Class PolynomialCountSketch

sklearn/kernel_approximation.py:40–250

Polynomial kernel approximation via Tensor Sketch. Implements Tensor Sketch, which approximates the feature map of the polynomial kernel:: K(X, Y) = (gamma * + coef0)^degree by efficiently computing a Count Sketch of the outer product of a vector with itself using F

class PolynomialCountSketch(
    ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator
):
    """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
    :ref:`User Guide <polynomial_kernel_approx>`.

    .. versionadded:: 0.24

    Parameters
    ----------
    gamma : float, default=1.0
        Parameter of the polynomial kernel whose feature map
        will be approximated.

    degree : int, default=2
        Degree of the polynomial kernel whose feature map
        will be approximated.

    coef0 : int, default=0
        Constant term of the polynomial kernel whose feature map
        will be approximated.

    n_components : int, 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 around `n_components` = 10 * `n_features`,
        but this depends on the specific dataset being used.

    random_state : int, RandomState instance, default=None
        Determines random number generation for indexHash and bitHash
        initialization. Pass an int for reproducible results across multiple
        function calls. See :term:`Glossary <random_state>`.

    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 :term:`fit`.

        .. versionadded:: 0.24

    feature_names_in_ : ndarray of shape (`n_features_in_`,)