def testRCA(self):
ds = testdata.loadTestDB()
ds = transform(ds, 'removevl')
ds = transform(ds, 'fixlength')
ds = transform(ds, 'remove', { 'descriptorNames': '*cov' })
ds = transform(ds, 'cleaner')
ds = transform(ds, 'normalize')
ds = transform(ds, 'pca', { 'resultName': 'pca15',
'dimension': 15 })
ds_rca = transform(ds, 'rca', { 'resultName': 'rca10',
'dimension': 10,
'classFile': testdata.RCA_GENRE_GT })
v = View(ds_rca)
dist = MetricFactory.create('euclidean', ds_rca.layout())
self.compareResults(v.nnSearch('01 Cigarettes And Alcohol - Oasis.mp3', dist).get(10),
testdata.RCA_GENRE_RESULTS)
# try by passing directly the groundtruth map
import gaia2.fastyaml as yaml
ds_rca = transform(ds, 'rca', { 'resultName': 'rca10',
'dimension': 10,
'classMap': yaml.load(open(testdata.RCA_GENRE_GT).read()) })
v = View(ds_rca)
dist = MetricFactory.create('euclidean', ds_rca.layout())
self.compareResults(v.nnSearch('01 Cigarettes And Alcohol - Oasis.mp3', dist).get(10),
testdata.RCA_GENRE_RESULTS)
def testParserStillInValidStateAfterParserError(self):
'''ticket #20: parser is in invalid state after parser error'''
ds = testdata.createSimpleDataSet()
dist = MetricFactory.create('null', ds.layout())
v = View(ds)
result = v.nnSearch(ds.samplePoint(), dist, 'WHERE true').get(1)
clause = 'WHERE label.tonal_key_mode.value = \\"major"'
try:
result = v.nnSearch(ds.samplePoint(), dist, clause).get(1)
except:
pass # filter correctly failed to compile
result = v.nnSearch(ds.samplePoint(), dist, 'WHERE true').get(1)
def testParsedVsConstructedFilters(self):
ds = testdata.loadTestDB()
ds = transform(ds, 'fixlength')
p = ds.samplePoint()
p2 = ds.point('Higher State of Consciousness.mp3')
queries = [
(p, '', ''), (p2, '', ''),
(p2, 'WHERE value.tempotap_bpm.value > 140',
Filter('tempotap_bpm.value', '>', 140)),
(p, 'WHERE value.tempotap_bpm > 110',
Filter('tempotap_bpm', '>', 110)),
(p, 'WHERE value.tempotap_bpm > -10',
Filter('tempotap_bpm', '>', -10)),
(p, 'WHERE value.tempotap_bpm > 23000',
Filter('tempotap_bpm', '>', 23000)),
(p, 'WHERE value.tempotap_bpm > 120 AND value.tempotap_bpm < 130',
AndFilter([
Filter('tempotap_bpm', '>', 120),
Filter('tempotap_bpm', '<', 130)
])),
(p, 'WHERE value.tempotap_bpm BETWEEN 130 AND 120',
Filter('tempotap_bpm', 'between', [130, 120])),
(p, 'WHERE label.key_key = "C"', Filter('key_key', '==', 'C')),
(p2,
'''WHERE ((label.key_key = "A" AND label.key_mode = "major") OR
(label.key_key = "E" AND label.key_mode = "minor"))
AND value.tempotap_bpm < 90''',
AndFilter([
OrFilter([
AndFilter([
Filter('key_key', '==', 'A'),
Filter('key_mode', '==', 'major')
]),
AndFilter([
Filter('key_key', '==', 'E'),
Filter('key_mode', '==', 'minor')
])
]),
Filter('tempotap_bpm', '<', 90)
]))
]
dist = MetricFactory.create('euclidean', ds.layout(),
{'descriptorNames': '*.mean'})
v = View(ds)
for (pt, filtstr, filt) in queries:
self.assertEqualSearchSpace(v.nnSearch(pt, dist, filtstr),
v.nnSearch(pt, dist, filt))
def testKullbackLeibler(self):
ds = transform(testdata.loadTestDB(), 'fixlength')
# creates a test with more than 1000 points otherwise the test is useless because
# we split the workload in chunks of 1000 points when computing the distance
dstest = DataSet()
ncopy = 20
for cidx in range(ncopy):
points = list(ds.points())
for p in points:
p.setName(p.name() + '-%d' % cidx)
dstest.addPoints(points)
# test whether KL doesn't break with multithreading (did in 2.2.1)
v = View(dstest)
dist = MetricFactory.create('kullbackleibler',
dstest.layout(),
{ 'descriptorName': 'mfcc' })
results = v.nnSearch(ds.samplePoint(), dist).get(6*ncopy)
expected = [ 0.0 ]*2*ncopy + [ 6.1013755798339844 ]*ncopy
expected += [ 6.4808731079101562 ]*2*ncopy + [ 6.7828292846679688 ]*ncopy
for r, e in zip(results, expected):
self.assertAlmostEqual(r[1], e, 5)
def testDeleteUnderlyingDataSet(self):
ds = testdata.loadTestDB()
params = {'descriptorNames': ['*.mean', '*.var']}
ds = transform(ds, 'fixlength', params)
ds = transform(ds, 'cleaner', params)
ds = transform(ds, 'normalize', params)
