Source code for ribs.archives._archive_base

"""Provides ArchiveBase."""
from abc import ABC, abstractmethod

import numpy as np

from ribs._utils import (check_batch_shape, check_finite, check_is_1d,
                         check_shape, parse_float_dtype, validate_batch,
from ribs.archives._archive_data_frame import ArchiveDataFrame
from ribs.archives._archive_stats import ArchiveStats
from ribs.archives._array_store import ArrayStore
from ribs.archives._cqd_score_result import CQDScoreResult
from ribs.archives._transforms import (batch_entries_with_threshold,

_ARCHIVE_FIELDS = {"index", "solution", "objective", "measures", "threshold"}

[docs]class ArchiveBase(ABC): # pylint: disable = too-many-instance-attributes, too-many-public-methods """Base class for archives. This class composes archives using an :class:`ArrayStore` that has "solution", "objective", "measures", and "threshold" fields. Child classes typically override the following methods: - ``__init__``: Child classes must invoke this class's ``__init__`` with the appropriate arguments. - :meth:`index_of`: Returns integer indices into the arrays above when given a batch of measures. Usually, each index has a meaning, e.g. in :class:`~ribs.archives.CVTArchive` it is the index of a centroid. Documentation for this method should describe the meaning of the index. .. note:: Attributes beginning with an underscore are only intended to be accessed by child classes (i.e. they are "protected" attributes). .. note:: The idea of archive thresholds was introduced in `Fontaine 2022 <>`_. Refer to our `CMA-MAE tutorial <../../tutorials/cma_mae.html>`_ for more info on thresholds, including the ``learning_rate`` and ``threshold_min`` parameters. Args: solution_dim (int): Dimension of the solution space. cells (int): Number of cells in the archive. This is used to create the numpy arrays described above for storing archive info. measure_dim (int): The dimension of the measure space. 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): Data type of the solutions, objectives, and measures. We only support ``"f"`` / ``np.float32`` and ``"d"`` / ``np.float64``. 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. Attributes: _rng (numpy.random.Generator): Random number generator, used in particular for generating random elites. _store (ribs.archives.ArrayStore): The underlying ArrayStore containing data for the archive. Raises: ValueError: Invalid values for learning_rate and threshold_min. ValueError: Invalid names in extra_fields. """ def __init__(self, *, solution_dim, cells, measure_dim, learning_rate=None, threshold_min=-np.inf, qd_score_offset=0.0, seed=None, dtype=np.float64, extra_fields=None): self._dtype = parse_float_dtype(dtype) self._seed = seed self._rng = np.random.default_rng(seed) self._cells = cells self._solution_dim = solution_dim self._measure_dim = measure_dim self._qd_score_offset = self._dtype(qd_score_offset) if threshold_min != -np.inf and learning_rate is None: raise ValueError( "You set threshold_min without setting learning_rate. " "Please note that threshold_min is only used in CMA-MAE; " "it is not intended to be used for only filtering archive " "solutions. If you would like to run CMA-MAE, please also set " "learning_rate.") if learning_rate is None: learning_rate = 1.0 # Default value. if threshold_min == -np.inf and learning_rate != 1.0: raise ValueError("threshold_min can only be -np.inf if " "learning_rate is 1.0") self._learning_rate = self._dtype(learning_rate) self._threshold_min = self._dtype(threshold_min) self._stats = None self._best_elite = None # Sum of all objective values in the archive; useful for computing # qd_score and obj_mean. self._objective_sum = None self._stats_reset() extra_fields = extra_fields or {} if _ARCHIVE_FIELDS & extra_fields.keys(): raise ValueError("The following names are not allowed in " f"extra_fields: {_ARCHIVE_FIELDS}") self._store = ArrayStore( field_desc={ "solution": ((solution_dim,), self.dtype), "objective": ((), self.dtype), "measures": ((measure_dim,), self.dtype), "threshold": ((), self.dtype), **extra_fields, }, capacity=self._cells, ) @property def field_list(self): """list: List of data fields in the archive.""" return self._store.field_list @property def cells(self): """int: Total number of cells in the archive.""" return self._cells @property def measure_dim(self): """int: Dimensionality of the measure space.""" return self._measure_dim @property def solution_dim(self): """int: Dimensionality of the solutions in the archive.""" return self._solution_dim @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 @property def stats(self): """:class:`ArchiveStats`: Statistics about the archive. See :class:`ArchiveStats` for more info. """ return self._stats @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 dtype(self): """data-type: The dtype of the solutions, objective, and measures.""" return self._dtype @property def empty(self): """bool: Whether the archive is empty.""" return len(self._store) == 0
[docs] def __len__(self): """Number of elites in the archive.""" return len(self._store)
[docs] def __iter__(self): """Creates an iterator over the elites in the archive. Example: :: for elite in archive: elite["solution"] elite["objective"] ... """ return iter(self._store)
