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 to the archive.
Returns:
tuple: The result of calling :meth:`ArchiveBase.add` on the last
item in the buffer.
"""
# Sort all behavior values along the axis of each bc.
sorted_bc = self._buffer.sorted_behavior_values
# Calculate new boundaries.
SlidingBoundariesArchive._remap_numba_helper(sorted_bc,
self._buffer.size,
self._boundaries,
self._behavior_dim,
self.dims)
index_columns = tuple(map(list, zip(*self._occupied_indices)))
old_sols = self._solutions[index_columns].copy()
old_objs = self._objective_values[index_columns].copy()
old_behs = self._behavior_values[index_columns].copy()
self._reset_archive()
for sol, obj, beh in zip(old_sols, old_objs, old_behs):
# Add solutions from old archive.
status, value = ArchiveBase.add(self, sol, obj, beh)
for sol, obj, beh in self._buffer:
# Add solutions from buffer.
status, value = ArchiveBase.add(self, sol, obj, beh)
return status, value
def __init__(self, dims, ranges, seed=None, dtype=np.float64):
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,
storage_dims=tuple(self._dims),
behavior_dim=len(self._dims),
seed=seed,
dtype=dtype,
)
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._boundaries = []
for dim, lower_bound, upper_bound in zip(self._dims,
self._lower_bounds,
self._upper_bounds):
self._boundaries.append(
np.linspace(lower_bound, upper_bound, dim + 1))
def add(self, solution, objective_value, behavior_values):
"""Attempts to insert a new solution into the archive.
This method remaps the archive after every ``self.remap_frequency``
solutions are added. Remapping involves changing the boundaries of the
archive to the percentage marks of the behavior values stored in the
buffer and re-adding all of the solutions stored in the buffer `and` the
current archive.
Args:
solution (array-like): See :meth:`ArchiveBase.add`
objective_value (float): See :meth:`ArchiveBase.add`
behavior_values (array-like): See :meth:`ArchiveBase.add`
Returns:
See :meth:`ArchiveBase.add`
"""
solution = np.asarray(solution)
behavior_values = np.asarray(behavior_values)
self._buffer.add(solution, objective_value, behavior_values)
self._total_num_sol += 1
if self._total_num_sol % self._remap_frequency == 0:
status, value = 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)
])
else:
status, value = ArchiveBase.add(self, solution, objective_value,
behavior_values)
return status, value
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 to the archive.
Returns:
tuple: The result of calling :meth:`ArchiveBase.add` on the last
item in the buffer.
"""
# Sort all behavior values along the axis of each bc.
sorted_bc = self._buffer.sorted_behavior_values
# Calculate new boundaries.
SlidingBoundariesArchive._remap_numba_helper(sorted_bc,
self._buffer.size,
self._boundaries,
self._behavior_dim,
self.dims)
# TODO (btjanaka): Add an option that allows adding solutions from the
# previous archive that are not in the buffer.
# Add all solutions to the new empty archive.
self._reset_archive()
for solution, objective_value, behavior_value in self._buffer:
status, value = ArchiveBase.add(self, solution, objective_value,
behavior_value)
return status, value
def __init__(self,
dims,
ranges,
seed=None,
dtype=np.float64,
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,
storage_dims=tuple(self._dims),
behavior_dim=len(self._dims),
seed=seed,
dtype=dtype,
)
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
# 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._behavior_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._behavior_dim)
# Total number of solutions encountered.
self._total_num_sol = 0
def initialize(self, solution_dim):
"""Initializes the archive storage space and centroids.
This method may take a while to run. In addition to allocating storage
space, it runs :func:`~sklearn.cluster.k_means` to create an approximate
CVT, and it constructs a :class:`~scipy.spatial.cKDTree` object
containing the centroids found by k-means. k-means is not run if
``custom_centroids`` was passed in during construction.
Args:
solution_dim (int): The dimension of the solution space.
Raises:
RuntimeError: The archive is already initialized.
RuntimeError: The number of centroids returned by k-means clustering
was fewer than the number of bins specified during construction.
This is most likely caused by having too few samples and too
many bins.
"""
ArchiveBase.initialize(self, solution_dim)
if self._centroids is None:
self._samples = self._rng.uniform(
self._lower_bounds,
self._upper_bounds,
size=(self._samples, self._behavior_dim),
).astype(self.dtype) if isinstance(self._samples,
int) else self._samples
self._centroids = k_means(self._samples, self._bins,
**self._k_means_kwargs)[0]
if self._centroids.shape[0] < self._bins:
raise RuntimeError(
"While generating the CVT, k-means clustering found "
f"{self._centroids.shape[0]} centroids, but this archive "
f"needs {self._bins} bins. This most likely happened "
"because there are too few samples and/or too many bins.")
if self._use_kd_tree:
self._centroid_kd_tree = cKDTree(self._centroids,
**self._ckdtree_kwargs)
def as_pandas(self, include_solutions=True):
"""Converts the archive into a Pandas dataframe.
Args:
include_solutions (bool): Whether to include solution columns.
Returns:
pandas.DataFrame: A dataframe where each row is an elite in the
archive. The dataframe has ``behavior_dim`` columns called
``index_{i}`` for the archive index, ``behavior_dim`` columns called
``behavior_{i}`` for the behavior values, 1 column for the objective
function value called ``objective``, and ``solution_dim`` columns
called ``solution_{i}`` for the solution values.
"""
return ArchiveBase.as_pandas(self, include_solutions)
def as_pandas(self, include_solutions=True):
"""Converts the archive into a Pandas dataframe.
Args:
include_solutions (bool): Whether to include solution columns.
Returns:
pandas.DataFrame: A dataframe where each row is an elite in the
archive. The dataframe consists of 1 ``index`` column indicating the
index of the centroid in ``self._centroids``, ``behavior_dim``
columns called ``behavior_{i}`` for the behavior values, 1 column
for the objective function value called ``objective``, and
``solution_dim`` columns called ``solution_{i}`` for the solution
values.
"""
df = ArchiveBase.as_pandas(self, include_solutions)
df.rename(columns={"index_0": "index"}, inplace=True)
return df
the wrong shape.
"""
def __init__(self,
bins,
ranges,
seed=None,
dtype=np.float64,
samples=100_000,
custom_centroids=None,
k_means_kwargs=None,
use_kd_tree=False,
ckdtree_kwargs=None):
ArchiveBase.__init__(
self,
storage_dims=(bins, ),
behavior_dim=len(ranges),
seed=seed,
dtype=dtype,
)
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._bins = bins
# Apply default args for k-means. Users can easily override these,
# particularly if they want higher quality clusters.
self._k_means_kwargs = ({} if k_means_kwargs is None else
k_means_kwargs.copy())
if "n_init" not in self._k_means_kwargs: