在 python 图形数据结构中找到所有可能的路径而不使用递归 function

Question

I have a serious issue with finding all possible paths in my csv file that looks like this:

Source	Target	Source_repo	Target_repo
SOURCE1	Target2	repo-1	repo-2
SOURCE5	Target3	repo-5	repo-3
SOURCE8	Target5	repo-8	repo-5

There a large amount of lines in the datasets, more than 5000 lines. I want to generate all possible paths like this in and return a list (Target5 is equal to SOURCE5):

• SOURCE1 Target2
• SOURCE8 Target5 Target3

I want to implement this solution without using recursive functions, since causes problems (maximum recursion depth exceeded).

This is the current code example:

def attach_co_changing_components(base_component):
    co_changes = df_depends_on.loc[df_depends_on["Source_repo"] ==
                                   base_component, "Target_repo"].values
    result = {base_component: list(co_changes)}
    return result


def dfs(data, path, paths):
    datum = path[-1]
    if datum in data:
        for val in data[datum]:
            new_path = path + [val]
            paths = dfs(data, new_path, paths)
    else:
        paths += [path]
    return paths



def enumerate_paths(graph, nodes=[]):
    nodes = graph.keys()
    all_paths = []
    for node in nodes:
        node_paths = dfs(graph, [node], [])
        all_paths += node_paths
    return all_paths


if __name__ == "__main__":

    df = pd.read_csv("clean_openstack_evolution.csv")

    co_changing_components = df[["Source"]].copy()

    co_changing_components = co_changing_components.drop_duplicates(
    ).reset_index(drop=True)

    co_changing_components = co_changing_components["Source"].map(
        attach_co_changing_components)

    co_changing_components = co_changing_components.rename("Path")

    co_changing_components = co_changing_components.reset_index(drop=True)

    newdict = {}
    for k, v in [(key, d[key]) for d in co_changing_components for key in d]:
        if k not in newdict: newdict[k] = v
        else: newdict[k].append(v)

    graph_keys = df_depends_on["Source_repo"].drop_duplicates().to_dict(
    ).values()
    graph_keys = {*graph_keys}
    graph_keys = set([
        k for k in graph_keys
        if len(df_depends_on[df_depends_on["Target"] == k]) > 0
    ])

    result = enumerate_paths(new_dict)

Here is the output after executing the preceding code:

Here is the data link Google drive

I tried to solve the problem using recursive function, but the code failed with the problem of depth exceeded. I aim to solve it without recursive functions.

Answer 1

I'm not sure if you want all paths or paths specifically from node to another node. Either way this looks like a job for networkx .

Setup ( nx.from_pandas_edgelist )

import networkx as nx
import pandas as pd


df = pd.read_csv("...")

graph = nx.from_pandas_edgelist(df, create_using=nx.DiGraph)

All paths ( nx.all_simple_paths )

from itertools import chain, product, starmap
from functools import partial


roots = (node for node, d in graph.in_degree if d == 0)

leaves = (node for node, d in graph.out_degree if d == 0)

all_paths = partial(nx.all_simple_paths, graph)

paths = list(chain.from_iterable(starmap(all_paths, product(roots, leaves))))

From one node to another

source_node = "some_node_in_graph"
target_node = "some_other_node_in_graph"
list(nx.all_simple_paths(graph, source=source_node, target=target_node))