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()
