def eval_reranker(res_fname="svm.test.res", pred_fname="svm.train.pred",
format="trec",
th=50,
verbose=False,
reranking_th=-100.0,
ignore_noanswer=False, ignore_allanswer=False):
ir, svm = read_res_pred_files(res_fname, pred_fname, format, verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer,
ignore_allanswer=ignore_allanswer)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir)
map_svm = metrics.map(svm)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
'''
print "%13s %5s" %("IR", "SVM")
print "MRR: %5.2f %5.2f" %(mrr_se, mrr_svm)
print "MAP: %5.4f %5.4f" %(map_se, map_svm)
print "AvgRec: %5.2f %5.2f" %(avg_acc1_ir, avg_acc1_svm)
print "%16s %6s %14s %6s %14s %6s %12s %4s" % ("IR", "SVM", "IR", "SVM", "IR", "SVM", "IR", "SVM")
'''
rec1_se =-10
rec1_svm = -10
for i, (p_se, p_svm, a_se, a_svm, a_se1, a_svm1, a_se2, a_svm2) in enumerate(zip(prec_se, prec_svm, acc_se, acc_svm, acc_se1, acc_svm1, acc_se2, acc_svm2), 1):
#print "REC-1@%02d: %6.2f %6.2f ACC@%02d: %6.2f %6.2f AC1@%02d: %6.2f %6.2f AC2@%02d: %4.0f %4.0f" %(i, p_se, p_svm, i, a_se, a_svm, i, a_se1, a_svm1, i, a_se2, a_svm2)
if (rec1_se<-5):
rec1_se = p_se
rec1_svm = p_svm
'''
print "REC-1 - percentage of questions with at least 1 correct answer in the top @X positions (useful for tasks were questions have at most one correct answer)"
print "ACC - accuracy, i.e. number of correct answers retrieved at rank @X normalized by the rank and the total number of questions"
print "AC1 - the number of correct answers at @X normalized by the number of maximum possible answers (perfect re-ranker)"
print "AC2 - the absolute number of correct answers at @X"
'''
print "Table view"
print " MRR MAP P@1"
print "REF_FILE %5.2f %5.2f %5.2f" % (mrr_se, map_se*100, rec1_se)
print "SVM %5.2f %5.2f %5.2f" % (mrr_svm, map_svm*100, rec1_svm)
def eval_reranker(res_fname="svm.test.res",
pred_fname="svm.train.pred",
format="trec",
th=10,
verbose=False,
reranking_th=0.0,
ignore_noanswer=False):
ir, svm, conf_matrix = read_res_pred_files(res_fname,
pred_fname,
format,
verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer)
# Calculate standard P, R, F1, Acc
acc = 1.0 * (
conf_matrix['true']['true'] + conf_matrix['false']['false']) / (
conf_matrix['true']['true'] + conf_matrix['false']['false'] +
conf_matrix['true']['false'] + conf_matrix['false']['true'])
p = 0
if (conf_matrix['true']['true'] + conf_matrix['false']['true']) > 0:
p = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['false']['true'])
r = 0
if (conf_matrix['true']['true'] + conf_matrix['true']['false']) > 0:
r = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['true']['false'])
f1 = 0
if (p + r) > 0:
f1 = 2.0 * p * r / (p + r)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir, th)
map_svm = metrics.map(svm, th)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
print "acc\tf1\tMAP\tMRR\tAvgRec"
print "%.4f %4.4f %4.4f %4.4f %4.4f" % (acc, f1, map_svm, mrr_svm,
avg_acc1_svm)
def eval_reranker(res_fname="svm.test.res", pred_fname="svm.train.pred",
format="trec",
th=50,
verbose=False,
reranking_th=0.0,
ignore_noanswer=False):
ir, svm = read_res_pred_files(res_fname, pred_fname, format, verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir)
map_svm = metrics.map(svm)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
print "%13s %5s" %("IR", "SVM")
print "MRR: %5.2f %5.2f" %(mrr_se, mrr_svm)
print "MAP: %5.4f %5.4f" %(map_se, map_svm)
print "AvgRec: %5.2f %5.2f" %(avg_acc1_ir, avg_acc1_svm)
print "%16s %6s %14s %6s %14s %6s %12s %4s" % ("IR", "SVM", "IR", "SVM", "IR", "SVM", "IR", "SVM")
for i, (p_se, p_svm, a_se, a_svm, a_se1, a_svm1, a_se2, a_svm2) in enumerate(zip(prec_se, prec_svm, acc_se, acc_svm, acc_se1, acc_svm1, acc_se2, acc_svm2), 1):
print "REC-1@%02d: %6.2f %6.2f ACC@%02d: %6.2f %6.2f AC1@%02d: %6.2f %6.2f AC2@%02d: %4.0f %4.0f" %(i, p_se, p_svm, i, a_se, a_svm, i, a_se1, a_svm1, i, a_se2, a_svm2)
print
print "REC-1 - percentage of questions with at least 1 correct answer in the top @X positions (useful for tasks were questions have at most one correct answer)"
print "ACC - accuracy, i.e. number of correct answers retrieved at rank @X normalized by the rank and the total number of questions"
print "AC1 - the number of correct answers at @X normalized by the number of maximum possible answers (perfect re-ranker)"
