toys¶

Core protocols¶

The core protocols are pure abstract classes. They provide no functionality and are for documentation purpose only. There is no requirement to subclass them; however doing so provides certain runtime protections through Python’s abstract base class (abc) functionality.

`Dataset`
`Estimator`
`Model`

Common classes¶

`BaseEstimator`
`TorchModel`

Dataset utilities¶

`toys.batches`
`toys.zip`
`toys.concat`
`toys.flatten`
`toys.subset`
`toys.shape`

Argument parsers¶

`parse_activation`
`parse_initializer`
`parse_optimizer`
`parse_loss`
`parse_dtype`
`parse_metric`

Type aliases¶

These type aliases exist to aid in documentation and static analysis. They are irrelevant at runtime.

class toys.ColumnShape = Optional[Tuple[Optional[int], ...]]¶

The shape of a single datum in a column. None is used for dimensions of variable length, and when the total number of dimensions is variable.

Note that the shape of a column does not include the index dimension.

class toys.RowShape = Optional[Tuple[ColumnShape, ...]]¶: The shape of a row is the sequence of (possibly variable) shapes of its columns. The dataset shape may be None to indicate that the number of columns is variable.

For example, the CIFAR10 dataset has two columns. The first contains 32x32 RGB images; it’s shape is (32, 32, 3). The second contains scalar class labels; it’s shape is (). The shape of the whole row is thus ((32, 32, 3), ()).

>>> from toys.datasets import CIFAR10
>>> cifar = CIFAR10()
>>> toys.shape(cifar)
((32, 32, 3), ())