cuvis_ai.node.node.Node

class cuvis_ai.node.node.Node[source]

Bases: ABC

Abstract class for data preprocessing.

Methods

`__init__`()
`check_input_dim`(X)	Check that the parameters for the input data data match user expectations
`check_output_dim`(X)	Check that the parameters for the output data data match user expectations
`forward`(X)	Transform the input data.
`get_fit_requested_meta`()
`get_forward_requested_meta`()
`load`(params, serial_dir)	Load from serialized format into an object
`serialize`(serial_dir)	Convert the class into a serialized representation
`set_fit_meta_request`(**kwargs)
`set_forward_meta_request`(**kwargs)

Attributes

`input_dim`	Returns the needed shape for the input data.
`output_dim`	Returns the shape for the output data.

check_input_dim(X)[source]

Check that the parameters for the input data data match user expectations

Parameters: X (array-like): Input data.

Returns: (Bool) Valid data

check_output_dim(X)[source]

Check that the parameters for the output data data match user expectations

Parameters: X (array-like): Input data.

Returns: (Bool) Valid data

abstract forward(X)[source]

Transform the input data.

Parameters: X (array-like): Input data.

Returns: Transformed data.

abstract property input_dim: tuple[int, int, int]

Returns the needed shape for the input data. If a dimension is not important, it will return -1 in the specific position.

Returns: (tuple) needed shape for data

abstract load(params: dict, serial_dir: str) → None[source]: Load from serialized format into an object

abstract property output_dim: tuple[int, int, int]

Returns the shape for the output data. If a dimension is dependent on the input, it will return -1 in the specific position.

Returns: (tuple) expected output shape for data

abstract serialize(serial_dir: str) → dict[source]: Convert the class into a serialized representation