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 chisquared” 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 crossvalidated.
 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
Pseudorandom 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=1e3) >>> 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
 Xarraylike, 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
 X{arraylike, sparse matrix, dataframe} of shape (n_samples, n_features)
Input samples.
 yndarray of shape (n_samples,), 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
 paramsmapping of string to any
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 pipelines). 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
 selfobject
Estimator instance.

transform
(X)[source]¶ Apply the approximate feature map to X.
 Parameters
 Xarraylike, 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_newarraylike, shape (n_samples, n_components)