I just started doing my bachelor thesis and what I am trying to do is to create an algorithm that will provide personalized suggestions regarding course selection based on past academic performance. I am learning and using python and till now I have created a list of courses and a list of prerequisites that looks like this:
[['CS101'], ['CS105'], ['CS106', 'CS105'], ['CS107'], ['CS130'], ['CS151', 'CS105', 'MATH101'], ['CS180'], ['CS201', 'CS151'], ['CS205', 'CS105'], ...]
So for example in order to take CS101 you do not need to have taken any other course, but for you to take CS106 you have to already have taken CS105. Now, I have to do topological sort and even though I found some code from a similar problem that was provided here in Stack Overflow, it does not work. The solution was this (I modified only the part of my_lists in order to work for my code):
import networkx as nx
my_lists = data.prerequisites
my_graph = nx.DiGraph()
for path in my_lists:
my_graph.add_nodes_from(path)
my_graph.add_path(path)
ts = nx.topological_sort(my_graph)
print(ts)
where data.prerequisites is the list that was created in another python file(data.py). The problem is that when I run this it gives me this error:
AttributeError: 'DiGraph' object has no attribute 'add_path'
I searched for documentation of networkx 2.4 and there is nothing indicating with what they replaced the add_path(). There was one solution for this error but it does not work anymore since the solution was given in 2011 and the add_path() was completely removed in 2019. I tried to use other examples from the internet but I couldn't find a proper one for lists. Should I try to find a completely different approach or there is a small easy solution like this one? Because a lot of them I cannot really understand.
See the documentation of add_path the new way to call the method is
nx.add_path(my_graph, path)
and as additional hint, you don't need to call my_graph.add_nodes_from(path) since the nodes are added together with the edges.