dist = MetricFactory.create('euclidean', ds.layout(), params)
v = View(ds)
del ds
#self.assertRaises(Exception, v.nnSearch, '01 Respect.mp3')
# this doesn't throw anymore, as the View keeps a ref to the dataset
v.nnSearch('01 Respect.mp3', dist)
def testSubspaceSearch(self):
ds = testdata.loadTestDB()
ds = transform(ds, 'fixlength')
dist = MetricFactory.create('euclidean', ds.layout(),
{'descriptorNames': '*.mean'})
v = View(ds)
pid = 'Higher State of Consciousness.mp3'
key_a_minor = v.nnSearch(
pid, dist,
'WHERE label.key_key = "A" AND label.key_mode = "minor"')
key_a = v.nnSearch(pid, dist, 'WHERE label.key_key = "A"')
key_minor = v.nnSearch(pid, dist, 'WHERE label.key_mode = "minor"')
key_a_minor_sspace1 = v.nnSearch(pid, key_minor, dist,
'WHERE label.key_key = "A"')
key_a_minor_sspace2 = v.nnSearch(pid, key_a, dist,
'WHERE label.key_mode = "minor"')
self.assertEqualSearchSpace(key_a_minor, key_a_minor_sspace1)
self.assertEqualSearchSpace(key_a_minor, key_a_minor_sspace2)
def testValidPoint(dataset, clause, fromList=None):
# search the point using the clause:
# if we have a result, the clause was true
# if we have no result, the clause was false
v = View(dataset)
dist = MetricFactory.create('null', dataset.layout())
filtr = 'WHERE ' + clause
if fromList:
filtr = 'FROM ' + fromList + ' ' + filtr
result = v.nnSearch(dataset.samplePoint(), dist, filtr).get(1)
if len(result) == 1:
return True
return False
def testComplete(self):
# load 2.0 dataset, history, apply history to dataset
# check nn-search results are the same as the ones we get when doing it from gaia 2.0
ds = DataSet()
ds.load(testdata.GAIA_20_BACKWARDS_COMPAT_DATASET)
h = TransfoChain()
self.assertRaises(Exception, h.load, testdata.GAIA_20_BACKWARDS_COMPAT_HISTORY)
return
h.load(testdata.GAIA_20_BACKWARDS_COMPAT_HISTORY)
ds = h.mapDataSet(ds)
v = View(ds)
dist = MetricFactory.create('euclidean', ds.layout())
results = v.nnSearch('01 Respect.mp3', dist).get(5)
self.compareResults(results, testdata.GAIA_20_BACKWARDS_COMPAT_RESULTS)
ds21 = DataSet()
ds21.load(testdata.TEST_DATABASE)
results = v.nnSearch(h.mapPoint(ds21.point('01 Respect.mp3')), dist).get(5)
self.compareResults(results, testdata.GAIA_20_BACKWARDS_COMPAT_RESULTS)
def get_neighbours(self, dataset, encoded_filename, number,
encoded_request=None):
"""Get a number of nearest neighbours."""
view = View(dataset)
request_point = self.gaia_db.point(
encoded_request) if encoded_request else None
try:
total = view.nnSearch(
encoded_filename, self.metric).get(number + 1)[1:]
except Exception as e:
print(e)
return []
result = sorted([
(self.compute_score(
score, name, request_point=request_point) * 1000,
name)
for name, score in total])
if request_point:
# Filter out the worst matches for the requested track
return result[:max(1, number / 2)]
return result
def testCreatedInputSpace(self):
ds = testdata.createSimpleDataSet()
ds.point('p')['a.1'] = 23.0
for i in range(5):
p = Point()
p.setName('p%d' % i)
p.setLayout(ds.originalLayout())
p['a.1'] = float(i)
ds.addPoint(p)
ds = transform(ds, 'fixlength')
dist = MetricFactory.create('euclidean', ds.layout())
v = View(ds)
p = ds.point('p')
RS_remove = v.nnSearch(p, dist)
RS_remove.removePoints(['p2', 'p4'])
RS_create = InputSpace()
RS_create.addPoints(ds, ['p', 'p0', 'p1', 'p3'])
rsc = v.nnSearch(p, RS_remove, dist)
rsa = v.nnSearch(p, RS_create, dist)
self.assertEqual((('p', 0.), ('p3', 20.), ('p1', 22.), ('p0', 23.)),
v.nnSearch(p, rsc, dist).get(10))
self.assertEqual((('p', 0.), ('p3', 20.), ('p1', 22.), ('p0', 23.)),
v.nnSearch(p, rsa, dist).get(10))
# test thresholdLimit method
self.assertEqual((('p', 0.), ),
v.nnSearch(p, rsa, dist).thresholdLimit(10).get(10))
self.assertEqual((('p', 0.), ('p3', 20.)),
v.nnSearch(p, rsa, dist).thresholdLimit(20).get(10))
self.assertEqual((('p', 0.), ('p3', 20.), ('p1', 22.)),
v.nnSearch(p, rsa,
dist).thresholdLimit(22.01).get(10))
def get_neighbours(self,
dataset,
encoded_filename,
number,
encoded_request=None):
"""Get a number of nearest neighbours."""
