def merge_final_log_probs(self, source1_decoder_attention_score,
source2_decoder_attention_score,
source1_local_words_ids,
source2_local_words_ids,
gate_score):
"""
根据三个概率，计算全词表上的对数似然。
"""
# 获取group_size和两个序列的长度
group_size, seq_max_len_1 = source1_decoder_attention_score.size()
group_size, seq_max_len_2 = source2_decoder_attention_score.size()
# 需要和source1相乘的gate概率，shape: (group_size, seq_max_len_1)
gate_1 = gate_score.expand(seq_max_len_1, -1).t()
# 需要和source2相乘的gate概率，shape: (group_size, seq_max_len_2)
gate_2 = (1 - gate_score).expand(seq_max_len_2, -1).t()
# 加权后的source1分值，shape: (group_size, seq_max_len_1)
source1_decoder_attention_score = source1_decoder_attention_score * gate_1
# 加权后的source2分值，shape: (group_size, seq_max_len_2)
source2_decoder_attention_score = source2_decoder_attention_score * gate_2
# shape: (group_size, seq_max_len_1)
log_probs_1 = (source1_decoder_attention_score + 1e-45).log()
# shape: (group_size, seq_max_len_2)
log_probs_2 = (source2_decoder_attention_score + 1e-45).log()
# 初始化全词表上的概率为全0, shape: (group_size, target_vocab_size)
final_log_probs = (source1_decoder_attention_score.new_zeros((group_size,
2 * self.max_seq_len)) + 1e-45).log()
for i in range(seq_max_len_1): # 遍历source1的所有时间步
# 当前时间步的预测概率，shape: (group_size, 1)
log_probs_slice = log_probs_1[:, i].unsqueeze(-1)
#print(log_probs_slice)
# 当前时间步的token ids，shape: (group_size, 1)
source_to_target_slice = source1_local_words_ids[:, i].unsqueeze(-1)
#print(source_to_target_slice)
# 选出要更新位置，原有的词表概率，shape: (group_size, 1)
#print(source_to_target_slice.shape,"\t",final_log_probs.shape)
selected_log_probs = final_log_probs.gather(-1, source_to_target_slice)
# 更新后的概率值（原有概率+更新概率，混合），shape: (group_size, 1)
combined_scores = logsumexp(torch.cat((selected_log_probs,
log_probs_slice), dim=-1)).unsqueeze(-1)
# 将combined_scores设置回final_log_probs中
final_log_probs = final_log_probs.scatter(-1, source_to_target_slice, combined_scores)
# 对source2也同样做一遍
for i in range(seq_max_len_2):
log_probs_slice = log_probs_2[:, i].unsqueeze(-1)
source_to_target_slice = source2_local_words_ids[:, i].unsqueeze(-1)
selected_log_probs = final_log_probs.gather(-1, source_to_target_slice)
combined_scores = logsumexp(torch.cat((selected_log_probs,
log_probs_slice), dim=-1)).unsqueeze(-1)
final_log_probs = final_log_probs.scatter(-1, source_to_target_slice, combined_scores)
return final_log_probs
def loss(self, input, target, mask=None):
'''
Args:
input: Tensor
the input tensor with shape = [batch, length, input_size]
target: Tensor
the tensor of target labels with shape [batch, length]
mask:Tensor or None
the mask tensor with shape = [batch, length]
Returns: Tensor
A 1D tensor for minus log likelihood loss
'''
batch, length, _ = input.size()
energy = self.forward(input, mask=mask)
# shape = [length, batch, num_label, num_label]
energy_transpose = energy.transpose(0, 1)
# shape = [length, batch]
target_transpose = target.transpose(0, 1)
# shape = [length, batch, 1]
mask_transpose = None
if mask is not None:
mask_transpose = mask.unsqueeze(2).transpose(0, 1)
# shape = [batch, num_label]
partition = None
if input.is_cuda:
# shape = [batch]
batch_index = torch.arange(0, batch).long().cuda()
prev_label = torch.cuda.LongTensor(batch).fill_(self.num_labels - 1)
tgt_energy = torch.zeros(batch).cuda()
else:
# shape = [batch]
batch_index = torch.arange(0, batch).long()
prev_label = torch.LongTensor(batch).fill_(self.num_labels - 1)
tgt_energy = torch.zeros(batch)
for t in range(length):
# shape = [batch, num_label, num_label]
curr_energy = energy_transpose[t]
if t == 0:
partition = curr_energy[:, -1, :]
else:
# shape = [batch, num_label]
partition_new = utils.logsumexp(curr_energy + partition.unsqueeze(2), dim=1)
if mask_transpose is None:
partition = partition_new
else:
mask_t = mask_transpose[t]
partition = partition + (partition_new - partition) * mask_t
tgt_energy += curr_energy[batch_index, prev_label, target_transpose[t].data]
prev_label = target_transpose[t].data
return utils.logsumexp(partition, dim=1) - tgt_energy
def gammaKsi(self, logB):
""" Compute gamma (posterior distribution) and Ksi (joint succesive
posterior distrbution) values.
gamma [i,n] = conditional probability of the event state 'i'
at time 'n', given the complete observation sequence.
ksi[n,i,j] = joint posterior probability of two succesive hidden
states 'i' and 'j' at time 'n'.
Parameters
----------
logB : ndarray
The observation probability matrix in logarithmic space.
Returns
-------
llh : float
The normalized log-likelihood.
logGamma : ndarray
The log posterior distribution.
logKsi : ndarray
The log joint posterior probability distribution.
logAlpha : ndarray
The log scaled alpha distribution.
logBeta : ndarray
The log scaled beta distribution.
