def KNN_reg_leave_one_out(x,t,k=1,norm=1):
N = len(x)
vals = [None]*N
for i in range(N):
traind = np.array(list(x[:i]) + list(x[i+1:]))
testd = np.array(x[i])
traint = np.array(t[:i] + t[i+1:])
kd_tree = getKDTree(x=traind, t=traint)
val = KNN_reg(testd=testd, kd_tree=kd_tree, k=k, norm=norm)
vals[i] = val
return vals
def KNN_label_leave_one_out(x,t,k=1,norm=1):
tp = 0
fp = 0
tn = 0
fn = 0
N = len(x)
labels = [None]*N
for i in range(N):
traind = np.array(list(x[:i]) + list(x[i+1:]))
traint = np.array(t[:i] + t[i+1:])
testd = np.array(x[i])
testt = t[i]
kd_tree = getKDTree(x=traind, t=traint)
testlabel = KNN_get_label(testd=testd, kd_tree=kd_tree, k=k, norm=norm)
labels[i] = testlabel
if (testlabel == 1):
if (testt[0] == 1):
tp += 1
else:
fp += 1
elif (testlabel == 0):
if (testt[0] == 0):
tn += 1
else:
fn += 1
try:
acc = (tp+tn)/N
precision = tp/(tp+fp)
recall = tp/(tp+fn)
sensitivity = recall
specificity = tn/(tn+fp)
f1_measure = (2*(precision*recall))/(precision+recall)
conf_matrix = np.matrix([[tn,fp],[fn,tp]])
return labels,acc,precision,recall,sensitivity,specificity,f1_measure,conf_matrix
except ZeroDivisionError:
print("One of the evaluation metrics is 0")
def UA_leave_one_out(path,features1,features2,T=50000,k=1,ita=0.3,th=0.5):
tp = 0
fp = 0
tn = 0
fn = 0
xMLP,t = import_data(path,features1)
xKNN,t = import_data(path,features2)
N = len(xMLP)
m = len(xMLP[0])
labels = [None]*N
for i in range(N):
print("Training MLP model #:",i+1)
traind = np.array(list(xMLP[:i]) + list(xMLP[i+1:]))
traint = np.array(t[:i] + t[i+1:])
testt = t[i]
#train on training data
wih,who = trainMLP(x=traind, t=traint, T=T, n1=3, n2=1, ita=ita)
#get values for entire data
valsMLP=[None]*N
for i in range(N):
valsMLP[i] = MLP_reg(testd=np.array(xMLP[i]), wih=wih, who=who, n1=3, n2=1)
#insert absolute errors as feature
err = abs(np.array(valsMLP)-t[:][0])
xKNN = np.insert(arr=xKNN, obj=m-1, values=err, axis=1)
traind = np.array(list(xKNN[:i]) + list(xKNN[i+1:]))
traint = np.array(t[:i] + t[i+1:])
#make tree with training data
kd_tree = getKDTree(x=traind, t=traint)
valsKNN=[None]*N
for i in range(N):
valsKNN[i] = KNN_reg(testd=np.array(xKNN[i]), kd_tree=kd_tree, k=k)
#inputs for MLP2
x2 = np.ndarray([N,2])
x2[:,0] = valsMLP
x2[:,1] = valsKNN
traind = np.array(list(x2[:i]) + list(x2[i+1:]))
traint = np.array(t[:i] + t[i+1:])
testd=np.array(x2[i])
#train on training data
wih,who = trainMLP(x=traind, t=traint, T=T, n1=1, n2=1, ita=ita)
#get value for test point
label = MLP_getLabel(testd, wih=wih, who=who, th=th, n1=1, n2=1)
labels[i] = label
if(label==1):
if(testt[0] == 1):
tp+=1
else:
fp+=1
elif(label==0):
if(testt[0] == 0):
tn+=1
else:
fn+=1
try:
acc = (tp+tn)/N
precision = tp/(tp+fp)
recall = tp/(tp+fn)
sensitivity = recall
specificity = tn/(tn+fp)
f1_measure = (2*(precision*recall))/(precision+recall)
conf_matrix = np.matrix([[tn,fp],[fn,tp]])
return labels,acc,precision,recall,sensitivity,specificity,f1_measure,conf_matrix
except ZeroDivisionError:
print("One of the evaluation metrics is 0")
