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"""
Unit tests for mir_eval.beat
"""
import numpy as np
import json
import mir_eval
import glob
import pytest
A_TOL = 1e-12
# Path to the fixture files
REF_GLOB = "data/beat/ref*.txt"
EST_GLOB = "data/beat/est*.txt"
SCORES_GLOB = "data/beat/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.fixture
def beat_data(request):
ref_f, est_f, sco_f = request.param
with open(sco_f) as f:
expected_scores = json.load(f)
reference_beats = mir_eval.io.load_events(ref_f)
estimated_beats = mir_eval.io.load_events(est_f)
return reference_beats, estimated_beats, expected_scores
def test_trim_beats():
# Construct dummy beat times [0., 1., ...]
dummy_beats = np.arange(10, dtype=np.float64)
# We expect trim_beats to remove all beats < 5s
expected_beats = np.arange(5, 10, dtype=np.float64)
assert np.allclose(mir_eval.beat.trim_beats(dummy_beats), expected_beats)
@pytest.mark.parametrize(
"metric",
[
mir_eval.beat.f_measure,
mir_eval.beat.cemgil,
mir_eval.beat.goto,
mir_eval.beat.p_score,
mir_eval.beat.continuity,
mir_eval.beat.information_gain,
],
)
def test_beat_empty_warnings(metric):
with pytest.warns(UserWarning, match="Reference beats are empty."):
metric(np.array([]), np.arange(10))
with pytest.warns(UserWarning, match="Estimated beats are empty."):
metric(np.arange(10), np.array([]))
with pytest.warns(UserWarning, match="beats are empty."):
assert np.allclose(metric(np.array([]), np.array([])), 0)
@pytest.mark.parametrize(
"metric",
[
mir_eval.beat.f_measure,
mir_eval.beat.cemgil,
mir_eval.beat.goto,
mir_eval.beat.p_score,
mir_eval.beat.continuity,
mir_eval.beat.information_gain,
],
)
@pytest.mark.parametrize(
"beats",
[
np.array([[1.0, 2.0]]), # beats must be a 1d array
np.array([1e10, 1e11]), # beats must be not huge
np.array([2.0, 1.0]), # must be sorted
],
)
@pytest.mark.xfail(raises=ValueError)
def test_beat_fail(metric, beats):
metric(beats, beats)
@pytest.mark.parametrize(
"metric",
[
mir_eval.beat.f_measure,
mir_eval.beat.cemgil,
mir_eval.beat.goto,
mir_eval.beat.p_score,
mir_eval.beat.continuity,
mir_eval.beat.information_gain,
],
)
def test_beat_perfect(metric):
beats = np.arange(10, dtype=np.float64)
assert np.allclose(metric(beats, beats), 1)
@pytest.mark.parametrize("beat_data", file_sets, indirect=True)
def test_beat_functions(beat_data):
reference_beats, estimated_beats, expected_scores = beat_data
# Compute scores
scores = mir_eval.beat.evaluate(reference_beats, estimated_beats)
# Compare them
assert scores.keys() == expected_scores.keys()
for metric in scores:
assert np.allclose(scores[metric], expected_scores[metric], atol=A_TOL)
# Unit tests for specific behavior not covered by the above
def test_goto_proportion_correct():
# This covers the case when over 75% of the beat tracking is correct, and
# more than 3 beats are incorrect
assert mir_eval.beat.goto(
np.arange(100), np.append(np.arange(80), np.arange(80, 100) + 0.2)
)
@pytest.mark.parametrize(
"metric",
[mir_eval.beat.p_score, mir_eval.beat.continuity, mir_eval.beat.information_gain],
)
def test_warning_on_one_beat(metric):
# This tests the metrics where passing only a single beat raises a warning
# and returns 0
with pytest.warns(UserWarning, match="Only one reference beat"):
metric(np.array([10]), np.arange(10))
with pytest.warns(UserWarning, match="Only one estimated beat"):
metric(np.arange(10), np.array([10]))
def test_continuity_edge_cases():
# There is some special-case logic for when there are few beats
assert np.allclose(
mir_eval.beat.continuity(np.array([6.0, 6.0]), np.array([6.0, 7.0])), 0.0
)
assert np.allclose(
mir_eval.beat.continuity(np.array([6.0, 6.0]), np.array([6.5, 7.0])), 0.0
)
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