Update the GED method, use new gedlibpy library.

preimage/iam.py

@@ -12,23 +12,19 @@ import networkx as nx
 from tqdm import tqdm
 
 import sys
-#from Cython_GedLib_2 import librariesImport, script
-import librariesImport, script
+from gedlibpy import librariesImport, gedlibpy
 sys.path.insert(0, "../")
-from pygraph.utils.graphfiles import saveDataset
 from pygraph.utils.graphdataset import get_dataset_attributes
 from pygraph.utils.utils import graph_isIdentical, get_node_labels, get_edge_labels
-#from pygraph.utils.utils import graph_deepcopy
 
-def iam_moreGraphsAsInit_tryAllPossibleBestGraphs(Gn_median, Gn_candidate, 
-        c_ei=3, c_er=3, c_es=1, ite_max=50, epsilon=0.001, 
-        node_label='atom', edge_label='bond_type', 
+
+def iam_upgraded(Gn_median, Gn_candidate, c_ei=3, c_er=3, c_es=1, ite_max=50, 
+        epsilon=0.001, node_label='atom', edge_label='bond_type', 
         connected=False, removeNodes=True, allBestInit=False, allBestNodes=False,
-        allBestEdges=False,
+        allBestEdges=False, allBestOutput=False,
         params_ged={'ged_cost': 'CHEM_1', 'ged_method': 'IPFP', 'saveGXL': 'benoit'}):
     """See my name, then you know what I do.
     """
-    from tqdm import tqdm
 #    Gn_median = Gn_median[0:10]
 #    Gn_median = [nx.convert_node_labels_to_integers(g) for g in Gn_median]
     if removeNodes:
@@ -150,16 +146,6 @@ def iam_moreGraphsAsInit_tryAllPossibleBestGraphs(Gn_median, Gn_candidate,
                                 h_ij0 += h_ij0_p
                             h_ij0_list.append(h_ij0)
                             label_list.append(label)
-    #                    # case when the edge is to be removed.
-    #                    h_ij0_remove = 0
-    #                    for idx, g in enumerate(Gn_median):
-    #                        pi_i = pi_p_forward[idx][nd1i]
-    #                        pi_j = pi_p_forward[idx][nd2i]
-    #                        if g.has_node(pi_i) and g.has_node(pi_j) and not 
-    #                            g.has_edge(pi_i, pi_j):
-    #                            h_ij0_remove += 1
-    #                    h_ij0_list.append(h_ij0_remove)
-    #                    label_list.append(label_r)
                         
                         # get the best labels.
                         idx_max = np.argwhere(h_ij0_list == np.max(h_ij0_list)).flatten().tolist()
@@ -370,7 +356,9 @@ def iam_moreGraphsAsInit_tryAllPossibleBestGraphs(Gn_median, Gn_candidate,
                 idx_list.append(idx)
         return Gn_new, idx_list
 
-   
+    
+    ###########################################################################
+    
     # phase 1: initilize.
     # compute set-median.
     dis_min = np.inf
@@ -421,8 +409,6 @@ def iam_moreGraphsAsInit_tryAllPossibleBestGraphs(Gn_median, Gn_candidate,
 #        print(g.edges(data=True))
     
     # get the best median graphs
-#    dis_list, pi_forward_list = median_distance(G_list, Gn_median,
-#        **params_ged)
     G_min_list, pi_forward_min_list, dis_min = best_median_graphs(
             G_list, pi_forward_list, dis_list)
 #    for g in G_min_list:
@@ -430,9 +416,11 @@ def iam_moreGraphsAsInit_tryAllPossibleBestGraphs(Gn_median, Gn_candidate,
 #        plt.show()
 #        print(g.nodes(data=True))
 #        print(g.edges(data=True))
-    # randomly choose one graph.
-    idx_rdm = random.randint(0, len(G_min_list) - 1)
-    G_min_list = [G_min_list[idx_rdm]]   
+    
+    if not allBestOutput:
+        # randomly choose one graph.
+        idx_rdm = random.randint(0, len(G_min_list) - 1)
+        G_min_list = [G_min_list[idx_rdm]]
     
     return G_min_list, dis_min
 
@@ -445,13 +433,91 @@ def iam_moreGraphsAsInit_tryAllPossibleBestGraphs(Gn_median, Gn_candidate,
 
 
 
+###############################################################################
+# Useful functions.
 
