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"""
Unit tests for mir_eval.alignment
"""
import glob
import json
import pytest
import numpy as np
import mir_eval
A_TOL = 1e-12
# Path to the fixture files
REF_GLOB = "data/alignment/ref*.txt"
EST_GLOB = "data/alignment/est*.txt"
SCORES_GLOB = "data/alignment/output*.json"
ref_files = sorted(glob.glob(REF_GLOB))
est_files = sorted(glob.glob(EST_GLOB))
sco_files = sorted(glob.glob(SCORES_GLOB))
assert len(ref_files) == len(est_files) == len(sco_files) > 0
file_sets = list(zip(ref_files, est_files, sco_files))
@pytest.mark.xfail(raises=ValueError)
@pytest.mark.parametrize(
"metric",
[
mir_eval.alignment.absolute_error,
mir_eval.alignment.percentage_correct,
mir_eval.alignment.percentage_correct_segments,
(
lambda ref_ts, est_ts: mir_eval.alignment.percentage_correct_segments(
ref_ts, est_ts, duration=max(np.max(ref_ts), np.max(est_ts))
)
),
mir_eval.alignment.karaoke_perceptual_metric,
],
)
@pytest.mark.parametrize(
"est_alignment, pred_alignment",
[
(
np.array([[1.0, 2.0]]),
np.array([[1.0, 2.0]]),
), # alignments must be 1d ndarray
(
np.array([[-1.0, 2.0]]),
np.array([[1.0, 2.0]]),
), # alignments must be non-negative
(np.array([[2.0, 1.0]]), np.array([[1.0, 2.0]])), # alignments must be sorted
(
np.array([[1.0, 2.0]]),
np.array([[1.0]]),
), # alignments must have the same length
],
)
def test_alignment_functions_fail(metric, est_alignment, pred_alignment):
metric(est_alignment, pred_alignment)
@pytest.fixture
def alignment_data(request):
ref_f, est_f, sco_f = request.param
with open(sco_f) as f:
expected_scores = json.load(f)
reference_alignments = mir_eval.io.load_events(ref_f)
estimated_alignments = mir_eval.io.load_events(est_f)
return reference_alignments, estimated_alignments, expected_scores
@pytest.mark.parametrize("alignment_data", file_sets, indirect=True)
def test_alignment_functions(alignment_data):
reference_alignments, estimated_alignments, expected_scores = alignment_data
scores = mir_eval.alignment.evaluate(reference_alignments, estimated_alignments)
assert scores.keys() == expected_scores.keys()
for metric in scores:
assert np.allclose(scores[metric], expected_scores[metric], atol=A_TOL)
|
