问题描述
我正在开发交互式网络，以便将数据集发送给合作者。我发现 HoloViews 是交互式网络最直观的选项。我在后端使用 Bokeh 不是出于任何原因，除了上面的教程使用的内容，而且我对它非常熟悉。
我已经获得了适用于我的网络的悬停工具，它看起来很棒。为了这篇文章，下面是使用 iris 数据集的方法的改编。
我遇到的问题是除了已经显示的那些之外，还获得了自定义悬停字段。例如，我希望所有节点都具有 [Node,Species] DataFrame 中的 df_nodes 字段。但是，在图下方代码的第二部分中，我为每个节点生成了 0-5 个类别的自定义字段。我想将其附加到现有的 Hover 选项中。
例如，iris_1 将具有以下内容，其中 * 表示已经存在的内容，# 表示需要添加的内容：
* Node iris_1
* Species Setosa
# Category_2 0.734694
# Category_9 0.489796
# Category_8 0.469388
# Category_4 0.122449
iris_2 只有 [Node,Species]，因为它有 0 个类别（如果你索引 node_to_custom 字典，你会看到）。 iris_3 将包含 [Node,Species,Category_4,Category_5] 字段。
如何在 Holoviews 绘图上添加相对于节点的可变数量的自定义悬停字段？ 最好使用 bokeh，但如果 Plot.ly 是更好的选择，那么让我们开始吧
我尝试过换行，但没有渲染。不过，这应该是一种黑客行为，而不是我真正想要的。
# Iris
import pandas as pd
import networkx as nx
import holoviews as hv
from holoviews import opts
hv.extension('bokeh')
defaults = dict(width=500,height=500)
hv.opts.defaults(
opts.EdgePaths(**defaults),opts.Graph(**defaults),opts.Nodes(**defaults),)
X_iris = pd.DataFrame({'sepal_length': {'iris_0': 5.1,'iris_1': 4.9,'iris_2': 4.7,'iris_3': 4.6,'iris_4': 5.0,'iris_5': 5.4,'iris_6': 4.6,'iris_7': 5.0,'iris_8': 4.4,'iris_9': 4.9,'iris_10': 5.4,'iris_11': 4.8,'iris_12': 4.8,'iris_13': 4.3,'iris_14': 5.8,'iris_15': 5.7,'iris_16': 5.4,'iris_17': 5.1,'iris_18': 5.7,'iris_19': 5.1,'iris_20': 5.4,'iris_21': 5.1,'iris_22': 4.6,'iris_23': 5.1,'iris_24': 4.8,'iris_25': 5.0,'iris_26': 5.0,'iris_27': 5.2,'iris_28': 5.2,'iris_29': 4.7,'iris_30': 4.8,'iris_31': 5.4,'iris_32': 5.2,'iris_33': 5.5,'iris_34': 4.9,'iris_35': 5.0,'iris_36': 5.5,'iris_37': 4.9,'iris_38': 4.4,'iris_39': 5.1,'iris_40': 5.0,'iris_41': 4.5,'iris_42': 4.4,'iris_43': 5.0,'iris_44': 5.1,'iris_45': 4.8,'iris_46': 5.1,'iris_47': 4.6,'iris_48': 5.3,'iris_49': 5.0,'iris_50': 7.0,'iris_51': 6.4,'iris_52': 6.9,'iris_53': 5.5,'iris_54': 6.5,'iris_55': 5.7,'iris_56': 6.3,'iris_57': 4.9,'iris_58': 6.6,'iris_59': 5.2,'iris_60': 5.0,'iris_61': 5.9,'iris_62': 6.0,'iris_63': 6.1,'iris_64': 5.6,'iris_65': 6.7,'iris_66': 5.6,'iris_67': 5.8,'iris_68': 6.2,'iris_69': 5.6,'iris_70': 5.9,'iris_71': 6.1,'iris_72': 6.3,'iris_73': 6.1,'iris_74': 6.4,'iris_75': 6.6,'iris_76': 6.8,'iris_77': 6.7,'iris_78': 6.0,'iris_79': 5.7,'iris_80': 5.5,'iris_81': 5.5,'iris_82': 5.8,'iris_83': 6.0,'iris_84': 5.4,'iris_85': 6.0,'iris_86': 6.7,'iris_87': 6.3,'iris_88': 5.6,'iris_89': 5.5,'iris_90': 5.5,'iris_91': 6.1,'iris_92': 5.8,'iris_93': 5.0,'iris_94': 5.6,'iris_95': 5.7,'iris_96': 5.7,'iris_97': 6.2,'iris_98': 5.1,'iris_99': 5.7,'iris_100': 6.3,'iris_101': 5.8,'iris_102': 7.1,'iris_103': 6.3,'iris_104': 