view = View(dataset)
request_point = self.gaia_db.point(
encoded_request) if encoded_request else None
try:
total = view.nnSearch(encoded_filename,
self.metric).get(number + 1)[1:]
except Exception as e:
print(e)
return []
result = sorted([
(self.compute_score(score, name, request_point=request_point) *
1000, name) for name, score in total
])
if request_point:
# Filter out the worst matches for the requested track
return result[:max(1, number / 2)]
return result
def testSimple(self):
ds = testdata.createSimpleDataSet()
p2 = Point(ds.point('p'))
p2.setName('p2')
p3 = Point(ds.point('p'))
p3.setName('p3')
p3['a.1'] = 1
p4 = Point(ds.point('p'))
p4.setName('p4')
p4['a.1'] = 1
p4['a.2'] = 1
ds.addPoint(p2)
ds.addPoint(p3)
ds.addPoint(p4)
ds = transform(ds, 'fixlength')
dist = MetricFactory.create('euclidean', ds.layout())
v = View(ds)
results = v.nnSearch('p', dist).get(10)
self.assertEqual(results[0][1], 0.0)
self.assertEqual(results[1][1], 0.0)
self.assertSearchResultEqual(results[2], ('p3', 1.0))
self.assertSearchResultEqual(results[3], ('p4', math.sqrt(2)))
class GaiaWrapper:
def __init__(self, indexing_only_mode=False):
self.indexing_only_mode = indexing_only_mode
self.index_path = sim_settings.INDEX_DIR
self.original_dataset = DataSet()
self.pca_dataset = DataSet()
if not self.indexing_only_mode:
self.original_dataset_path = self.__get_dataset_path(
sim_settings.INDEX_NAME)
else:
self.original_dataset_path = self.__get_dataset_path(
sim_settings.INDEXING_SERVER_INDEX_NAME)
self.descriptor_names = {}
self.metrics = {}
self.view = None
self.view_pca = None
self.transformations_history = None
self.__load_dataset()
def __get_dataset_path(self, ds_name):
return os.path.join(sim_settings.INDEX_DIR, ds_name + '.db')
def __load_dataset(self):
"""
Loads the dataset, does all the necessary steps to make it available for similarity queries and creates the PCA
version of it. If dataset does not exist, creates a new empty one.
NOTE: we assume that loaded datasets will have been prepared and normalized (see_
_prepare_original_dataset() and __normalize_original_dataset()) on due time (see add_point() method below),
therefore this function does not prepare or normalize loaded datasets.
"""
if not os.path.exists(sim_settings.INDEX_DIR):
os.makedirs(sim_settings.INDEX_DIR)
# load original dataset
if os.path.exists(self.original_dataset_path):
self.original_dataset.load(self.original_dataset_path)
self.__calculate_descriptor_names()
if self.original_dataset.size(
) >= sim_settings.SIMILARITY_MINIMUM_POINTS and not self.indexing_only_mode:
# Save transformation history so we do not need to compute it every time we need it
self.transformations_history = self.original_dataset.history(
).toPython()
# Build metrics for the different similarity presets, create a Gaia view
self.__build_metrics()
view = View(self.original_dataset)
self.view = view
# Compute PCA and create pca view and metric
# NOTE: this step may take a long time if the dataset is big, but it only needs to be performed once
# when the similarity server is loaded-
self.pca_dataset = transform(
self.original_dataset, 'pca', {
'descriptorNames': sim_settings.PCA_DESCRIPTORS,
'dimension': sim_settings.PCA_DIMENSIONS,
'resultName': 'pca'
})
self.pca_dataset.setReferenceDataSet(self.original_dataset)
self.view_pca = View(self.pca_dataset)
self.__build_pca_metric()
if self.original_dataset.history().size() <= 0:
logger.info('Dataset loaded, size: %s points' %
(self.original_dataset.size()))
else:
logger.info(
'Dataset loaded, size: %s points (%i fixed-length desc., %i variable-length desc.)'
% (self.original_dataset.size(),
len(self.descriptor_names['fixed-length']),
len(self.descriptor_names['variable-length'])))
else:
# If there is no existing dataset we create an empty one.
# For the moment we do not create any distance metric nor a view because search won't be possible until
# the DB has a minimum of SIMILARITY_MINIMUM_POINTS
self.original_dataset.save(self.original_dataset_path)
self.__calculate_descriptor_names()
logger.info('Created new dataset, size: %s points (should be 0)' %
(self.original_dataset.size()))
def __prepare_original_dataset(self):
logger.info('Preparing the original dataset.')
self.original_dataset = self.prepare_original_dataset_helper(
self.original_dataset)
self.__calculate_descriptor_names()
def __normalize_original_dataset(self):
logger.info('Normalizing the original dataset.')
self.original_dataset = self.normalize_dataset_helper(
self.original_dataset, self.descriptor_names['fixed-length'])
def __calculate_descriptor_names(self):
layout = self.original_dataset.layout()
all_descriptor_names = layout.descriptorNames()
fixed_length_descritpor_names = []
variable_length_descritpor_names = []
multidimensional_descriptor_names = []
for name in all_descriptor_names:
region = layout.descriptorLocation(name)
if region.lengthType() == VariableLength:
variable_length_descritpor_names.append(name)
else:
fixed_length_descritpor_names.append(name)
try:
if region.dimension() > 1:
multidimensional_descriptor_names.append(name)
except: # TODO: exception too broad here...
pass
self.descriptor_names = {
'all': all_descriptor_names,
'fixed-length': fixed_length_descritpor_names,
'variable-length': variable_length_descritpor_names,
'multidimensional': multidimensional_descriptor_names
}
@staticmethod
def prepare_original_dataset_helper(ds):
ds = transform(
ds, 'FixLength'
) # Needed to optimize use of fixed-length descriptors and save memory
ds = transform(
ds, 'Cleaner'
) # Remove descriptors that will cause problems in further transformations
try:
ds = transform(ds, 'enumerate',
{'descriptorNames': ['.tonal.chords_progression']})
except: # TODO: exception too broad here...
logger.info(
'WARNING: enumerate transformation to .tonal.chords_progression could not be performed.'
