pysindy.feature_library.base.BaseFeatureLibrary

class pysindy.feature_library.base.BaseFeatureLibrary[source]

Base class for feature libraries.

Forces subclasses to implement fit, transform, and get_feature_names methods.

Methods

`calc_trajectory`
`correct_shape`	Correct the shape of x, given what we know of the problem
`fit`	Compute number of output features.
`get_feature_names`	Return feature names for output features.
`get_spatial_grid`
`reshape_samples_to_spatial_grid`	Adapt predictions to fitted spatial grid.
`set_fit_request`	Configure whether metadata should be requested to be passed to the `fit` method.
`set_transform_request`	Configure whether metadata should be requested to be passed to the `transform` method.
`transform`	Transform data.
`validate_input`

Attributes

`size`
`n_features_in_`
`n_output_features_`

correct_shape(x: AxesArray)[source]: Correct the shape of x, given what we know of the problem

abstractmethod fit(x, y=None)[source]

Compute number of output features.

Parameters:: x (array-like, shape (n_samples, n_features)) – The data.
Returns:: self
Return type:: instance

abstractmethod get_feature_names(input_features=None) → list[str][source]

Return feature names for output features.

Parameters:: input_features (list of string, length n_features, optional) – String names for input features if available. By default, “x0”, “x1”, … “xn_features” is used.
Returns:: output_feature_names
Return type:: list of string, length n_output_features

reshape_samples_to_spatial_grid(x: ndarray) → AxesArray[source]: Adapt predictions to fitted spatial grid.

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

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_transform_request(*, x: bool | None | str = '$UNCHANGED$') → BaseFeatureLibrary

Configure whether metadata should be requested to be passed to the transform 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 transform 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 transform.
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 transform.
Returns:: self – The updated object.
Return type:: object

abstractmethod transform(x)[source]

Transform data.

Parameters:: x (array-like, shape [n_samples, n_features]) – The data to transform, row by row.
Returns:: xp – The matrix of features, where n_output_features is the number of features generated from the combination of inputs.
Return type:: np.ndarray, [n_samples, n_output_features]