#drama_all = np.array(drama_all)
#np.save('../outputs/uns_drama_'+file_name+'_'+str(i),drama_all)
#np.save('../outputs/uns_lof_'+file_name+'_'+str(i),lof_all)
#np.save('../outputs/uns_ifr_'+file_name+'_'+str(i),ifr_all)
cond = True
while cond:
try:
X, y = batch()
res = drm.unsupervised_outlier_finder_all(X)
arr, drts, metrs = drm.result_array(res, y, 'real')
drama_all.append(arr)
df = drm.sk_check(X, X, y, [1])
for k, scr in enumerate(['AUC', 'MCC', 'RWS']):
lof_all[k] = df[scr][0]
ifr_all[k] = df[scr][1]
drama_all = np.array(drama_all)
np.save('../outputs/uns_drama_' + file_name + '_' + str(i), drama_all)
np.save('../outputs/uns_lof_' + file_name + '_' + str(i), lof_all)
np.save('../outputs/uns_ifr_' + file_name + '_' + str(i), ifr_all)
cond = False
except:
pass
#outliers = X[y!=i_sig]
#outliers_y = y[y!=i_sig]
#for i in range(0,45,5):
# ax1.plot(inliers[i],'b')
# ax2.plot(outliers[i],drm.COLORS[outliers_y[i]])
#
#plt.subplots_adjust(hspace=0.3,left=0.1, right=0.9, top=0.9, bottom=0.1)
#plt.savefig('1.jpg')
y = (y != i_sig).astype(int)
y = y[:, None]
if n_train == 0:
res = drm.unsupervised_outlier_finder_all(X)
df = drm.sk_check(X, X, y, [1])
auc = []
mcc = []
rws = []
for i in range(50):
for j in ['real', 'latent']:
o1 = res[j][i]
auc.append(drm.roc_auc_score(y == 1, o1))
mcc.append(drm.MCC(y == 1, o1))
rws.append(drm.rws_score(y == 1, o1))
# print(y==1)
auc = np.array(auc)
mcc = np.array(mcc)
rws = np.array(rws)
drm.save(dir_add + str(i_sig) + '_' + str(n_train) + '_' + str(nn),