r"""Hourly energy output of windmills."""
from __future__ import annotations
import numpy as np
from stgraph.dataset.temporal.stgraph_temporal_dataset import STGraphTemporalDataset
[docs]class WindmillOutputDataLoader(STGraphTemporalDataset):
r"""Hourly energy output of windmills.
This class provides functionality for loading, processing, and accessing
the Windmill output dataset for use in deep learning such as
regression tasks.
.. list-table:: gdata for Windmill Output Small
:widths: 33 33 33
:header-rows: 1
* - num_nodes
- num_edges
- total_timestamps
* - 11
- 121
- 17472
.. list-table:: gdata for Windmill Output Medium
:widths: 33 33 33
:header-rows: 1
* - num_nodes
- num_edges
- total_timestamps
* - 26
- 676
- 17472
.. list-table:: gdata for Windmill Output Large
:widths: 33 33 33
:header-rows: 1
* - num_nodes
- num_edges
- total_timestamps
* - 319
- 101761
- 17472
Example
-------
.. code-block:: python
from stgraph.dataset import WindmillOutputDataLoader
wind_small = WindmillOutputDataLoader(verbose=True, size="small")
num_nodes = wind_small.gdata["num_nodes"]
num_edges = wind_small.gdata["num_edges"]
total_timestamps = wind_small.gdata["total_timestamps"]
edge_list = wind_small.get_edges()
edge_weights = wind_small.get_edge_weights()
targets = wind_small.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)
size : str, optional
The dataset size among large, medium and small (default is large)
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: WindmillOutputDataLoader,
verbose: bool = False,
lags: int = 8,
cutoff_time: int | None = None,
size: str = "large",
redownload: bool = False,
) -> None:
r"""Hourly energy output of windmills."""
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")
if not isinstance(size, str):
raise TypeError("size must be of type string")
if size not in ["large", "medium", "small"]:
raise ValueError(
"size must take either of the following values : "
"large, medium or small",
)
self.name = "WindMill_" + size
self._verbose = verbose
self._lags = lags
self._cutoff_time = cutoff_time
self._size = size
size_urls = {
"large": "https://graphmining.ai/temporal_datasets/windmill_output.json",
"medium": "https://graphmining.ai/temporal_datasets/windmill_output_medium.json",
"small": "https://graphmining.ai/temporal_datasets/windmill_output_small.json",
}
self._url = size_urls[self._size]
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: WindmillOutputDataLoader) -> None:
self._set_total_timestamps()
self._set_num_nodes()
self._set_num_edges()
self._set_edges()
self._set_edge_weights()
self._set_targets()
def _set_total_timestamps(self: WindmillOutputDataLoader) -> 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: WindmillOutputDataLoader) -> 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: WindmillOutputDataLoader) -> None:
r"""Set the total number of edges present in the dataset."""
self.gdata["num_edges"] = len(self._dataset["edges"])
def _set_edges(self: WindmillOutputDataLoader) -> 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: WindmillOutputDataLoader) -> 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: WindmillOutputDataLoader) -> None:
r"""Calculate and sets the target attributes."""
stacked_target = np.stack(self._dataset["block"])
standardized_target = (stacked_target - np.mean(stacked_target, axis=0)) / (
np.std(stacked_target, axis=0) + 10**-10
)
self._all_targets = [
standardized_target[i, :].T for i in range(self.gdata["total_timestamps"])
]
def _set_features(self: WindmillOutputDataLoader) -> None:
pass
[docs] def get_edges(self: WindmillOutputDataLoader) -> list:
r"""Return the edge list."""
return self._edge_list
[docs] def get_edge_weights(self: WindmillOutputDataLoader) -> np.ndarray:
r"""Return the edge weight."""
return self._edge_weights
[docs] def get_all_targets(self: WindmillOutputDataLoader) -> list:
r"""Return the targets for each timestamp."""
return self._all_targets