timesead.data.minismd_dataset

Attributes

`FILENAMES`
`TRAIN_LENS`
`TEST_LENS`

Classes

MiniSMDDataset

This is a condensed version of the SMDDataset containing only shortened time

Module Contents

timesead.data.minismd_dataset.FILENAMES = ['machine-1-3.txt', 'machine-1-7.txt']

timesead.data.minismd_dataset.TRAIN_LENS = [500, 1000]

timesead.data.minismd_dataset.TEST_LENS = [500, 1000]

class timesead.data.minismd_dataset.MiniSMDDataset(server_id: int = 0, path: str = os.path.join(DATA_DIRECTORY, 'mini_smd'), training: bool = True, standardize: bool | Callable = True, preprocess: bool = True)

Bases: timesead.data.dataset.BaseTSDataset

This is a condensed version of the SMDDataset containing only shortened time series for two different machines. Mostly used for testing purposes.

Parameters:

server_id (int) – ID of the server to load. Must be 0 or 1.
path (str) – Path to the data
training (bool) – Whether to load the training or the test set.
standardize (Union[bool, Callable]) – Can be either a bool that decides whether to apply the dataset-dependent default standardization or a function with signature (dataframe, stats) -> dataframe, where stats is a dictionary of common statistics on the training dataset (i.e., mean, std, median, etc. for each feature)
preprocess (bool)

server_id = 0

path

training = True

standardize = True

inputs = None

targets = None

processed_dir

load_data() → Tuple[numpy.ndarray, numpy.ndarray]

Return type:: Tuple[numpy.ndarray, numpy.ndarray]

__getitem__(item: int) → Tuple[Tuple[torch.Tensor], Tuple[torch.Tensor]]

Parameters:: item (int)
Return type:: Tuple[Tuple[torch.Tensor], Tuple[torch.Tensor]]

__len__() → int | None

Return type:: Optional[int]

property seq_len: int | List[int]

Return type:: Union[int, List[int]]

property num_features: int

Return type:: int

static get_default_pipeline() → Dict[str, Dict[str, Any]]

Return type:: Dict[str, Dict[str, Any]]

static get_feature_names()