Dakota
Version 6.20
Explore and Predict with Confidence
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The Python class constructs a surrogate via python and has it ready for Dakota use. More...
Public Member Functions | |
Python (const std::string &module_and_class_name) | |
Constructor that sets moduleAndClassName and does not build. More... | |
Python (const MatrixXd &samples, const MatrixXd &response, const std::string &module_and_class_name) | |
Constructor sets moduleAndClassName and builds the python surrogate. More... | |
~Python () | |
Default destructor. | |
void | default_options () override |
Construct and populate the defaultConfigOptions. | |
void | build (const MatrixXd &samples, const MatrixXd &response) override |
Build the python surrogate using specified build data. More... | |
VectorXd | value (const MatrixXd &eval_points, const int qoi) override |
Evaluate the python surrogate at a set of prediction points for a single QoI. More... | |
VectorXd | value (const MatrixXd &eval_points) |
Evaluate the python surrogate at a set of prediction points for QoI index 0. More... | |
MatrixXd | gradient (const MatrixXd &eval_points, const int qoi) override |
Evaluate the gradient of the python surrogate at a set of prediction points for a single QoI. More... | |
MatrixXd | gradient (const MatrixXd &eval_points) |
Evaluate the gradient of the python surrogate at a set of prediction points for QoI index 0. More... | |
MatrixXd | hessian (const MatrixXd &eval_point, const int qoi) override |
Evaluate the Hessian of the python surrogate at a single point for a single QoI. More... | |
MatrixXd | hessian (const MatrixXd &eval_point) |
Evaluate the Hessian of the python surrogate at a single point for QoI index 0. More... | |
std::shared_ptr< Surrogate > | clone () const override |
clone derived Surrogate class for use in cross-validation | |
Public Member Functions inherited from Surrogate | |
Surrogate () | |
Constructor that uses defaultConfigOptions and does not build. | |
Surrogate (const ParameterList ¶m_list) | |
Constructor that sets configOptions but does not build. More... | |
Surrogate (const MatrixXd &samples, const MatrixXd &response, const ParameterList ¶m_list) | |
Constructor for the Surrogate that sets configOptions and builds the surrogate (does nothing in the base class). More... | |
virtual | ~Surrogate () |
Default destructor. | |
VectorXd | value (const MatrixXd &eval_points) |
Evaluate the Surrogate at a set of prediction points for QoI index 0. More... | |
MatrixXd | gradient (const MatrixXd &eval_points) |
Evaluate the gradient of the Surrogate at a set of prediction points for QoI index 0. More... | |
MatrixXd | hessian (const MatrixXd &eval_point) |
Evaluate the Hessian of the Surrogate at a single point for QoI index 0. More... | |
void | variable_labels (const std::vector< std::string > &var_labels) |
Set the variable/feature names. More... | |
const std::vector< std::string > & | variable_labels () const |
Get the (possibly empty) variable/feature names. More... | |
void | response_labels (const std::vector< std::string > &resp_labels) |
Set the response/QoI names. More... | |
const std::vector< std::string > & | response_labels () const |
Get the (possibly empty) response/QoI names. More... | |
void | set_options (const ParameterList &options) |
Set the Surrogate's configOptions. More... | |
void | get_options (ParameterList &options) |
Get the Surrogate's configOptions. More... | |
void | print_options () |
Print the Surrogate's configOptions. | |
VectorXd | evaluate_metrics (const StringArray &mnames, const MatrixXd &points, const MatrixXd &ref_values) |
Evalute metrics at specified points (within surrogates) | |
VectorXd | cross_validate (const MatrixXd &samples, const MatrixXd &response, const StringArray &mnames, const int num_folds=5, const int seed=20) |
Perform K-folds cross-validation (within surrogates) | |
template<typename DerivedSurr > | |
void | save (const DerivedSurr &surr_out, const std::string &outfile, const bool binary) |
Serialize a derived (i.e. non-base) surrogate model. More... | |
template<typename DerivedSurr > | |
void | load (const std::string &infile, const bool binary, DerivedSurr &surr_in) |
Load a derived (i.e. non-base) surrogate model. More... | |
Private Member Functions | |
void | initialize_python () |
Initialize python interpreter and callback module. | |
Private Attributes | |
std::string | moduleAndClassName |
Name of python callback module file. | |
bool | ownPython |
true if this class created the interpreter instance | |
bool | pyModuleActive |
true if python callback module is valid | |
py::object | pySurrogate |
python Surrogate class | |
int | verbosity |
