Challenge

class gaitmap_challenges.stride_segmentation.sensor_position_comparison_instep.Challenge(dataset: Optional[Union[str, Path, SensorPositionComparison2019Segmentation]], cv_iterator: Optional[Union[int, BaseCrossValidator, Iterator]] = 5, cv_params: Optional[Dict] = None)

The SensorPositionComparison Stride Segmentaion Validation Challenge.

Parameters:

dataset

A instance of SensorPositionComparison2019Segmentation or a path to a directory containing the dataset.

cv_iterator

A cross-validation iterator or the number of folds to use.

cv_params

Additional parameters to pass to the tpcp cross-validation function.

Other Parameters:

match_tolerance_s

(Class Constant) The tolerance in seconds that is allowed between the calculated stride borders and the reference stride borders.

sensor_pos

(Class Constant) The sensor position to use for the challenge.

Attributes:

cv_results_

The results of the cross-validation. This can be passed directly to the pandas DataFrame constructor to get a dataframe with the results.

See also

gaitmap_challenges.challenge_base.BaseChallenge

For common parameters and attributes of all challenges.

Methods

clone()	Create a new instance of the class with all parameters copied over.
get_core_results()	Get the main results of the challenge.
get_params([deep])	Get parameters for this algorithm.
load_core_results(folder_path)	Load the core results from a folder that have been stored using save_core_results.
run(optimizer)	Run the challenge.
save_core_results(folder_path)	Save the main results of the challenge to a folder.
set_params(**params)	Set the parameters of this Algorithm.

get_imu_data
get_reference_stride_list
get_scorer

__init__(dataset: Optional[Union[str, Path, SensorPositionComparison2019Segmentation]], cv_iterator: Optional[Union[int, BaseCrossValidator, Iterator]] = 5, cv_params: Optional[Dict] = None) → None

get_core_results() → ResultType

Get the main results of the challenge.

classmethod load_core_results(folder_path) → ResultType

Load the core results from a folder that have been stored using save_core_results.

The assumption is, that the output is identical to calling get_core_results on the challenge instance directly.

When implementing this method, make sure that it remains compatible with results saved using older versions of the challenge_class.

run(optimizer: BaseOptimize)

Run the challenge.

save_core_results(folder_path) → None

Save the main results of the challenge to a folder.