Sampler¶

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

AbstractSampler¶

class AbstractSampler(indices=None)[source]¶

Bases: object

Class to define an abstract Sampling API

_get_indices(n_indices)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

Bases: delira.data_loading.sampler.abstract_sampler.AbstractSampler

Implements Random Sampling from whole Dataset

_get_indices(n_indices)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

Classmethod to initialize the sampler from a given dataset

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

StoppingPrevalenceRandomSampler¶

class StoppingPrevalenceRandomSampler(indices, shuffle_batch=True)[source]¶

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)[source]¶

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)[source]¶

Classmethod to initialize the sampler from a given dataset

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

SequentialSampler¶

class SequentialSampler(indices)[source]¶

Bases: delira.data_loading.sampler.abstract_sampler.AbstractSampler

Implements Sequential Sampling from whole Dataset

_get_indices(n_indices)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

Bases: delira.data_loading.sampler.abstract_sampler.AbstractSampler

Implements Weighted Random Sampling

_get_indices(n_indices)[source]¶

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
ValueError – if weights or cum_weights don’t match the population

classmethod from_dataset(dataset: delira.data_loading.dataset.AbstractDataset, **kwargs)[source]¶

Classmethod to initialize the sampler from a given dataset

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