aepsych.acquisition¶
Submodules¶
aepsych.acquisition.bvn module¶
- aepsych.acquisition.bvn.bvn_cdf(xu, yu, r)[source]¶
Evaluate the bivariate normal CDF.
WARNING: Implements only the routine for moderate levels of correlation. Will be inaccurate and should not be used for correlations larger than 0.925.
Standard (mean 0, var 1) bivariate normal distribution with correlation r. Evaluated from -inf to xu, and -inf to yu.
Based on function developed by Alan Genz: http://www.math.wsu.edu/faculty/genz/software/matlab/bvn.m
based in turn on Drezner, Z and G.O. Wesolowsky, (1989), On the computation of the bivariate normal inegral, Journal of Statist. Comput. Simul. 35, pp. 101-107.
- Parameters
xu (Tensor) – Upper limits for cdf evaluation in x
yu (Tensor) – Upper limits for cdf evaluation in y
r (Tensor) – BVN correlation
- Return type
Tensor
Returns: Tensor of cdf evaluations of same size as xu, yu, and r.
aepsych.acquisition.lookahead module¶
- aepsych.acquisition.lookahead.Hb(p)[source]¶
Binary entropy.
- Parameters
p (Tensor) – Tensor of probabilities.
Returns: Binary entropy for each probability.
- aepsych.acquisition.lookahead.MI_fn(Px, P1, P0, py1)[source]¶
Average mutual information. H(p) - E_y*[H(p | y*)]
- Parameters
Px (Tensor) – (b x m) Level-set posterior before observation
P1 (Tensor) – (b x m) Level-set posterior given observation of 1
P0 (Tensor) – (b x m) Level-set posterior given observation of 0
py1 (Tensor) – (b x 1) Probability of observing 1
- Return type
Tensor
Returns: (b) tensor of mutual information averaged over Xq.
- aepsych.acquisition.lookahead.ClassErr(p)[source]¶
Expected classification error, min(p, 1-p).
- Parameters
p (Tensor) –
- Return type
Tensor
- aepsych.acquisition.lookahead.SUR_fn(Px, P1, P0, py1)[source]¶
Stepwise uncertainty reduction.
Expected reduction in expected classification error given observation at Xstar, averaged over Xq.
- Parameters
Px (Tensor) – (b x m) Level-set posterior before observation
P1 (Tensor) – (b x m) Level-set posterior given observation of 1
P0 (Tensor) – (b x m) Level-set posterior given observation of 0
py1 (Tensor) – (b x 1) Probability of observing 1
- Return type
Tensor
Returns: (b) tensor of SUR values.
- aepsych.acquisition.lookahead.EAVC_fn(Px, P1, P0, py1)[source]¶
Expected absolute value change.
Expected absolute change in expected level-set volume given observation at Xstar.
- Parameters
Px (Tensor) – (b x m) Level-set posterior before observation
P1 (Tensor) – (b x m) Level-set posterior given observation of 1
P0 (Tensor) – (b x m) Level-set posterior given observation of 0
py1 (Tensor) – (b x 1) Probability of observing 1
- Return type
Tensor
Returns: (b) tensor of EAVC values.
- class aepsych.acquisition.lookahead.LookaheadAcquisitionFunction(*args, **kwargs)[source]¶
Bases:
AcquisitionFunction
A localized look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.lookahead.GlobalLookaheadAcquisitionFunction(*args, **kwargs)[source]¶
Bases:
LookaheadAcquisitionFunction
A global look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
Xq – (m x d) global reference set.
args (Any) –
kwargs (Any) –
- Return type
Any
- forward(X)¶
Evaluate acquisition function at X.
- Parameters
X (Tensor) – (b x 1 x d) point at which to evalaute acquisition function.
- Return type
Tensor
Returns: (b) tensor of acquisition values.