print "AC2 - the absolute number of correct answers at @X"
def eval_reranker(res_fname="svm.test.res",
pred_fname="svm.train.pred",
format="trec",
th=10,
verbose=False,
reranking_th=0.0,
ignore_noanswer=False):
ir, svm, conf_matrix = read_res_pred_files(res_fname,
pred_fname,
format,
verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer)
# Calculate standard P, R, F1, Acc
acc = 1.0 * (
conf_matrix['true']['true'] + conf_matrix['false']['false']) / (
conf_matrix['true']['true'] + conf_matrix['false']['false'] +
conf_matrix['true']['false'] + conf_matrix['false']['true'])
p = 0
if (conf_matrix['true']['true'] + conf_matrix['false']['true']) > 0:
p = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['false']['true'])
r = 0
if (conf_matrix['true']['true'] + conf_matrix['true']['false']) > 0:
r = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['true']['false'])
f1 = 0
if (p + r) > 0:
f1 = 2.0 * p * r / (p + r)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir, th)
map_svm = metrics.map(svm, th)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
print("")
print("*** Official score (MAP for SYS): %5.4f" % (map_svm))
print("")
print("")
print("******************************")
print("*** Classification results ***")
print("******************************")
print("")
print("Acc = %5.4f" % (acc))
print("P = %5.4f" % (p))
print("R = %5.4f" % (r))
print("F1 = %5.4f" % (f1))
print("")
print("")
print("********************************")
print("*** Detailed ranking results ***")
print("********************************")
print("")
print("IR -- Score for the output of the IR system (baseline).")
print("SYS -- Score for the output of the tested system.")
print("")
print("%13s %5s" % ("IR", "SYS"))
print("MAP : %5.4f %5.4f" % (map_se, map_svm))
print("AvgRec: %5.4f %5.4f" % (avg_acc1_ir, avg_acc1_svm))
print("MRR : %6.2f %6.2f" % (mrr_se, mrr_svm))
print("%16s %6s %14s %6s %14s %6s %12s %4s" %
("IR", "SYS", "IR", "SYS", "IR", "SYS", "IR", "SYS"))
for i, (p_se, p_svm, a_se, a_svm, a_se1, a_svm1, a_se2,
a_svm2) in enumerate(
zip(prec_se, prec_svm, acc_se, acc_svm, acc_se1, acc_svm1,
acc_se2, acc_svm2), 1):
print(
"REC-1@%02d: %6.2f %6.2f ACC@%02d: %6.2f %6.2f AC1@%02d: %6.2f %6.2f AC2@%02d: %4.0f %4.0f"
% (i, p_se, p_svm, i, a_se, a_svm, i, a_se1, a_svm1, i, a_se2,
a_svm2))
print()
print(
"REC-1 - percentage of questions with at least 1 correct answer in the top @X positions (useful for tasks where questions have at most one correct answer)"
)
print(
"ACC - accuracy, i.e., number of correct answers retrieved at rank @X normalized by the rank and the total number of questions"
)
print(
"AC1 - the number of correct answers at @X normalized by the number of maximum possible answers (perfect re-ranker)"
)
print("AC2 - the absolute number of correct answers at @X")
def eval_reranker(res_fname="svm.test.res",
pred_fname="svm.train.pred",
format="trec",
th=10,
verbose=False,
reranking_th=0.0,
ignore_noanswer=False):
ir, svm, conf_matrix = read_res_pred_files(res_fname,
pred_fname,
format,
verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer)
# Calculate standard P, R, F1, Acc
acc = 1.0 * (
conf_matrix['true']['true'] + conf_matrix['false']['false']) / (
conf_matrix['true']['true'] + conf_matrix['false']['false'] +
conf_matrix['true']['false'] + conf_matrix['false']['true'])
p = 0
if (conf_matrix['true']['true'] + conf_matrix['false']['true']) > 0:
p = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['false']['true'])
r = 0
if (conf_matrix['true']['true'] + conf_matrix['true']['false']) > 0:
r = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['true']['false'])
f1 = 0
if (p + r) > 0:
f1 = 2.0 * p * r / (p + r)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir, th)
map_svm = metrics.map(svm, th)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
#print ""
#print "*** Official score (MAP for SYS): %5.4f" %(map_svm)
#print ""
#print ""
#print "******************************"
#print "*** Classification results ***"
#print "******************************"
#print ""
#print "Acc = %5.4f" %(acc)
#print "P = %5.4f" %(p)
#print "R = %5.4f" %(r)
#print "F1 = %5.4f" %(f1)
#print ""
#print ""
#print "********************************"
#print "*** Detailed ranking results ***"
#print "********************************"
#print ""
#print "IR -- Score for the output of the IR system (baseline)."
