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iris_test_reducer.py~
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#!/usr/bin/env python
import sys
import base64
import cPickle as pickle
from mrjob.job import MRJob
import numpy as np
def main():
tp=0
tn=0
fn=0
fp=0
count2 = 0
old_key = 0
classes_pos_list = []
classes_neg_list = []
accuracy_list = []
omegalist = []
gammalist = []
for line in sys.stdin:
#print line
line = line.strip()
line = line.replace('[', '')
line = line.replace(']', '')
line = line.split('$')
#print line
key = int(line[0])
#print key
if(key == old_key):
#print line
#print line[7]
tp = tp + int(line[1])
fp = fp + int(line[2])
fn = fn + int(line[3])
tn = tn + int(line[4])
#x =line[1]
#y=line[2]
#om = line[7]
#gam = line[8]
old_key = key
#print tp,fp,fn,tn
else:
#omegalist.append(om)
#gammalist.append(gam)
'''
classes_pos_list.append(x)
x = classes_pos_list[-1]
x = x.replace('\'', '')
x = x.replace(' ', '')
classes_pos_list[-1] = x
classes_neg_list.append(y)
x = classes_neg_list[-1]
x = x.replace('\'', '')
x = x.replace(' ', '')
classes_neg_list[-1] = x
'''
if (tp==0 and fp ==0):
precision = 0.0
else:
precision = float(tp)/(tp+fp)
if (tp==0 and fn ==0):
recall = 0.0
else:
recall = float(tp)/(tp+fn)
if (precision ==0.0 and recall ==0.0):
f1measure = 0
else:
f1measure = 2*precision*recall/(precision+recall)
accuracy = float(tp+tn)/(tp+tn+fn+fp)
accuracy_list.append(str(accuracy))
#print line[1], line[2], precision, recall, f1measure, accuracy
tp=0
tn=0
fn=0
fp=0
tp = tp + int(line[1])
fp = fp + int(line[2])
fn = fn + int(line[3])
tn = tn + int(line[4])
#x = line[1]
#y=line[2]
#om = line[7]
#gam = line[8]
#print tp,fp,fn,tn
old_key = key
'''
omegalist.append(om)
gammalist.append(gam)
classes_pos_list.append(x)
x = classes_pos_list[-1]
x = x.replace('\'', '')
x = x.replace(' ', '')
classes_pos_list[-1] = x
classes_neg_list.append(y)
x = classes_neg_list[-1]
x = x.replace('\'', '')
x = x.replace(' ', '')
classes_neg_list[-1] = x
'''
if (tp==0 and fp ==0):
precision = 0.0
else:
precision = float(tp)/(tp+fp)
if (tp==0 and fn ==0):
recall = 0.0
else:
recall = float(tp)/(tp+fn)
if (precision ==0.0 and recall ==0.0):
f1measure = 0
else:
f1measure = 2*precision*recall/(precision+recall)
accuracy = float(tp+tn)/(tp+tn+fn+fp)
accuracy_list.append(str(accuracy))
#print accuracy_list, omegalist, gammalist
max=0
index = 0
for i in range(len(accuracy_list)):
accuracy_list[i] = float(accuracy_list[i])
if(accuracy_list[i]>max):
max=accuracy_list[i]
index = i
print accuracy_list[index]
#print classes_pos_list, classes_neg_list
#print "sas", classes_list, "lkl", accuracy_list
#print line[1], line[2], precision, recall, f1measure, accuracy
#print key, precision, recall, f1measure, accuracy
main()