"""Provides the Scheduler."""
import warnings
from collections import defaultdict
import numpy as np
[docs]class Scheduler:
"""A basic class that composes an archive with multiple emitters.
To use this class, first create an archive and list of emitters for the
QD algorithm. Then, construct the Scheduler with these arguments. Finally,
repeatedly call :meth:`ask` to collect solutions to analyze, and return the
objective and measures of those solutions **in the same order** using
:meth:`tell`.
As all solutions go into the same archive, the emitters passed in must emit
solutions with the same dimension (that is, their ``solution_dim`` attribute
must be the same).
.. warning:: If you are constructing many emitters at once, do not do
something like ``[EmitterClass(...)] * 5``, as this creates a list with
the same instance of ``EmitterClass`` in each position. Instead, use
``[EmitterClass(...) for _ in range 5]``, which creates 5 unique
instances of ``EmitterClass``.
Args:
archive (ribs.archives.ArchiveBase): An archive object, e.g. one
selected from :mod:`ribs.archives`.
emitters (list of ribs.archives.EmitterBase): A list of emitter objects,
e.g. :class:`ribs.emitters.GaussianEmitter`.
add_mode (str): Indicates how solutions should be added to the archive.
The default is "batch", which adds all solutions with one call to
:meth:`~ribs.archives.ArchiveBase.add`. Alternatively, use "single"
to add the solutions one at a time with
:meth:`~ribs.archives.ArchiveBase.add_single`. "single" mode is
included for legacy reasons, as it was the only mode of operation in
pyribs 0.4.0 and before. We highly recommend using "batch" mode
since it is significantly faster.
result_archive (ribs.archives.ArchiveBase): In some algorithms, such as
CMA-MAE, the archive does not store all the best-performing
solutions. The ``result_archive`` is a secondary archive where we
can store all the best-performing solutions.
Raises:
TypeError: The ``emitters`` argument was not a list of emitters.
ValueError: The emitters passed in do not have the same solution
dimensions.
ValueError: There is no emitter passed in.
ValueError: The same emitter instance was passed in multiple times. Each
emitter should be a unique instance (see the warning above).
ValueError: Invalid value for ``add_mode``.
ValueError: The ``result_archive`` and ``archive`` are the same object
(``result_archive`` should not be passed in in this case).
ValueError: The ``result_archive`` and ``archive`` have different
fields.
"""
def __init__(self,
archive,
emitters,
*,
result_archive=None,
add_mode="batch"):
try:
if len(emitters) == 0:
raise ValueError(
"Pass in at least one emitter to the scheduler.")
except TypeError as exception:
# TypeError will be raised by len(). We avoid directly checking if
# `emitters` is an instance of list since we do not want to be too
# restrictive.
raise TypeError(
"`emitters` must be a list of emitter objects.") from exception
emitter_ids = set(id(e) for e in emitters)
if len(emitter_ids) != len(emitters):
raise ValueError(
"Not all emitters passed in were unique (i.e. some emitters "
"had the same id). If emitters were created with something "
"like [EmitterClass(...)] * n, instead use "
"[EmitterClass(...) for _ in range(n)] so that all emitters "
"are unique instances.")
self._solution_dim = emitters[0].solution_dim
for idx, emitter in enumerate(emitters[1:]):
if emitter.solution_dim != self._solution_dim:
raise ValueError(
"All emitters must have the same solution dim, but "
f"Emitter {idx} has dimension {emitter.solution_dim}, "
f"while Emitter 0 has dimension {self._solution_dim}")
if add_mode not in ["single", "batch"]:
raise ValueError("add_mode must either be 'batch' or 'single', but "
f"it was '{add_mode}'")
if archive is result_archive:
raise ValueError("`archive` has same id as `result_archive` -- "
"Note that `Scheduler.result_archive` already "
"defaults to be the same as `archive` if you pass "
"`result_archive=None`")
if (result_archive is not None and
set(archive.field_list) != set(result_archive.field_list)):
raise ValueError("`archive` and `result_archive` should have the "
"same set of fields. This may be the result of "
"passing extra_fields to archive but not to "
"result_archive.")
