🔨 Introduces two new examples of CVT

examples/cvt_bin_packing_generator.py

@@ -0,0 +1,117 @@
+#!/usr/bin/env python
+# -*-coding:utf-8 -*-
+"""
+@File    :   generate_instances.py
+@Time    :   2024/09/30 09:19:06
+@Author  :   Alejandro Marrero
+@Version :   1.0
+@Contact :   amarrerd@ull.edu.es
+@License :   (C)Copyright 2024, Alejandro Marrero
+@Desc    :   None
+"""
+
+import argparse
+
+import numpy as np
+
+import digneapy as dpy
+from digneapy.solvers import best_fit, first_fit, next_fit, worst_fit
+
+
+def save_instances(filename, generated_instances, dimension):
+    """Writes the generated instances into a CSV file
+
+    Args:
+        filename (str): Filename
+        generated_instances (iterable): Iterable of instances
+    """
+    header = ["target", "N", "capacity", *[f"w_{i}" for i in range(dimension - 1)]]
+    with open(filename, "w") as file:
+        file.write(",".join(header) + "\n")
+        for solver, instances in generated_instances.items():
+            for instance in instances:
+                assert len(instance) == dimension + 1
+                vars = ",".join(str(x) for x in instance)
+                content = solver + "," + str(dimension) + "," + vars + "\n"
+                file.write(content)
+
+
+def generate_instances_for_target(
+    portfolio,
+    dimension: int,
+    generations: int = 1000,
+    population_size: int = 128,
+    regions: int = 1_000,
+    n_samples: int = 100_000,
+):
+    cvt_centroids = np.load(f"BPP_CVTArchive_dimension_{dimension}_centroids.npy")
+    cvt_samples = np.load(f"BPP_CVTArchive_dimension_{dimension}_samples.npy")
+    domain = dpy.domains.BPPDomain(
+        dimension=dimension,
+        min_i=20,
+        max_i=100,
+        max_capacity=150,
+        capacity_approach="fixed",
+    )
+
+    # Create an empty archive with the previous centroids and samples
+    # The genotype of the BPP is [capacity, w_i x N]
+    # The ranges must be [150, (20, 100)]
+    cvt_archive = dpy.CVTArchive(
+        k=regions,
+        ranges=[(150, 150), *[(20, 100) for _ in range(dimension)]],
+        n_samples=n_samples,
+        centroids=cvt_centroids,
+        samples=cvt_samples,
+    )
+    map_elites = dpy.generators.MapElitesGenerator(
+        domain,
+        portfolio=portfolio,
+        archive=cvt_archive,
+        initial_pop_size=population_size,
+        mutation=dpy.operators.uniform_one_mutation,
+        generations=generations,
+        descriptor="instance",
+        repetitions=1,
+    )
+    archive = map_elites()
+    del map_elites
+    return archive
+
+
+if __name__ == "__main__":
+    expected_dimensions = (120, 240, 560, 1080)
+    parser = argparse.ArgumentParser(
+        prog="cvt_bpp_generator", description="CVTMAP-Elites for Bin Packing instances"
+    )
+    parser.add_argument(
+        "dimension",
+        choices=expected_dimensions,
+        type=int,
+        help="dimension of the Bin Packing instances",
+    )
+    parser.add_argument(
+        "repetition",
+        choices=list(range(10)),
+        type=int,
+        help="Number of the run to append in the final CSV file",
+    )
+    args = parser.parse_args()
+    n_run = args.repetition
+    dimension = args.dimension
+
+    portfolios = [
+        [best_fit, first_fit, next_fit, worst_fit],
+        [first_fit, best_fit, next_fit, worst_fit],
+        [next_fit, best_fit, first_fit, worst_fit],
+        [worst_fit, best_fit, first_fit, next_fit],
+    ]
+
+    results = {}
+    for portfolio in portfolios:
+        archive = generate_instances_for_target(portfolio, dimension)
+        results[portfolio[0].__name__] = archive
+
+    save_instances(
+        f"bpp_cvtmapelites_N_{dimension}_{n_run}.csv", results, dimension=dimension
+    )

