在StellarGraph返回NaNs中使用Hinsage/Graphsage的链接预测

Question

我正在尝试使用stellargraph python包中的HinSAGE运行链接预测。

我有一个人和产品的网络，有人到人(知道)和人到产品(购买)的边缘。人员和产品都附加了一个属性向量，尽管每种类型都有不同的属性向量(人员向量是1024，产品是200)。我正在尝试创建一个从个人到产品的链接预测算法，基于网络中的所有信息。对我来说，使用HinSAGE的原因是可以选择归纳学习。

我有下面的代码，并且我认为我所做的与示例类似

https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/hinsage-link-prediction.html https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/graphsage-link-prediction.html

但是我一直得到"nan“作为我的输出预测，有谁有建议我可以尝试一下吗？

import networkx as nx
import pandas as pd
import numpy as np
from tensorflow.keras import Model, optimizers, losses, metrics
import stellargraph as sg
from stellargraph.data import EdgeSplitter
from stellargraph.mapper import HinSAGELinkGenerator
from stellargraph.layer import HinSAGE, link_classification, link_regression
from sklearn.model_selection import train_test_split


graph.info()
#StellarGraph: Undirected multigraph
# Nodes: 54226, Edges: 259120
#
# Node types:
#  products: [45027]
#    Features: float32 vector, length 200
#    Edge types: products-BOUGHT->person
#  person: [9199]
#    Features: float32 vector, length 1024
#    Edge types: person-KNOWS->person, person-BOUGHT->product
#
# Edge types:
#    person-KNOWS->person: [246131]
#        Weights: all 1 (default)
#        Features: none
#    person-BOUGHT->product: [12989]
#        Weights: all 1 (default)
#        Features: none



import networkx as nx
import pandas as pd
import numpy as np
import os
import random
from tensorflow.keras import Model, optimizers, losses, metrics
import stellargraph as sg
from stellargraph.data import EdgeSplitter
from stellargraph.mapper import HinSAGELinkGenerator
from stellargraph.layer import HinSAGE, link_classification
from stellargraph.data import UniformRandomWalk
from stellargraph.data import UnsupervisedSampler
from sklearn.model_selection import train_test_split

from stellargraph.layer import HinSAGE, link_regression



edge_splitter_test = EdgeSplitter(graph)
graph_test, edges_test, labels_test = edge_splitter_test.train_test_split(
    p=0.1, method="global", edge_label="BOUGHT"
)
edge_splitter_train = EdgeSplitter(graph_test, graph)

graph_train, edges_train, labels_train = edge_splitter_train.train_test_split(
    p=0.1, method="global", edge_label="BOUGHT"
)


num_samples = [8, 4]

G = graph

batch_size = 20
epochs = 20


generator = HinSAGELinkGenerator(
    G, batch_size, num_samples, head_node_types=["person", "product"]
)
train_gen = generator.flow(edges_train, labels_train, shuffle=True)
test_gen = generator.flow(edges_test, labels_test)


hinsage_layer_sizes = [32, 32]
assert len(hinsage_layer_sizes) == len(num_samples)

hinsage = HinSAGE(
    layer_sizes=hinsage_layer_sizes, generator=generator, bias=True, dropout=0.0
)


# Expose input and output sockets of hinsage:
x_inp, x_out = hinsage.in_out_tensors()



    
# Final estimator layer
prediction = link_classification(
    output_dim=1, output_act="sigmoid", edge_embedding_method="concat"
)(x_out)

model = Model(inputs=x_inp, outputs=prediction)

model.compile(
    optimizer=optimizers.Adam(),
    loss=losses.binary_crossentropy,
    metrics=["acc"],
)

history = model.fit(train_gen, epochs=epochs, validation_data=test_gen, verbose=2)

Answer 1

所以我发现了这个问题，可能对其他人有用。如果有任何节点包含丢失的数据，则只会生成NAs。如果您通过连接pandas数据帧来创建图形，这尤其危险，我在一个集成的文件中有一个拼写错误，这导致了问题。