6.5,'iris_105': 7.6,'iris_106': 4.9,'iris_107': 7.3,'iris_108': 6.7,'iris_109': 7.2,'iris_110': 6.5,'iris_111': 6.4,'iris_112': 6.8,'iris_113': 5.7,'iris_114': 5.8,'iris_115': 6.4,'iris_116': 6.5,'iris_117': 7.7,'iris_118': 7.7,'iris_119': 6.0,'iris_120': 6.9,'iris_121': 5.6,'iris_122': 7.7,'iris_123': 6.3,'iris_124': 6.7,'iris_125': 7.2,'iris_126': 6.2,'iris_127': 6.1,'iris_128': 6.4,'iris_129': 7.2,'iris_130': 7.4,'iris_131': 7.9,'iris_132': 6.4,'iris_133': 6.3,'iris_134': 6.1,'iris_135': 7.7,'iris_136': 6.3,'iris_137': 6.4,'iris_138': 6.0,'iris_139': 6.9,'iris_140': 6.7,'iris_141': 6.9,'iris_142': 5.8,'iris_143': 6.8,'iris_144': 6.7,'iris_145': 6.7,'iris_146': 6.3,'iris_147': 6.5,'iris_148': 6.2,'iris_149': 5.9},'sepal_width': {'iris_0': 3.5,'iris_1': 3.0,'iris_2': 3.2,'iris_3': 3.1,'iris_4': 3.6,'iris_5': 3.9,'iris_6': 3.4,'iris_7': 3.4,'iris_8': 2.9,'iris_9': 3.1,'iris_10': 3.7,'iris_11': 3.4,'iris_12': 3.0,'iris_13': 3.0,'iris_14': 4.0,'iris_15': 4.4,'iris_16': 3.9,'iris_17': 3.5,'iris_18': 3.8,'iris_19': 3.8,'iris_20': 3.4,'iris_21': 3.7,'iris_22': 3.6,'iris_23': 3.3,'iris_24': 3.4,'iris_25': 3.0,'iris_26': 3.4,'iris_27': 3.5,'iris_28': 3.4,'iris_29': 3.2,'iris_30': 3.1,'iris_31': 3.4,'iris_32': 4.1,'iris_33': 4.2,'iris_34': 3.1,'iris_35': 3.2,'iris_36': 3.5,'iris_37': 3.6,'iris_38': 3.0,'iris_39': 3.4,'iris_40': 3.5,'iris_41': 2.3,'iris_42': 3.2,'iris_43': 3.5,'iris_44': 3.8,'iris_45': 3.0,'iris_46': 3.8,'iris_47': 3.2,'iris_48': 3.7,'iris_49': 3.3,'iris_50': 3.2,'iris_51': 3.2,'iris_52': 3.1,'iris_53': 2.3,'iris_54': 2.8,'iris_55': 2.8,'iris_56': 3.3,'iris_57': 2.4,'iris_58': 2.9,'iris_59': 2.7,'iris_60': 2.0,'iris_61': 3.0,'iris_62': 2.2,'iris_63': 2.9,'iris_64': 2.9,'iris_65': 3.1,'iris_66': 3.0,'iris_67': 2.7,'iris_68': 2.2,'iris_69': 2.5,'iris_70': 3.2,'iris_71': 2.8,'iris_72': 2.5,'iris_73': 2.8,'iris_74': 2.9,'iris_75': 3.0,'iris_76': 2.8,'iris_77': 3.0,'iris_78': 2.9,'iris_79': 2.6,'iris_80': 2.4,'iris_81': 2.4,'iris_82': 2.7,'iris_83': 2.7,'iris_84': 3.0,'iris_85': 3.4,'iris_86': 3.1,'iris_87': 2.3,'iris_88': 3.0,'iris_89': 2.5,'iris_90': 2.6,'iris_91': 3.0,'iris_92': 2.6,'iris_93': 2.3,'iris_94': 2.7,'iris_95': 3.0,'iris_96': 2.9,'iris_97': 2.9,'iris_98': 2.5,'iris_99': 2.8,'iris_100': 3.3,'iris_101': 2.7,'iris_102': 3.0,'iris_103': 2.9,'iris_104': 3.0,'iris_105': 3.0,'iris_106': 2.5,'iris_107': 2.9,'iris_108': 2.5,'iris_109': 3.6,'iris_110': 3.2,'iris_111': 2.7,'iris_112': 3.0,'iris_113': 2.5,'iris_114': 2.8,'iris_115': 3.2,'iris_116': 3.0,'iris_117': 3.8,'iris_118': 2.6,'iris_119': 2.2,'iris_120': 