def baseline_demo(info):
fout_baseline = open(info.fname_baseline, 'w')
fout_q = open(info.fname_baseline_q, 'w')
fout_w = open(info.fname_baseline_w, 'w')
w, winAuctions, winbid, losebid = getTrainData_b(info.fname_trainlog,
info.fname_trainbid)
print "trainData read success."
wt = getTestData_b(info.fname_testlog)
print "testData read success."
# get priceSet
wcount = [0] * UPPER
if info.mode == NORMAL or info.mode == SURVIVAL:
for i in range(0, len(winAuctions)):
if winAuctions[i] == 1:
wcount[w[i]] += 1
if info.mode == FULL:
for i in range(0, len(winAuctions)):
wcount[w[i]] += 1
minPrice = 0
maxPrice = UPPER
q = calProbDistribution(wcount, winbid, losebid, minPrice, maxPrice, info)
w = q2w(q)
print "q calculation success."
if len(q) != 0:
fout_q.write(str(q[0]))
for i in range(1, len(q)):
fout_q.write(' ' + str(q[i]))
fout_q.close()
if len(w) != 0:
fout_w.write(str(w[0]))
for i in range(1, len(w)):
fout_w.write(' ' + str(w[i]))
fout_w.close()
# n calculation
priceSet = wt
minPrice = 0
maxPrice = max(priceSet)
n = [0.] * UPPER
for i in range(0, len(priceSet)):
pay_price = priceSet[i]
n[pay_price] += 1
print "n calculation success."
# qt,wt calculation
qt = calProbDistribution_n(n, minPrice, maxPrice, info)
wt = q2w(qt)
# evaluation
# ANLP
fout_baseline.write("baseline campaign " + str(info.campaign) + " mode " +
MODE_NAME_LIST[info.mode] + " basebid " +
info.basebid + '\n')
fout_baseline.write("laplace " + str(info.laplace) + "\n")
print "baseline campaign " + str(
info.campaign) + " mode " + MODE_NAME_LIST[
info.mode] + " basebid " + info.basebid
print "laplace " + str(info.laplace)
for step in STEP_LIST:
qi = changeBucketUniform(q, step)
ni = deepcopy(n)
bucket = len(qi)
anlp, N = getANLP(qi, ni, minPrice, maxPrice)
fout_baseline.write("bucket " + str(bucket) + " step " + str(step) +
"\n")
fout_baseline.write("Average negative log probability = " + str(anlp) +
" N = " + str(N) + "\n")
print "bucket " + str(bucket) + " step " + str(step)
print "Average negative log probability = " + str(
anlp) + " N = " + str(N)
# KLD & pearsonr
bucket = len(q)
step = STEP_LIST[0]
KLD = KLDivergence(q, qt)
N = sum(n)
fout_baseline.write("bucket " + str(bucket) + " step " + str(step) + "\n")
fout_baseline.write("KLD = " + str(KLD) + " N = " + str(N) + "\n")
print "bucket " + str(bucket) + " step " + str(step)
print "KLD = " + str(KLD) + " N = " + str(N)
fout_baseline.close()
fout_q.close()
fout_w.close()
return q, w
def baseline_kdd15_Rversion0(info):
w = []
for day in DAY_LIST:
tmpw = getWinningPrice(info.fname_trainlog+day+'.txt')
w.extend(tmpw)
wt = getTestData_yzx(info.fname_testlog)
# q calculation
q = getQ(w,info)
w = q2w(q)
fout_q = open(info.fname_baseline_kdd15_q,'w')
fout_w = open(info.fname_baseline_kdd15_w,'w')
if len(q)!=0:
fout_q.write(str(q[0]))
for i in range(1,len(q)):
fout_q.write(' '+str(q[i]))
fout_q.close()
if len(w)!=0:
fout_w.write(str(w[0]))
for i in range(1,len(w)):
fout_w.write(' '+str(w[i]))
fout_w.close()
# n calculation
print time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())),"n calculation begins."
