# Revision history [back]

I have searched more ;)

From Sagemath source code, we can spot the following that leaves no room for hope:

    Traceback (most recent call last):
...
ValueError: Algorithm 'Floyd-Warshall-Cython' does not work with weights.


But all is not lost. For each of Numpy (via the NetworkX library) and Scipy, there is an efficient implementation of Floyd algorithm (for Scipy, the implementation is as efficient as pure C code). The following code shows how to use them within SageMath:

import networkx as nx
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import floyd_warshall
import numpy as np

def wedges2matrix(wedges, n, loop=True):

M=[[float('inf')]*n for _ in range(n)]
for a, b, w in wedges:
M[a][b]=w
if loop:
for a in range(n):
M[a][a]=0
return M

def floyd_scipy(wedges, n):
M=wedges2matrix(wedges, n)
return floyd_warshall(np.array(M))

def floyd_np(wedges, n):
G = nx.DiGraph()
M=wedges2matrix(wedges, n)
dist=nx.floyd_warshall_numpy(G, nodelist=range(n))
return dist

n=10 #nb of vertices
wedges=[(9, 3, 38.0), (6, 0, 6.0), (0, 2, 53.0), (4, 7, 66.0),
(1, 9, 84.0), (8, 5, 24.0), (2, 0, 73.0), (4, 2, 79.0),
(6, 7, 78.0), (2, 4, 90.0), (7, 2, 81.0), (7, 0, 57.0),
(1, 3, 89.0), (0, 3, 17.0)]

A=floyd_np(wedges, n)
B=floyd_scipy(wedges, n)

print A
print "-------------"
print B


outputting identical matrices.

This is an opportunity to benchmark Sage implementation:

from time import clock

def dist2matrix(dist,n):
M=matrix(n, n, [[float('inf')]*n for _ in range(n)])
for a in dist:
for b in dist[a]:
M[a,b]=dist[a][b]
for i in range(n):
M[i,i]=0
return M

def random_edges(n, density, max_weight=100):
M=n*(n-1)//2
m=int(density*M)
edges=set()
wedges=[]
while len(edges)<m:
L=(randrange(n),randrange(n))
if L[0]!=L[1] and L not in edges:
w=float(randrange(max_weight))
wedges.append(L+(w,))
return wedges

def floyd(wedges, n):
M=wedges2matrix(wedges, n)
for k in range(n):
A=M[k]
for i in range(n):
B=M[i]
for j in range(n):
if B[k] + A[j]<B[j]:
B[j]=B[k] + A[j]
return M

def floyd_sage(wedges, n):
G = DiGraph(wedges)
dist, _ = G.shortest_path_all_pairs(by_weight = True, algorithm='Floyd-Warshall-Python')
return dist

n=300
density=0.33

wedges=random_edges(n, density)

start=clock()
M=floyd_scipy(wedges, n)
end=clock()
print("Scipy Floyd \t: %.2f" %(end-start))

start=clock()
M=floyd_np(wedges, n)
end=clock()
print("Numpy Floyd  \t: %.2f" %(end-start))

start=clock()
A=floyd(wedges, n)
end=clock()
print("Usual Floyd  \t: %.2f" %(end-start))

start=clock()
dist=floyd_sage(wedges, n)
end=clock()
print("Sage Floyd \t: %.2f" %(end-start))

check(A, dist2matrix(dist,n))


outputting on my machine:

Scipy Floyd     : 0.03
Numpy Floyd     : 0.11
Usual Floyd     : 2.52
Sage Floyd  : 10.43
True


It seems that Sage pure Python implementation is not very efficient...

I have searched more ;)

From Sagemath source code, we can spot the following that leaves no room for hope:

    Traceback (most recent call last):
...
ValueError: Algorithm 'Floyd-Warshall-Cython' does not work with weights.


But all is not lost. For each of Numpy (via the NetworkX library) and Scipy, there is an efficient implementation of Floyd algorithm (for Scipy, the implementation is as efficient as pure C code). The following code shows how to use them within SageMath:

import networkx as nx
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import floyd_warshall
import numpy as np

def wedges2matrix(wedges, n, loop=True):

M=[[float('inf')]*n for _ in range(n)]
for a, b, w in wedges:
M[a][b]=w
if loop:
for a in range(n):
M[a][a]=0
return M

def floyd_scipy(wedges, n):
M=wedges2matrix(wedges, n)
return floyd_warshall(np.array(M))

def floyd_np(wedges, n):
G = nx.DiGraph()
M=wedges2matrix(wedges, n)
dist=nx.floyd_warshall_numpy(G, nodelist=range(n))
return dist

n=10 #nb of vertices
wedges=[(9, 3, 38.0), (6, 0, 6.0), (0, 2, 53.0), (4, 7, 66.0),
(1, 9, 84.0), (8, 5, 24.0), (2, 0, 73.0), (4, 2, 79.0),
(6, 7, 78.0), (2, 4, 90.0), (7, 2, 81.0), (7, 0, 57.0),
(1, 3, 89.0), (0, 3, 17.0)]

A=floyd_np(wedges, n)
B=floyd_scipy(wedges, n)

print A
print "-------------"
print B


outputting identical matrices.

This is an opportunity to benchmark Sage implementation:

from time import clock

def dist2matrix(dist,n):
M=matrix(n, n, [[float('inf')]*n for _ in range(n)])
for a in dist:
for b in dist[a]:
M[a,b]=dist[a][b]
for i in range(n):
M[i,i]=0
return M

def random_edges(n, density, max_weight=100):
M=n*(n-1)//2
m=int(density*M)
edges=set()
wedges=[]
while len(edges)<m:
L=(randrange(n),randrange(n))
if L[0]!=L[1] and L not in edges:
w=float(randrange(max_weight))
wedges.append(L+(w,))
return wedges

def floyd(wedges, n):
M=wedges2matrix(wedges, n)
for k in range(n):
A=M[k]
for i in range(n):
B=M[i]
for j in range(n):
if B[k] + A[j]<B[j]:
B[j]=B[k] + A[j]
return M

def floyd_sage(wedges, n):
G = DiGraph(wedges)
dist, _ = G.shortest_path_all_pairs(by_weight = True, algorithm='Floyd-Warshall-Python')
return dist

n=300
density=0.33

wedges=random_edges(n, density)

start=clock()
M=floyd_scipy(wedges, n)
end=clock()
print("Scipy Floyd \t: %.2f" %(end-start))

start=clock()
M=floyd_np(wedges, n)
end=clock()
print("Numpy Floyd  \t: %.2f" %(end-start))

start=clock()
A=floyd(wedges, n)
end=clock()
print("Usual Floyd  \t: %.2f" %(end-start))

start=clock()
dist=floyd_sage(wedges, n)
end=clock()
print("Sage Floyd \t: %.2f" %(end-start))

check(A, dist2matrix(dist,n))
matrix(RR, A)==dist2matrix(dist,n)


outputting on my machine:

Scipy Floyd     : 0.03
Numpy Floyd     : 0.11
Usual Floyd     : 2.52
Sage Floyd  : 10.43
True


It seems that Sage pure Python implementation is not very efficient...

Edit: final check was missing.