]))
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g1 = eg.Graph()
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g1.add_edges_from(edge_list)
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# print(g.edges)
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# print(g.__dir__())
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model = LINE(
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dimension=128,
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walk_length=80,
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walk_num=20,
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negative=5,
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batch_size=128,
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init_alpha=0.025,
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order=3,
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)
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print(model)
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model.train()
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out = model(g1, return_dict=True)
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keylist = sorted(out)
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tmp = torch.cat(
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(
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torch.unsqueeze(torch.tensor(out[keylist[0]]), -2),
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torch.unsqueeze(torch.tensor(out[keylist[1]]), -2),
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),
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0,
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)
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for i in range(2, len(keylist)):
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tmp = torch.cat((tmp, torch.unsqueeze(torch.tensor(out[keylist[i]]), -2)), 0)
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torch.save(tmp, "line.emb")
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print(tmp, tmp.shape)
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line_emb = []
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for i in range(0, len(tmp)):
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line_emb.append(list(tmp[i]))
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line_emb = np.array(line_emb)
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# tsne = TSNE(n_components=2)
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# z = tsne.fit_transform(line_emb)
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# z_data = np.vstack((z.T, labels)).T
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# df_tsne = pd.DataFrame(z_data, columns=['Dim1', 'Dim2', 'class'])
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# df_tsne['class'] = df_tsne['class'].astype(int)
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# df_tsne.head()
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#
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# plt.figure(figsize=(8, 8))
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# sns.scatterplot(data=df_tsne, hue='class', x='Dim1', y='Dim2', palette=['green','orange','brown','red', 'blue','black'])
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# plt.savefig('torch_line_citeseer.pdf', bbox_inches='tight')
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# plt.show()
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#
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#
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