def binary_logloss(p, y):
epsilon = 1e-15
p = sp.maximum(epsilon, p)
p = sp.minimum(1-epsilon, p)
res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
res *= -1.0/len(y)
return res
python类subtract()的实例源码
def binary_logloss(p, y):
epsilon = 1e-15
p = sp.maximum(epsilon, p)
p = sp.minimum(1-epsilon, p)
res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
res *= -1.0/len(y)
return res
def loglossl(act, pred):
epsilon = 1e-15
pred = sp.maximum(epsilon, pred)
pred = sp.minimum(1-epsilon, pred)
ll = sum(act*sp.log(pred) + sp.subtract(1,act)*sp.log(sp.subtract(1,pred)))
ll = ll * -1.0/len(act)
return ll
def logloss(act, pred):
'''
????????
:param act:
:param pred:
:return:
'''
epsilon = 1e-15
pred = sp.maximum(epsilon, pred)
pred = sp.minimum(1 - epsilon, pred)
ll = sum(act * sp.log(pred) + sp.subtract(1, act) * sp.log(sp.subtract(1, pred)))
ll = ll * -1.0 / len(act)
return ll
def binary_logloss(p, y):
epsilon = 1e-15
p = sp.maximum(epsilon, p)
p = sp.minimum(1-epsilon, p)
res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
res *= -1.0/len(y)
return res
def binary_logloss(p, y):
epsilon = 1e-15
p = sp.maximum(epsilon, p)
p = sp.minimum(1-epsilon, p)
res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
res *= -1.0/len(y)
return res
def logloss(act, pred):
epsilon = 1e-15
pred = sp.maximum(epsilon, pred)
pred = sp.minimum(1-epsilon, pred)
ll = sum(act*sp.log(pred) + sp.subtract(1,act)*sp.log(sp.subtract(1,pred)))
ll = ll * -1.0/len(act)
return ll
def logloss(act, pred):
epsilon = 1e-15
pred = sp.maximum(epsilon, pred)
pred = sp.minimum(1-epsilon, pred)
ll = sum(act*sp.log(pred) + sp.subtract(1,act)*sp.log(sp.subtract(1,pred)))
ll = ll * -1.0/len(act)
return ll
def binary_logloss(p, y):
epsilon = 1e-15
p = sp.maximum(epsilon, p)
p = sp.minimum(1-epsilon, p)
res = sum(y*sp.log(p) + sp.subtract(1,y)*sp.log(sp.subtract(1,p)))
res *= -1.0/len(y)
return res
def binary_logloss(p, y):
epsilon = 1e-15
p = sp.maximum(epsilon, p)
p = sp.minimum(1-epsilon, p)
res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
res *= -1.0/len(y)
return res
def logloss(act, preds):
epsilon = 1e-15
preds = sp.maximum(epsilon, preds)
preds = sp.minimum(1 - epsilon, preds)
ll = sum(act * sp.log(preds) + sp.subtract(1, act) * sp.log(sp.subtract(1, preds)))
ll = ll * -1.0 / len(act)
return ll
def logloss(act, preds):
epsilon = 1e-15
preds = sp.maximum(epsilon, preds)
preds = sp.minimum(1 - epsilon, preds)
ll = sum(act * sp.log(preds) + sp.subtract(1, act) * sp.log(sp.subtract(1, preds)))
ll = ll * -1.0 / len(act)
return ll
def logloss(act, preds):
epsilon = 1e-15
preds = sp.maximum(epsilon, preds)
preds = sp.minimum(1 - epsilon, preds)
ll = sum(act * sp.log(preds) + sp.subtract(1, act) * sp.log(sp.subtract(1, preds)))
ll = ll * -1.0 / len(act)
return ll
utils.py 文件源码
项目：Tencent_Social_Advertising_Algorithm_Competition
作者: guicunbin
项目源码
文件源码
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def logloss(act, pred):
epsilon = 1e-15
pred = sp.maximum(epsilon, pred)
pred = sp.minimum(1-epsilon, pred)
ll = sum(act*sp.log(pred) + sp.subtract(1,act)*sp.log(sp.subtract(1,pred)))
ll = ll * -1.0/len(act)
return ll
def binary_logloss(p, y):
epsilon = 1e-15
p = sp.maximum(epsilon, p)
p = sp.minimum(1-epsilon, p)
res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
res *= -1.0/len(y)
return res
def logloss(act, pred):
epsilon = 1e-15
pred = sp.maximum(epsilon, pred)
pred = sp.minimum(1-epsilon, pred)
ll = sum(act*sp.log(pred) + sp.subtract(1,act)*sp.log(sp.subtract(1,pred)))
ll = ll * -1.0/len(act)
return ll
def logloss(act, pred):
epsilon = 1e-15
pred = sp.maximum(epsilon, pred)
pred = sp.minimum(1-epsilon, pred)
ll = sum(act*sp.log(pred) + sp.subtract(1,act)*sp.log(sp.subtract(1,pred)))
ll = ll * -1.0/len(act)
return ll
def binary_logloss(p, y):
epsilon = 1e-15
p = sp.maximum(epsilon, p)
p = sp.minimum(1-epsilon, p)
res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
res *= -1.0/len(y)
return res
def binary_logloss(p, y):
epsilon = 1e-15
p = sp.maximum(epsilon, p)
p = sp.minimum(1-epsilon, p)
res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
res *= -1.0/len(y)
return res
def binary_logloss(p, y):
epsilon = 1e-15
p = sp.maximum(epsilon, p)
p = sp.minimum(1-epsilon, p)
res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
res *= -1.0/len(y)
return res
def logloss(act, pred):
epsilon = 1e-15
pred = sp.maximum(epsilon, pred)
pred = sp.minimum(1-epsilon, pred)
ll = sum(act*sp.log(pred) + sp.subtract(1,act)*sp.log(sp.subtract(1,pred)))
ll = ll * -1.0/len(act)
return ll
def binary_logloss(p, y):
epsilon = 1e-15
p = sp.maximum(epsilon, p)
p = sp.minimum(1-epsilon, p)
res = sum(y*sp.log(p) + sp.subtract(1,y)*sp.log(sp.subtract(1,p)))
res *= -1.0/len(y)
return res
