sklearn.kernel_approximation
.SkewedChi2Sampler¶
- class sklearn.kernel_approximation.SkewedChi2Sampler(*, skewedness=1.0, n_components=100, random_state=None)[source]¶
Approximate feature map for “skewed chi-squared” kernel.
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.
- 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.
See also
AdditiveChi2Sampler
Approximate feature map for additive chi2 kernel.
Nystroem
Approximate a kernel map using a subset of the training data.
RBFSampler
Approximate a RBF kernel feature map using random Fourier features.
SkewedChi2Sampler
Approximate feature map for “skewed chi-squared” kernel.
sklearn.metrics.pairwise.chi2_kernel
The exact chi squared kernel.
sklearn.metrics.pairwise.kernel_metrics
List of built-in kernels.
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_feature_names_out
([input_features])Get output feature names for transformation.
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
set_output
(*[, transform])Set output container.
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
is the number of samples andn_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 instance itself.
- fit_transform(X, y=None, **fit_params)[source]¶
Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and returns a transformed version ofX
.- 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_feature_names_out(input_features=None)[source]¶
Get output feature names for transformation.
The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are:
["class_name0", "class_name1", "class_name2"]
.- 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_metadata_routing()[source]¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- 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_output(*, transform=None)[source]¶
Set output container.
See Introducing the set_output API for an example on how to use the API.
- Parameters:
- transform{“default”, “pandas”}, default=None
Configure output of
transform
andfit_transform
."default"
: Default output format of a transformer"pandas"
: DataFrame output"polars"
: Polars outputNone
: Transform configuration is unchanged
New in version 1.4:
"polars"
option was added.
- Returns:
- selfestimator instance
Estimator instance.
- 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
is the number of samples andn_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)
Returns the instance itself.