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_frozen_analyze():
"""Frozen analyze compare the results of a previously "frozen" run with
a new one, asserting that the code did not change in behaviour.
"""
try:
if not os.path.exists('test_items'):
os.makedirs('test_items')
item_file = frozen_file('item')
feature_file = frozen_file('features')
distance_file = 'test_items/data.distance'
scorefilename = 'test_items/data.score'
taskfilename = 'test_items/data.abx'
analyzefilename = 'test_items/data.csv'
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)
# assert items.h5cmp(taskfilename, frozen_file('abx'))
# assert items.h5cmp(distance_file, frozen_file('distance'))
assert items.h5cmp(scorefilename, frozen_file('score'))
assert items.cmp(analyzefilename, frozen_file('csv'))
finally:
try:
os.remove(taskfilename)
os.remove(distance_file)
os.remove(scorefilename)
os.remove(analyzefilename)
except:
pass
def test_multiple_across():
items.generate_testitems(2, 3, name='data.item')
try:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
task = ABXpy.task.Task('data.item', 'c0', ['c1', 'c2'])
stats = task.stats
assert stats['nb_blocks'] == 8
assert stats['nb_triplets'] == 8
assert stats['nb_by_levels'] == 1
task.generate_triplets()
f = h5py.File('data.abx', 'r')
triplets_block = get_triplets(f, '0')
triplets = np.array([[0, 1, 6], [1, 0, 7], [2, 3, 4], [3, 2, 5],
[4, 5, 2], [5, 4, 3], [6, 7, 0], [7, 6, 1]])
assert tables_equivalent(triplets, triplets_block)
finally:
try:
os.remove('data.abx')
os.remove('data.item')
except:
pass
def test_multiple_across():
items.generate_testitems(2, 3, name='data.item')
try:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
task = ABXpy.task.Task('data.item', 'c0', ['c1', 'c2'])
stats = task.stats
assert stats['nb_blocks'] == 8
assert stats['nb_triplets'] == 8
assert stats['nb_by_levels'] == 1
task.generate_triplets()
f = h5py.File('data.abx', 'r')
triplets_block = get_triplets(f, '0')
triplets = np.array([[0, 1, 6], [1, 0, 7], [2, 3, 4], [3, 2, 5],
[4, 5, 2], [5, 4, 3], [6, 7, 0], [7, 6, 1]])
assert tables_equivalent(triplets, triplets_block)
finally:
try:
os.remove('data.abx')
os.remove('data.item')
except:
pass
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_basic():
items.generate_testitems(2, 3, name='data.item')
try:
task = ABXpy.task.Task('data.item', 'c0', 'c1', 'c2')
stats = task.stats
assert stats['nb_blocks'] == 8, "incorrect stats: number of blocks"
assert stats['nb_triplets'] == 8
assert stats['nb_by_levels'] == 2
task.generate_triplets()
f = h5py.File('data.abx', 'r')
triplets = f['triplets']['data'][...]
by_indexes = f['triplets']['by_index'][...]
triplets_block0 = triplets[slice(*by_indexes[0])]
triplets_block1 = triplets[slice(*by_indexes[1])]
triplets_block0 = get_triplets(f, '0')
triplets_block1 = get_triplets(f, '1')
triplets = np.array([[0, 1, 2], [1, 0, 3], [2, 3, 0], [3, 2, 1]])
assert tables_equivalent(triplets, triplets_block0), error_triplets
assert tables_equivalent(triplets, triplets_block1), error_triplets
pairs = [2, 6, 7, 3, 8, 12, 13, 9]
pairs_block0 = get_pairs(f, '0')
pairs_block1 = get_pairs(f, '1')
assert (set(pairs) == set(pairs_block0[:, 0])), error_pairs
assert (set(pairs) == set(pairs_block1[:, 0])), error_pairs
finally:
try:
os.remove('data.abx')
os.remove('data.item')
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 test_filter():
items.generate_testitems(2, 4, name='data.item')
try:
task = ABXpy.task.Task('data.item', 'c0', 'c1', 'c2',
filters=["[attr == 0 for attr in c3]"])
stats = task.stats
assert stats['nb_blocks'] == 8, "incorrect stats: number of blocks"
assert stats['nb_triplets'] == 8
assert stats['nb_by_levels'] == 2
task.generate_triplets(output='data.abx')
f = h5py.File('data.abx', 'r')
triplets_block0 = get_triplets(f, '0')
triplets_block1 = get_triplets(f, '1')
triplets = np.array([[0, 1, 2], [1, 0, 3], [2, 3, 0], [3, 2, 1]])
assert tables_equivalent(triplets, triplets_block0), error_triplets
assert tables_equivalent(triplets, triplets_block1), error_triplets
pairs = [2, 6, 7, 3, 8, 12, 13, 9]
pairs_block0 = get_pairs(f, '0')
pairs_block1 = get_pairs(f, '1')
assert (set(pairs) == set(pairs_block0[:, 0])), error_pairs
assert (set(pairs) == set(pairs_block1[:, 0])), error_pairs
finally:
try:
os.remove('data.abx')
os.remove('data.item')
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_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_basic():
items.generate_testitems(2, 3, name='data.item')
try:
task = ABXpy.task.Task('data.item', 'c0', 'c1', 'c2')
stats = task.stats
assert stats['nb_blocks'] == 8, "incorrect stats: number of blocks"
assert stats['nb_triplets'] == 8
assert stats['nb_by_levels'] == 2
task.generate_triplets()
f = h5py.File('data.abx', 'r')
triplets = f['triplets']['data'][...]
