import numpy as np
import scipy.stats
[docs]def score(mode, predictor, test, training=None, **kwargs):
"""
Calculate scores (acquisition function) for test data.
Parameters
----------
mode: str
Kind of score.
"EI", "PI", and "TS" are available.
predictor: predictor object
Base class is defined in physbo.predictor.
training: physbo.variable
Training dataset.
If the predictor is not trained, use this for training.
test: physbo.variable
Inputs
Other Parameters
----------------
fmax: float
Max value of mean of posterior probability distribution.
If not set, the maximum value of posterior mean for training is used.
Used only for mode == "EI" and "PI"
alpha: float
noise for sampling source (default: 1.0)
Used only for mode == "TS"
Returns
-------
score: numpy.ndarray
Raises
------
NotImplementedError
If unknown mode is given
"""
if test.X.shape[0] == 0:
return np.zeros(0)
if mode == "EI":
fmax = kwargs.get("fmax", None)
return EI(predictor, training, test, fmax)
elif mode == "PI":
fmax = kwargs.get("fmax", None)
return PI(predictor, training, test, fmax)
elif mode == "TS":
alpha = kwargs.get("alpha", 1.0)
return TS(predictor, training, test, alpha)
else:
raise NotImplementedError("ERROR: mode must be EI, PI or TS.")
[docs]def EI(predictor, training, test, fmax=None):
"""
Maximum expected improvement.
Parameters
----------
predictor: predictor object
Base class is defined in physbo.predictor.
training: physbo.variable
Training dataset.
If the predictor is not trained, use this for training.
test: physbo.variable
Inputs
fmax: float
Max value of posterior probability distribution.
If not set, the maximum value of posterior mean for training is used.
Returns
-------
score: numpy.ndarray
"""
fmean = predictor.get_post_fmean(training, test)
fcov = predictor.get_post_fcov(training, test)
fstd = np.sqrt(fcov)
if fmax is None:
fmax = np.max(predictor.get_post_fmean(training, training))
temp1 = fmean - fmax
temp2 = temp1 / fstd
score = temp1 * scipy.stats.norm.cdf(temp2) + fstd * scipy.stats.norm.pdf(temp2)
return score
[docs]def PI(predictor, training, test, fmax=None):
"""
Maximum probability of improvement.
Parameters
----------
predictor: predictor object
Base class is defined in physbo.predictor.
training: physbo.variable
Training dataset.
If the predictor is not trained, use this for training.
test: physbo.variable
Inputs
fmax: float
Max value of posterior probability distribution.
If not set, the maximum value of posterior mean for training is used.
Returns
-------
score: numpy.ndarray
"""
fmean = predictor.get_post_fmean(training, test)
fcov = predictor.get_post_fcov(training, test)
fstd = np.sqrt(fcov)
if fmax is None:
fmax = np.max(predictor.get_post_fmean(training, training))
temp = (fmean - fmax) / fstd
score = scipy.stats.norm.cdf(temp)
return score
[docs]def TS(predictor, training, test, alpha=1):
"""
Thompson sampling (See Sec. 2.1 in Materials Discovery Volume 4, June 2016, Pages 18-21)
Parameters
----------
predictor: predictor object
Base class is defined in physbo.predictor.
training: physbo.variable
Training dataset.
If the predictor is not trained, use this for training.
test: physbo.variable
Inputs
alpha: float
noise for sampling source
(default: 1.0)
Returns
-------
score: numpy.ndarray
"""
return (predictor.get_post_samples(training, test, alpha=alpha)).flatten()