Binary Class の測定
logloss
l_pred = [0.5, 0.5, 0.5, 0.5]
l_label = [0, 0, 0, 0]
def logloss(l_pred, l_label):
n = len(l_pred)
score = 0
for t in range(n):
i = l_pred[t]
k = l_label[t]
score += k * np.log(i) + (1 - k) * np.log(i)
return - score / n
logloss(l_pred, l_label)
auc
from io import StringIO
a = StringIO('''target prediction
1 0.38
0 0.51
1 0.90
1 0.84
1 0.48
0 0.03
0 0.45''')
def auc(df):
df_ = df_ = df.sort_values('prediction', ascending=False).reset_index(drop=True)
all_ = df_.target.value_counts()[0] * df_.target.value_counts()[1]
target_list = df_.target.values
n = len(target_list)
count = 0
for i in range(n):
for k in range(i + 1, n):
if target_list[i] < target_list[k]:
count += 1
return(1 - count / all_)
auc(df)