- class aepsych.acquisition.lookahead.GlobalMI(*args, **kwargs)[source]¶
Bases:
GlobalLookaheadAcquisitionFunction
A global look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
Xq – (m x d) global reference set.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.lookahead.GlobalSUR(*args, **kwargs)[source]¶
Bases:
GlobalLookaheadAcquisitionFunction
A global look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
Xq – (m x d) global reference set.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.lookahead.ApproxGlobalSUR(*args, **kwargs)[source]¶
Bases:
GlobalSUR
A global look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
Xq – (m x d) global reference set.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.lookahead.EAVC(*args, **kwargs)[source]¶
Bases:
GlobalLookaheadAcquisitionFunction
A global look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
Xq – (m x d) global reference set.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.lookahead.LocalLookaheadAcquisitionFunction(*args, **kwargs)[source]¶
Bases:
LookaheadAcquisitionFunction
A localized look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
args (Any) –
kwargs (Any) –
- Return type
Any
- forward(X)¶
Evaluate acquisition function at X.
- Parameters
X (Tensor) – (b x 1 x d) point at which to evalaute acquisition function.
- Return type
Tensor
Returns: (b) tensor of acquisition values.
- class aepsych.acquisition.lookahead.LocalMI(*args, **kwargs)[source]¶
Bases:
LocalLookaheadAcquisitionFunction
A localized look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.lookahead.LocalSUR(*args, **kwargs)[source]¶
Bases:
LocalLookaheadAcquisitionFunction
A localized look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.lookahead.MOCU(*args, **kwargs)[source]¶
Bases:
GlobalLookaheadAcquisitionFunction
MOCU acquisition function given in expr. 4 of:
Zhao, Guang, et al. “Uncertainty-aware active learning for optimal Bayesian classifier.” International Conference on Learning Representations (ICLR) 2021.
A global look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
Xq – (m x d) global reference set.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.lookahead.SMOCU(*args, **kwargs)[source]¶
Bases:
GlobalLookaheadAcquisitionFunction
SMOCU acquisition function given in expr. 11 of:
Zhao, Guang, et al. “Bayesian active learning by soft mean objective cost of uncertainty.” International Conference on Artificial Intelligence and Statistics (AISTATS) 2021.
A global look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
Xq – (m x d) global reference set.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.lookahead.BEMPS(*args, **kwargs)[source]¶
Bases:
GlobalLookaheadAcquisitionFunction
BEMPS acquisition function given in:
Tan, Wei, et al. “Diversity Enhanced Active Learning with Strictly Proper Scoring Rules.” Advances in Neural Information Processing Systems 34 (2021).
A global look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
Xq – (m x d) global reference set.
args (Any) –
kwargs (Any) –
- Return type
Any
aepsych.acquisition.lookahead_utils module¶
- aepsych.acquisition.lookahead_utils.posterior_at_xstar_xq(model, Xstar, Xq, posterior_transform=None)[source]¶
Evaluate the posteriors of f at single point Xstar and set of points Xq.
- Parameters
model (GP) – The model to evaluate.
Xstar (Tensor) – (b x 1 x d) tensor.
Xq (Tensor) – (b x m x d) tensor.
posterior_transform (Optional[botorch.acquisition.objective.PosteriorTransform]) –
- Returns
(b x 1) mean at Xstar. Sigma2_s: (b x 1) variance at Xstar. Mu_q: (b x m) mean at Xq. Sigma2_q: (b x m) variance at Xq. Sigma_sq: (b x m) covariance between Xstar and each point in Xq.
- Return type
Mu_s
- aepsych.acquisition.lookahead_utils.lookahead_levelset_at_xstar(model, Xstar, Xq, posterior_transform=None, **kwargs)[source]¶
Evaluate the look-ahead level-set posterior at Xq given observation at xstar.
- Parameters
model (GP) – The model to evaluate.
Xstar (Tensor) – (b x 1 x d) observation point.
