pysindy.feature_library.CustomLibrary

class pysindy.feature_library.CustomLibrary(library_functions, function_names=None, interaction_only=True, include_bias=False)[source]

Generate a library with custom functions.

Parameters:

library_functions (list of mathematical functions) – Functions to include in the library. Default is to use same functions for all variables. Can also be used so that each variable has an associated library, in this case library_functions is shape (n_input_features, num_library_functions)
function_names (list of functions, optional (default None)) – List of functions used to generate feature names for each library function. Each name function must take a string input (representing a variable name), and output a string depiction of the respective mathematical function applied to that variable. For example, if the first library function is sine, the name function might return $\sin(x)$ given $x$ as input. The function_names list must be the same length as library_functions. If no list of function names is provided, defaults to using $[ f_0(x),f_1(x), f_2(x), \ldots ]$.
interaction_only (boolean, optional (default True)) – Whether to omit self-interaction terms. If True, function evaulations of the form $f(x,x)$ and $f(x,y,x)$ will be omitted, but those of the form $f(x,y)$ and $f(x,y,z)$ will be included. If False, all combinations will be included.
include_bias (boolean, optional (default False)) – If True (default), then include a bias column, the feature in which all polynomial powers are zero (i.e. a column of ones - acts as an intercept term in a linear model). This is hard to do with just lambda functions, because if the system is not 1D, lambdas will generate duplicates.

Attributes:

functions (list of functions) – Mathematical library functions to be applied to each input feature.
function_names (list of functions) – Functions for generating string representations of each library function.
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 product of the number of library functions and the number of input features.

Examples

>>> import numpy as np
>>> from pysindy.feature_library import CustomLibrary
>>> x = np.array([[0.,-1],[1.,0.],[2.,-1.]])
>>> functions = [lambda x : np.exp(x), lambda x,y : np.sin(x+y)]
>>> lib = CustomLibrary(library_functions=functions).fit(x)
>>> lib.transform(x)
array([[ 1.        ,  0.36787944, -0.84147098],
       [ 2.71828183,  1.        ,  0.84147098],
       [ 7.3890561 ,  0.36787944,  0.84147098]])
>>> lib.get_feature_names()
['f0(x0)', 'f0(x1)', 'f1(x0,x1)']

Methods

`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 to custom features

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)) – Measurement 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 = '$UNCHANGED$') → CustomLibrary

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 = '$UNCHANGED$') → CustomLibrary

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 to custom features

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 applying the custom functions to the inputs.
Return type:: np.ndarray, shape (n_samples, n_output_features)