"""
K = self.K
N = logB.shape[1]
logKsi = np.zeros(( N-1, K, K), dtype=np.float)
logGamma = np.zeros((K,N), dtype = np.float)
logAlpha = self.alpha(logB)
logBeta = self.beta(logB)
loglikelihood = logsumexp(logAlpha[:,-1])
#compute gamma
logGamma = (logAlpha + logBeta)
logGamma-=logsumexp(logGamma,0)
#compute ksi
for n in xrange (N-1):
temp=logB[:,n+1] + logBeta[:,n+1]
logKsi[n,:,:] = (logAlpha[:,n][:,np.newaxis] +
self.logA[:-1][:,:] + temp)
logKsi[n, :, :] -= logsumexp(logKsi[n, :, :].flatten())
return (loglikelihood, logGamma , logKsi,logAlpha,logBeta)
def compute_estimator(self, log_p_all, log_q_all):
n_samples = tt.shape(log_p_all)[1]
# See equation 14, for definition of I see equation 2
f_x_h = log_p_all - log_q_all # f_x_h: (batch_size, n_samples)
sum_p_over_q = logsumexp(f_x_h, axis=1) # sum_p_over_q: (batch_size, )
L = sum_p_over_q - tt.log(n_samples) # L: (batch_size, )
# Equation 10
sum_min_i = logsubexp(sum_p_over_q.dimshuffle(0, 'x'), f_x_h)
sum_min_i_normalized = sum_min_i - np.log(n_samples - 1).astype(
theano.config.floatX)
L_h_given_h = L.dimshuffle(0,
'x') - sum_min_i_normalized # equation (10)
# Get gradient of log Q and scale
part_1 = L_h_given_h * log_q_all # equation 11, part 1
weights = f_x_h - sum_p_over_q.dimshuffle(0, 'x')
exp_weights = tt.exp(weights)
part_2 = exp_weights * f_x_h
estimator = (part_1 + part_2).sum() / self.batch_size
gradients = tt.grad(estimator,
self.params.values(),
consider_constant=[exp_weights, L_h_given_h])
likelihood = L.sum() / self.batch_size
return likelihood, gradients
def log_cond_prob(self, y, y_pred):
a = torch.sum(y * y_pred, 2)
b = logsumexp(y_pred, 2)
# print("y_pred",torch.sum(y_pred))
prob = a - b
# print("loss",prob)
return prob
def compute_estimator(self, log_p_all, log_q_all):
n_samples = tt.shape(log_p_all)[1]
# See equation 14, for definition of I see equation 2
f_x_h = log_p_all - log_q_all # f_x_h: (batch_size, n_samples)
sum_p_over_q = logsumexp(f_x_h, axis=1) # sum_p_over_q: (batch_size, )
L = sum_p_over_q - tt.log(n_samples) # L: (batch_size, )
# Equation 10
sum_min_i = logsubexp(sum_p_over_q.dimshuffle(0, 'x'), f_x_h)
sum_min_i_normalized = sum_min_i - np.log(n_samples - 1).astype(theano.config.floatX)
L_h_given_h = L.dimshuffle(0, 'x') - sum_min_i_normalized # equation (10)
# Get gradient of log Q and scale
part_1 = L_h_given_h * log_q_all # equation 11, part 1
weights = f_x_h - sum_p_over_q.dimshuffle(0, 'x')
exp_weights = tt.exp(weights)
part_2 = exp_weights * f_x_h
estimator = (part_1 + part_2).sum() / self.batch_size
gradients = tt.grad(estimator,
self.params.values(),
consider_constant=[exp_weights, L_h_given_h])
likelihood = L.sum() / self.batch_size
return likelihood, gradients
def logProbability(self, x):
logProbability = [self.flow.logProbability(x)]
for op in self.symmetryList:
logProbability.append(self.flow.logProbability(op(x)))
logp = logsumexp(logProbability).view(-1)
logp = logp - math.log(len(self.symmetryList) + 1)
return logp
def dp_inside_batch(batch_size,sentence_len,tags_dim,weights):
inside_table = torch.DoubleTensor(batch_size, sentence_len * sentence_len * 8, tags_dim, tags_dim)
inside_table.fill_(-np.inf)
if torch.cuda.is_available():
inside_table = inside_table.cuda()
m = sentence_len
seed_spans, base_left_spans, base_right_spans, left_spans, right_spans, ijss, ikss, kjss, id_span_map, span_id_map = test_constituent_indexes(
m, False)
for ii in seed_spans:
inside_table[:, ii, :, :] = 0.0
for ii in base_right_spans:
(l, r, c) = id_span_map[ii]
swap_weights = weights.permute(0, 1, 4, 3, 2)
inside_table[:, ii, :, :] = swap_weights[:, r, :, l, :]
for ii in base_left_spans:
(l, r, c) = id_span_map[ii]
inside_table[:, ii, :, :] = weights[:, l, :, r, :]
for ij in ijss:
(l, r, c) = id_span_map[ij]
if ij in left_spans:
ids = span_id_map.get((l, r, get_state_code(0, 0, 0)), -1)
prob = inside_table[:, ids, :, :] + weights[:, l, :, r, :]
inside_table[:, ij, :, :] = utils.logaddexp(inside_table[:, ij, :, :], prob)
elif ij in right_spans:
ids = span_id_map.get((l, r, get_state_code(0, 0, 0)), -1)
swap_weights = weights.permute(0, 1, 4, 3, 2)
prob = inside_table[:, ids, :, :] + swap_weights[:, r, :, l, :]
inside_table[:, ij, :, :] = utils.logaddexp(inside_table[:, ij, :, :], prob)
else:
num_k = len(ikss[ij])
