Class - KCPipeline

class openfl.pipelines.kc_pipeline.KCPipeline(p_sparsity=0.01, n_clusters=6, **kwargs)

Bases: TransformationPipeline

A pipeline class to compress data lossly using k-means and GZIP methods.

p

The amount of sparsity for compression.

Type:

float

n_cluster

The number of K-mean clusters.

Type:

int

__init__(p_sparsity=0.01, n_clusters=6, **kwargs)

Initialize a pipeline of transformers.

Parameters:

• p_sparsity (float, optional) – The amount of sparsity for compression. Defaults to 0.01.

• n_clusters (int, optional) – The number of K-mean clusters. Defaults to 6.

• **kwargs – Additional keyword arguments.

Methods

__init__([p_sparsity, n_clusters])	Initialize a pipeline of transformers.
backward(data, transformer_metadata, **kwargs)	Backward pass of pipeline data transformer.
forward(data, **kwargs)	Forward pass of pipeline data transformer.
is_lossy()	If any of the transformers are lossy, then the pipeline is lossy.

backward(data, transformer_metadata, **kwargs)

Backward pass of pipeline data transformer.

Parameters:

• data – The transformed data.

• transformer_metadata – The metadata for the transformation.

• **kwargs – Additional keyword arguments for the transformation.

Returns:

The original data before the transformation.

forward(data, **kwargs)

Forward pass of pipeline data transformer.

Parameters:

• data – The data to be transformed.

• **kwargs – Additional keyword arguments for the transformation.

Returns:

The transformed data. transformer_metadata: The metadata for the transformation.

Return type:

data

is_lossy()

If any of the transformers are lossy, then the pipeline is lossy.

Returns:

True if any of the transformers in the pipeline are lossy, False

otherwise.