Challenge

class gaitmap_challenges.full_pipeline.sensor_position_comparison_instep.Challenge(dataset: Optional[Union[str, Path, SensorPositionComparison2019Mocap]], cv_iterator: Optional[Union[int, BaseCrossValidator, Iterator]] = GroupKFold(n_splits=5), cv_params: Optional[Dict] = None)

The SensorPositionComparison Full-Pipeline Challenge.

Parameters:

dataset

A instance of SensorPositionComparison2019Mocap 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:

ground_truth_marker

(Class Constant) The marker used to calculate the ground truth stride borders.

data_padding_s

(Class Constant) The amount of padding in seconds to add before and after each gait tests. This ensures that sufficient resting data is available before and after the gait test.

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_aggregated_ground_truth_parameter
get_ground_truth_parameter
get_imu_data
get_scorer

__init__(dataset: Optional[Union[str, Path, SensorPositionComparison2019Mocap]], cv_iterator: Optional[Union[int, BaseCrossValidator, Iterator]] = GroupKFold(n_splits=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.