profit.sur.linreg

Submodules

Package Contents

Classes

LinearRegression

Base class for all Linear Regression models.

ChaospyLinReg

Linear regression surrogate using polynomial expansions as basis
class profit.sur.linreg.LinearRegression[source]

Bases: profit.sur.Surrogate

Base class for all Linear Regression models.

trained

Flag that indicates if the model is already trained and ready to make predictions.

Type:

bool

# ToDo

Parameters:

class profit.sur.linreg.ChaospyLinReg(model=defaults['model'], order=defaults['order'], model_kwargs=defaults['model_kwargs'], sigma_n=defaults['sigma_n'], sigma_p=defaults['sigma_p'])[source]

Bases: profit.sur.linreg.LinearRegression

Linear regression surrogate using polynomial expansions as basis functions from chaospy https://chaospy.readthedocs.io/en/master/reference/polynomial/index.html

# ToDo
property model
set_model(model, order, model_kwargs=None)[source]

Sets model parameters for surrogate

Parameters:

  • model (str) – Name of chaospy model to use

  • order (int) – Highest order for polynomial basis functions

  • model_kwargs (dict) – Keyword arguments for the model

transform(X)[source]

Transforms input data on selected basis functions

Parameters:

X (ndarray) – Input points

Returns:

Basis functions evaluated at X

Return type:

Phi (ndarray)

train(X, y)[source]

Trains the surrogate on input points X and model outputs y.

Depending on the surrogate, the signature can vary.

Parameters:

  • X (ndarray) – Input training points.

  • y (ndarray) – Observed output data.

  • fixed_sigma_n (bool) – Whether the noise \(\sigma_n\) is fixed during optimization.

predict(Xpred, add_data_variance=False)[source]

Predicts model output y for input Xpred based on surrogate.

Parameters:

  • Xpred (ndarray/list) – Input points for prediction.

  • add_data_variance (bool) – Adds the data noise \(\sigma_n^2\) to the prediction variance. This is especially useful for plotting.

Returns:

a tuple containing:

  • ymean (ndarray) Predicted output values at the test input points.

  • yvar (ndarray): Generally the uncertainty of the fit. For Gaussian Processes this is

the diagonal of the posterior covariance matrix.

Return type:

tuple

as_dict()[source]

Converts class to dictionary

# ToDo

save_model(path)[source]

Save the model as dict to a .hdf5 file.

Parameters:

path (str) – Path including the file name, where the model should be saved.

classmethod load_model(path)[source]

Loads a saved model from a .hdf5 file and updates its attributes

Parameters:

path (str) – Path including the file name, from where the model should be loaded.

Returns:

# ToDo Instantiated surrogate model.

classmethod from_config(config, base_config)[source]

Instantiates a surrogate based on the parameters given in the configuration file and delegates to child.

Parameters:

  • config (dict) – Only the ‘fit’ part of the base_config.

  • base_config (dict) – The whole configuration parameters.