feat: add reevaluation function to compute corrected archives in unce…

…rtain domains

qdax/utils/uncertainty_metrics.py

@@ -0,0 +1,318 @@
+from functools import partial
+from typing import Callable, Tuple
+
+import jax
+import jax.numpy as jnp
+
+from qdax.core.containers.mapelites_repertoire import MapElitesRepertoire
+from qdax.types import Descriptor, ExtraScores, Fitness, Genotype, RNGKey
+from qdax.utils.sampling import (
+    dummy_extra_scores_extractor,
+    median,
+    multi_sample_scoring_function,
+    std,
+)
+
+
+@partial(
+    jax.jit,
+    static_argnames=(
+        "scoring_fn",
+        "num_reevals",
+        "fitness_extractor",
+        "descriptor_extractor",
+        "extra_scores_extractor",
+        "scan_size",
+    ),
+)
+def reevaluation_function(
+    repertoire: MapElitesRepertoire,
+    random_key: RNGKey,
+    empty_corrected_repertoire: MapElitesRepertoire,
+    scoring_fn: Callable[
+        [Genotype, RNGKey],
+        Tuple[Fitness, Descriptor, ExtraScores, RNGKey],
+    ],
+    num_reevals: int,
+    fitness_extractor: Callable[[jnp.ndarray], jnp.ndarray] = median,
+    descriptor_extractor: Callable[[jnp.ndarray], jnp.ndarray] = median,
+    extra_scores_extractor: Callable[
+        [ExtraScores, int], ExtraScores
+    ] = dummy_extra_scores_extractor,
+    scan_size: int = 0,
+) -> Tuple[MapElitesRepertoire, RNGKey]:
+    """
+    Perform reevaluation of a repertoire and construct a corrected repertoire from it.
+
+    Args:
+        repertoire: repertoire to reevaluate.
+        empty_corrected_repertoire: repertoire to be filled with reevaluated solutions,
+            allow to use a different type of repertoire than the one from the algorithm.
+        random_key: JAX random key.
+        scoring_fn: scoring function used for evaluation.
+        num_reevals: number of samples to generate for each individual.
+        fitness_extractor: function to extract the final fitness from
+            multiple samples of the same solution (default: median).
+        descriptor_extractor: function to extract the final descriptor from
+            multiple samples of the same solution (default: median).
+        extra_scores_extractor: function to extract the extra_scores from
+            multiple samples of the same solution (default: no effect).
+        scan_size: allow to split the reevaluations in multiple batch to reduce
+            the memory load of the reevaluation.
+    Returns:
+        The corrected repertoire and a random key.
+    """
+
+    # If no reevaluations, return copies of the original container
+    if num_reevals == 0:
+        return repertoire, random_key
+
+    # Perform reevaluation
+    (
+        all_fitnesses,
+        all_descriptors,
+        all_extra_scores,
+        random_key,
+    ) = _perform_reevaluation(
+        policies_params=repertoire.genotypes,
+        random_key=random_key,
+        scoring_fn=scoring_fn,
+        num_reevals=num_reevals,
+        scan_size=scan_size,
+    )
+
+    # Extract the final scores
+    extra_scores = extra_scores_extractor(all_extra_scores, num_reevals)
+    fitnesses = fitness_extractor(all_fitnesses)
+    descriptors = descriptor_extractor(all_descriptors)
+
+    # Set -inf fitness for all unexisting indivs
+    fitnesses = jnp.where(repertoire.fitnesses == -jnp.inf, -jnp.inf, fitnesses)
+
+    # Fill-in the corrected repertoire
+    corrected_repertoire = empty_corrected_repertoire.add(
+        batch_of_genotypes=repertoire.genotypes,
+        batch_of_descriptors=descriptors,
+        batch_of_fitnesses=fitnesses,
+        batch_of_extra_scores=extra_scores,
+    )
+
+    return corrected_repertoire, random_key
+
+
+@partial(
+    jax.jit,
+    static_argnames=(
+        "scoring_fn",
+        "num_reevals",
+        "fitness_extractor",
+        "fitness_reproducibility_extractor",
+        "descriptor_extractor",
+        "descriptor_reproducibility_extractor",