beta_ik, beta_kj = inside_table[:, ikss[ij], :, :], inside_table[:, kjss[ij], :, :]
probs = beta_ik.contiguous().view(batch_size, num_k, tags_dim, tags_dim, 1) +\
beta_kj.contiguous().view(batch_size, num_k, 1, tags_dim, tags_dim)
probs = utils.logsumexp(probs, axis=(1, 3))
inside_table[:, ij, :, :] = utils.logaddexp(inside_table[:, ij, :, :], probs)
id1 = span_id_map.get((0, m - 1, get_state_code(0, 1, 0)), -1)
id2 = span_id_map.get((0, m - 1, get_state_code(0, 1, 1)), -1)
score1 = inside_table[:, id1, 0, :].contiguous().view(batch_size, 1, tags_dim)
score2 = inside_table[:, id2, 0, :].contiguous().view(batch_size, 1, tags_dim)
ll = utils.logaddexp(utils.logsumexp(score1, axis=2), utils.logsumexp(score2, axis=2))
return inside_table, ll
def predict(self, data, mc_test):
out = self.likelihood.predict(self.layer_out)
nll = - tf.reduce_sum(-np.log(mc_test) + utils.logsumexp(self.likelihood.log_cond_prob(self.Y, self.layer_out), 0))
#nll = - tf.reduce_sum(tf.reduce_mean(self.likelihood.log_cond_prob(self.Y, self.layer_out), 0))
pred, neg_ll = self.session.run([out, nll], feed_dict={self.X:data.X, self.Y: data.Y, self.mc:mc_test})
mean_pred = np.mean(pred, 0)
return mean_pred, neg_ll
def _calculate_log_p(self, W, b):
log_p = 0.
log_p += logsumexp(
torch.stack([
float(np.log(self.pi)) +
self._calculate_log_gaussian(W, 0, self.sigma1),
float(np.log(1 - self.pi)) +
self._calculate_log_gaussian(W, 0, self.sigma2)
])).sum()
log_p += logsumexp(
torch.stack([
float(np.log(self.pi)) +
self._calculate_log_gaussian(b, 0, self.sigma1),
float(np.log(1 - self.pi)) +
self._calculate_log_gaussian(b, 0, self.sigma2)
])).sum()
return log_p
def estimatepostduration(self, logalpha, logbeta, logB, rankn, g , llh):
"""Estimate state durations based on the posterior distribution.
Since the durations are truncated by the timeout parameter, we use
a distribution free method.
Parameters
-----------
logalpha : ndarray
Log scaled alpha distribution.
logbeta : ndarray
Log scaled beta values.
logB : ndarray
Observation probability distribution in log-space.
rankn : ndarray
the top ranked 'n' for eah state 'k', used to estimate state durations.
g : ndarray
log scaled posterior distribution ('logGamma')
llh : float
the normalized log-likelihood.
Returns
--------
int
The estimated durations in each state.
ndarray
The expected value of the state duration at the 'rankn'.
Notes
------
The QDHMM EM algorithm requires good initial estimates of the model
parameters in order to converge to a good solution. We propose a
distribution free method to find the expected value of state durations
in a standard HMM model, which is then used to initialize the QDHMM
'tau' parameters.
"""
sub = len(rankn[0])
N = logalpha.shape[1]
K=self.K
durations = np.zeros((K))
res = np.zeros((K,sub))
for o,k in enumerate(range(K)):
inotk = set(range(K)) - set([k])
for idx,n in enumerate(rankn[o]):
const = np.zeros(len(inotk))
#Base Case
tmp = (N-1-n)*self.logA[k,k] - np.log(1-np.exp(self.logA[k,k]))
#Induction
for t in xrange(N-2, n,-1):
tmp = (np.logaddexp(logbeta[k,t], logB[k,t+1]
+ self.logA[k,k]+ tmp))
for x,i in enumerate(inotk):
const[x]=(logalpha[i,n] + self.logA[i,k]+logB[k,n+1]
- (g[i,n] + llh) )
res[o,idx] = logsumexp(const) + tmp
durations = np.max(np.exp(res), 1 )
return durations.astype(int), np.exp(res)
def predict(self, latent_val):
"""
return the probabilty for all the samples, datapoints and calsses
:param latent_val:
:return:
"""
logprob = latent_val - tf.expand_dims(utils.logsumexp(latent_val, 2),
2)
return tf.exp(logprob)
def predict(self, y_pred):
"""
return the probabilty for all the samples, datapoints and classes
param: y_pred
return:
"""
logprob = y_pred - logsumexp(y_pred, 2).unsqueeze(2).expand(
self.mc, self.batch_size, self.classes)
return torch.exp(logprob)
def log_likelihood(X_apps, theta_unconstrained, pi_unconstrained):
"""
X_apps.shape = [n, 1, d]
theta_unconstrained \in \mathbb{R}^{k, d}, where d is the number of app cats
"""
log_pi = logsoftmax(pi_unconstrained, dim=-1)
logp_theta = torch.sum(
Bernoulli(logits=theta_unconstrained).log_prob(X_apps), dim=-1)
logps = logsumexp(log_pi + logp_theta, dim=-1)
return logps.sum()
def logposterior(self, data):
"""
Calculate log-posterior over scales given the data points.