)
return ds
@staticmethod
def normalize_dataset_helper(ds, descriptor_names):
# NOTE: The "except" list of descriptors below should be reviewed if a new extractor is used. The point is to
# remove descriptors can potentially break normalize transform (e.g. descriptors with value = 0)
normalization_params = {
"descriptorNames": descriptor_names,
"except": [
"*.min",
"*.max",
"tonal.chords_histogram",
],
"independent": True,
"outliers": -1
}
ds = transform(ds, 'normalize', normalization_params)
return ds
def __build_metrics(self):
for preset in sim_settings.PRESETS:
if preset != 'pca': # PCA metric is built only after pca dataset is created so it should not be built here
logger.info('Bulding metric for preset %s' % preset)
name = preset
path = sim_settings.PRESET_DIR + name + ".yaml"
preset_file = yaml.safe_load(open(path))
distance = preset_file['distance']['type']
parameters = preset_file['distance']['parameters']
search_metric = DistanceFunctionFactory.create(
str(distance), self.original_dataset.layout(), parameters)
self.metrics[name] = search_metric
def __build_pca_metric(self):
logger.info('Bulding metric for preset pca')
preset_file = yaml.safe_load(open(sim_settings.PRESET_DIR +
"pca.yaml"))
distance = preset_file['distance']['type']
parameters = preset_file['distance']['parameters']
search_metric = DistanceFunctionFactory.create(
str(distance), self.pca_dataset.layout(), parameters)
self.metrics['pca'] = search_metric
def add_point(self, point_location, point_name):
if self.original_dataset.contains(str(point_name)):
self.original_dataset.removePoint(str(point_name))
p = Point()
if os.path.exists(str(point_location)):
try:
p.load(str(point_location))
p.setName(str(point_name))
if self.original_dataset.size(
) <= sim_settings.SIMILARITY_MINIMUM_POINTS:
# Add point to original_dataset because PCA dataset has not been created yet
self.original_dataset.addPoint(p)
msg = 'Added point with name %s. Index has now %i points.' % \
(str(point_name), self.original_dataset.size())
logger.info(msg)
else:
# Add point to PCA dataset because it has been already created.
# PCA dataset will take care of adding the point to the original dataset as well.
self.pca_dataset.addPoint(p)
msg = 'Added point with name %s. Index has now %i points (pca index has %i points).' % \
(str(point_name), self.original_dataset.size(), self.pca_dataset.size())
logger.info(msg)
except Exception as e:
msg = 'Point with name %s could NOT be added (%s).' % (
str(point_name), str(e))
logger.info(msg)
return {
'error': True,
'result': msg,
'status_code': sim_settings.SERVER_ERROR_CODE
}
else:
msg = 'Point with name %s could NOT be added because analysis file does not exist (%s).' % \
(str(point_name), str(point_location))
logger.info(msg)
return {
'error': True,
'result': msg,
'status_code': sim_settings.SERVER_ERROR_CODE
}
if self.original_dataset.size(
) == sim_settings.SIMILARITY_MINIMUM_POINTS:
# Do enumerate
try:
self.original_dataset = transform(
self.original_dataset, 'enumerate',
{'descriptorNames': ['.tonal.chords_progression']})
except: # TODO: exception too broad here...
logger.info(
'WARNING: enumerate transformation to .tonal.chords_progression could not be performed.'
)
# If when adding a new point we reach the minimum points for similarity, do the needful so that the dataset
# can be used for search. This includes preparing the dataset, normalizing it, saveing it and creating view and
# distance metrics. This will only happen once when the size of the dataset reaches SIMILARITY_MINIMUM_POINTS.
if self.original_dataset.size(
) == sim_settings.SIMILARITY_MINIMUM_POINTS and not self.indexing_only_mode:
self.__prepare_original_dataset()
self.__normalize_original_dataset()
self.transformations_history = self.original_dataset.history(
).toPython()
self.save_index(msg="(reaching %i points)" %
sim_settings.SIMILARITY_MINIMUM_POINTS)
# TODO: the code below is repeated from __load_dataset() method, should be moved into a util function
# Build metrics for the different similarity presets, create a Gaia view
self.__build_metrics()
view = View(self.original_dataset)
self.view = view
# Compute PCA and create pca view and metric
# NOTE: this step may take a long time if the dataset is big, but it only needs to be performed once
# when the similarity server is loaded-
self.pca_dataset = transform(
self.original_dataset, 'pca', {
'descriptorNames': sim_settings.PCA_DESCRIPTORS,
'dimension': sim_settings.PCA_DIMENSIONS,
'resultName': 'pca'
})
self.pca_dataset.setReferenceDataSet(self.original_dataset)
self.view_pca = View(self.pca_dataset)
self.__build_pca_metric()
return {'error': False, 'result': msg}
def delete_point(self, point_name):
if self.original_dataset.contains(str(point_name)):
if self.original_dataset.size(
) <= sim_settings.SIMILARITY_MINIMUM_POINTS:
# Remove from original dataset
self.original_dataset.removePoint(str(point_name))
else:
# Remove from pca dataset (pca dataset will take care of removing from original dataset too)
self.pca_dataset.removePoint(str(point_name))
logger.info(
'Deleted point with name %s. Index has now %i points (pca index has %i points).'
