"""Contains the CategoricalArchive."""
import numpy as np
from numpy_groupies import aggregate_nb as aggregate

from ribs._utils import (check_batch_shape, check_shape, validate_batch,
                         validate_single)
from ribs.archives._archive_base import ArchiveBase
from ribs.archives._archive_stats import ArchiveStats
from ribs.archives._array_store import ArrayStore
from ribs.archives._grid_archive import GridArchive
from ribs.archives._utils import (fill_sentinel_values, parse_dtype,
                                  validate_cma_mae_settings)


class CategoricalArchive(ArchiveBase):
    # pylint: disable = too-many-public-methods
    """An archive where each dimension is divided into categories.

    This archive is similar to a :class:`~ribs.archives.GridArchive`, except
    that each measure is a categorical variable. Just like GridArchive, it can
    be visualized as an n-dimensional grid in the measure space that is divided
    into cells along each dimension. Each cell contains an elite, i.e., a
    solution that *maximizes* the objective function and has measures that lie
    within that cell. This archive also implements the idea of *soft archives*
    that have *thresholds*, as introduced in `Fontaine 2023
    <https://arxiv.org/abs/2205.10752>`_.

    By default, this archive stores the following data fields: ``solution``,
    ``objective``, ``measures``, ``threshold``, and ``index``. The ``threshold``
    is the value that a solution's objective value must exceed to be inserted
    into a cell, while the integer ``index`` uniquely identifies each cell.

    Args:
        solution_dim (int or tuple of int): Dimensionality of the solution
            space. Scalar or multi-dimensional solution shapes are allowed by
            passing an empty tuple or tuple of integers, respectively.
        categories (list of list of any): The name of each category for each
            dimension of the measure space. The length of this list is the
            dimensionality of the measure space. An example is ``[["A", "B",
            "C"], ["One", "Two", "Three", "Four"]]``, which defines a 2D measure
            space where the first dimension has categories ``["A", "B", "C"]``
            and the second has categories ``["One", "Two", "Three", "Four"]``.
            While any object can be used for the category name, strings are
            expected to be the typical use case.
        learning_rate (float): The learning rate for threshold updates. Defaults
            to 1.0.
        threshold_min (float): The initial threshold value for all the cells.
        qd_score_offset (float): Archives often contain negative objective
            values, and if the QD score were to be computed with these negative
            objectives, the algorithm would be penalized for adding new cells
            with negative objectives. Thus, a standard practice is to normalize
            all the objectives so that they are non-negative by introducing an
            offset. This QD score offset will be *subtracted* from all
            objectives in the archive, e.g., if your objectives go as low as
            -300, pass in -300 so that each objective will be transformed as
            ``objective - (-300)``.
        seed (int): Value to seed the random number generator. Set to None to
            avoid a fixed seed.
        dtype (str or data-type or dict): There are two options for this
            parameter. First, it can be just the data type of the solutions and
            objectives, with the measures defaulting to a dtype of ``object``.
            In this case, ``dtype`` can be ``"f"`` / ``np.float32`` or ``"d"`` /
            ``np.float64``. Second, ``dtype`` can be a dict specifying separate
            dtypes, of the form ``{"solution": <dtype>, "objective": <dtype>,
            "measures": <dtype>}``.
        extra_fields (dict): Description of extra fields of data that is stored
            next to elite data like solutions and objectives. The description is
            a dict mapping from a field name (str) to a tuple of ``(shape,
            dtype)``. For instance, ``{"foo": ((), np.float32), "bar": ((10,),
            np.float32)}`` will create a "foo" field that contains scalar values
            and a "bar" field that contains 10D values. Note that field names
            must be valid Python identifiers, and names already used in the
            archive are not allowed.
    Raises:
        ValueError: Invalid values for learning_rate and threshold_min.
        ValueError: Invalid names in extra_fields.
    """

    def __init__(
        self,
        *,
        solution_dim,
        categories,
        learning_rate=None,
        threshold_min=-np.inf,
        qd_score_offset=0.0,
        seed=None,
        dtype=np.float64,
        extra_fields=None,
    ):
        self._rng = np.random.default_rng(seed)
        self._categories = [list(measure_dim) for measure_dim in categories]
        self._dims = np.array([len(measure_dim) for measure_dim in categories],
                              dtype=np.int32)

        ArchiveBase.__init__(
            self,
            solution_dim=solution_dim,
            objective_dim=(),
            measure_dim=len(self._categories),
        )