Verbosity level. | |
Additional Inherited Members | |
Static Public Member Functions inherited from Surrogate | |
template<typename SurrHandle > | |
static void | save (const SurrHandle &surr_out, const std::string &outfile, const bool binary) |
serialize Surrogate to file (typically through shared_ptr<Surrogate>, but Derived& or Derived* okay too) | |
template<typename SurrHandle > | |
static void | load (const std::string &infile, const bool binary, SurrHandle &surr_in) |
serialize Surrogate from file (typically through shared_ptr<Surrogate>, but Derived& or Derived* okay too) | |
static std::shared_ptr< Surrogate > | load (const std::string &infile, const bool binary) |
serialize Surrogate from file through pointer to base class (must have been saved via same data type) | |
Public Attributes inherited from Surrogate | |
util::DataScaler | dataScaler |
DataScaler class for a Surrogate's build samples. | |
double | responseOffset = 0. |
Response offset. | |
double | responseScaleFactor = 1. |
Response scale factor. | |
Protected Attributes inherited from Surrogate | |
int | numSamples |
Number of samples in the Surrogate's build samples. | |
int | numVariables |
Number of features/variables in the Surrogate's build samples. | |
std::vector< std::string > | variableLabels |
Names of the variables/features; need not be populated. | |
int | numQOI |
Number of quantities of interest predicted by the surrogate. For scalar-valued surrogates numQOI = 1. | |
std::vector< std::string > | responseLabels |
Names of the responses/QoIs; need not be populated. | |
ParameterList | defaultConfigOptions |
Default Key/value options to configure the surrogate. | |
ParameterList | configOptions |
Key/value options to configure the surrogate - will override defaultConfigOptions. | |
Python | ( | const std::string & | module_and_class_name | ) |
Constructor that sets moduleAndClassName and does not build.
[in] | module_and_class_name | Name of python module file containing callback functions |
References Python::initialize_python().
Python | ( | const MatrixXd & | samples, |
const MatrixXd & | response, | ||
const std::string & | module_and_class_name | ||
) |
Constructor sets moduleAndClassName and builds the python surrogate.
[in] | samples | Matrix of data for surrogate construction - (num_samples by num_features) |
[in] | response | Vector of targets for surrogate construction - (num_samples by num_qoi = 1; only 1 response is supported currently). |
[in] | module_and_class_name | Name of python module file containing callback functions |
References Python::build(), and Python::initialize_python().
Build the python surrogate using specified build data.
[in] | samples | Matrix of data for surrogate construction - (num_samples by num_features) |
[in] | response | Vector of targets for surrogate construction - (num_samples by num_qoi = 1; only 1 response is supported currently). |
Implements Surrogate.
References Surrogate::configOptions, Python::moduleAndClassName, Python::pyModuleActive, Python::pySurrogate, and Python::verbosity.
Referenced by Python::Python().
Evaluate the python surrogate at a set of prediction points for a single QoI.
[in] | eval_points | Matrix of prediction points - (num_pts by num_features). |
[in] | qoi | Index for surrogate QoI. |
Implements Surrogate.
References Python::pyModuleActive, Python::pySurrogate, and dakota::silence_unused_args().
Evaluate the python surrogate at a set of prediction points for QoI index 0.
[in] | eval_points | Matrix of prediction points - (num_pts by num_features). |
References Surrogate::value().
Evaluate the gradient of the python surrogate at a set of prediction points for a single QoI.
[in] | eval_points | Coordinates of the prediction points - (num_pts by num_features). |
[in] | qoi | Index of response/QOI for which to compute derivatives. |
Reimplemented from Surrogate.
References Python::moduleAndClassName, Python::pyModuleActive, Python::pySurrogate, and dakota::silence_unused_args().
Evaluate the gradient of the python surrogate at a set of prediction points for QoI index 0.
[in] | eval_points | Coordinates of the prediction points - (num_pts by num_features). |
References Surrogate::gradient().
Evaluate the Hessian of the python surrogate at a single point for a single QoI.
[in] | eval_point | Coordinates of the prediction point - (1 by num_features). |
[in] | qoi | Index of response/QOI for which to compute derivatives. |
Reimplemented from Surrogate.
References Python::pyModuleActive, and dakota::silence_unused_args().
Evaluate the Hessian of the python surrogate at a single point for QoI index 0.
[in] | eval_point | Coordinates of the prediction point - (1 by num_features). |
References Surrogate::hessian().