% (str(point_name), self.original_dataset.size(),
self.pca_dataset.size()))
return {'error': False, 'result': True}
else:
msg = 'Can\'t delete point with name %s because it does not exist.' % str(
point_name)
logger.info(msg)
return {
'error': True,
'result': msg,
'status_code': sim_settings.NOT_FOUND_CODE
}
def get_point(self, point_name):
logger.info('Getting point with name %s' % str(point_name))
if self.original_dataset.contains(str(point_name)):
return self.original_dataset.point(str(point_name))
def get_all_point_names(self):
point_names = sorted(
[int(name) for name in self.original_dataset.pointNames()])
logger.info('Getting all point names (%i points)' % len(point_names))
return {'error': False, 'result': point_names}
def save_index(self, filename=None, msg=""):
tic = time.time()
path = self.original_dataset_path
if filename:
path = sim_settings.INDEX_DIR + filename + ".db"
logger.info('Saving index to (%s)...' % path + msg)
self.original_dataset.save(path)
toc = time.time()
logger.info(
'Finished saving index (done in %.2f seconds, index has now %i points).'
% ((toc - tic), self.original_dataset.size()))
return {'error': False, 'result': path}
def contains(self, point_name):
logger.info('Checking if index has point with name %s' %
str(point_name))
return {
'error': False,
'result': self.original_dataset.contains(point_name)
}
def get_sounds_descriptors(self,
point_names,
descriptor_names=None,
normalization=True,
only_leaf_descriptors=False):
"""
Returns a list with the descriptor values for all requested point names
"""
logger.info('Getting descriptors for points %s' %
','.join([str(name) for name in point_names]))
# Add dot '.' at the beginning of descriptor names if not present
if descriptor_names:
descriptor_names_aux = list()
for name in descriptor_names:
if name[0] != '.':
descriptor_names_aux.append('.' + name)
else:
descriptor_names_aux.append(name)
descriptor_names = descriptor_names_aux[:]
data = dict()
required_descriptor_names = self.__calculate_complete_required_descriptor_names(
descriptor_names, only_leaf_descriptors=only_leaf_descriptors)
if type(required_descriptor_names) == dict:
return required_descriptor_names # There has been an error
for point_name in point_names:
sound_descriptors = self.__get_point_descriptors(
point_name, required_descriptor_names, normalization)
if 'error' not in sound_descriptors:
data[point_name] = sound_descriptors
return {'error': False, 'result': data}
def __calculate_complete_required_descriptor_names(
self, descriptor_names, only_leaf_descriptors=False):
if not descriptor_names:
descriptor_names = self.descriptor_names['all'][:]
try:
structured_layout = generate_structured_dict_from_layout(
self.descriptor_names['all'][:])
processed_descriptor_names = []
for name in descriptor_names:
nested_descriptors = get_nested_dictionary_value(
name.split('.')[1:], structured_layout)
if not nested_descriptors:
processed_descriptor_names.append(name)
else:
if only_leaf_descriptors:
# only return descriptors if nested descriptors are statistics
if len(
set(nested_descriptors.keys()).intersection([
'min', 'max', 'dvar2', 'dmean2', 'dmean',
'var', 'dvar', 'mean'
])) > 0:
for extra_name in nested_descriptors.keys():
processed_descriptor_names.append(
'%s.%s' % (name, extra_name))
else:
# Return all nested descriptor names
extra_names = []
get_nested_descriptor_names(nested_descriptors,
extra_names)
for extra_name in extra_names:
processed_descriptor_names.append(
'%s.%s' % (name, extra_name))
processed_descriptor_names = list(set(processed_descriptor_names))
return processed_descriptor_names
except:
return {
'error': True,
'result': 'Wrong descriptor names, unable to create layout.',
'status_code': sim_settings.BAD_REQUEST_CODE
}
def __get_point_descriptors(self,
point_name,
required_descriptor_names,
normalization=True):
"""
Get normalization coefficients to transform the input data (get info from the last transformation which has
been a normalization)
"""
normalization_coeffs = None
if not normalization:
trans_hist = self.transformations_history
for i in range(0, len(trans_hist)):
if trans_hist[-(i + 1)]['Analyzer name'] == 'normalize':
normalization_coeffs = trans_hist[-(
i + 1)]['Applier parameters']['coeffs']
required_layout = generate_structured_dict_from_layout(
required_descriptor_names)
try:
p = self.original_dataset.point(str(point_name))
except:
return {
'error': True,
'result': 'Sound does not exist in gaia index.',
'status_code': sim_settings.NOT_FOUND_CODE
}
for descriptor_name in required_descriptor_names:
try:
value = p.value(str(descriptor_name))
