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import numpy as np
import argparse
import pickle
import random
import time
import os
def generate_IS(N, M):
'''
Function Description:
Generate instances of the maximum independent set problem in a general graph.
Parameters:
- N: Number of vertices in the graph.
- M: Number of edges in the graph.
Return:
Relevant parameters of the generated maximum independent set problem.
'''
# n represents the number of decision variables, where each vertex in the graph corresponds to a decision variable.
# m represents the number of constraints, where each edge in the graph corresponds to a constraint.
# k[i] represents the number of decision variables in the i-th constraint.
n = N
m = M
k = []
# site[i][j] represents which decision variable the j-th decision variable corresponds to in the i-th constraint.
# value[i][j] represents the coefficient of the j-th decision variable in the i-th constraint.
# constraint[i] represents the right-hand side value of the i-th constraint.
# constraint_type[i] represents the type of the i-th constraint, where 1 represents <=, 2 represents >=, and 3 represents =.
# coefficient[i] represents the coefficient of the i-th decision variable in the objective function.
site = []
value = []
for i in range(m):
site.append([])
value.append([])
k.append(0)
constraint = np.zeros(m)
constraint_type = np.zeros(m)
coefficient = {}
# Add constraint: randomly generate an edge and impose a constraint that the vertices connected by the edge cannot be selected simultaneously.
for i in range(M):
x = random.randint(0, N - 1)
y = random.randint(0, N - 1)
while(x == y) :
x = random.randint(0, N - 1)
y = random.randint(0, N - 1)
site[i].append(x)
value[i].append(1)
site[i].append(y)
value[i].append(1)
constraint[i] = 1
constraint_type[i] = 1
k[i] = 2
# Set the coefficients of the objective function, where the coefficient value of each decision variable corresponding to a vertex is a random value.
for i in range(N):
coefficient[i] = random.random()
return(n, m, k, site, value, constraint, constraint_type, coefficient)
def generate_MVC(N, M):
'''
Function Description:
Generate instances of the minimum vertex cover problem in a general graph.
Parameters:
- N: Number of vertices in the graph.
- M: Number of edges in the graph.
Return:
Relevant parameters of the generated minimum vertex cover problem.
'''
# n represents the number of decision variables, where each vertex in the graph corresponds to a decision variable.
# m represents the number of constraints, where each edge in the graph corresponds to a constraint.
# k[i] represents the number of decision variables in the i-th constraint.
n = N
m = M
k = []
# site[i][j] represents which decision variable the j-th decision variable corresponds to in the i-th constraint.
# value[i][j] represents the coefficient of the j-th decision variable in the i-th constraint.
# constraint[i] represents the right-hand side value of the i-th constraint.
# constraint_type[i] represents the type of the i-th constraint, where 1 represents <=, 2 represents >=, and 3 represents =.
# coefficient[i] represents the coefficient of the i-th decision variable in the objective function.
site = []
value = []
for i in range(m):
site.append([])
value.append([])
k.append(0)
constraint = np.zeros(m)
constraint_type = np.zeros(m)
coefficient = {}
# Add constraint: randomly generate an edge and impose a constraint that at least one of the vertices connected by the edge must be selected.
for i in range(M):
x = random.randint(0, N - 1)
y = random.randint(0, N - 1)
while(x == y) :
x = random.randint(0, N - 1)
y = random.randint(0, N - 1)
k[i] = 2
site[i].append(x)
value[i].append(1)
site[i].append(y)
value[i].append(1)
constraint[i] = 1
constraint_type[i] = 2
# Set the coefficients of the objective function, where the coefficient value of each decision variable corresponding to a vertex is a random value.
for i in range(N):
coefficient[i] = random.random()
return(n, m, k, site, value, constraint, constraint_type, coefficient)
def generate_SC(N, M):
'''
Function Description:
Generate instances of the set cover problem, where each item is guaranteed to appear in exactly 4 sets.
Parameters:
- N: Number of sets.
- M: Number of items.
Return:
Relevant parameters of the generated set cover problem.
'''
# n represents the number of decision variables, where each set corresponds to a decision variable.
# m represents the number of constraints, where each item corresponds to a constraint.
# k[i] represents the number of decision variables in the i-th constraint.
n = N
m = M
k = []
# site[i][j] represents which decision variable the j-th decision variable corresponds to in the i-th constraint.
# value[i][j] represents the coefficient of the j-th decision variable in the i-th constraint.
# constraint[i] represents the right-hand side value of the i-th constraint.
# constraint_type[i] represents the type of the i-th constraint, where 1 represents <=, 2 represents >=, and 3 represents =.
# coefficient[i] represents the coefficient of the i-th decision variable in the objective function.
site = []
value = []
for i in range(m):
site.append([])
value.append([])
k.append(0)
constraint = np.zeros(m)
constraint_type = np.zeros(m)
coefficient = {}
# Add constraint: At least one of the four sets in which each item appears must be selected.
for i in range(M):
vis = {}
for j in range(4):
now = random.randint(0, N - 1)
while(now in vis.keys()):
now = random.randint(0, N - 1)
vis[now] = 1
site[i].append(now)
value[i].append(1)
k[i] = 4
for i in range(M):
constraint[i] = 1
constraint_type[i] = 2
# Set the coefficients of the objective function, where the coefficient value of each decision variable corresponding to a set is a random value.
for i in range(N):
coefficient[i] = random.random()
return(n, m, k, site, value, constraint, constraint_type, coefficient)
def generate_CAT(N, M):
'''
Function Description:
Generate instances of the set cover problem, where each item is guaranteed to appear in exactly 5 sets.