        # Set up the ArrayStore, which is a data structure that stores all the
        # elites' data in arrays sharing a common index.
        extra_fields = extra_fields or {}
        reserved_fields = {
            "solution", "objective", "measures", "threshold", "index"
        }
        if reserved_fields & extra_fields.keys():
            raise ValueError("The following names are not allowed in "
                             f"extra_fields: {reserved_fields}")
        if not isinstance(dtype, dict):
            # Make measures default to `object` dtype.
            dtype = {
                "solution": dtype,
                "measures": object,
                "objective": dtype,
            }
        dtype = parse_dtype(dtype)
        self._store = ArrayStore(
            field_desc={
                "solution": (self.solution_dim, dtype["solution"]),
                "objective": ((), dtype["objective"]),
                "measures": (self.measure_dim, dtype["measures"]),
                # Must be same dtype as the objective since they share
                # calculations.
                "threshold": ((), dtype["objective"]),
                **extra_fields,
            },
            capacity=np.prod(self._dims),
        )

        # Set up constant properties.
        self._category_to_idx = [
            # Map from the category names in each dimension to integer indices.
            dict(zip(measure_dim, range(len(measure_dim))))
            for measure_dim in categories
        ]
        self._learning_rate, self._threshold_min = validate_cma_mae_settings(
            learning_rate, threshold_min, self.dtypes["threshold"])
        self._qd_score_offset = self.dtypes["objective"](qd_score_offset)

        # Set up statistics -- objective_sum is the sum of all objective values
        # in the archive; it is useful for computing qd_score and obj_mean.
        self._best_elite = None
        self._objective_sum = None
        self._stats = None
        self._stats_reset()

    ## Properties inherited from ArchiveBase ##

    @property
    def field_list(self):
        return self._store.field_list_with_index

    @property
    def dtypes(self):
        return self._store.dtypes_with_index

    @property
    def stats(self):
        return self._stats

    @property
    def empty(self):
        return len(self._store) == 0

    ## Properties that are not in ArchiveBase ##
    ## Roughly ordered by the parameter list in the constructor. ##

    @property
    def best_elite(self):
        """dict: The elite with the highest objective in the archive.

        None if there are no elites in the archive.

        .. note::
            If the archive is non-elitist (this occurs when using the archive
            with a learning rate which is not 1.0, as in CMA-MAE), then this
            best elite may no longer exist in the archive because it was
            replaced with an elite with a lower objective value. This can happen
            because in non-elitist archives, new solutions only need to exceed
            the *threshold* of the cell they are being inserted into, not the
            *objective* of the elite currently in the cell. See :pr:`314` for
            more info.

        .. note::
            The best elite will contain a "threshold" key. This threshold is the
            threshold of the best elite's cell after the best elite was inserted
            into the archive.
        """
        return self._best_elite

    @property
    def categories(self):
        """list of list of any: The categories in each dimension of the measure
        space."""
        return self._categories

    @property
    def dims(self):
        """(measure_dim,) numpy.ndarray: Number of cells in each dimension."""
        return self._dims

    @property
    def cells(self):
        """int: Total number of cells in the archive."""
        return self._store.capacity

    @property
    def learning_rate(self):
        """float: The learning rate for threshold updates."""
        return self._learning_rate

    @property
    def threshold_min(self):
        """float: The initial threshold value for all the cells."""
        return self._threshold_min

    @property
    def qd_score_offset(self):
        """float: The offset which is subtracted from objective values when
        computing the QD score."""
        return self._qd_score_offset

    ## dunder methods ##

    def __len__(self):
        return len(self._store)

    def __iter__(self):
        return iter(self._store)

    ## Utilities ##

    def _stats_reset(self):
        """Resets the archive stats."""
        self._best_elite = None
        self._objective_sum = self.dtypes["objective"](0.0)
        self._stats = ArchiveStats(
            num_elites=0,
            coverage=self.dtypes["objective"](0.0),
            qd_score=self.dtypes["objective"](0.0),
            norm_qd_score=self.dtypes["objective"](0.0),
            obj_max=None,
            obj_mean=None,
        )

    def _stats_update(self, new_objective_sum, new_best_index):
        """Updates statistics based on a new sum of objective values
        (new_objective_sum) and the index of a potential new best elite
        (new_best_index)."""
        _, new_best_elite = self._store.retrieve([new_best_index])
        new_best_elite = {k: v[0] for k, v in new_best_elite.items()}

