pysindy.optimizers.SINDyPI

class pysindy.optimizers.SINDyPI(reg_weight_lam=0.1, regularizer='l1', tol=1e-05, max_iter=10000, copy_X=True, model_subset=None, normalize_columns=False, verbose_cvxpy=False, unbias=False)[source]

SINDy-PI optimizer

Attempts to minimize the objective function

\[0.5\|X-Xw\|^2_2 + \lambda R(w)\]

over w where $R(v)$ is a regularization function. See the following reference for more details:

Kaheman, Kadierdan, J. Nathan Kutz, and Steven L. Brunton. SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics. Proceedings of the Royal Society A 476.2242 (2020): 20200279.

Parameters:

regularizer (string, optional (default 'l1')) – Regularization function to use. Currently implemented options are ‘l1’ (l1 norm), ‘weighted_l1’ (weighted l1 norm), l2, and ‘weighted_l2’ (weighted l2 norm)
model_subset (np.ndarray, shape(n_models), optional (default None)) – List of indices to compute models for. If list is not provided, the default is to compute SINDy-PI models for all possible candidate functions. This can take a long time for 4D systems or larger.
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.

Attributes:

coef_ (array, shape (n_features,) or (n_targets, n_features)) – Regularized weight vector(s). This is the v in the objective function.
unbias (bool) – Required to be false, maintained for supertype compatibility

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 = '$UNCHANGED$') → SINDyPI

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 = '$UNCHANGED$') → SINDyPI

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