Python 使用星图创建嵌入是不可复制的

我正在使用StellarGraph（一个奇妙的图形神经网络包），并试图为特定的图形/功能集创建嵌入。不幸的是，尽管每次都提供相同的信息，但每次创建/训练图形时嵌入都是不同的

是这个bug，还是我用错了星图

下面是演示该问题的代码：

import networkx as nx
import random
import numpy as np
import pandas as pd
import keras
import stellargraph as sg
from stellargraph.mapper import GraphSAGELinkGenerator, GraphSAGENodeGenerator
from stellargraph.layer import GraphSAGE, link_classification
from stellargraph.data import UnsupervisedSampler

# Establish random seed
RANDOM_SEED = 42
random.seed(RANDOM_SEED)

# Create a graph from well-known karate club data
print(f"Creating graph")
graph = nx.karate_club_graph()

# Create features for each node
print(f"Creating features")
features = []
nodes = list(graph.nodes)
columns = ["c-" + str(x) for x in range(10)]
nodes.sort()
for node in nodes:
    f = {c: random.random() for c in columns}
    features.append(f)

features_df = pd.DataFrame(features)
print(f"features_df: \n{features_df}")

for i in range(2):
    print(f"----- Iteration: {i} -----")

    # Create the model and generators
    print(f"Creating the model and generators")
    Gs = sg.StellarGraph(graph, node_features=features_df)
    unsupervisedSamples = UnsupervisedSampler(Gs, nodes=graph.nodes(), length=5, number_of_walks=3, seed=RANDOM_SEED)
    train_gen = GraphSAGELinkGenerator(Gs, 50, [5, 5], seed=RANDOM_SEED).flow(unsupervisedSamples)
    graphsage = GraphSAGE(layer_sizes=[100, 100], generator=train_gen, bias=True, dropout=0.0, normalize="l2")
    x_inp_src, x_out_src = graphsage.node_model(flatten_output=False)
    x_inp_dst, x_out_dst = graphsage.node_model(flatten_output=False)

    x_inp = [x for ab in zip(x_inp_src, x_inp_dst) for x in ab]
    x_out = [x_out_src, x_out_dst]
    edge_embedding_method = "l2"
    prediction = link_classification(output_dim=1, output_act="sigmoid", edge_embedding_method=edge_embedding_method)(x_out)

    # Create and train the Keras model
    model = keras.Model(inputs=x_inp, outputs=prediction)
    learning_rate = 1e-2
    model.compile(
        optimizer=keras.optimizers.Adam(lr=learning_rate),
        loss=keras.losses.binary_crossentropy,
        metrics=[keras.metrics.binary_accuracy])

    _ = model.fit_generator(train_gen, epochs=5, verbose=2, use_multiprocessing=False, workers=1, shuffle=False)

    # Create the embeddings
    print(f"Creating the embeddings")
    nodes = list(graph.nodes)
    nodes.sort()
    print(f"Nodes: {nodes}")

    # Create a generator that serves up nodes for use in embedding prediction / creation
    node_gen = GraphSAGENodeGenerator(Gs, 50, [5, 5], seed=RANDOM_SEED).flow(nodes)

    embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)
    embeddings = embedding_model.predict_generator(node_gen, workers=4, verbose=1)
    embeddings = embeddings[:, 0, :]

    np.set_printoptions(threshold=10)
    print(f"embeddings: {embeddings.shape} \n{embeddings}")

执行代码时，有许多调试（打印输出）语句。（示例输出如下所示）。请注意，尽管输入、图形配置、模型配置和随机see值相同，但嵌入是不同的

----- Iteration: 0 -----
:
:
1/1 [==============================] - 0s 58ms/step
embeddings: (34, 100) 
[[-0.10566715  0.02253576 -0.18743701 ... -0.1028127   0.03689012
  -0.02482301]
 [-0.03171733  0.01606975 -0.08616363 ... -0.11775644  0.0429472
  -0.02371055]
 [-0.05802531  0.03910012 -0.10229243 ... -0.15050544  0.06637941
  -0.01950052]
 ...
 [ 0.03011296  0.08852117 -0.01836969 ... -0.154132    0.03844732
  -0.08643046]
 [ 0.01052345 -0.0123206   0.08913474 ... -0.11741614  0.03202919
  -0.04432516]
 [ 0.01951274  0.06263477  0.07959272 ... -0.10350229  0.05735112
  -0.0368157 ]]
:
:
----- Iteration: 1 -----
embeddings: (34, 100) 
[[ 0.11182436 -0.02642134  0.01168384 ...  0.10322241 -0.01680471
  -0.03918815]
 [ 0.02391489  0.02674667 -0.00091334 ...  0.12946768 -0.02389602
  -0.01414653]
 [ 0.08718258 -0.01711811 -0.05704292 ...  0.13477756 -0.00658288
  -0.05889895]
 ...
 [ 0.06843725 -0.13134597 -0.10870655 ...  0.11091235 -0.05146989
  -0.06138216]
 [-0.00593233 -0.05901312 -0.02113489 ... -0.01590953 -0.02516254
  -0.02280537]
 [ 0.00871993 -0.04059998 -0.07237951 ... -0.01590569 -0.00954109
  -0.01116194]]

---

这以前是stellargraph中的一个错误，现在已在v0.9.0中解决

无监督图形图像现已更新，并进行了再现性测试。确保所有种子都已凝固，运行相同的管道应提供可复制的嵌入

目前，“确保所有种子都已设置”用于无监督的图形图像是指：

• 修复这些外部包的种子：

  numpy

  、

  tensorflow

  和

  random

• 在构造无监督的采样器和GraphShageLinkGenerator对象时提供种子。这些类用于执行随机游动和邻里抽样