我试图使用火炬的标签传播。 我有一个看起来像
ID Target Weight Label
1 12 0.4 1
2 24 0.1 0
4 13 0.5 1
4 12 0.3 1
12 1 0.1 1
12 4 0.4 1
13 4 0.2 1
17 1 0.1 0
等等
我按照如下方式构建网络:
G = nx.from_pandas_edgelist(df, source='ID', target='Target', edge_attr=['Weight'])
和邻接矩阵
adj_matrix = nx.adjacency_matrix(G).toarray()
我只有两个标签,0和1,还有一些未标记的数据。我创建了如下输入张量:
# Create input tensors
adj_matrix_t = torch.FloatTensor(adj_matrix)
labels_t = torch.LongTensor(df['Labels'].tolist())
正在尝试运行以下代码
# Learn with Label Propagation
label_propagation = LabelPropagation(adj_matrix_t)
label_propagation.fit(labels_t) # this is causing the error
我得到了错误:IndexError: The shape of the mask [196] at index 0 does not match the shape of the indexed tensor [207] at index 0
。
我检查了adj_matrix_t.shape
的大小,它当前是(207207207),而标签是196。
你知道我如何解决这个不一致的问题吗
请参见下面的错误跟踪:
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-42-cf4f88a4bb12> in <module>
2 label_propagation = LabelPropagation(adj_matrix_t)
3 print("Label Propagation: ", end="")
----> 4 label_propagation.fit(labels_t)
5 label_propagation_output_labels = label_propagation.predict_classes()
6
<ipython-input-1-54a7dbc30bd1> in fit(self, labels, max_iter, tol)
100
101 def fit(self, labels, max_iter=1000, tol=1e-3):
--> 102 super().fit(labels, max_iter, tol)
103
104 ## Label spreading
<ipython-input-1-54a7dbc30bd1> in fit(self, labels, max_iter, tol)
58 Convergence tolerance: threshold to consider the system at steady state.
59 """
---> 60 self._one_hot_encode(labels)
61
62 self.predictions = self.one_hot_labels.clone()
<ipython-input-1-54a7dbc30bd1> in _one_hot_encode(self, labels)
43 self.one_hot_labels = torch.zeros((self.n_nodes, self.n_classes), dtype=torch.float)
44 self.one_hot_labels = self.one_hot_labels.scatter(1, labels.unsqueeze(1), 1)
---> 45 self.one_hot_labels[unlabeled_mask, 0] = 0
46
47 self.labeled_mask = ~unlabeled_mask
下面的代码是我希望用于标签传播的示例。似乎错误是由标签引起的。我的数据集中有些节点没有标签(尽管在上面的示例中,我编写了所有标签)。这可能是导致错误消息的原因吗
原始代码(供参考:https://mybinder.org/v2/gh/thibaudmartinez/label-propagation/master?filepath=notebook.ipynb):
## Testing models on synthetic data
import pandas as pd
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
# Create caveman graph
n_cliques = 4
size_cliques = 5
caveman_graph = nx.connected_caveman_graph(n_cliques, size_cliques)
adj_matrix = nx.adjacency_matrix(caveman_graph).toarray()
# Create labels
labels = np.full(n_cliques * size_cliques, -1.)
# Only one node per clique is labeled. Each clique belongs to a different class.
labels[0] = 0
labels[size_cliques] = 1
labels[size_cliques * 2] = 2
labels[size_cliques * 3] = 3
# Create input tensors
adj_matrix_t = torch.FloatTensor(adj_matrix)
labels_t = torch.LongTensor(labels)
# Learn with Label Propagation
label_propagation = LabelPropagation(adj_matrix_t)
print("Label Propagation: ", end="")
label_propagation.fit(labels_t)
label_propagation_output_labels = label_propagation.predict_classes()
# Learn with Label Spreading
label_spreading = LabelSpreading(adj_matrix_t)
print("Label Spreading: ", end="")
label_spreading.fit(labels_t, alpha=0.8)
label_spreading_output_labels = label_spreading.predict_classes()
# Plot graphs
color_map = {-1: "orange", 0: "blue", 1: "green", 2: "red", 3: "cyan"}
input_labels_colors = [color_map[l] for l in labels]
lprop_labels_colors = [color_map[l] for l in label_propagation_output_labels.numpy()]
lspread_labels_colors = [color_map[l] for l in label_spreading_output_labels.numpy()]
plt.figure(figsize=(14, 6))
ax1 = plt.subplot(1, 4, 1)
ax2 = plt.subplot(1, 4, 2)
ax3 = plt.subplot(1, 4, 3)
ax1.title.set_text("Raw data (4 classes)")
ax2.title.set_text("Label Propagation")
ax3.title.set_text("Label Spreading")
pos = nx.spring_layout(G)
nx.draw(G, ax=ax1, pos=pos, node_color=input_labels_colors, node_size=50)
nx.draw(G, ax=ax2, pos=pos, node_color=lprop_labels_colors, node_size=50)
nx.draw(G, ax=ax3, pos=pos, node_color=lspread_labels_colors, node_size=50)
# Legend
ax4 = plt.subplot(1, 4, 4)
ax4.axis("off")
legend_colors = ["orange", "blue", "green", "red", "cyan"]
legend_labels = ["unlabeled", "class 0", "class 1", "class 2", "class 3"]
dummy_legend = [ax4.plot([], [], ls='-', c=c)[0] for c in legend_colors]
plt.legend(dummy_legend, legend_labels)
plt.show()
当然,如果由于标签的原因,我在本文顶部的dataset示例不适合原始代码,如果您能给我另一个示例,以了解dataset中的标签(确定节点类)应该是什么样子的(即使缺少预测值),我将不胜感激
对于这里的其他读者来说,似乎this是在这个问题中被问及的实现
用于尝试预测标签的方法适用于节点的标签,而不是边。为了可视化这一点,我绘制了示例数据,并用
Weight
和Label
列(下面附加了生成绘图的代码)给绘图上色,其中Weight
是边缘的线条厚度,Label
是颜色:为了使用此方法,您需要生成如下所示的数据,其中每个节点(由
ID
表示)正好得到一个node_label
:为了清楚起见,您仍然需要上面的原始数据来构建网络和邻接矩阵,但您必须确定一些逻辑规则,以便将边标签转换为节点标签。然后,预测未标记的节点后,如果需要,可以反转规则以获取边标签
这不是一个严格严格的方法,但它是实用的,如果你的数据不仅仅是随机噪声,它可能会产生一些合理的结果
代码附录:
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