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Commit 6d157f9c authored by Imène Lajili's avatar Imène Lajili
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made changes in isotonic regression widgets

parent ad2e75d3
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context-aware/library.py
View file @ 6d157f9c
......@@ -76,8 +76,6 @@ def ca_optimal_binary_threshold_selection(input_dict):
performance = input_dict['score']
method = input_dict['method']
#print method
cost_fn=input_dict['cost_false_neg']
cost_fp=input_dict['cost_false_pos']
list_score = []
labels = ''
n = len(performance['actual'])
......@@ -89,51 +87,38 @@ def ca_optimal_binary_threshold_selection(input_dict):
#print counter_neg
counter_pos = len([score for score in list_score if score[0] == 1])
#print counter_pos
output_dict['bin_thres'] = find_best_roc_weight(method,sorted_score,counter_pos,counter_neg,cost_fn,cost_fp)
output_dict['bin_thres'] = find_best_roc_weight(method,sorted_score,counter_pos,counter_neg)
return output_dict
def find_best_roc_weight(method,a_list,a_num_positives,a_num_negatives,cost_fn,cost_fp):
def find_best_roc_weight(method,a_list,a_num_positives,a_num_negatives):
previous = float('inf')
xpos = 0
xneg = a_num_negatives
#return the best threshold
the_best_value = get_value(method,xpos,xneg,a_num_positives,a_num_negatives,cost_fn,cost_fp)
#print the_best_value
#print the_best_value
#at the beginning the best is inf
the_best_value = get_value(method,xpos,xneg,a_num_positives,a_num_negatives)
best = previous
for the_elt in a_list:
the_roc = the_elt
current = the_roc[1]
#print current
#print the_roc
if current != previous:
possible_best_value = get_value(method,xpos,xneg,a_num_positives,a_num_negatives,cost_fn,cost_fp)
#print '%f > %f' %(possible_best_value,the_best_value)
possible_best_value = get_value(method,xpos,xneg,a_num_positives,a_num_negatives)
print '%f > %f' %(possible_best_value,the_best_value)
if possible_best_value > the_best_value:
the_best_value = possible_best_value
#print '%f -> %f' %(best,(previous + current) / float(2))
print '%f -> %f' %(best,(previous + current) / float(2))
best = (previous + current) / float(2)
if the_roc[0] == 1:
xpos += 1
'''else:
xneg -= 1'''
previous = current;
#print best
print xpos
print xneg
possible_best_value = get_value(method,xpos,xneg,a_num_positives,a_num_negatives,cost_fn,cost_fp)
print possible_best_value
else:
xneg -= 1
previous = current;
possible_best_value = get_value(method,xpos,xneg,a_num_positives,a_num_negatives)
if possible_best_value > the_best_value:
the_best_value = possible_best_value
best = (previous + float('-inf')) / float(2)
print previous
#print best
best = (previous + float('-inf')) / float(2)
return best
def get_value(method, TP, TN, P, N,CN,CP):
def get_value(method, TP, TN, P, N):
if method == 'accuracy':
accuracy = (TP + TN) / float(P+N)
return accuracy
......@@ -145,17 +130,6 @@ def get_value(method, TP, TN, P, N,CN,CP):
recall = TP / float(P)
if method == 'recall':
return recall
Cn=N/(P+N)
Cp=P/(P+N)
'''print FP
print TN
print FN
print TP'''
#print TP
#print type (FP/(FP+TN))
Cost=((FP/(FP+TN))*float(CN))+((FN/(TP+FN))*float(CP))
if method=='Cost':
return Cost
if TP + FP > 0:
precision = TP / float(TP + FP)
if method == 'precision':
......@@ -174,19 +148,21 @@ def Context_Bcalibration(input_dict):
old_actual=non_calibrated_scores['actual']
old_predicted=non_calibrated_scores['predicted']
g2=sort_list2(old_predicted,old_actual)
print g2
#print g2
Z=g2['actual']
print len(Z)
#print len(Z)
#print Z
Z2=g2['predicted']
print len(Z2)
output_dict={}
list3=[]
for i in Z:
list3.append(i)
#print list3
L=search_for_position(list3)
#print L
#print old_actual
while non_decreasing(L)!=True:
L=search_for_position(L)
g2=sort_list2(old_predicted,old_actual)
output_dict['builded_scores']= { 'noncalibrated_scr':Z2,'class':Z,'calibrated_scr':L}
print output_dict
......@@ -194,13 +170,14 @@ def Context_Bcalibration(input_dict):
def Context_Acalibration(input_dict):
from numpy import inf
import math
dict=input_dict['test_scores']
sc=input_dict['builded_scores']
X1=sc['calibrated_scr']
print len(X1)
print dict['predicted']
#print len(X1)
#print dict['predicted']
k=0
......@@ -233,24 +210,16 @@ def Context_Acalibration(input_dict):
list_max.append(j)
max=j
print "what's up man!"