@type data: array_like
@param data: data stored in columns
"""
jnt = self.logjoint(data)
return jnt - logsumexp(jnt, 0)
def predict_log_proba(self, X):
"""
Estimate log-probability
:param X_test: array-like of shape (n_samples, n_features) -- Input data
:return: ndarray of shape (n_samples, n_features) -- Estimated log-probabilities
"""
values = self._decision_function(X)
loglikelihood = (values - values.max(axis=1)[:, np.newaxis])
normalization = logsumexp(loglikelihood, axis=1)
return loglikelihood - normalization[:, np.newaxis]
def get_const(log_lambdas, desired_k):
base_inc_probs = np.log(desired_k) + log_lambdas
remaining_prob = 1 - np.exp(utils.logsumexp(log_lambdas))
c = desired_k / expected_k(base_inc_probs)
start = c * desired_k
results = opt.minimize(
lambda x:
(desired_k -
(expected_k(log_lambdas + x) + desired_k * remaining_prob))**2,
np.log(start))
return np.exp(results.x[0])
def step(obs, prev):
# The "outer sum" of the previous log-probabilities and the
# observation log-probabilities gives a (num_states,
# num_states) matrix of the paired log-probabilities, and
# adding in the transition log-probabilities gives the matrix
# representing the log-probabilities of moving from each state
# to each other and generating the observation. Summing these
# (in the standard [0,1] probability space, not the log space)
# along the 1st axis then gives the vector with the total
# log-probability of being in each state after the step.
return logsumexp(outer_sum(prev, obs, batch) + transitions, axis=axis)
def get_agg_kl(data, model, meta_optimizer):
model.eval()
criterion = nn.NLLLoss().cuda()
means = []
logvars = []
all_z = []
for i in range(len(data)):
sents, length, batch_size = data[i]
if args.gpu >= 0:
sents = sents.cuda()
mean, logvar = model._enc_forward(sents)
z_samples = model._reparameterize(mean, logvar)
if args.model == 'savae':
mean_svi = Variable(mean.data, requires_grad=True)
logvar_svi = Variable(logvar.data, requires_grad=True)
var_params_svi = meta_optimizer.forward([mean_svi, logvar_svi],
sents)
mean_svi_final, logvar_svi_final = var_params_svi
z_samples = model._reparameterize(mean_svi_final, logvar_svi_final)
preds = model._dec_forward(sents, z_samples)
nll_svi = sum([
criterion(preds[:, l], sents[:, l + 1]) for l in range(length)
])
kl_svi = utils.kl_loss_diag(mean_svi_final, logvar_svi_final)
mean, logvar = mean_svi_final, logvar_svi_final
means.append(mean.data)
logvars.append(logvar.data)
all_z.append(z_samples.data)
means = torch.cat(means, 0)
logvars = torch.cat(logvars, 0)
all_z = torch.cat(all_z, 0)
N = float(means.size(0))
mean_prior = torch.zeros(1, means.size(1)).cuda()
logvar_prior = torch.zeros(1, means.size(1)).cuda()
agg_kl = 0.
count = 0.
for i in range(all_z.size(0)):
z_i = all_z[i].unsqueeze(0).expand_as(means)
log_agg_density = utils.log_gaussian(z_i, means,
logvars) # log q(z|x) for all x
log_q = utils.logsumexp(log_agg_density, 0)
log_q = -np.log(N) + log_q
log_p = utils.log_gaussian(all_z[i].unsqueeze(0), mean_prior,
logvar_prior)
agg_kl += log_q.sum() - log_p.sum()
count += 1
mean_var = mean.var(0)
print('active units', (mean_var > 0.02).float().sum())
print(mean_var)
return agg_kl / count
def log_sample_poisson(log_lambdas, k=1, normalize=True, seed=0):
np.random.seed(seed=seed)
J = []
inc_probs = np.log(k) + log_lambdas
if normalize:
inc_probs -= utils.logsumexp(log_lambdas)
for i, l in enumerate(inc_probs):
u = np.random.uniform()
if np.log(u) < l:
J.append(i)
#print(len(J))
return J, inc_probs
def logdrcdf(norm): """ Logarithm of the derivative of the radial CDF. """ # allocate memory result = zeros([self.gsm.num_scales, len(norm)]) tmp = sqrt(self.gsm.scales) for j in range(self.gsm.num_scales): result[j, :] = log(self.gsm.priors[j]) + logdgrcdf(tmp[j] * norm, self.gsm.dim) + log(tmp[j]) return logsumexp(result, 0)
def posterior(self, data):
"""
Compute posterior over component indices.
"""
log_post = empty([len(self), data.shape[1]])
for k in range(len(self.components)):
log_post[k] = self.components[k].expected_log_likelihood(data)
log_post += (psi(self.gamma) - psi(sum(self.gamma)))
log_post -= logsumexp(log_post, 0)
return exp(log_post)
def logdrcdf(norm):
"""
Logarithm of the derivative of the radial CDF.
"""
# allocate memory
result = zeros([self.gsm.num_scales, len(norm)])
tmp = sqrt(self.gsm.scales)
for j in range(self.gsm.num_scales):
result[j, :] = log(self.gsm.priors[j]) + logdgrcdf(
tmp[j] * norm, self.gsm.dim) + log(tmp[j])
return logsumexp(result, 0)
def compute_log_model_probability(scores, ranking, gpu_id=None):
"""
more stable version
if rel is provided, use it to calculate probability only till
all the relevant documents are found in the ranking
"""
subtracts = torch.zeros_like(scores)
log_probs = torch.zeros_like(scores)
if gpu_id is not None:
subtracts, log_probs = convert_vars_to_gpu([subtracts, log_probs],
gpu_id)
for j in range(scores.size()[0]):
posj = ranking[j]
log_probs[j] = scores[posj] - logsumexp(scores - subtracts, dim=0)
subtracts[posj] = scores[posj] + 1e6
return torch.sum(log_probs)
def sample_nav_article_nav_assignments(self, article_id):
nav_article_log_proportions = np.log(
self.nav_article_proportions[-1][article_id])
updated_assignments = []
for nav_id in self.training_data.article_navs[article_id]:
assignment_proportions = nav_article_log_proportions + np.array([
self.calculate_topic_nav_logprob(topic_id, nav_id) \
for topic_id in range(self.num_nav_topics)
])
assignment_probabilities = np.exp(
assignment_proportions - logsumexp(assignment_proportions))
updated_assignments.append(
choice(self.num_nav_topics, p=assignment_probabilities))
return updated_assignments
def xe(z, targets, predict=False, error=False, addon=0):
"""
Cross entropy error.
