Source code for gemseo.disciplines.surrogate

# Copyright 2021 IRT Saint Exupéry,
# 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
# 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
"""Surrogate discipline."""

from __future__ import annotations

import logging
from typing import TYPE_CHECKING
from typing import Any

from gemseo.core.discipline import MDODiscipline
from gemseo.mlearning.quality_measures.error_measure_factory import (
from gemseo.mlearning.regression.factory import RegressionModelFactory
from gemseo.mlearning.regression.regression import MLRegressionAlgo
from import MLRegressorQualityViewer
from gemseo.utils.string_tools import MultiLineString
from gemseo.utils.string_tools import pretty_str

    from import Iterable
    from import Mapping

    from numpy import ndarray

    from gemseo.datasets.io_dataset import IODataset
    from gemseo.mlearning.core.ml_algo import MLAlgoParameterType
    from gemseo.mlearning.core.ml_algo import TransformerType
    from gemseo.mlearning.quality_measures.error_measure import MLErrorMeasure

LOGGER = logging.getLogger(__name__)

[docs] class SurrogateDiscipline(MDODiscipline): """A discipline wrapping a regression model built from a dataset. Examples: >>> import numpy as np >>> from gemseo.datasets.io_dataset import IODataset >>> from gemseo.disciplines.surrogate import SurrogateDiscipline >>> >>> # Create an input-output dataset. >>> dataset = IODataset() >>> dataset.add_input_variable("x", np.array([[1.0], [2.0], [3.0]])) >>> dataset.add_output_variable("y", np.array([[3.0], [5.0], [6.0]])) >>> >>> # Build a surrogate discipline relying on a linear regression model. >>> surrogate_discipline = SurrogateDiscipline("LinearRegressor", dataset) >>> >>> # Assess its quality with the R2 measure. >>> r2 = surrogate_discipline.get_error_measure("R2Measure") >>> learning_r2 = r2.evaluate_learn() >>> >>> # Execute the surrogate discipline, with default or custom input values. >>> surrogate_discipline.execute() >>> surrogate_discipline.execute({"x": np.array([1.5])}) """ regression_model: MLRegressionAlgo """The regression model called by the surrogate discipline.""" __error_measure_factory: MLErrorMeasureFactory """The factory of error measures.""" def __init__( self, surrogate: str | MLRegressionAlgo, data: IODataset | None = None, transformer: TransformerType = MLRegressionAlgo.DEFAULT_TRANSFORMER, disc_name: str | None = None, default_inputs: dict[str, ndarray] | None = None, input_names: Iterable[str] | None = None, output_names: Iterable[str] | None = None, **parameters: MLAlgoParameterType, ) -> None: """ Args: surrogate: Either the name of a class deriving from :class:`.MLRegressionAlgo` or the instance of an :class:`.MLRegressionAlgo`. data: The learning dataset to train the regression model. If ``None``, the regression model is supposed to be trained. transformer: The strategies to transform the variables. The values are instances of :class:`.Transformer` while the keys are the names of either the variables or the groups of variables, e.g. ``"inputs"`` or ``"outputs"`` in the case of the regression algorithms. If a group is specified, the :class:`.Transformer` will be applied to all the variables of this group. If :attr:`~.MLAlgo.IDENTITY, do not transform the variables. The :attr:`.MLRegressionAlgo.DEFAULT_TRANSFORMER` uses the :class:`.MinMaxScaler` strategy for both input and output variables. disc_name: The name to be given to the surrogate discipline. If ``None``, concatenate :attr:`.SHORT_ALGO_NAME` and ````. default_inputs: The default values of the inputs. If ``None``, use the center of the learning input space. input_names: The names of the input variables. If ``None``, consider all input variables mentioned in the learning dataset. output_names: The names of the output variables. If ``None``, consider all input variables mentioned in the learning dataset. **parameters: The parameters of the machine learning algorithm. Raises: ValueError: If the learning dataset is missing whilst the regression model is not trained. """ # noqa: D205, D212, D415 self.__error_measure_factory = MLErrorMeasureFactory() if isinstance(surrogate, MLRegressionAlgo): self.regression_model = surrogate 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 = f"{self.regression_model.SHORT_ALGO_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 size: {}", data.n_samples) msg.add("Surrogate model: {}", self.regression_model.__class__.__name__)"%s", msg) if not name.startswith(self.regression_model.SHORT_ALGO_NAME): disc_name = f"{self.regression_model.SHORT_ALGO_NAME}_{disc_name}" msg = MultiLineString() msg.add("Use the surrogate discipline: {}", disc_name) msg.indent() super().__init__(disc_name) self._initialize_grammars(input_names, output_names) msg.add("Inputs: {}", pretty_str(self.get_input_data_names())) msg.add("Outputs: {}", pretty_str(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 = self.LinearizationMode.AUTO msg.add("Jacobian: use surrogate model jacobian") except NotImplementedError: self.linearization_mode = self.LinearizationMode.FINITE_DIFFERENCES msg.add("Jacobian: use finite differences")"%s", msg) @property def _string_representation(self) -> MultiLineString: """The string representation of the object.""" mls = MultiLineString() mls.add("Surrogate discipline: {}", mls.indent() mls.add("Dataset name: {}", mls.add("Dataset size: {}", len(self.regression_model.learning_set)) mls.add("Surrogate model: {}", self.regression_model.__class__.__name__) mls.add("Inputs: {}", pretty_str(self.regression_model.input_names)) mls.add("Outputs: {}", pretty_str(self.regression_model.output_names)) mls.add("Linearization mode: {}", self.linearization_mode) return mls def __repr__(self) -> str: return str(self._string_representation) def _repr_html_(self) -> str: return self._string_representation._repr_html_() def _initialize_grammars( self, input_names: Iterable[str] | None = None, output_names: Iterable[str] | None = None, ) -> None: """Initialize the input and output grammars from the regression model. Args: input_names: The names of the inputs to consider. If ``None``, use all the inputs of the regression model. output_names: The names of the inputs to consider. If ``None``, use all the inputs of the regression model. """ self.input_grammar.update_from_names( input_names or self.regression_model.input_names ) self.output_grammar.update_from_names( output_names or self.regression_model.output_names ) def _set_default_inputs( self, default_inputs: Mapping[str, ndarray] | None = None, ) -> None: """Set the default values of the inputs. Args: default_inputs: The default values of the inputs. If ``None``, use the center of the learning input space. """ if default_inputs is None: self.default_inputs = self.regression_model.input_space_center else: self.default_inputs = default_inputs def _run(self) -> None: for name, value in self.regression_model.predict(self.get_input_data()).items(): self.local_data[name] = value.flatten() def _compute_jacobian( self, inputs: Iterable[str] | None = None, outputs: Iterable[str] | None = None, ) -> None: self._init_jacobian(inputs, outputs, MDODiscipline.InitJacobianType.EMPTY) self.jac = self.regression_model.predict_jacobian(self.get_input_data())
[docs] def get_quality_viewer(self) -> MLRegressorQualityViewer: """Return a viewer of the quality of the underlying regressor. Returns: A viewer of the quality of the underlying regressor. """ return MLRegressorQualityViewer(self.regression_model)
[docs] def get_error_measure( self, measure_name: str, **measure_options: Any, ) -> MLErrorMeasure: """Return an error measure. Args: measure_name: The class name of the error measure. **measure_options: The options of the error measure. Returns: The error measure. """ return self.__error_measure_factory.create( measure_name, algo=self.regression_model, **measure_options )