temp.delete(i) folds_train.append(temp) f = open(''.join([directory , ''.join(['fold_train_' , str(i/len_fold) , '.arff'])]) , "w") f.write(str(folds_train[-1])); f.close() ## Prediction buffers = [] ## List of per fold predictions weights = [] ## List of per fold weights per attribute for fld in range(0,folds): train = folds_train[fld] test = folds_test[fld] train.setClassIndex(data.numAttributes() - 1) test.setClassIndex(data.numAttributes() - 1) lr = LR() lr.buildClassifier(train) buf= StringBuffer() # buffer for the predictions attRange = Range() # no additional attributes output outputDistribution = Boolean(False) evaluation = Evaluation(test) evaluation.evaluateModel(lr, test, [buf, attRange, outputDistribution]) buffers.append(buf) ## Writing Evaluation Summaries f = open(''.join([directory , ''.join(['summary_',str(fld),'.report'])]) , 'w') f.write(evaluation.toSummaryString(True)) f.close() f = open(''.join([directory , ''.join(['coeff_',str(fld),'.report'])]) , 'w')
f = open( ''.join([ directory, ''.join(['fold_train_', str(i / len_fold), '.arff']) ]), "w") f.write(str(folds_train[-1])) f.close() ## Prediction buffers = [] ## List of per fold predictions weights = [] ## List of per fold weights per attribute for fld in range(0, folds): train = folds_train[fld] test = folds_test[fld] train.setClassIndex(data.numAttributes() - 1) test.setClassIndex(data.numAttributes() - 1) lr = LR() lr.buildClassifier(train) buf = StringBuffer() # buffer for the predictions attRange = Range() # no additional attributes output outputDistribution = Boolean(False) evaluation = Evaluation(test) evaluation.evaluateModel(lr, test, [buf, attRange, outputDistribution]) buffers.append(buf) ## Writing Evaluation Summaries f = open( ''.join([directory, ''.join(['summary_', str(fld), '.report'])]), 'w') f.write(evaluation.toSummaryString(True)) f.close()