pysindy.deeptime.SINDyModel

class pysindy.deeptime.SINDyModel(feature_library, optimizer, n_control_features_=0)[source]

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

The model is represented as a Scikit-learn pipeline object with three steps: 1. Map the raw input data to nonlinear features according to the selected feature_library 2. Reshape the data from input shape to an optimization problem 3. Multiply the nonlinear features with a coefficient matrix encapuslated in optimizer.

This class expects the feature library and optimizer to already be fit with a SINDyEstimator. It is best to instantiate a SINDyModel object via the SINDyEstimator.fetch_model rather than calling the SINDyModel constructor directly.

Parameters:

  • optimizer (optimizer object) – Optimization method used to fit the SINDy model. This must be an (already fit) object extending pysindy.optimizers.BaseOptimizer.

  • feature_library (feature library object) – Feature library object used to specify candidate right-hand side features. This must be an (already fit) object extending pysindy.feature_library.BaseFeatureLibrary.

  • differentiation_method (differentiation object) – Method for differentiating the data. This must be an object extending pysindy.differentiation_methods.BaseDifferentiation. 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

copy

Makes a deep copy of this model.

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

feature_library

optimizer

feature_names

copy()[source]

Makes a deep copy of this model.

Returns:

copy – A new copy of this model.

Return type:

SINDyModel

set_fit_request(*, feature_names: 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 = '$UNCHANGED$') → SINDyModel

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:

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

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

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

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

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

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

Returns:

self – The updated object.

Return type:

object

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

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 = '$UNCHANGED$') → SINDyModel

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