def test_threshold_analyze():
try:
if not os.path.exists('test_items'):
os.makedirs('test_items')
item_file = 'test_items/data.item'
feature_file = 'test_items/data.features'
distance_file = 'test_items/data.distance'
scorefilename = 'test_items/data.score'
taskfilename = 'test_items/data.abx'
analyzefilename = 'test_items/data.csv'
threshold = 2
items.generate_db_and_feat(3, 3, 1, item_file, 2, 3, feature_file)
task = ABXpy.task.Task(item_file, 'c0', 'c1', 'c2')
task.generate_triplets(taskfilename, threshold=threshold)
distances.compute_distances(feature_file,
'/features/',
taskfilename,
distance_file,
dtw_cosine_distance,
normalized=True,
n_cpu=1)
score.score(taskfilename, distance_file, scorefilename)
analyze.analyze(taskfilename, scorefilename, analyzefilename)
number_triplets = np.loadtxt(analyzefilename,
dtype=int,
delimiter='\t',
skiprows=1,
usecols=[-1])
assert np.all(number_triplets == threshold)
finally:
shutil.rmtree('test_items', ignore_errors=True)
def test_analyze():
try:
if not os.path.exists('test_items'):
os.makedirs('test_items')
item_file = 'test_items/data.item'
feature_file = 'test_items/data.features'
distance_file = 'test_items/data.distance'
scorefilename = 'test_items/data.score'
taskfilename = 'test_items/data.abx'
analyzefilename = 'test_items/data.csv'
items.generate_db_and_feat(3, 3, 1, item_file, 2, 3, feature_file)
task = ABXpy.task.Task(item_file, 'c0', 'c1', 'c2')
task.generate_triplets(taskfilename)
distances.compute_distances(feature_file, '/features/', taskfilename,
distance_file, dtw_cosine_distance)
score.score(taskfilename, distance_file, scorefilename)
analyze.analyze(taskfilename, scorefilename, analyzefilename)
finally:
try:
os.remove(item_file)
os.remove(feature_file)
os.remove(taskfilename)
os.remove(distance_file)
os.remove(scorefilename)
os.remove(analyzefilename)
# pass
except:
pass
def test_analyze():
try:
if not os.path.exists('test_items'):
os.makedirs('test_items')
item_file = 'test_items/data.item'
feature_file = 'test_items/data.features'
distance_file = 'test_items/data.distance'
scorefilename = 'test_items/data.score'
taskfilename = 'test_items/data.abx'
analyzefilename = 'test_items/data.csv'
items.generate_db_and_feat(3, 3, 1, item_file, 2, 3, feature_file)
task = ABXpy.task.Task(item_file, 'c0', 'c1', 'c2')
task.generate_triplets(taskfilename)
distances.compute_distances(feature_file,
'/features/',
taskfilename,
distance_file,
dtw_cosine_distance,
normalized=True,
n_cpu=1)
score.score(taskfilename, distance_file, scorefilename)
analyze.analyze(taskfilename, scorefilename, analyzefilename)
finally:
shutil.rmtree('test_items', ignore_errors=True)
def test_threshold_analyze():
try:
if not os.path.exists('test_items'):
os.makedirs('test_items')
item_file = 'test_items/data.item'
feature_file = 'test_items/data.features'
distance_file = 'test_items/data.distance'
scorefilename = 'test_items/data.score'
taskfilename = 'test_items/data.abx'
analyzefilename = 'test_items/data.csv'
threshold = 2
items.generate_db_and_feat(3, 3, 1, item_file, 2, 3, feature_file)
task = ABXpy.task.Task(item_file, 'c0', 'c1', 'c2')
task.generate_triplets(taskfilename, threshold=threshold)
distances.compute_distances(
feature_file, '/features/', taskfilename,
distance_file, dtw_cosine_distance,
normalized = True, n_cpu=1)
score.score(taskfilename, distance_file, scorefilename)
analyze.analyze(taskfilename, scorefilename, analyzefilename)
number_triplets = np.loadtxt(analyzefilename, dtype=int,
delimiter='\t', skiprows=1, usecols=[-1])
