pysindy.feature_library.PolynomialLibrary

class pysindy.feature_library.PolynomialLibrary(degree=2, include_interaction=True, interaction_only=False, include_bias=True, order='C')[source]

Generate polynomial and interaction features.

This is the same as sklearn.preprocessing.PolynomialFeatures, but also adds the option to omit interaction features from the library.

Parameters:

degree (integer, optional (default 2)) – The degree of the polynomial features.
include_interaction (boolean, optional (default True)) – Determines whether interaction features are produced. If false, features are all of the form x[i] ** k.
interaction_only (boolean, optional (default False)) – If true, only interaction features are produced: features that are products of at most degree distinct input features (so not x[1] ** 2, x[0] * x[2] ** 3, etc.).
include_bias (boolean, optional (default True)) – 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).
order (str in {'C', 'F'}, optional (default 'C')) – Order of output array in the dense case. ‘F’ order is faster to compute, but may slow down subsequent estimators.

Attributes:

powers_ (array, shape (n_output_features, n_input_features)) – powers_[i, j] is the exponent of the jth input in the ith output.
n_features_in_ (int) – The total number of input features.
n_output_features_ (int) – The total number of output features. This number is computed by iterating over all appropriately sized combinations of input features.

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 polynomial features.

Attributes

`powers_`	The exponents of the polynomial as an array of shape (n_features_out, n_features_in), where each item is the exponent of the jth input variable in the ith polynomial term.
`n_features_in_`
`n_output_features_`

fit(x_full: list[AxesArray], 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

property powers_: ndarray[tuple[Any, ...], dtype[int64]]: The exponents of the polynomial as an array of shape (n_features_out, n_features_in), where each item is the exponent of the jth input variable in the ith polynomial term.

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

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 = '$UNCHANGED$') → PolynomialLibrary

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 polynomial features.

Parameters:: x_full ({array-like, sparse matrix} of shape (n_samples, n_features)) – The data to transform, row by row.
Returns:: xp – shape (n_samples, n_output_features) The matrix of features, where n_output_features is the number of polynomial features generated from the combination of inputs.
Return type:: np.ndarray or CSR/CSC sparse matrix,