Source code for selene_sdk.samplers.multi_sampler

"""
This module provides the `MultiSampler` class, which accepts
either an online sampler or a file sampler for each mode of
sampling (train, test, validation).
MultiSampler is a subclass of Sampler.
"""
import numpy as np
from torch.utils.data import DataLoader

from .sampler import Sampler


[docs]def MultiFileSampler(*args, **kwargs): """ `MultiFileSampler` is deprecated and will be removed from future versions of Selene. Please use `MultiSampler` instead. This function maintains backward compatibility for code that uses `MultiFileSampler`, but we will remove this function in future. Please refer to the `MultiSampler` documentation for usage. """ from warnings import warn warn("MultiFileSampler is deprecated and will be removed from future " "versions of Selene. Please use MultiSampler instead.") return MultiSampler(*args, **kwargs)
class MultiSampler(Sampler): """ This sampler draws samples from individual file samplers or data loaders that corresponds to training, validation, and testing (optional) modes. MultiSampler calls on the correct file sampler or data loader to draw samples for a given mode. Example file samplers are under `selene_sdk.samplers.file_samplers` and example data loaders are under `selene_sdk.samplers.dataloaders`. MultiSampler can use either file samplers or data loaders for different modes. Using data loaders for some modes while using file samplers for other modes are also allowed. The file samplers parse data files (e.g. bed, mat, or hdf5). The data loaders provide multi-worker iterators that draw samples from online samplers (i.e. on-the-fly sampling). As data loaders support parallel sampling, they are generally recommended for sampling speed. Parameters ---------- train_sampler : selene_sdk.samplers.file_samplers.FileSampler or \ selene_sdk.samplers.dataloader.DataLoader Load your training data as a `FileSampler` or `DataLoader` validate_sampler : FileSampler or DataLoader The validation dataset file sampler or data loader. features : list(str) The list of features the model should predict test_sampler : None or FileSampler or DataLoader, optional Default is None. The test file sampler is optional. mode : str, optional Default is "train". Must be one of `{train, validate, test}`. The starting mode in which to run the sampler. save_datasets : list(str) or None, optional Default is None. Currently, we are only including this parameter so that `MultiSampler` is consistent with the `Sampler` interface. The save dataset functionality for MultiSampler has not been defined yet. output_dir : str or None, optional Default is None. Only used if the sampler has any data or logging statements to save to file. Currently not used in `MultiSampler`. Attributes ---------- modes : list(str) A list of the modes that the object may operate in. mode : str or None Default is `None`. The current mode that the object is operating in. """ def __init__(self, train_sampler, validate_sampler, features, test_sampler=None, mode="train", save_datasets=[], output_dir=None): """ Constructs a new `MultiSampler` object. """ super(MultiSampler, self).__init__( features, save_datasets=save_datasets, output_dir=output_dir) self._samplers = { "train": train_sampler if isinstance(train_sampler, Sampler) \ else None, "validate": validate_sampler if isinstance(validate_sampler, Sampler) \ else None } self._dataloaders = { "train": train_sampler if isinstance(train_sampler, DataLoader) \ else None, "validate": validate_sampler if isinstance(validate_sampler, DataLoader) \ else None } self._iterators = { "train": iter(self._dataloaders["train"]) \ if self._dataloaders["train"] else None, "validate": iter(self._dataloaders["validate"]) \ if self._dataloaders["validate"] else None } self._index_to_feature = {i: f for (i, f) in enumerate(features)} if test_sampler is not None: self.modes.append("test") self._samplers["test"] = \ test_sampler if isinstance(test_sampler, Sampler) else None self._dataloaders["test"] = \ test_sampler if isinstance(test_sampler, DataLoader) else None self.mode = mode def set_mode(self, mode): """ Sets the sampling mode. Parameters ---------- mode : str The name of the mode to use. It must be one of `Sampler.BASE_MODES` ("train", "validate") or "test" if the test data is supplied. Raises ------ ValueError If `mode` is not a valid mode. """ if mode not in self.modes: raise ValueError( "Tried to set mode to be '{0}' but the only valid modes are " "{1}".format(mode, self.modes)) self.mode = mode def _set_batch_size(self, batch_size, mode=None): """ Sets the batch size for DataLoader for the specified mode, if the specified batch_size does not equal the current batch_size. Parameters ---------- batch_size : int The batch size for the mode. mode : str, optional Default is None. The mode to set batch_size If None, will use the current mode `self.mode`. """ if mode is None: mode = self.mode if self._dataloaders[mode]: batch_size_matched = True if self._dataloaders[mode].batch_sampler: if self._dataloaders[mode].batch_sampler.batch_size != batch_size: self._dataloaders[mode].batch_sampler.batch_size = batch_size batch_size_matched = False else: if self._dataloaders[mode].batch_size != batch_size: self._dataloaders[mode].batch_size = batch_size batch_size_matched = False if not batch_size_matched: print("Reset data loader for mode {0} to use the new batch " "size: {1}.".format(mode, batch_size)) self._iterators[mode] = iter(self._dataloaders[mode]) def get_feature_from_index(self, index): """ Returns the feature corresponding to an index in the feature vector. Parameters ---------- index : int The index of the feature to retrieve the name for. Returns ------- str The name of the feature occurring at the specified index. """ return self._index_to_feature[index] def sample(self, batch_size=1, mode=None): """ Fetches a