gaitmap.data_transform.ParallelTransformer

class gaitmap.data_transform.ParallelTransformer(transformers: list[tuple[Union[collections.abc.Hashable, str], gaitmap.data_transform._base.BaseTransformer]])

Apply multiple different transformation to the input, resulting in multiple outputs.

Each transformer is expected to output a Dataframe with one or more columns. Note, all outputs of all transformers must have the same second dimension, so that they can all be combined into a single dataframe.

Parameters:

transformers

A list of tuples of the form (name, transformer) that specify the transformers that should be applied. The name is used as a prefix ({name}__) for the output columns of the transformers to avoid duplicated column names in the output when multiple transformers return the same output names.

Other Parameters:

data

The data passed to the transform method.

Attributes:

transformed_data_

The transformed data.

Methods

clone()	Create a new instance of the class with all parameters copied over.
from_json(json_str)	Import an gaitmap object from its json representation.
get_params([deep])	Get parameters for this algorithm.
self_optimize(data, **kwargs)	Optimize all transformer.
set_params(**params)	Set the parameters of this Algorithm.
to_json()	Export the current object parameters as json.
transform(data, **kwargs)	Transform the data.

__init__(transformers: list[tuple[Union[collections.abc.Hashable, str], gaitmap.data_transform._base.BaseTransformer]]) → None

clone() → Self

Create a new instance of the class with all parameters copied over.

This will create a new instance of the class itself and all nested objects

classmethod from_json(json_str: str) → Self

Import an gaitmap object from its json representation.

For details have a look at the this example.

You can use the to_json method of a class to export it as a compatible json string.

Parameters:

json_str

json formatted string

get_params(deep: bool = True) → dict[str, Any]

Get parameters for this algorithm.

Parameters:

deep

Only relevant if object contains nested algorithm objects. If this is the case and deep is True, the params of these nested objects are included in the output using a prefix like nested_object_name__ (Note the two “_” at the end)

Returns:

params

Parameter names mapped to their values.

self_optimize(data: Sequence[DataFrame], **kwargs) → Self

Optimize all transformer.

Each transformer’s self_optimize method will be called individually with the data.

Parameters:

data

The data to be transformed

kwargs

All kwargs will be passed to all transformers self_optimize methods

Returns:

self

The trained instance of the transformer

set_params(**params: Any) → Self

Set the parameters of this Algorithm.

To set parameters of nested objects use nested_object_name__para_name=.

to_json() → str

Export the current object parameters as json.

For details have a look at the this example.

You can use the from_json method of any gaitmap algorithm to load the object again.

Warning

This will only export the Parameters of the instance, but not any results!

transform(data: DataFrame, **kwargs) → Self

Transform the data.

The results of all transformations will be concatenated into one dataframe.

Parameters:

data

A dataframe representing single sensor data.

Returns:

self

The instance of the transformer with the results attached