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mix_functions.py
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mix_functions.py
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# -*- coding: utf-8 -*-
"""
mix_functions.py
@author: Mengting Wan
"""
from __future__ import division
import numpy as np
import basic_functions as bsf
import time as time
import KDEm as KDEm
import RKDE as RKDE
def compute_AUC(fpr, tpr):
tmp = np.zeros((len(fpr),2))
tmp[:,0] = fpr
tmp[:,1] = tpr
tmp.sort(axis=0)
auc = tmp[0,0]*tmp[0,1]/2
for i in range(1,len(tpr)):
auc = auc + (tmp[i,0]-tmp[i-1,0])*(tmp[i,1]+tmp[i-1,1])/2
auc = auc + (1-tmp[i,0])*(1+tmp[i,0])/2
return(auc)
def twist_AUC(x, x_i, wi_vec, argmax=False, h=-1, tol=1e-3):
l = len(x)
center = np.array([x[0]])
if(h<0):
h = bsf.MAD(x_i)
#h = np.std(x_i)
conf = np.array([bsf.get_density(x[0],x_i,wi_vec,h)])
ind = np.zeros(l)
if(sum(wi_vec)==0):
wi_vec = wi_vec + 1e-5
for i in range(1,l):
tmp = abs((x[i]-center)/center)
if(tmp.min()>tol):
center = np.append(center, x[i])
tmp = bsf.get_density(x[i],x_i,wi_vec,h)
conf = np.append(conf, tmp)
ind[i] = len(center)-1
else:
ind[i] = tmp.argmin()
conf = conf/sum(conf)
thr = np.arange(0,1,0.05)
center_list = []
conf_list = []
ind_list = []
if(argmax):
for i in range(len(center)):
tmp0 = x_i[ind==i]
tmp1 = np.zeros(len(tmp0))
for j in range(len(tmp0)):
tmp1[j] = bsf.get_density(tmp0[j],x_i,wi_vec,h)
center[i] = tmp0[np.argmax(tmp1)]
for i in range(len(thr)):
tmp = np.where(conf>thr[i])[0]
center_new = center[list(tmp)]
ind_new = -np.ones(l)
for j in range(len(center_new)):
ind_new[ind==tmp[j]] = j
center_list.append(center_new)
ind_list.append(np.copy(ind_new))
tmp = np.copy(conf[conf>thr[i]])
conf_list.append(tmp/sum(tmp))
return([center_list,np.array(ind_list),conf_list])
def evaluation(out1, out2, truth, c_max=2):
l = len(out1)
n_mark = len(truth)
mae = []
rmse = []
truth_sort = []
for j in range(n_mark):
truth_sort.append(np.array(sorted(truth[j])))
for k in range(c_max):
mae.append(np.zeros((l,2)))
rmse.append(np.zeros((l,2)))
count = np.zeros(c_max)
for i in range(l):
for j in range(n_mark):
if len(out1[i][j])==len(out2[i][j]) and len(out2[i][j])==len(truth[j]):
k = len(truth[j])-1
tmp1 = np.array(sorted(out1[i][j]))
tmp2 = np.array(sorted(out2[i][j]))
mae[k][i,0] += sum(abs(tmp1-truth_sort[j]))
mae[k][i,1] += sum(abs(tmp2-truth_sort[j]))
rmse[k][i,0] += sum((tmp1-truth_sort[j])**2)
rmse[k][i,1] += sum((tmp2-truth_sort[j])**2)
count[k] += k+1
for k in range(c_max):
mae[k] = mae[k]/count[k]
rmse[k] = np.sqrt(rmse[k]/count[k])
return([mae, rmse])
def wKDE_twist(data, m, n, w_M, method, argmax=False, h=-1, p_unre=0.1):
index, claim, count = bsf.extract(data, m, n)
n = len(claim)
truth = []
ind_c = []
conf = []
evl = np.zeros((20,4))
for i in range(n):
x_new = bsf.DENCLUE(claim[i], w_M[i], method, h=h)
center,ind,conf_i = twist_AUC(x_new, claim[i], w_M[i], argmax, h)
tmp0 = ind<0
tmp1 = data[i][:,0]>=np.int(m*(1-p_unre))
evl[:,0] = evl[:,0] + np.sum(tmp0*tmp1, axis=1)
evl[:,1] = evl[:,1] + np.sum(tmp0*(tmp1==False), axis=1)
evl[:,2] = evl[:,2] + np.sum((tmp0==False)*tmp1, axis=1)
evl[:,3] = evl[:,3] + np.sum((tmp0==False)*(tmp1==False), axis=1)
truth.append(center)
ind_c.append(ind)
conf.append(conf_i)
tpr = evl[:,0]/(evl[:,0]+evl[:,2])
fpr = evl[:,1]/(evl[:,1]+evl[:,3])
auc = compute_AUC(fpr,tpr)
rtn_truth = []
rtn_ind = []
rtn_conf = []
for j in range(20):
tmp1 = []
tmp2 = []
tmp3 = []
for i in range(n):
tmp1.append(np.copy(truth[i][j]))
tmp2.append(np.copy(ind_c[i][j]))
tmp3.append(np.copy(conf[i][j]))
