3.4. Solver

FitSNAP uses a Solver class which is a parent of all the different types of solvers, e.g. SVD and ARD for linear regression, PYTORCH and JAX for neural networks, etc.

class fitsnap3lib.solvers.solver.Solver(name, pt, config, linear=True)

This class declares the method to solve the machine learning problem, e.g. linear regression, nonlinear regression, etc.

fit

Numpy array containing coefficients of fit.

error_analysis(a=None, b=None, w=None, fs_dict=None)

If linear fit: extracts and stores fitting data, such as descriptor values, truths, and predictions, into a Pandas dataframe.

If nonlinear fit: evaluate NN on all configurations to get truth values for error calculation.

The optional arguments are for calculating errors on a given set of inputs. For linear models these inputs are A matrix, truth array, weights, and a fs dictionary which contains group info. Care must be taken to ensure that these data structures are already processed and lined up properly.

Parameters:

  • a – Optional A matrix numpy array.

  • b – Optional truth matrix numpy array.

  • w – Optional weight matrix numpy array.

  • fs_dict – Optional fs dictionary from a fs.pt.fitsnap_dict

perform_fit()

Base class function for performing a fit.

prepare_data(a, b, w, fs_dict)

Prepare a, b, w data for fitting by applying weight arrays w to the a and b arrays.

Parameters:

  • a (np.array) – design matrix

  • b (np.array) – truth array

  • w (np.array) – weight array

  • fs_dict (dict) – dictionary with Testing key of bools for which structures to test on.

Returns:

design matrix and truth array multiplied by weights.

Return type:

aw, bw (np.array)

Specific solvers are inherited from the base Solver class.

3.4.1. SVD

This class is for performing SVD fits on linear systems.

class fitsnap3lib.solvers.svd.SVD(name, pt, config)
perform_fit(a=None, b=None, w=None, fs_dict=None, trainall=False)

Perform fit on a linear system. If no args are supplied, will use fitting data in pt.shared_arrays.

Parameters:

  • a (np.array) – Optional “A” matrix.

  • b (np.array) – Optional Truth array.

  • w (np.array) – Optional Weight array.

  • fs_dict (dict) – Optional dictionary containing a Testing key of which A matrix rows should not be trained.

  • trainall (bool) – Optional boolean declaring whether to train on all samples in the A matrix.

The fit is stored as a member fs.solver.fit.

3.4.2. RIDGE

This class is for performing ridge regression fits on linear systems.

class fitsnap3lib.solvers.ridge.RIDGE(name, pt, config)
perform_fit(a=None, b=None, w=None, fs_dict=None, trainall=False)

Perform fit on a linear system. If no args are supplied, will use fitting data in pt.shared_arrays.

Parameters:

  • a (np.array) – Optional “A” matrix.

  • b (np.array) – Optional Truth array.

  • w (np.array) – Optional Weight array.

  • fs_dict (dict) – Optional dictionary containing a Testing key of which A matrix rows should not be trained.

  • trainall (bool) – Optional boolean declaring whether to train on all samples in the A matrix.

The fit is stored as a member fs.solver.fit.

3.4.3. PYTORCH

This class inherits from the Solver class, since it is a particular solver option.

class fitsnap3lib.solvers.pytorch.PYTORCH(name)

Dummy class for factory to read if torch is not available for import.