def test_display_hierarchy_label():
plt.figure()
# Load some chord data
int0, lab0 = load_labeled_intervals("tests/data/hierarchy/ref00.lab")
int1, lab1 = load_labeled_intervals("tests/data/hierarchy/ref01.lab")
# Plot reference and estimate with a common label set
mir_eval.display.hierarchy([int0, int1], [lab0, lab1], levels=["Large", "Small"])
plt.legend()
def test_display_hierarchy_nolabel():
plt.figure()
# Load some chord data
int0, lab0 = load_labeled_intervals('tests/data/hierarchy/ref00.lab')
int1, lab1 = load_labeled_intervals('tests/data/hierarchy/ref01.lab')
# Plot reference and estimate with a common label set
mir_eval.display.hierarchy([int0, int1], [lab0, lab1])
plt.legend()
def test_display_hierarchy_nolabel():
plt.figure()
# Load some chord data
int0, lab0 = load_labeled_intervals('data/hierarchy/ref00.lab')
int1, lab1 = load_labeled_intervals('data/hierarchy/ref01.lab')
# Plot reference and estimate with a common label set
mir_eval.display.hierarchy([int0, int1],
[lab0, lab1])
plt.legend()
def test_display_labeled_intervals_compare():
plt.figure()
# Load some chord data
ref_int, ref_labels = load_labeled_intervals("tests/data/chord/ref01.lab")
est_int, est_labels = load_labeled_intervals("tests/data/chord/est01.lab")
# Plot reference and estimates using label set extension
mir_eval.display.labeled_intervals(ref_int, ref_labels, alpha=0.5, label="Reference")
mir_eval.display.labeled_intervals(est_int, est_labels, alpha=0.5, label="Estimate")
plt.legend()
def test_display_labeled_intervals_compare_common():
plt.figure()
# Load some chord data
ref_int, ref_labels = load_labeled_intervals("tests/data/chord/ref01.lab")
est_int, est_labels = load_labeled_intervals("tests/data/chord/est01.lab")
label_set = list(sorted(set(ref_labels) | set(est_labels)))
# Plot reference and estimate with a common label set
mir_eval.display.labeled_intervals(ref_int, ref_labels, label_set=label_set, alpha=0.5, label="Reference")
mir_eval.display.labeled_intervals(est_int, est_labels, label_set=label_set, alpha=0.5, label="Estimate")
plt.legend()
def test_display_labeled_intervals_compare():
plt.figure()
# Load some chord data
ref_int, ref_labels = load_labeled_intervals('data/chord/ref01.lab')
est_int, est_labels = load_labeled_intervals('data/chord/est01.lab')
# Plot reference and estimates using label set extension
mir_eval.display.labeled_intervals(ref_int, ref_labels,
alpha=0.5, label='Reference')
mir_eval.display.labeled_intervals(est_int, est_labels,
alpha=0.5, label='Estimate')
plt.legend()
def test_display_labeled_intervals_compare_noextend():
plt.figure()
# Load some chord data
ref_int, ref_labels = load_labeled_intervals('data/chord/ref01.lab')
est_int, est_labels = load_labeled_intervals('data/chord/est01.lab')
# Plot reference and estimate, but only use the reference labels
mir_eval.display.labeled_intervals(ref_int, ref_labels,
alpha=0.5, label='Reference')
mir_eval.display.labeled_intervals(est_int, est_labels,
extend_labels=False,
alpha=0.5, label='Estimate')
plt.legend()
def test_display_segment_text():
plt.figure()
# Load some segment data
intervals, labels = load_labeled_intervals('data/segment/ref00.lab')
# Plot the segments with no labels
mir_eval.display.segments(intervals, labels, text=True)
def test_display_labeled_intervals():
plt.figure()
# Load some chord data
intervals, labels = load_labeled_intervals('data/chord/ref01.lab')
# Plot the chords with nothing fancy
mir_eval.display.labeled_intervals(intervals, labels)
def test_display_labeled_intervals_compare_common():
plt.figure()
# Load some chord data
ref_int, ref_labels = load_labeled_intervals('data/chord/ref01.lab')
est_int, est_labels = load_labeled_intervals('data/chord/est01.lab')
label_set = list(sorted(set(ref_labels) | set(est_labels)))
# Plot reference and estimate with a common label set
mir_eval.display.labeled_intervals(ref_int, ref_labels,
label_set=label_set,
alpha=0.5, label='Reference')
mir_eval.display.labeled_intervals(est_int, est_labels,
label_set=label_set,
alpha=0.5, label='Estimate')
plt.legend()
def test_display_labeled_intervals_noextend():
plt.figure()
# Load some chord data
intervals, labels = load_labeled_intervals("tests/data/chord/ref01.lab")
# Plot the chords with nothing fancy
ax = plt.axes()
ax.set_yticklabels([])
mir_eval.display.labeled_intervals(intervals, labels, label_set=[], extend_labels=False, ax=ax)
def test_display_labeled_intervals_compare_noextend():
plt.figure()