minPrice = 0
maxPrice = max(wt)
n = [0.]*UPPER
for pay_price in wt:
n[pay_price] += 1
# qt,wt calculation
qt = calProbDistribution_n(n,minPrice,maxPrice,info)
wt = q2w(qt)
# evaluation
fout_baseline_kdd15 = open(info.fname_baseline_kdd15,'w')
fout_baseline_kdd15.write("baseline_kdd15 campaign "+str(info.campaign)+" basebid "+info.basebid+'\n')
print "baseline campaign "+str(info.campaign)+" basebid "+info.basebid
for step in STEP_LIST:
qi = changeBucketUniform(q,step)
ni = deepcopy(n)
bucket = len(qi)
anlp,N = getANLP(qi,ni,minPrice,maxPrice)
fout_baseline_kdd15.write("bucket "+str(bucket)+" step "+str(step)+"\n")
fout_baseline_kdd15.write("Average negative log probability = "+str(anlp)+" N = "+str(N)+"\n")
print "bucket "+str(bucket)+" step "+str(step)
print "Average negative log probability = "+str(anlp)+" N = "+str(N)
# KLD
bucket = len(q)
step = STEP_LIST[0]
KLD = KLDivergence(q,qt)
N = sum(n)
fout_baseline_kdd15.write("bucket "+str(bucket)+" step "+str(step)+"\n")
fout_baseline_kdd15.write("KLD = "+str(KLD)+" N = "+str(N)+"\n")
print "bucket "+str(bucket)+" step "+str(step)
print "KLD = "+str(KLD)+" N = "+str(N)
fout_baseline_kdd15.close()
fout_q.close()
fout_w.close()
return q,w
def baseline_kdd15_Rversion0(info):
w = []
for day in DAY_LIST:
tmpw = getWinningPrice(info.fname_trainlog + day + '.txt')
w.extend(tmpw)
wt, ground_truth, bt = getTestData_yzbx(info.fname_testlog)
# q calculation
q = getQ(w, info)
w = q2w(q)
fout_q = open(info.fname_baseline_kdd15_q, 'w')
fout_w = open(info.fname_baseline_kdd15_w, 'w')
if len(q) != 0:
fout_q.write(str(q[0]))
for i in range(1, len(q)):
fout_q.write(' ' + str(q[i]))
fout_q.close()
if len(w) != 0:
fout_w.write(str(w[0]))
for i in range(1, len(w)):
fout_w.write(' ' + str(w[i]))
fout_w.close()
# n calculation
print time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time())), "n calculation begins."
minPrice = 0
maxPrice = max(wt)
maxPriceB = max(bt)
n = [0.] * UPPER
for i in range(len(wt)):
pay_price = wt[i]
n[pay_price] += 1
nb = [0.] * UPPER
g = {}
for i in range(len(bt)):
pay_price = bt[i]
nb[pay_price] += 1
if not g.has_key(pay_price):
g[pay_price] = [ground_truth[i]]
else:
g[pay_price].append(ground_truth[i])
# qt,wt calculation
qt = calProbDistribution_n(n, minPrice, maxPrice, info)
wt = q2w(qt)
qb = calProbDistribution_n(nb, minPrice, maxPriceB, info)
wb = q2w(qb)
# evaluation
fout_baseline_kdd15 = open(info.fname_baseline_kdd15, 'w')
fout_baseline_kdd15.write("baseline_kdd15 campaign " + str(info.campaign) +
" basebid " + info.basebid + '\n')
print "baseline campaign " + str(
info.campaign) + " basebid " + info.basebid
for step in STEP_LIST:
qi = changeBucketUniform(q, step)
ni = deepcopy(n)
bucket = len(qi)
anlp, N = getANLP(qi, ni, minPrice, maxPrice)
auc, cross = getAUC_CROSS(wb, g)
fout_baseline_kdd15.write("bucket " + str(bucket) + " step " +
str(step) + "\n")
fout_baseline_kdd15.write("Average negative log probability = " +
str(anlp) + " N = " + str(N) + "\n")
fout_baseline_kdd15.write("AUC = " + str(auc) + "\n")
fout_baseline_kdd15.write("CROSS = " + str(cross) + "\n")
print "bucket " + str(bucket) + " step " + str(step)
print "Average negative log probability = " + str(
anlp) + " N = " + str(N)
print "AUC = " + str(auc)
print "CROSS = " + str(cross)
# KLD
bucket = len(q)
step = STEP_LIST[0]
KLD = KLDivergence(q, qt)
N = sum(n)
fout_baseline_kdd15.write("bucket " + str(bucket) + " step " + str(step) +
"\n")
fout_baseline_kdd15.write("KLD = " + str(KLD) + " N = " + str(N) + "\n")
print "bucket " + str(bucket) + " step " + str(step)
print "KLD = " + str(KLD) + " N = " + str(N)
fout_baseline_kdd15.close()
fout_q.close()
fout_w.close()
return q, w
def baseline_kdd15_Rversion0(info):
w = []
for day in DAY_LIST:
tmpw = getWinningPrice(info.fname_trainlog + day + '.txt')
w.extend(tmpw)
wt = getTestData_yzx(info.fname_testlog)
# q calculation
q = getQ(w, info)
w = q2w(q)
fout_q = open(info.fname_baseline_kdd15_q, 'w')
fout_w = open(info.fname_baseline_kdd15_w, 'w')
if len(q) != 0:
fout_q.write(str(q[0]))
for i in range(1, len(q)):
fout_q.write(' ' + str(q[i]))
fout_q.close()
if len(w) != 0:
fout_w.write(str(w[0]))
for i in range(1, len(w)):
fout_w.write(' ' + str(w[i]))
fout_w.close()
# n calculation
print time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time())), "n calculation begins."
