sklearn.kernel_approximation
.PolynomialCountSketch¶
- class sklearn.kernel_approximation.PolynomialCountSketch(*, gamma=1.0, degree=2, coef0=0, n_components=100, random_state=None)[source]¶
Polynomial kernel approximation via Tensor Sketch.
Implements Tensor Sketch, which approximates the feature map of the polynomial kernel:
K(X, Y) = (gamma * <X, Y> + coef0)^degree
by efficiently computing a Count Sketch of the outer product of a vector with itself using Fast Fourier Transforms (FFT). Read more in the User Guide.
New in version 0.24.
- Parameters:
- gammafloat, default=1.0
Parameter of the polynomial kernel whose feature map will be approximated.
- degreeint, default=2
Degree of the polynomial kernel whose feature map will be approximated.
- coef0int, default=0
Constant term of the polynomial kernel whose feature map will be approximated.
- n_componentsint, default=100
Dimensionality of the output feature space. Usually,
n_components
should be greater than the number of features in input samples in order to achieve good performance. The optimal score / run time balance is typically achieved aroundn_components
= 10 *n_features
, but this depends on the specific dataset being used.- random_stateint, RandomState instance, default=None
Determines random number generation for indexHash and bitHash initialization. Pass an int for reproducible results across multiple function calls. See Glossary.
- Attributes:
- indexHash_ndarray of shape (degree, n_features), dtype=int64
Array of indexes in range [0, n_components) used to represent the 2-wise independent hash functions for Count Sketch computation.
- bitHash_ndarray of shape (degree, n_features), dtype=float32
Array with random entries in {+1, -1}, used to represent the 2-wise independent hash functions for Count Sketch computation.
- 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.kernel_metrics
List of built-in kernels.
Examples
>>> from sklearn.kernel_approximation import PolynomialCountSketch >>> from sklearn.linear_model import SGDClassifier >>> X = [[0, 0], [1, 1], [1, 0], [0, 1]] >>> y = [0, 0, 1, 1] >>> ps = PolynomialCountSketch(degree=3, random_state=1) >>> X_features = ps.fit_transform(X) >>> clf = SGDClassifier(max_iter=10, tol=1e-3) >>> clf.fit(X_features, y) SGDClassifier(max_iter=10) >>> clf.score(X_features, y) 1.0
For a more detailed example of usage, see Scalable learning with polynomial kernel approximation
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)Generate the feature map approximation for X.
- fit(X, y=None)[source]¶
Fit the model with X.
Initializes the internal variables. The method needs no information about the distribution of data, so we only care about n_features in X.
- Parameters:
- X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data, where
n_samples
is the number of samples andn_features
is the number of features.- yarray-like of 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]¶
Generate the feature map approximation for X.
- Parameters:
- X{array-like}, shape (n_samples, n_features)
New data, where
n_samples
is the number of samples andn_features
is the number of features.
- Returns:
- X_newarray-like, shape (n_samples, n_components)
Returns the instance itself.
Examples using sklearn.kernel_approximation.PolynomialCountSketch
¶
Release Highlights for scikit-learn 0.24
Scalable learning with polynomial kernel approximation