+def GED(g1, g2, lib='gedlibpy', cost='CHEM_1', method='IPFP', saveGXL='benoit', 
+        stabilizer='min'):
+    """
+    Compute GED.
+    """
+    if lib == 'gedlibpy':
+        def convertGraph(G):
+            """Convert a graph to the proper NetworkX format that can be
+            recognized by library gedlibpy.
+            """
+            G_new = nx.Graph()
+            for nd, attrs in G.nodes(data=True):
+                G_new.add_node(str(nd), chem=attrs['atom'])
+            for nd1, nd2, attrs in G.edges(data=True):
+#                G_new.add_edge(str(nd1), str(nd2), valence=attrs['bond_type'])
+                G_new.add_edge(str(nd1), str(nd2))
+                
+            return G_new
+        
+        gedlibpy.restart_env()
+        gedlibpy.add_nx_graph(convertGraph(g1), "")
+        gedlibpy.add_nx_graph(convertGraph(g2), "")
 
+        listID = gedlibpy.get_all_graph_ids()
+        gedlibpy.set_edit_cost(cost)
+        gedlibpy.init()
+        gedlibpy.set_method(method, "")
+        gedlibpy.init_method()
+
+        g = listID[0]
+        h = listID[1]
+        if stabilizer == None:
+            gedlibpy.run_method(g, h)
+            pi_forward = gedlibpy.get_forward_map(g, h)
+            pi_backward = gedlibpy.get_backward_map(g, h)
+            upper = gedlibpy.get_upper_bound(g, h)
+            lower = gedlibpy.get_lower_bound(g, h)        
+        elif stabilizer == 'min':
+            upper = np.inf
+            for itr in range(50):                
+                gedlibpy.run_method(g, h)                
+                upper_tmp = gedlibpy.get_upper_bound(g, h)                
+                if upper_tmp < upper:
+                    upper = upper_tmp
+                    pi_forward = gedlibpy.get_forward_map(g, h)
+                    pi_backward = gedlibpy.get_backward_map(g, h)
+                    lower = gedlibpy.get_lower_bound(g, h)
+                if upper == 0:
+                    break
+                    
+        dis = upper
+        
+        # make the map label correct (label remove map as np.inf)
+        nodes1 = [n for n in g1.nodes()]
+        nodes2 = [n for n in g2.nodes()]
+        nb1 = nx.number_of_nodes(g1)
+        nb2 = nx.number_of_nodes(g2)
+        pi_forward = [nodes2[pi] if pi < nb2 else np.inf for pi in pi_forward]
+        pi_backward = [nodes1[pi] if pi < nb1 else np.inf for pi in pi_backward]      
+        
+    return dis, pi_forward, pi_backward
 
 
+def median_distance(Gn, Gn_median, measure='ged', verbose=False, 
+                    ged_cost='CHEM_1', ged_method='IPFP', saveGXL='benoit'):
+    dis_list = []
+    pi_forward_list = []
+    for idx, G in tqdm(enumerate(Gn), desc='computing median distances', 
+                       file=sys.stdout) if verbose else enumerate(Gn):
+        dis_sum = 0
+        pi_forward_list.append([])
+        for G_p in Gn_median:
+            dis_tmp, pi_tmp_forward, pi_tmp_backward = GED(G, G_p, 
+                cost=ged_cost, method=ged_method, saveGXL=saveGXL)
+            pi_forward_list[idx].append(pi_tmp_forward)
+            dis_sum += dis_tmp
+        dis_list.append(dis_sum)
+    return dis_list, pi_forward_list
 
 
 ###############################################################################
+# Old implementations.
     
 def iam(Gn, c_ei=3, c_er=3, c_es=1, node_label='atom', edge_label='bond_type', 
         connected=True):
@@ -579,73 +645,6 @@ def iam(Gn, c_ei=3, c_er=3, c_es=1, node_label='atom', edge_label='bond_type',
     
