def test_sparse_ule():
skip_if_no_data()
# Test loading of transfer data
train, valid, test, transfer = utlc.load_sparse_dataset("ule",
normalize=True,
transfer=True)
assert train.shape[0] == transfer.shape[0]
def test_all_sparse_utlc():
skip_if_no_data()
for name in ['harry', 'terry', 'ule']:
print "Loading sparse ", name
train, valid, test = utlc.load_sparse_dataset(name, normalize=True)
nb_elem = numpy.prod(train.shape)
mi = train.data.min()
ma = train.data.max()
mi = min(0, mi)
ma = max(0, ma)
su = train.data.sum()
mean = float(su) / nb_elem
print name, "dtype, max, min, mean, nb non-zero, nb element, %sparse"
print train.dtype, ma, mi, mean, train.nnz, nb_elem, (
nb_elem - float(train.nnz)) / nb_elem
print name, "max, min, mean, std (all stats on non-zero element)"
print train.data.max(), train.data.min(), train.data.mean(
), train.data.std()
assert scipy.sparse.issparse(
train), "train is not sparse for %s dataset" % name
assert scipy.sparse.issparse(
valid), "valid is not sparse for %s dataset" % name
assert scipy.sparse.issparse(
test), "test is not sparse for %s dataset" % name
assert train.shape[1] == test.shape[1] == valid.shape[
1], "shapes of sparse %s dataset do not match" % name
def test_sparse_ule():
skip_if_no_data()
# Test loading of transfer data
train, valid, test, transfer = utlc.load_sparse_dataset("ule",
normalize=True,
transfer=True)
assert train.shape[0] == transfer.shape[0]
def load_data(conf):
"""
Loads a specified dataset according to the parameters in the dictionary
Parameters
----------
conf : WRITEME
Returns
-------
WRITEME
"""
logger.info('... loading dataset')
# Special case for sparse format
if conf.get('sparse', False):
expected = inspect.getargspec(load_sparse_dataset)[0][1:]
data = load_sparse_dataset(conf['dataset'], **subdict(conf, expected))
valid, test = data[1:3]
# Sparse TERRY data on LISA servers contains an extra null first row in
# valid and test subsets.
if conf['dataset'] == 'terry':
valid = valid[1:]
test = test[1:]
assert valid.shape[0] == test.shape[0] == 4096, \
'Sparse TERRY data loaded has wrong number of examples'
if len(data) == 3:
return [data[0], valid, test]
else:
return [data[0], valid, test, data[3]]
# Load as the usual ndarray
expected = inspect.getargspec(load_ndarray_dataset)[0][1:]
data = load_ndarray_dataset(conf['dataset'], **subdict(conf, expected))
# Special case for on-the-fly normalization
if conf.get('normalize_on_the_fly', False):
return data
# Allocate shared variables
def shared_dataset(data_x):
"""Function that loads the dataset into shared variables"""
if conf.get('normalize', True):
return sharedX(data_x, borrow=True)
else:
return theano.shared(theano._asarray(data_x), borrow=True)
return map(shared_dataset, data)
def load_data(conf):
"""
Loads a specified dataset according to the parameters in the dictionary
Parameters
----------
conf : WRITEME
Returns
-------
WRITEME
"""
print '... loading dataset'
# Special case for sparse format
if conf.get('sparse', False):
expected = inspect.getargspec(load_sparse_dataset)[0][1:]
data = load_sparse_dataset(conf['dataset'], **subdict(conf, expected))
valid, test = data[1:3]
# Sparse TERRY data on LISA servers contains an extra null first row in
# valid and test subsets.
if conf['dataset'] == 'terry':
valid = valid[1:]
test = test[1:]
assert valid.shape[0] == test.shape[0] == 4096, \
'Sparse TERRY data loaded has wrong number of examples'
if len(data) == 3:
return [data[0], valid, test]
else:
return [data[0], valid, test, data[3]]
# Load as the usual ndarray
expected = inspect.getargspec(load_ndarray_dataset)[0][1:]
data = load_ndarray_dataset(conf['dataset'], **subdict(conf, expected))
# Special case for on-the-fly normalization
if conf.get('normalize_on_the_fly', False):
return data
# Allocate shared variables
def shared_dataset(data_x):
"""Function that loads the dataset into shared variables"""
if conf.get('normalize', True):
return sharedX(data_x, borrow=True)
else:
return theano.shared(theano._asarray(data_x), borrow=True)
return map(shared_dataset, data)
def test_all_sparse_utlc():
skip_if_no_data()
for name in ['harry','terry','ule']:
print "Loading sparse ", name
train, valid, test = utlc.load_sparse_dataset(name, normalize=True)
nb_elem = numpy.prod(train.shape)
mi = train.data.min()
ma = train.data.max()
mi = min(0, mi)
ma = max(0, ma)
su = train.data.sum()
mean = float(su)/nb_elem
print name,"dtype, max, min, mean, nb non-zero, nb element, %sparse"
print train.dtype, ma, mi, mean, train.nnz, nb_elem, (nb_elem-float(train.nnz))/nb_elem
print name,"max, min, mean, std (all stats on non-zero element)"
print train.data.max(), train.data.min(), train.data.mean(), train.data.std()
assert scipy.sparse.issparse(train), "train is not sparse for %s dataset" % name
assert scipy.sparse.issparse(valid), "valid is not sparse for %s dataset" % name
assert scipy.sparse.issparse(test), "test is not sparse for %s dataset" % name
assert train.shape[1]==test.shape[1]==valid.shape[1], "shapes of sparse %s dataset do not match" % name
def test_all_sparse_utlc():
skip_if_no_data()
for name in ['harry', 'terry', 'ule']:
print("Loading sparse ", name)
train, valid, test = utlc.load_sparse_dataset(name, normalize=True)
nb_elem = numpy.prod(train.shape)
mi = train.data.min()
ma = train.data.max()
mi = min(0, mi)
ma = max(0, ma)
su = train.data.sum()
mean = float(su) / nb_elem
print(name, "dtype, max, min, mean, nb non-zero, nb element, %sparse")
print(train.dtype, ma, mi, mean, train.nnz, end='')
print(nb_elem, (nb_elem - float(train.nnz)) / nb_elem)
print(name, "max, min, mean, std (all stats on non-zero element)")
print(train.data.max(), train.data.min(), end='')
print(train.data.mean(), train.data.std())
assert scipy.sparse.issparse(train)
assert scipy.sparse.issparse(valid)
assert scipy.sparse.issparse(test)
assert train.shape[1] == test.shape[1] == valid.shape[1]
