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CLGP_theano_opt.py
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CLGP_theano_opt.py
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import CLGP_theano_model
from CLGP_theano_model import CLGP_model
import numpy as np
class CLGP_opt:
def __init__(self, params, Y, mask = None, sx2 = 1, linear_model = False, samples = 20, use_hat = False):
self.Y = Y
if mask is None:
self.mask = np.zeros((Y[0].shape[0], len(Y)), dtype=bool)
else:
self.mask = mask
self.clgp = CLGP_model(params, sx2, linear_model=linear_model, samples=samples, use_hat=use_hat)
self.ELBO, self.f = self.clgp.ELBO, self.clgp.f
self.params, self.KmmInv = self.clgp.params, self.clgp.KmmInv
self.exec_f, self.estimate = self.clgp.exec_f, self.clgp.estimate
self.param_updates = {n: np.zeros_like(v) for n, v in params.iteritems()}
self.moving_mean_squared = {n: np.zeros_like(v) for n, v in params.iteritems()}
self.learning_rates = {n: 1e-2*np.ones_like(v) for n, v in params.iteritems()}
def get_KmmInv_grad(self, Y, mask, (M, Q)):
dKL_U_dKmmInv, dLS_dKmmInv = {}, {}
for modality in xrange(len(Y)):
dKL_U_dKmmInv[modality] = self.exec_f(self.clgp.g['KmmInv']['KL_U'], Y, modality, mask[:, modality])
dLS_dKmmInv[modality] = self.estimate(self.clgp.g['KmmInv']['LS'], Y, modality, mask[:, modality])[0]
df_dn_i = {'Z': {'KL_U': {}, 'LS': {}}, 'lhyp': {'KL_U': {}, 'LS': {}}}
dKmm_dlhyp = self.clgp.dKmm_d['lhyp'](self.params['Z'], self.params['lhyp']).reshape(M, M, -1)
dKmm_dn_KmmInv = np.dot(dKmm_dlhyp.transpose((2, 0, 1)), self.clgp.KmmInv)
KmmInv_dKmm_dn_KmmInv = np.dot(dKmm_dn_KmmInv.transpose((0,2,1)),self.clgp.KmmInv.T).transpose((0,2,1))
dKmmInv_dlhyp = -1.0 * KmmInv_dKmm_dn_KmmInv.transpose((1,2,0))
dKmm_dZ = self.clgp.dKmm_d['Z'](self.params['Z'], self.params['lhyp']).reshape(M, M, M, Q)
dKmm_dn_KmmInv = np.dot(dKmm_dZ.transpose((2,3,0,1)), self.clgp.KmmInv)
KmmInv_dKmm_dn_KmmInv = np.dot(dKmm_dn_KmmInv.transpose((0,1,3,2)),self.clgp.KmmInv.T).transpose((0,1,3,2))
dKmmInv_dZ = -1.0 * KmmInv_dKmm_dn_KmmInv.transpose((2,3,0,1))
for modality in xrange(len(Y)):
df_dn_i['lhyp']['KL_U'][modality] = (dKL_U_dKmmInv[modality][:,:,None] * dKmmInv_dlhyp).sum(0).sum(0)
df_dn_i['lhyp']['LS'][modality] = (dLS_dKmmInv[modality][:,:,None] * dKmmInv_dlhyp).sum(0).sum(0)
df_dn_i['Z']['KL_U'][modality] = (dKL_U_dKmmInv[modality][:,:,None,None] * dKmmInv_dZ).sum(0).sum(0)
df_dn_i['Z']['LS'][modality] = (dLS_dKmmInv[modality][:,:,None,None] * dKmmInv_dZ).sum(0).sum(0)
return df_dn_i
def get_grad(self, param_name, Y, KmmInv_grad, mask = None):
if 'mu' in param_name:
modality = int(param_name[2:])
return (self.exec_f(self.clgp.g['mu']['KL_U'], Y, modality, mask[:, modality])
+ self.estimate(self.clgp.g['mu']['LS'], Y, modality, mask[:, modality])[0])
grad = []
for modality in xrange(len(Y)):
m = mask[:, modality]
if param_name in ['m', 'ls']:
g = np.zeros_like(self.clgp.params[param_name])
g[~m] = (self.exec_f(self.clgp.g[param_name]['KL_U'], Y, modality, m)
+ self.estimate(self.clgp.g[param_name]['LS'], Y, modality, m)[0])
grad += [g]
else:
# grad += [(self.exec_f(self.clgp.g[param_name]['KL_U'], Y, modality, m)
# + self.estimate(self.clgp.g[param_name]['LS'], Y, modality, m)[0])]
grad_ls, grad_std = self.estimate(self.clgp.g[param_name]['LS'], Y, modality, m)
grad += [self.exec_f(self.clgp.g[param_name]['KL_U'], Y, modality, m) + grad_ls]
if param_name in ['Z', 'lhyp']:
grad[modality] += (KmmInv_grad[param_name]['KL_U'][modality]
+ KmmInv_grad[param_name]['LS'][modality])
if param_name in ['Z', 'lhyp', 'm', 'ls']:
grad = np.sum(grad, 0)
if param_name in ['m', 'ls']:
m = ~np.any(~mask, axis=1)
grad[~m] += self.exec_f(self.clgp.g[param_name]['KL_X'], Y, mask=m)
# DEBUG
if param_name == 'lhyp' and np.any(np.abs(grad) < grad_std / np.sqrt(self.clgp.samples)):
#print 'Large noise, recomputing. lhyp grad mean:', grad, ', std:', grad_std / np.sqrt(self.clgp.samples)
samples = self.clgp.samples * 10
grad = []
for modality in xrange(len(Y)):
m = mask[:, modality]