Xq (Tensor) – (b x m x d) reference points.
gamma – Threshold in f-space.
posterior_transform (Optional[botorch.acquisition.objective.PosteriorTransform]) –
kwargs (Dict[str, Any]) –
- Returns
(b x m) Level-set posterior at Xq, before observation at xstar. P1: (b x m) Level-set posterior at Xq, given observation of 1 at xstar. P0: (b x m) Level-set posterior at Xq, given observation of 0 at xstar. py1: (b x 1) Probability of observing 1 at xstar.
- Return type
Px
- aepsych.acquisition.lookahead_utils.lookahead_p_at_xstar(model, Xstar, Xq, posterior_transform=None, **kwargs)[source]¶
Evaluate the look-ahead response probability posterior at Xq given observation at xstar.
Uses the approximation given in expr. 9 in: Zhao, Guang, et al. “Efficient active learning for Gaussian process classification by error reduction.” Advances in Neural Information Processing Systems 34 (2021): 9734-9746.
- Parameters
model (GP) – The model to evaluate.
Xstar (Tensor) – (b x 1 x d) observation point.
Xq (Tensor) – (b x m x d) reference points.
kwargs (Dict[str, Any]) – ignored (here for compatibility with other kinds of lookahead)
posterior_transform (Optional[botorch.acquisition.objective.PosteriorTransform]) –
- Returns
(b x m) Response posterior at Xq, before observation at xstar. P1: (b x m) Response posterior at Xq, given observation of 1 at xstar. P0: (b x m) Response posterior at Xq, given observation of 0 at xstar. py1: (b x 1) Probability of observing 1 at xstar.
- Return type
Px
- aepsych.acquisition.lookahead_utils.approximate_lookahead_levelset_at_xstar(model, Xstar, Xq, gamma, posterior_transform=None)[source]¶
The look-ahead posterior approximation of Lyu et al.
- Parameters
model (GP) – The model to evaluate.
Xstar (Tensor) – (b x 1 x d) observation point.
Xq (Tensor) – (b x m x d) reference points.
gamma (float) – Threshold in f-space.
posterior_transform (Optional[botorch.acquisition.objective.PosteriorTransform]) –
- Returns
(b x m) Level-set posterior at Xq, before observation at xstar. P1: (b x m) Level-set posterior at Xq, given observation of 1 at xstar. P0: (b x m) Level-set posterior at Xq, given observation of 0 at xstar. py1: (b x 1) Probability of observing 1 at xstar.
- Return type
Px
aepsych.acquisition.lse module¶
- class aepsych.acquisition.lse.MCLevelSetEstimation(*args, **kwargs)[source]¶
Bases:
MCAcquisitionFunction
Monte-carlo level set estimation.
- Parameters
model – A fitted model.
target – the level set (after objective transform) to be estimated
beta – a parameter that governs explore-exploit tradeoff
objective – An MCAcquisitionObjective representing the link function (e.g., logistic or probit.) applied on the samples. Can be implemented via GenericMCObjective.
sampler – The sampler used for drawing MC samples.
args (Any) –
kwargs (Any) –
- Return type
Any
- acquisition(obj_samples)[source]¶
Evaluate the acquisition based on objective samples.
Usually you should not call this directly unless you are subclassing this class and modifying how objective samples are generated.
- Parameters
obj_samples (torch.Tensor) – Samples from the model, transformed by the objective. Should be samples x batch_shape.
- Returns
Acquisition function at the sampled values.
- Return type
torch.Tensor
- forward(X)¶
Evaluate the acquisition function
- Parameters
X (torch.Tensor) – Points at which to evaluate.
- Returns
Value of the acquisition functiona at these points.
- Return type
torch.Tensor
aepsych.acquisition.mc_posterior_variance module¶
- aepsych.acquisition.mc_posterior_variance.balv_acq(obj_samps)[source]¶
Evaluate BALV (posterior variance) on a set of objective samples.
- Parameters
obj_samps (torch.Tensor) – Samples from the GP, transformed by the objective. Should be samples x batch_shape.
