Clean up similarity.py and use dataclasses for storing state (network…

…x#5831)

* Clean up similarity.py and use dataclasses for storing state

* use nonlocal to stop using an object to store maxcost value

networkx/algorithms/similarity.py

@@ -16,9 +16,8 @@
 import math
 import time
 import warnings
-from functools import reduce
+from dataclasses import dataclass
 from itertools import product
-from operator import mul
 
 import networkx as nx
 
@@ -187,7 +186,7 @@ def graph_edit_distance(
 
     """
     bestcost = None
-    for vertex_path, edge_path, cost in optimize_edit_paths(
+    for _, _, cost in optimize_edit_paths(
         G1,
         G2,
         node_match,
@@ -503,7 +502,7 @@ def optimize_graph_edit_distance(
        <10.5220/0005209202710278>. <hal-01168816>
        https://hal.archives-ouvertes.fr/hal-01168816
     """
-    for vertex_path, edge_path, cost in optimize_edit_paths(
+    for _, _, cost in optimize_edit_paths(
         G1,
         G2,
         node_match,
@@ -672,18 +671,12 @@ def optimize_edit_paths(
     import scipy as sp
     import scipy.optimize  # call as sp.optimize
 
+    @dataclass
     class CostMatrix:
-        def __init__(self, C, lsa_row_ind, lsa_col_ind, ls):
-            # assert C.shape[0] == len(lsa_row_ind)
-            # assert C.shape[1] == len(lsa_col_ind)
-            # assert len(lsa_row_ind) == len(lsa_col_ind)
-            # assert set(lsa_row_ind) == set(range(len(lsa_row_ind)))
-            # assert set(lsa_col_ind) == set(range(len(lsa_col_ind)))
-            # assert ls == C[lsa_row_ind, lsa_col_ind].sum()
-            self.C = C
-            self.lsa_row_ind = lsa_row_ind
-            self.lsa_col_ind = lsa_col_ind
-            self.ls = ls
+        C: ...
+        lsa_row_ind: ...
+        lsa_col_ind: ...
+        ls: ...
 
     def make_CostMatrix(C, m, n):
         # assert(C.shape == (m + n, m + n))
@@ -724,7 +717,7 @@ def reduce_ind(ind, i):
             rind[rind >= k] -= 1
         return rind
 
-    def match_edges(u, v, pending_g, pending_h, Ce, matched_uv=[]):
+    def match_edges(u, v, pending_g, pending_h, Ce, matched_uv=None):
         """
         Parameters:
             u, v: matched vertices, u=None or v=None for
@@ -748,7 +741,10 @@ def match_edges(u, v, pending_g, pending_h, Ce, matched_uv=[]):
         # only attempt to match edges after one node match has been made
         # this will stop self-edges on the first node being automatically deleted
         # even when a substitution is the better option
-        if matched_uv:
+        if matched_uv is None or len(matched_uv) == 0:
+            g_ind = []
+            h_ind = []
+        else:
             g_ind = [
                 i
                 for i in range(M)
@@ -765,9 +761,6 @@ def match_edges(u, v, pending_g, pending_h, Ce, matched_uv=[]):
                     pending_h[j][:2] in ((q, v), (v, q), (q, q)) for p, q in matched_uv
                 )
             ]
-        else:
-            g_ind = []
-            h_ind = []
 
         m = len(g_ind)
         n = len(h_ind)
@@ -778,9 +771,9 @@ def match_edges(u, v, pending_g, pending_h, Ce, matched_uv=[]):
 
             # Forbid structurally invalid matches
             # NOTE: inf remembered from Ce construction
-            for k, i in zip(range(m), g_ind):
+            for k, i in enumerate(g_ind):
                 g = pending_g[i][:2]
-                for l, j in zip(range(n), h_ind):
+                for l, j in enumerate(h_ind):
                     h = pending_h[j][:2]
                     if nx.is_directed(G1) or nx.is_directed(G2):
                         if any(
@@ -822,8 +815,7 @@ def reduce_Ce(Ce, ij, m, n):
             m_i = m - sum(1 for t in i if t < m)
             n_j = n - sum(1 for t in j if t < n)
             return make_CostMatrix(reduce_C(Ce.C, i, j, m, n), m_i, n_j)
-        else:
-            return Ce
+        return Ce
 
     def get_edit_ops(
         matched_uv, pending_u, pending_v, Cv, pending_g, pending_h, Ce, matched_cost
@@ -982,8 +974,9 @@ def get_edit_paths(
             # assert not len(pending_g)
             # assert not len(pending_h)
             # path completed!
-            # assert matched_cost <= maxcost.value
-            maxcost.value = min(maxcost.value, matched_cost)
+            # assert matched_cost <= maxcost_value
+            nonlocal maxcost_value
+            maxcost_value = min(maxcost_value, matched_cost)
             yield matched_uv, matched_gh, matched_cost
 
         else:
@@ -1051,7 +1044,7 @@ def get_edit_paths(
                 for y, h in zip(sortedy, reversed(H)):
                     if h is not None:
                         pending_h.insert(y, h)
-                for t in xy:
+                for _ in xy:
                     matched_gh.pop()
 
     # Initialization
@@ -1167,13 +1160,7 @@ def get_edit_paths(
     # debug_print(Ce.C)
     # debug_print()
 
-    class MaxCost:
-        def __init__(self):
-            # initial upper-bound estimate
-            # NOTE: should work for empty graph
-            self.value = Cv.C.sum() + Ce.C.sum() + 1
-
-    maxcost = MaxCost()
+    maxcost_value = Cv.C.sum() + Ce.C.sum() + 1
 
     if timeout is not None:
         if timeout <= 0:
@@ -1187,10 +1174,11 @@ def prune(cost):
         if upper_bound is not None:
             if cost > upper_bound:
                 return True
-        if cost > maxcost.value:
+        if cost > maxcost_value:
             return True
-        elif strictly_decreasing and cost >= maxcost.value:
+        if strictly_decreasing and cost >= maxcost_value:
             return True
+        return False
 
     # Now go!
 
@@ -1204,7 +1192,7 @@ def prune(cost):
         # assert sorted(G1.edges) == sorted(g for g, h in edge_path if g is not None)
         # assert sorted(G2.edges) == sorted(h for g, h in edge_path if h is not None)
         # print(vertex_path, edge_path, cost, file = sys.stderr)
-        # assert cost == maxcost.value
+        # assert cost == maxcost_value
         yield list(vertex_path), list(edge_path), cost
 
 
@@ -1324,9 +1312,9 @@ def simrank(G, u, v):
 
     if isinstance(x, np.ndarray):
         if x.ndim == 1:
-            return {node: val for node, val in zip(G, x)}
-        else:  # x.ndim == 2:
-            return {u: dict(zip(G, row)) for u, row in zip(G, x)}
+            return dict(zip(G, x))
+        # else x.ndim == 2
+        return {u: dict(zip(G, row)) for u, row in zip(G, x)}
     return x