self._archive = archive
self._emitters = emitters
self._add_mode = add_mode
self._result_archive = result_archive
# Keeps track of whether the scheduler should be receiving a call to
# ask() or tell().
self._last_called = None
# The last set of solutions returned by ask().
self._cur_solutions = []
# The number of solutions created by each emitter.
self._num_emitted = [None for _ in self._emitters]
@property
def archive(self):
"""ribs.archives.ArchiveBase: Archive for storing solutions found in
this scheduler."""
return self._archive
@property
def emitters(self):
"""list of ribs.archives.EmitterBase: Emitters for generating solutions
in this scheduler."""
return self._emitters
@property
def result_archive(self):
"""ribs.archives.ArchiveBase: Another archive for storing solutions
found in this optimizer.
If `result_archive` was not passed to the constructor, this property is
the same as :attr:`archive`.
"""
return (self._archive
if self._result_archive is None else self._result_archive)
[docs] def ask_dqd(self):
"""Generates a batch of solutions by calling ask_dqd() on all DQD
emitters.
.. note:: The order of the solutions returned from this method is
important, so do not rearrange them.
Returns:
(batch_size, dim) array: An array of n solutions to evaluate. Each
row contains a single solution.
Raises:
RuntimeError: This method was called without first calling
:meth:`tell`.
"""
if self._last_called in ["ask", "ask_dqd"]:
raise RuntimeError("ask_dqd cannot be called immediately after " +
self._last_called)
self._last_called = "ask_dqd"
self._cur_solutions = []
for i, emitter in enumerate(self._emitters):
emitter_sols = emitter.ask_dqd()
self._cur_solutions.append(emitter_sols)
self._num_emitted[i] = len(emitter_sols)
# In case the emitters didn't return any solutions.
self._cur_solutions = np.concatenate(
self._cur_solutions, axis=0) if self._cur_solutions else np.empty(
(0, self._solution_dim))
return self._cur_solutions
[docs] def ask(self):
"""Generates a batch of solutions by calling ask() on all emitters.
.. note:: The order of the solutions returned from this method is
important, so do not rearrange them.
Returns:
(batch_size, dim) array: An array of n solutions to evaluate. Each
row contains a single solution.
Raises:
RuntimeError: This method was called without first calling
:meth:`tell`.
"""
if self._last_called in ["ask", "ask_dqd"]:
raise RuntimeError("ask cannot be called immediately after " +
self._last_called)
self._last_called = "ask"
self._cur_solutions = []
for i, emitter in enumerate(self._emitters):
emitter_sols = emitter.ask()
self._cur_solutions.append(emitter_sols)
self._num_emitted[i] = len(emitter_sols)
# In case the emitters didn't return any solutions.
self._cur_solutions = np.concatenate(
self._cur_solutions, axis=0) if self._cur_solutions else np.empty(
(0, self._solution_dim))
return self._cur_solutions
def _check_length(self, name, arr):
"""Raises a ValueError if array does not have the same length as the
solutions."""
if len(arr) != len(self._cur_solutions):
raise ValueError(
f"{name} should have length {len(self._cur_solutions)} "
"(this is the number of solutions output by ask()) but "
f"has length {len(arr)}")
def _validate_tell_data(self, data):
"""Preprocesses data passed into tell methods."""
for name, arr in data.items():
data[name] = np.asarray(arr)
self._check_length(name, arr)
# Convenient to have solutions be part of data, so that everything is
# just one dict.
data["solution"] = self._cur_solutions
return data
EMPTY_WARNING = (
"`{name}` was empty before adding solutions, and it is still empty "
"after adding solutions. "
"One potential cause is that `threshold_min` is too high in this "
"archive, i.e., solutions are not being inserted because their "
"objective value does not exceed `threshold_min`.")
def _add_to_archives(self, data):
"""Adds solutions to both the regular archive and the result archive."""