     return G
 
-
-def GED(g1, g2, lib='gedlib', cost='CHEM_1', method='IPFP', saveGXL='benoit', 
-        stabilizer='min'):
-    """
-    Compute GED.
-    """
-    if lib == 'gedlib':
-        # transform dataset to the 'xml' file as the GedLib required.
-        saveDataset([g1, g2], [None, None], group='xml', filename='ged_tmp/tmp',
-                    xparams={'method': saveGXL})
-    #        script.appel()
-        script.PyRestartEnv()
-        script.PyLoadGXLGraph('ged_tmp/', 'ged_tmp/tmp.xml')
-        listID = script.PyGetGraphIds()
-        script.PySetEditCost(cost) #("CHEM_1")
-        script.PyInitEnv()
-        script.PySetMethod(method, "")
-        script.PyInitMethod()
-        g = listID[0]
-        h = listID[1]
-        if stabilizer == None:
-            script.PyRunMethod(g, h)
-            pi_forward, pi_backward = script.PyGetAllMap(g, h)
-            upper = script.PyGetUpperBound(g, h)
-            lower = script.PyGetLowerBound(g, h)        
-        elif stabilizer == 'min':
-            upper = np.inf
-            for itr in range(50):                
-                script.PyRunMethod(g, h)                
-                upper_tmp = script.PyGetUpperBound(g, h)                
-                if upper_tmp < upper:
-                    upper = upper_tmp
-                    pi_forward, pi_backward = script.PyGetAllMap(g, h)
-                    lower = script.PyGetLowerBound(g, h)
-                if upper == 0:
-                    break
-                    
-        dis = upper
-        
-        # make the map label correct (label remove map as np.inf)
-        nodes1 = [n for n in g1.nodes()]
-        nodes2 = [n for n in g2.nodes()]
-        nb1 = nx.number_of_nodes(g1)
-        nb2 = nx.number_of_nodes(g2)
-        pi_forward = [nodes2[pi] if pi < nb2 else np.inf for pi in pi_forward]
-        pi_backward = [nodes1[pi] if pi < nb1 else np.inf for pi in pi_backward]      
-        
-    return dis, pi_forward, pi_backward
-
-
-def median_distance(Gn, Gn_median, measure='ged', verbose=False, 
-                    ged_cost='CHEM_1', ged_method='IPFP', saveGXL='benoit'):
-    dis_list = []
-    pi_forward_list = []
-    for idx, G in tqdm(enumerate(Gn), desc='computing median distances', 
-                       file=sys.stdout) if verbose else enumerate(Gn):
-        dis_sum = 0
-        pi_forward_list.append([])
-        for G_p in Gn_median:
-            dis_tmp, pi_tmp_forward, pi_tmp_backward = GED(G, G_p, 
-                cost=ged_cost, method=ged_method, saveGXL=saveGXL)
-            pi_forward_list[idx].append(pi_tmp_forward)
-            dis_sum += dis_tmp
-        dis_list.append(dis_sum)
-    return dis_list, pi_forward_list
-
-
 # --------------------------- These are tests --------------------------------#
     
 def test_iam_with_more_graphs_as_init(Gn, G_candidate, c_ei=3, c_er=3, c_es=1, 
@@ -785,9 +784,6 @@ def test_iam_with_more_graphs_as_init(Gn, G_candidate, c_ei=3, c_er=3, c_es=1,
 
 ###############################################################################
 
-
-
-
 if __name__ == '__main__':
     from pygraph.utils.graphfiles import loadDataset
     ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG.mat',

preimage/librariesImport.py

@@ -1,5 +0,0 @@
-from ctypes import *
-lib1 = cdll.LoadLibrary('lib/fann/libdoublefann.so')
-lib2 = cdll.LoadLibrary('lib/libsvm.3.22/libsvm.so')
-lib3 = cdll.LoadLibrary('lib/nomad/libnomad.so')
-lib4 = cdll.LoadLibrary('lib/nomad/libsgtelib.so')

preimage/librariesImport2.py

@@ -1,5 +0,0 @@
-from ctypes import *
-lib1 = cdll.LoadLibrary('Cython_GedLib_2/lib/fann/libdoublefann.so')
-lib2 = cdll.LoadLibrary('Cython_GedLib_2/lib/libsvm.3.22/libsvm.so')
-lib3 = cdll.LoadLibrary('Cython_GedLib_2/lib/nomad/libnomad.so')
-lib4 = cdll.LoadLibrary('Cython_GedLib_2/lib/nomad/libsgtelib.so')

preimage/preimage.py → preimage/preimage_random.py

@@ -57,7 +57,7 @@ def compute_kernel(Gn, graph_kernel, verbose):
     return Kmatrix
 
