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

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

n_componentsint

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

random_stateint, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

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

__init__(self, skewedness=1.0, n_components=100, random_state=None)[source]¶

Initialize self. See help(type(self)) for accurate signature.

fit(self, 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(self, 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

Xnumpy array of shape [n_samples, n_features]

Training set.

ynumpy array of shape [n_samples]

Target values.

Returns

X_newnumpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(self, deep=True)[source]¶

Get parameters for this estimator.

Parameters

deepboolean, optional

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

Returns

paramsmapping of string to any

Parameter names mapped to their values.

set_params(self, **params)[source]¶

Set the parameters of this estimator.

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

Returns

self

transform(self, 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)