Use dataset.Dataset in the tests of graph kernel classes.

gklearn/tests/test_graph_kernels.py

@@ -19,44 +19,38 @@ def test_list_graph_kernels():
 def chooseDataset(ds_name):
 	"""Choose dataset according to name.
 	"""
-	from gklearn.utils import Dataset
-
-	dataset = Dataset()
+	from gklearn.dataset import Dataset
 
 	# no node labels (and no edge labels).
 	if ds_name == 'Alkane':
-		dataset.load_predefined_dataset(ds_name)
+		dataset = Dataset('Alkane_unlabeled')
 		dataset.trim_dataset(edge_required=False)
-		irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']}
-		dataset.remove_labels(**irrelevant_labels)
 		dataset.cut_graphs(range(1, 10))
 	# node symbolic labels.
 	elif ds_name == 'Acyclic':
-		dataset.load_predefined_dataset(ds_name)
+		dataset = Dataset('Acyclic')
 		dataset.trim_dataset(edge_required=False)
-		irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']}
-		dataset.remove_labels(**irrelevant_labels)
 	# node non-symbolic labels.
 	elif ds_name == 'Letter-med':
-		dataset.load_predefined_dataset(ds_name)
+		dataset = Dataset('Letter-med')
 		dataset.trim_dataset(edge_required=False)
 	# node symbolic and non-symbolic labels (and edge symbolic labels).
 	elif ds_name == 'AIDS':
-		dataset.load_predefined_dataset(ds_name)
+		dataset = Dataset('AIDS')
 		dataset.trim_dataset(edge_required=False)
 	# edge non-symbolic labels (no node labels).
 	elif ds_name == 'Fingerprint_edge':
-		dataset.load_predefined_dataset('Fingerprint')
+		dataset = Dataset('Fingerprint')
 		dataset.trim_dataset(edge_required=True)
 		irrelevant_labels = {'edge_attrs': ['orient', 'angle']}
 		dataset.remove_labels(**irrelevant_labels)
 	# edge non-symbolic labels (and node non-symbolic labels).
 	elif ds_name == 'Fingerprint':
-		dataset.load_predefined_dataset(ds_name)
+		dataset = Dataset('Fingerprint')
 		dataset.trim_dataset(edge_required=True)
 	# edge symbolic and non-symbolic labels (and node symbolic and non-symbolic labels).
 	elif ds_name == 'Cuneiform':
-		dataset.load_predefined_dataset(ds_name)
+		dataset = Dataset('Cuneiform')
 		dataset.trim_dataset(edge_required=True)
 
 	dataset.cut_graphs(range(0, 3))
@@ -544,4 +538,4 @@ if __name__ == "__main__":
 #	test_RandomWalk('Acyclic', 'fp', None, None)
 #	test_RandomWalk('Acyclic', 'spectral', 'exp', 'imap_unordered')
 # 	test_CommonWalk('Alkane', 0.01, 'geo')
-# 	test_ShortestPath('Acyclic')
+ 	# test_ShortestPath('Acyclic')