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iris_test.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():
#f1 = open("/home/aditi/Downloads/SVM_MR/Snabler-master/psvm/testdata.txt")
f = open('SVMvalues.txt', 'r')
#f2 = open('cases', 'r')
omatrixList = []
gammaList =[]
pos_class_list = []
neg_class_list = []
count =0
O = 0
data = f.readlines()
#print data
#cases = f2.readlines()
tp=0
tn=0
fp=0
fn=0
'''
for line in f:
#current_case = cases[count]
print data
count+=1
line = line.strip()
svm = line.split()
#print svm
pos_classes = svm[0].split(',')
pos_class_list = pos_classes[2:]
neg_classes = svm[1].split(',')
neg_class_list = neg_classes[2:]
current_case = current_case.strip()
class_t = current_case.split('\t',1)
class_t[0]=class_t[0].rstrip(',')
class_t[1]=class_t[1].rstrip(',')
#print class_t
class_1 = str(class_t[0]).split(',',1000)
class_2 = str(class_t[1]).split(',',1000)
#print "line_in",line_in
#pos_class_list.append(class_1)
#neg_class_list.append(class_2)
omega = svm[3:-1]
omegalist=[]
for o in omega:
o=float(o)
omegalist.append(o)
#print "omegalist ", omegalist
O = np.matrix(np.reshape(np.array(omegalist),(1, len(omegalist))))
#print "Omega", O
gamma = svm[-1]
gamma=float(gamma)
tp=0
tn=0
fp=0
fn=0
#G = np.matrix(np.reshape(np.array(gamma),(1, len(gamma))))
'''
for line in sys.stdin:
#print "A", A
#print "THIS IS IN MAIN", line
tp, tn, fp, fn = classify_recursive(data, 1, line, tp, tn, fp, fn)
#print "THIS IS IN MAIN", tp, tn, fp, fn
#print len(data)
#print classifyList
#print '0',"\t", pos_class_list,"\t", neg_class_list,"\t", tp,"\t", fp,"\t", fn,"\t", tn, "\t", str(O.tolist()), "\t", str(gamma)
print "0", "$", tp,"$", fp,"$", fn,"$", tn
def map_category(category):
if (category == 'Iris-setosa'):
return '1'
elif (category == 'Iris-versicolor'):
return '2'
else:
return '3'
def numerify_feature(feature):
if feature == '?':
feature = 0.0
return float(feature)
def extract_features(array):
features = array[0:-1]
return [numerify_feature(f) for f in features]
def extract_category(array, neg, pos):
categ = array[-1].strip()
category = map_category(categ)
if category in neg:
return -1.0
elif category in pos:
return 1.0
else:
return 0
def transform_input(value,pos,neg):
array = value.split(',')
features = extract_features(array)
category = extract_category(array, neg, pos)
return(category, features)
def get_svm(line):
line = line.strip()
svm = line.split('$')
omega = svm[3:-1]
omegalist=[]
for o in omega:
o=float(o)
omegalist.append(o)
#print "omegalist ", omegalist
O = np.matrix(np.reshape(np.array(omegalist),(1, len(omegalist))))
#print "Omega", O
gamma = svm[-1]
gamma=float(gamma)
return (O, gamma)
def get_class(line):
line = line.strip()
svm = line.split('$')
#print svm
pos_classes = svm[0].split(',')
pos_class_list = pos_classes[2:]
neg_classes = svm[1].split(',')
neg_class_list = neg_classes[2:]
return(pos_class_list, neg_class_list)
def confusion_matrix(classify, category, tp, tn, fp, fn):
if(float(classify)>0 and category > 0):
tp+=1
if(float(classify)>0 and category < 0):
fp+=1
if(float(classify)<0 and category > 0):
fn+=1
if(float(classify)<0 and category < 0):
tn+=1
return (tp,tn,fp,fn)
def classify_recursive(data, i, line, tp, tn, fp, fn):
pos_class_list,neg_class_list = get_class(data[i-1])
category,features = transform_input(line ,pos_class_list,neg_class_list)
#print "category", category
#print "pos_class_list[i]", pos_class_list
#print "neg_class_list[i]", neg_class_list
num_training_features = len(features)
#print num_training_features
A = np.matrix(np.reshape(np.array(features),(1, num_training_features)))
O, gamma = get_svm(data[i-1])
#print O
#print gamma
#print A
classify = A * O.T - gamma
#print "classify, categ, i, len(data)", classify, category, i, len(data)
if(float(classify)>0):
#print "here now"
if(2*i <= len(data)):
#print 2*i, data[2*i-1]
tp, tn, fp, fn = classify_recursive(data, 2*i, line, tp, tn, fp, fn)
return (tp,tn,fp,fn)
else:
#print "final class, categ", classify, category
tp,tn,fp,fn = confusion_matrix(classify, category, tp, tn, fp, fn)
return (tp,tn,fp,fn)
if(float(classify)<0):
#print "here 2 now"
if(2*i+1 <=len(data)):
#print 2*i+1, data[2*i+1-1]
tp, tn, fp, fn = classify_recursive(data, 2*i+1, line, tp, tn, fp, fn)
return (tp,tn,fp,fn)
else:
#print "final class, categ", classify, category
tp,tn,fp,fn = confusion_matrix(classify, category, tp, tn, fp, fn)
#print tp,tn,fp,fn
return (tp,tn,fp,fn)
main()