by_indexes = f['triplets']['by_index'][...]
triplets_block0 = triplets[slice(*by_indexes[0])]
triplets_block1 = triplets[slice(*by_indexes[1])]
triplets_block0 = get_triplets(f, '0')
triplets_block1 = get_triplets(f, '1')
triplets = np.array([[0, 1, 2], [1, 0, 3], [2, 3, 0], [3, 2, 1]])
assert tables_equivalent(triplets, triplets_block0), error_triplets
assert tables_equivalent(triplets, triplets_block1), error_triplets
pairs = [2, 6, 7, 3, 8, 12, 13, 9]
pairs_block0 = get_pairs(f, '0')
pairs_block1 = get_pairs(f, '1')
assert (set(pairs) == set(pairs_block0[:, 0])), error_pairs
assert (set(pairs) == set(pairs_block1[:, 0])), error_pairs
finally:
try:
os.remove('data.abx')
os.remove('data.item')
except:
pass
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_frozen_analyze():
"""Frozen analyze compare the results of a previously "frozen" run with
a new one, asserting that the code did not change in behaviour.
"""
try:
if not os.path.exists('test_items'):
os.makedirs('test_items')
item_file = frozen_file('item')
feature_file = frozen_file('features')
distance_file = 'test_items/data.distance'
scorefilename = 'test_items/data.score'
taskfilename = 'test_items/data.abx'
analyzefilename = 'test_items/data.csv'
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)
# assert items.h5cmp(taskfilename, frozen_file('abx'))
# assert items.h5cmp(distance_file, frozen_file('distance'))
# assert items.h5cmp(scorefilename, frozen_file('score'))
assert items.csv_cmp(analyzefilename, frozen_file('csv'))
finally:
shutil.rmtree('test_items', ignore_errors=True)
def test_filter():
items.generate_testitems(2, 4, name='data.item')
try:
task = ABXpy.task.Task('data.item',
'c0',
'c1',
'c2',
filters=["[attr == 0 for attr in c3]"])
stats = task.stats
assert stats['nb_blocks'] == 8, "incorrect stats: number of blocks"
assert stats['nb_triplets'] == 8
assert stats['nb_by_levels'] == 2
task.generate_triplets(output='data.abx')
f = h5py.File('data.abx', 'r')
triplets_block0 = get_triplets(f, '0')
triplets_block1 = get_triplets(f, '1')
triplets = np.array([[0, 1, 2], [1, 0, 3], [2, 3, 0], [3, 2, 1]])
assert tables_equivalent(triplets, triplets_block0), error_triplets
assert tables_equivalent(triplets, triplets_block1), error_triplets
pairs = [2, 6, 7, 3, 8, 12, 13, 9]
pairs_block0 = get_pairs(f, '0')
pairs_block1 = get_pairs(f, '1')
assert (set(pairs) == set(pairs_block0[:, 0])), error_pairs
assert (set(pairs) == set(pairs_block1[:, 0])), error_pairs
finally:
try:
os.remove('data.abx')
os.remove('data.item')
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)
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(itemfile,
featurefile,
args,
taskfile=None,
distance=None,
distancefile=None,
scorefile=None,
analyzefile=None,
filename=None):
on = get_arg('on', args)
assert on, ("The 'on' argument was not found, this argument is mandatory"
"for the task")
across = get_arg('across', args)
by = get_arg('by', args)
filters = get_arg('filters', args)
reg = get_arg('reg', args)
if not filename:
filename = '_'.join(
filter(None, [
get_name(itemfile),
get_name(featurefile),
str(on),
str(across),
str(by)
]))
if not distancefile:
distancefile = filename + '.distance'
if not scorefile:
scorefile = filename + '.score'
if not analyzefile:
analyzefile = filename + '.csv'
task = ABXpy.task.Task(itemfile,
on,
across,
by,
filters,
reg,
features=featurefile)
task.generate_triplets()
if not distance:
distance = dtw_cosine_distance
distances.compute_distances(featurefile, '/features/', taskfile,
distancefile, distance)
score.score(taskfile, distancefile, scorefile)
analyze.analyze(scorefile, taskfile, analyzefile)
def test_no_across():
items.generate_testitems(2, 3, name='data.item')
try:
task = ABXpy.task.Task('data.item', 'c0', None, 'c2')
stats = task.stats