- Returns
Acquisition function value.
- Return type
torch.Tensor
- class aepsych.acquisition.mc_posterior_variance.MCPosteriorVariance(*args, **kwargs)[source]¶
Bases:
MCAcquisitionFunction
Posterior variance, computed using samples so we can use objective/transform
Posterior Variance of Link Function
- Parameters
model – A fitted model.
objective – An MCAcquisitionObjective representing the link function (e.g., logistic or probit.) applied on the difference of (usually 1-d) two samples. Can be implemented via GenericMCObjective.
sampler – The sampler used for drawing MC samples.
args (Any) –
kwargs (Any) –
- Return type
Any
- forward(X)¶
Evaluate MCPosteriorVariance on the candidate set X.
- Parameters
X (Tensor) – A batch_size x q x d-dim Tensor
- Returns
Posterior variance of link function at X that active learning hopes to maximize
- Return type
Tensor
- class aepsych.acquisition.mc_posterior_variance.MonotonicMCPosteriorVariance(*args, **kwargs)[source]¶
Bases:
MonotonicMCAcquisition
Initialize MonotonicMCAcquisition
- Parameters
model (Model) – Model to use, usually a MonotonicRejectionGP.
num_samples (int, optional) – Number of samples to keep from the rejection sampler. . Defaults to 32.
num_rejection_samples (int, optional) – Number of rejection samples to draw. Defaults to 1024.
objective (Optional[MCAcquisitionObjective], optional) – Objective transform of the GP output before evaluating the acquisition. Defaults to identity transform.
args (Any) –
kwargs (Any) –
- Return type
Any
aepsych.acquisition.monotonic_rejection module¶
- class aepsych.acquisition.monotonic_rejection.MonotonicMCAcquisition(*args, **kwargs)[source]¶
Bases:
AcquisitionFunction
- Acquisition function base class for use with the rejection sampling
monotonic GP. This handles the bookkeeping of the derivative constraint points – implement specific monotonic MC acquisition in subclasses.
Initialize MonotonicMCAcquisition
- Parameters
model (Model) – Model to use, usually a MonotonicRejectionGP.
num_samples (int, optional) – Number of samples to keep from the rejection sampler. . Defaults to 32.
num_rejection_samples (int, optional) – Number of rejection samples to draw. Defaults to 1024.
objective (Optional[MCAcquisitionObjective], optional) – Objective transform of the GP output before evaluating the acquisition. Defaults to identity transform.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.monotonic_rejection.MonotonicMCLSE(*args, **kwargs)[source]¶
Bases:
MonotonicMCAcquisition
Level set estimation acquisition function for use with monotonic models.
- Parameters
model (Model) – Underlying model object, usually should be MonotonicRejectionGP.
target (float) – Level set value to target (after the objective).
num_samples (int, optional) – Number of MC samples to draw in MC acquisition. Defaults to 32.
num_rejection_samples (int, optional) – Number of rejection samples from which to subsample monotonic ones. Defaults to 1024.
beta (float, optional) – Parameter of the LSE acquisition function that governs exploration vs exploitation (similarly to the same parameter in UCB). Defaults to 3.84 (1.96 ** 2), which maps to the straddle heuristic of Bryan et al. 2005.
objective (Optional[MCAcquisitionObjective], optional) – Objective transform. Defaults to identity transform.
args (Any) –
kwargs (Any) –
- Return type
Any
aepsych.acquisition.mutual_information module¶
- aepsych.acquisition.mutual_information.bald_acq(obj_samples)[source]¶
Evaluate Mutual Information acquisition function.
With latent function F and X a hypothetical observation at a new point, I(F; X) = I(X; F) = H(X) - H(X |F), H(X |F ) = E_{f} (H(X |F =f ) i.e., we take the posterior entropy of the (Bernoulli) observation X given the current model posterior and subtract the conditional entropy on F, that being the mean entropy over the posterior for F. This is equivalent to the BALD acquisition function in Houlsby et al. NeurIPS 2012.