print begin_score
print end_score
print list_max
for scr in dict['predicted']:
k+=1
scr2=0
print scr
for i in range(len(begin_score)):
if (scr >=begin_score[i] and scr<=end_score[i])or(scr==begin_score[i])or(scr==end_score[i]):
scr2=list_max[i]
if scr2==0:
scr2=0.1
elif scr2==1:
scr2=0.98
elif scr>end_score[i]and scr<begin_score[i+1]:
......@@ -258,35 +227,29 @@ def Context_Acalibration(input_dict):
scr2 = list_max[i]+((list_max[i+1]-list_max[i])*val1)
if scr2==0:
scr2=0.1
elif scr2==1.0:
scr2=0.98
list_scr.append(scr2)
print end_score[len(end_score)-1]
print len(list_scr)
print list_scr
for j in list_scr:
print j
probs.append(math.log10(j/(1-j)))
print len(test_cls)
#print len(probs)
#print probs
#print j
if j==0:
probs.append(float(-inf))
elif j==1:
probs.append(float(+inf))
else:
probs.append(math.log10(j/(1-j)))
#print len(test_cls)
output_dict={}
output_dict['calibrated_scores']= {'actual':test_cls , 'predicted':probs}
return output_dict
def search_for_position(list):
print len(list)
l1=list
l2=l1
print l2
#print l2
new_dict={}
new_dict['actual']=l2
for i in range(1,len(l1)):
#print 'etape n%s '%i
print 'etape n%s '%i
k=2
list2=[]
list3=[]
......@@ -296,7 +259,6 @@ def search_for_position(list):
j=1
if l1[i]<l1[i-1] :
j+=1
#print 'hello n 1'
print j
list2.append(l1[i])
list2.append(l1[i-1])
......@@ -305,42 +267,31 @@ def search_for_position(list):
for n in range(2,i):
if l1[i-1]==l1[i-n] and var==True and i!=1:
j+=1
#print 'here%f'%j
list2.append(l1[i-n])
list3.append(i-n)
aux=False
else:
var=False
if aux==False:
#print j
#print 'hello n 2'
c=sum(list2)
#print c
#print list3
for z in list3:
print z
l1[z]=float(c)/j
print l1
c=sum(list2)
#print c
#print list3
for z in list3:
print z
l1[z]=float(c)/j
else:
print list2
print j
print 'hello n 3'
#list[i-1]=list[i]=(list[i]+list[i-1]/j)
x=((float(l1[i]+l1[i-1]))/2)
l1[i]=l1[i-1]=x
print j
print x
print l1
list4=search_for_position(l1)
print "hi ! i am list n4"
print list4
#print list
print new_dict
print list2
#list[i-1]=list[i]=(list[i]+list[i-1]/j)
x=((float(l1[i]+l1[i-1]))/2)
l1[i]=l1[i-1]=x
print j
print x
print new_dict
return l1
def sort_list2(list1,list2):
n= len(list1)
#print n
Dict={}
for i in range(n-1):
#print i
......@@ -360,7 +311,10 @@ def sort_list2(list1,list2):
aux2=list2[i]
list2[i]=list2[pos]
list2[pos]=aux2
print list1
#print list1
Dict={'actual':list2,'predicted':list1}
return Dict
\ No newline at end of file
def non_decreasing(L):
return all(x<=y for x, y in zip(L, L[1:]))
\ No newline at end of file
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