pysindy.deeptime package

Submodules

pysindy.deeptime.deeptime module

Deeptime wrapper interface for PySINDy.

class pysindy.deeptime.deeptime.SINDyEstimator(optimizer=None, feature_library=None, differentiation_method=None, feature_names=None, t_default=1, discrete_time=False)[source]

Bases: SINDy

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', ...].

  • t_default (float, optional (default 1)) – Default value for the time step.

  • discrete_time (boolean, optional (default False)) – If True, dynamical system is treated as a map. Rather than predicting derivatives, the right hand side functions step the system forward by one time step. If False, dynamical system is assumed to be a flow (right-hand side functions predict continuous time derivatives).

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.

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

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

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

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see 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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

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

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see 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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

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 = '$UNCHANGED$', t: bool | None | str = '$UNCHANGED$', u: bool | None | str = '$UNCHANGED$', x: bool | None | str = '$UNCHANGED$', x_dot: bool | None | str = '$UNCHANGED$') → SINDyEstimator

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see 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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

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

class pysindy.deeptime.deeptime.SINDyModel(feature_library, optimizer, feature_names=None, t_default=1, discrete_time=False, n_control_features_=0)[source]

Bases: SINDy

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', ...].

  • t_default (float, optional (default 1)) – Default value for the time step.

  • discrete_time (boolean, optional (default False)) – If True, dynamical system is treated as a map. Rather than predicting derivatives, the right hand side functions step the system forward by one time step. If False, dynamical system is assumed to be a flow (right-hand side functions predict continuous time derivatives).

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.

copy()[source]

Makes a deep copy of this model.

Returns:

copy – A new copy of this model.

Return type:

SINDyModel

set_fit_request(*, t: bool | None | str = '$UNCHANGED$', u: bool | None | str = '$UNCHANGED$', x: bool | None | str = '$UNCHANGED$', x_dot: bool | None | str = '$UNCHANGED$') → SINDyModel

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see 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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

  • 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

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see 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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

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 = '$UNCHANGED$', t: bool | None | str = '$UNCHANGED$', u: bool | None | str = '$UNCHANGED$', x: bool | None | str = '$UNCHANGED$', x_dot: bool | None | str = '$UNCHANGED$') → SINDyModel

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see 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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

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

Module contents

class pysindy.deeptime.SINDyEstimator(optimizer=None, feature_library=None, differentiation_method=None, feature_names=None, t_default=1, discrete_time=False)[source]

Bases: SINDy

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', ...].

  • t_default (float, optional (default 1)) – Default value for the time step.

  • discrete_time (boolean, optional (default False)) – If True, dynamical system is treated as a map. Rather than predicting derivatives, the right hand side functions step the system forward by one time step. If False, dynamical system is assumed to be a flow (right-hand side functions predict continuous time derivatives).

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.

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

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

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

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see 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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

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

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see 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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

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 = '$UNCHANGED$', t: bool | None | str = '$UNCHANGED$', u: bool | None | str = '$UNCHANGED$', x: bool | None | str = '$UNCHANGED$', x_dot: bool | None | str = '$UNCHANGED$') → SINDyEstimator

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see 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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

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

class pysindy.deeptime.SINDyModel(feature_library, optimizer, feature_names=None, t_default=1, discrete_time=False, n_control_features_=0)[source]

Bases: SINDy

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', ...].

  • t_default (float, optional (default 1)) – Default value for the time step.

  • discrete_time (boolean, optional (default False)) – If True, dynamical system is treated as a map. Rather than predicting derivatives, the right hand side functions step the system forward by one time step. If False, dynamical system is assumed to be a flow (right-hand side functions predict continuous time derivatives).

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.

copy()[source]

Makes a deep copy of this model.

Returns:

copy – A new copy of this model.

Return type:

SINDyModel

set_fit_request(*, t: bool | None | str = '$UNCHANGED$', u: bool | None | str = '$UNCHANGED$', x: bool | None | str = '$UNCHANGED$', x_dot: bool | None | str = '$UNCHANGED$') → SINDyModel

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see 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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

  • 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

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see 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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

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 = '$UNCHANGED$', t: bool | None | str = '$UNCHANGED$', u: bool | None | str = '$UNCHANGED$', x: bool | None | str = '$UNCHANGED$', x_dot: bool | None | str = '$UNCHANGED$') → SINDyModel

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see 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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

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