# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
# Contributors:
# INITIAL AUTHORS - initial API and implementation and/or initial
# documentation
# :author: Matthias De Lozzo
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""
Surrogate discipline baseclass
******************************
"""
from __future__ import division, unicode_literals
import logging
from gemseo.core.discipline import MDODiscipline
from gemseo.core.jacobian_assembly import JacobianAssembly
from gemseo.mlearning.regression.factory import RegressionModelFactory
from gemseo.mlearning.regression.regression import MLRegressionAlgo
from gemseo.utils.data_conversion import DataConversion
from gemseo.utils.string_tools import MultiLineString, pretty_repr
LOGGER = logging.getLogger(__name__)
[docs]class SurrogateDiscipline(MDODiscipline):
"""Surrogate discipline class."""
def __init__(
self,
surrogate,
data=None,
transformer=MLRegressionAlgo.DEFAULT_TRANSFORMER,
disc_name=None,
default_inputs=None,
input_names=None,
output_names=None,
**parameters
):
"""Constructor.
:param surrogate: name of the surrogate model algorithm.
:type surrogate: str or MLRegressionAlgo
:param Dataset data: dataset to train the surrogate. If None,
assumes that the surrogate is trained. Default: None.
:param dict(Transformer) transformer: transformation strategy for data groups.
If None, do not scale data.
Default: MLRegressionAlgo.DEFAULT_TRANSFORMER,
which is a min/max scaler applied to the inputs
and a min/max scaler applied to the outputs.
:param str disc_name: name of the surrogate discipline.
If None, use surrogate.ABBR + data.name . Default: None
:param dict default_inputs: default inputs. If None, use the center of the
learning input space. Default: None.
:param list(str) input_names: list of input names.
If None, use all inputs. Default: None.
:param list(str) output_names: list of output names.
If None, use all outputs. Default: None.
:param parameters: surrogate model parameters.
"""
if isinstance(surrogate, MLRegressionAlgo):
self.regression_model = surrogate
name = self.regression_model.learning_set.name
elif data is None:
raise ValueError("data is required to train the surrogate model.")
else:
factory = RegressionModelFactory()
self.regression_model = factory.create(
surrogate,
data=data,
transformer=transformer,
input_names=input_names,
output_names=output_names,
**parameters
)
name = self.regression_model.ABBR + "_" + data.name
disc_name = disc_name or name
if not self.regression_model.is_trained:
self.regression_model.learn()
msg = MultiLineString()
msg.add("Build the surrogate discipline: {}", disc_name)
msg.indent()
msg.add("Dataset name: {}", data.name)
msg.add("Dataset size: {}", data.length)
msg.add("Surrogate model: {}", self.regression_model.__class__.__name__)
LOGGER.info("%s", msg)
if not name.startswith(self.regression_model.ABBR):
disc_name = self.regression_model.ABBR + "_" + disc_name
msg = MultiLineString()
msg.add("Use the surrogate discipline: {}", disc_name)
msg.indent()
super(SurrogateDiscipline, self).__init__(disc_name)
self._initialize_grammars(input_names, output_names)
msg.add("Inputs: {}", pretty_repr(self.get_input_data_names()))
msg.add("Outputs: {}", pretty_repr(self.get_output_data_names()))
self._set_default_inputs(default_inputs)
self.add_differentiated_inputs()
self.add_differentiated_outputs()
try:
self.regression_model.predict_jacobian(self.default_inputs)
self.linearization_mode = JacobianAssembly.AUTO_MODE
msg.add("Jacobian: use surrogate model jacobian")
except NotImplementedError:
self.linearization_mode = self.FINITE_DIFFERENCES
msg.add("Jacobian: use finite differences")
LOGGER.info("%s", msg)
def __repr__(self):
model = self.regression_model.__class__.__name__
data_name = self.regression_model.learning_set.name
length = len(self.regression_model.learning_set)
inputs = sorted(self.regression_model.input_names)
outputs = sorted(self.regression_model.output_names)
arguments = [
"name={}".format(self.name),
"algo={}".format(model),
"data={}".format(data_name),
"size={}".format(length),
"inputs=[{}]".format(pretty_repr(inputs)),
"outputs=[{}]".format(pretty_repr(outputs)),
"jacobian={}".format(self.linearization_mode),
]
msg = "SurrogateDiscipline({})".format(", ".join(arguments))
return msg
def __str__(self):
data_name = self.regression_model.learning_set.name
length = len(self.regression_model.learning_set)
msg = MultiLineString()
msg.add("Surrogate discipline: {}", self.name)
msg.indent()
msg.add("Dataset name: {}", data_name)
msg.add("Dataset size: {}", length)
msg.add("Surrogate model: {}", self.regression_model.__class__.__name__)
inputs = sorted(self.regression_model.input_names)
outputs = sorted(self.regression_model.output_names)
msg.add("Inputs: {}", pretty_repr(inputs))
msg.add("Outputs: {}", pretty_repr(outputs))
return str(msg)
def _initialize_grammars(self, input_names=None, output_names=None):
"""Initializes the inputs and outputs grammars from data."""
learning_set = self.regression_model.learning_set
in_grp = learning_set.INPUT_GROUP
out_grp = learning_set.OUTPUT_GROUP
if input_names is None:
inputs = learning_set.get_data_by_group(in_grp)[0, :]
input_names = learning_set.get_names(in_grp)
else:
inputs = learning_set.get_data_by_names(input_names, False)[0, :]
if output_names is None:
outputs = learning_set.get_data_by_group(out_grp)[0, :]
output_names = learning_set.get_names(out_grp)
else:
outputs = learning_set.get_data_by_names(output_names, False)[0, :]
inputs = DataConversion.array_to_dict(inputs, input_names, learning_set.sizes)
outputs = DataConversion.array_to_dict(
outputs, output_names, learning_set.sizes
)
self.input_grammar.initialize_from_base_dict(inputs)
self.output_grammar.initialize_from_base_dict(outputs)
def _set_default_inputs(self, default_inputs=None):
"""Set default inputs either from the center of the learning input space or from
user specification.
:param dict default_inputs: user default inputs.
If None, use the learning input space center. Default: None.
"""
if default_inputs is None:
self._default_inputs = self.regression_model.input_space_center
else:
self._default_inputs = default_inputs
def _run(self):
input_data = self.get_input_data()
output_data = self.regression_model.predict(input_data)
output_data = {key: val.flatten() for key, val in output_data.items()}
self.local_data.update(output_data)
def _compute_jacobian(self, inputs=None, outputs=None):
input_data = self.get_input_data()
self._init_jacobian(inputs, outputs)
self.jac = self.regression_model.predict_jacobian(input_data)
