singleline

generate_data.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 theta_generator(n_line, n_dataset):
+    theta_noadd = np.column_stack([np.random.uniform(size = (n_dataset))*2*np.pi, np.random.default_rng().dirichlet(np.ones(n_line), size=n_dataset)[:, :-1]*(2 - n_line/3)*np.pi])
+    theta_add = theta_noadd @ np.triu(np.ones((n_line, n_line)),0)
+    add_matrix = np.column_stack([np.ones(n_dataset)*np.pi*2/6*i for i in range(n_line)])
+    theta = theta_add + add_matrix 
+    return theta 
+
+
+
+def generate_vrp_data(dataset_size, vrp_size):
+    CAPACITIES = {
+        10: 20.,
+        20: 30.,
+        50: 40.,
+        100: 50.
+    }
+    line_number = 1
+    dataset_size = 100
+    theta = theta_generator(line_number, dataset_size)
+    vrp_size = 20 
+    eps = 1e-8
+    uni_eps = 0.5
+    eps_demand = 1e-04
+    r_data_single = np.array([(1 << (i+1)) -1 for i in range(vrp_size//2)]) 
+    r_data_single = r_data_single/r_data_single[-1] - eps 
+    r_data_rev = np.hstack((-r_data_single[::-1], r_data_single))/2
+    r_data = np.tile(r_data_rev,(dataset_size,1))
+    r_data = r_data * np.random.uniform(1-uni_eps, 1, size = (dataset_size, 1))
+    r_data = r_data / np.maximum(np.abs(np.cos(theta)), np.abs(np.sin(theta)))
+    data_point = 0.5*np.ones((dataset_size, vrp_size, 2)) + np.stack([r_data * np.cos(theta), r_data*np.sin(theta)], axis = 2)
+    demand_point = np.ones((dataset_size, vrp_size)) * 2 * CAPACITIES[vrp_size] / vrp_size - eps_demand
+
+    return list(zip(
+        (0.5*np.ones((dataset_size, 2))).tolist(),  # Depot location
+        data_point.tolist(),  # Node locations
+        demand_point.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__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--filename", help="Filename of the dataset to create (ignores datadir)")
+    parser.add_argument("--data_dir", default='data', 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=[10, 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)
+                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)

outputs_singleline/cvrp_20/org/vrp20_20230313T145506/args.json

@@ -0,0 +1,64 @@
+{
+ "problem": "cvrp",
+ "graph_size": 20,
+ "batch_size": 512,
+ "epoch_size": 1280000,
+ "val_size": 10000,
+ "val_dataset": "/home/gridsan/cpark/routing/data/vrp/vrp20_validation_2route_line_seed1234.pkl",
+ "model": "attention",
+ "embedding_dim": 128,
+ "hidden_dim": 128,
+ "n_encode_layers": 3,
+ "tanh_clipping": 10.0,
+ "normalization": "batch",
+ "lr_model": 0.0001,
+ "lr_critic": 0.0001,
+ "lr_decay": 1.0,
+ "eval_only": false,
+ "n_epochs": 100,
+ "seed": 1234,
+ "max_grad_norm": 1.0,
+ "no_cuda": false,
+ "exp_beta": 0.8,
+ "baseline": "rollout",
+ "bl_alpha": 0.05,
+ "bl_warmup_epochs": 1,
+ "eval_batch_size": 1024,
+ "checkpoint_encoder": false,
+ "shrink_size": null,
+ "data_distribution": null,
+ "log_step": 50,
+ "log_dir": "log_singleline_test",
+ "run_dir": "run_single_line",
+ "run_name": "vrp20_20230313T145506",
+ "output_dir": "outputs_singleline",
+ "image_dir": "images",
+ "epoch_start": 0,
+ "checkpoint_epochs": 1,
+ "load_path": null,
+ "resume": null,
+ "no_tensorboard": false,
+ "no_progress_bar": false,
+ "use_symmetry_ll": false,
+ "supervise_symmetry": false,
+ "restrict_symmetry": false,
+ "angle_threshold": 0.5,
+ "alternate": false,
+ "alternate_freq": 5,
+ "plot_freq": 0,
+ "alex_symmetric_loss": false,
+ "both_losses": false,
+ "plot_symmetric_ll": false,
+ "cap_50": 40,
+ "decay_mode": -1,
+ "num_equivariant_samples": 0,
+ "supervise_lambda": 0.01,
+ "use_starts": false,
+ "label_aug": false,
+ "num_input_augmentations": 0,
+ "wandb_run_name": "run",
+ "use_cuda": true,
+ "variant_name": "org",
+ "save_dir": "outputs_singleline/cvrp_20/org/vrp20_20230313T145506",
+ "plot_dir": "images/cvrp_20/org/vrp20_20230313T145506"
+}

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