"""
if predict:
return gpu.argmax(z, axis=1)
_xe = z - logsumexp(z, axis=1)
n, _ = _xe.shape
xe = -gpu.mean(_xe[np.arange(n), targets])
if error:
err = gpu.exp(_xe)
err[np.arange(n), targets] -= 1
return xe + addon, err / n
else:
return xe + addon
def calc_marg_log_prob(X, obs_rvs, w, all_rvs_params=None, g=None):
"""
Calculate marginal log probabilities of observations
:param g:
:param X: M x N_o matrix of (partial) observations, where N_o is the number of obs nodes; alternatively a N_o vector
:param obs_rvs: obs_rvs: length N_o list of observed rvs
:param params:
:return:
"""
comp_log_probs = calc_marg_comp_log_prob(X,
obs_rvs,
all_rvs_params=all_rvs_params,
g=g) # M x K
out = utils.logsumexp(np.log(w) + comp_log_probs,
axis=-1) # reduce along the last dimension
return out
def logLikelihood(self, X, Xcov):
"""
Compute the log-likelihood of data given the model
Parameters
----------
X: array_like
data, shape = (n_samples, n_features)
Xcov: array_like
covariances, shape = (n_samples, n_features, n_features)
Returns
-------
logL : float
log-likelihood
"""
return np.sum(logsumexp(self.logprob_a(X, Xcov), -1))
def logLikelihood(self, X, Xcov):
"""
Compute the log-likelihood of data given the model
Parameters
----------
X: array_like
data, shape = (n_samples, n_features)
Xcov: array_like
covariances, shape = (n_samples, n_features, n_features)
Returns
-------
logL : float
log-likelihood
"""
return np.sum(logsumexp(self.logprob_a(X, Xcov), -1))
def logposterior(self, data): """ Computes the log-posterior distribution over components. @type data: array_like @param data: data points stored in columns @rtype: ndarray @return: a posterior distribution for each data point """ # compute unnormalized log-posterior def logposterior_(i): return self[i].loglikelihood(data) + log(self.priors[i]) logpost = vstack(map(logposterior_, range(len(self)))) # normalize posterior return asarray(logpost) - logsumexp(logpost, 0)
def loglikelihood(self, data): """ Computes the log-likelihood of the model for the given data. @type data: array_like @param data: data points stored in columns @rtype: ndarray @return: a log-likelihood for each data point """ # compute joint density over components and data points def loglikelihood_(i): return self[i].loglikelihood(data) + log(self.priors[i]) logjoint = vstack(map(loglikelihood_, range(len(self)))) # marginalize return logsumexp(logjoint, 0).flatten()
def get_negative_log_likelihood(self, source1_decoder_attention_score, source2_decoder_attention_score,
source1_token_mask, source2_token_mask, target_to_source1,
target_to_source2, target_tokens, gate_score):
# shape: (batch_size, seq_max_len_1)
combined_log_probs_1 = ((source1_decoder_attention_score * target_to_source1.float()).sum(-1) + 1e-20).log()
# shape: (batch_size, seq_max_len_2)
combined_log_probs_2 = ((source2_decoder_attention_score * target_to_source2.float()).sum(-1) + 1e-20).log()
# 计算 log(p1 * gate + p2 * (1-gate))
log_gate_score_1 = (gate_score + 1e-20).log() # shape: (batch_size,)
log_gate_score_2 = (1 - gate_score + 1e-20).log() # shape: (batch_size,)
item_1 = (log_gate_score_1 + combined_log_probs_1).unsqueeze(-1)
item_2 = (log_gate_score_2 + combined_log_probs_2).unsqueeze(-1)
step_log_likelihood = logsumexp(torch.cat((item_1, item_2), -1))
return step_log_likelihood
def alpha(self, logB):
""" Compute alpha (forward) distribution.
alpha [i,n] = joint probability of being in state i,
after observing 1..N observations. .
Parameters
----------
logB : ndarray
The observation probability matrix in logarithmic space.
Returns
-------
logalpha : ndarray
The log scaled alpha distribution.
Notes
-----
Refer to Tobias Man's paper [1]_ for the motivation behind the
scaling factors used here. Note that this scaling methods is suitable
when the dynamics of the system is not highly sparse. Adaptation of
log-scaling in the QDHMM would require the use to construct a new
sparse data structure
References
----------
.. [1] Mann, T. P. Numerically Stable Hidden Markov Model
Implementation 2006.
"""
K = self.K
N = logB.shape[1]
assert logB.shape == (K,N)
logAlpha = np.zeros((K, N), dtype=np.float)
# Base case, when n=0
logAlpha[:,0] = self.logA[-1] + logB[:,0]
#induction
for n in xrange(1, N):
logAlpha[:,n] = logsumexp(self.logA[:-1][:,:].T + \
logAlpha[:,n-1],1) + logB[:,n]
return logAlpha
def beta(self, logB):
""" Compute beta (backward) distribution.
beta [i,n] = conditional probability generating observations
Y_n+1..Y_N, given Z_n.