if normalization_coeffs:
if descriptor_name in normalization_coeffs:
a = normalization_coeffs[descriptor_name]['a']
b = normalization_coeffs[descriptor_name]['b']
if len(a) == 1:
value = float(value - b[0]) / a[0]
else:
normalized_value = []
for i in range(0, len(a)):
normalized_value.append(
float(value[i] - b[i]) / a[i])
value = normalized_value
except:
try:
value = p.label(str(descriptor_name))
except:
value = None
if descriptor_name[0] == '.':
descriptor_name = descriptor_name[1:]
set_nested_dictionary_value(descriptor_name.split('.'),
required_layout, value)
return required_layout
# SIMILARITY SEARCH and CONTENT SEARCH
def search_dataset(self,
query_point,
number_of_results,
preset_name,
offset=0):
preset_name = str(preset_name)
results = []
count = 0
size = self.original_dataset.size()
if size < sim_settings.SIMILARITY_MINIMUM_POINTS:
msg = 'Not enough datapoints in the dataset (%s < %s).' % (
size, sim_settings.SIMILARITY_MINIMUM_POINTS)
logger.info(msg)
return {
'error': True,
'result': msg,
'status_code': sim_settings.SERVER_ERROR_CODE
}
query_point = str(query_point)
logger.info('NN search for point with name %s (preset = %s)' %
(query_point, preset_name))
results = []
if not self.original_dataset.contains(query_point):
msg = "Sound with id %s doesn't exist in the dataset." % query_point
logger.info(msg)
return {
'error': True,
'result': msg,
'status_code': sim_settings.NOT_FOUND_CODE
}
if preset_name == 'pca':
# Search on PCA view
search = self.view_pca.nnSearch(query_point,
self.metrics[preset_name])
else:
# Search on original dataset view
search = self.view.nnSearch(query_point, self.metrics[preset_name])
results = search.get(int(number_of_results), offset=int(offset))
count = search.size()
return {'error': False, 'result': {'results': results, 'count': count}}
def api_search(self, target_type, target, filter, preset_name,
metric_descriptor_names, num_results, offset, in_ids):
# Check if index has sufficient points
size = self.original_dataset.size()
if size < sim_settings.SIMILARITY_MINIMUM_POINTS:
msg = 'Not enough datapoints in the dataset (%s < %s).' % (
size, sim_settings.SIMILARITY_MINIMUM_POINTS)
logger.info(msg)
return {
'error': True,
'result': msg,
'status_code': sim_settings.SERVER_ERROR_CODE
}
# Get some dataset parameters that will be useful later
trans_hist = self.transformations_history
layout = self.original_dataset.layout()
pca_layout = self.pca_dataset.layout()
coeffs = None # Get normalization coefficients
for i in range(0, len(trans_hist)):
if trans_hist[-(i + 1)]['Analyzer name'] == 'normalize':
coeffs = trans_hist[-(i + 1)]['Applier parameters']['coeffs']
# Process target
if target:
if target_type == 'sound_id':
query_point = str(target)
if not self.original_dataset.contains(query_point):
msg = "Sound with id %s doesn't exist in the dataset and can not be set as similarity target." \
% query_point
logger.info(msg)
return {
'error': True,
'result': msg,
'status_code': sim_settings.NOT_FOUND_CODE
}
else:
query = query_point
elif target_type == 'descriptor_values':
# Transform input params to the normalized feature space and add them to a query point
# If there are no params specified in the target, the point is set as empty (probably random sounds
# are returned)
feature_names = []
query = Point()
query.setLayout(layout)
try:
for param in target.keys():
# Only add numerical parameters. Non numerical ones (like key) are only used as filters
if param in coeffs.keys():
feature_names.append(str(param))
value = target[param]
if coeffs:
a = coeffs[param]['a']
b = coeffs[param]['b']
if len(a) == 1:
norm_value = a[0] * value + b[0]
else:
norm_value = []
for i in range(0, len(a)):
norm_value.append(a[i] * value[i] +
b[i])
query.setValue(str(param), norm_value)
else:
query.setValue(str(param), value)
except:
return {
'error': True,
'result':
'Invalid target (descriptor values could not be correctly parsed)',
'status_code': sim_settings.BAD_REQUEST_CODE
}
# Overwrite metric with present descriptors in target
metric = DistanceFunctionFactory.create(
'euclidean', layout, {'descriptorNames': feature_names})
elif target_type == 'file':
# Target is specified as the attached file
# Create a point with the data in 'descriptors_data' and search for it
target_file_parsing_type = '-'
try:
# Try directly loading the file
p, query = Point(), Point()
p.loadFromString(yaml.dump(target))
if preset_name == 'pca':
query = self.pca_dataset.history().mapPoint(
p) # map point to pca dataset
else:
query = self.original_dataset.history().mapPoint(
p) # map point to original dataset