#print "SYS -- Score for the output of the tested system."
#print ""
#print "%13s %5s" %("IR", "SYS")
#print "MAP : %5.4f %5.4f" %(map_se, map_svm)
#print "AvgRec: %5.4f %5.4f" %(avg_acc1_ir, avg_acc1_svm)
#print "MRR : %6.2f %6.2f" %(mrr_se, mrr_svm)
print "MAP : %5.4f\tMRR : %5.4f\tAvgRec: %5.4f" % (map_svm, mrr_svm,
avg_acc1_svm)
#print "Acc : %5.4f" %(acc)
#print "P : %5.4f" %(p)
#print "R : %5.4f" %(r)
#print "F1 : %5.4f" %(f1)
"""
def eval_reranker(res_fname="svm.test.res", pred_fname="svm.train.pred",
format="trec",
th=10,
verbose=False,
reranking_th=0.0,
ignore_noanswer=False):
ir, svm, conf_matrix = read_res_pred_files(res_fname, pred_fname, format, verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer)
# Calculate standard P, R, F1, Acc
acc = 1.0 * (conf_matrix['true']['true'] + conf_matrix['false']['false']) / (conf_matrix['true']['true'] + conf_matrix['false']['false'] + conf_matrix['true']['false'] + conf_matrix['false']['true'])
p = 0
if (conf_matrix['true']['true'] + conf_matrix['false']['true']) > 0:
p = 1.0 * (conf_matrix['true']['true']) / (conf_matrix['true']['true'] + conf_matrix['false']['true'])
r = 0
if (conf_matrix['true']['true'] + conf_matrix['true']['false']) > 0:
r = 1.0 * (conf_matrix['true']['true']) / (conf_matrix['true']['true'] + conf_matrix['true']['false'])
f1 = 0
if (p + r) > 0:
f1 = 2.0 * p * r / (p + r)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir, th)
map_svm = metrics.map(svm, th)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
print ("")
print ("*** Official score (MAP for SYS): %5.4f" %(map_svm))
print ("")
print ("")
print( "******************************")
print( "*** Classification results ***")
print( "******************************")
print( "")
print( "Acc = %5.4f" %(acc))
print( "P = %5.4f" %(p))
print( "R = %5.4f" %(r))
print( "F1 = %5.4f" %(f1))
print( "")
print( "")
print( "********************************")
print( "*** Detailed ranking results ***")
print( "********************************")
print( "")
print( "IR -- Score for the output of the IR system (baseline).")
print( "SYS -- Score for the output of the tested system.")
print( "")
print( "%13s %5s" %("IR", "SYS"))
print( "MAP : %5.4f %5.4f" %(map_se, map_svm))
print( "AvgRec: %5.4f %5.4f" %(avg_acc1_ir, avg_acc1_svm))
print( "MRR : %6.2f %6.2f" %(mrr_se, mrr_svm))
print( "%16s %6s %14s %6s %14s %6s %12s %4s" % ("IR", "SYS", "IR", "SYS", "IR", "SYS", "IR", "SYS"))
for i, (p_se, p_svm, a_se, a_svm, a_se1, a_svm1, a_se2, a_svm2) in enumerate(zip(prec_se, prec_svm, acc_se, acc_svm, acc_se1, acc_svm1, acc_se2, acc_svm2), 1):
print( "REC-1@%02d: %6.2f %6.2f ACC@%02d: %6.2f %6.2f AC1@%02d: %6.2f %6.2f AC2@%02d: %4.0f %4.0f" %(i, p_se, p_svm, i, a_se, a_svm, i, a_se1, a_svm1, i, a_se2, a_svm2))
print( "REC-1 - percentage of questions with at least 1 correct answer in the top @X positions (useful for tasks where questions have at most one correct answer)")
print( "ACC - accuracy, i.e., number of correct answers retrieved at rank @X normalized by the rank and the total number of questions")
print( "AC1 - the number of correct answers at @X normalized by the number of maximum possible answers (perfect re-ranker)")
print( "AC2 - the absolute number of correct answers at @X")
return map_svm