[docs] def clear(self): """Removes all elites from the archive. After this method is called, the archive will be :attr:`empty`. """ self._store.clear() self._stats_reset()
[docs] @abstractmethod def index_of(self, measures): """Returns archive indices for the given batch of measures. If you need to retrieve the index of the measures for a *single* solution, consider using :meth:`index_of_single`. Args: measures (array-like): (batch_size, :attr:`measure_dim`) array of coordinates in measure space. Returns: (numpy.ndarray): (batch_size,) array with the indices of the batch of measures in the archive's storage arrays. """
[docs] def index_of_single(self, measures): """Returns the index of the measures for one solution. While :meth:`index_of` takes in a *batch* of measures, this method takes in the measures for only *one* solution. If :meth:`index_of` is implemented correctly, this method should work immediately (i.e. `"out of the box" <>`_). 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) check_shape(measures, "measures", self.measure_dim, "measure_dim") check_finite(measures, "measures") return self.index_of(measures[None])[0]
def _stats_reset(self): """Resets the archive stats.""" self._stats = ArchiveStats( num_elites=0, coverage=self.dtype(0.0), qd_score=self.dtype(0.0), norm_qd_score=self.dtype(0.0), obj_max=None, obj_mean=None, ) self._best_elite = None self._objective_sum = self.dtype(0.0) 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).""" self._objective_sum = new_objective_sum new_qd_score = (self._objective_sum - self.dtype(len(self)) * self._qd_score_offset) _, new_best_elite = self._store.retrieve([new_best_index]) if (self._stats.obj_max is None or new_best_elite["objective"] > self._stats.obj_max): # Convert batched values to single values. new_best_elite = {k: v[0] for k, v in new_best_elite.items()} new_obj_max = new_best_elite["objective"] self._best_elite = new_best_elite else: new_obj_max = self._stats.obj_max self._stats = ArchiveStats( num_elites=len(self), coverage=self.dtype(len(self) / self.cells), qd_score=new_qd_score, norm_qd_score=self.dtype(new_qd_score / self.cells), obj_max=new_obj_max, obj_mean=self._objective_sum / self.dtype(len(self)), )
[docs] 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 2022 <>`_. .. note:: The indices of all arguments should "correspond" to each other, i.e. ``solution[i]``, ``objective[i]``, ``measures[i]``, and 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:`int`): 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`` which 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:`dtype`): 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 <>`_): - ``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, }, ) add_info = self._store.add( self.index_of(data["measures"]), data, { "dtype": self._dtype, "learning_rate": self._learning_rate, "threshold_min": self._threshold_min, "objective_sum": self._objective_sum, }, [ batch_entries_with_threshold, compute_objective_sum, compute_best_index, ], ) objective_sum = add_info.pop("objective_sum") best_index = add_info.pop("best_index") if not np.all(add_info["status"] == 0): self._stats_update(objective_sum, best_index) return add_info
[docs] 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:: To make it more amenable to modifications, this method's implementation is designed to be readable at the cost of performance, e.g., none of its operations are modified. If you need performance, we recommend using :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, }, ) for name, arr in data.items(): data[name] = np.expand_dims(arr, axis=0) add_info = self._store.add( np.expand_dims(self.index_of_single(measures), axis=0), data, { "dtype": self._dtype, "learning_rate": self._learning_rate, "threshold_min": self._threshold_min, "objective_sum": self._objective_sum, }, [ single_entry_with_threshold, compute_objective_sum, compute_best_index, ], ) objective_sum = add_info.pop("objective_sum") best_index = add_info.pop("best_index") for name, arr in add_info.items(): add_info[name] = arr[0] if add_info["status"]: self._stats_update(objective_sum, best_index) return add_info
[docs] def retrieve(self, measures): """Retrieves the elites with measures in the same cells as the measures specified. This method operates in batch, i.e., it takes in a batch of measures and outputs the batched data for the elites:: occupied, elites = archive.retrieve(...) elites["solution"] # Shape: (batch_size, solution_dim) elites["objective"] elites["measures"] elites["threshold"] elites["index"] If the cell associated with ``elites["measures"][i]`` has an elite in it, then ``occupied[i]`` will be True. Furthermore, ``elites["solution"][i]``, ``elites["objective"][i]``, ``elites["measures"][i]``, ``elites["threshold"][i]``, and ``elites["index"][i]`` will be set to the properties of the elite. Note that ``elites["measures"][i]`` may not be equal to the ``measures[i]`` passed as an argument, since the measures only need to be in the same archive cell. If the cell associated with ``measures[i]`` *does not* have any elite in it, then ``occupied[i]`` will be set to False. Furthermore, the corresponding outputs will be set to empty values -- namely: * NaN for floating-point fields * -1 for the "index" field * 0 for integer fields * None for object fields If you need to retrieve a *single* elite associated with some measures, consider using :meth:`retrieve_single`. Args: measures (array-like): (batch_size, :attr:`measure_dim`) array of coordinates in measure space. Returns: tuple: 2-element tuple of (occupied array, dict). The occupied array indicates whether each of the cells indicated by the coordinates in ``measures`` has an elite, while the dict contains the data of those elites. The dict maps from field name to the corresponding array. 