import torch
import gplvm
import numpy as np
import scipy.stats as st
def ei(fm, fv, ybest, xi=0.01, eps=1e-12):
fsd = torch.sqrt(fv) + eps
gamma = (fm - ybest - xi)/fsd
return fsd * (torch.distributions.Normal(0,1).cdf(gamma) * gamma
+ torch.distributions.Normal(0,1).log_prob(gamma).exp())
def init_l1(Ytrain, Ftrain, ftest, n_init=5):
dis = np.abs(Ftrain - ftest).sum(axis=1)
ix_closest = np.argsort(dis)[:n_init]
ix_nonnan_pipelines \
= np.where(np.invert(np.isnan(Ytrain[:,ix_closest].sum(axis=1))))[0]
ranks = np.apply_along_axis(st.rankdata, 0,
Ytrain[ix_nonnan_pipelines[:,None],ix_closest])
ave_pipeline_ranks = ranks.mean(axis=1)
ix_init = ix_nonnan_pipelines[np.argsort(ave_pipeline_ranks)[::-1]]
return ix_init[:n_init]
class BO(gplvm.GP):
def __init__(self, dim, kernel, acq_func, **kwargs):
super(BO, self).__init__(dim, np.asarray([]), np.asarray([]), kernel,
**kwargs)
self.acq_func = acq_func
self.ybest = None
self.xbest = None
def add(self, xnew, ynew):
xnew_ = torch.tensor(xnew, dtype=self.X.dtype).reshape((1,-1))
self.X = torch.cat((self.X, xnew_))
ynew_ = torch.tensor([ynew], dtype=self.y.dtype)
self.y = torch.cat((self.y, ynew_))
if self.ybest is None or ynew_ > self.ybest:
self.ybest = ynew_
self.xbest = xnew_
self.N += 1
def next(self, Xcandidates):
if not self.N:
return torch.randperm(Xcandidates.size()[0])[0]
fmean, fvar = self.posterior(Xcandidates)
return torch.argmax(self.acq_func(fmean, fvar, self.ybest))
