IcdEmulationPipeline#

class mobgap.initial_contacts.pipeline.IcdEmulationPipeline(
algo: BaseIcDetector,
*,
convert_to_body_frame: bool = True,
)[source]#

Run an ICD algorithm in isolation on a Gait Dataset.

This wraps any ICD algorithm and allows to apply it to a single datapoint of a Gait Dataset or optimize it based on a whole dataset.

This pipeline can be used in combination with the tpcp.validate and tpcp.optimize modules to evaluate or improve the performance of an ICD algorithm.

Parameters:
algo

The ICD algorithm that should be run/evaluated.

convert_to_body_frame

If True, the data will be converted to the body frame before running the algorithm. This is the default, as most algorithm expect the data in the body frame. If your data is explicitly not aligned and your algorithm supports sensor frame/unaligned input you might want to set this to False.

Attributes:
ic_list_

A pandas dataframe with the indices of the detected initial contacts in the input data. It only has one column, ic, which contains the indices of the detected initial contacts.

algo_

The ICD algo instance with all results after running the algorithm. This can be helpful for debugging or further analysis.

Notes

All emulation pipelines pass available metadata of the dataset to the algorithm. This includes the recording metadata (recording_metadata) and the participant metadata (participant_metadata), which are passed as keyword arguments to the detect method of the algorithm. In addition, we pass the group label of the datapoint as dp_group to the algorithm. This is usually not required by algorithms (because this would mean that the algorithm changes behaviour based on the exact recording provided). However, it can be helpful when working with “dummy” algorithms, that simply return some fixed pre-defined results or to be used as cache key, when the algorithm has internal caching mechanisms.

For the self_optimize method, we pass the same metadata to the algorithm, but each value is actually a list of values, one for each datapoint in the dataset.

Methods

clone()

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

get_params([deep])

Get parameters for this algorithm.

run(datapoint)

Run the pipeline on a single data point.

safe_run(datapoint)

Run the pipeline with some additional checks.

self_optimize(dataset, **kwargs)

Optimize the input parameters of the pipeline or algorithm using any logic.

self_optimize_with_info(dataset, **kwargs)

Optimize the input parameters of the pipeline or algorithm using any logic.

set_params(**params)

Set the parameters of this Algorithm.
__init__(
algo: BaseIcDetector,
*,
convert_to_body_frame: bool = True,
) → None[source]#
clone() → Self[source]#

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

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

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.

run(
datapoint: BaseGaitDatasetWithReference,
) → Self[source]#

Run the pipeline on a single data point.

This extracts the imu_data (data_ss) and the sampling rate (sampling_rate_hz) from the datapoint and uses the detect method of the ICD algorithm to detect the gait sequences.

Parameters:
datapoint

A single datapoint of a Gait Dataset with reference information.

Returns:
self

The pipeline instance with the detected initial contacts stored in the ic_list_ attribute.

safe_run(
datapoint: DatasetT,
) → Self[source]#

Run the pipeline with some additional checks.

It is preferred to use this method over run, as it can catch some simple implementation errors of custom pipelines.

The following things are checked:

  • The run method must return self (or at least an instance of the pipeline)

  • The run method must set result attributes on the pipeline

  • All result attributes must have a trailing _ in their name

  • The run method must not modify the input parameters of the pipeline

Parameters:
datapoint

An instance of a tpcp.Dataset containing only a single datapoint. The structure of the data will depend on the dataset.

Returns:
self

The class instance with all result attributes populated

self_optimize(
dataset: DatasetT,
**kwargs,
) → Self[source]#

Optimize the input parameters of the pipeline or algorithm using any logic.

This method can be used to adapt the input parameters (values provided in the init) based on any data driven heuristic.

Note

The optimizations must only modify the input parameters (aka self.clone should retain the optimization results). If you need to return further information, implement self_optimize_with_info instead.

Parameters:
dataset

An instance of a tpcp.Dataset containing one or multiple data points that can be used for training. The structure of the data and the available reference information will depend on the dataset.

kwargs

Additional parameters required for the optimization process.

Returns:
self

The class instance with optimized input parameters.

self_optimize_with_info(
dataset: DatasetT,
**kwargs,
) → tuple[Self, Any][source]#

Optimize the input parameters of the pipeline or algorithm using any logic.

This is equivalent to self_optimize, but allows you to return additional information as a second return value. If you implement this method, there is no need to implement self_optimize as well.

Parameters:
dataset

An instance of a tpcp.Dataset containing one or multiple data points that can be used for training. The structure of the data and the available reference information will depend on the dataset.

kwargs

Additional parameters required for the optimization process.

Returns:
self

The class instance with optimized input parameters.

info

An arbitrary piece of information

set_params(**params: Any) → Self[source]#

Set the parameters of this Algorithm.

To set parameters of nested objects use nested_object_name__para_name=.

Examples using mobgap.initial_contacts.pipeline.IcdEmulationPipeline#

Revalidation of the initial contact detection algorithms

Revalidation of the initial contact detection algorithms

Revalidation of the laterality classification algorithms

Revalidation of the laterality classification algorithms