target_file_parsing_type = 'mapPoint'
except Exception as e:
logger.info(
'Unable to create gaia point from uploaded file (%s). '
'Trying adding descriptors one by one.' % e)
# If does not work load descriptors one by one
try:
query = Point()
#query.setLayout(layout)
feature_names = []
get_nested_descriptor_names(target, feature_names)
feature_names = [
'.%s' % item for item in feature_names
]
nonused_features = []
for param in feature_names:
if param in coeffs.keys():
value = get_nested_dictionary_value(
param[1:].split('.'), target)
if coeffs:
try:
a = coeffs[param]['a']
b = coeffs[param]['b']
if len(a) == 1:
norm_value = a[0] * value + b[0]
else:
norm_value = []
for i in range(0, len(a)):
norm_value.append(a[i] *
value[i] +
b[i])
query.setValue(str(param[1:]),
norm_value)
except:
nonused_features.append(param)
else:
query.setValue(str(param[1:]), value)
else:
nonused_features.append(param)
if preset_name == 'pca':
query = self.pca_dataset.history().mapPoint(
query) # map point to pca dataset
else:
query = self.original_dataset.history().mapPoint(
p) # map point to original dataset
target_file_parsing_type = 'walkDict'
except Exception as e:
logger.info(
'Unable to create gaia point from uploaded file and adding descriptors one by '
'one (%s)' % e)
return {
'error':
True,
'result':
'Unable to create gaia point from uploaded file. Probably the '
'file does not have the required layout. Are you using the '
'correct version of Essentia\'s Freesound extractor?',
'status_code':
sim_settings.SERVER_ERROR_CODE
}
else:
query = Point() # Empty target
if preset_name == 'pca':
query.setLayout(pca_layout)
else:
query.setLayout(layout)
# Process filter
if filter:
filter = parse_filter_list(filter, coeffs)
else:
filter = "" # Empty filter
# log
log_message = 'Similarity search'
if target:
if target_type == 'sound_id':
log_target = '%s (sound id)' % str(target)
elif target_type == 'descriptor_values':
log_target = '%s (descriptor values)' % str(target)
elif target_type == 'file':
log_target = 'uploaded file (%s)' % target_file_parsing_type
log_message += ' with target: %s' % log_target
if filter:
log_message += ' with filter: %s' % str(filter)
logger.info(log_message)
# if in_ids is specified, edit the filter accordingly
if in_ids:
if not filter:
filter = 'WHERE point.id IN ("' + '", "'.join(in_ids) + '")'
else:
filter += ' AND point.id IN ("' + '", "'.join(in_ids) + '")'
# Set query metric
metric = self.metrics[preset_name]
if metric_descriptor_names:
metric = DistanceFunctionFactory.create(
'euclidean', layout,
{'descriptorNames': metric_descriptor_names})
# Do query!
try:
if target_type == 'descriptor_values' and target:
search = self.view.nnSearch(query, metric, str(filter))
else:
if preset_name == 'pca':
search = self.view_pca.nnSearch(query, metric, str(filter))
else:
search = self.view.nnSearch(query, metric, str(filter))
results = search.get(num_results, offset=offset)
count = search.size()
except Exception as e:
return {
'error': True,
'result': 'Similarity server error',
'status_code': sim_settings.SERVER_ERROR_CODE
}
note = None
if target_type == 'file':
if target_file_parsing_type == 'walkDict':
note = 'The layout of the given analysis file differed from what we expected. Similarity results ' \
'might not be accurate. Was the file generated with the last version of Essentia\'s ' \
'Freesound extractor?'
return {
'error': False,
'result': {
'results': results,
'count': count,
'note': note
}
}
def testRegressionIndexing(self):
ds = testdata.loadTestDB()
ds = transform(ds, 'removevl')
ds = transform(ds, 'fixlength')
p = ds.samplePoint().name()
p2 = 'Higher State of Consciousness.mp3'
queries = [
(p, ''), (p2, ''),
(p, 'WHERE (True AND True) and (true and TRUE)'),
(p, 'WHERE (false AND True) OR (true and false)'),
(p2, 'WHERE value.tempotap_bpm.value > 140'),
(p, 'WHERE true AND value.tempotap_bpm.value > 140'),
(p, 'WHERE value.tempotap_bpm > 110'),
(p, 'WHERE value.tempotap_bpm > -10'),
(p, 'WHERE value.tempotap_bpm < -10'),
(p, 'WHERE value.tempotap_bpm > 23000'),
(p, 'WHERE value.tempotap_bpm < 23000'),
(p, 'WHERE value.tempotap_bpm > 120 AND value.tempotap_bpm < 130'),
(p, 'WHERE value.tempotap_bpm BETWEEN 120 AND 130'),
(p, 'WHERE value.tempotap_bpm BETWEEN 130 AND 120'),
(p, 'WHERE value.tempotap_bpm BETWEEN 120 AND 120'),
(p, 'WHERE value.tempotap_bpm BETWEEN -2.3 AND 4096'),
(p, "WHERE value.tempotap_bpm BETWEEN -2.3 AND -1.4"),
(p, "WHERE value.tempotap_bpm BETWEEN 2048 AND 4096"),
(p, 'WHERE label.key_key = "C"'), (p,
'WHERE label.key_key != "C"'),
(p, 'WHERE label.key_key = "X"'),
(p, 'WHERE label.key_key != "X"'),
