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Description: Allow more test_chord warnings
Author: Josenilson Ferreira da silva <nilsonfsilva@hotmail.com>
Forwarded: https://github.com/craffel/mir_eval/issues/352
Last-Update: 2023-01-16
@@ -540,7 +540,7 @@ def _contingency_matrix(reference_indice
return scipy.sparse.coo_matrix((np.ones(ref_class_idx.shape[0]),
(ref_class_idx, est_class_idx)),
shape=(n_ref_classes, n_est_classes),
- dtype=np.int).toarray()
+ dtype=int).toarray()
def _adjusted_rand_index(reference_indices, estimated_indices):
@@ -720,7 +720,7 @@ def _entropy(labels):
if len(labels) == 0:
return 1.0
label_idx = np.unique(labels, return_inverse=True)[1]
- pi = np.bincount(label_idx).astype(np.float)
+ pi = np.bincount(label_idx).astype(float)
pi = pi[pi > 0]
pi_sum = np.sum(pi)
# log(a / b) should be calculated as log(a) - log(b) for
@@ -510,7 +510,7 @@ def encode(chord_label, reduce_extended_
semitone_bitmap += scale_degree_to_bitmap(scale_degree,
reduce_extended_chords)
- semitone_bitmap = (semitone_bitmap > 0).astype(np.int)
+ semitone_bitmap = (semitone_bitmap > 0).astype(int)
if not semitone_bitmap[bass_number] and strict_bass_intervals:
raise InvalidChordException(
"Given bass scale degree is absent from this chord: "
@@ -544,8 +544,8 @@ def encode_many(chord_labels, reduce_ext
"""
num_items = len(chord_labels)
- roots, basses = np.zeros([2, num_items], dtype=np.int)
- semitones = np.zeros([num_items, 12], dtype=np.int)
+ roots, basses = np.zeros([2, num_items], dtype=int)
+ semitones = np.zeros([num_items, 12], dtype=int)
local_cache = dict()
for i, label in enumerate(chord_labels):
result = local_cache.get(label, None)
@@ -749,7 +749,7 @@ def thirds(reference_labels, estimated_l
eq_roots = ref_roots == est_roots
eq_thirds = ref_semitones[:, 3] == est_semitones[:, 3]
- comparison_scores = (eq_roots * eq_thirds).astype(np.float)
+ comparison_scores = (eq_roots * eq_thirds).astype(float)
# Ignore 'X' chords
comparison_scores[np.any(ref_semitones < 0, axis=1)] = -1.0
@@ -797,7 +797,7 @@ def thirds_inv(reference_labels, estimat
eq_root = ref_roots == est_roots
eq_bass = ref_bass == est_bass
eq_third = ref_semitones[:, 3] == est_semitones[:, 3]
- comparison_scores = (eq_root * eq_third * eq_bass).astype(np.float)
+ comparison_scores = (eq_root * eq_third * eq_bass).astype(float)
# Ignore 'X' chords
comparison_scores[np.any(ref_semitones < 0, axis=1)] = -1.0
@@ -845,7 +845,7 @@ def triads(reference_labels, estimated_l
eq_roots = ref_roots == est_roots
eq_semitones = np.all(
np.equal(ref_semitones[:, :8], est_semitones[:, :8]), axis=1)
- comparison_scores = (eq_roots * eq_semitones).astype(np.float)
+ comparison_scores = (eq_roots * eq_semitones).astype(float)
# Ignore 'X' chords
comparison_scores[np.any(ref_semitones < 0, axis=1)] = -1.0
@@ -894,7 +894,7 @@ def triads_inv(reference_labels, estimat
eq_basses = ref_bass == est_bass
eq_semitones = np.all(
np.equal(ref_semitones[:, :8], est_semitones[:, :8]), axis=1)
- comparison_scores = (eq_roots * eq_semitones * eq_basses).astype(np.float)
+ comparison_scores = (eq_roots * eq_semitones * eq_basses).astype(float)
# Ignore 'X' chords
comparison_scores[np.any(ref_semitones < 0, axis=1)] = -1.0
@@ -941,7 +941,7 @@ def tetrads(reference_labels, estimated_
eq_roots = ref_roots == est_roots
eq_semitones = np.all(np.equal(ref_semitones, est_semitones), axis=1)
- comparison_scores = (eq_roots * eq_semitones).astype(np.float)
+ comparison_scores = (eq_roots * eq_semitones).astype(float)
# Ignore 'X' chords
comparison_scores[np.any(ref_semitones < 0, axis=1)] = -1.0
@@ -989,7 +989,7 @@ def tetrads_inv(reference_labels, estima
eq_roots = ref_roots == est_roots
eq_basses = ref_bass == est_bass
eq_semitones = np.all(np.equal(ref_semitones, est_semitones), axis=1)
- comparison_scores = (eq_roots * eq_semitones * eq_basses).astype(np.float)
+ comparison_scores = (eq_roots * eq_semitones * eq_basses).astype(float)
# Ignore 'X' chords
comparison_scores[np.any(ref_semitones < 0, axis=1)] = -1.0
@@ -1035,7 +1035,7 @@ def root(reference_labels, estimated_lab
validate(reference_labels, estimated_labels)
ref_roots, ref_semitones = encode_many(reference_labels, False)[:2]
est_roots = encode_many(estimated_labels, False)[0]
- comparison_scores = (ref_roots == est_roots).astype(np.float)
