#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import warnings
from typing import Any, Dict, Optional
import torch
from aepsych.config import Config
from aepsych.generators.base import AEPsychGenerator
from aepsych.models.base import AEPsychModelMixin
from aepsych.utils import _process_bounds
from torch.quasirandom import SobolEngine
[docs]class ManualGenerator(AEPsychGenerator):
"""Generator that generates points from a predefined list."""
_requires_model = False
def __init__(
self,
lb: torch.Tensor,
ub: torch.Tensor,
points: torch.Tensor,
dim: Optional[int] = None,
shuffle: bool = True,
seed: Optional[int] = None,
) -> None:
"""Iniatialize ManualGenerator.
Args:
lb (torch.Tensor): Lower bounds of each parameter.
ub (torch.Tensor): Upper bounds of each parameter.
points (torch.Tensor): The points that will be generated.
dim (int, optional): Dimensionality of the parameter space. If None, it is inferred from lb and ub.
shuffle (bool): Whether or not to shuffle the order of the points. True by default.
seed (int, optional): Random seed. Defaults to None.
"""
self.seed = seed
self.lb, self.ub, self.dim = _process_bounds(lb, ub, dim)
self.points = points
if shuffle:
if seed is not None:
torch.manual_seed(seed)
self.points = points[torch.randperm(len(points))]
self.max_asks = len(self.points)
self._idx = 0
[docs] def gen(
self,
num_points: int = 1,
model: Optional[AEPsychModelMixin] = None, # included for API compatibility
fixed_features: Optional[Dict[int, float]] = None,
**kwargs, # Ignored
) -> torch.Tensor:
"""Query next point(s) to run by quasi-randomly sampling the parameter space.
Args:
num_points (int): Number of points to query. Defaults to 1.
model (AEPsychModelMixin, optional): Model to use for generating points. Not used in this generator. Defaults to None.
fixed_features (Dict[int, float], optional): Ignored, kept for consistent
API.
**kwargs: Ignored, API compatibility
Returns:
torch.Tensor: Next set of point(s) to evaluate, [num_points x dim].
"""
if num_points > (len(self.points) - self._idx):
warnings.warn(
"Asked for more points than are left in the generator! Giving everthing it has!",
RuntimeWarning,
)
if fixed_features is not None:
warnings.warn(
f"Cannot fix features when generating from {self.__class__.__name__}"
)
points = self.points[self._idx : self._idx + num_points]
self._idx += num_points
return points
[docs] @classmethod
def get_config_options(
cls,
config,
name: Optional[str] = None,
options: Optional[Dict[str, Any]] = None,
) -> Dict[str, Any]:
"""Find the config options for the generator.
Args:
config (Config): Config to look for options in.
name (str, optional): Unused, kept for API conformity.
options (Dict[str, Any], optional): Existing options, any key in options
will be ignored from the config.
Return:
Dict[str, Any]: A dictionary of options to initialize the generator.
"""
options = super().get_config_options(config, name, options)
# Configs have a reasonable natural input method that produces incorrect tensors
if len(options["points"].shape) == 3:
options["points"] = options["points"].swapaxes(-1, -2)
return options
@property
def finished(self) -> bool:
return self._idx >= len(self.points)
[docs]class SampleAroundPointsGenerator(ManualGenerator):
"""Generator that samples in a window around reference points in a predefined list."""
def __init__(
self,
lb: torch.Tensor,
ub: torch.Tensor,
window: torch.Tensor,
points: torch.Tensor,
samples_per_point: int,
dim: Optional[int] = None,
shuffle: bool = True,
seed: Optional[int] = None,
) -> None:
"""Iniatialize SampleAroundPointsGenerator.
Args:
lb (torch.Tensor): Lower bounds of each parameter.
ub (torch.Tensor): Upper bounds of each parameter.
window (torch.Tensor): How far away to sample from the reference point along each dimension.
points (torch.Tensor): The points that will be generated.
samples_per_point (int): How many samples around each point to take.
dim (int, optional): Dimensionality of the parameter space. If None, it is inferred from lb and ub.
shuffle (bool): Whether or not to shuffle the order of the points. True by default.
seed (int, optional): Random seed.
"""
lb, ub, dim = _process_bounds(lb, ub, dim)
self.engine = SobolEngine(dimension=dim, scramble=True, seed=seed)
gen_points = []
if len(points.shape) > 2:
# We need to determine how many stimuli there are per trial to maintain the proper tensor shape
n_draws = points.shape[-1]
else:
n_draws = 1
for point in points:
if len(points.shape) > 2:
point = point.T
p_lb = torch.max(point - window, lb)
p_ub = torch.min(point + window, ub)
for _ in range(samples_per_point):
grid = self.engine.draw(n_draws)
grid = p_lb + (p_ub - p_lb) * grid
gen_points.append(grid)
if len(points.shape) > 2:
generated = torch.stack(gen_points)
generated = generated.swapaxes(-2, -1)
else:
generated = torch.vstack(gen_points)
super().__init__(lb, ub, generated, dim, shuffle, seed) # type: ignore
