torchsig.transforms.dataset_transforms.RandomDropSamples¶

class torchsig.transforms.dataset_transforms.RandomDropSamples(drop_rate=(0.01, 0.05), size=(1, 10), fill: List[str] = ['ffill', 'bfill', 'mean', 'zero'], **kwargs)[source]¶

Bases: DatasetTransform

Randomly drop IQ samples from the input data of specified durations and with specified fill techniques:

ffill (front fill): replace drop samples with the last previous value.

bfill (back fill): replace drop samples with the next value.

mean: replace drop samples with the mean value of the full data.

zero: replace drop samples with zeros.

Transform is based off of the TSAug Dropout Transform.

drop_rate¶

drop_rate sets the rate at which to drop samples * If int or float, drop_rate is fixed at the value provided. * If list, drop_rate is any element in the list. * If tuple, drop_rate is in range of (tuple[0], tuple[1]).

Type:: int, float, list, tuple

drop_rate_distribution¶

Random draw from drop_rate distribution.

Type:: Callable[[], float]

size¶

size sets the size of each instance of dropped samples * If int or float, size is fixed at the value provided. * If list, size is any element in the list. * If tuple, size is in range of (tuple[0], tuple[1]).

Type:: int, float, list, tuple

size_distribution¶

Random draw from size distribution.

Type:: Callable[[], int]

fill¶

fill sets the method of how the dropped samples should be filled * If list, fill is any element in the list. * If str, fill is fixed at the method provided.

Type:: list, str

fill_distribution¶

Random draw from fill distribution.

Type:: Callable[[], str]

Methods

`add_parent`	Add parent Seedable object and set up RNGs accordingly
`get_distribution`
`get_second_seed`	Gets second seed, usually used to seed both torch and numpy generators with slightly different seeds
`seed`	Seed number generators with given seed.
`setup_rngs`	Initialize torch and numpy number generators, and update its children.
`update`	Updates bookkeeping to transforms in DatasetSignal's SignalMetadata and checks signal valididty.
`update_from_parent`	Update numpy and torch number generators with parent seed

__init__(drop_rate=(0.01, 0.05), size=(1, 10), fill: List[str] = ['ffill', 'bfill', 'mean', 'zero'], **kwargs) → None[source]¶: Transform initialization as Seedable.

__call__(signal: DatasetSignal) → DatasetSignal[source]¶

Performs transforms.

Parameters:: signal (DatasetSignal) – DatasetSignal to be transformed.
Raises:: NotImplementedError – Inherited classes must override this method.
Returns:: Transformed DatasetSignal.
Return type:: DatasetSignal

__repr__() → str¶

Transform string representation. Should be able to recreate class from this string.

Returns:: Transform representation.
Return type:: str

__str__() → str¶: Return str(self).

add_parent(parent) → None¶: Add parent Seedable object and set up RNGs accordingly

get_second_seed(seed: int) → int¶

Gets second seed, usually used to seed both torch and numpy generators with slightly different seeds

Parameters:: seed (int) – Seed to use.
Returns:: New seed.
Return type:: int

seed(seed: int) → None¶

Seed number generators with given seed.

Parameters:: seed (int) – Seed to use.

setup_rngs() → None¶: Initialize torch and numpy number generators, and update its children.

update(signal: DatasetSignal) → None¶

Updates bookkeeping to transforms in DatasetSignal’s SignalMetadata and checks signal valididty. Inherited classes should always call self.update() after performing transform operation (inside __call__).

Parameters:: signal (DatasetSignal) – transformed DatasetSignal.

update_from_parent() → None¶: Update numpy and torch number generators with parent seed