# sklearn.kernel_approximation.AdditiveChi2Sampler¶

Approximate feature map for additive chi2 kernel.

Uses sampling the fourier transform of the kernel characteristic at regular intervals.

Since the kernel that is to be approximated is additive, the components of the input vectors can be treated separately. Each entry in the original space is transformed into 2*sample_steps-1 features, where sample_steps is a parameter of the method. Typical values of sample_steps include 1, 2 and 3.

Optimal choices for the sampling interval for certain data ranges can be computed (see the reference). The default values should be reasonable.

Read more in the User Guide.

Parameters:
sample_stepsint, default=2

Gives the number of (complex) sampling points.

sample_intervalfloat, default=None

Sampling interval. Must be specified when sample_steps not in {1,2,3}.

Attributes:
sample_interval_float

Stored sampling interval. Specified as a parameter if sample_steps not in {1,2,3}.

n_features_in_int

Number of features seen during fit.

New in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.

SkewedChi2Sampler

A Fourier-approximation to a non-additive variant of the chi squared kernel.

sklearn.metrics.pairwise.chi2_kernel

The exact chi squared kernel.

sklearn.metrics.pairwise.additive_chi2_kernel

The exact additive chi squared kernel.

Notes

This estimator approximates a slightly different version of the additive chi squared kernel then metric.additive_chi2 computes.

References

See “Efficient additive kernels via explicit feature maps” A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence, 2011

Examples

>>> from sklearn.datasets import load_digits
>>> from sklearn.linear_model import SGDClassifier
>>> X_transformed = chi2sampler.fit_transform(X, y)
>>> clf = SGDClassifier(max_iter=5, random_state=0, tol=1e-3)
>>> clf.fit(X_transformed, y)
SGDClassifier(max_iter=5, random_state=0)
>>> clf.score(X_transformed, y)
0.9499...


Methods

 fit(X[, y]) Set the parameters. fit_transform(X[, y]) Fit to data, then transform it. get_feature_names_out([input_features]) Get output feature names for transformation. get_params([deep]) Get parameters for this estimator. set_params(**params) Set the parameters of this estimator. Apply approximate feature map to X.
fit(X, y=None)[source]

Set the parameters.

Parameters:
Xarray-like, shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.

yarray-like, shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

Returns:
selfobject

Returns the transformer.

fit_transform(X, y=None, **fit_params)[source]

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:
Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Returns:
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)[source]

Get output feature names for transformation.

Parameters:
input_featuresarray-like of str or None, default=None

Only used to validate feature names with the names seen in fit.

Returns:
feature_names_outndarray of str objects

Transformed feature names.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

set_params(**params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

transform(X)[source]

Apply approximate feature map to X.

Parameters:
X{array-like, sparse matrix}, shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.

Returns:
X_new{ndarray, sparse matrix}, shape = (n_samples, n_features * (2*sample_steps - 1))

Whether the return value is an array or sparse matrix depends on the type of the input X.