archive_empty_before = self.archive.empty
if self._result_archive is not None:
# Check self._result_archive here since self.result_archive is a
# property that always provides a proper archive.
result_archive_empty_before = self.result_archive.empty
# Add solutions to the archive.
if self._add_mode == "batch":
add_info = self.archive.add(**data)
# Add solutions to result_archive.
if self._result_archive is not None:
self._result_archive.add(**data)
elif self._add_mode == "single":
add_info = defaultdict(list)
for i in range(len(self._cur_solutions)):
single_data = {name: arr[i] for name, arr in data.items()}
single_info = self.archive.add_single(**single_data)
for name, val in single_info.items():
add_info[name].append(val)
# Add solutions to result_archive.
if self._result_archive is not None:
self._result_archive.add_single(**single_data)
for name, arr in add_info.items():
add_info[name] = np.asarray(arr)
# Warn the user if nothing was inserted into the archives.
if archive_empty_before and self.archive.empty:
warnings.warn(self.EMPTY_WARNING.format(name="archive"))
if self._result_archive is not None:
if result_archive_empty_before and self.result_archive.empty:
warnings.warn(self.EMPTY_WARNING.format(name="result_archive"))
return add_info
[docs] def tell_dqd(self, objective, measures, jacobian, **fields):
"""Returns info for solutions from :meth:`ask_dqd`.
.. note:: The objective, measures, and jacobian arrays must be in the
same order as the solutions created by :meth:`ask_dqd`; i.e.
``objective[i]``, ``measures[i]``, and ``jacobian[i]`` should be the
objective, measures, and jacobian for ``solution[i]``.
Args:
objective ((batch_size,) array): Each entry of this array contains
the objective function evaluation of a solution.
measures ((batch_size, measure_dim) array): Each row of this array
contains a solution's coordinates in measure space.
jacobian (numpy.ndarray): ``(batch_size, 1 + measure_dim,
solution_dim)`` array consisting of Jacobian matrices of the
solutions obtained from :meth:`ask_dqd`. Each matrix should
consist of the objective gradient of the solution followed by
the measure gradients.
fields (keyword arguments): Additional data for each solution. Each
argument should be an array with batch_size as the first
dimension.
Raises:
RuntimeError: This method is called without first calling
:meth:`ask`.
ValueError: One of the inputs has the wrong shape.
"""
if self._last_called != "ask_dqd":
raise RuntimeError(
"tell_dqd() was called without calling ask_dqd().")
self._last_called = "tell_dqd"
data = self._validate_tell_data({
"objective": objective,
"measures": measures,
**fields,
})
jacobian = np.asarray(jacobian)
self._check_length("jacobian", jacobian)
add_info = self._add_to_archives(data)
# Keep track of pos because emitters may have different batch sizes.
pos = 0
for emitter, n in zip(self._emitters, self._num_emitted):
end = pos + n
emitter.tell_dqd(
**{
name: arr[pos:end] for name, arr in data.items()
},
jacobian=jacobian[pos:end],
add_info={
name: arr[pos:end] for name, arr in add_info.items()
},
)
pos = end
[docs] def tell(self, objective, measures, **fields):
"""Returns info for solutions from :meth:`ask`.
.. note:: The objective and measures arrays must be in the same order as
the solutions created by :meth:`ask_dqd`; i.e. ``objective[i]`` and
``measures[i]`` should be the objective and measures for
``solution[i]``.
Args:
objective ((batch_size,) array): Each entry of this array contains
the objective function evaluation of a solution.
measures ((batch_size, measures_dm) array): Each row of this array
contains a solution's coordinates in measure space.
fields (keyword arguments): Additional data for each solution. Each
argument should be an array with batch_size as the first
dimension.
Raises:
RuntimeError: This method is called without first calling
:meth:`ask`.
ValueError: One of the inputs has the wrong shape.
"""
if self._last_called != "ask":
raise RuntimeError("tell() was called without calling ask().")
self._last_called = "tell"
data = self._validate_tell_data({
"objective": objective,
"measures": measures,
**fields,
})
add_info = self._add_to_archives(data)
# Keep track of pos because emitters may have different batch sizes.
pos = 0
for emitter, n in zip(self._emitters, self._num_emitted):
end = pos + n
emitter.tell(
**{
name: arr[pos:end] for name, arr in data.items()
},
add_info={
name: arr[pos:end] for name, arr in add_info.items()
},
)
pos = end