minPrice = 0
maxPrice = max(wt)
n = [0.] * UPPER
for pay_price in wt:
n[pay_price] += 1
# qt,wt calculation
qt = calProbDistribution_n(n, minPrice, maxPrice, info)
wt = q2w(qt)
# evaluation
fout_baseline_kdd15 = open(info.fname_baseline_kdd15, 'w')
fout_baseline_kdd15.write("baseline_kdd15 campaign " + str(info.campaign) +
" basebid " + info.basebid + '\n')
print "baseline campaign " + str(
info.campaign) + " basebid " + info.basebid
for step in STEP_LIST:
qi = changeBucketUniform(q, step)
ni = deepcopy(n)
bucket = len(qi)
anlp, N = getANLP(qi, ni, minPrice, maxPrice)
fout_baseline_kdd15.write("bucket " + str(bucket) + " step " +
str(step) + "\n")
fout_baseline_kdd15.write("Average negative log probability = " +
str(anlp) + " N = " + str(N) + "\n")
print "bucket " + str(bucket) + " step " + str(step)
print "Average negative log probability = " + str(
anlp) + " N = " + str(N)
# KLD
bucket = len(q)
step = STEP_LIST[0]
KLD = KLDivergence(q, qt)
N = sum(n)
fout_baseline_kdd15.write("bucket " + str(bucket) + " step " + str(step) +
"\n")
fout_baseline_kdd15.write("KLD = " + str(KLD) + " N = " + str(N) + "\n")
print "bucket " + str(bucket) + " step " + str(step)
print "KLD = " + str(KLD) + " N = " + str(N)
fout_baseline_kdd15.close()
fout_q.close()
fout_w.close()
return q, w
def baseline(info):
fout_baseline = open(info.fname_baseline,'w')
fout_q = open(info.fname_baseline_q,'w')
fout_w = open(info.fname_baseline_w,'w')
w,winAuctions,winbid,losebid = getTrainData_b(info.fname_trainlog,info.fname_trainbid)
print "trainData read success."
wt = getTestData_b(info.fname_testlog)
print "testData read success."
# get priceSet
wcount = [0]*UPPER
if info.mode==NORMAL or info.mode==SURVIVAL:
for i in range(0,len(winAuctions)):
if winAuctions[i]==1:
wcount[w[i]] += 1
if info.mode==FULL :
for i in range(0,len(winAuctions)):
wcount[w[i]] += 1
minPrice = 0
maxPrice = UPPER
q = calProbDistribution(wcount,winbid,losebid,minPrice,maxPrice,info)
w = q2w(q)
print "q calculation success."
if len(q)!=0:
fout_q.write(str(q[0]))
for i in range(1,len(q)):
fout_q.write(' '+str(q[i]))
fout_q.close()
if len(w)!=0:
fout_w.write(str(w[0]))
for i in range(1,len(w)):
fout_w.write(' '+str(w[i]))
fout_w.close()
# n calculation
priceSet = wt
minPrice = 0
maxPrice = max(priceSet)
n = [0.]*UPPER
for i in range(0,len(priceSet)):
pay_price = priceSet[i]
n[pay_price] += 1
print "n calculation success."
# qt,wt calculation
qt = calProbDistribution_n(n,minPrice,maxPrice,info)
wt = q2w(qt)
# evaluation
# ANLP
fout_baseline.write("baseline campaign "+str(info.campaign)+" mode "+MODE_NAME_LIST[info.mode]+" basebid "+info.basebid+'\n')
fout_baseline.write("laplace "+str(info.laplace)+"\n")
print "baseline campaign "+str(info.campaign)+" mode "+MODE_NAME_LIST[info.mode]+" basebid "+info.basebid
print "laplace "+str(info.laplace)
for step in STEP_LIST:
qi = changeBucketUniform(q,step)
ni = deepcopy(n)
bucket = len(qi)
anlp,N = getANLP(qi,ni,minPrice,maxPrice)
fout_baseline.write("bucket "+str(bucket)+" step "+str(step)+"\n")
fout_baseline.write("Average negative log probability = "+str(anlp)+" N = "+str(N)+"\n")
print "bucket "+str(bucket)+" step "+str(step)
print "Average negative log probability = "+str(anlp)+" N = "+str(N)
# KLD & pearsonr
bucket = len(q)
step = STEP_LIST[0]
KLD = KLDivergence(q,qt)
N = sum(n)
fout_baseline.write("bucket "+str(bucket)+" step "+str(step)+"\n")
fout_baseline.write("KLD = "+str(KLD)+" N = "+str(N)+"\n")
print "bucket "+str(bucket)+" step "+str(step)
print "KLD = "+str(KLD)+" N = "+str(N)
fout_baseline.close()
fout_q.close()
fout_w.close()
return q,w