Source code for gemseo.problems.scalable.data_driven.model

# 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
"""Scalable model.

This module implements the abstract concept of scalable model
which is used by scalable disciplines. A scalable model is built
from an input-output learning dataset associated with a function
and generalizing its behavior to a new user-defined problem dimension,
that is to say new user-defined input and output dimensions.

The concept of scalable model is implemented
through :class:`.ScalableModel`, an abstract class which is instantiated from:

- data provided as a :class:`.Dataset`
- variables sizes provided as a dictionary
  whose keys are the names of inputs and outputs
  and values are their new sizes.
  If a variable is missing, its original size is considered.

Scalable model parameters can also be filled in.
Otherwise, the model uses default values.

.. seealso::

   The :class:`.ScalableDiagonalModel` class overloads :class:`.ScalableModel`.

from __future__ import annotations

from typing import TYPE_CHECKING
from typing import Any

from numpy import array
from numpy import full
from numpy import ndarray

    from import Mapping

    from gemseo.datasets.io_dataset import IODataset

[docs] class ScalableModel: """A scalable model.""" ABBR = "sm" data: IODataset """The learning dataset.""" def __init__( self, data: IODataset, sizes: Mapping[str, int] | None = None, **parameters: Any, ) -> None: """ Args: data: The learning dataset. sizes: The sizes of the input and output variables. If ``None``, use the original sizes. **parameters: The parameters of the model. """ # noqa: D205 D212 sizes = sizes or {} = self.ABBR + "_" + = data self.sizes = self._set_sizes(sizes) self.parameters = parameters self.lower_bounds, self.upper_bounds = self.compute_bounds() self.normalize_data() self.lower_bounds, self.upper_bounds = self.compute_bounds() self.default_inputs = self._set_default_inputs() self.model = self.build_model() def _set_default_inputs(self) -> dict[str, ndarray]: """Set the default values of the inputs from the model. Returns: The default inputs. """ return {name: full(self.sizes[name], 0.5) for name in self.input_names}
[docs] def scalable_function(self, input_value=None) -> None: """Evaluate the scalable function. Args: input_value: The input values. If ``None``, use the default inputs. Returns: The evaluations of the scalable function. """ raise NotImplementedError
[docs] def scalable_derivatives(self, input_value=None) -> None: """Evaluate the scalable derivatives. Args: input_value: The input values. If ``None``, use the default inputs. Returns: The evaluations of the scalable derivatives. """ raise NotImplementedError
[docs] def compute_bounds(self) -> tuple[dict[str, int], dict[str, int]]: """Compute lower and upper bounds of both input and output variables. Returns: The lower and upper bounds. """ inputs ="list") outputs ="list") lower_bounds = { column[1]: array(value).min(0) for column, value in inputs.items() } lower_bounds.update({ column[1]: array(value).min(0) for column, value in outputs.items() }) upper_bounds = { column[1]: array(value).max(0) for column, value in inputs.items() } upper_bounds.update({ column[1]: array(value).max(0) for column, value in outputs.items() }) return lower_bounds, upper_bounds
[docs] def normalize_data(self) -> None: """Normalize the dataset from lower and upper bounds.""" =
[docs] def build_model(self) -> None: """Build model with original sizes for input and output variables.""" raise NotImplementedError
@property def original_sizes(self) -> Mapping[str, int]: """The original sizes of variables.""" return @property def output_names(self) -> list[str]: """The output names.""" return @property def input_names(self) -> list[str]: """The input names.""" return def _set_sizes(self, sizes: Mapping[str, int]) -> Mapping[str, int]: """Set the new sizes of input and output variables. Args: sizes: The sizes of some of the variables. Returns: The new sizes of all the variables. """ for group in [,]: for name in original_size = self.original_sizes.get(name) sizes[name] = sizes.get(name, original_size) return sizes