Source code for ribs.archives._sliding_boundaries_archive

"""Contains the SlidingBoundariesArchive."""

from collections import deque

import numpy as np
from sortedcontainers import SortedList

from ribs._utils import (check_batch_shape, check_finite, validate_batch,
from ribs.archives._archive_base import ArchiveBase
from ribs.archives._grid_archive import GridArchive

class SolutionBuffer:
    """An internal class that stores relevant data to re-add after remapping.

    Maintains two data structures:
    - Queue storing the buffer_capacity last entries (solution + objective +
      measures). When new items are inserted, the oldest ones are
    - Sorted lists with the sorted measures in each dimension. Measures are
      removed from these lists when they are removed from the queue.

    def __init__(self, buffer_capacity, measure_dim):
        self._buffer_capacity = buffer_capacity
        self._queue = deque()
        self._measure_lists = [SortedList() for _ in range(measure_dim)]
        self._iter_idx = 0

    def __iter__(self):
        """Enables iterating over solutions stored in the buffer."""
        return self

    def __next__(self):
        """Returns the next entry in the buffer."""
        if self._iter_idx >= self.size:
            self._iter_idx = 0
            raise StopIteration
        result = self._queue[self._iter_idx]
        self._iter_idx += 1
        return result

    def add(self, data):
        """Inserts a new entry.

        Pops the oldest if it is full.
        if self.full():
            # Remove item from the deque.
            deleted_data = self._queue.popleft()
            # Remove measures from sorted lists.
            for i, m in enumerate(deleted_data["measures"]):


        # Add measures to sorted lists.
        for i, m in enumerate(data["measures"]):

    def full(self):
        """Whether buffer is full."""
        return len(self._queue) >= self._buffer_capacity

    def sorted_measures(self):
        """(measure_dim, self.size) numpy.ndarray: Sorted measures of each
        return np.array(self._measure_lists, dtype=np.float64)

    def size(self):
        """Number of solutions stored in the buffer."""
        return len(self._queue)

    def capacity(self):
        """Capacity of the buffer."""
        return self._buffer_capacity