examples/cvt_kp_generator.py

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+#!/usr/bin/env python
+# -*-coding:utf-8 -*-
+"""
+@File    :   generate_instances.py
+@Time    :   2024/09/30 09:19:06
+@Author  :   Alejandro Marrero
+@Version :   1.0
+@Contact :   amarrerd@ull.edu.es
+@License :   (C)Copyright 2024, Alejandro Marrero
+@Desc    :   None
+"""
+
+import argparse
+import itertools
+
+import numpy as np
+
+import digneapy as dpy
+from digneapy.solvers import default_kp, map_kp, miw_kp, mpw_kp
+
+
+def save_instances(filename, generated_instances, dimension):
+    """Writes the generated instances into a CSV file
+
+    Args:
+        filename (str): Filename
+        generated_instances (iterable): Iterable of instances
+    """
+    header = (
+        ["target", "N"]
+        + ["capacity"]
+        + list(
+            itertools.chain.from_iterable(
+                [(f"w_{i}", f"p_{i}") for i in range(dimension)]
+            )
+        )
+    )
+    with open(filename, "w") as file:
+        file.write(",".join(header) + "\n")
+        for solver, instances in generated_instances.items():
+            for instance in instances:
+                assert len(instance) == (dimension * 2) + 1
+                vars = ",".join(str(x) for x in instance)
+                content = solver + "," + str(dimension) + "," + vars + "\n"
+                file.write(content)
+
+
+def generate_instances_for_target(
+    portfolio,
+    dimension: int,
+    generations: int = 1000,
+    population_size: int = 128,
+    regions: int = 1_000,
+    n_samples: int = 100_000,
+):
+    genotype = (dimension) * 2
+    cvt_centroids = np.load(f"CVTArchive_dimension_{dimension}_centroids.npy")
+    cvt_samples = np.load(f"CVTArchive_dimension_{dimension}_samples.npy")
+    domain = dpy.domains.KnapsackDomain(dimension=dimension)
+
+    # Create an empty archive with the previous centroids and samples
+    cvt_archive = dpy.CVTArchive(
+        k=regions,
+        ranges=[(1.0, 10_000), *[(1.0, 1_000) for _ in range(genotype)]],
+        n_samples=n_samples,
+        centroids=cvt_centroids,
+        samples=cvt_samples,
+    )
+    map_elites = dpy.generators.MapElitesGenerator(
+        domain,
+        portfolio=portfolio,
+        archive=cvt_archive,
+        initial_pop_size=population_size,
+        mutation=dpy.operators.uniform_one_mutation,
+        generations=generations,
+        descriptor="instance",
+        repetitions=1,
+    )
+    archive = map_elites()
+    del map_elites
+    return archive
+
+
+if __name__ == "__main__":
+    expected_dimensions = (50, 100, 500, 1000)
+    parser = argparse.ArgumentParser(
+        prog="generate_instances", description="CVTMAP-Elites for KP instances"
+    )
+    parser.add_argument(
+        "dimension",
+        choices=expected_dimensions,
+        type=int,
+        help="dimension of the KP instances",
+    )
+    parser.add_argument(
+        "repetition",
+        choices=list(range(10)),
+        type=int,
+        help="Number of the run to append in the final CSV file",
+    )
+    args = parser.parse_args()
+    n_run = args.repetition
+    dimension = args.dimension
+
+    portfolios = [
+        [default_kp, map_kp, miw_kp, mpw_kp],
+        [map_kp, default_kp, miw_kp, mpw_kp],
+        [miw_kp, default_kp, map_kp, mpw_kp],
+        [mpw_kp, default_kp, map_kp, miw_kp],
+    ]
+
+    results = {}
+    for portfolio in portfolios:
+        archive = generate_instances_for_target(portfolio, dimension)
+        results[portfolio[0].__name__] = archive
+
+    save_instances(
+        f"kp_cvtmapelites_N_{dimension}_{n_run}.csv", results, dimension=dimension
+    )