Sampler

Sampler define the way of iterating over the dataset and returning samples.

AbstractSampler

class AbstractSampler(indices=None)

Bases: object

Class to define an abstract Sampling API

_get_indices(n_indices)

Function to return a specific number of indices. Implements the actual sampling strategy.

Parameters:n_indices (int) – Number of indices to return Returns:List with sampled indices Return type:list

classmethod from_dataset(dataset: delira.data_loading.dataset.AbstractDataset, **kwargs)

Classmethod to initialize the sampler from a given dataset

Parameters:dataset (AbstractDataset) – the given dataset Returns:The initialzed sampler Return type:AbstractSampler

LambdaSampler

class LambdaSampler(indices, sampling_fn)

Bases: delira.data_loading.sampler.abstract_sampler.AbstractSampler

Implements Arbitrary Sampling methods specified by a function which takes the index_list and the number of indices to return

_get_indices(n_indices)

Actual Sampling

Parameters:n_indices (int) – number of indices to return Returns:list of sampled indices Return type:list Raises:StopIteration – Maximum number of indices sampled

classmethod from_dataset(dataset: delira.data_loading.dataset.AbstractDataset, **kwargs)

Classmethod to initialize the sampler from a given dataset

Parameters:dataset (AbstractDataset) – the given dataset Returns:The initialzed sampler Return type:AbstractSampler

RandomSampler

class RandomSampler(indices)

Bases: delira.data_loading.sampler.abstract_sampler.AbstractSampler

Implements Random Sampling from whole Dataset

_get_indices(n_indices)

Actual Sampling

Parameters:n_indices (int) – number of indices to return Returns:list of sampled indices Return type:list Raises:StopIteration – If maximal number of samples is reached

classmethod from_dataset(dataset: delira.data_loading.dataset.AbstractDataset, **kwargs)

Classmethod to initialize the sampler from a given dataset

Parameters:dataset (AbstractDataset) – the given dataset Returns:The initialzed sampler Return type:AbstractSampler

PrevalenceRandomSampler

class PrevalenceRandomSampler(indices, shuffle_batch=True)

Bases: delira.data_loading.sampler.abstract_sampler.AbstractSampler

Implements random Per-Class Sampling and ensures same number of samplers per batch for each class

_get_indices(n_indices)

Actual Sampling

Parameters:n_indices (int) – number of indices to return Returns:list of sampled indices Return type:list Raises:StopIteration – If maximal number of samples is reached

classmethod from_dataset(dataset: delira.data_loading.dataset.AbstractDataset, **kwargs)

Classmethod to initialize the sampler from a given dataset

Parameters:dataset (AbstractDataset) – the given dataset Returns:The initialzed sampler Return type:AbstractSampler

StoppingPrevalenceRandomSampler

class StoppingPrevalenceRandomSampler(indices, shuffle_batch=True)

Bases: delira.data_loading.sampler.abstract_sampler.AbstractSampler

Implements random Per-Class Sampling and ensures same number of samplers per batch for each class; Stops if out of samples for smallest class

_get_indices(n_indices)

Actual Sampling

Parameters:n_indices (int) – number of indices to return Raises:StopIteration: If end of class indices is reached for one class Returns:list Return type:list of sampled indices

classmethod from_dataset(dataset: delira.data_loading.dataset.AbstractDataset, **kwargs)

Classmethod to initialize the sampler from a given dataset

Parameters:dataset (AbstractDataset) – the given dataset Returns:The initialzed sampler Return type:AbstractSampler

SequentialSampler

class SequentialSampler(indices)

Bases: delira.data_loading.sampler.abstract_sampler.AbstractSampler

Implements Sequential Sampling from whole Dataset

_get_indices(n_indices)

Actual Sampling

Parameters:n_indices (int) – number of indices to return Raises:StopIteration : If end of dataset reached Returns:list of sampled indices Return type:list

classmethod from_dataset(dataset: delira.data_loading.dataset.AbstractDataset, **kwargs)

Classmethod to initialize the sampler from a given dataset

Parameters:dataset (AbstractDataset) – the given dataset Returns:The initialzed sampler Return type:AbstractSampler

PrevalenceSequentialSampler

class PrevalenceSequentialSampler(indices, shuffle_batch=True)

Bases: delira.data_loading.sampler.abstract_sampler.AbstractSampler

Implements Per-Class Sequential sampling and ensures same number of samples per batch for each class; If out of samples for one class: restart at first sample

_get_indices(n_indices)

Actual Sampling

Parameters:n_indices (int) – number of indices to return Raises:StopIteration : If end of class indices is reached Returns:list of sampled indices Return type:list

classmethod from_dataset(dataset: delira.data_loading.dataset.AbstractDataset, **kwargs)

Classmethod to initialize the sampler from a given dataset

Parameters:dataset (AbstractDataset) – the given dataset Returns:The initialzed sampler Return type:AbstractSampler

StoppingPrevalenceSequentialSampler

class StoppingPrevalenceSequentialSampler(indices, shuffle_batch=True)

Bases: delira.data_loading.sampler.abstract_sampler.AbstractSampler

Implements Per-Class Sequential sampling and ensures same number of samples per batch for each class; Stops if all samples of first class have been sampled

_get_indices(n_indices)

Actual Sampling

Parameters:n_indices (int) – number of indices to return Raises:StopIteration : If end of class indices is reached for one class Returns:list of sampled indices Return type:list

classmethod from_dataset(dataset: delira.data_loading.dataset.AbstractDataset)

Classmethod to initialize the sampler from a given dataset

Parameters:dataset (AbstractDataset) – the given dataset Returns:The initialzed sampler Return type:AbstractSampler

WeightedRandomSampler

class WeightedRandomSampler(indices, weights=None, cum_weights=None)

Bases: delira.data_loading.sampler.abstract_sampler.AbstractSampler

Implements Weighted Random Sampling

_get_indices(n_indices)

Actual Sampling

Parameters:

n_indices (int) – number of indices to return

Returns:

list of sampled indices

Return type:

list

Raises:

• StopIteration – If maximal number of samples is reached
• TypeError – if weights and cum_weights are specified at the same time
• ValueError – if weights or cum_weights don’t match the population

classmethod from_dataset(dataset: delira.data_loading.dataset.AbstractDataset, **kwargs)

Classmethod to initialize the sampler from a given dataset

Parameters:dataset (AbstractDataset) – the given dataset Returns:The initialzed sampler Return type:AbstractSampler