mindspore.dataset.WeightedRandomSampler

class mindspore.dataset.WeightedRandomSampler(weights, num_samples=None, replacement=True)[source]

Samples the elements from [0, len(weights) - 1] randomly with the given weights (probabilities).

Parameters

weights (list[float, int]) – A sequence of weights, not necessarily summing up to 1.
num_samples (int, optional) – Number of elements to sample (default=None, all elements).
replacement (bool) – If True, put the sample ID back for the next draw (default=True).

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "path/to/imagefolder_directory"
>>>
>>> weights = [0.9, 0.01, 0.4, 0.8, 0.1, 0.1, 0.3]
>>>
>>> # creates a WeightedRandomSampler that will sample 4 elements without replacement
>>> sampler = ds.WeightedRandomSampler(weights, 4)
>>> data = ds.ImageFolderDataset(dataset_dir, num_parallel_workers=8, sampler=sampler)

Raises

ValueError – If num_samples is not positive.
ValueError – If replacement is not boolean.

get_num_samples()

All samplers can contain a numeric num_samples value (or it can be set to None). A child sampler can exist or be None. If a child sampler exists, then the child sampler count can be a numeric value or None. These conditions impact the resultant sampler count that is used. The following table shows the possible results from calling this function.

child sampler	num_samples	child_samples	result
T	x	y	min(x, y)
T	x	None	x
T	None	y	y
T	None	None	None
None	x	n/a	x
None	None	n/a	None

Returns: int, the number of samples, or None