pysindy.optimizers.StableLinearSR3

class pysindy.optimizers.StableLinearSR3(reg_weight_lam=0.1, regularizer='l1', relax_coeff_nu=1.0, tol=1e-05, max_iter=30, trimming_fraction=0.0, trimming_step_size=1.0, constraint_lhs=None, constraint_rhs=None, constraint_order='target', normalize_columns=False, copy_X=True, initial_guess=None, equality_constraints=False, inequality_constraints=False, constraint_separation_index=0, verbose=False, verbose_cvxpy=False, gamma=-1e-08, unbias=False)[source]

Sparse relaxed regularized regression for building a-priori stable linear models. This requires making a matrix negative definite, which can be challenging. Here we use a similar method to the TrappingOptimizer algorithm. Linear equality and linear inequality constraints are both allowed, as in the ConstrainedSR3 optimizer.

Attempts to minimize the objective function

\[0.5\|y-Xw\|^2_2 + \lambda R(u) + (0.5 / \nu)\|w-u\|^2_2\]

\[\text{subject to } Cu = d, Du = e, w negative definite\]

over u and w, where $R(u)$ is a regularization function, C and D are constraint matrices, and d and e are vectors of values. NOTE: This optimizer is intended for building purely linear models that are guaranteed to be stable.

Parameters:

regularizer (string, optional (default 'l1')) – Regularization function to use. Currently implemented options are ‘l1’ (l1 norm), ‘l2’ (l2 norm), ‘weighted_l1’ (weighted l1 norm), and ‘weighted_l2’ (weighted l2 norm). Note that the regularizer must be convex here.
constraint_lhs (numpy ndarray, optional (default None)) – Shape should be (n_constraints, n_features * n_targets), The left hand side matrix C of Cw <= d. There should be one row per constraint.
constraint_rhs (numpy ndarray, shape (n_constraints,), optional (default None)) – The right hand side vector d of Cw <= d.
constraint_order (string, optional (default "target")) – The format in which the constraints constraint_lhs were passed. Must be one of “target” or “feature”. “target” indicates that the constraints are grouped by target: i.e. the first n_features columns correspond to constraint coefficients on the library features for the first target (variable), the next n_features columns to the library features for the second target (variable), and so on. “feature” indicates that the constraints are grouped by library feature: the first n_targets columns correspond to the first library feature, the next n_targets columns to the second library feature, and so on.
inequality_constraints (bool, optional (default False)) – If True, CVXPY methods are used to solve the problem.
verbose_cvxpy (bool, optional (default False)) – Boolean flag which is passed to CVXPY solve function to indicate if output should be verbose or not. Only relevant for optimizers that use the CVXPY package in some capabity.
arguments (See base class for additional)

Attributes:

coef_ (array, shape (n_features,) or (n_targets, n_features)) – Regularized weight vector(s). This is the v in the objective function.
coef_full_ (array, shape (n_features,) or (n_targets, n_features)) – Weight vector(s) that are not subjected to the regularization. This is the w in the objective function.

Methods

`set_fit_request`	Configure whether metadata should be requested to be passed to the `fit` method.
`set_score_request`	Configure whether metadata should be requested to be passed to the `score` method.

Attributes

`max_iter`
`normalize_columns`
`initial_guess`
`copy_X`
`unbias`
`coef_`
`intercept_`

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$', x_: bool | None | str = '$UNCHANGED$') → StableLinearSR3

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for x_ parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → StableLinearSR3

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:: sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.
Returns:: self – The updated object.
Return type:: object