+ comparison_scores = (ref_roots == est_roots).astype(float)
# Ignore 'X' chords
comparison_scores[np.any(ref_semitones < 0, axis=1)] = -1.0
@@ -1087,7 +1087,7 @@ def mirex(reference_labels, estimated_la
eq_chroma = (ref_chroma * est_chroma).sum(axis=-1)
# Chroma matching for set bits
- comparison_scores = (eq_chroma >= min_intersection).astype(np.float)
+ comparison_scores = (eq_chroma >= min_intersection).astype(float)
# No-chord matching; match -1 roots, SKIP_CHORDS dropped next
no_root = np.logical_and(ref_data[0] == -1, est_data[0] == -1)
@@ -1150,7 +1150,7 @@ def majmin(reference_labels, estimated_l
eq_root = ref_roots == est_roots
eq_quality = np.all(np.equal(ref_semitones[:, :8],
est_semitones[:, :8]), axis=1)
- comparison_scores = (eq_root * eq_quality).astype(np.float)
+ comparison_scores = (eq_root * eq_quality).astype(float)
# Test for Major / Minor / No-chord
is_maj = np.all(np.equal(ref_semitones[:, :8], maj_semitones), axis=1)
@@ -1217,7 +1217,7 @@ def majmin_inv(reference_labels, estimat
eq_root_bass = (ref_roots == est_roots) * (ref_bass == est_bass)
eq_semitones = np.all(np.equal(ref_semitones[:, :8],
est_semitones[:, :8]), axis=1)
- comparison_scores = (eq_root_bass * eq_semitones).astype(np.float)
+ comparison_scores = (eq_root_bass * eq_semitones).astype(float)
# Test for Major / Minor / No-chord
is_maj = np.all(np.equal(ref_semitones[:, :8], maj_semitones), axis=1)
@@ -1280,7 +1280,7 @@ def sevenths(reference_labels, estimated
eq_root = ref_roots == est_roots
eq_semitones = np.all(np.equal(ref_semitones, est_semitones), axis=1)
- comparison_scores = (eq_root * eq_semitones).astype(np.float)
+ comparison_scores = (eq_root * eq_semitones).astype(float)
# Test for reference chord inclusion
is_valid = np.array([np.all(np.equal(ref_semitones, semitones), axis=1)
@@ -1335,7 +1335,7 @@ def sevenths_inv(reference_labels, estim
eq_roots_basses = (ref_roots == est_roots) * (ref_basses == est_basses)
eq_semitones = np.all(np.equal(ref_semitones, est_semitones), axis=1)
- comparison_scores = (eq_roots_basses * eq_semitones).astype(np.float)
+ comparison_scores = (eq_roots_basses * eq_semitones).astype(float)
# Test for Major / Minor / No-chord
is_valid = np.array([np.all(np.equal(ref_semitones, semitones), axis=1)
@@ -19,9 +19,9 @@ SCORES_GLOB = 'data/beat/output*.json'
def test_trim_beats():
# Construct dummy beat times [0., 1., ...]
- dummy_beats = np.arange(10, dtype=np.float)
+ dummy_beats = np.arange(10, dtype=float)
# We expect trim_beats to remove all beats < 5s
- expected_beats = np.arange(5, 10, dtype=np.float)
+ expected_beats = np.arange(5, 10, dtype=float)
assert np.allclose(mir_eval.beat.trim_beats(dummy_beats), expected_beats)
@@ -51,7 +51,7 @@ def __unit_test_beat_function(metric):
nose.tools.assert_raises(ValueError, metric, beats, beats)
# Valid beats which are the same produce a score of 1 for all metrics
- beats = np.arange(10, dtype=np.float)
+ beats = np.arange(10, dtype=float)
assert np.allclose(metric(beats, beats), 1)
@@ -43,7 +43,7 @@ def __unit_test_onset_function(metric):
nose.tools.assert_raises(ValueError, metric, onsets, onsets)
# Valid onsets which are the same produce a score of 1 for all metrics
- onsets = np.arange(10, dtype=np.float)
+ onsets = np.arange(10, dtype=float)
assert np.allclose(metric(onsets, onsets), 1)
@@ -386,14 +386,14 @@ def p_score(reference_beats,
estimated_beats = np.array(estimated_beats - offset)
reference_beats = np.array(reference_beats - offset)
# Get the largest time index
- end_point = np.int(np.ceil(np.max([np.max(estimated_beats),
+ end_point = int(np.ceil(np.max([np.max(estimated_beats),
np.max(reference_beats)])))
# Make impulse trains with impulses at beat locations
reference_train = np.zeros(end_point*sampling_rate + 1)
- beat_indices = np.ceil(reference_beats*sampling_rate).astype(np.int)
+ beat_indices = np.ceil(reference_beats*sampling_rate).astype(int)
reference_train[beat_indices] = 1.0
estimated_train = np.zeros(end_point*sampling_rate + 1)
- beat_indices = np.ceil(estimated_beats*sampling_rate).astype(np.int)
+ beat_indices = np.ceil(estimated_beats*sampling_rate).astype(int)
estimated_train[beat_indices] = 1.0
# Window size to take the correlation over
# defined as .2*median(inter-annotation-intervals)
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