mini-batch of the data from the sampler. Parameters ---------- batch_size : int, optional Default is 1. The size of the batch to retrieve. mode : str, optional Default is None. The operating mode that the object should run in. If None, will use the current mode `self.mode`. """ mode = mode if mode else self.mode if self._samplers[mode]: return self._samplers[mode].sample(batch_size) else: self._set_batch_size(batch_size, mode=mode) try: data, targets = next(self._iterators[mode]) return data.numpy(), targets.numpy() except StopIteration: #If DataLoader iterator reaches its length, reinitialize self._iterators[mode] = iter(self._dataloaders[mode]) data, targets = next(self._iterators[mode]) return data.numpy(), targets.numpy() def get_data_and_targets(self, batch_size, n_samples=None, mode=None): """ This method fetches a subset of the data from the sampler, divided into batches. This method also allows the user to specify what operating mode to run the sampler in when fetching the data. Parameters ---------- batch_size : int The size of the batches to divide the data into. n_samples : int or None, optional Default is None. The total number of samples to retrieve. If `n_samples` is None, if a FileSampler is specified for the mode, the number of samplers returned is defined by the FileSample, or if a Dataloader is specified, will set `n_samples` to 32000 if the mode is `validate`, or 640000 if the mode is `test`. If the mode is `train` you must have specified a value for `n_samples`. mode : str, optional Default is None. The operating mode that the object should run in. If None, will use the current mode `self.mode`. """ mode = mode if mode else self.mode if self._samplers[mode]: return self._samplers[mode].get_data_and_targets( batch_size, n_samples) else: if n_samples is None: if mode == 'validate': n_samples = 32000 elif mode == 'test': n_samples = 640000 self._set_batch_size(batch_size, mode=mode) data_and_targets = [] targets_mat = [] count = batch_size while count < n_samples: data, tgts = self.sample(batch_size=batch_size, mode=mode) data_and_targets.append((data, tgts)) targets_mat.append(tgts) count += batch_size remainder = batch_size - (count - n_samples) data, tgts = self.sample(batch_size=remainder) data_and_targets.append((data, tgts)) targets_mat.append(tgts) targets_mat = np.vstack(targets_mat) return data_and_targets, targets_mat def get_validation_set(self, batch_size, n_samples=None): """ This method returns a subset of validation data from the sampler, divided into batches. Parameters ---------- batch_size : int The size of the batches to divide the data into. n_samples : int, optional Default is None. The total number of validation examples to retrieve. If `n_samples` is None, then if a FileSampler is specified for the 'validate' mode, the number of samplers returned is defined by the FileSample, or if a Dataloader is specified, will set `n_samples` to 32000. Returns ------- sequences_and_targets, targets_matrix : \ tuple(list(tuple(numpy.ndarray, numpy.ndarray)), numpy.ndarray) Tuple containing the list of sequence-target pairs, as well as a single matrix with all targets in the same order. Note that `sequences_and_targets` sequence elements are of the shape :math:`B \\times L \\times N` and its target elements are of the shape :math:`B \\times F`, where :math:`B` is `batch_size`, :math:`L` is the sequence length, :math:`N` is the size of the sequence type's alphabet, and :math:`F` is the number of features. Further, `target_matrix` is of the shape :math:`S \\times F`, where :math:`S = n_samples`. Raises ------ ValueError If no test partition of the data was specified during sampler initialization. """ return self.get_data_and_targets( batch_size, n_samples, mode="validate") def get_test_set(self, batch_size, n_samples=None): """ This method returns a subset of testing data from the sampler, divided into batches. Parameters ---------- batch_size : int The size of the batches to divide the data into. n_samples : int or None, optional Default is None. The total number of test examples to retrieve. If `n_samples` is None, then if a FileSampler is specified for the 'test' mode, the number of samplers returned is defined by the FileSample, or if a Dataloader is specified, will set `n_samples` to 640000. Returns ------- sequences_and_targets, targets_matrix : \ tuple(list(tuple(numpy.ndarray, numpy.ndarray)), numpy.ndarray) Tuple containing the list of sequence-target pairs, as well as a single matrix with all targets in the same order. Note that `sequences_and_targets` sequence elements are of the shape :math:`B \\times L \\times N` and its target elements are of the shape :math:`B \\times F`, where :math:`B` is `batch_size`, :math:`L` is the sequence length, :math:`N` is the size of the sequence type's alphabet, and :math:`F` is the number of features. Further, `target_matrix` is of the shape :math:`S \\times F`, where :math:`S = n_samples`. Raises ------ ValueError If no test partition of the data was specified during sampler initialization. """ return self.get_data_and_targets( batch_size, n_samples, mode="test") def save_dataset_to_file(self, mode, close_filehandle=False): """ We implement this function in this class only because the TrainModel class calls this method. In the future, we will likely remove this method or implement a different way of "saving the data" for file samplers. For example, we may only output the row numbers sampled so that users may reproduce exactly what order the data was sampled. Parameters ---------- mode : str Must be one of the modes specified in `save_datasets` during sampler initialization. close_filehandle : bool, optional Default is False. `close_filehandle=True` assumes that all data corresponding to the input `mode` has been saved to file and `save_dataset_to_file` will not be called with `mode` again. """ return None