Parameters:
- N: Number of sets.
- M: Number of items.
Return:
Relevant parameters of the generated set cover problem.
'''
# n represents the number of decision variables, where each set corresponds to a decision variable.
# m represents the number of constraints, where each item corresponds to a constraint.
# k[i] represents the number of decision variables in the i-th constraint.
n = N
m = M
k = []
# site[i][j] represents which decision variable the j-th decision variable corresponds to in the i-th constraint.
# value[i][j] represents the coefficient of the j-th decision variable in the i-th constraint.
# constraint[i] represents the right-hand side value of the i-th constraint.
# constraint_type[i] represents the type of the i-th constraint, where 1 represents <=, 2 represents >=, and 3 represents =.
# coefficient[i] represents the coefficient of the i-th decision variable in the objective function.
site = []
value = []
for i in range(m):
site.append([])
value.append([])
k.append(0)
constraint = np.zeros(m)
constraint_type = np.zeros(m)
coefficient = {}
# Add constraints.
for i in range(M):
vis = {}
for j in range(5):
now = random.randint(0, N - 1)
while(now in vis.keys()):
now = random.randint(0, N - 1)
vis[now] = 1
site[i].append(now)
value[i].append(1)
k[i] = 5
for i in range(M):
constraint[i] = 1
constraint_type[i] = 1
# Set the coefficients of the objective function, where the coefficient value of each decision variable corresponding to a set is a random value.
for i in range(N):
coefficient[i] = random.random() * 1000
return(n, m, k, site, value, constraint, constraint_type, coefficient)
def generate_samples(
problem_type : str,
difficulty_mode : str,
seed : int,
number : int
):
'''
Function Description:
Generate problem instances based on the provided parameters and package the output as data.pickle.
Parameters:
- problem_type: Available options are ['IS', 'MVC', 'MAXCUT', 'SC'], representing the maximum independent set problem, minimum vertex cover problem, maximum cut problem, minimum set cover problem, and Meituan flash sale problem, respectively.
- difficulty_mode: Available options are ['easy', 'medium', 'hard'], representing easy (small-scale), medium (medium-scale), and hard (large-scale) difficulties.
- seed: Integer value indicating the starting random seed used for problem generation.
- number: Integer value indicating the number of instances to generate.
Return:
The problem instances are generated and packaged as data.pickle. The function does not have a return value.
'''
# Set the random seed.
random.seed(seed)
# Check and create using the os module.
dir_name = 'example'
if not os.path.exists(dir_name):
os.mkdir(dir_name)
for i in range(number):
# Randomly generate instances of the maximum independent set problem and package the output.
if(problem_type == 'IS'):
if(difficulty_mode == 'easy'):
N = 10000
M = 30000
elif(difficulty_mode == 'medium'):
N = 100000
M = 300000
else:
N = 1000000
M = 3000000
n, m, k, site, value, constraint, constraint_type, coefficient = generate_IS(N, M)
with open('./example/data' + str(i) + '.pickle', 'wb') as f:
pickle.dump(['maximize', n, m, k, site, value, constraint, constraint_type, coefficient], f)
# Randomly generate instances of the minimum vertex cover problem and package the output.
if(problem_type == 'MVC'):
if(difficulty_mode == 'easy'):
N = 10000
M = 30000
elif(difficulty_mode == 'medium'):
N = 100000
M = 300000
else:
N = 1000000
M = 3000000
n, m, k, site, value, constraint, constraint_type, coefficient = generate_MVC(N, M)
with open('./example/data' + str(i) + '.pickle', 'wb') as f:
pickle.dump(['minimize', n, m, k, site, value, constraint, constraint_type, coefficient], f)
# Randomly generate instances of the minimum set cover problem and package the output.
if(problem_type == 'SC'):
if(difficulty_mode == 'easy'):
N = 20000
M = 20000
elif(difficulty_mode == 'medium'):
N = 200000
M = 200000
else:
N = 2000000
M = 2000000
n, m, k, site, value, constraint, constraint_type, coefficient = generate_SC(N, M)
with open('./example/data' + str(i) + '.pickle', 'wb') as f:
pickle.dump(['minimize', n, m, k, site, value, constraint, constraint_type, coefficient], f)
# Randomly generate instances of the combinatorial auction problem and package the output.
if(problem_type == 'CAT'):
if(difficulty_mode == 'easy'):
N = 10000
M = 10000
elif(difficulty_mode == 'medium'):
N = 100000
M = 100000
else:
N = 1000000
M = 1000000
n, m, k, site, value, constraint, constraint_type, coefficient = generate_CAT(N, M)
with open('./example/data' + str(i) + '.pickle', 'wb') as f:
pickle.dump(['maximize', n, m, k, site, value, constraint, constraint_type, coefficient], f)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--problem_type", choices = ['IS', 'MVC', 'SC', 'CAT'], default = 'SC', help = "Problem type selection")
parser.add_argument("--difficulty_mode", choices = ['easy', 'medium', 'hard'], default = 'easy', help = "Difficulty level.")
parser.add_argument('--seed', type = int, default = 0, help = 'Random generator seed.')
parser.add_argument("--number", type = int, default = 10, help = 'The number of instances.')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
#print(vars(args))
generate_samples(**vars(args)) |