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"""
Unit tests for mir_eval.multipitch
"""
import numpy as np
import json
import mir_eval
import glob
import warnings
import pytest
A_TOL = 1e-12
# Path to the fixture files
REF_GLOB = "data/multipitch/ref*.txt"
EST_GLOB = "data/multipitch/est*.txt"
SCORES_GLOB = "data/multipitch/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 multipitch_data(request):
ref_f, est_f, sco_f = request.param
with open(sco_f) as f_handle:
expected_score = json.load(f_handle)
ref_times, ref_freqs = mir_eval.io.load_ragged_time_series(ref_f)
est_times, est_freqs = mir_eval.io.load_ragged_time_series(est_f)
return ref_times, ref_freqs, est_times, est_freqs, expected_score
def __frequencies_equal(freqs_a, freqs_b):
if len(freqs_a) != len(freqs_b):
return False
else:
equal = True
for freq_a, freq_b in zip(freqs_a, freqs_b):
if freq_a.size != freq_b.size:
return False
equal = equal and np.allclose(freq_a, freq_b, atol=A_TOL)
return equal
def __scores_equal(score_a, score_b):
keys_a = set(list(score_a.keys()))
keys_b = set(list(score_b.keys()))
if keys_a != keys_b:
return False
else:
equal = True
for k in keys_a:
value_a = score_a[k]
value_b = score_b[k]
equal = equal and np.allclose(value_a, value_b, atol=A_TOL)
return equal
def test_resample_multif0():
times = np.array([0.00, 0.01, 0.02, 0.03])
empty_times = np.array([])
freqs = [
np.array([200.0]),
np.array([]),
np.array([300.0, 400.0, 500.0]),
np.array([300.0, 500.0]),
]
empty_freqs = []
target_times1 = times
target_times2 = np.array([0.001, 0.002, 0.01, 0.029, 0.05])
target_times3 = empty_times
expected_freqs1 = freqs
expected_freqs2 = [
np.array([200.0]),
np.array([200.0]),
np.array([]),
np.array([300.0, 500.0]),
np.array([]),
]
expected_freqs3 = empty_freqs
expected_freqs4 = [np.array([])] * 4
actual_freqs1 = mir_eval.multipitch.resample_multipitch(times, freqs, target_times1)
actual_freqs2 = mir_eval.multipitch.resample_multipitch(times, freqs, target_times2)
actual_freqs3 = mir_eval.multipitch.resample_multipitch(times, freqs, target_times3)
actual_freqs4 = mir_eval.multipitch.resample_multipitch(
empty_times, empty_freqs, target_times1
)
assert __frequencies_equal(actual_freqs1, expected_freqs1)
assert __frequencies_equal(actual_freqs2, expected_freqs2)
assert __frequencies_equal(actual_freqs3, expected_freqs3)
assert __frequencies_equal(actual_freqs4, expected_freqs4)
def test_frequencies_to_midi():
frequencies = [
np.array([440.0]),
np.array([]),
np.array([220.0, 660.0, 512.0]),
np.array([300.0, 512.0]),
]
expected = [
np.array([69.0]),
np.array([]),
np.array([57.0, 76.01955000865388, 71.623683437704088]),
np.array([62.369507723654657, 71.623683437704088]),
]
actual = mir_eval.multipitch.frequencies_to_midi(frequencies)
assert __frequencies_equal(actual, expected)
def test_midi_to_chroma():
midi_frequencies = [
np.array([69.0]),
np.array([]),
np.array([57.0, 76.01955000865388, 71.623683437704088]),
np.array([62.369507723654657, 71.623683437704088]),
]
expected = [
np.array([9.0]),
np.array([]),
np.array([9.0, 4.01955000865388, 11.623683437704088]),
np.array([2.3695077236546567, 11.623683437704088]),
]
actual = mir_eval.multipitch.midi_to_chroma(midi_frequencies)
assert __frequencies_equal(actual, expected)
def test_compute_num_freqs():
frequencies = [
np.array([256.0]),
np.array([]),
np.array([362.03867196751236, 128.0, 512.0]),
np.array([300.0, 512.0]),
]
expected = np.array([1, 0, 3, 2])
actual = mir_eval.multipitch.compute_num_freqs(frequencies)
assert np.allclose(actual, expected, atol=A_TOL)
def test_compute_num_true_positives():
ref_freqs = [
np.array([96.0, 100.0]),
np.array([]),
np.array([81.0]),
np.array([102.0, 84.0, 108.0]),
np.array([98.745824285950576, 108.0]),
]
est_freqs = [
np.array([96.0]),
np.array([]),
np.array([200.0, 82.0]),
np.array([102.0, 84.0, 108.0]),
np.array([99.0, 108.0]),
]
expected = np.array([1, 0, 0, 3, 2])
actual = mir_eval.multipitch.compute_num_true_positives(ref_freqs, est_freqs)
assert np.allclose(actual, expected, atol=A_TOL)
ref_freqs_chroma = [