assert np.all(number_triplets == threshold)
finally:
try:
shutil.rmtree('test_items')
# os.remove(item_file)
# os.remove(feature_file)
# os.remove(taskfilename)
# os.remove(distance_file)
# os.remove(scorefilename)
# os.remove(analyzefilename)
except:
pass
def test_score():
try:
if not os.path.exists('test_items'):
os.makedirs('test_items')
item_file = 'test_items/data.item'
feature_file = 'test_items/data.features'
distance_file = 'test_items/data.distance'
scorefilename = 'test_items/data.score'
taskfilename = 'test_items/data.abx'
items.generate_db_and_feat(3, 3, 1, item_file, 2, 3, feature_file)
task = ABXpy.task.Task(item_file, 'c0', 'c1', 'c2')
task.generate_triplets()
distances.compute_distances(
feature_file, '/features/', taskfilename,
distance_file, dtw_cosine_distance,
normalized = True, n_cpu=3)
score.score(taskfilename, distance_file, scorefilename)
finally:
try:
shutil.rmtree('test_items')
# os.remove(item_file)
# os.remove(feature_file)
# os.remove(taskfilename)
# os.remove(distance_file)
# os.remove(scorefilename)
except:
pass
def test_score():
try:
if not os.path.exists('test_items'):
os.makedirs('test_items')
item_file = 'test_items/data.item'
feature_file = 'test_items/data.features'
distance_file = 'test_items/data.distance'
scorefilename = 'test_items/data.score'
taskfilename = 'test_items/data.abx'
items.generate_db_and_feat(3, 3, 1, item_file, 2, 3, feature_file)
task = ABXpy.task.Task(item_file, 'c0', 'c1', 'c2')
task.generate_triplets()
distances.compute_distances(feature_file,
'/features/',
taskfilename,
distance_file,
dtw_cosine_distance,
normalized=True,
n_cpu=3)
score.score(taskfilename, distance_file, scorefilename)
finally:
try:
shutil.rmtree('test_items')
# os.remove(item_file)
# os.remove(feature_file)
# os.remove(taskfilename)
# os.remove(distance_file)
# os.remove(scorefilename)
except:
pass
def fullrun():
if not os.path.exists('example_items'):
os.makedirs('example_items')
item_file = 'example_items/data.item'
feature_file = 'example_items/data.features'
distance_file = 'example_items/data.distance'
scorefilename = 'example_items/data.score'
taskfilename = 'example_items/data.abx'
analyzefilename = 'example_items/data.csv'
# deleting pre-existing files
for f in [item_file, feature_file, distance_file,
scorefilename, taskfilename, analyzefilename]:
try:
os.remove(f)
except OSError:
pass
# running the evaluation
items.generate_db_and_feat(3, 3, 5, item_file, 2, 2, feature_file)
task = ABXpy.task.Task(item_file, 'c0', across='c1', by='c2')
task.generate_triplets(taskfilename)
distances.compute_distances(
feature_file, '/features/', taskfilename,
distance_file, dtw_cosine_distance,
normalized=True, n_cpu=1)
score.score(taskfilename, distance_file, scorefilename)
analyze.analyze(taskfilename, scorefilename, analyzefilename)
def fullrun():
if not os.path.exists('example_items'):
os.makedirs('example_items')
item_file = 'example_items/data.item'
feature_file = 'example_items/data.features'
distance_file = 'example_items/data.distance'
scorefilename = 'example_items/data.score'
taskfilename = 'example_items/data.abx'
analyzefilename = 'example_items/data.csv'
items.generate_db_and_feat(3, 3, 1, item_file, 2, 2, feature_file)
task = ABXpy.task.Task(item_file, 'c0', 'c1', 'c2',
features=feature_file)
task.generate_triplets()
distances.compute_distances(feature_file, '/features/', taskfilename,
distance_file, dtw_cosine_distance)
score.score(taskfilename, distance_file, scorefilename)
analyze.analyze(scorefilename, taskfilename, analyzefilename)