Parameters
----------
logB : ndarray
The observation probability matrix in logarithmic space.
Returns
-------
logbeta : ndarray
The log scaled beta distribution.
Notes
-----
Refer to Tobias Man's paper [1]_ for the motivation behind the
scaling factors used here. Note that this scaling methods is suitable
when the dynamics of the system is not highly sparse. Adaptation of
log-scaling in the QDHMM would require the use to construct a new
sparse data structure
References
----------
.. [1] Mann, T. P. Numerically Stable Hidden Markov Model
Implementation 2006.
"""
K = self.K
N = logB.shape[1]
logBeta = np.zeros((K, N), dtype=np.float)
#Base case when n = N
logBeta[:,-1] = 0.0
#Induction
for n in xrange(N-2, -1, -1):
logBeta[:,n] = logsumexp(logBeta[:,n+1]+\
self.logA[:-1][:,:] \
+ logB[:,n+1],1)
return logBeta
def get_agg_kl(q_data, test_data, model):
model.eval()
means = []
logvars = []
all_z = []
for datum in q_data:
img, _ = datum
batch_size = img.size(0)
img = Variable(img.cuda())
mean, logvar = model._enc_forward(img)
z_samples = model._reparameterize(mean, logvar)
means.append(mean.data)
logvars.append(logvar.data)
all_z.append(z_samples.data)
means = torch.cat(means, 0)
logvars = torch.cat(logvars, 0)
N = float(means.size(0))
mean_prior = torch.zeros(1, means.size(1)).cuda()
logvar_prior = torch.zeros(1, means.size(1)).cuda()
agg_kl = 0.
count = 0.
for datum in test_data:
img, _ = datum
batch_size = img.size(0)
img = Variable(img.cuda())
mean, logvar = model._enc_forward(img)
z_samples = model._reparameterize(mean, logvar).data
for i in range(z_samples.size(0)):
z_i = z_samples[i].unsqueeze(0).expand_as(means)
log_agg_density = utils.log_gaussian(
z_i, means, logvars) # log q(z|x) for all x
log_q = utils.logsumexp(log_agg_density, 0)
log_q = -np.log(N) + log_q
log_p = utils.log_gaussian(z_samples[i].unsqueeze(0), mean_prior,
logvar_prior)
agg_kl += log_q.sum() - log_p.sum()
count += 1
mean_var = means.var(0)
print('active units', (mean_var > 0.02).float().sum())
print(mean_var)
return agg_kl / count
def fit(self, obs, logweights):
"""Fit a Gaussian to the state distributions after observing the data.
Parameters
-----------
obs : ndarray
Observation sequence.
logweights : ndarray
The weights attached to each state (posterior distribution).
In log-space.
"""
#oldmeans = self.mu.copy()
logGamma = np.concatenate(logweights, 1)
normalizer = np.exp(logGamma - logsumexp(logGamma,1)[:,np.newaxis])
for k in range(self.K):
self.mu[:,k] = np.dot(normalizer[k,:][np.newaxis,:] , obs.T)
obs_bar = obs - self.mu[:,k][:,np.newaxis]
self.covar[k,:,:] = np.dot(obs_bar * normalizer[k,:]
, obs_bar.T)
def forward(observations, transitions, sequence_len, batch=False):
"""Implementation of the forward algorithm in Keras.
Returns the log probability of the given observations and transitions
by recursively summing over the probabilities of all paths through
the state space. All probabilities are in logarithmic space.
See e.g. https://en.wikipedia.org/wiki/Forward_algorithm .
Args:
observations (tensor): A tensor of the observation log
probabilities, shape (sequence_len, num_states) if
batch is False, (batch_size, sequence_len, num_states)
otherwise.
transitions (tensor): A (num_states, num_states) tensor of
the transition weights (log probabilities).
sequence_len (int): The number of steps in the sequence.
This must be given because unrolling scan() requires a
definite (not tensor) value.
batch (bool): Whether to run in batchwise mode. If True, the
first dimension of observations corresponds to the batch.
Returns:
Total log probability if batch is False or vector of log
probabiities otherwise.