assert stats['nb_blocks'] == 8
assert stats['nb_triplets'] == 16
assert stats['nb_by_levels'] == 2
task.generate_triplets()
finally:
try:
os.remove('data.abx')
os.remove('data.item')
except:
pass
def test_no_across():
items.generate_testitems(2, 3, name='data.item')
try:
task = ABXpy.task.Task('data.item', 'c0', None, 'c2')
stats = task.stats
assert stats['nb_blocks'] == 8
assert stats['nb_triplets'] == 16
assert stats['nb_by_levels'] == 2
task.generate_triplets()
finally:
try:
os.remove('data.abx')
os.remove('data.item')
except:
pass
def test_multiple_bys():
items.generate_testitems(3, 4, name='data.item')
try:
task = ABXpy.task.Task('data.item', 'c0', None, ['c1', 'c2', 'c3'])
stats = task.stats
assert stats['nb_blocks'] == 81
assert stats['nb_triplets'] == 0
assert stats['nb_by_levels'] == 27
task.generate_triplets()
finally:
try:
os.remove('data.abx')
os.remove('data.item')
except:
pass
def test_multiple_bys():
items.generate_testitems(3, 4, name='data.item')
try:
task = ABXpy.task.Task('data.item', 'c0', None, ['c1', 'c2', 'c3'])
stats = task.stats
assert stats['nb_blocks'] == 81
assert stats['nb_triplets'] == 0
assert stats['nb_by_levels'] == 27
with warnings.catch_warnings():
warnings.simplefilter("ignore")
task.generate_triplets()
finally:
try:
os.remove('data.abx')
os.remove('data.item')
except:
pass
def test_sampling_task():
items.generate_testitems(4, 6, name='data.item')
try:
task = ABXpy.task.Task('data.item', 'c0', 'c1', ['c2', 'c3'])
print "stats computed"
# stats = task.stats
task.generate_triplets(sample=0.2)
print "first sample"
os.remove('data.abx')
task.generate_triplets(sample=200)
print "second sample"
finally:
try:
os.remove('data.abx')
os.remove('data.item')
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'
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)
def test_filter_on_B():
items.generate_testitems(2, 2, name='data.item')
try:
task = ABXpy.task.Task('data.item', 'c0',
filters=["[attr == 0 for attr in c1_B]"])
stats = task.stats
assert stats['nb_blocks'] == 4, "incorrect stats: number of blocks"
assert stats['nb_triplets'] == 4
assert stats['nb_by_levels'] == 1
task.generate_triplets()
f = h5py.File('data.abx', 'r')
triplets_block0 = get_triplets(f, '0')
triplets = np.array([[0, 1, 2], [1, 0, 3], [2, 1, 0], [3, 0, 1]])
assert tables_equivalent(triplets, triplets_block0), error_triplets
finally:
try:
os.remove('data.abx')
os.remove('data.item')
except:
pass
def test_filter_on_C():
items.generate_testitems(2, 2, name='data.item')
try:
task = ABXpy.task.Task('data.item',
'c0',
filters=["[attr == 0 for attr in c1_X]"])
stats = task.stats
assert stats['nb_blocks'] == 4, "incorrect stats: number of blocks"
assert stats['nb_triplets'] == 4
assert stats['nb_by_levels'] == 1
task.generate_triplets()
f = h5py.File('data.abx', 'r')
triplets_block0 = get_triplets(f, '0')
triplets = np.array([[2, 1, 0], [2, 3, 0], [3, 0, 1], [3, 2, 1]])
assert tables_equivalent(triplets, triplets_block0), error_triplets
finally:
try:
os.remove('data.abx')
os.remove('data.item')
except:
pass
def memorizable_abx(data_file, on, across, by, njobs, tmpdir=None,
distance=cosine_distance, item_features_hash='0'):
''' wrap ABXpy funcions and compute the scores
'''
item_file = '{}.item'.format(data_file)
feature_file = '{}.features'.format(data_file)
if not os.path.isfile(item_file) or not os.path.isfile(feature_file):
raise ValueError('item_file or feature_file doesnt exist')
distance_file = '{}.distance'.format(data_file)
score_file = '{}.score'.format(data_file)
task_file = '{}.abx'.format(data_file)
analyze_file = '{}.csv'.format(data_file)
# clean up before compute ABX
remove_files = [distance_file,
score_file,
task_file,
analyze_file]
map(os.remove, filter(os.path.exists, remove_files))
