数据集的官方描述：
class ChickenpoxDatasetLoader(object):
"""A dataset of county level chicken pox cases in Hungary between 2004
and 2014. We made it public during the development of PyTorch Geometric
Temporal. The underlying graph is static - vertices are counties and
edges are neighbourhoods. Vertex features are lagged weekly counts of the
chickenpox cases (we included 4 lags). The target is the weekly number of
cases for the upcoming week (signed integers). Our dataset consist of more
than 500 snapshots (weeks).
"""
翻译：
2004年到2014年期间匈牙利县级水痘病例数据集。
图是静态的——顶点是县；边是邻居；
顶点的特征是滞后的每周计数水痘病例（包含4个滞后）；
目标：下一周的水痘的数量；
数据集包含了超过500个时间快照；
官方数据集的加载：
1 def _read_web_data(self): 2 #url = "https://raw.githubusercontent.com/bene?dekrozemberczki/pytorch_geometric_temporal/master/dataset/chickenpox.json" 3 #self._dataset = json.loads(urllib.request.urlopen(url).read())
1 def _get_edges(self): #加载边 2 self._edges = np.array(self._dataset["edges"]).T 3 4 5 def _get_edge_weights(self): #一共有多少条边 6 self._edge_weights = np.ones(self._edges.shape[1]) 7 8 9 def _get_targets_and_features(self): #节点的属性;构建时间序列 10 stacked_target = np.array(self._dataset["FX"]) #m*n维，其中m是时间，n是节点的数量
#时间切片 11 self.features = [stacked_target[i:i+self.lags,:].T for i in range(stacked_target.shape[0]-self.lags)] 12 self.targets = [stacked_target[i+self.lags,:].T for i in range(stacked_target.shape[0]-self.lags)] 13 14 15 def get_dataset(self, lags: int=4) -> StaticGraphTemporalSignal: 16 """Returning the Chickenpox Hungary data iterator. 17 18 Args types: 19 * **lags** *(int)* - The number of time lags. 20 Return types: 21 * **dataset** *(StaticGraphTemporalSignal)* - The Chickenpox Hungary dataset. 22 """ 23 self.lags = lags 24 self._get_edges() 25 self._get_edge_weights() 26 self._get_targets_and_features() 27 dataset = StaticGraphTemporalSignal(self._edges, self._edge_weights, self.features, self.targets) 28 return dataset
数据集解析：
dataset：
原文：https://www.cnblogs.com/xmd-home/p/14814675.html