Challenge

class gaitmap_challenges.stride_segmentation.egait_segmentation_validation_2014.Challenge(dataset: Optional[Union[str, Path, EgaitSegmentationValidation2014]], cv_iterator: Optional[Union[int, BaseCrossValidator, Iterator]] = StratifiedKFold(n_splits=5, random_state=None, shuffle=False), cv_params: Optional[Dict] = None)

The EgaitSegmentation Validation Challenge.

This challenge uses the new labels by default.

Parameters:

dataset

A instance of EgaitSegmentationValidation2014 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.

use_original_labels

(Class Constant) If True, the original stride labels are used for the reference stride list. The original labels only contain straight strides. If False, the new labels are used for the reference stride list. The new labels contain all strides including turns and stairs.

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.

gaitmap_challenges.stride_segmentation.egait_segmentation_validation_2014_original_label.Challenge

The same challenge, but with the original labels

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, EgaitSegmentationValidation2014]], cv_iterator: Optional[Union[int, BaseCrossValidator, Iterator]] = StratifiedKFold(n_splits=5, random_state=None, shuffle=False), 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.