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import torch.nn as nn
import numpy as np
class CFTourGenerator(nn.Module):
def __init__(self, cf_solver):
super().__init__()
self.solver = cf_solver
self.problem = cf_solver.problem
def forward(self, factual_tour, vehicle_id, cf_step, cf_next_node_id, node_feats, dist_matrix=None):
"""
solve an input instance with visited edges fixed
Parameters
----------
factual_tour: list [seq_length]
cf_step: int
cf_next_node_id: int
node_feats:
Returns
-------
cf_tour: np.array [seq_length]
"""
fixed_paths = self.get_fixed_paths(factual_tour, vehicle_id, cf_step, cf_next_node_id)
cf_tours = self.solver.solve(node_feats, fixed_paths, dist_matrix=dist_matrix)
if cf_tours is None:
return
if (cf_step > 0):
for vehicle_id, cf_tour in enumerate(cf_tours):
if cf_next_node_id in cf_tour:
if cf_step == 1:
if cf_tour[1] != cf_next_node_id:
cf_tours[vehicle_id] = np.flipud(cf_tour)
break
else:
if (factual_tour[vehicle_id][1] != cf_tour[1]):
cf_tours[vehicle_id] = np.flipud(cf_tour) # make direction of the cf tour the same as factual one
break
print("aaaa", cf_tours)
return cf_tours
def get_fixed_paths(self, factual_tour, vehicle_id, cf_step, cf_next_node_id):
visited_paths = np.append(factual_tour[vehicle_id][:cf_step], cf_next_node_id)
return visited_paths
# def get_avail_edges(self, factual_tour, cf_step, cf_next_node_id):
# visited_paths = np.append(factual_tour[:cf_step], cf_next_node_id)
# avail_edges = []
# # add fixed edges
# for i in range(len(visited_paths) - 1):
# avail_edges.append([visited_paths[i], visited_paths[i + 1]])
# print(avail_edges)
# # add rest avaialbel edges
# num_nodes = np.max(factual_tour) + 1
# visited = np.array([0] * num_nodes)
# for id in visited_paths:
# visited[id] = 1
# visited[factual_tour[0]] = 0
# visited[cf_next_node_id] = 0
# mask = visited < 1
# node_id = np.arange(num_nodes)
# feasible_node_id = node_id[mask]
# for j in range(len(feasible_node_id) - 1):
# for i in range(j + 1, len(feasible_node_id)):
# if ((feasible_node_id[j] == factual_tour[0]) and (feasible_node_id[i] == cf_next_node_id)) or ((feasible_node_id[i] == factual_tour[0]) and (feasible_node_id[j] == cf_next_node_id)):
# continue
# avail_edges.append([feasible_node_id[j], feasible_node_id[i]])
# return np.array(avail_edges)
#-----------
# unit test
#-----------
if __name__ == "__main__":
import argparse
import random
import matplotlib.pyplot as plt
# FYI:
# - https://yu-nix.com/archives/python-path-get/
# - https://www.delftstack.com/ja/howto/python/python-get-parent-directory/
# - https://stackoverflow.com/questions/2817264/how-to-get-the-parent-dir-location
import os
import sys
CURR_DIR = os.path.dirname(os.path.abspath(__file__))
PARENT_DIR = os.path.abspath(os.path.join(CURR_DIR, os.pardir))
sys.path.append(PARENT_DIR)
from utils.util_vis import visualize_factual_and_cf_tours
from lkh.lkh import LKH
from models.ortools.ortools import ORTools
from data_generator.tsptw.tsptw_dataset import generate_tsptw_instance
parser = argparse.ArgumentParser(description='')
# general settings
parser.add_argument("--problem", type=str, default="tsptw")
parser.add_argument("--random_seed", type=int, default=1234)
parser.add_argument("--num_samples", type=int, default=5)
parser.add_argument("--num_nodes", type=int, default=100)
parser.add_argument("--coord_dim", type=int, default=2)
# LKH settings
parser.add_argument("--max_trials", type=int, default=1000)
parser.add_argument("--lkh_dir", type=str, default="lkh", help="Path to the binary of LKH")
parser.add_argument("--io_dir", type=str, default="lkh_io_files")
args = parser.parse_args()
# models
# cf_solver = LKH(args.problem, args.max_trials, args.random_seed, lkh_dir=args.lkh_dir, io_dir=args.io_dir)
cf_solver = ORTools(args.problem)
cf_generator = CFTourGenerator(cf_solver)
# dataset
if args.problem == "tsp":
np.random.seed(args.random_seed)
node_feats = np.random.uniform(size=[args.num_samples, args.num_nodes, args.coord_dim])
elif args.problem == "tsptw":
coords, time_window, grid_size = generate_tsptw_instance(num_nodes=args.num_nodes, grid_size=100, max_tw_gap=10, max_tw_size=1000, is_integer_instance=True, da_silva_style=True)
node_feats = np.concatenate([coords, time_window], -1)
node_feats = node_feats[None, :, :]
# function ot automatically generate couterfactual visit
def get_random_cf_visit(factual_tour, random_seed=1234):
# random.seed(random_seed)
num_nodes = np.max(factual_tour) + 1
step = random.randrange(len(factual_tour) - 2) # remove the last step (returning to the start-point)
visited = np.array([0] * num_nodes)
for i in range(step+1):
visited[factual_tour[i]] = 1
mask = visited < 1
node_id = np.arange(num_nodes)
feasible_node_id = node_id[mask]
cf_next_id = random.choice(feasible_node_id) # select counterfactual
return step, cf_next_id
for i in range(len(node_feats)):
# obtain a factual tour
factual_tour = cf_solver.solve(node_feats[i])
# counterfactual visit
cf_step, cf_next_node_id = get_random_cf_visit(factual_tour, random_seed=args.random_seed)
print(cf_step, cf_next_node_id)
# obtain a counterfactual tour
cf_tour = cf_generator(factual_tour, cf_step, cf_next_node_id, node_feats[i])
print(factual_tour)
print(cf_tour)
# visualize the factual and counterfactual tours
if args.problem == "tsp":
coords = node_feats[i]
elif args.problem == "tsptw":
coord_dim = 2
coords = node_feats[i, :, :coord_dim]
visualize_factual_and_cf_tours(factual_tour, cf_tour, coords, cf_step, f"test{i}.png")
break |