EnglandCovidDataLoader

class stgraph.dataset.EnglandCovidDataLoader(verbose: bool = False, lags: int = 8, cutoff_time: int | None = None, redownload: bool = False)

Bases: STGraphDynamicDataset

COVID-19 cases in England’s NUTS3 regions.

This dataset captures the interplay between COVID-19 cases and mobility in England’s NUTS3 regions from March 3rd to May 12th. It is a directed and weighted graph that offers daily case count and movement of people between each region through node and edge features respectively.

This class provides functionality for loading, processing, and accessing the England Covid dataset for use in deep learning tasks such as predicting the COVID cases in a region.

Example

from stgraph.dataset import EnglandCovidDataLoader

eng_covid = EnglandCovidDataLoader(verbose=True)
num_nodes_dict = eng_covid.gdata["num_nodes"]
num_edges_dict = eng_covid.gdata["num_edges"]
total_timestamps = eng_covid.gdata["total_timestamps"]

edge_list = eng_covid.get_edges()
edge_weights = eng_covid.get_edge_weights()
feats = eng_covid.get_all_features()
targets = eng_covid.get_all_targets()

Parameters:

• verbose (bool, optional) – Flag to control whether to display verbose info (default is False)

• lags (int, optional) – The number of time lags (default is 8)

• cutoff_time (int, optional) – The cutoff timestamp for the temporal dataset (default is None)

• redownload (bool, optional (default is False)) – Redownload the dataset online and save to cache

name

The name of the dataset.

Type:

str

gdata

Graph meta data.

Type:

dict

get_all_features() → list

Return the features for each timestamp.

get_all_targets() → list

Return the targets for each timestamp.

get_edge_weights() → list

Return the edge weights.

get_edges() → list

Return the edge list.