r"""Vital mathematical articles sourced from Wikipedia."""
from __future__ import annotations
import numpy as np
from stgraph.dataset.temporal.stgraph_temporal_dataset import STGraphTemporalDataset
[docs]class WikiMathDataLoader(STGraphTemporalDataset):
r"""Vital mathematical articles sourced from Wikipedia.
The graph dataset is static, with vertices representing Wikipedia pages and
edges representing links. The graph is both directed and weighted, where
the weights indicate the number of links originating from the source page
connecting to the target page. The target is the daily user visits to the
Wikipedia pages between March 16th 2019 and March 15th 2021 which results
in 731 periods.
This class provides functionality for loading, processing, and accessing
the Hungary Chickenpox dataset for use in deep learning tasks such as County
level case count prediction.
.. list-table:: gdata
:widths: 33 33 33
:header-rows: 1
* - num_nodes
- num_edges
- total_timestamps
* - 1068
- 27079
- 731
Example
-------
.. code-block:: python
from stgraph.dataset import WikiMathDataLoader
wiki = WikiMathDataLoader(verbose=True)
num_nodes = wiki.gdata["num_nodes"]
num_edges = wiki.gdata["num_edges"]
total_timestamps = wiki.gdata["total_timestamps"]
edge_list = wiki.get_edges()
edge_weights = wiki.get_edge_weights()
targets = wiki.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
Attributes
----------
name : str
The name of the dataset.
gdata : dict
Graph meta data.
"""
def __init__(
self: WikiMathDataLoader,
verbose: bool = False,
lags: int = 8,
cutoff_time: int | None = None,
redownload: bool = False,
) -> None:
r"""Vital mathematical articles sourced from Wikipedia."""
super().__init__()
if not isinstance(lags, int):
raise TypeError("lags must be of type int")
if lags < 0:
raise ValueError("lags must be a positive integer")
if cutoff_time is not None and not isinstance(cutoff_time, int):
raise TypeError("cutoff_time must be of type int")
if cutoff_time is not None and cutoff_time < 0:
raise ValueError("cutoff_time must be a positive integer")
self.name = "WikiMath"
self._url = "https://raw.githubusercontent.com/bfGraph/STGraph-Datasets/main/wikivital_mathematics.json"
self._verbose = verbose
self._lags = lags
self._cutoff_time = cutoff_time
if redownload and self._has_dataset_cache():
self._delete_cached_dataset()
if self._has_dataset_cache():
self._load_dataset()
else:
self._download_dataset()
self._save_dataset()
self._process_dataset()
def _process_dataset(self: WikiMathDataLoader) -> None:
self._set_total_timestamps()
self._set_num_nodes()
self._set_num_edges()
self._set_edges()
self._set_edge_weights()
self._set_targets()
self._set_features()
def _set_total_timestamps(self: WikiMathDataLoader) -> None:
r"""Set the total timestamps present in the dataset.
It sets the total timestamps present in the dataset into the
gdata attribute dictionary. It is the minimum of the cutoff time
choosen by the user and the total time periods present in the
original dataset.
"""
if self._cutoff_time is not None:
self.gdata["total_timestamps"] = min(
self._dataset["time_periods"],
self._cutoff_time,
)
else:
self.gdata["total_timestamps"] = self._dataset["time_periods"]
def _set_num_nodes(self: WikiMathDataLoader) -> None:
r"""Set the total number of nodes present in the dataset."""
node_set = set()
max_node_id = 0
for edge in self._dataset["edges"]:
node_set.add(edge[0])
node_set.add(edge[1])
max_node_id = max(max_node_id, edge[0], edge[1])
if max_node_id != len(node_set) - 1:
raise ValueError("Node ID labelling is not continuous")
self.gdata["num_nodes"] = len(node_set)
def _set_num_edges(self: WikiMathDataLoader) -> None:
r"""Set the total number of edges present in the dataset."""
self.gdata["num_edges"] = len(self._dataset["edges"])
def _set_edges(self: WikiMathDataLoader) -> None:
r"""Set the edge list of the dataset."""
self._edge_list = [(edge[0], edge[1]) for edge in self._dataset["edges"]]
def _set_edge_weights(self: WikiMathDataLoader) -> None:
r"""Set the edge weights of the dataset."""
edges = self._dataset["edges"]
edge_weights = self._dataset["weights"]
comb_edge_list = [
(edges[i][0], edges[i][1], edge_weights[i]) for i in range(len(edges))
]
comb_edge_list.sort(key=lambda x: (x[1], x[0]))
self._edge_weights = np.array([edge_det[2] for edge_det in comb_edge_list])
def _set_targets(self: WikiMathDataLoader) -> None:
r"""Calculate and set the target attributes."""
targets = [
np.array(self._dataset[str(time)]["y"])
for time in range(self.gdata["total_timestamps"])
]
stacked_target = np.stack(targets)
standardized_target = (stacked_target - np.mean(stacked_target, axis=0)) / (
np.std(stacked_target, axis=0) + 10**-10
)
self._all_targets = np.array(
[standardized_target[i, :].T for i in range(len(targets))],
)
def _set_features(self: WikiMathDataLoader) -> None:
pass
[docs] def get_edges(self: WikiMathDataLoader) -> list:
r"""Return the edge list."""
return self._edge_list
[docs] def get_edge_weights(self: WikiMathDataLoader) -> np.ndarray:
r"""Return the edge weights."""
return self._edge_weights
[docs] def get_all_targets(self: WikiMathDataLoader) -> np.ndarray:
r"""Return the targets for each timestamp."""
return self._all_targets