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Evaluation.py
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Evaluation.py
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import argparse
import cPickle as pickle
import numpy as np
def euclidian(a,b):
return np.linalg.norm(a-b, axis=1)
def kl_divergence(a,b):
aux = np.where(b != 0, np.divide(a,b), 0)
return np.sum(np.where(aux != 0, a * np.log10(aux), 0),axis = 1)
def norm(a):
return a/np.sum(a)
def totalUtility(a,amostra,sol):
u = np.zeros(a.shape)
amostra = np.reshape(amostra, (a.shape[0],1))
i = 0
for elem in u:
if sol[i] < a.shape[1]:
elem[sol[i]] = 1.0
i = i + 1
#mm = np.sqrt((a)*(a))
#return np.sum(u*(a)/mm)/float(a.shape[0])
return np.sum(u*(a)/amostra)/float(a.shape[0])
def compTotalUtility(a,b,amostra,sol):
u = np.zeros(a.shape)
amostra = np.reshape(amostra, (a.shape[0],1))
i = 0
for elem in u:
if sol[i] < a.shape[1]:
elem[sol[i]] = 1.0
i = i + 1
#mm = np.sqrt((a-b)*(a-b))
#return np.sum(u*(a-b)/mm)/float(a.shape[0])
return np.sum(u*(a-b)/amostra)/float(a.shape[0])
def utility(a,amostra,sol,n,random_sample):
u = np.zeros(a.shape)
amostra_soma = np.zeros(len(amostra))
for i in range(len(amostra)):
amostra_soma[i] = np.sum(amostra[:i+1])
i = 0
for elem in u:
if sol[i] < a.shape[1]:
elem[sol[i]] = 1.0
i = i + 1
utili = 0.0
for i in range(n):
index = np.searchsorted(amostra_soma, random_sample[i], side='left')
utili += np.sum(u[index]*(a[index])/amostra[index])
return utili/float(n)
def compUtility(a,b,amostra,sol,n,random_sample):
u = np.zeros(a.shape)
amostra_soma = np.zeros(len(amostra))
for i in range(len(amostra)):
amostra_soma[i] = np.sum(amostra[:i+1])
i = 0
for elem in u:
if sol[i] < a.shape[1]:
elem[sol[i]] = 1.0
i = i + 1
utili = 0.0
for i in range(n):
index = np.searchsorted(amostra_soma, random_sample[i], side='left')
utili += np.sum(u[index]*(a[index]-b[index])/amostra[index])
return utili/float(n)
def main():
parser = argparse.ArgumentParser();
evaluationStrategyChoices = ["euclidian", "kl_divergence", "all"]
parser.add_argument('--evaluation', dest='evaluation', action='store', default=evaluationStrategyChoices[2], choices=evaluationStrategyChoices,
help='Set the EVALUATION strategy. EUCLIDIAN and KL_DIVERGENCE and ALL are the options available')
samplingStrategyChoices = ["divergent", "all"]
parser.add_argument('--sampling', dest='sampling', action='store', default=samplingStrategyChoices[0], choices=samplingStrategyChoices,
help='Set the SAMPLING strategy. DIVERGENT and ALL are the options available')
parser.add_argument('--sampling_size', dest='samplingSize', action='store', type=float, default=1.0,
help='The size of the sampling'
+ 'Number between 0 and 1 for percentage, number > 1 for literal number of samples')
parser.add_argument('--systemFiles', dest='systemFiles',
action='store', default=[], nargs='*',
help='This argument set the system files that will be used in Evaluation')
args = parser.parse_args();
k = 0
if len(args.systemFiles) < 2:
print 'Error: not enough files to evaluate'
return
elif len(args.systemFiles) == 2:
k = 1
else :
k = len(args.systemFiles)
sysPred_y_given_x = []
sysPreds = []
sysSol = []
lexiconOfLabel = []
a_result = []
b_result = []
a_dictLabel = {}
a_lexiLabel = []
a_preds = []
b_dictLabel = {}
b_lexiLabel = []
b_preds = []
lexiPos = []
if args.systemFiles:
print 'Loading system files ...'
