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 run_testset(kernel_type='cosine',
zero_diag_flag=True,
kernel_normal=False,
log2_b=None,
log2_w=None,
log2_p=None):
dc = DataClass(valid_flag=False, kernel_normal=kernel_normal)
dc.kernel_type = kernel_type
dc.zero_diag_flag = zero_diag_flag
# dc.source_data_type = 'parallel' # CorrNet test
y, I, K, offset = dc.get_TL_Kernel()
# run eigen decomposition on K
v_s, Q_s, v_t, Q_t = eigen_decompose(K, offset, max_k=128)
# v_s, Q_s, v_t, Q_t = eigen_decompose(K, offset, max_k=5)
beta = 2**log2_b
K_exp = K.copy()
K_exp = get_K_exp(K_exp, offset, v_s, Q_s, v_t, Q_t, beta, kernel_normal)
if log2_p == -1:
K_sp = K_exp
else:
K_sp = DataClass.sym_sparsify_K(K_exp, 2**log2_p)
auc, ap, rl = solve_and_eval(y, I, K_sp, offset, 2**log2_w)
print('test set: auc %6f ap %6f rl %6f' % (auc, ap, rl))
def run_testset(kernel_type='cosine',
zero_diag_flag=False,
kernel_normal=False,
alpha=None,
log2_w=None,
log2_p=None):
dc = DataClass(valid_flag=False, kernel_normal=kernel_normal)
dc.kernel_type = kernel_type
dc.zero_diag_flag = zero_diag_flag
y, I, K, offset = dc.get_TL_Kernel()
K_rw = K.copy()
K_rw = get_K_rw(K_rw, offset, alpha=alpha)
if log2_p == -1:
K_sp = K_rw
else:
K_sp = DataClass.sym_sparsify_K(K_rw, 2**log2_p)
auc, ap, rl = solve_and_eval(y, I, K_sp, offset, 2**log2_w)
print('test set: auc %8f ap %6f rl %6f' % (auc, ap, rl))
def grid(kernel_type='cosine',
zero_diag_flag=True,
kernel_normal=False,
bList=None,
wList=None,
pList=None):
dc = DataClass(valid_flag=True, kernel_normal=kernel_normal)
dc.kernel_type = kernel_type
dc.zero_diag_flag = zero_diag_flag
y, I, K, offset = dc.get_TL_Kernel()
# run eigen decomposition on K
v_s, Q_s, v_t, Q_t = eigen_decompose(K, offset, max_k=128)
best_b = -1
best_w = -1
best_p = -1
best_auc = -1
for log2_b in bList:
beta = 2**log2_b
K_exp = K.copy()
K_exp = get_K_exp(K_exp, offset, v_s, Q_s, v_t, Q_t, beta,
kernel_normal)
for log2_w in wList:
for log2_p in pList:
if log2_p == -1:
K_sp = K_exp
else:
K_sp = DataClass.sym_sparsify_K(K_exp, 2**log2_p)
auc, ap, rl = solve_and_eval(y, I, K_sp, offset, 2**log2_w)
print(
'log2_b %3d log2_w %3d log2_p %3d auc %8f ap %6f rl %6f' %
(log2_b, log2_w, log2_p, auc, ap, rl))
if best_auc < auc:
best_b = log2_b
best_w = log2_w
best_p = log2_p
return
print('best parameters: log2_b %3d log2_w %3d log2_p %3d auc %6f' \
% (best_b, best_w, best_p, best_auc))
def grid(kernel_type='cosine',
zero_diag_flag=False,
kernel_normal=False,
aList=None,
wList=None,
pList=None):
dc = DataClass(valid_flag=True, kernel_normal=kernel_normal)
dc.kernel_type = kernel_type
dc.zero_diag_flag = zero_diag_flag
y, I, K, offset = dc.get_TL_Kernel()
best_a = -1
best_w = -1
best_p = -1
best_auc = -1
for alpha in aList:
K_rw = K.copy()
K_rw = get_K_rw(K_rw, offset, alpha=alpha)
for log2_w in wList:
for log2_p in pList:
if log2_p == -1:
K_sp = K_rw
else:
K_sp = DataClass.sym_sparsify_K(K_rw, 2**log2_p)
#print DataClass.sparse_K_stat(K_sp, offset)
auc, ap, rl = solve_and_eval(y, I, K_sp, offset, 2**log2_w)
print('alpha %3d log2_w %3d log2_p %3d auc %8f ap %6f rl %6f' %
(alpha, log2_w, log2_p, auc, ap, rl))
if best_auc < auc:
best_a = alpha
best_w = log2_w
best_p = log2_p
best_auc = auc
print('best parameters: alpha %3d log2_w %3d log2_p %3d auc %6f' \
% (best_a, best_w, best_p, best_auc))