# SPDX-License-Identifier: MPL-2.0
# Copyright (C) 2020- The University of Tokyo
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at https://mozilla.org/MPL/2.0/.
import pickle as pickle
from physbo import gp
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class BasePredictor(object):
"""
Base predictor is defined in this class.
"""
def __init__(self, config, model=None):
"""
Parameters
----------
config: SetConfig object (physbo.misc.SetConfig)
model: model object
A default model is set as gp.core.Model
"""
self.config = config
self.model = model
if self.model is None:
self.model = gp.core.Model(
cov=gp.cov.Gauss(num_dim=None, ard=False),
mean=gp.mean.Const(),
lik=gp.lik.Gauss(),
)
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def fit(self, *args, **kwds):
"""
Default fit function.
This function must be overwritten in each model.
Parameters
----------
args
kwds
Returns
-------
"""
raise NotImplementedError
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def prepare(self, *args, **kwds):
"""
Default prepare function.
This function must be overwritten in each model.
Parameters
----------
args
kwds
Returns
-------
"""
raise NotImplementedError
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def delete_stats(self, *args, **kwds):
"""
Default function to delete status.
This function must be overwritten in each model.
Parameters
----------
args
kwds
Returns
-------
"""
raise NotImplementedError
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def get_basis(self, *args, **kwds):
"""
Default function to get basis
This function must be overwritten in each model.
Parameters
----------
args
kwds
Returns
-------
"""
raise NotImplementedError
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def get_post_fmean(self, *args, **kwds):
"""
Default function to get a mean value of the score.
This function must be overwritten in each model.
Parameters
----------
args
kwds
Returns
-------
"""
raise NotImplementedError
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def get_post_fcov(self, *args, **kwds):
"""
Default function to get a covariance of the score.
This function must be overwritten in each model.
Parameters
----------
args
kwds
Returns
-------
"""
raise NotImplementedError
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def get_post_params(self, *args, **kwds):
"""
Default function to get parameters.
This function must be overwritten in each model.
Parameters
----------
args
kwds
Returns
-------
"""
raise NotImplementedError
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def get_post_samples(self, *args, **kwds):
"""
Default function to get samples.
This function must be overwritten in each model.
Parameters
----------
args
kwds
Returns
-------
"""
raise NotImplementedError
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def get_predict_samples(self, *args, **kwds):
"""
Default function to get prediction variables of samples.
This function must be overwritten in each model.
Parameters
----------
args
kwds
Returns
-------
"""
raise NotImplementedError
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def get_post_params_samples(self, *args, **kwds):
"""
Default function to get parameters of samples.
This function must be overwritten in each model.
Parameters
----------
args
kwds
Returns
-------
"""
raise NotImplementedError
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def get_permutation_importance(
self, training, num_permutations=10, comm=None, split_features_parallel=False
):
"""
Calculate permutation importance of the predictor.
Parameters
----------
training: physbo.variable
training dataset. If already trained, the model does not use this.
test: physbo.variable
input X
num_permutations: int
number of permutations
comm: MPI.Comm
MPI communicator
split_features_parallel: bool
If true, split features in parallel.
Returns
-------
numpy.ndarray
importance_mean
numpy.ndarray
importance_std
"""
return self.model.get_permutation_importance(
training.X,
training.t,
num_permutations,
comm=comm,
split_features_parallel=split_features_parallel,
)
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def update(self, *args, **kwds):
"""
Default function to update variables.
This function must be overwritten in each model.
Parameters
----------
args
kwds
Returns
-------
"""
raise NotImplementedError
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def save(self, file_name):
"""
Default function to save information by using pickle.dump function.
The protocol version is set as 3.
Parameters
----------
file_name: str
A file name to save self.__dict__ object.
Returns
-------
"""
with open(file_name, "wb") as f:
pickle.dump(self.__dict__, f, 4)
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def load(self, file_name):
"""
Default function to load variables.
The information is updated using self.update function.
Parameters
----------
file_name: str
A file name to load variables from the file.
Returns
-------
"""
with open(file_name, "rb") as f:
tmp_dict = pickle.load(f)
self.config = tmp_dict["config"]
self.model = tmp_dict["model"]