def _block_proximal_gradient_descent(self, W, P):
WX, XP, XSPS = self._get_XC_prods(self.train_X, W, P)
err = self._cal_err(WX, XP, XSPS, self.train_Y)
objs = [self._obj(err, W, P)]
WtX, tXP, tXSPS = self._get_XC_prods(self.test_X, W, P)
test_err = self._cal_err(WtX, tXP, tXSPS, self.test_Y)
rmses = [cal_rmse(test_err)]
maes = [cal_mae(test_err)]
start = time.time()
eta = self.eta
for t in range(self.max_iters):
start = time.time()
l_obj, eta, lt, W, P = self._get_updated_paras(eta, W, P)
if lt == self.ln:
logging.info('!!!stopped by line_search, lt=%s!!!', lt)
break
objs.append(l_obj)
WtX, tXP, tXSPS = self._get_XC_prods(self.test_X, W, P)
test_err = self._cal_err(WtX, tXP, tXSPS, self.test_Y)
rmses.append(cal_rmse(test_err))
maes.append(cal_mae(test_err))
end = time.time()
dr = abs(objs[t] - objs[t + 1]) / objs[t]
logging.info(
'exp_id=%s, iter=%s, lt,eta,dr=(%s,%s, %.7f), obj=%.5f, rmse=%.5f, mae=%.5f, cost=%.2f seconds',
self.exp_id, t, lt, eta, dr, objs[t], rmses[t], maes[t],
(end - start))
if dr < self.eps:
logging.info(
'*************stopping criterion satisfied*********')
break
logging.info('train process finished, total iters=%s', t + 1)
self.rmses, self.maes = rmses, maes
self._save_paras(W, P)
def _block_nonmono_acc_proximal_gradient_descent(self, W, P):
'''
non-monotone accelerated pg
'''
logging.info(
'start solving by _block_nonmono_acc_proximal_gradient_descent')
WX, XP, XSPS = self._get_XC_prods(self.train_X, W, P)
err = self._cal_err(WX, XP, XSPS, self.train_Y)
objs = [None] * (self.max_iters + 1)
objs[0] = self._obj(err, W, P)
WtX, tXP, tXSPS = self._get_XC_prods(self.test_X, W, P)
test_err = self._cal_err(WtX, tXP, tXSPS, self.test_Y)
rmses = [cal_rmse(test_err)]
maes = [cal_mae(test_err)]
start = time.time()
A = np.hstack((W.reshape(-1, 1), P))
A0, A1, C1 = A.copy(), A.copy(), A.copy()
c = objs[0]
r0, r1, q, qeta = 0.0, 1.0, 1.0, 0.5
eta1 = eta2 = self.eta
lt1, lt2 = 0, 0
XS = np.square(self.train_X)
for t in range(self.max_iters):
start = time.time()
self._update_bias(W, P)
use_acc = False
B = A1 + r0 / r1 * (C1 - A1) + (r0 - 1) / r1 * (A1 - A0)
W, P = B[:, 0].flatten(), B[:, 1:]
y_obj, y_eta, y_lt, yW, yP = self._get_updated_paras(eta1, W, P)
lt1, eta1 = y_lt, y_eta
C1 = np.hstack((yW.reshape(-1, 1), yP))
if y_obj < c:
objs[t + 1] = y_obj
W, P = yW, yP
use_acc = True
else:
W, P = A1[:, 0].flatten(), A1[:, 1:]
v_obj, v_eta, v_lt, vW, vP = self._get_updated_paras(
eta2, W, P)
lt2, eta2 = v_lt, v_eta
if y_obj < v_obj:
objs[t + 1] = y_obj
W, P = yW, yP
else:
objs[t + 1] = v_obj
W, P = vW, vP
if lt1 == self.ln or lt2 == self.ln:
logging.info('!!!stopped by line_search, lt1=%s, lt2=%s!!!',
lt1, lt2)
break
A0 = A1
