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"""
Tests for mir_eval.key
"""
import mir_eval
import pytest
import glob
import json
import numpy as np
A_TOL = 1e-12
# Path to the fixture files
REF_GLOB = "data/key/ref*.txt"
EST_GLOB = "data/key/est*.txt"
SCORES_GLOB = "data/key/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.parametrize(
"good_key",
["C major", "c major", "C# major", "Bb minor", "db minor", "X", "x", "C other"],
)
@pytest.mark.parametrize(
"bad_key", ["C maj", "Cb major", "C", "K major", "F## minor" "X other", "x minor"]
)
def test_key_function_fail(good_key, bad_key):
score = mir_eval.key.weighted_score(good_key, good_key)
assert score == 1.0
with pytest.raises(ValueError):
mir_eval.key.weighted_score(good_key, bad_key)
with pytest.raises(ValueError):
mir_eval.key.weighted_score(bad_key, good_key)
@pytest.fixture
def key_data(request):
ref_f, est_f, sco_f = request.param
with open(sco_f) as f:
expected_scores = json.load(f)
reference_key = mir_eval.io.load_key(ref_f)
estimated_key = mir_eval.io.load_key(est_f)
return reference_key, estimated_key, expected_scores
@pytest.mark.parametrize("key_data", file_sets, indirect=True)
def test_key_functions(key_data):
reference_key, estimated_key, expected_scores = key_data
# Compute scores
scores = mir_eval.key.evaluate(reference_key, estimated_key)
# Compare them
assert scores.keys() == expected_scores.keys()
for metric in scores:
assert np.allclose(scores[metric], expected_scores[metric], atol=A_TOL)
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