def eval_all_metrics_lab(refs_URI, detected_URI, tolerance=0.3):
"""
run all eval metrics on one file
"""
ref_intervals, ref_labels = load_labeled_intervals(refs_URI)
detected_intervals, use_end_ts = load_detected_intervals(detected_URI)
# metric 1: alignment error
alignmentErrors = _eval_alignment_error(ref_intervals, detected_intervals, ref_labels, use_end_ts)
mean, stDev, median = getMeanAndStDevError(alignmentErrors)
# metric 2: percentage correct
initialTimeOffset_refs = ref_intervals[0][0]
finalts_refs = ref_intervals[-1][1]
durationCorrect, totalLength = _eval_percentage_correct(reference_token_list=ref_intervals,
detected_token_List=detected_intervals,
final_ts_anno=finalts_refs,
initial_time_offset_refs=initialTimeOffset_refs,
reference_labels=ref_labels)
percentage_correct = durationCorrect / totalLength
# metric 3: percentage tolerance
percentage_tolerance = _eval_percentage_tolerance(ref_intervals=ref_intervals,
detected_intervals=detected_intervals,
reference_labels=ref_labels,
tolerance=tolerance)
return mean, percentage_correct, percentage_tolerance
def eval_all_metrics_lab(refs_URI, detected_URI):
"""
run all eval metrics on one file
"""
ref_intervals, ref_labels = load_labeled_intervals(refs_URI)
detected_intervals, detected_labels = load_labeled_intervals(detected_URI)
# metric 1: alignment error
alignmentErrors = _eval_alignment_error(ref_intervals, detected_intervals,
tierAliases.phrases, ref_labels)
mean, stDev, median = getMeanAndStDevError(alignmentErrors)
# metric 2: percentage correct
initialTimeOffset_refs = ref_intervals[0][0]
finalts_refs = ref_intervals[-1][1]
durationCorrect, totalLength = _eval_percentage_correct(
reference_token_list=ref_intervals,
detected_token_List=detected_intervals,
final_ts_anno=finalts_refs,
initial_time_offset_refs=initialTimeOffset_refs,
reference_labels=ref_labels)
percentage_correct = durationCorrect / totalLength
# metric 3: percentage tolerance
percentage_tolerance = _eval_percentage_tolerance(
ref_intervals=ref_intervals,
detected_intervals=detected_intervals,
reference_labels=ref_labels,
tolerance=0.3)
return mean, percentage_correct, percentage_tolerance
def test_eval_percentage_tolerance_lab_generic():
"""
test the accuracy of tokens with a tolerance window tau loading the .lab files
"""
ref_intervals, detected_intervals, ref_labels = load_ref_and_detections(dataset='generic')
accuracy = _eval_percentage_tolerance(ref_intervals=ref_intervals,
detected_intervals=detected_intervals,
reference_labels=ref_labels,
tolerance=0.3)
assert accuracy == 0.0
def test_eval_percentage_tolerance_lab_generic():
"""
test the accuracy of tokens with a tolerance window tau loading the .lab files
"""
ref_intervals, detected_intervals, ref_labels = load_ref_and_detections(
dataset='generic')
accuracy = _eval_percentage_tolerance(
ref_intervals=ref_intervals,
detected_intervals=detected_intervals,
reference_labels=ref_labels,
tolerance=0.3)
assert accuracy == 0.0