# running the evaluation
task = ABXpy.task.Task(item_file, on, across=across, by=by, verbose=False)
task.generate_triplets(task_file, tmpdir=tmpdir)
distances.compute_distances(feature_file, '/features/', task_file,
distance_file, distance, normalized=True,
n_cpu=njobs)
score.score(task_file, distance_file, score_file)
analyze.analyze(task_file, score_file, analyze_file)
# I will keep only the ABX scores
remove_files = [distance_file, score_file, task_file]
map(os.remove, filter(os.path.exists, remove_files))
analyze_data = open(analyze_file, 'r').read()
return analyze_data
def test_analyze(
itemfile,
featurefile,
args,
taskfile=None,
distance=None,
distancefile=None,
scorefile=None,
analyzefile=None,
filename=None,
):
on = get_arg("on", args)
assert on, "The 'on' argument was not found, this argument is mandatory" "for the task"
across = get_arg("across")
by = get_arg("by")
filters = get_arg("filters")
reg = get_arg("reg")
if not filename:
filename = "_".join(filter(None, [get_name(itemfile), get_name(featurefile), str(on), str(across), str(by)]))
if not distancefile:
distancefile = filename + ".distance"
if not scorefile:
scorefile = filename + ".score"
if not analyzefile:
analyzefile = filename + ".csv"
task = ABXpy.task.Task(itemfile, on, across, by, filters, reg, features=featurefile)
task.generate_triplets()
if not distance:
distance = dtw_cosine_distance
distances.compute_distances(featurefile, "/features/", taskfile, distancefile, distance)
score.score(taskfile, distancefile, scorefile)
analyze.analyze(scorefile, taskfile, analyzefile)
def fullrun():
if type(BY) == list:
out = '/' + 'on_' + ON[0:2] + '_ac_' + ACROSS[0:2] + '_by_' + BY[0][
0:2] + '_' + BY[1][0:2]
else:
out = '/' + 'on_' + ON[0:2] + '_ac_' + ACROSS[0:2] + '_by_' + BY[0:2]
output_folder = input_folder + out
print("the input folder is " + input_folder + "\n")
print("the ABX task id done :" + out + "\n")
print(feature)
if not os.path.exists(output_folder):
os.makedirs(output_folder)
item_file = input_folder + '/' + ON + '.item'
feature_file = input_folder + '/' + feature
distance_file = output_folder + '/' + out + '.distance'
scorefilename = output_folder + '/' + out + '.score'
taskfilename = output_folder + '/' + out + '.abx'
analyzefilename = output_folder + '/' + out + '.csv'
statsfilename = output_folder + '/' + out + '.stats'
# running the evaluation:
if not os.path.exists(taskfilename):
if ACROSS == "na" and BY != "na":
task = ABXpy.task.Task(item_file, ON, by=BY)
elif BY == "na" and ACROSS != "na":
task = ABXpy.task.Task(item_file, ON, across=ACROSS)
elif ACROSS == "na" and BY == "na":
task = ABXpy.task.Task(item_file, ON)
else:
task = ABXpy.task.Task(item_file, ON, across=ACROSS, by=BY)
task.generate_triplets(taskfilename)
try:
task.print_stats(statsfilename)
except:
pass
print("the abx task file is created")
print("number of cpu used is " + str(NB_CPU))
if not os.path.exists(distance_file):
if distance == 'cosine':
distances.compute_distances(feature_file,
'/features/',
taskfilename,
distance_file,
dtw_cosine_distance,
normalized=True,
n_cpu=NB_CPU)
elif distance == 'kl':
distances.compute_distances(feature_file,
'/features/',
taskfilename,
distance_file,
dtw_kl_divergence,
normalized=True,
n_cpu=NB_CPU)
else:
raise ValueError('distance must be either cosine or kl')
print("Computing %(distance)s distance has been computed")
else:
print("distance has already been computed")
if not os.path.exists(scorefilename):
score.score(taskfilename, distance_file, scorefilename)
print("Score is computed")
if not os.path.exists(analyzefilename):
analyze.analyze(taskfilename, scorefilename, analyzefilename)
print("Raw results are available in the csv file !")
eval_abx.avg(analyzefilename, out_res, ON, ACROSS, ponderate)
print('evaluation done')
