Fix bugs in gklearn.dataset.

gklearn/dataset/data_fetcher.py

@@ -74,6 +74,8 @@ class DataFetcher():
 			message = 'Invalid Dataset name "' + self._name + '".'
 			message += '\nAvailable datasets are as follows: \n\n'
 			message += '\n'.join(ds for ds in sorted(DATASET_META))
+			message += '\n\nFollowing special suffices can be added to the name:'
+			message += '\n\n' + '\n'.join(['_unlabeled'])
 			raise ValueError(message)
 		else:
 			self.write_archive_file(self._name)
@@ -127,9 +129,9 @@ class DataFetcher():
 	
 	def write_archive_file(self, ds_name):
 		path = osp.join(self._root, ds_name)
-		url = DATASET_META[ds_name]['url']
 # 		filename_dir = osp.join(path,filename)
 		if not osp.exists(path) or self._reload:
+			url = DATASET_META[ds_name]['url']
 			response = self.download_file(url)
 			if response is None: 
 				return False
@@ -152,7 +154,7 @@ class DataFetcher():
 				with tarfile.open(filename_archive, 'r:gz') as tar:
 					if self._reload and self._verbose: 
 						print(filename + ' Downloaded.')
-					subpath = os.path.join(path, tar.getnames()[0])
+					subpath = os.path.join(path, tar.getnames()[0].split('/')[0])
 					if not osp.exists(subpath) or self._reload:
 						tar.extractall(path = path)
 					return subpath

gklearn/dataset/dataset.py

@@ -14,7 +14,33 @@ from gklearn.dataset import DATASET_META, DataFetcher, DataLoader
 class Dataset(object):
 	
 	
-	def __init__(self, inputs=None, root='datasets', filename_targets=None, targets=None,  mode='networkx', clean_labels=True, reload=False, verbose=False, **kwargs):
+	def __init__(self, inputs=None, root='datasets', filename_targets=None, targets=None, mode='networkx', clean_labels=True, reload=False, verbose=False, **kwargs):
+		self._substructures = None
+		self._node_label_dim = None
+		self._edge_label_dim = None
+		self._directed = None
+		self._dataset_size = None
+		self._total_node_num = None
+		self._ave_node_num = None
+		self._min_node_num = None
+		self._max_node_num = None
+		self._total_edge_num = None
+		self._ave_edge_num = None
+		self._min_edge_num = None
+		self._max_edge_num = None
+		self._ave_node_degree = None
+		self._min_node_degree = None
+		self._max_node_degree = None
+		self._ave_fill_factor = None
+		self._min_fill_factor = None
+		self._max_fill_factor = None
+		self._node_label_nums = None
+		self._edge_label_nums = None
+		self._node_attr_dim = None
+		self._edge_attr_dim = None
+		self._class_number = None
+		self._ds_name = None
+		
 		if inputs is None:
 			self._graphs = None
 			self._targets = None
@@ -38,38 +64,26 @@ class Dataset(object):
 			# If inputs is predefined dataset name.
 			if inputs in DATASET_META:
 				self.load_predefined_dataset(inputs, root=root, clean_labels=clean_labels, reload=reload, verbose=verbose)
+				self._ds_name = inputs
+				
+			elif inputs.endswith('_unlabeled'):
+				self.load_predefined_dataset(inputs[:len(inputs) - 10], root=root, clean_labels=clean_labels, reload=reload, verbose=verbose)
+				self._ds_name = inputs
+
+				# Deal with special suffices.
+				self.check_special_suffices()
+				
+			# If inputs is a file name.
+			elif os.path.isfile(inputs):
+				self.load_dataset(inputs, filename_targets=filename_targets, clean_labels=clean_labels, **kwargs)			
 				
 			# If inputs is a file name.
 			else:
-				self.load_dataset(inputs, filename_targets=filename_targets, clean_labels=clean_labels, **kwargs)
+				raise ValueError('The "inputs" argument "' + inputs + '" is not a valid dataset name or file name.')
 				