[docs]class SlidingBoundariesArchive(ArchiveBase): """An archive with a fixed number of sliding boundaries on each dimension. This archive is the container described in `Fontaine 2019 <>`_. Just like the :class:`~ribs.archives.GridArchive`, it can be visualized as an n-dimensional grid in the measure space that is divided into a certain number of cells in each dimension. Internally, this archive stores a buffer with the ``buffer_capacity`` most recent solutions and uses them to determine the boundaries of each dimension of the measure space. After every ``remap_frequency`` solutions are inserted, the archive remaps the boundaries based on the solutions in the buffer. Initially, the archive has no solutions, so it cannot automatically calculate the boundaries. Thus, until the first remap, this archive divides the measure space defined by ``ranges`` into equally sized cells. Overall, this archive attempts to make the distribution of the space illuminated by the archive more accurately match the true distribution of the measures when they are not uniformly distributed. .. note:: Unlike other archives, this archive does not currently support `thresholds <../../tutorials/cma_mae.html>`_ or batched addition (see :meth:`add`). Args: solution_dim (int): Dimension of the solution space. dims (array-like): Number of cells in each dimension of the measure space, e.g. ``[20, 30, 40]`` indicates there should be 3 dimensions with 20, 30, and 40 cells. (The number of dimensions is implicitly defined in the length of this argument). ranges (array-like of (float, float)): `Initial` upper and lower bound of each dimension of the measure space, e.g. ``[(-1, 1), (-2, 2)]`` indicates the first dimension should have bounds :math:`[-1,1]` (inclusive), and the second dimension should have bounds :math:`[-2,2]` (inclusive). ``ranges`` should be the same length as ``dims``. epsilon (float): Due to floating point precision errors, we add a small epsilon when computing the archive indices in the :meth:`index_of` method -- refer to the implementation `here <../_modules/ribs/archives/_sliding_boundaries_archive.html#SlidingBoundariesArchive.index_of>`_. Pass this parameter to configure that epsilon. 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. remap_frequency (int): Frequency of remapping. Archive will remap once after ``remap_frequency`` number of solutions has been found. buffer_capacity (int): Number of solutions to keep in the buffer. Solutions in the buffer will be reinserted into the archive after remapping. """ def __init__(self, *, solution_dim, dims, ranges, epsilon=1e-6, qd_score_offset=0.0, seed=None, dtype=np.float64, extra_fields=None, remap_frequency=100, buffer_capacity=1000): self._dims = np.array(dims) if len(self._dims) != len(ranges): raise ValueError(f"dims (length {len(self._dims)}) and ranges " f"(length {len(ranges)}) must be the same length") ArchiveBase.__init__( self, solution_dim=solution_dim,, measure_dim=len(self._dims), qd_score_offset=qd_score_offset, seed=seed, dtype=dtype, extra_fields=extra_fields, ) ranges = list(zip(*ranges)) self._lower_bounds = np.array(ranges[0], dtype=self.dtype) self._upper_bounds = np.array(ranges[1], dtype=self.dtype) self._interval_size = self._upper_bounds - self._lower_bounds self._epsilon = self.dtype(epsilon) # Specifics for sliding boundaries. self._remap_frequency = remap_frequency # Allocate an extra entry in each row so we can put the upper bound at # the end. self._boundaries = np.full((self.measure_dim, np.max(self._dims) + 1), np.nan, dtype=self.dtype) # Initialize the boundaries. for i, (dim, lower_bound, upper_bound) in enumerate( zip(self._dims, self._lower_bounds, self._upper_bounds)): self._boundaries[i, :dim + 1] = np.linspace(lower_bound, upper_bound, dim + 1) # Create buffer. self._buffer = SolutionBuffer(buffer_capacity, self.measure_dim) # Total number of solutions encountered. self._total_num_sol = 0 @property def dims(self): """(measure_dim,) numpy.ndarray: Number of cells in each dimension.""" return self._dims @property def lower_bounds(self): """(measure_dim,) numpy.ndarray: Lower bound of each dimension.""" return self._lower_bounds @property def upper_bounds(self): """(measure_dim,) numpy.ndarray: Upper bound of each dimension.""" return self._upper_bounds @property def interval_size(self): """(measure_dim,) numpy.ndarray: The size of each dim (upper_bounds - lower_bounds).""" return self._interval_size @property def epsilon(self): """:attr:`dtype`: Epsilon for computing archive indices. Refer to the documentation for this class.""" return self._epsilon @property def remap_frequency(self): """int: Frequency of remapping. Archive will remap once after ``remap_frequency`` number of solutions has been found. """ return self._remap_frequency @property def buffer_capacity(self): """int: Maximum capacity of the buffer.""" return self._buffer.capacity @property def boundaries(self): """list of numpy.ndarray: The dynamic boundaries of the cells in each dimension. Entry ``i`` in this list is an array that contains the boundaries of the cells in dimension ``i``. The array contains ``self.dims[i] + 1`` entries laid out like this:: Archive cells: | 0 | 1 | ... | self.dims[i] | boundaries[i]: 0 1 2 self.dims[i] - 1 self.dims[i] Thus, ``boundaries[i][j]`` and ``boundaries[i][j + 1]`` are the lower and upper bounds of cell ``j`` in dimension ``i``. To access the lower bounds of all the cells in dimension ``i``, use ``boundaries[i][:-1]``, and to access all the upper bounds, use ``boundaries[i][1:]``. """ return [ bound[:dim + 1] for bound, dim in zip(self._boundaries, self._dims) ]