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) check_batch_shape(measures, "measures", self.measure_dim, "measure_dim") check_finite(measures, "measures") occupied, data = self._store.retrieve(self.index_of(measures)) unoccupied = ~occupied for name, arr in data.items(): if arr.dtype == object: fill_val = None elif name == "index": fill_val = -1 elif np.issubdtype(arr.dtype, np.integer): fill_val = 0 else: # Floating-point and other fields. fill_val = np.nan arr[unoccupied] = fill_val return occupied, data
[docs] def retrieve_single(self, measures): """Retrieves the elite with measures in the same cell as the measures specified. While :meth:`retrieve` takes in a *batch* of measures, this method takes in the measures for only *one* solution and returns a single bool and a dict with single entries. Args: measures (array-like): (:attr:`measure_dim`,) array of measures. Returns: tuple: If there is an elite with measures in the same cell as the measures specified, then this method returns a True value and a dict where all the fields hold the info of the elite. Otherwise, this method returns a False value and a dict filled with the same "empty" values described in :meth:`retrieve`. Raises: ValueError: ``measures`` is not of shape (:attr:`measure_dim`,). ValueError: ``measures`` has non-finite values (inf or NaN). """ measures = np.asarray(measures) check_shape(measures, "measures", self.measure_dim, "measure_dim") check_finite(measures, "measures") occupied, data = self.retrieve(measures[None]) return occupied[0], {field: arr[0] for field, arr in data.items()}
[docs] def sample_elites(self, n): """Randomly samples elites from the archive. Currently, this sampling is done uniformly at random. Furthermore, each sample is done independently, so elites may be repeated in the sample. Additional sampling methods may be supported in the future. Example: :: elites = archive.sample_elites(16) elites["solution"] # Shape: (16, solution_dim) elites["objective"] ... Args: n (int): Number of elites to sample. Returns: dict: Holds a batch of elites randomly selected from the archive. Raises: IndexError: The archive is empty. """ 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
[docs] def data(self, fields=None, return_type="dict"): """Retrieves data for all elites in the archive. Args: fields (str or array-like of str): List of fields to include. By default, all fields will be included, with an additional "index" as the last field ("index" can also be placed anywhere in this list). This can also be a single str indicating a field name. return_type (str): Type of data to return. See below. Ignored if ``fields`` is a str. Returns: The data for all entries in the archive. If ``fields`` was a single str, this will just be an array holding data for the given field. Otherwise, this data can take the following forms, depending on the ``return_type`` argument: - ``return_type="dict"``: Dict mapping from the field name to the field data at the given indices. An example is:: { "solution": [[1.0, 1.0, ...], ...], "objective": [1.5, ...], "measures": [[1.0, 2.0], ...], "threshold": [0.8, ...], "index": [4, ...], } Observe that we also return the indices as an ``index`` entry in the dict. The keys in this dict can be modified with the ``fields`` arg; duplicate fields will be ignored since the dict stores unique keys. - ``return_type="tuple"``: Tuple of arrays matching the field order given in ``fields``. For instance, if ``fields`` was ``["objective", "measures"]``, we would receive a tuple of ``(objective_arr, measures_arr)``. In this case, the results from ``retrieve`` could be unpacked as:: objective, measures =["objective", "measures"], return_type="tuple") Unlike with the ``dict`` return type, duplicate fields will show up as duplicate entries in the tuple, e.g., ``fields=["objective", "objective"]`` will result in two objective arrays being returned. By default, (i.e., when ``fields=None``), the fields in the tuple will be ordered according to the :attr:`field_list` along with ``index`` as the last field. - ``return_type="pandas"``: A :class:`~ribs.archives.ArchiveDataFrame` with the following columns: - For fields that are scalars, a single column with the field name. For example, ``objective`` would have a single column called ``objective``. - For fields that are 1D arrays, multiple columns with the name suffixed by its index. For instance, if we have a ``measures`` field of length 10, we create 10 columns with names ``measures_0``, ``measures_1``, ..., ``measures_9``. We do not currently support fields with >1D data. - 1 column of integers (``np.int32``) for the index, named ``index``. In short, the dataframe might look like this by default: +------------+------+-----------+------------+------+-----------+-------+ | solution_0 | ... | objective | measures_0 | ... | threshold | index | +============+======+===========+============+======+===========+=======+ | | ... | | | ... | | | +------------+------+-----------+------------+------+-----------+-------+ Like the other return types, the columns can be adjusted with the ``fields`` parameter. All data returned by this method will be a copy, i.e., the data will not update as the archive changes. """ # pylint: disable = line-too-long data =, return_type) if return_type == "pandas": data = ArchiveDataFrame(data) return data