A1 = np.hstack((W.reshape(-1, 1), P))
r0 = r1
r1 = (np.sqrt(4 * pow(r0, 2) + 1) + 1) / 2.0
tq = qeta * q + 1.0
c = (qeta * q * c + objs[t + 1]) / tq
q = tq
WtX, tXP, tXSPS = self._get_XC_prods(self.test_X, W, P)
test_err = self._cal_err(WtX, tXP, tXSPS, self.test_Y)
rmses.append(cal_rmse(test_err))
maes.append(cal_mae(test_err))
end = time.time()
dr = abs(objs[t] - objs[t + 1]) / objs[t]
logging.info(
'exp_id=%s, iter=%s, use_acc=%s, (lt1,eta1,lt2,eta2)=(%s,%s,%s,%s), obj=%.5f(%.8f), rmse=%.5f, mae=%.5f, cost=%.2f seconds',
self.exp_id, t, use_acc, lt1, eta1, lt2, eta2, objs[t], dr,
rmses[t], maes[t], (end - start))
if dr < self.eps:
logging.info(
'*************stopping criterion satisfied*********')
break
logging.info('train process finished, total iters=%s', t + 1)
self.rmses, self.maes = rmses, maes
self._save_paras(W, P)
def _block_mono_acc_proximal_gradient_descent(self, W, P):
'''
monotone accelerated pg
'''
logging.info(
'start solving by _block_mono_acc_proximal_gradient_descent')
WX, XP, XSPS = self._get_XC_prods(self.train_X, W, P)
err = self._cal_err(WX, XP, XSPS, self.train_Y)
objs = [self._obj(err, W, P)]
WtX, tXP, tXSPS = self._get_XC_prods(self.test_X, W, P)
test_err = self._cal_err(WtX, tXP, tXSPS, self.test_Y)
rmses = [cal_rmse(test_err)]
maes = [cal_mae(test_err)]
start = time.time()
A = np.hstack((W.reshape(-1, 1), P))
A0, A1, C1 = A.copy(), A.copy(), A.copy()
r0, r1 = 0, 1
eta = self.eta
XS = np.square(self.train_X)
for t in range(self.max_iters):
start = time.time()
v_obj, v_eta, v_lt, vW, vP = self._get_updated_paras(eta, W, P)
B = A1 + r0 / r1 * (C1 - A1) + (r0 - 1) / r1 * (A1 - A0)
W, P = B[:, 0].flatten(), B[:, 1:]
y_obj, y_eta, y_lt, yW, yP = self._get_updated_paras(eta, W, P)
C1 = np.hstack((yW.reshape(-1, 1), yP))
if v_obj > y_obj:
objs.append(y_obj)
lt = y_lt
eta = y_eta
W, P = yW, yP
else:
objs.append(v_obj)
lt = v_lt
eta = v_eta
W, P = vW, vP
if lt == self.ln:
logging.info('!!!stopped by line_search, lt=%s!!!', lt)
break
A0 = A1
A1 = np.hstack((W.reshape(-1, 1), P))
r1 = (np.sqrt(4 * pow(r0, 2) + 1) + 1) / 2.0
WtX, tXP, tXSPS = self._get_XC_prods(self.test_X, W, P)
test_err = self._cal_err(WtX, tXP, tXSPS, self.test_Y)
rmses.append(cal_rmse(test_err))
maes.append(cal_mae(test_err))
end = time.time()
dr = abs(objs[t] - objs[t + 1]) / objs[t]
logging.info(
'exp_id=%s, iter=%s, lt,eta,dr=(%s,%s, %.7f), obj=%.5f, rmse=%.5f, mae=%.5f, cost=%.2f seconds',
self.exp_id, t, lt, eta, dr, objs[t], rmses[t], maes[t],
(end - start))
if dr < self.eps:
logging.info(
'*************stopping criterion satisfied*********')
break
logging.info('train process finished, total iters=%s', t + 1)
self.rmses, self.maes = rmses, maes
self._save_paras(W, P)