        if (self._stats.obj_max is None or
                new_best_elite["objective"] > self._stats.obj_max):
            self._best_elite = new_best_elite
            new_obj_max = new_best_elite["objective"]
        else:
            new_obj_max = self._stats.obj_max

        self._objective_sum = new_objective_sum
        new_qd_score = (
            self._objective_sum -
            self.dtypes["objective"](len(self)) * self._qd_score_offset)
        self._stats = ArchiveStats(
            num_elites=len(self),
            coverage=self.dtypes["objective"](len(self) / self.cells),
            qd_score=new_qd_score,
            norm_qd_score=self.dtypes["objective"](new_qd_score / self.cells),
            obj_max=new_obj_max,
            obj_mean=self.dtypes["objective"](self._objective_sum / len(self)),
        )

    def index_of(self, measures):
        """Returns archive indices for the given batch of measures.

        This is by done by mapping from the category name to the cell indices,
        and then converting to integer indices with :meth:`grid_to_int_index`.

        Args:
            measures (array-like): (batch_size, :attr:`measure_dim`) array of
                coordinates in measure space.
        Returns:
            numpy.ndarray: (batch_size,) array of integer indices representing
            the flattened grid coordinates.
        Raises:
            ValueError: ``measures`` is not of shape (batch_size,
                :attr:`measure_dim`).
            ValueError: ``measures`` has non-finite values (inf or NaN).
        """
        measures = np.asarray(measures, dtype=self.dtypes["measures"])
        check_batch_shape(measures, "measures", self.measure_dim, "measure_dim")

        # yapf: disable
        grid_indices = [
            [self._category_to_idx[i][m] for i, m in enumerate(measure)]
            for measure in measures
        ]
        # yapf: enable

        return self.grid_to_int_index(grid_indices)

    def index_of_single(self, measures):
        """Returns the index of the measures for one solution.

        See :meth:`index_of`.

        Args:
            measures (array-like): (:attr:`measure_dim`,) array of measures for
                a single solution.
        Returns:
            int or numpy.integer: Integer index of the measures in the archive's
            storage arrays.
        Raises:
            ValueError: ``measures`` is not of shape (:attr:`measure_dim`,).
            ValueError: ``measures`` has non-finite values (inf or NaN).
        """
        measures = np.asarray(measures, dtype=self.dtypes["measures"])
        check_shape(measures, "measures", self.measure_dim, "measure_dim")
        return self.index_of(measures[None])[0]

    # Copy these methods from GridArchive.
    int_to_grid_index = GridArchive.int_to_grid_index
    grid_to_int_index = GridArchive.grid_to_int_index

    ## Methods for writing to the archive ##

    @staticmethod
    def _compute_thresholds(indices, objective, cur_threshold, learning_rate,
                            dtype):
        """Computes new thresholds with the CMA-MAE batch threshold update rule.

        If entries in `indices` are duplicated, they receive the same threshold.
        """
        if len(indices) == 0:
            return np.array([], dtype=dtype)

        # Compute the number of objectives inserted into each cell. Note that we
        # index with `indices` to place the counts at all relevant indices. For
        # instance, if we had an array [1,2,3,1,5], we would end up with
        # [2,1,1,2,1] (there are 2 1's, 1 2, 1 3, 2 1's, and 1 5).
        #
        # All objective_sizes should be > 0 since we only retrieve counts for
        # indices in `indices`.
        objective_sizes = aggregate(indices, 1, func="len",
                                    fill_value=0)[indices]

        # Compute the sum of the objectives inserted into each cell -- again, we
        # index with `indices`.
        objective_sums = aggregate(indices,
                                   objective,
                                   func="sum",
                                   fill_value=np.nan)[indices]

        # Update the threshold with the batch update rule from Fontaine 2023
        # (https://arxiv.org/abs/2205.10752).
        #
        # Unlike in single_entry_with_threshold, we do not need to worry about
        # cur_threshold having -np.inf here as a result of threshold_min being
        # -np.inf. This is because the case with threshold_min = -np.inf is
        # handled separately since we compute the new threshold based on the max
        # objective in each cell in that case.
        ratio = dtype(1.0 - learning_rate)**objective_sizes
        new_threshold = (ratio * cur_threshold +
                         (objective_sums / objective_sizes) * (1 - ratio))

        return new_threshold

    def add(self, solution, objective, measures, **fields):
        """Inserts a batch of solutions into the archive.

