sklearn.kernel_approximation.SkewedChi2Sampler

class sklearn.kernel_approximation.SkewedChi2Sampler(*, skewedness=1.0, n_components=100, random_state=None) [source]

Approximates feature map of the “skewed chi-squared” kernel by Monte Carlo approximation of its Fourier transform.

Read more in the User Guide.

Parameters

skewednessfloat, default=1.0

“skewedness” parameter of the kernel. Needs to be cross-validated.

n_componentsint, default=100

number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space.

random_stateint, RandomState instance or None, default=None

Pseudo-random number generator to control the generation of the random weights and random offset when fitting the training data. Pass an int for reproducible output across multiple function calls. See Glossary.

Attributes

random_weights_ndarray of shape (n_features, n_components)

Weight array, sampled from a secant hyperbolic distribution, which will be used to linearly transform the log of the data.

random_offset_ndarray of shape (n_features, n_components)

Bias term, which will be added to the data. It is uniformly distributed between 0 and 2*pi.

See also

AdditiveChi2Sampler

A different approach for approximating an additive variant of the chi squared kernel.

sklearn.metrics.pairwise.chi2_kernel

The exact chi squared kernel.

References

See “Random Fourier Approximations for Skewed Multiplicative Histogram Kernels” by Fuxin Li, Catalin Ionescu and Cristian Sminchisescu.

Examples

>>> from sklearn.kernel_approximation import SkewedChi2Sampler
>>> from sklearn.linear_model import SGDClassifier
>>> X = [[0, 0], [1, 1], [1, 0], [0, 1]]
>>> y = [0, 0, 1, 1]
>>> chi2_feature = SkewedChi2Sampler(skewedness=.01,
...                                  n_components=10,
...                                  random_state=0)
>>> X_features = chi2_feature.fit_transform(X, y)
>>> clf = SGDClassifier(max_iter=10, tol=1e-3)
>>> clf.fit(X_features, y)
SGDClassifier(max_iter=10)
>>> clf.score(X_features, y)
1.0

Methods

fit(X[, y])	Fit the model with X.
fit_transform(X[, y])	Fit to data, then transform it.
get_params([deep])	Get parameters for this estimator.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Apply the approximate feature map to X.

fit(X, y=None) [source]

Fit the model with X.

Samples random projection according to n_features.

Parameters

Xarray-like, shape (n_samples, n_features)

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

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

Additional fit parameters.

Returns

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

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 the approximate feature map to X.

Parameters

Xarray-like, shape (n_samples, n_features)

New data, where n_samples in the number of samples and n_features is the number of features. All values of X must be strictly greater than “-skewedness”.

Returns

X_newarray-like, shape (n_samples, n_components)