Source code for selene_sdk.samplers.sampler

"""This module provides the `Sampler` base class, which defines the
interface for sampling classes. These sampling classes should provide
a way to query some training/validation/test data for examples.
"""
from abc import ABCMeta
from abc import abstractmethod
import os


[docs]class Sampler(metaclass=ABCMeta): """ The base class for sampler currently enforces that all samplers have modes for drawing training and validation samples to train a model. Parameters ---------- features : list(str) The list of features (classes) the model predicts. save_datasets : list(str), optional Default is `[]` the empty list. The list of modes for which we should save sampled data to file (1 or more of ['train', 'validate', 'test']). output_dir : str or None, optional Default is None. Path to the output directory. Used if we save any of the data sampled. If `save_datasets` is non-empty, `output_dir` must be a valid path. If the directory does not yet exist, it will be created for you. Attributes ---------- modes : list(str) A list of the names of the modes that the object may operate in. mode : str or None The current mode that the object is operating in. """ BASE_MODES = ("train", "validate") """ The types of modes that the `Sampler` object can run in. """ def __init__(self, features, save_datasets=[], output_dir=None): """ Constructs a new `Sampler` object. """ self.modes = list(self.BASE_MODES) self.mode = None self._features = features self._save_datasets = {} for mode in save_datasets: self._save_datasets[mode] = [] self._output_dir = output_dir if output_dir is not None: os.makedirs(output_dir, exist_ok=True)
[docs] 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`. 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
[docs] @abstractmethod 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. """ raise NotImplementedError()
[docs] @abstractmethod def sample(self, batch_size=1): """ 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. """ raise NotImplementedError()
[docs] @abstractmethod def get_data_and_targets(self, batch_size, n_samples, 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 The total number of samples 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`. """ raise NotImplementedError()
[docs] @abstractmethod 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. Handling for `n_samples=None` should be done by all classes that subclass `selene_sdk.samplers.Sampler`. """ raise NotImplementedError()
[docs] @abstractmethod 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 validation examples to retrieve. If `None`, 640000 examples are retrieved. 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`'s 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. """ raise NotImplementedError()
[docs] @abstractmethod def save_dataset_to_file(self, mode, close_filehandle=False): """ Save samples for each partition (i.e. train/validate/test) to disk. 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. """ raise NotImplementedError()