3.2,'iris_121': 2.8,'iris_122': 2.8,'iris_123': 2.7,'iris_124': 3.3,'iris_125': 3.2,'iris_126': 2.8,'iris_127': 3.0,'iris_128': 2.8,'iris_129': 3.0,'iris_130': 2.8,'iris_131': 3.8,'iris_132': 2.8,'iris_133': 2.8,'iris_134': 2.6,'iris_135': 3.0,'iris_136': 3.4,'iris_137': 3.1,'iris_138': 3.0,'iris_139': 3.1,'iris_140': 3.1,'iris_141': 3.1,'iris_142': 2.7,'iris_143': 3.2,'iris_144': 3.3,'iris_145': 3.0,'iris_146': 2.5,'iris_147': 3.0,'iris_148': 3.4,'iris_149': 3.0},'petal_length': {'iris_0': 1.4,'iris_1': 1.4,'iris_2': 1.3,'iris_3': 1.5,'iris_4': 1.4,'iris_5': 1.7,'iris_6': 1.4,'iris_7': 1.5,'iris_8': 1.4,'iris_9': 1.5,'iris_10': 1.5,'iris_11': 1.6,'iris_12': 1.4,'iris_13': 1.1,'iris_14': 1.2,'iris_15': 1.5,'iris_16': 1.3,'iris_17': 1.4,'iris_18': 1.7,'iris_19': 1.5,'iris_20': 1.7,'iris_21': 1.5,'iris_22': 1.0,'iris_23': 1.7,'iris_24': 1.9,'iris_25': 1.6,'iris_26': 1.6,'iris_27': 1.5,'iris_28': 1.4,'iris_29': 1.6,'iris_30': 1.6,'iris_31': 1.5,'iris_32': 1.5,'iris_33': 1.4,'iris_34': 1.5,'iris_35': 1.2,'iris_36': 1.3,'iris_37': 1.4,'iris_38': 1.3,'iris_39': 1.5,'iris_40': 1.3,'iris_41': 1.3,'iris_42': 1.3,'iris_43': 1.6,'iris_44': 1.9,'iris_45': 1.4,'iris_46': 1.6,'iris_47': 1.4,'iris_48': 1.5,'iris_49': 1.4,'iris_50': 4.7,'iris_51': 4.5,'iris_52': 4.9,'iris_53': 4.0,'iris_54': 4.6,'iris_55': 4.5,'iris_56': 4.7,'iris_57': 3.3,'iris_58': 4.6,'iris_59': 3.9,'iris_60': 3.5,'iris_61': 4.2,'iris_62': 4.0,'iris_63': 4.7,'iris_64': 3.6,'iris_65': 4.4,'iris_66': 4.5,'iris_67': 4.1,'iris_68': 4.5,'iris_69': 3.9,'iris_70': 4.8,'iris_71': 4.0,'iris_72': 4.9,'iris_73': 4.7,'iris_74': 4.3,'iris_75': 4.4,'iris_76': 4.8,'iris_77': 5.0,'iris_78': 4.5,'iris_79': 3.5,'iris_80': 3.8,'iris_81': 3.7,'iris_82': 3.9,'iris_83': 5.1,'iris_84': 4.5,'iris_85': 4.5,'iris_86': 4.7,'iris_87': 4.4,'iris_88': 4.1,'iris_89': 4.0,'iris_90': 4.4,'iris_91': 4.6,'iris_92': 4.0,'iris_93': 3.3,'iris_94': 4.2,'iris_95': 4.2,'iris_96': 4.2,'iris_97': 4.3,'iris_98': 3.0,'iris_99': 4.1,'iris_100': 6.0,'iris_101': 5.1,'iris_102': 5.9,'iris_103': 5.6,'iris_104': 5.8,'iris_105': 6.6,'iris_106': 4.5,'iris_107': 6.3,'iris_108': 5.8,'iris_109': 6.1,'iris_110': 5.1,'iris_111': 5.3,'iris_112': 5.5,'iris_113': 5.0,'iris_114': 5.1,'iris_115': 5.3,'iris_116': 5.5,'iris_117': 6.7,'iris_118': 6.9,'iris_119': 5.0,'iris_120': 5.7,'iris_121': 4.9,'iris_122': 6.7,'iris_123': 4.9,'iris_124': 5.7,'iris_125': 6.0,'iris_126': 4.8,'iris_127': 4.9,'iris_128': 5.6,'iris_129': 5.8,'iris_130': 6.1,'iris_131': 6.4,'iris_132': 5.6,'iris_133': 5.1,'iris_134': 5.6,'iris_135': 6.1,'iris_136': 