#!/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 Dict, Optional
import torch
from aepsych.config import Config
from aepsych.generators.base import AEPsychGenerator
from aepsych.models.base import AEPsychMixin
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.
"""
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[AEPsychMixin] = None, # included for API compatibility
) -> torch.Tensor:
"""Query next point(s) to run by quasi-randomly sampling the parameter space.
Args:
num_points (int): Number of points to query.
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,
)
points = self.points[self._idx : self._idx + num_points]
self._idx += num_points
return points
[docs] @classmethod
def from_config(
cls, config: Config, name: Optional[str] = None
) -> "ManualGenerator":
return cls(**cls.get_config_options(config, name))
[docs] @classmethod
def get_config_options(cls, config: Config, name: Optional[str] = None) -> Dict:
if name is None:
name = cls.__name__
lb = config.gettensor(name, "lb")
ub = config.gettensor(name, "ub")
dim = config.getint(name, "dim", fallback=None)
points = config.gettensor(name, "points")
shuffle = config.getboolean(name, "shuffle", fallback=True)
seed = config.getint(name, "seed", fallback=None)
if len(points.shape) == 3:
# Configs have a reasonable natural input method that produces incorrect tensors
points = points.swapaxes(-1, -2)
options = {
"lb": lb,
"ub": ub,
"dim": dim,
"points": points,
"shuffle": shuffle,
"seed": seed,
}
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 (Union[np.ndarray, torch.Tensor]): Lower bounds of each parameter.
ub (Union[np.ndarray, torch.Tensor]): Upper bounds of each parameter.
window (Union[np.ndarray, torch.Tensor]): How far away to sample from the
reference point along each dimension. If the parameters are transformed,
the proportion of the range (based on ub/lb given) covered by the window
will be preserved (and not the absolute distance from the reference points).
points (Union[np.ndarray, 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
[docs] @classmethod
def get_config_options(cls, config: Config, name: Optional[str] = None) -> Dict:
if name is None:
name = cls.__name__
options = super().get_config_options(config)
window = config.gettensor(name, "window")
samples_per_point = config.getint(name, "samples_per_point")
options.update({"window": window, "samples_per_point": samples_per_point})
return options