"""
step = make_forward_step(transitions, batch)
if not batch:
first, rest = observations[0, :], observations[1:, :]
else:
first, rest = observations[:, 0, :], observations[:, 1:, :]
sequence_len -= 1 # exclude first
outputs, _ = scan(step, rest, first, n_steps=sequence_len, batch=batch)
if not batch:
last, axis = outputs[sequence_len - 1], 0
else:
last, axis = outputs[:, sequence_len - 1], 1
return logsumexp(last, axis=axis)
def log_likelihood(self, reward_hypothesis, q_values, demonstrations):
#input is reward weights, q_values as a list [q(s0,a0), q(s1,a0), ..., q(sn,am)]
# and demonstrations = [(s0,a0), ..., (sm,am)] list of state-action pairs
#if self.prior is None:
log_sum = 0.0
if self.prior == "non-pos":
#check if weights are all non-pos
for r in reward_hypothesis:
if r > 0:
return -np.inf
for s, a in demonstrations:
if s not in self.mdp_env.terminals and a is not None: #there are no counterfactuals in a terminal state
if self.likelihood == "birl":
Z_exponents = []
for b in range(self.num_actions):
Z_exponents.append(self.beta *
q_values[s + self.num_states * b])
#print Z_exponents
log_sum += self.beta * q_values[
s + self.num_states * a] - utils.logsumexp(Z_exponents)
#print "likelihood:", np.exp(self.beta * placement_reward - scipy.misc.logsumexp(Z_exponents))
#plt.show()
elif self.likelihood == "uniform":
#print(s,self.mdp_env.get_readable_actions(a))
hinge_losses = 0.0
for b in range(self.num_actions):
# print(b)
# print(q_values[s + self.num_states * b])
# print(a)
# print(q_values[s + self.num_states * a])
hinge_losses += max(
q_values[s + self.num_states * b] -
q_values[s + self.num_states * a], 0.0)
# print(hinge_losses)
log_sum += -self.beta * hinge_losses
else:
raise NotImplementedError
return log_sum
def runSmc(args):
smcData, settings, do_metrics = args
print '\nInitializing SMC\n'
# precomputation
(particles, param, log_weights, cache, cache_tmp) = bdtsmc.init_smc(smcData, settings)
# Run smc
print '\nRunning SMC'
(particles, ess_itr, log_weights_itr, log_pd, particle_stats_itr_d, particles_itr_d, log_pd_islands) = \
bdtsmc.run_smc(particles, smcData, settings, param, log_weights, cache)
# Printing some diagnostics
print
print 'Estimate of log marginal probability i.e. log p(Y|X) = %s ' % log_pd
print 'Estimate of log marginal probability for different islands = %s' % log_pd_islands
print 'logsumexp(log_pd_islands) - np.max(log_pd_islands) = %s\n' % \
(logsumexp(log_pd_islands) - np.max(log_pd_islands))
if settings.debug == 1:
print 'log_weights_itr = \n%s' % log_weights_itr
# check if log_weights are computed correctly
for i_, p in enumerate(particles):
log_w = log_weights_itr[-1, i_] + np.log(settings.n_particles) - np.log(settings.n_islands)
logprior_p = p.compute_logprior()
loglik_p = p.compute_loglik()
logprob_p = p.compute_logprob()
if (np.abs(settings.ess_threshold) < 1e-15) and (settings.proposal == 'prior'):
# for the criterion above, only loglik should contribute to the weight update
try:
check_if_zero(log_w - loglik_p)
except AssertionError:
print 'Incorrect weight computation: log_w (smc) = %s, loglik_p = %s' % (log_w, loglik_p)
raise AssertionError
try:
check_if_zero(logprob_p - loglik_p - logprior_p)
except AssertionError:
print 'Incorrect weight computation'
print 'check if 0: %s, logprior_p = %s, loglik_p = %s' % (logprob_p - loglik_p - logprior_p, logprior_p, loglik_p)
raise AssertionError
# Evaluate
print 'Results on training data (log predictive prob is bogus)'
# log_predictive on training data is bogus ... you are computing something like \int_{\theta} p(data|\theta) p(\theta|data)
if settings.weight_islands == 1:
# each island's prediction is weighted by its marginal likelihood estimate which is equivalent to micro-averaging globally
weights_prediction = softmax(log_weights_itr[-1, :])
assert('islandv1' in settings.tag)
else:
# correction for macro-averaging predictions across islands
weights_prediction = np.ones(settings.n_particles) / settings.n_islands
n_particles_tmp = settings.n_particles / settings.n_islands
for i_ in range(settings.n_islands):
pid_min, pid_max = i_ * n_particles_tmp, (i_ + 1) * n_particles_tmp - 1
pid_range_tmp = range(pid_min, pid_max+1)
weights_prediction[pid_range_tmp] *= softmax(log_weights_itr[-1, pid_range_tmp])
(pred_prob_overall_train, metrics_train) = \
evaluate_predictions_smc(particles, smcData, smcData['x_train'], smcData['y_train'], settings, param, weights_prediction, do_metrics)
print '\nResults on test data'
(pred_prob_overall_test, metrics_test) = \
evaluate_predictions_smc(particles, smcData, smcData['x_test'], smcData['y_test'], settings, param, weights_prediction, do_metrics)
#return pred_prob_overall_test, particles, param, weights_prediction
return pred_prob_overall_test,
def s82_star_galaxy_classification(model_parms_file, epoch, Nstar,
features, filters, r_pmm, figname,
threshold=0., Nthreads=4):
"""
Compare quality of classifcation for a model with the s82 coadd. Should
be a model trained on s82 single epoch data.
"""
# get the data
single, singlecov = fetch_matched_s82data(epoch, features=features,
filters=filters)
coadd, coaddecov = fetch_matched_s82data(epoch, features=features,
filters=filters, use_single=False)
# classfy the single epoch data
single_class = np.zeros(single.shape[0])
ind = np.abs(single[:, r_pmm]) < 0.145
single_class[ind] = 1.
alpha, mu, V, _, _ = load_xd_parms(model_parms_file)
logls = log_multivariate_gaussian_Nthreads(single, mu, V, singlecov,
Nthreads)
logls += np.log(alpha)
logodds = logsumexp(logls[:, :Nstar], axis=1)
logodds -= logsumexp(logls[:, Nstar:], axis=1)
ind = logodds > threshold
model_class = np.zeros(single.shape[0])
model_class[ind] = 1.
coadd_class = np.zeros(single.shape[0])
ind = np.abs(coadd[:, r_pmm]) < 0.03
coadd_class[ind] = 1.
