def _split(self, data, constrained_clustering=None):
particle = self.kernel.create_particle(0, data[0], None, log_q={0: 0})
particle = self.kernel.create_particle(1,
data[1],
particle,
log_q={1: 0})
if constrained_clustering is None:
for data_point in data[2:]:
particle = self.kernel.propose(data_point, particle)
else:
constrained_clustering = relabel_clustering(constrained_clustering)
for block_idx, data_point in zip(constrained_clustering[2:],
data[2:]):
particle = self.kernel.create_particle(block_idx, data_point,
particle)
clustering = get_cluster_labels(particle)
init_params = [
self.dist.create_params_from_data(data[0]),
self.dist.create_params_from_data(data[1]),
]
log_mh_factor = get_log_normalisation(
particle) + self.kernel.log_target_density(init_params)
return clustering, log_mh_factor
def _get_updated_clustering(self, clustering, particle, sigma):
restricted_clustering = get_cluster_labels(particle)
max_idx = clustering.max()
clustering[sigma] = restricted_clustering + max_idx + 1
return relabel_clustering(clustering)
def update(self, clustering):
clustering = relabel_clustering(clustering)
clusters = np.unique(clustering)
num_clusters = len(np.unique(clustering))
self.cluster_probs = np.zeros((num_clusters, num_clusters))
self.clusters_to_data = {}
self.data_to_clusters = {}
margs = {}
for c in clusters:
cluster_data = self.data[clustering == c]
cluster_params = self.dist.create_params_from_data(cluster_data)
margs[c] = self.dist.log_marginal_likelihood(cluster_params)
if self.use_prior_weight:
margs[c] += self.partition_prior.log_tau_2(cluster_params.N)
self.clusters_to_data[c] = np.where(clustering == c)[0].flatten()
for i in self.clusters_to_data[c]:
self.data_to_clusters[i] = c
for c_i in clusters:
log_p = np.ones(num_clusters) * float('-inf')
for c_j in clusters:
if c_i == c_j:
continue
merged_data = self.data[(clustering == c_i) |
(clustering == c_j)]
merged_params = self.dist.create_params_from_data(merged_data)
merge_marg = self.dist.log_marginal_likelihood(merged_params)
if self.use_prior_weight:
merge_marg += self.partition_prior.log_tau_2(
merged_params.N)
log_p[c_j] = merge_marg - (margs[c_i] + margs[c_j])
if num_clusters == 1:
log_p[c_i] = 0
else:
log_p[c_i] = -np.log(num_clusters - 1) + log_sum_exp(log_p)
self.cluster_probs[c_i], _ = exp_normalize(log_p)
def _sample(self, clustering, data):
anchors, sigma = self.split_merge_setup_kernel.setup_split_merge(
clustering, 2)
self.kernel.setup(anchors,
clustering,
data,
sigma,
set_constrained_path=False)
clustering_sigma = clustering[sigma]
data_sigma = data[sigma]
propose_merge = (clustering_sigma[0] != clustering_sigma[1])
if propose_merge:
merge_clustering, merge_mh_factor = self._merge(data_sigma)
split_clustering, split_mh_factor = self._split(
data_sigma, constrained_clustering=clustering_sigma)
forward_factor = merge_mh_factor
reverse_factor = split_mh_factor
restricted_clustering = merge_clustering
else:
merge_clustering, merge_mh_factor = self._merge(data_sigma)
split_clustering, split_mh_factor = self._split(data_sigma)
forward_factor = split_mh_factor
reverse_factor = merge_mh_factor
restricted_clustering = split_clustering
log_ratio = forward_factor - reverse_factor
# print split_mh_factor, merge_mh_factor, log_ratio
u = np.random.random()
if log_ratio >= np.log(u):
max_idx = clustering.max()
clustering[sigma] = restricted_clustering + max_idx + 1
clustering = relabel_clustering(clustering)
return clustering
def get_constrained_path(clustering, data, kernel):
constrained_path = []
clustering = relabel_clustering(clustering)
particle = None
for c, x in zip(clustering, data):
particle = kernel.create_particle(c, x, particle)
constrained_path.append(particle)
return constrained_path
def sample(self, clustering, data, num_iters=1):
for _ in range(num_iters):
anchors, sigma = self._setup_split_merge(clustering)
self.smc_kernel.setup(anchors, clustering, data, sigma)
particles_weights = self.smc_sampler.sample(
data[sigma], self.smc_kernel)
sampled_particle = self._sample_particle(particles_weights)
self._get_updated_clustering(clustering, sampled_particle, sigma)
clustering = relabel_clustering(clustering)
return clustering
def get_exact_posterior(data, dist, partition_prior):
'''
Compute the exact posterior of the clustering model.
Returns a dictionary mapping clusterings to posterior probability.
'''
log_p = []
clusterings = []
for c in get_all_clusterings(data.shape[0]):
clusterings.append(tuple(relabel_clustering(c).astype(int)))
log_p.append(log_joint_probability(c, data, dist, partition_prior))
p, _ = exp_normalize(np.array(log_p))
return dict(zip(clusterings, p))
def update(self, clustering):
self.cluster_params = {}
self.clusters_to_data = {}
self.data_to_clusters = {}
self.clustering = relabel_clustering(clustering)
for c in np.unique(clustering):
cluster_data = self.data[clustering == c]
self.cluster_params[c] = self.dist.create_params()
for data_point in cluster_data:
self.cluster_params[c].increment(data_point)
self.clusters_to_data[c] = np.where(clustering == c)[0].flatten()
def _run_sampler_posterior(self,
data,
sampler,
burnin=int(1e2),
num_iters=int(1e4)):
clustering = np.zeros(data.shape[0], dtype=int)
test_counts = Counter()
for i in range(num_iters):
clustering = sampler.sample(clustering, data)
if i >= burnin:
test_counts[tuple(relabel_clustering(clustering))] += 1
posterior_probs = defaultdict(float)
norm_const = sum(test_counts.values())
for key in test_counts:
posterior_probs[key] = test_counts[key] / norm_const
return posterior_probs