for i in args.systemFiles:
print i
print '\n'
for files in args.systemFiles:
f = open(files, "rb")
sysPYX, sysP, sysS, lexiL = pickle.load(f)
f.close()
if len(sysPreds) == 0:
sysSol = np.array(sysS)
a_dictLabel = lexiL.getLexiconDict()
a_lexiLabel = lexiL.getAllLexicon()
a_result = np.array(sysPYX)
a_preds = np.array(sysP)
sysPreds = sysP
else :
if len(np.where(np.array(sysS) != sysSol)[0]) > 0 :
print 'Error: different target file'
return
b_dictLabel = lexiL.getLexiconDict()
b_lexiLabel = lexiL.getAllLexicon()
b_preds = np.array(sysP)
if a_dictLabel != b_dictLabel:
a_result = np.transpose(a_result)
m = len(a_dictLabel)
for i in range(m):
idx = b_dictLabel.pop(a_lexiLabel[i], None)
if idx == None:
if len(b_result) == 0:
b_result = np.zeros(len(sysS))
else:
b_result = np.vstack((b_result,np.zeros(len(sysS))))
else:
b_lexiLabel.remove(a_lexiLabel[i])
if len(b_result) == 0:
b_result = sysPYX[:,idx]
elif idx >= len(sysPYX[0]):
b_result = np.vstack((b_result,np.zeros(len(sysS))))
else:
b_result = np.vstack((b_result,sysPYX[:,idx]))
for i in range(len(b_dictLabel)):
idx = b_dictLabel[b_lexiLabel[i]]
b_result = np.vstack((b_result,sysPYX[:,idx]))
a_dictLabel[b_lexiLabel[i]] = len(a_dictLabel)
a_lexiLabel.append(b_lexiLabel[i])
a_result = np.vstack((a_result,np.zeros(len(sysS))))
a_result = np.transpose(a_result)
b_result = np.transpose(b_result)
else:
b_result = np.array(sysPYX)
#sysPred_y_given_x = [sysPred_y_given_x ,sysPYX]
sysPreds = [sysPreds, sysP]
#sysSol = [sysSol, sysS]
if args.sampling == 'divergent':
print 'Apenas as predicoes divergentes'
idx = np.where(a_preds!=b_preds)[0]
a_result = a_result[idx]
b_result = b_result[idx]
sysSol = sysSol[idx]
print '\nNumero de predicoes diferentes'
print len(idx)
else:
print 'Todas as predicoes'
num = 0
if args.samplingSize <=1.0:
num = int(args.samplingSize*len(a_result))
elif args.samplingSize < len(a_result):
num = int(args.samplingSize)
else :
num = len(a_result)
if num == 0:
print 'Nothing to be compared'
return
Q = np.zeros(len(sysS))
random_sample = np.random.random_sample((num,))
print 'A: ', args.systemFiles[0]
print 'B: ', args.systemFiles[1]
if args.evaluation == 'euclidian' or args.evaluation =='all':
Q = euclidian(a_result, b_result)
Q = norm(Q)
print '\n\nUtilidade Total Euclidiana'
print 'A: ', totalUtility(a_result,Q,sysSol)
print 'B: ', totalUtility(b_result,Q,sysSol)
print 'Diferenca das Utilidades (UA-UB): ', compTotalUtility(a_result,b_result,Q,sysSol)
print '\nUtilidade Amostrada Euclidiana'
print 'A: ', utility(a_result,Q,sysSol,num,random_sample)
print 'B: ', utility(b_result,Q,sysSol,num,random_sample)
print 'Diferenca das Utilidades Amostradas (UA-UB): ', compUtility(a_result,b_result,Q,sysSol,num,random_sample)
if args.evaluation == 'kl_divergence' or args.evaluation =='all':
Q = kl_divergence(a_result, b_result)
Q = norm(Q)
print '\n\nUtilidade Total KL_divergencia'
print 'A: ', totalUtility(a_result,Q,sysSol)
print 'B: ', totalUtility(b_result,Q,sysSol)
print 'Diferenca das Utilidades (UA-UB): ', compTotalUtility(a_result,b_result,Q,sysSol)
print '\nUtilidade Amostrada KL_divergencia'
print 'A: ', utility(a_result,Q,sysSol,num,random_sample)
print 'B: ', utility(b_result,Q,sysSol,num,random_sample)
print 'Diferenca das Utilidades Amostradas (UA-UB):', compUtility(a_result,b_result,Q,sysSol,num,random_sample)
if __name__ == '__main__':
main()