fs = 10
f = pl.figure(figsize=(2 * fs, 2 * fs))
pl.subplot(221)
pl.plot(single[single_class==0, 0], single[single_class==0, r_pmm], '.',
color='#ff6633', alpha=0.2)
pl.plot(single[single_class==1, 0], single[single_class==1, r_pmm], '.',
color='#3b5998', alpha=0.2)
pl.ylim(-0.2, 0.5)
pl.subplot(222)
pl.plot(coadd[single_class==0, 0], coadd[single_class==0, r_pmm], '.',
color='#ff6633', alpha=0.2)
pl.plot(coadd[single_class==1, 0], coadd[single_class==1, r_pmm], '.',
color='#3b5998', alpha=0.2)
pl.plot([17.5, 22.], [0.03, 0.03], 'k')
pl.ylim(-0.2, 0.5)
pl.subplot(223)
pl.plot(single[model_class==0, 0], single[model_class==0, r_pmm], '.',
color='#ff6633', alpha=0.2)
pl.plot(single[model_class==1, 0], single[model_class==1, r_pmm], '.',
color='#3b5998', alpha=0.2)
pl.ylim(-0.2, 0.5)
pl.subplot(224)
pl.plot(coadd[model_class==0, 0], coadd[model_class==0, r_pmm], '.',
color='#ff6633', alpha=0.2)
pl.plot(coadd[model_class==1, 0], coadd[model_class==1, r_pmm], '.',
color='#3b5998', alpha=0.2)
pl.plot([17.5, 22.], [0.03, 0.03], 'k')
pl.ylim(-0.2, 0.5)
f.savefig(figname, bbox_inches='tight')
def hmmFit(self, obs, maxiter = 50 , epsilon = 0.0001, debug= False):
"""Fit the standard HMM to the given data using the (adapted Baum-Welch)
EM algorithm.
Parameters
----------
obs : list
The list of observations sequences where every sequence is a
ndarray. The sequences can be of different length, but
the dimension of the features needs to be identical.
maxiter : int, optional
The maximum number of iterations of the EM algorithm. Default = 50.
epsilon : float, optional
The minimum allowed threshold in the variation of the log-likelihood
between succesive iterations of the EM algorithm. Once the variation
exceeds 'epsilon' the algorithm is said to have converged.
Default = 1e-6.
debug : bool, optional
Display verbose On/off.
Returns
-------
float
The normalized log-likelihood.
list
The list of log-likelihoods for each iteration of the EM algorithm.
To check for monotonicity of the log-likelihoods.
int
The duration estimates of each HMM state from the posterior
distribution.
ndarray
The top ranked 'n' which are used to estimate the state
durations.
ndarray
The expected value of the state durations obtained at
the top ranked 'n'.
"""
if debug:
logger.setLevel(logging.DEBUG)
logger.debug('Running the HMM EM algorithm..')
ll=[]
numseq = len(obs)
lastavgloglikelihood = -np.inf
logksilist = [None] * numseq
logGammalist = [None] * numseq
logAlphalist = [None] * numseq
logBetalist = [None] * numseq
llhlist = [None] * numseq
obsmatrix = [None] * numseq
logB = [None] * numseq
duration = [None]* numseq
res = [None]* numseq
rankn = [None]* numseq
N=[None] * numseq
for iteration in xrange(maxiter):
start_time=time.time()
logger.debug('-------------------------------------------')
logger.debug('iter: %d'% iteration )
logger.debug('E step..' )
for seq,obsseq in enumerate(obs):
N[seq] = obsseq.shape[1]
if self.O.__class__.__name__=='Discrete':
obsmatrix[seq] = np.zeros((len(self.O.c),N))
obsmatrix[seq][ np.int_(obsseq),xrange(N)] = 1
#E-step
#calcualte the posterior probability for each sequence
logB[seq] = self.O.loglikelihood(obsseq)
llhlist[seq], logGammalist[seq], logksilist[seq],\
logAlphalist[seq],logBetalist[seq] \
= self.gammaKsi(logB[seq])
normalizellhlist = np.divide(llhlist, N)
loglikelihood = np.sum(normalizellhlist)
ll.append(loglikelihood)
logger.debug('LLH: %0.10f' % (loglikelihood))
if abs(loglikelihood - lastavgloglikelihood) < epsilon:
for seq in range(len(obs)):
rankn[seq] = self.rankn(logksilist[seq])
duration[seq], res[seq] = \
self.estimatepostduration(logAlphalist[seq], \
logBetalist[seq], logB[seq],
rankn[seq], logGammalist[seq], llhlist[seq] )
logger.info("Convergence after %d iterations" % iteration)
break
lastavgloglikelihood = loglikelihood
obsarray = np.concatenate(obs, 1)
if self.O.__class__.__name__=='Discrete':
obsarray = np.concatenate(obsmatrix,1)
#M-step
logger.debug('M step..' )
self.logA[-1] = (logsumexp(np.array([g[:,0]
for g in logGammalist]),axis = 0) -
np.log(np.double(numseq)))
logKsiarray = np.concatenate(logksilist, axis = 0)
logGammasArray = (np.concatenate(map(lambda x: x[:,:-1],
logGammalist),axis = 1))
self.logA[:-1] = (logsumexp(logKsiarray,axis = 0) -
logsumexp(logGammasArray, axis= 1)
[:,np.newaxis])
self.O.fit(obsarray, logGammalist)
end_time=time.time()
logger.debug('Time to run iter : %.5f s' %(end_time-start_time))
if iteration == maxiter-1:
for seq in range(len(obs)):
rankn[seq] = self.rankn(logksilist[seq])
duration[seq], res[seq] = \
self.estimatepostduration(logAlphalist[seq], \
logBetalist[seq], logB[seq],
rankn[seq], logGammalist[seq], llhlist[seq] )
logger.info('No convergence after %d iterations'%(iteration+1))
break
return (loglikelihood,ll, duration, rankn, res)
def fit(self,obs,logweights):
logGamma = np.concatenate(logweights, 1)
normalizer = np.exp(logGamma - logsumexp(logGamma, axis = 1)[:, np.newaxis])
for k in range(self.K):
self.p[k]=np.exp(np.log(np.sum(normalizer[k,:]*obs, 1))-\
np.log(np.sum(normalizer[k,:])))
