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generate_newdata.py
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generate_newdata.py
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import argparse
import os
import numpy as np
from utils.data_utils import check_extension, save_dataset
def generate_tsp_data(dataset_size, tsp_size):
return np.random.uniform(size=(dataset_size, tsp_size, 2)).tolist()
def generate_cluster_tsp(dataset_size, tsp_size, n_samples_per_cluster=10, cluster_std=0.03, random_seed=None):
"""
Generate a random dataset with clusters of points.
Parameters:
tsp_size (int): The total number of samples to generate.
n_clusters (int): The number of clusters to generate.
cluster_std (float): The standard deviation of the clusters.
random_seed (int): The random seed to use for reproducibility. Optional.
Returns:
A numpy array of shape (n_samples, 2) representing the generated dataset.
"""
# Set the random seed if provided
if random_seed is not None:
np.random.seed(random_seed)
# Compute the number of samples per cluster
# n_samples_per_cluster = n_samples // n_clusters
n_clusters = tsp_size // n_samples_per_cluster
# Generate the cluster centers randomly
cluster_centers = np.random.uniform(size=(n_clusters, 2))
xy = []
# Generate the samples by adding noise to each cluster center
for xx in range(0, dataset_size):
X = np.zeros((tsp_size, 2))
for i in range(n_clusters):
start_idx = i * n_samples_per_cluster
end_idx = start_idx + n_samples_per_cluster
X[start_idx:end_idx, :] = np.random.normal(
loc=cluster_centers[i], scale=cluster_std, size=(n_samples_per_cluster, 2)
)
# Shuffle the samples randomly
np.random.shuffle(X)
xy.append(X.tolist())
# Return the generated dataset
return xy
def generate_vrp_data(dataset_size, vrp_size):
CAPACITIES = {
10: 20.,
20: 30.,
50: 40.,
100: 50.
}
return list(zip(
np.random.uniform(size=(dataset_size, 2)).tolist(), # Depot location
np.random.uniform(size=(dataset_size, vrp_size, 2)).tolist(), # Node locations
np.random.randint(1, 10, size=(dataset_size, vrp_size)).tolist(), # Demand, uniform integer 1 ... 9
np.full(dataset_size, CAPACITIES[vrp_size]).tolist() # Capacity, same for whole dataset
))
def generate_op_data(dataset_size, op_size, prize_type='const'):
depot = np.random.uniform(size=(dataset_size, 2))
loc = np.random.uniform(size=(dataset_size, op_size, 2))
# Methods taken from Fischetti et al. 1998
if prize_type == 'const':
prize = np.ones((dataset_size, op_size))
elif prize_type == 'unif':
prize = (1 + np.random.randint(0, 100, size=(dataset_size, op_size))) / 100.
else: # Based on distance to depot
assert prize_type == 'dist'
prize_ = np.linalg.norm(depot[:, None, :] - loc, axis=-1)
prize = (1 + (prize_ / prize_.max(axis=-1, keepdims=True) * 99).astype(int)) / 100.
# Max length is approximately half of optimal TSP tour, such that half (a bit more) of the nodes can be visited
# which is maximally difficult as this has the largest number of possibilities
MAX_LENGTHS = {
20: 2.,
50: 3.,
100: 4.
}
return list(zip(
depot.tolist(),
loc.tolist(),
prize.tolist(),
np.full(dataset_size, MAX_LENGTHS[op_size]).tolist() # Capacity, same for whole dataset
))
def generate_pctsp_data(dataset_size, pctsp_size, penalty_factor=3):
depot = np.random.uniform(size=(dataset_size, 2))
loc = np.random.uniform(size=(dataset_size, pctsp_size, 2))
# For the penalty to make sense it should be not too large (in which case all nodes will be visited) nor too small
# so we want the objective term to be approximately equal to the length of the tour, which we estimate with half
# of the nodes by half of the tour length (which is very rough but similar to op)
# This means that the sum of penalties for all nodes will be approximately equal to the tour length (on average)
# The expected total (uniform) penalty of half of the nodes (since approx half will be visited by the constraint)
# is (n / 2) / 2 = n / 4 so divide by this means multiply by 4 / n,
# However instead of 4 we use penalty_factor (3 works well) so we can make them larger or smaller
MAX_LENGTHS = {
20: 2.,
50: 3.,
100: 4.
