KernelCenterer

class cuml.preprocessing.KernelCenterer(*args, **kwargs)

Center a kernel matrix

Let K(x, z) be a kernel defined by phi(x)^T phi(z), where phi is a function mapping x to a Hilbert space. KernelCenterer centers (i.e., normalize to have zero mean) the data without explicitly computing phi(x). It is equivalent to centering phi(x) with cuml.preprocessing.StandardScaler(with_std=False).

Attributes:

K_fit_rows_array, shape (n_samples,)

Average of each column of kernel matrix

K_fit_all_float

Average of kernel matrix

Methods

fit(K[, y])	Fit KernelCenterer
transform(K[, copy])	Center kernel matrix.

Examples

>>> import cupy as cp
>>> from cuml.preprocessing import KernelCenterer
>>> from cuml.metrics import pairwise_kernels
>>> X = cp.array([[ 1., -2.,  2.],
...               [ -2.,  1.,  3.],
...               [ 4.,  1., -2.]])
>>> K = pairwise_kernels(X, metric='linear')
>>> K
array([[  9.,   2.,  -2.],
       [  2.,  14., -13.],
       [ -2., -13.,  21.]])
>>> transformer = KernelCenterer().fit(K)
>>> transformer
KernelCenterer()
>>> transformer.transform(K)
array([[  5.,   0.,  -5.],
       [  0.,  14., -14.],
       [ -5., -14.,  19.]])

fit(

K,

y=None,

) → KernelCenterer

Fit KernelCenterer

Parameters:

Knumpy array of shape [n_samples, n_samples]

Kernel matrix.

Returns:

selfreturns an instance of self.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X{array-like, sparse matrix, dataframe} of shape (n_samples, n_features)

yndarray of shape (n_samples,), default=None

Target values.

**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)

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_params(deep=True)

Returns a dict of all params owned by this class. If the child class has appropriately overridden the _get_param_names method and does not need anything other than what is there in this method, then it doesn’t have to override this method

set_params(**params)

Accepts a dict of params and updates the corresponding ones owned by this class. If the child class has appropriately overridden the _get_param_names method and does not need anything other than what is, there in this method, then it doesn’t have to override this method

transform(K, copy=True)

Center kernel matrix.

Parameters:

Knumpy array of shape [n_samples1, n_samples2]

Kernel matrix.

copyboolean, optional, default True

Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.

Returns:

K_newnumpy array of shape [n_samples1, n_samples2]