ENH : improved `all_pairs_bellman_ford_path`

[Schefflera-Arboricola]

build upon: https://github.com/networkx/nx-parallel/pull/14

Improvements

1. Yielding instead of returning a generator

The updated all_pairs_bellman_ford_path algorithm yields and the old(current) version returns a generator object. The difference is subtle but there is a difference. And, the speedups are also pretty good(for some cases). And, this way it is more aligned with networkx's implementation.

Speedup Heatmap(yielding Vs returning a generator object)

* The title says it compares nx-parallel and networkx implementations but it actually compares the old implementation and new implementation, I just forgot to change the title.

2. Added Chunking

I think it's good to have chunking whenever possible, as it increases user control. The chunking version of the algorithm gives better speed-ups(compared to the no_chunking version). It also gives better overall speed-ups(ref. the updated heatmap in PR). I've also added get_chunks which lets the user adjust the nodes inside the chunks.

Speedup Heatmap(default chunking Vs no chunking)

Timing script used

import time
import random
import types
import networkx as nx
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
import nx_parallel as nxp

heatmapDF = pd.DataFrame()
number_of_nodes_list = [10, 50, 100, 300, 500]
pList = [1, 0.8, 0.6, 0.4, 0.2]
currFun = nx.all_pairs_bellman_ford_path
for p in pList:
    for num in number_of_nodes_list:
        # create original and parallel graphs
        G = nx.fast_gnp_random_graph(num, p, seed=42, directed=False)

        # for weighted graphs
        random.seed(42)
        for u, v in G.edges():
            G[u][v]["weight"] = random.random()

        H = nxp.ParallelGraph(G)

        # time both versions and update heatmapDF
        t1 = time.time()
        c = nxp.all_pairs_bellman_ford_path_new(H)
        # c = nxp.all_pairs_bellman_ford_path_chunk(H) # when timing chunking VS no-chunking
        if isinstance(c, types.GeneratorType):
            d = dict(c)
        t2 = time.time()
        newTime = t2 - t1
        t1 = time.time()
        c = nxp.all_pairs_bellman_ford_path_old(H)
        # c = nxp.all_pairs_bellman_ford_path_no_chunk(H) # when timing chunking VS no-chunking
        if isinstance(c, types.GeneratorType):
            d = dict(c)
        t2 = time.time()
        oldTime = t2 - t1
        timesFaster = oldTime / newTime
        heatmapDF.at[num, p] = timesFaster
        print("Finished " + str(currFun))

plt.figure(figsize=(20, 4))
hm = sns.heatmap(data=heatmapDF.T, annot=True, cmap="Greens", cbar=True)
hm.set_yticklabels(pList)
hm.set_xticklabels(number_of_nodes_list)
plt.xticks(rotation=45)
plt.yticks(rotation=20)
plt.title("Small Scale Demo: Times Speedups of " + currFun.__name__ + " compared to networkx")
plt.xlabel("Number of Vertices")
plt.ylabel("Edge Probability")
print(currFun.__name__)
plt.tight_layout()
plt.savefig("timing/" + "heatmap_" + currFun.__name__ + "_timing.png")