5.6,'iris_137': 5.5,'iris_138': 4.8,'iris_139': 5.4,'iris_140': 5.6,'iris_141': 5.1,'iris_142': 5.1,'iris_143': 5.9,'iris_144': 5.7,'iris_145': 5.2,'iris_146': 5.0,'iris_147': 5.2,'iris_148': 5.4,'iris_149': 5.1},'petal_width': {'iris_0': 0.2,'iris_1': 0.2,'iris_2': 0.2,'iris_3': 0.2,'iris_4': 0.2,'iris_5': 0.4,'iris_6': 0.3,'iris_7': 0.2,'iris_8': 0.2,'iris_9': 0.1,'iris_10': 0.2,'iris_11': 0.2,'iris_12': 0.1,'iris_13': 0.1,'iris_14': 0.2,'iris_15': 0.4,'iris_16': 0.4,'iris_17': 0.3,'iris_18': 0.3,'iris_19': 0.3,'iris_20': 0.2,'iris_21': 0.4,'iris_22': 0.2,'iris_23': 0.5,'iris_24': 0.2,'iris_25': 0.2,'iris_26': 0.4,'iris_27': 0.2,'iris_28': 0.2,'iris_29': 0.2,'iris_30': 0.2,'iris_31': 0.4,'iris_32': 0.1,'iris_33': 0.2,'iris_34': 0.2,'iris_35': 0.2,'iris_36': 0.2,'iris_37': 0.1,'iris_38': 0.2,'iris_39': 0.2,'iris_40': 0.3,'iris_41': 0.3,'iris_42': 0.2,'iris_43': 0.6,'iris_44': 0.4,'iris_45': 0.3,'iris_46': 0.2,'iris_47': 0.2,'iris_48': 0.2,'iris_49': 0.2,'iris_50': 1.4,'iris_51': 1.5,'iris_52': 1.5,'iris_53': 1.3,'iris_54': 1.5,'iris_55': 1.3,'iris_56': 1.6,'iris_57': 1.0,'iris_58': 1.3,'iris_59': 1.4,'iris_60': 1.0,'iris_61': 1.5,'iris_62': 1.0,'iris_63': 1.4,'iris_64': 1.3,'iris_65': 1.4,'iris_66': 1.5,'iris_67': 1.0,'iris_68': 1.5,'iris_69': 1.1,'iris_70': 1.8,'iris_71': 1.3,'iris_72': 1.5,'iris_73': 1.2,'iris_74': 1.3,'iris_75': 1.4,'iris_76': 1.4,'iris_77': 1.7,'iris_78': 1.5,'iris_79': 1.0,'iris_80': 1.1,'iris_81': 1.0,'iris_82': 1.2,'iris_83': 1.6,'iris_84': 1.5,'iris_85': 1.6,'iris_86': 1.5,'iris_87': 1.3,'iris_88': 1.3,'iris_89': 1.3,'iris_90': 1.2,'iris_91': 1.4,'iris_92': 1.2,'iris_93': 1.0,'iris_94': 1.3,'iris_95': 1.2,'iris_96': 1.3,'iris_97': 1.3,'iris_98': 1.1,'iris_99': 1.3,'iris_100': 2.5,'iris_101': 1.9,'iris_102': 2.1,'iris_103': 1.8,'iris_104': 2.2,'iris_105': 2.1,'iris_106': 1.7,'iris_107': 1.8,'iris_108': 1.8,'iris_109': 2.5,'iris_110': 2.0,'iris_111': 1.9,'iris_112': 2.1,'iris_113': 2.0,'iris_114': 2.4,'iris_115': 2.3,'iris_116': 1.8,'iris_117': 2.2,'iris_118': 2.3,'iris_119': 1.5,'iris_120': 2.3,'iris_121': 2.0,'iris_122': 2.0,'iris_123': 1.8,'iris_124': 2.1,'iris_125': 1.8,'iris_126': 1.8,'iris_127': 1.8,'iris_128': 2.1,'iris_129': 1.6,'iris_130': 1.9,'iris_131': 2.0,'iris_132': 2.2,'iris_133': 1.5,'iris_134': 1.4,'iris_135': 2.3,'iris_136': 2.4,'iris_137': 1.8,'iris_138': 1.8,'iris_139': 2.1,'iris_140': 2.4,'iris_141': 2.3,'iris_142': 1.9,'iris_143': 2.3,'iris_144': 2.5,'iris_145': 2.3,'iris_146': 1.9,'iris_147': 2.0,'iris_148': 2.3,'iris_149': 1.8}})
