surrogate module¶
Surrogate discipline.
- class gemseo.disciplines.surrogate.SurrogateDiscipline(surrogate, data=None, transformer=mappingproxy({'inputs': <gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object>, 'outputs': <gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object>}), disc_name=None, default_inputs=None, input_names=None, output_names=None, **parameters)[source]
Bases:
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])})
Initialize self. See help(type(self)) for accurate signature.
- Parameters:
surrogate (str | MLRegressionAlgo) – Either the name of a class deriving from
MLRegressionAlgo
or the instance of anMLRegressionAlgo
.data (IODataset | None) – The learning dataset to train the regression model. If
None
, the regression model is supposed to be trained.transformer (TransformerType) –
The strategies to transform the variables. The values are instances of
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, theTransformer
will be applied to all the variables of this group. IfThe :attr:
.MLRegressionAlgo.DEFAULT_TRANSFORMER` uses theMinMaxScaler
strategy for both input and output variables.By default it is set to {‘inputs’: <gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object at 0x7f8b2358ab50>, ‘outputs’: <gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object at 0x7f8b2358abe0>}.
disc_name (str | None) – The name to be given to the surrogate discipline. If
None
, concatenateSHORT_ALGO_NAME
anddata.name
.default_inputs (dict[str, ndarray] | None) – The default values of the inputs. If
None
, use the center of the learning input space.input_names (Iterable[str] | None) – The names of the input variables. If
None
, consider all input variables mentioned in the learning dataset.output_names (Iterable[str] | None) – The names of the output variables. If
None
, consider all input variables mentioned in the learning dataset.**parameters (MLAlgoParameterType) – The parameters of the machine learning algorithm.
- Raises:
ValueError – If the learning dataset is missing whilst the regression model is not trained.
- get_error_measure(measure_name, **measure_options)[source]
Return an error measure.
- Parameters:
measure_name (str) – The class name of the error measure.
**measure_options (Any) – The options of the error measure.
- Returns:
The error measure.
- Return type:
- get_quality_viewer()[source]
Return a viewer of the quality of the underlying regressor.
- Returns:
A viewer of the quality of the underlying regressor.
- Return type:
- cache: AbstractCache | None
The cache containing one or several executions of the discipline according to the cache policy.
- data_processor: DataProcessor
A tool to pre- and post-process discipline data.
- exec_for_lin: bool
Whether the last execution was due to a linearization.
- input_grammar: BaseGrammar
The input grammar.
- jac: MutableMapping[str, MutableMapping[str, ndarray | csr_array | JacobianOperator]]
The Jacobians of the outputs wrt inputs.
The structure is
{output: {input: matrix}}
.
- name: str
The name of the discipline.
- output_grammar: BaseGrammar
The output grammar.
- re_exec_policy: ReExecutionPolicy
The policy to re-execute the same discipline.
- regression_model: MLRegressionAlgo
The regression model called by the surrogate discipline.
- residual_variables: dict[str, str]
The output variables mapping to their inputs, to be considered as residuals; they shall be equal to zero.
- run_solves_residuals: bool
Whether the run method shall solve the residuals.