        Each solution is only inserted if it has a higher ``objective`` than the
        threshold of the corresponding cell. For the default values of
        ``learning_rate`` and ``threshold_min``, this threshold is simply the
        objective value of the elite previously in the cell.  If multiple
        solutions in the batch end up in the same cell, we only insert the
        solution with the highest objective. If multiple solutions end up in the
        same cell and tie for the highest objective, we insert the solution that
        appears first in the batch.

        For the default values of ``learning_rate`` and ``threshold_min``, the
        threshold for each cell is updated by taking the maximum objective value
        among all the solutions that landed in the cell, resulting in the same
        behavior as in the vanilla MAP-Elites archive. However, for other
        settings, the threshold is updated with the batch update rule described
        in the appendix of `Fontaine 2023 <https://arxiv.org/abs/2205.10752>`_.

        .. note:: The indices of all arguments should "correspond" to each
            other, i.e., ``solution[i]``, ``objective[i]``, and ``measures[i]``
            should be the solution parameters, objective, and measures for
            solution ``i``.

        Args:
            solution (array-like): (batch_size, :attr:`solution_dim`) array of
                solution parameters.
            objective (array-like): (batch_size,) array with objective function
                evaluations of the solutions.
            measures (array-like): (batch_size, :attr:`measure_dim`) array with
                measure space coordinates of all the solutions.
            fields (keyword arguments): Additional data for each solution. Each
                argument should be an array with batch_size as the first
                dimension.

        Returns:
            dict: Information describing the result of the add operation. The
            dict contains the following keys:

            - ``"status"`` (:class:`numpy.ndarray` of :class:`numpy.int32`): An
              array of integers that represent the "status" obtained when
              attempting to insert each solution in the batch. Each item has the
              following possible values:

              - ``0``: The solution was not added to the archive.
              - ``1``: The solution improved the objective value of a cell
                which was already in the archive.
              - ``2``: The solution discovered a new cell in the archive.

              All statuses (and values, below) are computed with respect to the
              *current* archive. For example, if two solutions both introduce
              the same new archive cell, then both will be marked with ``2``.

              The alternative is to depend on the order of the solutions in the
              batch -- for example, if we have two solutions ``a`` and ``b``
              that introduce the same new cell in the archive, ``a`` could be
              inserted first with status ``2``, and ``b`` could be inserted
              second with status ``1`` because it improves upon ``a``. However,
              our implementation does **not** do this.

              To convert statuses to a more semantic format, cast all statuses
              to :class:`AddStatus`, e.g., with ``[AddStatus(s) for s in
              add_info["status"]]``.

            - ``"value"`` (:class:`numpy.ndarray` of
              :attr:`dtypes` ["objective"]): An array with values for each
              solution in the batch. With the default values of ``learning_rate
              = 1.0`` and ``threshold_min = -np.inf``, the meaning of each value
              depends on the corresponding ``status`` and is identical to that
              in CMA-ME (`Fontaine 2020 <https://arxiv.org/abs/1912.02400>`_):

              - ``0`` (not added): The value is the "negative improvement,"
                i.e., the objective of the solution passed in minus the
                objective of the elite still in the archive (this value is
                negative because the solution did not have a high enough
                objective to be added to the archive).
              - ``1`` (improve existing cell): The value is the "improvement,"
                i.e., the objective of the solution passed in minus the
                objective of the elite previously in the archive.
              - ``2`` (new cell): The value is just the objective of the
                solution.

              In contrast, for other values of ``learning_rate`` and
              ``threshold_min``, each value is equivalent to the objective value
              of the solution minus the threshold of its corresponding cell in
              the archive.

        Raises:
            ValueError: The array arguments do not match their specified shapes.
            ValueError: ``objective`` or ``measures`` has non-finite values (inf
                or NaN).
        """
        data = validate_batch(
            self,
            {
                "solution": solution,
                "objective": objective,
                "measures": measures,
                **fields,
            },
        )

        # Delete these so that we only use the clean, validated data in `data`.
        del solution, objective, measures, fields

        # Information to return about the addition.
        add_info = {}

        # Retrieve indices of the archive cells.
        indices = self.index_of(data["measures"])
        batch_size = len(indices)

        # Retrieve current data and thresholds. Unoccupied cells default to
        # threshold_min.
        cur_occupied, cur_data = self._store.retrieve(indices)
        cur_threshold = cur_data["threshold"]
        cur_threshold[~cur_occupied] = self.threshold_min