 
-def random_preimage(Gn_init, Gn_median, alpha, idx_gi, Kmatrix, k, r_max, l, gkernel):
+def preimage_random(Gn_init, Gn_median, alpha, idx_gi, Kmatrix, k, r_max, l, gkernel):
     Gn_init = [nx.convert_node_labels_to_integers(g) for g in Gn_init]
     
     # compute k nearest neighbors of phi in DN.

preimage/run_gk_iam.py

@@ -402,7 +402,7 @@ def test_iam_letter_h():
     
     
 def test_random_preimage_letter_h():
-    from preimage import random_preimage, compute_kernel
+    from preimage_random import preimage_random, compute_kernel
     ds = {'name': 'Letter-high', 'dataset': '../datasets/Letter-high/Letter-high_A.txt',
           'extra_params': {}} # node nsymb
 #    ds = {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt',
@@ -443,7 +443,7 @@ def test_random_preimage_letter_h():
         # for each alpha
         for alpha in alpha_range:
             print('alpha =', alpha)
-            dhat, ghat_list = random_preimage(Gn_let, Gn_let, [alpha] * len(Gn_let), 
+            dhat, ghat_list = preimage_random(Gn_let, Gn_let, [alpha] * len(Gn_let), 
                                                    range(len(Gn_let), len(Gn_mix)), km, 
                                                    k, r_max, gkernel, c_ei=1.7, 
                                                    c_er=1.7, c_es=1.7)

preimage/setup.py

@@ -1,26 +0,0 @@
-#from distutils.core import setup
-from distutils.extension import Extension
-#from Cython.Distutils import build_ext
-
-from distutils.core import setup
-from Cython.Build import cythonize
-
-#setup(ext_modules=cythonize("script.pyx"))
-
-extensions = [Extension("script",
-                        sources=["script.pyx", "src/essai.cpp"],
-                        include_dirs=["include","include/lsape", "include/Eigen", "include/nomad", "include/sgtelib", "include/libsvm.3.22", "include/fann", "include/boost_1_69_0"],
-                        library_dirs=["lib/fann","lib/gedlib", "lib/libsvm.3.22","lib/nomad"],
-                        libraries=["doublefann","sgtelib", "svm", "nomad"],
-                        language="c++",
-                        extra_compile_args=["-std=c++11"],
-                        extra_link_args=["-std=c++11"])]
-
-setup(ext_modules=cythonize(extensions))
-
-#extensions = [Extension("script", sources=["script.pyx", "include/gedlib-master/src/env/ged_env.ipp"],  include_dirs=["."], language="c++")]
- 
-#setup(name = "script", ext_modules = extensions, cmdclass = {'build_ext':build_ext},)
-
-
-# Commande Bash : python setup.py build_ext --inplace

preimage/test.py

@@ -1,57 +1,84 @@
-#export LD_LIBRARY_PATH=.:/export/home/lambertn/Documents/Cython_GedLib_2/lib/fann/:/export/home/lambertn/Documents/Cython_GedLib_2/lib/libsvm.3.22:/export/home/lambertn/Documents/Cython_GedLib_2/lib/nomad
+#export LD_LIBRARY_PATH=.:/export/home/lambertn/Documents/gedlibpy/lib/fann/:/export/home/lambertn/Documents/gedlibpy/lib/libsvm.3.22:/export/home/lambertn/Documents/gedlibpy/lib/nomad
 
 #Pour que "import script" trouve les librairies qu'a besoin GedLib
 #Equivalent à définir la variable d'environnement LD_LIBRARY_PATH sur un bash
-#Permet de fonctionner sur Idle et autre sans définir à chaque fois la variable d'environnement
-#os.environ ne fonctionne pas dans ce cas
-import librariesImport, script
+import gedlibpy.librariesImport
+from  gedlibpy import gedlibpy
+import networkx as nx
 
-#import script
 
-#truc = script.computeEditDistanceOnGXlGraphs('include/gedlib-master/data/datasets/Mutagenicity/data/','collections/MUTA_10.xml',"CHEM_1", "BIPARTITE", "") 
-#print(truc)
-#script.PyRestartEnv()
-#script.appel()
-
-def test() :
-#    script.appel()
-    
-    script.PyRestartEnv()
-    
-    print("Here is the Python function !")
-    
+def init() :
     print("List of Edit Cost Options : ")
-    for i in script.listOfEditCostOptions :
+    for i in gedlibpy.list_of_edit_cost_options :
         print (i)
     print("")
 
     print("List of Method Options : ")
-    for j in script.listOfMethodOptions :
+    for j in gedlibpy.list_of_method_options :
         print (j)
     print("")
+
+    print("List of Init Options : ")
+    for k in gedlibpy.list_of_init_options :
+        print (k)
+    print("")
     
