def load_from_dir(cls, d):
'''
This class method returns a FeatureMatrix object that has been
constructed using data loaded from a feature matrix directory.
Args:
| **d** *(str)*: The path to the feature matrix directory.
Raises:
'''
# initilaze empty feature matrix object
fm = cls()
# first load object ids, if available
f = os.path.join(d, cls.OBJECT_IDS_F)
if(os.path.exists(f)):
fm.load_object_ids(f)
# read and add labelings
lab_d = os.path.join(d, cls.LABELING_D)
if(os.path.exists(lab_d)):
for f in glob.glob(os.path.join(lab_d, '*.txt')):
lname = os.path.splitext(os.path.basename(f))[0]
if not(lname == cls.ONE_CLASS_LABELING):
(label_dict, class_names) = file_io.read_labeling(f)
fm.add_labeling(lname, label_dict, class_names)
fids = None
fnames = None
featmat = None
# read feature ids
f = os.path.join(d, cls.FEATURE_IDS_F)
if(os.path.exists(f)):
with open(f, 'r') as fin:
fids = [i for i in file_io.read_ids(fin)]
# read feature names
f = os.path.join(d, cls.FEATURE_NAMES_F)
if(os.path.exists(f)):
with open(f, 'r') as fin:
fnames = [n for n in file_io.read_names(fin)]
# read feature matrix
f = os.path.join(d, cls.FEATURE_MATRIX_F)
if(os.path.exists(f)):
featmat = numpy.loadtxt(f)
# in case of 1D matrix, reshape to single column 2D matrix
fm_shape = featmat.shape
if(len(fm_shape) == 1):
n = fm_shape[0]
featmat = featmat.reshape((n, 1))
if not(featmat is None):
fm.add_features(fids, featmat, fnames)
return fm
def load_from_dir(cls, d):
'''
This class method returns a FeatureMatrix object that has been
constructed using data loaded from a feature matrix directory.
Args:
| **d** *(str)*: The path to the feature matrix directory.
Raises:
'''
# initilaze empty feature matrix object
fm = cls()
# first load object ids, if available
f = os.path.join(d, cls.OBJECT_IDS_F)
if (os.path.exists(f)):
fm.load_object_ids(f)
# read and add labelings
lab_d = os.path.join(d, cls.LABELING_D)
if (os.path.exists(lab_d)):
for f in glob.glob(os.path.join(lab_d, '*.txt')):
lname = os.path.splitext(os.path.basename(f))[0]
if not (lname == cls.ONE_CLASS_LABELING):
(label_dict, class_names) = file_io.read_labeling(f)
fm.add_labeling(lname, label_dict, class_names)
fids = None
fnames = None
featmat = None
# read feature ids
f = os.path.join(d, cls.FEATURE_IDS_F)
if (os.path.exists(f)):
with open(f, 'r') as fin:
fids = [i for i in file_io.read_ids(fin)]
# read feature names
f = os.path.join(d, cls.FEATURE_NAMES_F)
if (os.path.exists(f)):
with open(f, 'r') as fin:
fnames = [n for n in file_io.read_names(fin)]
# read feature matrix
f = os.path.join(d, cls.FEATURE_MATRIX_F)
if (os.path.exists(f)):
featmat = numpy.loadtxt(f)
# in case of 1D matrix, reshape to single column 2D matrix
fm_shape = featmat.shape
if (len(fm_shape) == 1):
n = fm_shape[0]
featmat = featmat.reshape((n, 1))
if not (featmat is None):
fm.add_features(fids, featmat, fnames)
return fm
def add_labeling(self, labeling_name, labeling_f):
# read labeling
try:
label_dict, class_names = file_io.read_labeling(labeling_f)
except:
return 'Error in labeling file.'
finally:
labeling_f.close()
# add to feature matrix
fm = self.get_feature_matrix()
try:
fm.add_labeling(labeling_name, label_dict, class_names)
except ValueError as e:
return str(e)
# save if everything went well
fm.save_to_dir(self.fm_dir)
return ''
def add_labeling(self, labeling_name, labeling_f):
# read labeling
try:
label_dict, class_names = file_io.read_labeling(labeling_f)
except:
return 'Error in labeling file.'
finally:
labeling_f.close()
# add to feature matrix
fm = self.get_feature_matrix()
try:
fm.add_labeling(labeling_name, label_dict, class_names)
except ValueError as e:
return str(e)
# save if everything went well
fm.save_to_dir(self.fm_dir)
return ''
def load_from_file(cls, labeling_name, f):
(label_dict, class_names) = file_io.read_labeling(f)
object_ids = sorted(label_dict.keys())
labels = [label_dict[oid] for oid in object_ids]
return cls(labeling_name, object_ids, labels, class_names)
def load_from_file(cls, labeling_name, f):
(label_dict, class_names) = file_io.read_labeling(f)
object_ids = sorted(label_dict.keys())
labels = [label_dict[oid] for oid in object_ids]
return cls(labeling_name, object_ids, labels, class_names)
