GSD TVS Evaluation

This script shows the actual performance evaluation that is run to benchmark the GSD algorithms on the TVS dataset. As this is run on the TVS dataset and has a long runtime, this example needs to be downloaded and run locally.

from pathlib import Path

from joblib import Memory
from mobgap import PROJECT_ROOT
from mobgap.data import TVSFreeLivingDataset
from mobgap.utils.misc import get_env_var

dataset_path = Path(get_env_var("MOBGAP_TVS_DATASET_PATH").strip('"'))
n_jobs = get_env_var("MOBGAP_N_JOBS", 1)

free_living_data = TVSFreeLivingDataset(
    dataset_path,
    reference_system="INDIP",
    memory=Memory(PROJECT_ROOT / ".cache"),
    missing_reference_error_type="skip",
).get_subset(recording="Recording4")

Run the non-CV evaluation for all implemented algorithms

from mobgap.gait_sequences import GsdAdaptiveIonescu, GsdIluz, GsdIonescu
from mobgap.gait_sequences.evaluation import (
    GsdEvaluation,
    gsd_evaluation_scorer,
)
from mobgap.gait_sequences.pipeline import GsdEmulationPipeline

results = {}
for algo in (GsdIluz(), GsdIonescu(), GsdAdaptiveIonescu()):
    print(f"Running evaluation for {algo.__class__.__name__}")
    pipe = GsdEmulationPipeline(algo)
    eval_pipe = GsdEvaluation(
        free_living_data,
        scoring=gsd_evaluation_scorer,
        validate_paras={"n_jobs": n_jobs},
    ).run(pipe)
    results[algo.__class__.__name__] = eval_pipe.results_

Estimated memory usage: 0 MB