-    script.PyLoadGXLGraph('include/gedlib-master/data/datasets/Mutagenicity/data/', 'collections/MUTA_10.xml')
-    listID = script.PyGetGraphIds()
+def test():
     
-    afficheId = ""
-    for i in listID :
-        afficheId+=str(i) + " "
-    print("Number of graphs = " + str(len(listID)) + ", list of Ids = " + afficheId)
+    gedlibpy.load_GXL_graphs('include/gedlib-master/data/datasets/Mutagenicity/data/', 'collections/MUTA_10.xml')
+    listID = gedlibpy.get_all_graph_ids()
+    gedlibpy.set_edit_cost("CHEM_1")
+    gedlibpy.init()
+    gedlibpy.set_method("IPFP", "")
+    gedlibpy.init_method()
+    g = listID[0]
+    h = listID[1]
+    gedlibpy.run_method(g, h)
+    print("Node Map : ", gedlibpy.get_node_map(g,h))
+    print("Forward map : " , gedlibpy.get_forward_map(g, h), ", Backward map : ", gedlibpy.get_backward_map(g, h))
+    print("Assignment Matrix : ")
+    print(gedlibpy.get_assignment_matrix(g, h))
+    print ("Upper Bound = " + str(gedlibpy.get_upper_bound(g,h)) + ", Lower Bound = " + str(gedlibpy.get_lower_bound(g, h)) + ", Runtime = " + str(gedlibpy.get_runtime(g, h)))
 
-    script.PySetEditCost("CHEM_1")
 
-    script.PyInitEnv()
+def convertGraph(G):
+    G_new = nx.Graph()
+    for nd, attrs in G.nodes(data=True):
+        G_new.add_node(str(nd), chem=attrs['atom'])
+    for nd1, nd2, attrs in G.edges(data=True):
+        G_new.add_edge(str(nd1), str(nd2), valence=attrs['bond_type'])
+        
+    return G_new
 
-    script.PySetMethod("BIPARTITE", "")
-    script.PyInitMethod()
 
+def testNxGrapĥ():
+    import sys
+    sys.path.insert(0, "../")
+    from pygraph.utils.graphfiles import loadDataset
+    ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt',
+          'extra_params': {}}  # node/edge symb
+    Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params'])
+    
+    gedlibpy.restart_env()
+    for graph in Gn:
+        g_new = convertGraph(graph)
+        gedlibpy.add_nx_graph(g_new, "")
+        
+    listID = gedlibpy.get_all_graph_ids()
+    gedlibpy.set_edit_cost("CHEM_1")
+    gedlibpy.init()
+    gedlibpy.set_method("IPFP", "")
+    gedlibpy.init_method()
+
+    print(listID)
     g = listID[0]
     h = listID[1]
 
-    script.PyRunMethod(g,h)
-    liste = script.PyGetAllMap(g,h)
-    print("Forward map : " ,liste[0], ", Backward map : ", liste[1])
-    print ("Upper Bound = " + str(script.PyGetUpperBound(g,h)) + ", Lower Bound = " + str(script.PyGetLowerBound(g,h)) + ", Runtime = " + str(script.PyGetRuntime(g,h)))
+    gedlibpy.run_method(g, h)
 
+    print("Node Map : ", gedlibpy.get_node_map(g, h))
+    print("Forward map : " , gedlibpy.get_forward_map(g, h), ", Backward map : ", gedlibpy.get_backward_map(g, h))
+    print ("Upper Bound = " + str(gedlibpy.get_upper_bound(g, h)) + ", Lower Bound = " + str(gedlibpy.get_lower_bound(g, h)) + ", Runtime = " + str(gedlibpy.get_runtime(g, h)))
 
-test()
+init() 
+#testNxGrapĥ()

preimage/test_random_mutag.py

@@ -22,7 +22,7 @@ from pygraph.utils.graphfiles import loadDataset
 # random pre-image paper.)
 
