pysindy.deeptime.SINDyEstimator

class pysindy.deeptime.SINDyEstimator(optimizer=None, feature_library=None, differentiation_method=None)[source]

Implementation of SINDy conforming to the API of a Deeptime Estimator.

Parameters:

  • optimizer (optimizer object, optional) – Optimization method used to fit the SINDy model. This must be an object extending pysindy.optimizers.BaseOptimizer. Default is sequentially thresholded least squares with a threshold of 0.1.

  • feature_library (feature library object, optional) – Feature library object used to specify candidate right-hand side features. This must be an object extending the pysindy.feature_library.base.BaseFeatureLibrary. Default is polynomial features of degree 2.

  • differentiation_method (differentiation object, optional) – Method for differentiating the data. This must be an object extending the pysindy.differentiation_methods.base.BaseDifferentiation class. Default is centered difference.

  • feature_names (list of string, length n_input_features, optional) – Names for the input features (e.g. ['x', 'y', 'z']). If None, will use ['x0', 'x1', ...].

Attributes:

  • model (sklearn.multioutput.MultiOutputRegressor object) – The fitted SINDy model.

  • n_input_features_ (int) – The total number of input features.

  • n_output_features_ (int) – The total number of output features. This number is a function of self.n_input_features and the feature library being used.

Methods

fetch_model

Yields the estimated model.

fit

Fit the SINDyEstimator to data, learning a dynamical systems model for the data.

set_fit_request

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

set_predict_request

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

set_score_request

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

Attributes

has_model

Property reporting whether this estimator contains an estimated model.

feature_library

optimizer

feature_names

fetch_model()[source]

Yields the estimated model. Can be none if fit was not called.

Returns:

model – The estimated SINDy model or none

Return type:

SINDyModel or None

fit(x, **kwargs)[source]

Fit the SINDyEstimator to data, learning a dynamical systems model for the data.

Parameters:

  • x (array-like or list of array-like, shape (n_samples, n_input_features)) – Training data. If training data contains multiple trajectories, x should be a list containing data for each trajectory. Individual trajectories may contain different numbers of samples.

  • **kwargs (dict, optional) – Optional keyword arguments to pass to fit method.

Returns:

self

Return type:

fitted SINDyEstimator instance

property has_model

Property reporting whether this estimator contains an estimated model. This assumes that the model is initialized with None otherwise.

Type:

bool

set_fit_request(*, x: bool | None | str = '$UNCHANGED$') → SINDyEstimator

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:

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_predict_request(*, u: bool | None | str = '$UNCHANGED$', x: bool | None | str = '$UNCHANGED$') → SINDyEstimator

Configure whether metadata should be requested to be passed to the predict 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 predict 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 predict.

  • 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:

  • u (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for u parameter in predict.

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

Returns:

self – The updated object.

Return type:

object

set_score_request(*, metric: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$', t: bool | None | str = '$UNCHANGED$', u: bool | None | str = '$UNCHANGED$', x: bool | None | str = '$UNCHANGED$', x_dot: bool | None | str = '$UNCHANGED$') → SINDyEstimator

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:

  • metric (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for metric parameter in score.

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

  • t (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for t parameter in score.

  • u (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for u parameter in score.

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

  • x_dot (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for x_dot parameter in score.

Returns:

self – The updated object.

Return type:

object