PolynomialCountSketch (original) (raw)
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 theUser Guide.
Added 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_componentsshould 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 around n_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.
Added in version 0.24.
**feature_names_in_**ndarray of shape (n_features_in_,)
Names of features seen during fit. Defined only when Xhas feature names that are all strings.
Added in version 1.0.
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, seeScalable learning with polynomial kernel approximation
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 and n_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 and y with optional parameters fit_paramsand returns a transformed version of X.
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 APIfor an example on how to use the API.
Parameters:
transform{“default”, “pandas”, “polars”}, default=None
Configure output of transform and fit_transform.
"default": Default output format of a transformer"pandas": DataFrame output"polars": Polars outputNone: Transform configuration is unchanged
Added in version 1.4: "polars" option was added.
Returns:
selfestimator instance
Estimator instance.
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.
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 and n_features is the number of features.
Returns:
X_newarray-like, shape (n_samples, n_components)
Returns the instance itself.