PWMK

PWMK is a heuristic MKL algorithm that assigns the weights according to the individual kernels performance. The algorithm runs multiple SVM (or base learners) to compute the accuracy for each base kernel.

The algorithm returns the following combination of base kernels

$$k_{\mu}(x,z)=\sum_r^P\mu_rk_r(x,z),\quad \mbox{ where } \mu_r = \frac{acc(K_r)-m\delta}{\sum_h\left(acc(K_h)-m\delta\right)}$$

Specifically, $m$ is the minimum accuracy achieved, and $\delta$ is a hyper-parameter.

Paper

If you need additional details about PWMK, please refer to the following paper:
Tanabe, H., Ho, T. B., Nguyen, C. H., & Kawasaki, S: "Simple but effective methods for combining kernels in computational biology". IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies (2008, July)

MKLpy.algorithms.PWMK(
    delta=.4,
    cv=3,
    **kwargs,
    )

Parameters (init)

Parameter	Type	Description
delta	double
cv	int or cv-splitter	the cross-validation used to compute the accuracy
**kwargs	args	MKL parameters, see here

Attributes

Attribute	Type	Description
n_kernels	int	number of combined kernels
KL	list	the training kernels list
func_form	callable	the combination function (e.g. summation, average...)
solution	dict	the solution of the optimization

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Methods

See standard MKL methods here

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Examples

In the following example, we show two ways to use a 5-fold cv to determine the combination weights

from MKLpy.algorithms import PWMK
mkl = PWMK(delta=0, cv=5).fit(KLtr, Ytr)

from sklearn.model_selection import KFold
cv = KFold(n_splits=5, shuffle=True, random_state=42)
mkl = PWMK(delta=0, cv=cv).fit(KLtr, Ytr)