Datasets

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Sequentia provides a selection of sample sequential datasets for quick experimentation.

Each dataset follows the interface described below.

API reference

Class

SequentialDataset

Utility wrapper for a generic sequential dataset.

Methods

__init__(X[, y, lengths, classes])	Initialize a SequentialDataset.
copy()	Create a copy of the dataset.
iter_by_class()	Subset the observation sequences by class.
load(path, /)	Load a stored dataset in .npz format.
save(path, /, *[, compress])	Store the dataset in .npz format.
split(*[, test_size, train_size, ...])	Split the dataset into two partitions (train/test).

Properties

X	Observation sequences.
X_lengths	Observation sequences and corresponding lengths.
X_y	Observation sequences and corresponding outputs.
X_y_lengths	Observation sequences and corresponding outputs and lengths.
classes	Set of unique classes in y.
idxs	Observation sequence start and end indices.
lengths	Lengths corresponding to X.
y	Outputs corresponding to X.

class sequentia.datasets.base.SequentialDataset

Utility wrapper for a generic sequential dataset.

__init__(X, y=None, *, lengths=None, classes=None)

Initialize a SequentialDataset.

Parameters:

• self (SequentialDataset) –

• X (ndarray[Any, dtype[float64]] | ndarray[Any, dtype[int64]]) – Sequence(s).

• y (ndarray[Any, dtype[float64]] | ndarray[Any, dtype[int64]] | None) – Outputs corresponding to sequence(s) in X.

• lengths (ndarray[Any, dtype[int64]] | None) –

  Lengths of the sequence(s) provided in X.

  • If None, then X is assumed to be a single sequence.

  • len(X) should be equal to sum(lengths).

• classes (list[int] | None) –

  Set of possible class labels (only if y was provided with categorical values).

  If not provided, these will be determined from the training data labels.

Return type:

SequentialDataset

copy()

Create a copy of the dataset.

Returns:

Dataset copy.

Return type:

SequentialDataset

Parameters:

self (SequentialDataset) –

iter_by_class()

Subset the observation sequences by class.

Returns:

Generator iterating over classes, yielding:

• X subset of sequences belonging to the class.

• Lengths corresponding to the X subset.

• Class used to subset X.

Return type:

Generator[tuple[numpy.ndarray, numpy.ndarray, int]]

Raises:

• AttributeError – If y was not provided to __init__().

• TypeError – If y was provided but was not categorical.

Parameters:

self (SequentialDataset) –

classmethod load(path, /)

Load a stored dataset in .npz format.

See numpy.load().

Parameters:

path (str | Path | IO) – Location to store the dataset.

Returns:

The loaded dataset.

Return type:

SequentialDataset

See also

save

Stores the dataset in .npz format.

save(path, /, *, compress=True)

Store the dataset in .npz format.

See numpy.savez() and numpy.savez_compressed().

Parameters:

• path (str | Path | IO) – Location to store the dataset.

• compress (bool) – Whether or not to compress the dataset.

• self (SequentialDataset) –

Return type:

None

See also

load

Loads a stored dataset in .npz format.

split(*, test_size=None, train_size=None, random_state=None, shuffle=True, stratify=False)

Split the dataset into two partitions (train/test).

See sklearn.model_selection.train_test_split().

Parameters:

• self (SequentialDataset) –

• test_size (int | float | None) – Size of the test partition.

• train_size (int | float | None) – Size of the training partition.

• random_state (int | RandomState | None) – Seed or numpy.random.RandomState object for reproducible pseudo-randomness.

• shuffle (bool) – Whether or not to shuffle the data before splitting. If shuffle=False then stratify must be False.

• stratify (bool) – Whether or not to stratify the partitions by class label.

Returns:

Dataset partitions.

Return type:

tuple[SequentialDataset, SequentialDataset]

property X: ndarray[Any, dtype[float64]] | ndarray[Any, dtype[int64]]

Observation sequences.

Returns:

Observation sequences.

Return type:

numpy.ndarray

property X_lengths: dict[str, ndarray[Any, dtype[float64]] | ndarray[Any, dtype[int64]]]

Observation sequences and corresponding lengths.

Returns:

Mapping with keys:

• "X" for observation sequences,

• "lengths" for lengths.

Return type:

dict[str, numpy.ndarray]

property X_y: dict[str, ndarray[Any, dtype[float64]] | ndarray[Any, dtype[int64]]]

Observation sequences and corresponding outputs.

Returns:

Mapping with keys:

• "X" for observation sequences,

• "y" for outputs.

Return type:

dict[str, numpy.ndarray]

Raises:

AttributeError – If y was not provided to __init__().

property X_y_lengths: dict[str, ndarray[Any, dtype[float64]] | ndarray[Any, dtype[int64]]]

Observation sequences and corresponding outputs and lengths.

Returns:

Mapping with keys:

• "X" for observation sequences,

• "y" for outputs,

• "lengths" for lengths.

Return type:

dict[str, numpy.ndarray]

Raises:

AttributeError – If y was not provided to __init__().

property classes: ndarray[Any, dtype[int64]] | None

Set of unique classes in y.

Returns:

Unique classes if y is categorical.

Return type:

numpy.ndarray | None

property idxs: ndarray[Any, dtype[int64]]

Observation sequence start and end indices.

Returns:

Start and end indices for each sequence in X.

Return type:

numpy.ndarray

property lengths: ndarray[Any, dtype[int64]]

Lengths corresponding to X.

Returns:

Lengths for each sequence in X.

Return type:

numpy.ndarray

property y: ndarray[Any, dtype[float64]] | ndarray[Any, dtype[int64]]

Outputs corresponding to X.

Returns:

Sequence outputs.

Return type:

numpy.ndarray

Raises:

AttributeError – If y was not provided to __init__().