 def test_preimage_mix_2combination_all_pairs():
-    from gk_iam import preimage_iam_random_mix, compute_kernel
+    from preimage_iam import preimage_iam_random_mix, compute_kernel
     from iam import median_distance
     ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt',
           'extra_params': {}}  # node/edge symb
@@ -37,6 +37,8 @@ def test_preimage_mix_2combination_all_pairs():
     alpha_range = np.linspace(0.5, 0.5, 1)
     k = 5 # k nearest neighbors
     epsilon = 1e-6
+    InitIAMWithAllDk = True
+    InitRandomWithAllDk = True
     # parameters for GED function
     ged_cost='CHEM_1'
     ged_method='IPFP'
@@ -127,7 +129,8 @@ def test_preimage_mix_2combination_all_pairs():
                     nb_updated_k_iam, nb_updated_k_random = \
                     preimage_iam_random_mix(Gn, [g1, g2],
                     [alpha, 1 - alpha], range(len(Gn), len(Gn) + 2), km, k, r_max, 
-                    l_max, gkernel, epsilon=epsilon, 
+                    l_max, gkernel, epsilon=epsilon, InitIAMWithAllDk=InitIAMWithAllDk, 
+                    InitRandomWithAllDk=InitRandomWithAllDk,
                     params_iam={'c_ei': c_ei, 'c_er': c_er, 'c_es': c_es, 
                                 'ite_max': ite_max_iam, 'epsilon': epsilon_iam,
                                 'removeNodes': removeNodes, 'connected': connected_iam},
@@ -194,7 +197,7 @@ def test_preimage_mix_2combination_all_pairs():
                 
 
 def test_gkiam_2combination_all_pairs():
-    from gk_iam import gk_iam_nearest_multi, compute_kernel
+    from preimage_iam import preimage_iam, compute_kernel
     from iam import median_distance
     ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt',
           'extra_params': {}}  # node/edge symb
@@ -206,8 +209,9 @@ def test_gkiam_2combination_all_pairs():
     lmbda = 0.03 # termination probalility
     r_max = 10 # iteration limit for pre-image.
     alpha_range = np.linspace(0.5, 0.5, 1)
-    k = 10 # k nearest neighbors
+    k = 5 # k nearest neighbors
     epsilon = 1e-6
+    InitIAMWithAllDk = False
     # parameters for GED function
     ged_cost='CHEM_1'
     ged_method='IPFP'
@@ -292,9 +296,9 @@ def test_gkiam_2combination_all_pairs():
                 print('alpha =', alpha)
                 time0 = time.time()
                 dhat, ghat_list, sod_ks, nb_updated, nb_updated_k = \
-                    gk_iam_nearest_multi(Gn, [g1, g2],
+                    preimage_iam(Gn, [g1, g2],
                     [alpha, 1 - alpha], range(len(Gn), len(Gn) + 2), km, k, r_max, 
-                    gkernel, epsilon=epsilon, 
+                    gkernel, epsilon=epsilon, InitIAMWithAllDk=InitIAMWithAllDk,
                     params_iam={'c_ei': c_ei, 'c_er': c_er, 'c_es': c_es, 
                                 'ite_max': ite_max_iam, 'epsilon': epsilon_iam,
                                 'removeNodes': removeNodes, 'connected': connected_iam},
@@ -463,7 +467,7 @@ def test_gkiam_2combination():
     
 def test_random_preimage_2combination():
 #    from gk_iam import compute_kernel
-    from preimage import random_preimage
+    from preimage_random import preimage_random
     ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt',
           'extra_params': {}}  # node/edge symb
     Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params'])
@@ -535,7 +539,7 @@ def test_random_preimage_2combination():
         print('\n-------------------------------------------------------\n')
         print('alpha =', alpha)
         time0 = time.time()
-        dhat, ghat, nb_updated = random_preimage(Gn, [g1, g2], [alpha, 1 - alpha], 
+        dhat, ghat, nb_updated = preimage_random(Gn, [g1, g2], [alpha, 1 - alpha], 
                                           range(len(Gn), len(Gn) + 2), km,
                                           k, r_max, l, gkernel)
         time_total = time.time() - time0 + time_km
@@ -610,5 +614,5 @@ if __name__ == '__main__':
 # random pre-image paper.)
 #    test_random_preimage_2combination()
 #    test_gkiam_2combination()
-    test_gkiam_2combination_all_pairs()
-#    test_preimage_mix_2combination_all_pairs()
+#    test_gkiam_2combination_all_pairs()
+    test_preimage_mix_2combination_all_pairs()