Debug Weisfeiler_lehman + ADD BagOfNode approach

gmatch4py/bow.pyx

+# coding = utf-8
+
+import networkx as nx
+import numpy as np
+cimport numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+from .base cimport Base
+
+cdef class BOW(Base):
+
+    def __init__(self):
+            Base.__init__(self,0,True)
+
+    cpdef np.ndarray compare(self,list graph_list, list selected):
+        nodes = list()
+        for g in graph_list:
+            nodes.extend(list(g.nodes()))
+
+        vocabulary = list(set(nodes))
+        hash_voc = {}
+        i = 0
+        for se in vocabulary:
+            hash_voc[se] = i
+            i += 1
+        n, m = len(graph_list), len(hash_voc)
+        bow_matrix = np.zeros((n, m))
+        i = 0
+        for g in range(len(graph_list)):
+            graph = graph_list[g]
+            nodes = list(graph.nodes())
+            for nod in nodes:
+                j = hash_voc[nod]
+                bow_matrix[i, j] = 1
+            i += 1
+
+        sim_matrix = cosine_similarity(bow_matrix)
+        np.fill_diagonal(sim_matrix, 1)
+        return sim_matrix

gmatch4py/kernels/weisfeiler_lehman.pyx

@@ -18,6 +18,8 @@ import networkx as nx
 import numpy as np
 cimport numpy as np
 from ..base cimport Base
+from ..base import minmax_scale
+from scipy.sparse import csc_matrix,lil_matrix
 
 cdef class WeisfeleirLehmanKernel(Base):
 
@@ -51,7 +53,6 @@ cdef class WeisfeleirLehmanKernel(Base):
         """
 
         cdef int n = len(graph_list)
-        cdef np.ndarray phi
         cdef int n_nodes = 0
         cdef int n_max = 0
         cdef int i,j
@@ -67,6 +68,7 @@ cdef class WeisfeleirLehmanKernel(Base):
                 n_max = graph_list[i].number_of_nodes()
 
         phi = np.zeros((n_nodes, n), dtype=np.uint64)
+        phi=lil_matrix(phi)
 
         # INITIALIZATION: initialize the nodes labels for each graph
         # with their labels or with degrees (for unlabeled graphs)
@@ -101,9 +103,9 @@ cdef class WeisfeleirLehmanKernel(Base):
 
             graph_list[i]=nx.relabel_nodes(graph_list[i],label_lookup)
 
-        cdef np.ndarray[np.float64_t] k
+        # cdef np.ndarray[np.float64_t] k
         k = np.dot(phi.transpose(), phi)
-
+        print(1)
         # MAIN LOOP
         cdef int it = 0
 
@@ -140,10 +142,4 @@ cdef class WeisfeleirLehmanKernel(Base):
             k += np.dot(phi.transpose(), phi)
             it = it + 1
 
-        # Compute the normalized version of the kernel
-        cdef  np.ndarray[np.float64_t] k_norm = np.zeros((k.shape[0],k.shape[1]))
-        for i in range(k.shape[0]):
-            for j in range(k.shape[1]):
-                k_norm[i, j] = k[i, j] / np.sqrt(k[i, i] * k[j, j])
-
-        return k_norm
\ No newline at end of file
+        return minmax_scale(k).todense()
\ No newline at end of file