- Parameters
obj_samples (torch.Tensor) – Objective samples from the GP, of shape num_samples x batch_shape x d_out
- Returns
Value of acquisition at samples.
- Return type
torch.Tensor
- class aepsych.acquisition.mutual_information.BernoulliMCMutualInformation(*args, **kwargs)[source]¶
Bases:
MCAcquisitionFunction
Mutual Information acquisition function for a bernoulli outcome.
Given a model and an objective link function, calculate the mutual information of a trial at a new point and the distribution on the latent function.
Objective here should give values in (0, 1) (e.g. logit or probit).
Single Bernoulli mutual information for active learning
- Parameters
model (Model) – A fitted model.
objective (MCAcquisitionObjective) – An MCAcquisitionObjective representing the link function (e.g., logistic or probit)
sampler (MCSampler, optional) – The sampler used for drawing MC samples.
args (Any) –
kwargs (Any) –
- Return type
Any
- forward(X)¶
Evaluate mutual information on the candidate set X.
- Parameters
X (Tensor) – A batch_size x q x d-dim Tensor.
- Returns
Tensor of shape batch_size x q representing the mutual information of a hypothetical trial at X that active learning hopes to maximize.
- Return type
Tensor
- class aepsych.acquisition.mutual_information.MonotonicBernoulliMCMutualInformation(*args, **kwargs)[source]¶
Bases:
MonotonicMCAcquisition
Initialize MonotonicMCAcquisition
- Parameters
model (Model) – Model to use, usually a MonotonicRejectionGP.
num_samples (int, optional) – Number of samples to keep from the rejection sampler. . Defaults to 32.
num_rejection_samples (int, optional) – Number of rejection samples to draw. Defaults to 1024.
objective (Optional[MCAcquisitionObjective], optional) – Objective transform of the GP output before evaluating the acquisition. Defaults to identity transform.
args (Any) –
kwargs (Any) –
- Return type
Any
aepsych.acquisition.objective module¶
- class aepsych.acquisition.objective.ProbitObjective(*args, **kwargs)[source]¶
Bases:
MCAcquisitionObjective
Probit objective
Transforms the input through the normal CDF (probit).
- Parameters
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.objective.FloorLinkObjective(*args, **kwargs)[source]¶
Bases:
MCAcquisitionObjective
Wrapper for objectives to add a floor, when the probability is known not to go below it.
- Parameters
args (Any) –
kwargs (Any) –
- Return type
Any
- forward(samples, X=None)[source]¶
Evaluates the objective for input x and floor f
- Parameters
samples (Tensor) – GP samples.
X (Optional[Tensor], optional) – ignored, here for compatibility with MCAcquisitionObjective.
- Returns
outcome probability.
- Return type
Tensor
- class aepsych.acquisition.objective.FloorLogitObjective(*args, **kwargs)[source]¶
Bases:
FloorLinkObjective
Logistic sigmoid (aka expit, aka logistic CDF), but with a floor so that its output is between floor and 1.0.
- Parameters
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.objective.FloorGumbelObjective(*args, **kwargs)[source]¶
Bases:
FloorLinkObjective
Gumbel CDF but with a floor so that its output is between floor and 1.0. Note that this is not the standard Gumbel distribution, but rather the left-skewed Gumbel that arises as the log of the Weibull distribution, e.g. Treutwein 1995, doi:10.1016/0042-6989(95)00016-X.
- Parameters
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.objective.FloorProbitObjective(*args, **kwargs)[source]¶
Bases:
FloorLinkObjective
Probit (aka Gaussian CDF), but with a floor so that its output is between floor and 1.0.
- Parameters
args (Any) –
kwargs (Any) –
- Return type
Any
aepsych.acquisition.rejection_sampler module¶
- class aepsych.acquisition.rejection_sampler.RejectionSampler(*args, **kwargs)[source]¶
Bases:
MCSampler
Samples from a posterior subject to the constraint that samples in constrained_idx should be >= 0.
