datasets.Features defines the internal structure of a dataset. Features are used to specify the underlying serialization format but also contain high-level information regarding the fields, e.g. column names, types, and conversion methods from names to integer values for a class label field.
A brief summary of how to use this class:
datasets.Featuresshould be only called once and instantiated with a
dict[str, FieldType], where keys are your desired column names, and values are the type of that column.
FieldType can be one of a few possibilities:
datasets.Valuefeature specifies a single typed value, e.g.
string. The dtypes supported are as follows:
float32 (alias float)
float64 (alias double)
dictspecifies that the field is a nested field containing a mapping of sub-fields to sub-fields features. It’s possible to have nested fields of nested fields in an arbitrary manner.
datasets.Sequencespecifies that the field contains a list of objects. The python
datasets.Sequenceshould be provided with a single sub-feature as an example of the feature type hosted in this list. Python
listare simplest to define and write while
datasets.Sequenceprovide a few more specific behaviors like the possibility to specify a fixed length for the list (slightly more efficient).
datasets.Sequence with a internal dictionary feature will be automatically converted into a dictionary of lists. This behavior is implemented to have a compatilbity layer with the TensorFlow Datasets library but may be un-wanted in some cases. If you don’t want this behavior, you can use a python
list instead of the
datasets.ClassLabelfeature specifies a field with a predefined set of classes which can have labels associated to them and will be stored as integers in the dataset. This field will be stored and retrieved as an integer value and two conversion methods,
datasets.ClassLabel.int2str()can be used to convert from the label names to the associate integer value and vice-versa.