+        "extra_scores_extractor",
+        "scan_size",
+    ),
+)
+def reevaluation_reproducibility_function(
+    repertoire: MapElitesRepertoire,
+    random_key: RNGKey,
+    empty_corrected_repertoire: MapElitesRepertoire,
+    scoring_fn: Callable[
+        [Genotype, RNGKey],
+        Tuple[Fitness, Descriptor, ExtraScores, RNGKey],
+    ],
+    num_reevals: int,
+    fitness_extractor: Callable[[jnp.ndarray], jnp.ndarray] = median,
+    fitness_reproducibility_extractor: Callable[[jnp.ndarray], jnp.ndarray] = std,
+    descriptor_extractor: Callable[[jnp.ndarray], jnp.ndarray] = median,
+    descriptor_reproducibility_extractor: Callable[[jnp.ndarray], jnp.ndarray] = std,
+    extra_scores_extractor: Callable[
+        [ExtraScores, int], ExtraScores
+    ] = dummy_extra_scores_extractor,
+    scan_size: int = 0,
+) -> Tuple[MapElitesRepertoire, MapElitesRepertoire, MapElitesRepertoire, RNGKey]:
+    """
+    Perform reevaluation of a repertoire and construct a corrected repertoire and a
+    reproducibility repertoire from it.
+
+    Args:
+        repertoire: repertoire to reevaluate.
+        empty_corrected_repertoire: repertoire to be filled with reevaluated solutions,
+            allow to use a different type of repertoire than the one from the algorithm.
+        random_key: JAX random key.
+        scoring_fn: scoring function used for evaluation.
+        num_reevals: number of samples to generate for each individual.
+        fitness_extractor: function to extract the final fitness from
+            multiple samples of the same solution (default: median).
+        fitness_reproducibility_extractor: function to extract the fitness
+            reproducibility from multiple samples of the same solution (default: std).
+        descriptor_extractor: function to extract the final descriptor from
+            multiple samples of the same solution (default: median).
+        descriptor_reproducibility_extractor: function to extract the descriptor
+            reproducibility from multiple samples of the same solution (default: std).
+        extra_scores_extractor: function to extract the extra_scores from
+            multiple samples of the same solution (default: no effect).
+        scan_size: allow to split the reevaluations in multiple batch to reduce
+            the memory load of the reevaluation.
+    Returns:
+        The corrected repertoire.
+        A repertoire storing reproducibility in fitness.
+        A repertoire storing reproducibility in descriptor.
+        A random key.
+    """
+
+    # If no reevaluations, return copies of the original container
+    if num_reevals == 0:
+        return (
+            repertoire,
+            repertoire,
+            repertoire,
+            random_key,
+        )
+
+    # Perform reevaluation
+    (
+        all_fitnesses,
+        all_descriptors,
+        all_extra_scores,
+        random_key,
+    ) = _perform_reevaluation(
+        policies_params=repertoire.genotypes,
+        random_key=random_key,
+        scoring_fn=scoring_fn,
+        num_reevals=num_reevals,
+        scan_size=scan_size,
+    )
+
+    # Extract the final scores
+    extra_scores = extra_scores_extractor(all_extra_scores, num_reevals)
+    fitnesses = fitness_extractor(all_fitnesses)
+    fitnesses_reproducibility = fitness_reproducibility_extractor(all_fitnesses)
+    descriptors = descriptor_extractor(all_descriptors)
+    descriptors_reproducibility = descriptor_reproducibility_extractor(all_descriptors)
+
+    # WARNING: in the case of descriptors_reproducibility, take average over dimensions
+    descriptors_reproducibility = jnp.average(descriptors_reproducibility, axis=-1)
+
+    # Set -inf fitness for all unexisting indivs
+    fitnesses = jnp.where(repertoire.fitnesses == -jnp.inf, -jnp.inf, fitnesses)
+    fitnesses_reproducibility = jnp.where(
