def _canonicalize_numpy_data()

in tensorflow_estimator/python/estimator/canned/timeseries/saved_model_utils.py [0:0]

• 54 lines of code
• 20 McCabe index (conditional complexity)

def _canonicalize_numpy_data(data, require_single_batch):
  """Do basic checking and reshaping for Numpy data.

  Args:
    data: A dictionary mapping keys to Numpy arrays, with several possible
      shapes (requires keys `TrainEvalFeatures.TIMES` and
      `TrainEvalFeatures.VALUES`): Single example; `TIMES` is a scalar and
        `VALUES` is either a scalar or a vector of length [number of features].
        Sequence; `TIMES` is a vector of shape [series length], `VALUES` either
        has shape [series length] (univariate) or [series length x number of
        features] (multivariate). Batch of sequences; `TIMES` is a vector of
        shape [batch size x series length], `VALUES` has shape [batch size x
        series length] or [batch size x series length x number of features]. In
        any case, `VALUES` and any exogenous features must have their shapes
        prefixed by the shape of the value corresponding to the `TIMES` key.
    require_single_batch: If True, raises an error if the provided data has a
      batch dimension > 1.

  Returns:
    A dictionary with features normalized to have shapes prefixed with [batch
    size x series length]. The sizes of dimensions which were omitted in the
    inputs are 1.
  Raises:
    ValueError: If dimensions are incorrect or do not match, or required
      features are missing.
  """
  features = {key: numpy.array(value) for key, value in data.items()}
  if (_feature_keys.TrainEvalFeatures.TIMES not in features or
      _feature_keys.TrainEvalFeatures.VALUES not in features):
    raise ValueError("{} and {} are required features.".format(
        _feature_keys.TrainEvalFeatures.TIMES,
        _feature_keys.TrainEvalFeatures.VALUES))
  times = features[_feature_keys.TrainEvalFeatures.TIMES]
  for key, value in features.items():
    if value.shape[:len(times.shape)] != times.shape:
      raise ValueError(
          ("All features must have their shapes prefixed by the shape of the"
           " times feature. Got shape {} for feature '{}', but shape {} for"
           " '{}'").format(value.shape, key, times.shape,
                           _feature_keys.TrainEvalFeatures.TIMES))
  if not times.shape:  # a single example
    if not features[_feature_keys.TrainEvalFeatures.VALUES].shape:  # univariate
      # Add a feature dimension (with one feature)
      features[_feature_keys.TrainEvalFeatures.VALUES] = features[
          _feature_keys.TrainEvalFeatures.VALUES][..., None]
    elif len(features[_feature_keys.TrainEvalFeatures.VALUES].shape) > 1:
      raise ValueError(
          ("Got an unexpected number of dimensions for the '{}' feature."
           " Was expecting at most 1 dimension"
           " ([number of features]) since '{}' does not "
           "have a batch or time dimension, but got shape {}").format(
               _feature_keys.TrainEvalFeatures.VALUES,
               _feature_keys.TrainEvalFeatures.TIMES,
               features[_feature_keys.TrainEvalFeatures.VALUES].shape))
    # Add trivial batch and time dimensions for every feature
    features = {key: value[None, None, ...] for key, value in features.items()}
  if len(times.shape) == 1:  # shape [series length]
    if len(features[_feature_keys.TrainEvalFeatures.VALUES].shape
          ) == 1:  # shape [series length]
      # Add a feature dimension (with one feature)
      features[_feature_keys.TrainEvalFeatures.VALUES] = features[
          _feature_keys.TrainEvalFeatures.VALUES][..., None]
    elif len(features[_feature_keys.TrainEvalFeatures.VALUES].shape) > 2:
      raise ValueError(
          ("Got an unexpected number of dimensions for the '{}' feature."
           " Was expecting at most 2 dimensions"
           " ([series length, number of features]) since '{}' does not "
           "have a batch dimension, but got shape {}").format(
               _feature_keys.TrainEvalFeatures.VALUES,
               _feature_keys.TrainEvalFeatures.TIMES,
               features[_feature_keys.TrainEvalFeatures.VALUES].shape))
    # Add trivial batch dimensions for every feature
    features = {key: value[None, ...] for key, value in features.items()}
  elif len(features[_feature_keys.TrainEvalFeatures.TIMES].shape
          ) != 2:  # shape [batch size, series length]
    raise ValueError(
        ("Got an unexpected number of dimensions for times. Was expecting at "
         "most two ([batch size, series length]), but got shape {}.").format(
             times.shape))
  if require_single_batch:
    # We don't expect input to be already batched; batching is done later
    if features[_feature_keys.TrainEvalFeatures.TIMES].shape[0] != 1:
      raise ValueError("Got batch input, was expecting unbatched input.")
  return features