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, which means sample all elements).

  • replacement (bool) – If True, put the sample ID back for the next draw (default=True).

Raises

  • TypeError – If elements of weights are not of type number.

  • TypeError – If num_samples is not of type int.

  • TypeError – If replacement is not of type bool.

  • RuntimeError – If weights is empty or all zero.

  • ValueError – If num_samples is a negative value.

Examples

>>> 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)
>>> dataset = ds.ImageFolderDataset(image_folder_dataset_dir,
...                                 num_parallel_workers=8,
...                                 sampler=sampler)
add_child(sampler)

Add a sub-sampler for given sampler. The parent will receive all data from the output of sub-sampler sampler and apply its sample logic to return new samples.

Parameters

sampler (Sampler) – Object used to choose samples from the dataset. Only builtin samplers(DistributedSampler, PKSampler, RandomSampler, SequentialSampler, SubsetRandomSampler, WeightedRandomSampler) are supported.

Examples

>>> sampler = ds.SequentialSampler(start_index=0, num_samples=3)
>>> sampler.add_child(ds.RandomSampler(num_samples=4))
>>> dataset = ds.Cifar10Dataset(cifar10_dataset_dir, sampler=sampler)
get_child()

Get the child sampler of given sampler.

Returns

Sampler, The child sampler of given sampler.

Examples

>>> sampler = ds.SequentialSampler(start_index=0, num_samples=3)
>>> sampler.add_child(ds.RandomSampler(num_samples=2))
>>> child_sampler = sampler.get_child()
get_num_samples()

Get num_samples value of the current sampler instance. This parameter can be optionally passed in when defining the Sampler (default is None). This method will return the num_samples value. If the current sampler has child samplers, it will continue to access the child samplers and process the obtained value according to certain rules.

The following table shows the various possible combinations, and the final results returned.

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.

Examples

>>> sampler = ds.SequentialSampler(start_index=0, num_samples=3)
>>> num_samplers = sampler.get_num_samples()