+        repertoire.fitnesses == -jnp.inf, -jnp.inf, fitnesses_reproducibility
+    )
+    descriptors_reproducibility = jnp.where(
+        repertoire.fitnesses == -jnp.inf, -jnp.inf, descriptors_reproducibility
+    )
+
+    # Fill-in corrected repertoire
+    corrected_repertoire = empty_corrected_repertoire.add(
+        batch_of_genotypes=repertoire.genotypes,
+        batch_of_descriptors=descriptors,
+        batch_of_fitnesses=fitnesses,
+        batch_of_extra_scores=extra_scores,
+    )
+
+    # Fill-in fit_reproducibility repertoire
+    fit_reproducibility_repertoire = empty_corrected_repertoire.add(
+        batch_of_genotypes=repertoire.genotypes,
+        batch_of_descriptors=repertoire.descriptors,
+        batch_of_fitnesses=fitnesses_reproducibility,
+        batch_of_extra_scores=extra_scores,
+    )
+
+    # Fill-in desc_reproducibility repertoire
+    desc_reproducibility_repertoire = empty_corrected_repertoire.add(
+        batch_of_genotypes=repertoire.genotypes,
+        batch_of_descriptors=repertoire.descriptors,
+        batch_of_fitnesses=descriptors_reproducibility,
+        batch_of_extra_scores=extra_scores,
+    )
+
+    return (
+        corrected_repertoire,
+        fit_reproducibility_repertoire,
+        desc_reproducibility_repertoire,
+        random_key,
+    )
+
+
+@partial(
+    jax.jit,
+    static_argnames=(
+        "scoring_fn",
+        "num_reevals",
+        "scan_size",
+    ),
+)
+def _perform_reevaluation(
+    policies_params: Genotype,
+    random_key: RNGKey,
+    scoring_fn: Callable[
+        [Genotype, RNGKey],
+        Tuple[Fitness, Descriptor, ExtraScores, RNGKey],
+    ],
+    num_reevals: int,
+    scan_size: int = 0,
+) -> Tuple[Fitness, Descriptor, ExtraScores, RNGKey]:
+    """
+    Sub-function used to perform reevaluation of a repertoire in uncertain applications.
+
+    Args:
+        policies_params: genotypes to reevaluate.
+        random_key: JAX random key.
+        scoring_fn: scoring function used for evaluation.
+        num_reevals: number of samples to generate for each individual.
+        scan_size: allow to split the reevaluations in multiple batch to reduce
+            the memory load of the reevaluation.
+    Returns:
+        The fitnesses, descriptors and extra score from the reevaluation,
+        and a randon key.
+    """
+
+    # If no need for scan, call the sampling function
+    if scan_size == 0:
+        (
+            all_fitnesses,
+            all_descriptors,
+            all_extra_scores,
+            random_key,
+        ) = multi_sample_scoring_function(
+            policies_params=policies_params,
+            random_key=random_key,
+            scoring_fn=scoring_fn,
+            num_samples=num_reevals,
+        )
+
+    # If need for scan, call the sampling function multiple times
+    else:
+        num_loops = num_reevals // scan_size
+
+        def _sampling_scan(
+            random_key: RNGKey,
+            unused: Tuple[()],
+        ) -> Tuple[Tuple[RNGKey], Tuple[Fitness, Descriptor, ExtraScores]]:
+            (
+                all_fitnesses,
+                all_descriptors,
+                all_extra_scores,
+                random_key,
+            ) = multi_sample_scoring_function(
+                policies_params=policies_params,
+                random_key=random_key,
+                scoring_fn=scoring_fn,
+                num_samples=scan_size,
+            )
+            return (random_key), (
+                all_fitnesses,
+                all_descriptors,
+                all_extra_scores,
+            )
+
+        (random_key), (
+            all_fitnesses,
+            all_descriptors,
+            all_extra_scores,
+        ) = jax.lax.scan(_sampling_scan, (random_key), (), length=num_loops)
+        all_fitnesses = jnp.hstack(all_fitnesses)
+        all_descriptors = jnp.hstack(all_descriptors)
+
+    return all_fitnesses, all_descriptors, all_extra_scores, random_key