EgaitSegmentationValidation2014OriginalLabel#

Hide code cell source 
from myst_nb_bokeh import glue_bokeh

from gaitmap_bench import config, is_config_set
from gaitmap_bench.docu_utils import set_docs_config
from gaitmap_challenges.results import load_run, get_latest_result, filter_results, get_all_result_paths, \
    generate_overview_table
from gaitmap_challenges.stride_segmentation.egait_segmentation_validation_2014_original_label import Challenge
from gaitmap_challenges.visualization import SingleMetricBoxplot, group_by_data_label

is_config_set() or set_docs_config()
all_runs = get_all_result_paths(Challenge, config().results_dir)
all_runs = filter_results(all_runs, challenge_version=Challenge.VERSION, is_debug_run=False)
latest_runs = get_latest_result(all_runs)
generate_overview_table(latest_runs).set_index("Entry").T
Entry (gaitmap, barth_dtw, default) (gaitmap, barth_dtw, optimized) (gaitmap, constrained_barth_dtw, default) (gaitmap, constrained_barth_dtw, optimized) (gaitmap, roth_hmm, default) (gaitmap, roth_hmm, trained_default)
Datetime 2023-09-19T15:55:47.569492+02:00 2023-09-19T15:55:49.357765+02:00 2023-09-19T15:55:47.570986+02:00 2023-09-19T15:55:49.361545+02:00 2023-09-19T15:55:48.952746+02:00 2023-09-19T15:55:48.963352+02:00
Description DTW based stride segmentation algorithm from B... DTW based stride segmentation algorithm from B... DTW based stride segmentation algorithm from B... DTW based stride segmentation algorithm from B... Hierarchical Hidden Markov Model for gait segm... Hierarchical Hidden Markov Model for gait segm...
References [https://www.mdpi.com/1424-8220/15/3/6419] [https://www.mdpi.com/1424-8220/15/3/6419] [https://www.mdpi.com/1424-8220/15/3/6419] [https://www.mdpi.com/1424-8220/15/3/6419] [https://jneuroengrehab.biomedcentral.com/arti... [https://jneuroengrehab.biomedcentral.com/arti...
Code Authors [MaD-DiGait] [MaD-DiGait] [MaD-DiGait] [MaD-DiGait] [MaD-DiGait] [MaD-DiGait]
Algorithm Authors [Jens Barth et al.] [Jens Barth et al.] [Jens Barth et al.] [Jens Barth et al.] [Nils Roth et al.] [Nils Roth et al.]
Implementation https://github.com/mad-lab-fau/gaitmap/blob/ma... https://github.com/mad-lab-fau/gaitmap/blob/ma... https://github.com/mad-lab-fau/gaitmap/blob/ma... https://github.com/mad-lab-fau/gaitmap/blob/ma... https://github.com/mad-lab-fau/gaitmap/tree/ma... https://github.com/mad-lab-fau/gaitmap/tree/ma...

Results per Participant and Test#

Hide code cell source 
from gaitmap_bench.docu_utils import glue_bokeh_md, tabs
from myst_nb import glue
from IPython.display import Markdown


run_info = {k: load_run(Challenge, v) for k, v in latest_runs.items()}
cv_results = {k: v.results["cv_results"]  for k, v in run_info.items()}

tab_items = {}
metrics = {
    "F1-Score": "f1_score",
    "Precision": "precision",
    "Recall": "recall",
}

for name, metric in metrics.items():
    p = SingleMetricBoxplot(cv_results, metric, "single", overlay_scatter=True, label_grouper=group_by_data_label(level="test", include_all="Combined"))
    glue_name = f"single_{metric}"
    glue_bokeh(glue_name, p.bokeh())
    tab_items[name] = glue_bokeh_md(glue_name)

glue("single_results", Markdown(tabs(tab_items, class_str="full-width")), display=False)

Results per CV Fold#

Hide code cell source 
tab_items = {}

for name, metric in metrics.items():
    p = SingleMetricBoxplot(cv_results, metric, "fold", overlay_scatter=True)
    glue_name = f"fold_{metric}"
    glue_bokeh(glue_name, p.bokeh())
    tab_items[name] = glue_bokeh_md(glue_name)

glue("fold_results", Markdown(tabs(tab_items, class_str="full-width")), display=False)