 		else:
-			raise TypeError('The "inputs" argument cannot be recoganized. "Inputs" can be a list of graphs, a predefined dataset name, or a file name of a dataset.')
-		
-		self._substructures = None
-		self._node_label_dim = None
-		self._edge_label_dim = None
-		self._directed = None
-		self._dataset_size = None
-		self._total_node_num = None
-		self._ave_node_num = None
-		self._min_node_num = None
-		self._max_node_num = None
-		self._total_edge_num = None
-		self._ave_edge_num = None
-		self._min_edge_num = None
-		self._max_edge_num = None
-		self._ave_node_degree = None
-		self._min_node_degree = None
-		self._max_node_degree = None
-		self._ave_fill_factor = None
-		self._min_fill_factor = None
-		self._max_fill_factor = None
-		self._node_label_nums = None
-		self._edge_label_nums = None
-		self._node_attr_dim = None
-		self._edge_attr_dim = None
-		self._class_number = None
+			raise TypeError('The "inputs" argument cannot be recognized. "Inputs" can be a list of graphs, a predefined dataset name, or a file name of a dataset.')
+	
 	
 	
 	def load_dataset(self, filename, filename_targets=None, clean_labels=True, **kwargs):
@@ -97,7 +111,10 @@ class Dataset(object):
 			fn_targets = None
 		else:
 			load_files = DATASET_META[ds_name]['load_files']
-			ds_file = os.path.join(path, load_files[0])
+			if isinstance(load_files[0], str):
+				ds_file = os.path.join(path, load_files[0])
+			else: # load_files[0] is a list of files.
+				ds_file = [os.path.join(path, fn) for fn in load_files[0]]
 			fn_targets = os.path.join(path, load_files[1]) if len(load_files) == 2 else None
 			
 		self._graphs, self._targets, label_names = DataLoader(ds_file, filename_targets=fn_targets).data
@@ -108,6 +125,11 @@ class Dataset(object):
 		self._edge_attrs = label_names['edge_attrs']
 		if clean_labels:
 			self.clean_labels()
+			
+		# Deal with specific datasets.
+		if ds_name == 'Alkane':
+			self.trim_dataset(edge_required=True)
+			self.remove_labels(node_labels=['atom_symbol'])
 	
 
 	def set_labels(self, node_labels=[], node_attrs=[], edge_labels=[], edge_attrs=[]):
@@ -536,6 +558,14 @@ class Dataset(object):
 		return dataset
 	
 	
+	def check_special_suffices(self):
+		if self._ds_name.endswith('_unlabeled'):
+			self.remove_labels(node_labels=self._node_labels, 
+					  edge_labels=self._edge_labels, 
+					  node_attrs=self._node_attrs, 
+					  edge_attrs=self._edge_attrs)
+	
+	
 	def get_all_node_labels(self):
 		node_labels = []
 		for g in self._graphs:

gklearn/dataset/file_managers.py

@@ -38,7 +38,11 @@ class DataLoader():
 		for details. Note here filename is the name of either .txt file in
 		the dataset directory.
 		"""
-		extension = splitext(filename)[1][1:]
+		if isinstance(filename, str):
+			extension = splitext(filename)[1][1:]
+		else: # filename is a list of files.
+			extension = splitext(filename[0])[1][1:]
+			
 		if extension == "ds":
 			self._graphs, self._targets, self._label_names = self.load_from_ds(filename, filename_targets)
 		elif extension == "cxl":
@@ -67,14 +71,24 @@ class DataLoader():
 	
 		Note these graph formats are checked automatically by the extensions of 
 		graph files.
-		"""		
-		dirname_dataset = dirname(filename)
+		"""
+		if isinstance(filename, str):
+			dirname_dataset = dirname(filename)
+			with open(filename) as f:
+				content = f.read().splitlines()
+		else: # filename is a list of files.
+			dirname_dataset = dirname(filename[0])
+			content = []
+			for fn in filename:
+				with open(fn) as f:
+					content += f.read().splitlines()
+			# to remove duplicate file names.
+
 		data = []
 		y = []
 		label_names = {'node_labels': [], 'edge_labels': [], 'node_attrs': [], 'edge_attrs': []}
-		with open(filename) as fn:
-			content = fn.read().splitlines()
-		content = [line for line in content if not line.endswith('.ds')]
+		content = [line for line in content if not line.endswith('.ds')] # Alkane
+		content = [line for line in content if not line.startswith('#')] # Acyclic
 		extension = splitext(content[0].split(' ')[0])[1][1:]
 		if extension == 'ct':
 			load_file_fun = self.load_ct

gklearn/dataset/metadata.py

@@ -165,7 +165,7 @@ GREYC_META = {
 		'domain': 'small molecules',
 		'train_valid_test': ['trainset_0.ds', None, 'testset_0.ds'],
 		'stereoisomerism': False,
-		'load_files': ['dataset.ds'],
+		'load_files': [['trainset_0.ds', 'testset_0.ds']],
 	},
 	'PTC': {
 		'database': 'greyc',