himalaya.kernel_ridge.Kernelizer

class himalaya.kernel_ridge.Kernelizer(kernel='linear', kernel_params=None, force_cpu=False)

Transform tabular data into a kernel.

Parameters

kernelstr or callable, default=”linear”

Kernel mapping. Available kernels are: ‘linear’, ‘polynomial, ‘poly’, ‘rbf’, ‘sigmoid’, ‘cosine’, or ‘precomputed’. Set to ‘precomputed’ in order to pass a precomputed kernel matrix to the estimator methods instead of samples. A callable should accept two arguments and the keyword arguments passed to this object as kernel_params, and should return a floating point number.

kernel_paramsdict or None

Additional parameters for the kernel function. See more details in the docstring of the function: Kernelizer.ALL_KERNELS[kernel]

force_cpubool

If True, computations will be performed on CPU, ignoring the current backend. If False, use the current backend.

Examples

>>> from himalaya.kernel_ridge import Kernelizer
>>> import numpy as np
>>> n_samples, n_features, n_targets = 10, 5, 3
>>> X = np.random.randn(n_samples, n_features)
>>> model = Kernelizer()
>>> model.fit_transform(X).shape
(10, 10)

Attributes

X_fit_array of shape (n_samples, n_features)

Training data. If kernel == “precomputed” this is None.

n_features_in_int

Number of features (or number of samples if kernel == “precomputed”) used during the fit.

dtype_str

Dtype of input data.

Methods

fit(X[, y])	Compute the kernel on the training set.
fit_transform(X[, y])	Compute the kernel on the training set.
get_X_fit()	Helper to get the input data X seen during the fit.
get_params([deep])	Get parameters for this estimator.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Compute the kernel on any data set.