}
penalty_max = MAX_LENGTHS[pctsp_size] * (penalty_factor) / float(pctsp_size)
penalty = np.random.uniform(size=(dataset_size, pctsp_size)) * penalty_max
# Take uniform prizes
# Now expectation is 0.5 so expected total prize is n / 2, we want to force to visit approximately half of the nodes
# so the constraint will be that total prize >= (n / 2) / 2 = n / 4
# equivalently, we divide all prizes by n / 4 and the total prize should be >= 1
deterministic_prize = np.random.uniform(size=(dataset_size, pctsp_size)) * 4 / float(pctsp_size)
# In the deterministic setting, the stochastic_prize is not used and the deterministic prize is known
# In the stochastic setting, the deterministic prize is the expected prize and is known up front but the
# stochastic prize is only revealed once the node is visited
# Stochastic prize is between (0, 2 * expected_prize) such that E(stochastic prize) = E(deterministic_prize)
stochastic_prize = np.random.uniform(size=(dataset_size, pctsp_size)) * deterministic_prize * 2
return list(zip(
depot.tolist(),
loc.tolist(),
penalty.tolist(),
deterministic_prize.tolist(),
stochastic_prize.tolist()
))
if __name__ == "__main__":
# x = generate_cluster_tsp(10, 4, 2)
# x = generate_tsp_data(10,2)
# print(np.shape(x))
parser = argparse.ArgumentParser()
parser.add_argument("--filename", help="Filename of the dataset to create (ignores datadir)")
parser.add_argument("--data_dir", default='newdata', help="Create datasets in data_dir/problem (default 'data')")
parser.add_argument("--name", type=str, required=True, help="Name to identify dataset")
parser.add_argument("--problem", type=str, default='all',
help="Problem, 'tsp', 'vrp', 'pctsp' or 'op_const', 'op_unif' or 'op_dist'"
" or 'all' to generate all")
parser.add_argument('--data_distribution', type=str, default='all',
help="Distributions to generate for problem, default 'all'.")
parser.add_argument("--dataset_size", type=int, default=10000, help="Size of the dataset")
parser.add_argument('--graph_sizes', type=int, nargs='+', default=[20, 50, 100],
help="Sizes of problem instances (default 20, 50, 100)")
parser.add_argument("-f", action='store_true', help="Set true to overwrite")
parser.add_argument('--seed', type=int, default=1234, help="Random seed")
opts = parser.parse_args()
assert opts.filename is None or (len(opts.problems) == 1 and len(opts.graph_sizes) == 1), \
"Can only specify filename when generating a single dataset"
distributions_per_problem = {
'tsp': [None],
'vrp': [None],
'pctsp': [None],
'op': ['const', 'unif', 'dist']
}
if opts.problem == 'all':
problems = distributions_per_problem
else:
problems = {
opts.problem:
distributions_per_problem[opts.problem]
if opts.data_distribution == 'all'
else [opts.data_distribution]
}
for problem, distributions in problems.items():
for distribution in distributions or [None]:
for graph_size in opts.graph_sizes:
datadir = os.path.join(opts.data_dir, problem)
os.makedirs(datadir, exist_ok=True)
if opts.filename is None:
filename = os.path.join(datadir, "{}{}{}_{}_seed{}.pkl".format(
problem,
"_{}".format(distribution) if distribution is not None else "",
graph_size, opts.name, opts.seed))
else:
filename = check_extension(opts.filename)
assert opts.f or not os.path.isfile(check_extension(filename)), \
"File already exists! Try running with -f option to overwrite."
np.random.seed(opts.seed)
if problem == 'tsp':
# dataset = generate_tsp_data(opts.dataset_size, graph_size)
dataset = generate_cluster_tsp(opts.dataset_size, graph_size)
elif problem == 'vrp':
dataset = generate_vrp_data(
opts.dataset_size, graph_size)
elif problem == 'pctsp':
dataset = generate_pctsp_data(opts.dataset_size, graph_size)
elif problem == "op":
dataset = generate_op_data(opts.dataset_size, graph_size, prize_type=distribution)
else:
assert False, "Unknown problem: {}".format(problem)
print(dataset[0])
save_dataset(dataset, filename)