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#
# Licensed under the Apache License, Version 2.0 (the "License").
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
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from typing import Optional
import logging
from syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.learncurve.likelihood import (
GaussAdditiveMarginalLikelihood,
LCModel,
)
from syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.constants import (
OptimizationConfig,
)
from syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.gp_model import (
GaussianProcessOptimizeModel,
)
from syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.likelihood import (
MarginalLikelihood,
)
from syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.kernel import (
KernelFunction,
)
from syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.mean import (
ScalarMeanFunction,
MeanFunction,
)
logger = logging.getLogger(__name__)
[docs]
class GaussianProcessLearningCurveModel(GaussianProcessOptimizeModel):
"""
Represents joint Gaussian model of learning curves over a number of
configurations. The model has an additive form:
f(x, r) = g(r | x) + h(x),
where h(x) is a Gaussian process model for function values at r_max, and
the g(r | x) are independent Gaussian models. Right now, g(r | x) can be:
- Innovation state space model (ISSM) of a particular power-law decay
form. For this one, g(r_max | x) = 0 for all x. Used if
``res_model`` is of type :class:`ISSModelParameters`
- Gaussian process model with exponential decay covariance function. This
is essentially the model from the Freeze Thaw paper, see also
:class:`ExponentialDecayResourcesKernelFunction`. Used if
``res_model`` is of type :class:`ExponentialDecayBaseKernelFunction`
Importantly, inference scales cubically only in the number of
configurations, not in the number of observations.
Details about ISSMs in general are found in
Hyndman, R. and Koehler, A. and Ord, J. and Snyder, R.
Forecasting with Exponential Smoothing: The State Space Approach
Springer, 2008
:param kernel: Kernel function k(X, X')
:param res_model: Model for g(r | x)
:param mean: Mean function mu(X)
:param initial_noise_variance: A scalar to initialize the value of the
residual noise variance
:param optimization_config: Configuration that specifies the behavior of
the optimization of the marginal likelihood.
:param random_seed: Random seed to be used (optional)
:param fit_reset_params: Reset parameters to initial values before running
'fit'? If False, 'fit' starts from the current values
"""
def __init__(
self,
kernel: KernelFunction,
res_model: LCModel,
mean: Optional[MeanFunction] = None,
initial_noise_variance: Optional[float] = None,
optimization_config: Optional[OptimizationConfig] = None,
random_seed=None,
fit_reset_params: bool = True,
):
super().__init__(
optimization_config=optimization_config,
random_seed=random_seed,
fit_reset_params=fit_reset_params,
)
if mean is None:
mean = ScalarMeanFunction()
self._likelihood = GaussAdditiveMarginalLikelihood(
kernel=kernel,
res_model=res_model,
mean=mean,
initial_noise_variance=initial_noise_variance,
)
self.reset_params()
@property
def likelihood(self) -> MarginalLikelihood:
return self._likelihood