R-MKL

R-MKL (Radius MKL) is a TwoStepMKL algorithm finding the kernels combination that maximizes the margin between classes. Differently to other approaches, R-MKL regularizes the solution by introducing radius information.

The algorithm learns a convex combination of base kernels with form

$$k_{\mu}(x,z)=\sum_r^P\mu_rk_r(x,z),\quad \mu_r\ge 0 \land\|\mu\|_1=1,$$

where $\mu$ is the weights vector learned by R-MKL.

Paper

If you need additional details about R-MKL, please refer to the following paper:
Do Huyen, Kalousis Alexandros, Woznica Adam, and Hilario Melanie: "Margin and radius based multiple kernel learning". Joint European Conference on Machine Learning and Knowledge Discovery in Databases (2009)

MKLpy.algorithms.RMKL(
    C=1.0
    **kwargs,
    )

Parameters (init)

Parameter	Type	Description
C	double	The SVM cost for non-separable data
**kwargs	args	TwoStepMKL 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
direction	str	direction of the optimization, min or max
convergence	bool	True iff the algorithm reaches the convergence
cache	dict	a dictionary containing intermediate results and data structures used to speed-up the computation

---

Methods

See standard TwoStepMKL methods here

---

Examples

from MKLpy.algorithms import RMKL
from MKLpy.scheduler  import ReduceOnWorsening
from MKLpy.callbacks  import EarlyStopping

earlystop = EarlyStopping(
    KLva, Yva,      #validation data, KL is a validation kernels list
    patience=5,     #max number of acceptable negative steps
    cooldown=1,     #how ofter we run a measurement, 1 means every optimization step
    metric='roc_auc',   #the metric we monitor
)

#ReduceOnWorsening automatically redure the learning rate when a worsening solution occurs
scheduler = ReduceOnWorsening()

mkl = RMKL(
    max_iter=1000,          
    learning_rate=.1,       
    callbacks=[earlystop],
    scheduler=ReduceOnWorsening()).fit(KLtr, Ytr)