np.array([0.0, 1.5]),
np.array([]),
np.array([2.0]),
np.array([5.1, 6.0, 11.0]),
np.array([11.9, 11.9]),
]
est_freqs_chroma = [
np.array([0.0]),
np.array([]),
np.array([5.0, 2.6]),
np.array([5.1, 6.0, 11.0]),
np.array([0.2, 11.5]),
]
expected = np.array([1, 0, 0, 3, 2])
actual = mir_eval.multipitch.compute_num_true_positives(
ref_freqs_chroma, est_freqs_chroma, chroma=True
)
assert np.allclose(actual, expected, atol=A_TOL)
def test_accuracy_metrics():
true_positives = np.array([1, 0, 0, 3, 2])
n_ref = np.array([2, 0, 1, 3, 2])
n_est = np.array([1, 0, 2, 3, 2])
expected_precision = 0.75
expected_recall = 0.75
expected_accuracy = 0.6
(
actual_precision,
actual_recall,
actual_accuarcy,
) = mir_eval.multipitch.compute_accuracy(true_positives, n_ref, n_est)
assert np.allclose(actual_precision, expected_precision, atol=A_TOL)
assert np.allclose(actual_recall, expected_recall, atol=A_TOL)
assert np.allclose(actual_accuarcy, expected_accuracy, atol=A_TOL)
def test_error_score_metrics():
true_positives = np.array([1, 0, 0, 3, 2])
n_ref = np.array([2, 0, 1, 3, 2])
n_est = np.array([1, 0, 2, 3, 2])
expected_esub = 0.125
expected_emiss = 0.125
expected_efa = 0.125
expected_etot = 0.375
(
actual_esub,
actual_emiss,
actual_efa,
actual_etot,
) = mir_eval.multipitch.compute_err_score(true_positives, n_ref, n_est)
assert np.allclose(actual_esub, expected_esub, atol=A_TOL)
assert np.allclose(actual_emiss, expected_emiss, atol=A_TOL)
assert np.allclose(actual_efa, expected_efa, atol=A_TOL)
assert np.allclose(actual_etot, expected_etot, atol=A_TOL)
def unit_test_metrics():
empty_array = np.array([])
ref_time = np.array([0.0, 0.1])
ref_freqs = [np.array([201.0]), np.array([])]
est_time = np.array([0.0, 0.1])
est_freqs = [np.array([200.0]), np.array([])]
# ref sizes unequal
with pytest.raises(ValueError):
mir_eval.multipitch.metrics(np.array([0.0]), ref_freqs, est_time, est_freqs)
# est sizes unequal
with pytest.raises(ValueError):
mir_eval.multipitch.metrics(ref_time, ref_freqs, np.array([0.0]), est_freqs)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
# Test for warnings on empty values
actual_score = mir_eval.multipitch.metrics(
ref_time, [empty_array, empty_array], est_time, [empty_array, empty_array]
)
assert len(w) == 6
assert issubclass(w[-1].category, UserWarning)
assert str(w[-1].message) == "Reference frequencies are all empty."
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
# Test for warnings on empty values
# test all inputs empty
mir_eval.multipitch.metrics(empty_array, [], empty_array, [])
assert len(w) == 10
assert issubclass(w[-1].category, UserWarning)
assert str(w[-1].message) == "Reference frequencies are all empty."
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
# reference empty
mir_eval.multipitch.metrics(empty_array, [], est_time, est_freqs)
assert len(w) == 9
assert issubclass(w[-1].category, UserWarning)
assert str(w[-1].message) == "Reference frequencies are all empty."
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
# estimate empty
mir_eval.multipitch.metrics(ref_time, ref_freqs, empty_array, [])
assert len(w) == 5
assert issubclass(w[-1].category, UserWarning)
assert str(w[-1].message) == "Estimate frequencies are all empty."
expected_score = (
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
)
assert np.allclose(actual_score, expected_score)
# test perfect estimate
ref_time = np.array([0.0, 0.1, 0.2])
ref_freqs = [np.array([201.0]), np.array([]), np.array([300.5, 87.1])]
actual_score = mir_eval.multipitch.metrics(ref_time, ref_freqs, ref_time, ref_freqs)
expected_score = (
1.0,
1.0,
1.0,
0.0,
0.0,
0.0,
0.0,
1.0,
1.0,
1.0,
0.0,
0.0,
0.0,
0.0,
)
assert np.allclose(actual_score, expected_score)
@pytest.mark.parametrize("multipitch_data", file_sets, indirect=True)
def test_evaluate_regression(multipitch_data):
ref_times, ref_freqs, est_times, est_freqs, expected_score = multipitch_data
actual_score = mir_eval.multipitch.evaluate(
ref_times, ref_freqs, est_times, est_freqs
)
assert __scores_equal(actual_score, expected_score)
|