[docs] def index_of(self, measures): """Returns archive indices for the given batch of measures. First, values are clipped to the bounds of the measure space. Then, the values are mapped to cells via a binary search along the boundaries in each dimension. At this point, we have "grid indices" -- indices of each measure in each dimension. Since indices returned by this method must be single integers (as opposed to a tuple of grid indices), we convert these grid indices into integer indices with :func:`numpy.ravel_multi_index` and return the result. It may be useful to have the original grid indices. Thus, we provide the :meth:`grid_to_int_index` and :meth:`int_to_grid_index` methods for converting between grid and integer indices. As an example, the grid indices can be used to access boundaries of a measure value's cell. For example, the following retrieves the lower and upper bounds of the cell along dimension 0:: # Access only element 0 since this method operates in batch. idx = archive.int_to_grid_index(archive.index_of(...))[0] lower = archive.boundaries[0][idx[0]] upper = archive.boundaries[0][idx[0] + 1] See :attr:`boundaries` for more info. 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`). """ measures = np.asarray(measures) check_batch_shape(measures, "measures", self.measure_dim, "measure_dim") check_finite(measures, "measures") # Clip measures + epsilon to the range # [lower_bounds, upper_bounds - epsilon]. measures = np.clip(measures + self._epsilon, self._lower_bounds, self._upper_bounds - self._epsilon) idx_cols = [] for boundary, dim, measures_col in zip(self._boundaries, self._dims, measures.T): idx_col = np.searchsorted(boundary[:dim], measures_col) # The maximum index returned by searchsorted is `dim`, and since we # subtract 1, the max will be dim - 1 which is within the range of # the archive indices. idx_cols.append(np.maximum(0, idx_col - 1)) # We cannot use `grid_to_int_index` since that takes in an array of # indices, not index columns. # # pylint seems to think that ravel_multi_index returns a list and thus # has no astype method. # pylint: disable = no-member return np.ravel_multi_index(idx_cols, self._dims).astype(np.int32)
# Copy these methods from GridArchive. int_to_grid_index = GridArchive.int_to_grid_index grid_to_int_index = GridArchive.grid_to_int_index def _remap(self): """Remaps the archive. The boundaries are relocated to the percentage marks of the distribution of solutions stored in the archive. Also re-adds all of the solutions in the buffer and the previous archive to the archive. Returns: tuple: The result of calling :meth:`ArchiveBase.add` on the last item in the buffer. """ # Sort each measure along its dimension. sorted_measures = self._buffer.sorted_measures # Calculate new boundaries. for i in range(self.measure_dim): for j in range(self.dims[i]): sample_idx = int(j * self._buffer.size / self.dims[i]) self._boundaries[i][j] = sorted_measures[i][sample_idx] # Set the upper bound to be the greatest BC. self._boundaries[i][self.dims[i]] = sorted_measures[i][-1] cur_data = # These fields are only computed by the archive. cur_data.pop("threshold") cur_data.pop("index") new_data_single = list(self._buffer) # List of dicts. new_data = {name: None for name in new_data_single[0]} for name in new_data: new_data[name] = [d[name] for d in new_data_single] # The last solution must be added on its own so that we get an accurate # status and value to return to the user; hence we pop it from all the # batches (note that pop removes the last value and returns it). last_data = {name: arr.pop() for name, arr in new_data.items()} self.clear() final_data = { name: np.concatenate((cur_data[name], new_data[name])) for name in cur_data } ArchiveBase.add(self, **final_data) add_info = ArchiveBase.add_single(self, **last_data) return add_info
[docs] def add(self, solution, objective, measures, **fields): """Inserts a batch of solutions into the archive. .. note:: Unlike in other archives, this method (currently) is not truly batched; rather, it is implemented by calling :meth:`add_single` on the solutions in the batch, in the order that they are passed in. As such, this method is *not* invariant to the ordering of the solutions in the batch. See :meth:`ArchiveBase.add` for arguments and return values. """ new_data = validate_batch( self, { "solution": solution, "objective": objective, "measures": measures, **fields, }, ) batch_size = new_data["solution"].shape[0] add_info = {} for i in range(batch_size): single_info = self.add_single(**{ name: arr[i] for name, arr in new_data.items() }) if i == 0: # Initialize add_info. for name, val in single_info.items(): add_info[name] = np.empty(batch_size, dtype=val.dtype) for name, val in single_info.items(): add_info[name][i] = val return add_info
[docs] def add_single(self, solution, objective, measures, **fields): """Inserts a single solution into the archive. This method remaps the archive after every :attr:`remap_frequency` solutions are added. Remapping involves changing the boundaries of the archive to the percentage marks of the measures stored in the buffer and re-adding all of the solutions stored in the buffer `and` the current archive. See :meth:`ArchiveBase.add_single` for arguments and return values. """ new_data = validate_single( self, { "solution": solution, "objective": objective, "measures": measures, **fields, }, ) self._buffer.add(new_data) self._total_num_sol += 1 if self._total_num_sol % self._remap_frequency == 0: add_info = self._remap() self._lower_bounds = np.array( [bound[0] for bound in self._boundaries]) self._upper_bounds = np.array([ bound[dim] for bound, dim in zip(self._boundaries, self._dims) ]) self._interval_size = self._upper_bounds - self._lower_bounds else: add_info = ArchiveBase.add_single(self, **new_data) return add_info