(p, 'WHERE label.key_key != "C" AND label.key_mode != "major"'),
(p2,
'''WHERE ((label.key_key = "A" AND label.key_mode = "major") OR
(label.key_key = "E" AND label.key_mode = "minor"))
AND value.tempotap_bpm < 90''')
]
# test with standard views
dist = MetricFactory.create('euclidean', ds.layout(),
{'descriptorNames': '*.mean'})
v = View(ds)
vidx = View(ds)
vidx.indexOn('tempotap_bpm')
vidx.indexOn('key_key')
vidx.indexOn('key_mode')
for (pt, filt) in queries:
self.assertEqualSearchSpace(v.nnSearch(pt, dist, filt),
vidx.nnSearch(pt, dist, filt))
# test with frozen views
dsr = transform(
ds, 'select',
{'descriptorNames': ds.layout().descriptorNames(RealType)})
dsnorm = transform(dsr, 'normalize')
dspca = transform(dsnorm, 'pca', {
'resultName': 'pca',
'dimension': 25
})
fds = FrozenDataSet()
fds.fromDataSet(dspca)
fds.setReferenceDataSet(ds)
dist = FrozenDistanceFactory.create('Euclidean', fds.layout(),
{'descriptorName': 'pca'})
v = FrozenView(fds)
vidx = FrozenView(fds)
vidx.indexOn('tempotap_bpm')
vidx.indexOn('key_key')
vidx.indexOn('key_mode')
for (pt, filt) in queries:
self.assertEqualSearchSpace(v.nnSearch(pt, dist, filt),
vidx.nnSearch(pt, dist, filt))
def search(dataset, id, n):
v = View(dataset)
dist = MetricFactory.create('euclidean', dataset.layout())
return v.nnSearch(id, dist).get(n)
class GaiaWrapper:
def __init__(self):
self.as_dataset = DataSet()
self.tag_dataset = DataSet()
self.fs_dataset = DataSet()
self.ac_dataset = DataSet()
self.gaia_similiarity = None
self.index_path = clust_settings.INDEX_DIR
self.as_view = None
self.as_metric = None
self.tag_view = None
self.tag_metric = None
self.fs_view = None
self.fs_metric = None
self.ac_view = None
self.ac_metric = None
self.__load_datasets()
def __get_dataset_path(self, ds_name):
return os.path.join(clust_settings.INDEX_DIR, ds_name + '.db')
def __load_datasets(self):
self.as_dataset.load(self.__get_dataset_path(clust_settings.INDEX_NAME_AS))
self.as_view = View(self.as_dataset)
# self.as_metric = DistanceFunctionFactory.create('euclidean', self.as_dataset.layout())
# self.as_metric = DistanceFunctionFactory.create('CosineSimilarity', self.as_dataset.layout())
# self.as_metric = DistanceFunctionFactory.create('CosineAngle', self.as_dataset.layout())
self.as_metric = DistanceFunctionFactory.create('Manhattan', self.as_dataset.layout())
self.tag_dataset.load(self.__get_dataset_path(clust_settings.INDEX_NAME_TAG))
self.tag_view = View(self.tag_dataset)
self.tag_metric = DistanceFunctionFactory.create('euclidean', self.tag_dataset.layout())
self.fs_dataset.load(self.__get_dataset_path(clust_settings.INDEX_NAME_FS))
self.fs_view = View(self.fs_dataset)
self.fs_metric = DistanceFunctionFactory.create('euclidean', self.fs_dataset.layout(), {'descriptorNames': 'pca'})
# self.gaia_similiarity = GaiaWrapperSimilarity()
self.__load_ac_descriptors_dataset()
def __load_ac_descriptors_dataset(self):
self.ac_dataset.load(self.__get_dataset_path('FS_AC_descriptors_normalized')) # TODO: add this in clustering settings
self.ac_view = View(self.ac_dataset)
self.ac_metric = DistanceFunctionFactory.create('euclidean', self.ac_dataset.layout(),
{'descriptorNames': [
'ac_brightness',
'ac_boominess',
'ac_depth',
'ac_hardness',
'ac_roughness',
'ac_sharpness',
'ac_warmth'
]})
def search_nearest_neighbors(self, sound_id, k, in_sound_ids=[], features='audio_as'):
if in_sound_ids:
filter = 'WHERE point.id IN ("' + '", "'.join(in_sound_ids) + '")'
else:
filter = None
try:
if features == 'audio_as':
nearest_neighbors = self.as_view.nnSearch(sound_id, self.as_metric, filter).get(k)[1:]
elif features == 'tag':
nearest_neighbors = self.tag_view.nnSearch(sound_id, self.tag_metric, filter).get(k)[1:]
elif features == 'audio_fs':
nearest_neighbors = self.gaia_similiarity.view_pca.nnSearch(sound_id,
self.gaia_similiarity.metrics['pca'],
filter).get(k)[1:]
elif features == 'audio_fs_selected':
nearest_neighbors = self.fs_view.nnSearch(sound_id, self.fs_metric, filter).get(k)[1:]
elif features == 'audio_ac':
nearest_neighbors = self.ac_view.nnSearch(sound_id, self.ac_metric, filter).get(k)[1:]
if not nearest_neighbors:
logger.info("No nearest neighbors found for point with id '{}'".format(sound_id))
return nearest_neighbors
except Exception as e:
logger.info(e)
return []
def return_sound_tag_features(self, sound_ids):
tag_features = []
for sound_id in sound_ids:
try:
tag_features.append(self.tag_dataset.point(sound_id).value('tags_lda')) # TODO: add this in clustering settings
except Exception as e:
#logger.info(e)
tag_features.append(None)
return tag_features