pysindy.feature_library.GeneralizedLibrary

class pysindy.feature_library.GeneralizedLibrary(libraries: list, tensor_array=None, inputs_per_library: Sequence[Sequence[int]] | None = None, exclude_libraries=[])[source]

Put multiple libraries into one library. All settings provided to individual libraries will be applied. Note that this class allows one to specifically choose which input variables are used for each library, and take tensor products of any pair of libraries. Tensored libraries inherit the same input variables specified for the individual libraries.

Parameters:

libraries (list of libraries) – Library instances to be applied to the input matrix.
tensor_array (2D list of booleans, optional, (default None)) – Default is to not tensor any of the libraries together. Shape equal to the # of tensor libraries and the # feature libraries. Indicates which pairs of libraries to tensor product together and add to the overall library. For instance if you have 5 libraries, and want to do two tensor products, you could use the list [[1, 0, 0, 1, 0], [0, 1, 0, 1, 1]] to indicate that you want two tensored libraries from tensoring libraries 0 and 3 and libraries 1, 3, and 4.
inputs_per_library (Sequence of Seqeunces of ints (default None)) – list that specifies which input indexes should be passed as inputs for each of the individual feature libraries. length must equal the number of feature libraries. Default is that all inputs are used for every library.

Attributes:

self.libraries_full_ (list[BaseFeatureLibrary]) – The fitted libraries
n_features_in_ (int) – The total number of input features.
n_output_features_ (int) – The total number of output features. The number of output features is the sum of the numbers of output features for each of the concatenated libraries.

Examples

>>> import numpy as np
>>> from pysindy.feature_library import FourierLibrary, CustomLibrary
>>> from pysindy.feature_library import GeneralizedLibrary
>>> x = np.array([[0.,-1],[1.,0.],[2.,-1.]])
>>> functions = [lambda x : np.exp(x), lambda x,y : np.sin(x+y)]
>>> lib_custom = CustomLibrary(library_functions=functions)
>>> lib_fourier = FourierLibrary()
>>> lib_generalized = GeneralizedLibrary([lib_custom, lib_fourier])
>>> lib_generalized.fit(x)
>>> lib_generalized.transform(x)

Methods

`calc_trajectory`
`fit`	Compute number of output features.
`get_feature_names`	Return feature names for output features.
`get_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 with libs provided below.

Attributes

`n_features_in_`
`n_output_features_`

fit(x_full, y=None)[source]

Compute number of output features.

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

get_feature_names(input_features=None)[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

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

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_full (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for x_full parameter in fit.
Returns:: self – The updated object.
Return type:: object

set_transform_request(*, x_full: bool | None | str = '$UNCHANGED$') → GeneralizedLibrary

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_full (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for x_full parameter in transform.
Returns:: self – The updated object.
Return type:: object

transform(x_full)[source]

Transform data with libs provided below.

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