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

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# Class ClassifiersTests

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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)

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Simple test if classifiers can generalize ok on simple data

Decorators:
  - `@sweepargs(clf= clfswh ['binary'])`

### testSummary(self, clf)

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Basic testing of the clf summary

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

### testDegenerateUsage(self, clf)

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Test how clf handles degenerate cases

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

### test_single_class(self, clf)

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

Decorators:
  - `@sweepargs(clf= clfswh ['!sg', '!plr', '!meta'])`

### testSplitClassifierExtended(self, clf_)

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Decorators:
  - `@sweepargs(clf_= clfswh ['binary', '!meta'])`

### testValues(self, clf)

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Decorators:
  - `@sweepargs(clf= clfswh [:])`

### testMulticlassClassifier(self, clf)

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

### testSVMs(self, clf)

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Decorators:
  - `@sweepargs(clf= clfswh ['svm', '!meta'])`

### testRetrainables(self, clf)

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Decorators:
  - `@sweepargs(clf= clfswh ['retrainable'])`

### testCorrectDimensionsOrder(self, clf)

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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'])`