y_iris = pd.Series({'iris_0': 'setosa','iris_1': 'setosa','iris_2': 'setosa','iris_3': 'setosa','iris_4': 'setosa','iris_5': 'setosa','iris_6': 'setosa','iris_7': 'setosa','iris_8': 'setosa','iris_9': 'setosa','iris_10': 'setosa','iris_11': 'setosa','iris_12': 'setosa','iris_13': 'setosa','iris_14': 'setosa','iris_15': 'setosa','iris_16': 'setosa','iris_17': 'setosa','iris_18': 'setosa','iris_19': 'setosa','iris_20': 'setosa','iris_21': 'setosa','iris_22': 'setosa','iris_23': 'setosa','iris_24': 'setosa','iris_25': 'setosa','iris_26': 'setosa','iris_27': 'setosa','iris_28': 'setosa','iris_29': 'setosa','iris_30': 'setosa','iris_31': 'setosa','iris_32': 'setosa','iris_33': 'setosa','iris_34': 'setosa','iris_35': 'setosa','iris_36': 'setosa','iris_37': 'setosa','iris_38': 'setosa','iris_39': 'setosa','iris_40': 'setosa','iris_41': 'setosa','iris_42': 'setosa','iris_43': 'setosa','iris_44': 'setosa','iris_45': 'setosa','iris_46': 'setosa','iris_47': 'setosa','iris_48': 'setosa','iris_49': 'setosa','iris_50': 'versicolor','iris_51': 'versicolor','iris_52': 'versicolor','iris_53': 'versicolor','iris_54': 'versicolor','iris_55': 'versicolor','iris_56': 'versicolor','iris_57': 'versicolor','iris_58': 'versicolor','iris_59': 'versicolor','iris_60': 'versicolor','iris_61': 'versicolor','iris_62': 'versicolor','iris_63': 'versicolor','iris_64': 'versicolor','iris_65': 'versicolor','iris_66': 'versicolor','iris_67': 'versicolor','iris_68': 'versicolor','iris_69': 'versicolor','iris_70': 'versicolor','iris_71': 'versicolor','iris_72': 'versicolor','iris_73': 'versicolor','iris_74': 'versicolor','iris_75': 'versicolor','iris_76': 'versicolor','iris_77': 'versicolor','iris_78': 'versicolor','iris_79': 'versicolor','iris_80': 'versicolor','iris_81': 'versicolor','iris_82': 'versicolor','iris_83': 'versicolor','iris_84': 'versicolor','iris_85': 'versicolor','iris_86': 'versicolor','iris_87': 'versicolor','iris_88': 'versicolor','iris_89': 'versicolor','iris_90': 'versicolor','iris_91': 'versicolor','iris_92': 'versicolor','iris_93': 'versicolor','iris_94': 'versicolor','iris_95': 'versicolor','iris_96': 'versicolor','iris_97': 'versicolor','iris_98': 'versicolor','iris_99': 'versicolor','iris_100': 'virginica','iris_101': 'virginica','iris_102': 'virginica','iris_103': 'virginica','iris_104': 'virginica','iris_105': 'virginica','iris_106': 'virginica','iris_107': 'virginica','iris_108': 'virginica','iris_109': 'virginica','iris_110': 'virginica','iris_111': 'virginica','iris_112': 'virginica','iris_113': 'virginica','iris_114': 'virginica','iris_115': 'virginica','iris_116': 