orngSVM

orngSVM provides accsess to orange Support Vector Machine functionality.

Important!!! On some datasets this learner can perform very badly. It is a known fact that SVM's can be very sensitive to the proper choice of the parameters. If you are having problems with learner's accuracy try scaling the data and using diffrent parameters or choose an easier approach and use the SVMLearnerEasy class whitch does this automatically.

SVMLearner

SVMLearner is a function that constructs a SVMLearner class and optionaly trains it on provided examples

Arguments

svm_type: Defines the type of SVM (can be SVMLearner.C_SVC, SVMLearner.Nu_SVC (default), SVMLearner.OneClass, SVMLearner.Epsilon_SVR, SVMLearner.Nu_SVR)
kernel_type: Defines the type of a kernel to use for learning (can be SVMLearner.RBF (default), SVMLearner.Linear, SVMLearner.Polynomial, SVMLearner.Sigmoid, SVMLearner.Custom)
degree: Kernel parameter (Polynomial) (default 3)
gamma: Kernel parameter (Polynomial/RBF/Sigmoid) (default 1/number_of_examples)
coef0: Kernel parameter (Polynomial/Sigmoid) (default 0)
kernelFunc: Function that will be called if kernel_type is SVMLearner.Custom. It must accept two orange.Example arguments and return a float.
C: C parameter for C_SVC, Epsilon_SVR, Nu_SVR
nu: Nu parameter for Nu_SVC, Nu_SVR and OneClass (default 0.5)
p: Epsilon in loss-function for Epsilon_SVR
cache_size: Cache memory size in MB (default 100)
eps: Tolerance of termination criterion (default 0.001)
shrinking: Determines whether to use shrinking heuristics (default True)
probability: Determines if a probability model should be build (default False)

SVMLearnerSparse

Same as SVMLearner except that it learns from the examples mata attributes. Note that meta attributes dont need to be registerd with the dataset domain, or present in all the examples. Use this if you are using large sparse datasets.

SVMLearnerEasy

Same as above except that it will automaticaly scale the data and perform parameter optimization using the parameter_selection similar to the easy.py script in libSVM package. Use this if the SVMLearner performs badly.

SVMLearnerSparseEasy

Same as SVMLearnerEasy except that it learns from the examples mata attributes. Note that meta attributes dont need to be registerd with the dataset domain, or present in all the examples. Use this if you are using large sparse datasets (and have absolutely no respect for the fourth dimension commonly named as time).

getLinearSVMWeights

Returns a list of weights of linear class vs. class classifiers for the linear multiclass svm classifier. The list is in the order of 1vs2, 1vs3 ... 1vsN, 2vs3 ...

KernelWrapper (DualKernelWrapper)

KernelWrapper (DualKernelWrapper) is an abstract wrapper class that take one (two) kernel function (functions) as a initalization parameters and uses them to compute a new kernel function. The available kernel wrappers are RBFKernelWrapper, PolyKernelWrapper, AdditionKernelWrapper, MultiplicationKernelWrapper.

Methods

__call__(example1, example2): Computes the kernel function for the two examples

RBFKernelWrapper

Takes one kernel function (K1) in initialization and uses it to compute a new kernel function: K(x,y)=exp(K1(x,y)^2/gamma)

Attributes

gamma: gamma to use in the kernel function

PolyKernelWrapper

Takes one kernel function (K1) in initialization and uses it to compute a new kernel function: K(x,y)=K1(x,y)^degree

Attributes

degree: degree to use in the kernel function

AdditionKernelWrapper

Takes two kernel functions (K1 and K2) in initialization and uses them to compute a new kernel function: K(x,y)=K1(x,y)+K2(x,y)

MultiplicationKernelWrapper

Takes two kernel functions (K1 and K2) in initialization and uses them to compute a new kernel function: K(x,y)=K1(x,y)*K2(x,y)

CompositeKernelWrapper

Takes two kernel functions (K1 and K2) in initialization and uses them to compute a new kernel function: K(x,y)=&lambda*K1(x,y)+(1-&lambda)*K2(x,y)

Attributes

_lambda: lambda to use in the kernel function

SparseLinKernel

A linear kernel function that uses the examples meta attributes (must be floats) that need not be present in all examples

Examples

import orange, orngSVM data=orange.ExampleTable("iris.tab") l1=orngSVM.SVMLearner() l1.kernelFunc=orngSVM.RBFKernelWrapper(orange.ExamplesDistanceConstructor_Euclidean(data), gamma=0.5) l1.kernel_type=orange.SVMLearner.Custom l1.probability=True c1=l1(data) l1.name="SVM - RBF(Euclidean)" l2=orngSVM.SVMLearner() l2.kernelFunc=orngSVM.RBFKernelWrapper(orange.ExamplesDistanceConstructor_Hamming(data), gamma=0.5) l2.kernel_type=orange.SVMLearner.Custom l2.probability=True c2=l2(data) l2.name="SVM - RBF(Hamming)" l3=orngSVM.SVMLearner() l3.kernelFunc=orngSVM.CompositeKernelWrapper(orngSVM.RBFKernelWrapper(orange.ExamplesDistanceConstructor_Euclidean(data), gamma=0.5),orngSVM.RBFKernelWrapper(orange.ExamplesDistanceConstructor_Hamming(data), gamma=0.5), l=0.5) l3.kernel_type=orange.SVMLearner.Custom l3.probability=True c3=l1(data) l3.name="SVM - Composite" import orngTest, orngStat tests=orngTest.crossValidation([l1, l2, l3], data, folds=5) [ca1, ca2, ca3]=orngStat.CA(tests) print l1.name, "CA:", ca1 print l2.name, "CA:", ca2 print l3.name, "CA:", ca3

LinearLearner

A wrapper around orange.LinearLearner with a default solver_type == L2Loss_SVM_Dual (the default in orange.LinearLearner is L2_LR).