Source code for

import os

import logging
import inspect
logger = logging.getLogger("")

from .FileSelector import FileSelector
from .preprocessor import read_preprocessed_data
from .. import utils

def train_extractor(extractor, preprocessor, allow_missing_files = False, force = False):
  """Trains the feature extractor using preprocessed data of the ``'world'`` group, if the feature extractor requires training.

  This function should only be called, when the ``extractor`` actually requires training.
  The given ``extractor`` is trained using preprocessed data.
  It writes the extractor to the file specified by the :py:class:``.
  By default, if the target file already exist, it is not re-created.


  extractor : py:class:`` or derived
    The extractor to be trained.

  preprocessor : py:class:`` or derived
    The preprocessor, used for reading the preprocessed data.

  allow_missing_files : bool
    If set to ``True``, preprocessed data files that are not found are silently ignored during training.

  force : bool
    If given, the extractor file is regenerated, even if it already exists.

  if not extractor.requires_training:
    logger.warn("The train_extractor function should not have been called, since the extractor does not need training.")

  # the file selector object
  fs = FileSelector.instance()
  # the file to write
  if utils.check_file(fs.extractor_file, force,
                      extractor.min_extractor_file_size):"- Extraction: extractor '%s' already exists.", fs.extractor_file)
    # read training files
    train_files = fs.training_list('preprocessed', 'train_extractor', arrange_by_client = extractor.split_training_data_by_client)
    train_data = read_preprocessed_data(train_files, preprocessor, extractor.split_training_data_by_client, allow_missing_files)
    if extractor.split_training_data_by_client:"- Extraction: training extractor '%s' using %d identities:", fs.extractor_file, len(train_files))
    else:"- Extraction: training extractor '%s' using %d training files:", fs.extractor_file, len(train_files))
    # train model
    extractor.train(train_data, fs.extractor_file)

def extract(extractor, preprocessor, groups=None, indices = None, allow_missing_files = False, force = False):
  """Extracts features from the preprocessed data using the given extractor.

  The given ``extractor`` is used to extract all features required for the current experiment.
  It writes the extracted data into the directory specified by the :py:class:``.
  By default, if target files already exist, they are not re-created.

  The preprocessor is only used to load the data in a coherent way.


  extractor : py:class:`` or derived
    The extractor, used for extracting and writing the features.

  preprocessor : py:class:`` or derived
    The preprocessor, used for reading the preprocessed data.

  groups : some of ``('world', 'dev', 'eval')`` or ``None``
    The list of groups, for which the data should be extracted.

  indices : (int, int) or None
    If specified, only the features for the given index range ``range(begin, end)`` should be extracted.
    This is usually given, when parallel threads are executed.

  allow_missing_files : bool
    If set to ``True``, preprocessed data files that are not found are silently ignored.

  force : bool
    If given, files are regenerated, even if they already exist.
  # the file selector object
  fs = FileSelector.instance()
  data_files = fs.preprocessed_data_list(groups=groups)
  feature_files = fs.feature_list(groups=groups)

  if utils.is_argument_available("metadata", extractor.__call__):
    metadata = fs.original_data_list(groups=groups)
    metadata = None

  # select a subset of indices to iterate
  if indices is not None:
    index_range = range(indices[0], indices[1])"- Extraction: splitting of index range %s" % str(indices))
    index_range = range(len(data_files))"- Extraction: extracting %d features from directory '%s' to directory '%s'", len(index_range), fs.directories['preprocessed'], fs.directories['extracted'])
  for i in index_range:
    data_file = data_files[i]
    feature_file = feature_files[i]

    if not os.path.exists(data_file) and preprocessor.writes_data:
      if allow_missing_files:
        logger.debug("... Cannot find preprocessed data file %s; skipping", data_file)
        logger.error("Cannot find preprocessed data file %s", data_file)

    if not utils.check_file(feature_file, force,
      logger.debug("... Extracting features for data file '%s' (%d/%d)",
          data_file, index_range.index(i)+1, len(index_range))
      # create output directory before reading the data file (is sometimes required, when relative directories are specified, especially, including a .. somewhere)
      # load data
      data = preprocessor.read_data(data_file)

      # extract feature
      if metadata is None:
        feature = extractor(data)
        feature = extractor(data, metadata=metadata[i])

      if feature is None:
        if allow_missing_files:
          logger.debug("... Feature extraction for data file %s failed; skipping", data_file)
          raise RuntimeError("Feature extraction  of file '%s' was not successful" % data_file)

      # write feature
      extractor.write_feature(feature, feature_file)
      logger.debug("... Skipping preprocessed data '%s' since feature file '%s' exists", data_file, feature_file)

[docs]def read_features(file_names, extractor, split_by_client = False, allow_missing_files = False): """read_features(file_names, extractor, split_by_client = False) -> extracted Reads the extracted features from ``file_names`` using the given ``extractor``. If ``split_by_client`` is set to ``True``, it is assumed that the ``file_names`` are already sorted by client. **Parameters:** file_names : [str] or [[str]] A list of names of files to be read. If ``split_by_client = True``, file names are supposed to be split into groups. extractor : py:class:`` or derived The extractor, used for reading the extracted features. split_by_client : bool Indicates if the given ``file_names`` are split into groups. allow_missing_files : bool If set to ``True``, extracted files that are not found are silently ignored. **Returns:** extracted : [object] or [[object]] The list of extracted features, in the same order as in the ``file_names``. """ file_names = utils.filter_missing_files(file_names, split_by_client, allow_missing_files) if split_by_client: return [[extractor.read_feature(f) for f in client_files] for client_files in file_names] else: return [extractor.read_feature(f) for f in file_names]