sklearn.kernel_approximation
.AdditiveChi2Sampler¶
- class sklearn.kernel_approximation.AdditiveChi2Sampler(*, sample_steps=2, sample_interval=None)[source]¶
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}.Deprecated since version 1.3:
sample_interval_
serves internal purposes only and will be removed in 1.5.- 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
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.This estimator is stateless and does not need to be fitted. However, we recommend to call
fit_transform
instead oftransform
, as parameter validation is only performed infit
.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 >>> from sklearn.kernel_approximation import AdditiveChi2Sampler >>> X, y = load_digits(return_X_y=True) >>> chi2sampler = AdditiveChi2Sampler(sample_steps=2) >>> 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])Only validates estimator's parameters.
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 approximate feature map to X.
- fit(X, y=None)[source]¶
Only validates estimator’s parameters.
This method allows to: (i) validate the estimator’s parameters and (ii) be consistent with the scikit-learn transformer API.
- 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 transformer.
- 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.
- 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 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 andn_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.