def displayResults(results):
for accuracy, cm in zip(orngStat.CA(results),
orngStat.confusionMatrices(results, classIndex=0)):
print "accuracy", accuracy
print " TP: %i, FP: %i, FN: %s, TN: %i" % (cm.TP, cm.FP, cm.FN, cm.TN)
print "precision", orngStat.precision(cm)
print "recall", orngStat.recall(cm)
print "f1", fScore(cm)
print
def cforange_confusion_matrix_computations(input_dict):
import orngStat
cm = input_dict['cm']
alpha = float(input_dict['alpha'])
output_dict = {}
output_dict['sens']=orngStat.sens(cm)
output_dict['spec']=orngStat.spec(cm)
output_dict['PPV']=orngStat.PPV(cm)
output_dict['NPV']=orngStat.NPV(cm)
output_dict['precision']=orngStat.precision(cm)
output_dict['recall']=orngStat.recall(cm)
output_dict['F1']=orngStat.F1(cm)
output_dict['Falpha']=orngStat.Falpha(cm,alpha=alpha)
output_dict['MCC']=orngStat.MCC(cm)
return output_dict
]
new_domain = Orange.data.Domain(attributes, data.domain.class_var)
new_data = Orange.data.Table(new_domain, data)
classIndex = list(new_data.domain.classVar.values).index("yes")
svm = Orange.classification.svm.SVMLearner()
res = Orange.evaluation.testing.cross_validation([svm], new_data, folds=10)
cm = orngStat.computeConfusionMatrices(res, classIndex=classIndex)
print "======"
print test[1]
print "attributes: ",
print ",".join([attr.name for attr in new_data.domain.features])
print "Accuracy: %.1f%%" % (100 * orngStat.CA(res)[0])
print "Precision: %.3f" % orngStat.precision(cm)[0]
print "Recall: %.3f" % orngStat.recall(cm)[0]
print "F1: %.3f" % orngStat.F1(cm)[0]
res.all_results = res.results
res.number_of_iterations = 1
CAs = []
Ps = []
Rs = []
F1s = []
for fold in range(10):
# select only examples from the fold
res.results = [
result for result in res.all_results
if result.iteration_number == fold