If not enough feasible samples are generated, will return the least violating samples.
Initialize RejectionSampler
- Parameters
num_samples (int) – Number of samples to return. Note that if fewer samples than this number are positive in the required dimension, the remaining samples returned will be the “least violating”, i.e. closest to 0.
num_rejection_samples (int) – Number of samples to draw before rejecting.
constrained_idx (Tensor) – Indices of input dimensions that should be constrained positive.
args (Any) –
kwargs (Any) –
- Return type
Any
Module contents¶
- class aepsych.acquisition.BernoulliMCMutualInformation(*args, **kwargs)[source]¶
Bases:
MCAcquisitionFunction
Mutual Information acquisition function for a bernoulli outcome.
Given a model and an objective link function, calculate the mutual information of a trial at a new point and the distribution on the latent function.
Objective here should give values in (0, 1) (e.g. logit or probit).
Single Bernoulli mutual information for active learning
- Parameters
model (Model) – A fitted model.
objective (MCAcquisitionObjective) – An MCAcquisitionObjective representing the link function (e.g., logistic or probit)
sampler (MCSampler, optional) – The sampler used for drawing MC samples.
args (Any) –
kwargs (Any) –
- Return type
Any
- forward(X)¶
Evaluate mutual information on the candidate set X.
- Parameters
X (Tensor) – A batch_size x q x d-dim Tensor.
- Returns
Tensor of shape batch_size x q representing the mutual information of a hypothetical trial at X that active learning hopes to maximize.
- Return type
Tensor
- class aepsych.acquisition.MonotonicBernoulliMCMutualInformation(*args, **kwargs)[source]¶
Bases:
MonotonicMCAcquisition
Initialize MonotonicMCAcquisition
- Parameters
model (Model) – Model to use, usually a MonotonicRejectionGP.
num_samples (int, optional) – Number of samples to keep from the rejection sampler. . Defaults to 32.
num_rejection_samples (int, optional) – Number of rejection samples to draw. Defaults to 1024.
objective (Optional[MCAcquisitionObjective], optional) – Objective transform of the GP output before evaluating the acquisition. Defaults to identity transform.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.MonotonicMCLSE(*args, **kwargs)[source]¶
Bases:
MonotonicMCAcquisition
Level set estimation acquisition function for use with monotonic models.
- Parameters
model (Model) – Underlying model object, usually should be MonotonicRejectionGP.
target (float) – Level set value to target (after the objective).
num_samples (int, optional) – Number of MC samples to draw in MC acquisition. Defaults to 32.
num_rejection_samples (int, optional) – Number of rejection samples from which to subsample monotonic ones. Defaults to 1024.
beta (float, optional) – Parameter of the LSE acquisition function that governs exploration vs exploitation (similarly to the same parameter in UCB). Defaults to 3.84 (1.96 ** 2), which maps to the straddle heuristic of Bryan et al. 2005.
objective (Optional[MCAcquisitionObjective], optional) – Objective transform. Defaults to identity transform.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.MCPosteriorVariance(*args, **kwargs)[source]¶
Bases:
MCAcquisitionFunction
Posterior variance, computed using samples so we can use objective/transform
Posterior Variance of Link Function
- Parameters
model – A fitted model.
objective – An MCAcquisitionObjective representing the link function (e.g., logistic or probit.) applied on the difference of (usually 1-d) two samples. Can be implemented via GenericMCObjective.
sampler – The sampler used for drawing MC samples.
args (Any) –
kwargs (Any) –
- Return type
Any
- forward(X)¶
Evaluate MCPosteriorVariance on the candidate set X.