'virginica','iris_117': 'virginica','iris_118': 'virginica','iris_119': 'virginica','iris_120': 'virginica','iris_121': 'virginica','iris_122': 'virginica','iris_123': 'virginica','iris_124': 'virginica','iris_125': 'virginica','iris_126': 'virginica','iris_127': 'virginica','iris_128': 'virginica','iris_129': 'virginica','iris_130': 'virginica','iris_131': 'virginica','iris_132': 'virginica','iris_133': 'virginica','iris_134': 'virginica','iris_135': 'virginica','iris_136': 'virginica','iris_137': 'virginica','iris_138': 'virginica','iris_139': 'virginica','iris_140': 'virginica','iris_141': 'virginica','iris_142': 'virginica','iris_143': 'virginica','iris_144': 'virginica','iris_145': 'virginica','iris_146': 'virginica','iris_147': 'virginica','iris_148': 'virginica','iris_149': 'virginica'})
c_iris = pd.Series({'setosa': '#66c2a5','versicolor': '#fc8d62','virginica': '#8da0cb'})
# Get edge to weight mapping
weights = X_iris.T.corr().stack()
weights.index = weights.index.map(frozenset)
print(weights.size)
# 22500 = 150**2
# Get rid of diagonal b/c the weights are non-informative
weights = weights[weights.index.map(lambda nodes: len(nodes) == 2)]
print(weights.size)
# 22350 = 150**2 - 150
# Get non-redundant edges ([upper/lower]triangle)
weights = pd.Series(weights.to_dict() )
print(weights.size)
# 11175 = (150**2 - 150)/2
# Create graph
tol = 0.99
graph = nx.Graph()
for edge,w in weights.abs().items(): # For sake of demonstration,just take absolute value though I wouldn't normally do this
if w > tol:
graph.add_edge(*edge,weight=w)
# Get positions
pos = nx.circular_layout(graph)#,seed=0)
# Prepare nodes for Holoviews
df_nodes = pd.DataFrame(pos,index=list("xy")).T
df_nodes.index.name = "Node"
df_nodes["Species"] = y_iris
df_nodes = df_nodes.reset_index()[["x","y","Node","Species"]]
df_nodes.head()
# x y Node Species
# 0 0.002421 -0.765592 iris_1 setosa
# 1 0.116149 -0.721862 iris_0 setosa
# 2 0.012620 -0.730962 iris_2 setosa
# 3 0.053972 -0.611302 iris_3 setosa
# 4 0.049840 -0.687669 iris_4 setosa
# Prepare edges for Holoviews
df_edges = list()
for node_a,node_b,edge_data in graph.edges(data=True):
df_edges.append([node_a,edge_data["weight"]])
df_edges = pd.DataFrame(df_edges,columns=["start","end","weight"])
df_edges.head()
# start end weight
# 0 iris_1 iris_0 0.995999
# 1 iris_1 iris_2 0.996607
# 2 iris_1 iris_3 0.997397
# 3 iris_1 iris_4 0.992233
# 4 iris_1 iris_5 0.993592
hv_nodes = hv.Nodes(df_nodes)
hv_graph = hv.Graph((df_edges,hv_nodes),label='Iris Dataset')