        # Compute status -- arrays below are all boolean arrays of length
        # batch_size.
        #
        # When we want CMA-ME behavior, the threshold defaults to -inf for new
        # cells, which satisfies the condition for can_insert.
        can_insert = data["objective"] > cur_threshold
        is_new = can_insert & ~cur_occupied
        improve_existing = can_insert & cur_occupied
        add_info["status"] = np.zeros(batch_size, dtype=np.int32)
        add_info["status"][is_new] = 2
        add_info["status"][improve_existing] = 1

        # If threshold_min is -inf, then we want CMA-ME behavior, which computes
        # the improvement value of new solutions w.r.t zero. Otherwise, we
        # compute improvement with respect to threshold_min.
        cur_threshold[is_new] = (self.dtypes["threshold"](0.0)
                                 if self.threshold_min == -np.inf else
                                 self.threshold_min)
        add_info["value"] = data["objective"] - cur_threshold

        # Return early if we cannot insert anything -- continuing throws a
        # ValueError in aggregate() since index[can_insert] would be empty.
        if not np.any(can_insert):
            return add_info

        # Select all solutions that _can_ be inserted -- at this point, there
        # are still conflicts in the insertions, e.g., multiple solutions can
        # map to index 0.
        indices = indices[can_insert]
        data = {name: arr[can_insert] for name, arr in data.items()}
        cur_threshold = cur_threshold[can_insert]

        # Compute the new threshold associated with each entry.
        if self.threshold_min == -np.inf:
            # Regular archive behavior: thresholds are just the objectives.
            new_threshold = data["objective"]
        else:
            # Batch threshold update described in Fontaine 2023
            # (https://arxiv.org/abs/2205.10752). This computation is based on
            # the mean objective of all solutions in the batch that could have
            # been inserted into each cell.
            new_threshold = self._compute_thresholds(indices, data["objective"],
                                                     cur_threshold,
                                                     self.learning_rate,
                                                     self.dtypes["threshold"])

        # Retrieve indices of solutions that _should_ be inserted into the
        # archive. Currently, multiple solutions may be inserted at each archive
        # index, but we only want to insert the maximum among these solutions.
        # Thus, we obtain the argmax for each archive index.
        #
        # We use a fill_value of -1 to indicate archive indices that were not
        # covered in the batch. Note that the length of archive_argmax is only
        # max(indices), rather than the total number of grid cells. However,
        # this is okay because we only need the indices of the solutions, which
        # we store in should_insert.
        #
        # aggregate() always chooses the first item if there are ties, so the
        # first elite will be inserted if there is a tie. See their default
        # numpy implementation for more info:
        # https://github.com/ml31415/numpy-groupies/blob/master/numpy_groupies/aggregate_numpy.py#L107
        archive_argmax = aggregate(indices,
                                   data["objective"],
                                   func="argmax",
                                   fill_value=-1)
        should_insert = archive_argmax[archive_argmax != -1]

        # Select only solutions that will be inserted into the archive.
        indices = indices[should_insert]
        data = {name: arr[should_insert] for name, arr in data.items()}
        data["threshold"] = new_threshold[should_insert]

        # Insert elites into the store.
        self._store.add(indices, data)

        # Compute statistics.
        cur_objective = cur_data["objective"]
        cur_objective[~cur_occupied] = 0.0
        cur_objective = cur_objective[can_insert][should_insert]
        objective_sum = (self._objective_sum +
                         np.sum(data["objective"] - cur_objective))
        best_index = indices[np.argmax(data["objective"])]
        self._stats_update(objective_sum, best_index)

        return add_info

    def add_single(self, solution, objective, measures, **fields):
        """Inserts a single solution into the archive.

        The solution is only inserted if it has a higher ``objective`` than the
        threshold of the corresponding cell. For the default values of
        ``learning_rate`` and ``threshold_min``, this threshold is simply the
        objective value of the elite previously in the cell.  The threshold is
        also updated if the solution was inserted.

        .. note::
            This method is provided as an easier-to-understand implementation
            that has less performance due to inserting only one solution at a
            time. For better performance, see :meth:`add`.

        Args:
            solution (array-like): Parameters of the solution.
            objective (float): Objective function evaluation of the solution.
            measures (array-like): Coordinates in measure space of the solution.
            fields (keyword arguments): Additional data for the solution.

        Returns:
            dict: Information describing the result of the add operation. The
            dict contains ``status`` and ``value`` keys; refer to :meth:`add`
            for the meaning of status and value.

