Package PyML :: Package demo :: Module demo
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Source Code for Module PyML.demo.demo

  1       
  2  import datafunc 
  3  import svm 
  4  import multi 
  5  import assess 
  6  import myio 
  7  import ker 
  8  import classifiers 
  9  import composite 
 10  import featsel 
 11  import modelSelection 
 12   
 13  # read data 
 14  d = datafunc.SparseCDataSet ('heartSparse.data') 
 15   
 16  # look at the data 
 17  print d 
 18   
 19  # construct svm classifier 
 20  s = svm.SVM() 
 21   
 22  # train 
 23  s.train(d) 
 24   
 25  # test by CV 
 26  r=s.cv(d) 
 27   
 28  # look at the results: 
 29  print r 
 30   
 31  # show ROC curve 
 32  r.plotROC() 
 33   
 34  # save results 
 35  r.save('test.pyd') 
 36  r=myio.load('test.pyd') 
 37   
 38  # try polynomial kernel 
 39  d.attachKernel('polynomial') 
 40  rp=s.cv(d) 
 41   
 42  # another way of doing this: 
 43  d.attachKernel('linear') 
 44  s = svm.SVM(ker.Polynomial()) 
 45  r=s.cv(d) 
 46   
 47  d.attachKernel('linear') 
 48  knn = classifiers.KNNC() 
 49  r=knn.cv(d) 
 50   
 51  d = datafunc.DataSet ('heartSparse.data') 
 52  ridge = classifiers.RidgeRegression() 
 53  r=ridge.cv(d) 
 54   
 55   
 56  d = datafunc.SparseCDataSet('iris.data', labelsColumn = -1) 
 57  mc = multi.OneAgainstOne (svm.SVM()) 
 58  r=mc.cv(d) 
 59   
 60  mc = multi.OneAgainstRest (svm.SVM()) 
 61  r=mc.cv(d) 
 62           
 63  s = svm.SVM() 
 64  d = datafunc.SparseDataSet ('yeast.data', labelsColumn = 0) 
 65  d = datafunc.oneAgainstRest(d, '2') 
 66  r=s.cv(d) 
 67   
 68  m = composite.FeatureSelect (s, featsel.RFE()) 
 69  r=m.cv(d, 3) 
 70           
 71  fs = featsel.FeatureScore ('golub') 
 72  f = featsel.Filter (fs, sigma = 2) 
 73  m = composite.FeatureSelect (s, f) 
 74  r=m.cv(d,3) 
 75   
 76  d = datafunc.SparseDataSet ('heart.data') 
 77  p = modelSelection.Param(svm.SVM(), 'C', [0.1, 1, 10, 100, 1000]) 
 78  m = modelSelection.ModelSelector(p) 
 79  m.train(d) 
 80   
 81   
 82  d = datafunc.SparseDataSet ('heartSparse.data') 
 83  p = modelSelection.Param(classifiers.KNN(), 'k', [1,2,3,5,10,15]) 
 84  m = modelSelection.ModelSelector(p) 
 85  m.train(d) 
 86   
 87   
 88   
 89  r = p.cv(d, numFolds = 10) 
 90  results = [r for r in p.cv(d, numFolds = 10)] 
 91  results = [r.successRate for r in p.cv(d, numFolds = 10)] 
 92   
 93  d = datafunc.SparseDataSet ('yeast.data', labelsColumn = 0) 
 94   
 95  d = datafunc.SparseDataSet ('yeast2.data', labelsColumn = 1) 
 96   
 97   
 98   
 99  from PyML import * 
100   
101  d = datafunc.VectorDataSet ('yeast3.data', labelsColumn = 1) 
102   
103  knn = classifiers.KNN() 
104  results = knn.stratifiedCV(d) 
105   
106  p = modelSelection.Param(classifiers.KNN(), 'k', [1,2,3,5,10,15]) 
107   
108  results = [r.successRate for r in p.stratifiedCV(d)] 
109   
110  m = modelSelection.ModelSelector(p) 
111