rtn_truth.append(tmp1)
rtn_ind.append(tmp2)
rtn_conf.append(tmp3)
return([rtn_truth,rtn_ind,rtn_conf, auc])
def KDE_twist(data, m, n, method, argmax=False, h=-1, p_unre=0.1):
index, claim, count = bsf.extract(data, m, n)
n = len(claim)
truth = []
ind_c = []
conf = []
evl = np.zeros((20,4))
for i in range(n):
l = len(claim[i])
x_new = bsf.DENCLUE(claim[i], np.ones(l)/l, method, h=h)
center,ind,conf_i = twist_AUC(x_new, claim[i], np.ones(l)/l, argmax, h)
tmp0 = ind<0
tmp1 = data[i][:,0]>=np.int(m*(1-p_unre))
evl[:,0] = evl[:,0] + np.sum(tmp0*tmp1, axis=1)
evl[:,1] = evl[:,1] + np.sum(tmp0*(tmp1==False), axis=1)
evl[:,2] = evl[:,2] + np.sum((tmp0==False)*tmp1, axis=1)
evl[:,3] = evl[:,3] + np.sum((tmp0==False)*(tmp1==False), axis=1)
truth.append(center)
ind_c.append(ind)
conf.append(conf_i)
tpr = evl[:,0]/(evl[:,0]+evl[:,2])
fpr = evl[:,1]/(evl[:,1]+evl[:,3])
auc = compute_AUC(fpr,tpr)
rtn_truth = []
rtn_ind = []
rtn_conf = []
for j in range(20):
tmp1 = []
tmp2 = []
tmp3 = []
for i in range(n):
tmp1.append(np.copy(truth[i][j]))
tmp2.append(np.copy(ind_c[i][j]))
tmp3.append(np.copy(conf[i][j]))
rtn_truth.append(tmp1)
rtn_ind.append(tmp2)
rtn_conf.append(tmp3)
return([rtn_truth,rtn_ind,rtn_conf, auc])
def test_KDEm(data_raw, m, n, kernel, norm=True, argmax=False, max_itr=99, h=-1, p_unre=0.1):
print "====Test with KDEm===="
print "Kernel:", kernel
if(norm):
print "Normalized: True"
data, data_mean, data_sd = bsf.normalize(data_raw)
else:
print "Normalized: False"
data = data_raw[:]
a = time.time()
source_score, weights_for_each, itr = KDEm.KDEm(data, m, n,method=kernel, h=h)
b = time.time() - a
print "Time cost for KDEm: "+str(b)+"s"
out_list, ind_list, conf_list, auc = wKDE_twist(data, m, n, weights_for_each, kernel, argmax, h, p_unre)
c = time.time() - a
print "Time cost for all: "+str(c)+"s"
if(norm):
truth_out = []
for i in range(20):
truth_out.append(bsf.normalize_ivr(out_list[i], data_mean, data_sd))
else:
truth_out = out_list[:]
print "====End test===="
return([truth_out, ind_list, conf_list, source_score, auc, [b/itr,c]])
def test_KDE(data_raw, m, n, kernel, norm=True, argmax=False, h=-1, p_unre=0.1):
print "====Test with KDE===="
print "Kernel:", kernel
if(norm):
print "Normalized: True"
data, data_mean, data_sd = bsf.normalize(data_raw)
else:
print "Normalized: False"
data = data_raw[:]
a = time.time()
out_list, ind_list, conf_list, auc = KDE_twist(data, m, n, kernel, argmax, h, p_unre)
c = time.time() - a
print "Time cost for all: "+str(c)+"s"
if(norm):
truth_out = []
for i in range(20):
truth_out.append(bsf.normalize_ivr(out_list[i], data_mean, data_sd))
else:
truth_out = out_list[:]
print "====End test===="
return([truth_out, ind_list, conf_list, auc, [c]])
def test_RKDE(data_raw, m, n, kernel, rho_para=np.array([0.5,0.75,0.85]), norm=True, argmax=False, h=-1, p_unre=0.1):
print "====Test with RKDE===="
print "Kernel:", kernel
if(norm):
print "Normalized: True"
data, data_mean, data_sd = bsf.normalize(data_raw)
else:
print "Normalized: False"
data = data_raw[:]
a = time.time()
weights_for_each=RKDE.RKDE(data, m, n, rho_para=rho_para, method=kernel)
b = time.time() - a
print "Time cost for RKDE: "+str(b)+"s"
out_list, ind_list, conf_list, auc = wKDE_twist(data, m, n, weights_for_each, kernel, argmax, h, p_unre)
c = time.time() - a
print "Time cost for all: "+str(c)+"s"
if(norm):
truth_out = []
for i in range(20):
truth_out.append(bsf.normalize_ivr(out_list[i], data_mean, data_sd))
else:
truth_out = out_list[:]
print "====End test===="
return([truth_out, ind_list, conf_list, auc, [b,c]])