def run_testset(kernel_type='cosine',
log_2gs=-12,
log_2gt=-12,
log_2w=-2,
log_2p=-1,
complete_flag=1,
zero_diag_flag=True):
dc = DataClass(valid_flag=False)
dc.kernel_type = kernel_type
dc.source_gamma = 2**log_2gs
dc.target_gamma = 2**log_2gt
dc.zero_diag_flag = zero_diag_flag
y, I, K, offset = dc.get_TL_Kernel()
w_2 = 2**log_2w
p = 2**log_2p
if complete_flag == 1:
K = DataClass.complete_TL_Kernel(K, offset)
# log_2p == -1 means that using full kernel w/o sparsify
if log_2p != -1:
K = DataClass.sym_sparsify_K(K, p)
auc, ap, rl = solve_and_eval(y, I, K, offset, w_2)
print('tst test: auc %6f ap %6f rl %6f' % (auc, ap, rl))
def grid_search(gList,
wList,
pList,
complete_flag=False,
zero_diag_flag=False):
dc = DataClass(valid_flag=True,
zero_diag_flag=zero_diag_flag,
kernel_normal=False)
y, I, K, offset = dc.get_TL_Kernel()
n = len(y)
f = np.zeros(n)
best_auc = 0.0
best_gs = 0.0
best_gt = 0.0
best_w = 0.0
best_p = -1
# pList padding
if pList[0] != -1:
pList = np.insert(pList, 0, -1)
if kernel_type == 'cosine' and len(gList) != 1:
raise Warning('For cosine kernel, no need to tune gamma!')
for g_s in gList:
for g_t in gList:
dc.source_gamma = 2.0**g_s
dc.target_gamma = 2.0**g_t
y, I, K, offset = dc.get_TL_Kernel()
if complete_flag == 1:
K = DataClass.complete_TL_Kernel(K, offset)
for w in wList:
w_2 = 2.0**w
for p in pList:
_p = 2**p
if p == -1:
K_sp = K
else:
K_sp = DataClass.sym_sparsify_K(K, _p)
auc, ap, rl = solve_and_eval(y, I, K_sp, offset, w_2)
print('log_2gs %3d log_2gt %3d log_2w %3d log_2p %3d auc %6f ap %6f rl %6f' \
% (g_s, g_t, w, p, auc, ap, rl))
if auc > best_auc:
best_auc = auc
best_gs = g_s
best_gt = g_t
best_w = w
best_p = p
print('best parameters: log_2gs %3d log_2gt %3d log_2w %3d log_2p %3d auc %6f' \
% (best_gs, best_gt, best_w, best_p, best_auc))