hv_graph.opts(cmap=c_iris.to_dict(),node_size=10,edge_line_width="weight",node_line_color='white',node_color='Species',xaxis=None,yaxis=None)
# Custom mapping
categories = list(map(lambda i: "Category_{}".format(i),range(10)))
range_of_values = np.linspace(0,1)
node_to_custom = dict()
for i,node in enumerate(graph.nodes()):
rng = np.random.RandomState(i)
# Get a random number of categories (real data will not be this obvIoUsly)
number_of_categories = rng.choice([0,1,2,3,4,5],size=1)[0]
# Grab N categories w/o replacement
categories_wrt_node = rng.choice(categories,size=number_of_categories,replace=False)
# Get values ranging from [0,1] for those categories
values_wrt_categories = rng.choice(range_of_values,size=number_of_categories )
# Get a mapping between categories and values
categories_to_values = pd.Series(dict(zip(categories_wrt_node,values_wrt_categories)),dtype=float)
# Get non-zero values,sort,and store
node_to_custom[node] = categories_to_values[lambda v: v > 0].sort_values(ascending=False)
# Example of {key:value} showing {node:series}
list(node_to_custom.items())[0]
# ('iris_1',# Category_2 0.734694
# Category_9 0.489796
# Category_8 0.469388
# Category_4 0.122449
# dtype: float64)
解决方法
我对你的问题没有明确的答案，但也许我仍然可以提供帮助。
据我所知，Holoviews 不支持可变数量的工具提示。它支持自定义工具提示。
自定义工具提示如下所示：
# each tuple will be a row in the tooltip
tooltips = [
('Name','@name'),('Symbol','@symbol'),('CPK','$color[hex,swatch]:CPK')
]
custom_hover_tool = HoverTool(tooltips=tooltips)
points.opts(tools=[custom_hover_tool])
此处的示例：
http://holoviews.org/user_guide/Plotting_with_Bokeh.html
有关可用 $variables 和 @variables 的更多详细信息：
https://docs.bokeh.org/en/latest/docs/user_guide/tools.html#hovertool
因此，如果这对您来说足够好，您可以将您的分类数据聚合到每个记录的字符串中，例如“Category_2：0.734694，Category_9：0.489796 ...”并将其显示为带有标签的工具提示中的一行比如“类别：”。
但是 tooltips 变量实际上也可以是 HTML 模板，如下所示：
tooltips = """
<div class="row">
<div class="col label">Node</div>
<div class="col value">@name</div>
</div>
<div class="row">
<div class="col label">Species</div>
<div class="col value">@species</div>
</div>
@categories{safe}
"""
{safe} 部分强制工具提示将该变量的内容显示为 HTML 内容。所以这次您必须事先将分类数据聚合到一个数据列中，该列已经包含每条记录的最终 HTML 代码，因此对于您的示例记录，它应该如下所示：
'\
<div class="row">\
<div class="col label">Category_2:</div>\
<div class="col value">0.734694</div>\
</div>\
<div class="row">\
<div class="col label">Category_9:</div>\
<div class="col value">0.489796</div>\
</div>\
...\
'
（很可能你会在彼此内部有两个 for 循环，一个用于每个节点，另一个用于其中的每个类别，并且当时只添加一个“行”，但这样的事情将是每个节点的最终结果节点。）
如果您在两个代码块中使用完全相同的 HTML/CSS 结构，它们应该无缝合并。
将这些代码块视为模型只是为了演示这个想法，因为我只是在这里即兴创作而没有进行测试，但我希望它有所帮助。 如果您尝试过，请告诉我，如果您遇到困难，请向我展示一个可行的示例，我会尝试深入了解细节。