grad_ls, grad_std = self.estimate(self.clgp.g[param_name]['LS'], Y, modality, m, samples=samples)
grad += [self.exec_f(self.clgp.g[param_name]['KL_U'], Y, modality, m) + grad_ls]
grad[modality] += (KmmInv_grad[param_name]['KL_U'][modality]
+ KmmInv_grad[param_name]['LS'][modality])
grad = np.sum(grad, 0)
self.grad_std = grad_std
return np.array(grad)
def opt_one_step(self, params, iteration, opt = 'rmsprop', learning_rate_adapt = 0.2, use_einsum = True):
KmmInv_grad = self.get_KmmInv_grad(self.Y, self.mask, self.params['Z'].shape)
for param_name in params:
# DEBUG
if opt == 'grad_ascent' or param_name in ['ls']:
self.grad_ascent_one_step(param_name, [param_name, self.Y, KmmInv_grad, self.mask],
learning_rate_decay = learning_rate_adapt * 100 / (iteration + 100.0))
elif opt == 'rmsprop':
self.rmsprop_one_step(param_name, [param_name, self.Y, KmmInv_grad, self.mask],
learning_rate_adapt = learning_rate_adapt)#, momentum = 0.9 - 0.4 * 100 / (iteration + 100.0))
if param_name in ['lhyp']:
self.params[param_name] = np.clip(self.params[param_name], -8, 8)
if param_name in ['lhyp', 'Z']:
self.clgp.update_KmmInv_cache()
def grad_ascent_one_step(self, param_name, grad_args, momentum = 0.9, learning_rate_decay = 1):
self.clgp.params[param_name] += (learning_rate_decay*self.learning_rates[param_name]* self.param_updates[param_name])
grad = self.get_grad(*grad_args)
if param_name in ['lhyp']:
self.param_updates[param_name] = momentum*self.param_updates[param_name] + (1. - momentum)*grad
else:
self.param_updates[param_name] = grad
def rmsprop_one_step(self, param_name, grad_args, decay = 0.9, momentum = 0, learning_rate_adapt = 0.05,
learning_rate_min = 1e-6, learning_rate_max = 10):
# RMSPROP: Tieleman, T. and Hinton, G. (2012), Lecture 6.5 - rmsprop, COURSERA: Neural Networks for Machine Learning
# Implementation based on https://github.com/BRML/climin/blob/master/climin/rmsprop.py
# We use Nesterov momentum: first, we make a step according to the momentum and then we calculate the gradient.
step1 = self.param_updates[param_name] * momentum
self.params[param_name] += step1
grad = self.get_grad(*grad_args)
self.moving_mean_squared[param_name] = (decay * self.moving_mean_squared[param_name] + (1 - decay) * grad ** 2)
step2 = self.learning_rates[param_name] * grad / (self.moving_mean_squared[param_name] + 1e-8)**0.5
# DEBUG
if param_name == 'lhyp':
step2 = np.clip(step2, -0.1, 0.1)
self.params[param_name] += step2
step = step1 + step2
# Step rate adaption. If the current step and the momentum agree, we slightly increase the step rate for that dimension.
if learning_rate_adapt:
# This code might look weird, but it makes it work with both numpy and gnumpy.
step_non_negative = step > 0
step_before_non_negative = self.param_updates[param_name] > 0
agree = (step_non_negative == step_before_non_negative) * 1.
adapt = 1 + agree * learning_rate_adapt * 2 - learning_rate_adapt
self.learning_rates[param_name] *= adapt
self.learning_rates[param_name] = np.clip(self.learning_rates[param_name], learning_rate_min, learning_rate_max)
self.param_updates[param_name] = step
def choose_best_z(self, ind, Y_true, mask, samples=20):
"""
Assign m[i] to the best location among all the inducing points.
"""
orig_params = {'m': self.params['m'], 'ls': self.params['ls']}
N = len(ind)
M = self.params['Z'].shape[0]
self.params['ls'] = self.params['ls'][ind]
f = np.zeros((M + 1, N))
for m in xrange(M + 1):
if m < M:
self.params['m'] = np.tile(self.params['Z'][m], (N, 1))
else:
self.params['m'] = orig_params['m'][ind]
# KL.
kl_x = self.exec_f(self.f['KL_X_all'])
f[m] += kl_x
# Likelihood.
for modality in xrange(len(Y_true)):
S, _ = self.estimate(self.f['S'], modality=modality, samples=samples)
Y_ind = Y_true[modality][ind]
mask_ind = mask[:, modality][ind]
f[m] += np.log(np.maximum(np.sum(S * Y_ind, 1), 1e-16)) * mask_ind
self.params['m'], self.params['ls'] = orig_params['m'], orig_params['ls']
best_z = np.argmax(f, 0)
# Do not change m if best_z == M.
self.params['m'][ind[best_z < M]] = self.params['Z'][best_z[best_z < M]]
return best_z