# Load some chord data
ref_int, ref_labels = load_labeled_intervals('tests/data/chord/ref01.lab')
est_int, est_labels = load_labeled_intervals('tests/data/chord/est01.lab')
# Plot reference and estimate, but only use the reference labels
mir_eval.display.labeled_intervals(ref_int,
ref_labels,
alpha=0.5,
label='Reference')
mir_eval.display.labeled_intervals(est_int,
est_labels,
extend_labels=False,
alpha=0.5,
label='Estimate')
plt.legend()
def test_display_segment():
plt.figure()
# Load some segment data
intervals, labels = load_labeled_intervals('tests/data/segment/ref00.lab')
# Plot the segments with no labels
mir_eval.display.segments(intervals, labels, text=False)
# Draw a legend
plt.legend()
def test_display_segment():
plt.figure()
# Load some segment data
intervals, labels = load_labeled_intervals("tests/data/segment/ref00.lab")
# Plot the segments with no labels
mir_eval.display.segments(intervals, labels, text=False)
# Draw a legend
plt.legend()
def test_display_labeled_intervals_noextend():
plt.figure()
# Load some chord data
intervals, labels = load_labeled_intervals('data/chord/ref01.lab')
# Plot the chords with nothing fancy
ax = plt.axes()
ax.set_yticklabels([])
mir_eval.display.labeled_intervals(intervals, labels,
label_set=[],
extend_labels=False,
ax=ax)
action='store',
help='path to the estimated annotation(s) in '
'.lab format, ordered from top to bottom of '
'the hierarchy')
return vars(parser.parse_args(sys.argv[1:]))
if __name__ == '__main__':
# Get the parameters
parameters = process_arguments()
# load the data
ref_files = parameters['reference_file']
est_files = parameters['estimated_file']
ref_intervals = [load_labeled_intervals(_)[0] for _ in ref_files]
est_intervals = [load_labeled_intervals(_)[0] for _ in est_files]
# Compute all the scores
scores = mir_eval.hierarchy.evaluate(ref_intervals, est_intervals,
window=parameters['window'])
print("{} [...] vs. {} [...]".format(
basename(parameters['reference_file'][0]),
basename(parameters['estimated_file'][0])))
eval_utilities.print_evaluation(scores)
if parameters['output_file']:
print('Saving results to: ', parameters['output_file'])
eval_utilities.save_results(scores, parameters['output_file'])
help='path to the estimated annotation(s) in '
'.lab format, ordered from top to bottom of '
'the hierarchy')
return vars(parser.parse_args(sys.argv[1:]))
if __name__ == '__main__':
# Get the parameters
parameters = process_arguments()
# load the data
ref_files = parameters['reference_file']
est_files = parameters['estimated_file']
ref = [load_labeled_intervals(_) for _ in ref_files]
est = [load_labeled_intervals(_) for _ in est_files]
ref_intervals = [seg[0] for seg in ref]
ref_labels = [seg[1] for seg in ref]
est_intervals = [seg[0] for seg in est]
est_labels = [seg[1] for seg in est]
# Compute all the scores
scores = mir_eval.hierarchy.evaluate(ref_intervals, ref_labels,
est_intervals, est_labels,
window=parameters['window'])
print("{} [...] vs. {} [...]".format(
basename(parameters['reference_file'][0]),
basename(parameters['estimated_file'][0])))
eval_utilities.print_evaluation(scores)
def loadGT(self, GTPath):
intervals, labels = load_labeled_intervals(GTPath, delimiter="\t")
intervals = intervals / 100.0
# ignore pitch shift like: <"chorus A" (-10)>
labels = [label.split("\t")[0].strip('"') for label in labels]
return intervals, labels
def loadGT(self, GTPath):
intervals, labels = load_labeled_intervals(GTPath, delimiter="\t")
labels = np.array([label.strip('"') for label in labels])
intervals = intervals / 100.0
return intervals, labels
def loadGT(self, GTPath):
intervals, labels = load_labeled_intervals(GTPath, delimiter="\t")
return intervals, labels
help='path to the estimated annotation(s) in '
'.lab format, ordered from top to bottom of '
'the hierarchy')
return vars(parser.parse_args(sys.argv[1:]))
if __name__ == '__main__':
# Get the parameters
parameters = process_arguments()
# load the data
ref_files = parameters['reference_file']
est_files = parameters['estimated_file']
ref = [load_labeled_intervals(_) for _ in ref_files]
est = [load_labeled_intervals(_) for _ in est_files]
ref_intervals = [seg[0] for seg in ref]
ref_labels = [seg[1] for seg in ref]
est_intervals = [seg[0] for seg in est]
est_labels = [seg[1] for seg in est]
# Compute all the scores
scores = mir_eval.hierarchy.evaluate(ref_intervals,
ref_labels,
est_intervals,
est_labels,
window=parameters['window'])
print("{} [...] vs. {} [...]".format(
basename(parameters['reference_file'][0]),
basename(parameters['estimated_file'][0])))
