def __init__(self, which_set='train', n_datapoints=None, fname="mnist.pkl.gz", preproc=[]):
super(MNIST, self).__init__(preproc)
_logger.info("Loading MNIST data")
fname = datapath(fname)
if fname[-3:] == ".gz":
open_func = gzip.open
else:
open_func = open
with open_func(fname) as f:
(train_x, train_y), (valid_x, valid_y), (test_x, test_y) = pickle.load(f)
if which_set == 'train':
self.X, self.Y = self.prepare(train_x, train_y, n_datapoints)
elif which_set == 'valid':
self.X, self.Y = self.prepare(valid_x, valid_y, n_datapoints)
elif which_set == 'test':
self.X, self.Y = self.prepare(test_x, test_y, n_datapoints)
elif which_set == 'salakhutdinov_train':
train_x = np.concatenate([train_x, valid_x])
train_y = np.concatenate([train_y, valid_y])
self.X, self.Y = self.prepare(train_x, train_y, n_datapoints)
elif which_set == 'salakhutdinov_valid':
train_x = np.concatenate([train_x, valid_x])[::-1]
train_y = np.concatenate([train_y, valid_y])[::-1]
self.X, self.Y = self.prepare(train_x, train_y, n_datapoints)
else:
raise ValueError("Unknown dataset %s" % which_set)
self.n_datapoints = self.X.shape[0]
def __init__(self, which_set='train', fname="chardata.mat", shuffle_seed=123, n_used_for_validation=1345, preproc=[]):
super(Omniglot, self).__init__(preproc)
def reshape_data(data):
return data.reshape((-1, 28, 28)).reshape((-1, 28*28), order='fortran')
_logger.info("Loading Omniglot data (28x28)")
fname = datapath(fname)
omni_raw = scipy.io.loadmat(fname)
train_data = reshape_data(omni_raw['data'].T.astype('float32'))
test_data = reshape_data(omni_raw['testdata'].T.astype('float32'))
permutation = np.random.RandomState(seed=shuffle_seed).permutation(train_data.shape[0])
train_data = train_data[permutation]
if which_set == 'train':
self.X = train_data[:-n_used_for_validation]
elif which_set == 'valid':
self.X = train_data[-n_used_for_validation:]
elif which_set == 'test':
self.X = test_data
else:
raise ValueError("Unknown dataset %s" % which_set)
self.n_datapoints = self.X.shape[0]
self.Y = np.zeros((self.n_datapoints, 2), dtype=floatX)
def __init__(self, which_set='train', n_datapoints=-1, path="caltech-silhouettes", preproc=[]):
super(CalTechSilhouettes, self).__init__(preproc)
_logger.info("Loading CalTech 101 Silhouettes data (28x28)")
path = datapath(path)
test_x = np.load(path+"/test_data.npy")
test_y = np.load(path+"/test_labels.npy")
if which_set == 'train':
X = np.load(path+"/train_data.npy")
Y = np.load(path+"/train_labels.npy")
elif which_set == 'valid':
X = np.load(path+"/val_data.npy")
Y = np.load(path+"/val_labels.npy")
elif which_set == 'test':
X = np.load(path+"/test_data.npy")
Y = np.load(path+"/test_labels.npy")
else:
raise ValueError("Unknown dataset %s" % which_set)
if n_datapoints > 0:
X = X[:n_datapoints]
Y = Y[:n_datapoints]
else:
n_datapoints = X.shape[0]
X = X.astype(floatX)
self.n_datapoints = n_datapoints
self.X = X
self.Y = Y
def __init__(self, which_set='train', n_datapoints=None, fname="mnist.pkl.gz", preproc=[]):
super(MNIST, self).__init__(preproc)
_logger.info("Loading MNIST data")
fname = datapath(fname)
if fname[-3:] == ".gz":
open_func = gzip.open
else:
open_func = open
with open_func(fname) as f:
(train_x, train_y), (valid_x, valid_y), (test_x, test_y) = pickle.load(f)
if which_set == 'train':
self.X, self.Y = self.prepare(train_x, train_y, n_datapoints)
elif which_set == 'valid':
self.X, self.Y = self.prepare(valid_x, valid_y, n_datapoints)
elif which_set == 'test':
self.X, self.Y = self.prepare(test_x, test_y, n_datapoints)
elif which_set == 'salakhutdinov_train':
train_x = np.concatenate([train_x, valid_x])
train_y = np.concatenate([train_y, valid_y])
self.X, self.Y = self.prepare(train_x, train_y, n_datapoints)
elif which_set == 'salakhutdinov_valid':
train_x = np.concatenate([train_x, valid_x])[::-1]
train_y = np.concatenate([train_y, valid_y])[::-1]
self.X, self.Y = self.prepare(train_x, train_y, n_datapoints)
else:
