def logisticregression(samplerecords, lowage, upage,agetype ):
lstrecords = getsample(samplerecords, lowage, upage, agetype )
prewt = orange.FloatVariable('PREPREGNANCYWEIGHT')
motherbmi = orange.FloatVariable('MOTHERPREPREGNANCYBMI')
#gestWtGain = orange.FloatVariable('NETGESTATIONWEIGHTGAIN')
gestWtGainRate = orange.FloatVariable('GESTATIONWEIGHTGAINRATE')
wtdelivery = orange.FloatVariable('WEIGHTATDELIVERY')
bweight = orange.FloatVariable('BIRTHWEIGHT')
ethinicity = orange.EnumVariable('MOTHERETHINICITY',values = ['BL','WH', 'AS', 'HI', 'AI','UN'])
obesity = orange.EnumVariable('OBESITY',values = ['1','0'])
classAttributes = [prewt,motherbmi, gestWtGainRate,wtdelivery, bweight,ethinicity]
domain = orange.Domain(classAttributes, obesity)
ft = getallfeatures(lstrecords,domain,lowage, upage,agetype)
data = orange.ExampleTable(domain, ft)
countvariousattributes(data)
lr = orngLR.LogRegLearner(data, removeSingular=1)
TP = TN = FP = FN = 0
for ex in data:
if ex.getclass() == '1':
if lr(ex) == '1':
TP = TP +1
else:
FN = FN + 1
elif ex.getclass() == '0':
if lr(ex) == '0':
TN = TN +1
else:
FP = FP + 1
countNumberOfObese(lstrecords, lowage,upage, agetype)
orngLR.printOUT(lr)
print TP, ' ', FP , ' ', TN , ' ', FN
# Description: Demonstrates the use of logistic regression
# Category: classification, logistic regression
# Classes: LogRegLearner
# Uses: titanic.tab
import orange
import orngLR
data = orange.ExampleTable("titanic")
lr = orngLR.LogRegLearner(data)
correct = 0
for ex in data:
if lr(ex) == ex.getclass():
correct += 1
print "Classification accuracy:", correct/len(data)
orngLR.printOUT(lr)
# Description: Demonstrates the use of logistic regression
# Category: classification, logistic regression
# Classes: LogRegLearner
# Uses: titanic.tab
import orange
import orngLR
data = orange.ExampleTable("titanic")
lr = orngLR.LogRegLearner(data)
correct = 0
for ex in data:
if lr(ex) == ex.getclass():
correct += 1
print "Classification accuracy:", correct / len(data)
orngLR.printOUT(lr)