- Parameters
X (Tensor) – A batch_size x q x d-dim Tensor
- Returns
Posterior variance of link function at X that active learning hopes to maximize
- Return type
Tensor
- class aepsych.acquisition.MonotonicMCPosteriorVariance(*args, **kwargs)[source]¶
Bases:
MonotonicMCAcquisition
Initialize MonotonicMCAcquisition
- Parameters
model (Model) – Model to use, usually a MonotonicRejectionGP.
num_samples (int, optional) – Number of samples to keep from the rejection sampler. . Defaults to 32.
num_rejection_samples (int, optional) – Number of rejection samples to draw. Defaults to 1024.
objective (Optional[MCAcquisitionObjective], optional) – Objective transform of the GP output before evaluating the acquisition. Defaults to identity transform.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.MCLevelSetEstimation(*args, **kwargs)[source]¶
Bases:
MCAcquisitionFunction
Monte-carlo level set estimation.
- Parameters
model – A fitted model.
target – the level set (after objective transform) to be estimated
beta – a parameter that governs explore-exploit tradeoff
objective – An MCAcquisitionObjective representing the link function (e.g., logistic or probit.) applied on the samples. Can be implemented via GenericMCObjective.
sampler – The sampler used for drawing MC samples.
args (Any) –
kwargs (Any) –
- Return type
Any
- acquisition(obj_samples)[source]¶
Evaluate the acquisition based on objective samples.
Usually you should not call this directly unless you are subclassing this class and modifying how objective samples are generated.
- Parameters
obj_samples (torch.Tensor) – Samples from the model, transformed by the objective. Should be samples x batch_shape.
- Returns
Acquisition function at the sampled values.
- Return type
torch.Tensor
- forward(X)¶
Evaluate the acquisition function
- Parameters
X (torch.Tensor) – Points at which to evaluate.
- Returns
Value of the acquisition functiona at these points.
- Return type
torch.Tensor
- class aepsych.acquisition.ProbitObjective(*args, **kwargs)[source]¶
Bases:
MCAcquisitionObjective
Probit objective
Transforms the input through the normal CDF (probit).
- Parameters
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.FloorProbitObjective(*args, **kwargs)[source]¶
Bases:
FloorLinkObjective
Probit (aka Gaussian CDF), but with a floor so that its output is between floor and 1.0.
- Parameters
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.FloorLogitObjective(*args, **kwargs)[source]¶
Bases:
FloorLinkObjective
Logistic sigmoid (aka expit, aka logistic CDF), but with a floor so that its output is between floor and 1.0.
- Parameters
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.FloorGumbelObjective(*args, **kwargs)[source]¶
Bases:
FloorLinkObjective
Gumbel CDF but with a floor so that its output is between floor and 1.0. Note that this is not the standard Gumbel distribution, but rather the left-skewed Gumbel that arises as the log of the Weibull distribution, e.g. Treutwein 1995, doi:10.1016/0042-6989(95)00016-X.
- Parameters
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.GlobalMI(*args, **kwargs)[source]¶
Bases:
GlobalLookaheadAcquisitionFunction
A global look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
Xq – (m x d) global reference set.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.GlobalSUR(*args, **kwargs)[source]¶
Bases:
GlobalLookaheadAcquisitionFunction
A global look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
Xq – (m x d) global reference set.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.ApproxGlobalSUR(*args, **kwargs)[source]¶
Bases:
GlobalSUR
A global look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
Xq – (m x d) global reference set.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.EAVC(*args, **kwargs)[source]¶
Bases:
GlobalLookaheadAcquisitionFunction
A global look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
Xq – (m x d) global reference set.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.LocalMI(*args, **kwargs)[source]¶
Bases:
LocalLookaheadAcquisitionFunction
A localized look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
args (Any) –
kwargs (Any) –
- Return type
Any
- class aepsych.acquisition.LocalSUR(*args, **kwargs)[source]¶
Bases:
LocalLookaheadAcquisitionFunction
A localized look-ahead acquisition function.
- Parameters
model – The gpytorch model.
target – Threshold value to target in p-space.
args (Any) –
kwargs (Any) –
- Return type
Any