pysindy.optimizers.BaseOptimizer

class pysindy.optimizers.BaseOptimizer(max_iter=20, normalize_columns=False, initial_guess=None, copy_X=True, unbias: bool = True)[source]

Base class for SINDy optimizers. Subclasses must implement a _reduce method for carrying out the bulk of the work of fitting a model.

Parameters:

  • normalize_columns (boolean, optional (default False)) – Normalize the columns of x (the SINDy library terms) before regression by dividing by the L2-norm.

  • copy_X (boolean, optional (default True)) – If True, X will be copied; else, it may be overwritten.

  • initial_guess (np.ndarray, shape (n_features,) or (n_targets, n_features),) – optional (default None) Initial guess for coefficients coef_. If None, the initial guess is obtained via a least-squares fit.

  • unbias (Whether to perform an extra step of unregularized linear) – regression to unbias the coefficients for the identified support. If an optimizer (self.optimizer) applies any type of regularization, that regularization may bias coefficients, improving the conditioning of the problem but harming the quality of the fit. Setting unbias==True enables an extra step wherein unregularized linear regression is applied, but only for the coefficients in the support identified by the optimizer. This helps to remove the bias introduced by regularization.

Attributes:

  • coef_ (array, shape (n_features,) or (n_targets, n_features)) – Weight vector(s).

  • ind_ (array, shape (n_features,) or (n_targets, n_features)) – Array of bools indicating which coefficients of the weight vector have not been masked out.

  • history_ (list) – History of coef_ over iterations of the optimization algorithm.

  • Theta_ (np.ndarray, shape (n_samples, n_features)) – The Theta matrix to be used in the optimization. We save it as an attribute because access to the full library of terms is sometimes needed for various applications.

Methods

fit

Fit the model.

set_fit_request

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

set_params

Set the parameters of this estimator.

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_

fit(x_, y, sample_weight=None, **reduce_kws)[source]

Fit the model.

Parameters:

  • x (array-like, shape (n_samples, n_features)) – Training data

  • y (array-like, shape (n_samples,) or (n_samples, n_targets)) – Target values

  • sample_weight (float or numpy array of shape (n_samples,), optional) – Individual weights for each sample

  • reduce_kws (dict) – Optional keyword arguments to pass to the _reduce method (implemented by subclasses)

Returns:

self

Return type:

returns an instance of self

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

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_params(**kwargs)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance

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

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