pysindy.feature_library.base.ConcatLibrary

class pysindy.feature_library.base.ConcatLibrary(libraries: list)[source]

Concatenate multiple libraries into one library. All settings provided to individual libraries will be applied.

Parameters:

libraries (list of libraries) – Library instances to be applied to the input matrix.

Attributes:

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 ConcatLibrary
>>> 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_concat = ConcatLibrary([lib_custom, lib_fourier])
>>> lib_concat.fit()
>>> lib.transform(x)

Methods

`calc_trajectory`
`fit`	Compute number of output features.
`get_feature_names`	Return feature names for output features.
`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 = '$UNCHANGED$') → ConcatLibrary

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 = '$UNCHANGED$') → ConcatLibrary

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]