Home

Trees

Indices

Help

Package mvpa :: Package tests :: Module test_clf :: Class ClassifiersTests

[hide private]

[frames] | no frames]

Class ClassifiersTests

source code

Instance Methods

[hide private]

setUp(self)

source code

testDummy(self)

source code

testBoosted(self)

source code

testBoostedStatePropagation(self)

source code

testBinaryDecorator(self)

source code

testClassifierGeneralization(self, clf)
Simple test if classifiers can generalize ok on simple data

source code

testSummary(self, clf)
Basic testing of the clf summary

source code

testDegenerateUsage(self, clf)
Test how clf handles degenerate cases

source code

test_single_class(self, clf)
Test if binary and multiclass can handle single class training/testing

source code

testSplitClassifier(self)

source code

testSplitClassifierExtended(self, clf_)

source code

testHarvesting(self)
Basic testing of harvesting based on SplitClassifier

source code

testMappedClassifier(self)

source code

testFeatureSelectionClassifier(self)

source code

testFeatureSelectionClassifierWithRegression(self)

source code

testTreeClassifier(self)
Basic tests for TreeClassifier

source code

testValues(self, clf)

source code

testMulticlassClassifier(self, clf)

source code

testSVMs(self, clf)

source code

testRetrainables(self, clf)

source code

testGenericTests(self)
Test all classifiers for conformant behavior

source code

testCorrectDimensionsOrder(self, clf)
To check if known/present Classifiers are working properly with samples being first dimension. Started to worry about possible problems while looking at sg where samples are 2nd dimension

source code

Method Details

[hide private]

testClassifierGeneralization(self, clf)

source code

Simple test if classifiers can generalize ok on simple data

Decorators:

@sweepargs(clf= clfswh ['binary'])

testSummary(self, clf)

source code

Basic testing of the clf summary

Decorators:

@sweepargs(clf= clfswh [:]+ regrswh [:])

testDegenerateUsage(self, clf)

source code

Test how clf handles degenerate cases

Decorators:

@sweepargs(clf= clfswh [:]+ regrswh [:])

test_single_class(self, clf)

source code

Test if binary and multiclass can handle single class training/testing

Decorators:

@sweepargs(clf= clfswh ['!sg', '!plr', '!meta'])

testSplitClassifierExtended(self, clf_)

source code

Decorators:

@sweepargs(clf_= clfswh ['binary', '!meta'])

testValues(self, clf)

source code

Decorators:

@sweepargs(clf= clfswh [:])

testMulticlassClassifier(self, clf)

source code

Decorators:

@sweepargs(clf= clfswh ['linear', 'svm', 'libsvm', '!meta'])

testSVMs(self, clf)

source code

Decorators:

@sweepargs(clf= clfswh ['svm', '!meta'])

testRetrainables(self, clf)

source code

Decorators:

@sweepargs(clf= clfswh ['retrainable'])

testCorrectDimensionsOrder(self, clf)

source code

To check if known/present Classifiers are working properly with samples being first dimension. Started to worry about possible problems while looking at sg where samples are 2nd dimension

Decorators:

@sweepargs(clf= clfswh ['!smlr', '!knn', '!gnb', '!lars', '!meta', '!ridge'])

Home

Trees

Indices

Help