gemseo / problems / scalable / data_driven

model module¶

Scalable model¶

This module implements the abstract concept of scalable model which is used by scalable disciplines. A scalable model is built from a 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 ScalableModel, an abstract class which is instantiated from:

data provided as a 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.

See also

The ScalableDiagonalModel class overloads ScalableModel.

Classes:

ScalableModel(data[, sizes])

Scalable model.

class gemseo.problems.scalable.data_driven.model.ScalableModel(data, sizes=None, **parameters)[source]¶

Bases: object

Scalable model.

Constructor.

Parameters

data (Dataset) – learning dataset.
sizes (dict) –
sizes of input and output variables. If None, use the original sizes. Default: None.

By default it is set to None.
parameters – model parameters

Attributes:

`ABBR`
`inputs_names`	Inputs names.
`original_sizes`	Original sizes of variables.
`outputs_names`	Outputs names.

Methods:

`build_model`()	Build model with original sizes for input and output variables.
`compute_bounds`()	Compute lower and upper bounds of both input and output variables.
`normalize_data`()	Normalize dataset from lower and upper bounds.
`scalable_derivatives`([input_value])	Evaluate the scalable derivatives.
`scalable_function`([input_value])	Evaluate the scalable function.

ABBR = 'sm'¶

build_model()[source]¶

Build model with original sizes for input and output variables.

compute_bounds()[source]¶

Compute lower and upper bounds of both input and output variables.

Returns: lower bounds, upper bounds.
Return type: dict, dict

property inputs_names¶

Inputs names.

Returns: names of the inputs.
Return type: list(str)

normalize_data()[source]¶

Normalize dataset from lower and upper bounds.

property original_sizes¶

Original sizes of variables.

Returns: original sizes of variables.
Return type: dict

property outputs_names¶

Outputs names.

Returns: names of the outputs.
Return type: list(str)

scalable_derivatives(input_value=None)[source]¶

Evaluate the scalable derivatives.

Parameters

input_value (dict) –

input values. If None, use default inputs. Default: None

By default it is set to None.

Returns

evaluation of the scalable derivatives.

Return type

dict

scalable_function(input_value=None)[source]¶

Evaluate the scalable function.

Parameters

input_value (dict) –

input values. If None, use default inputs. Default: None.

By default it is set to None.

Returns

evaluation of the scalable function.

Return type

dict