        Raises:
            ValueError: The array arguments do not match their specified shapes.
            ValueError: ``objective`` is non-finite (inf or NaN) or ``measures``
                has non-finite values.
        """
        data = validate_single(
            self,
            {
                "solution": solution,
                "objective": objective,
                "measures": measures,
                **fields,
            },
        )

        # Delete these so that we only use the clean, validated data in `data`.
        del solution, objective, measures, fields

        # Information to return about the addition.
        add_info = {}

        # Identify the archive cell.
        index = self.index_of_single(data["measures"])

        # Retrieve current data of the cell.
        cur_occupied, cur_data = self._store.retrieve([index])
        cur_occupied = cur_occupied[0]

        if cur_occupied:
            # If the cell is currently occupied, the threshold comes from the
            # current data of the elite in the cell.
            cur_threshold = cur_data["threshold"][0]
        else:
            # If the cell is not currently occupied, the threshold needs special
            # settings.
            #
            # If threshold_min is -inf, then we want CMA-ME behavior, which
            # computes the improvement value with a threshold of zero for new
            # solutions. Otherwise, we will set cur_threshold to threshold_min.
            cur_threshold = (self.dtypes["threshold"](0.0) if self.threshold_min
                             == -np.inf else self.threshold_min)

        # Retrieve candidate objective.
        objective = data["objective"]

        # Compute status and threshold.
        add_info["status"] = np.int32(0)  # NOT_ADDED

        # Now we check whether a solution should be added to the archive. We use
        # the addition rule from MAP-Elites (Fig. 2 of Mouret 2015
        # https://arxiv.org/pdf/1504.04909.pdf), with modifications for CMA-MAE.

        # This checks if a new solution is discovered in the archive. Note that
        # regular MAP-Elites only checks `not cur_occupied`. CMA-MAE has an
        # additional `threshold_min` that the objective must exceed for new
        # cells. If CMA-MAE is not being used, then `threshold_min` is -np.inf,
        # making this check identical to that of MAP-Elites.
        is_new = not cur_occupied and self.threshold_min < objective

        # This checks whether the solution improves an existing cell in the
        # archive, i.e., whether it performs better than the current elite in
        # this cell. Vanilla MAP-Elites compares to the objective of the cell's
        # current elite. CMA-MAE compares to a threshold value that updates
        # over time (i.e., cur_threshold). When learning_rate is set to 1.0 (the
        # default value), we recover the same rule as in MAP-Elites because
        # cur_threshold is equivalent to the objective of the solution in the
        # cell.
        improve_existing = cur_occupied and cur_threshold < objective

        if is_new or improve_existing:
            if improve_existing:
                add_info["status"] = np.int32(1)  # IMPROVE_EXISTING
            else:
                add_info["status"] = np.int32(2)  # NEW

            # This calculation works in the case where threshold_min is -inf
            # because cur_threshold will be set to 0.0 instead.
            data["threshold"] = [(cur_threshold * (1.0 - self.learning_rate) +
                                  objective * self.learning_rate)]

            # Insert elite into the store.
            self._store.add(index[None], {
                name: np.expand_dims(arr, axis=0) for name, arr in data.items()
            })

            # Update stats.
            cur_objective = (cur_data["objective"][0]
                             if cur_occupied else self.dtypes["objective"](0.0))
            self._stats_update(self._objective_sum + objective - cur_objective,
                               index)

        # Value is the improvement over the current threshold (can be negative).
        add_info["value"] = objective - cur_threshold

        return add_info

    def clear(self):
        """Removes all elites in the archive."""
        self._store.clear()
        self._stats_reset()

    ## Methods for reading from the archive ##
    ## Refer to ArchiveBase for documentation of these methods. ##

    def retrieve(self, measures):
        measures = np.asarray(measures, dtype=self.dtypes["measures"])
        check_batch_shape(measures, "measures", self.measure_dim, "measure_dim")

        occupied, data = self._store.retrieve(self.index_of(measures))
        fill_sentinel_values(occupied, data)

        return occupied, data

    def retrieve_single(self, measures):
        measures = np.asarray(measures, dtype=self.dtypes["measures"])
        check_shape(measures, "measures", self.measure_dim, "measure_dim")

        occupied, data = self.retrieve(measures[None])

        return occupied[0], {field: arr[0] for field, arr in data.items()}

    def data(self, fields=None, return_type="dict"):
        return self._store.data(fields, return_type)

    def sample_elites(self, n):
        if self.empty:
            raise IndexError("No elements in archive.")

        random_indices = self._rng.integers(len(self._store), size=n)
        selected_indices = self._store.occupied_list[random_indices]
        _, elites = self._store.retrieve(selected_indices)
        return elites