[docs] def as_pandas(self, include_solutions=True, include_metadata=False): """DEPRECATED.""" # pylint: disable = unused-argument raise RuntimeError( "as_pandas has been deprecated. Please use " ", return_type='pandas') instead, or consider " "retrieving individual fields, e.g., " "objective ='objective')")
[docs] def cqd_score(self, iterations, target_points, penalties, obj_min, obj_max, dist_max=None, dist_ord=None): """Computes the CQD score of the archive. The Continuous Quality Diversity (CQD) score was introduced in `Kent 2022 <>`_. .. note:: This method by default assumes that the archive has an ``upper_bounds`` and ``lower_bounds`` property which delineate the bounds of the measure space, as is the case in :class:`~ribs.archives.GridArchive`, :class:`~ribs.archives.CVTArchive`, and :class:`~ribs.archives.SlidingBoundariesArchive`. If this is not the case, ``dist_max`` must be passed in, and ``target_points`` must be an array of custom points. Args: iterations (int): Number of times to compute the CQD score. We return the mean CQD score across these iterations. target_points (int or array-like): Number of target points to generate, or an (iterations, n, measure_dim) array which lists n target points to list on each iteration. When an int is passed, the points are sampled uniformly within the bounds of the measure space. penalties (int or array-like): Number of penalty values over which to compute the score (the values are distributed evenly over the range [0,1]). Alternatively, this may be a 1D array which explicitly lists the penalty values. Known as :math:`\\theta` in Kent 2022. obj_min (float): Minimum objective value, used when normalizing the objectives. obj_max (float): Maximum objective value, used when normalizing the objectives. dist_max (float): Maximum distance between points in measure space. Defaults to the distance between the extremes of the measure space bounds (the type of distance is computed with the order specified by ``dist_ord``). Known as :math:`\\delta_{max}` in Kent 2022. dist_ord: Order of the norm to use for calculating measure space distance; this is passed to :func:`numpy.linalg.norm` as the ``ord`` argument. See :func:`numpy.linalg.norm` for possible values. The default is to use Euclidean distance (L2 norm). Returns: The mean CQD score obtained with ``iterations`` rounds of calculations. Raises: RuntimeError: The archive does not have the bounds properties mentioned above, and dist_max is not specified or the target points are not provided. ValueError: target_points or penalties is an array with the wrong shape. """ if (not (hasattr(self, "upper_bounds") and hasattr(self, "lower_bounds")) and (dist_max is None or np.isscalar(target_points))): raise RuntimeError( "When the archive does not have lower_bounds and " "upper_bounds properties, dist_max must be specified, " "and target_points must be an array") if np.isscalar(target_points): # pylint: disable = no-member target_points = self._rng.uniform( low=self.lower_bounds, high=self.upper_bounds, size=(iterations, target_points, self.measure_dim), ) else: # Copy since we return this. target_points = np.copy(target_points) if (target_points.ndim != 3 or target_points.shape[0] != iterations or target_points.shape[2] != self.measure_dim): raise ValueError( "Expected target_points to be a 3D array with " f"shape ({iterations}, n, {self.measure_dim}) " "(i.e. shape (iterations, n, measure_dim)) but it had " f"shape {target_points.shape}") if dist_max is None: # pylint: disable = no-member dist_max = np.linalg.norm(self.upper_bounds - self.lower_bounds, ord=dist_ord) if np.isscalar(penalties): penalties = np.linspace(0, 1, penalties) else: penalties = np.copy(penalties) # Copy since we return this. check_is_1d(penalties, "penalties") objective_batch ="objective") measures_batch ="measures") norm_objectives = objective_batch / (obj_max - obj_min) scores = np.zeros(iterations) for itr in range(iterations): # Distance calculation -- start by taking the difference between # each measure i and all the target points. distances = measures_batch[:, None] - target_points[itr] # (len(archive), n_target_points) array of distances. distances = np.linalg.norm(distances, ord=dist_ord, axis=2) norm_distances = distances / dist_max for penalty in penalties: # Known as omega in Kent 2022 -- a (len(archive), # n_target_points) array. values = norm_objectives[:, None] - penalty * norm_distances # (n_target_points,) array. max_values_per_target = np.max(values, axis=0) scores[itr] += np.sum(max_values_per_target) return CQDScoreResult( iterations=iterations, mean=np.mean(scores), scores=scores, target_points=target_points, penalties=penalties, obj_min=obj_min, obj_max=obj_max, dist_max=dist_max, dist_ord=dist_ord, )