import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score

scores = []
with open("result.txt") as f:
    lines = f.readlines()
    for line in lines:
    line = line.strip("/r/n")
    line = line.replace("[[","")
    line = line
 
.replace("]]","")
    score = float(line)
    scores.append(score)
y = []
for i in range(6000):
    if i<3000: #N
        y.append(0)
    else:
        y.append(1)
roc_x = []
roc_y = []
min_score = min(scores)
max_score = max(scores)
thr = np.linspace(min_score, max_score, 100)
FP = 0
TP = 0
TN = 0
N = 3000
P = 3000
 

acc_list = []
for(i, T) in enumerate(thr):
    for i in range(0, len(scores)):
    if(scores[i] > T):
        if(y[i] == 1):
            TP = TP + 1
        if(y[i] == 0):
            FP = FP + 1
    else:
        if(y[i] == 0)
            TN = TN + 1
    roc_x.append(FP/float(N))
    roc_y.append(TP/float(P))
    acc= (TP + TN)*1.0
 
/(N+P)
    acc_list.append(acc)
    FP = 0
    TP =0
    TN = 0
plt.plot(roc_x, roc_y, '--*b')
plt.savefig("roc.png")
auc = roc_auc_score(y, scores)
print "AUC:", auc
print "ACC:", max(acc_list)