raise ValueError("Unknown dataset %s" % which_set)
self.n_datapoints = self.X.shape[0]
def __init__(self,
which_set='train',
size=48,
fold=0,
n_datapoints=-1,
path="TFD",
preproc=[]):
super(TorontoFaceDataset, self).__init__(preproc)
_logger.info("Loading Toronto Face Dataset (48x48)")
fname = datapath(path)
if size == 48:
fname += "/TFD_48x48.mat"
elif size == 96:
fname += "/TFD_96x96.mat"
else:
raise ValueError("Unknown size %s. Allowerd options 48 or 96." %
size)
assert 0 <= fold and fold <= 4
# Load dataset
data = loadmat(fname)
if which_set == 'unlabeled':
idx = (data['folds'][:, fold] == 0)
elif which_set == 'train':
idx = (data['folds'][:, fold] == 1)
elif which_set == 'unlabeled+train':
idx = (data['folds'][:, fold] == 0)
idx += (data['folds'][:, fold] == 1)
elif which_set == 'valid':
idx = (data['folds'][:, fold] == 2)
elif which_set == 'test':
idx = (data['folds'][:, fold] == 3)
else:
raise ValueError("Unknown dataset %s" % which_set)
X = data['images'][idx, :, :]
#Y = data['labs_id'][idx,:]
if n_datapoints > 0:
X = X[:n_datapoints]
Y = Y[:n_datapoints]
else:
n_datapoints = X.shape[0]
# Normalize to 0..1
X = (X / 255.).astype(floatX)
# Flatten images
X = X.reshape([n_datapoints, -1])
self.n_datapoints = n_datapoints
self.X = X
self.Y = None
def __init__(self, which_set='train', size=48, fold=0, n_datapoints=-1, path="TFD", preproc=[]):
super(TorontoFaceDataset, self).__init__(preproc)
_logger.info("Loading Toronto Face Dataset (48x48)")
fname = datapath(path)
if size == 48:
fname += "/TFD_48x48.mat"
elif size == 96:
fname += "/TFD_96x96.mat"
else:
raise ValueError("Unknown size %s. Allowerd options 48 or 96." % size)
assert 0 <= fold and fold <= 4
# Load dataset
data = loadmat(fname)
if which_set == 'unlabeled':
idx = (data['folds'][:,fold] == 0)
elif which_set == 'train':
idx = (data['folds'][:,fold] == 1)
elif which_set == 'unlabeled+train':
idx = (data['folds'][:,fold] == 0)
idx += (data['folds'][:,fold] == 1)
elif which_set == 'valid':
idx = (data['folds'][:,fold] == 2)
elif which_set == 'test':
idx = (data['folds'][:,fold] == 3)
else:
raise ValueError("Unknown dataset %s" % which_set)
X = data['images'][idx,:,:]
#Y = data['labs_id'][idx,:]
if n_datapoints > 0:
X = X[:n_datapoints]
Y = Y[:n_datapoints]
else:
n_datapoints = X.shape[0]
# Normalize to 0..1
X = (X / 255.).astype(floatX)
# Flatten images
X = X.reshape([n_datapoints, -1])
self.n_datapoints = n_datapoints
self.X = X
self.Y = None
def __init__(self, which_set='train', fname="caltech101_silhouettes_28_split1.mat", preproc=[]):
super(CalTech101Silhouettes, self).__init__(preproc)
_logger.info("Loading CalTech 101 Silhouettes data (28x28)")
fname = datapath(fname)
if which_set == 'train':
self.X = scipy.io.loadmat(fname)['train_data'].astype(floatX)
elif which_set == 'valid':
self.X = scipy.io.loadmat(fname)['val_data'].astype(floatX)
elif which_set == 'test':
self.X = scipy.io.loadmat(fname)['test_data'].astype(floatX)
else:
raise ValueError("Unknown dataset %s" % which_set)
self.n_datapoints = self.X.shape[0]
self.Y = np.zeros((self.n_datapoints, 2), dtype=floatX)
def __init__(self, data_name, which_set='train', preproc=[]):
super(UCIBinary, self).__init__(preproc)
UCIdatasets = {
'adult': 'adult.h5',
'connect4': 'connect4.h5',
'dna': 'dna.h5',
'mushrooms': 'mushrooms.h5',
'nips': 'nips.h5',
'ocrletters': 'ocr_letters.h5',
'rcv1': 'rcv1.h5',
'web': 'web.h5'
}
assert data_name in UCIdatasets.keys()
_logger.info("Loading %s data" % data_name)
fname = datapath(UCIdatasets[data_name])
try:
with h5py.File(fname, "r") as h5:
if which_set == 'train':
train_x = h5['train']
self.X = np.array(train_x).astype(floatX)
elif which_set == 'valid':
valid_x = h5['valid']
self.X = np.array(valid_x).astype(floatX)
elif which_set == 'test':
test_x = h5['test']
self.X = np.array(test_x).astype(floatX)
else:
raise ValueError("Unknown dataset %s" % which_set)
except KeyError, e:
logger.info("Failed to read data from %s: %s" % (fname, e))
exit(1)