graph_tool.inference.PseudoCIsingBlockState#
- class graph_tool.inference.PseudoCIsingBlockState(*args, **kwargs)[source]#
Bases:
IsingBaseBlockState
State for network reconstruction based on the equilibrium configurations of the continuous Ising model, using the Pseudolikelihood approximation and the stochastic block model as a prior.
See documentation for
IsingBaseBlockState
for details. Note that in this model “time-series” should be interpreted as a set of uncorrelated samples, not a temporal sequence. Additionally, thes
parameter should contain property maps of typevector<double>
, with values in the range \([-1,1]\).Methods
collect_marginal
([g])Collect marginal inferred network during MCMC runs.
Collect marginal latent multigraph during MCMC runs.
copy
(**kwargs)Return a copy of the state.
entropy
([latent_edges, density])Return the entropy, i.e. negative log-likelihood.
Return the underlying block state, which can be either
BlockState
orNestedBlockState
.get_edge_prob
(u, v, x[, entropy_args, epsilon])Return conditional posterior log-probability of edge \((u,v)\).
get_edges_prob
(elist[, entropy_args, epsilon])Return conditional posterior log-probability of an edge list, with shape \((E,2)\).
Return the current inferred graph.
get_x
()Return edge couplings.
mcmc_sweep
([r, p, pstep, h, hstep, xstep, ...])Perform sweeps of a Metropolis-Hastings acceptance-rejection sampling MCMC to sample network partitions and latent edges.
multiflip_mcmc_sweep
(**kwargs)Alias for
mcmc_sweep()
withmultiflip=True
.set_params
(params)Sets the model parameters via the dictionary
params
.set_state
(g, w)virtual_add_edge
(u, v[, entropy_args])virtual_remove_edge
(u, v[, entropy_args])- collect_marginal(g=None)#
Collect marginal inferred network during MCMC runs.
- Parameters:
- g
Graph
(optional, default:None
) Previous marginal graph.
- g
- Returns:
- g
Graph
New marginal graph, with internal edge
EdgePropertyMap
"eprob"
, containing the marginal probabilities for each edge.
- g
Notes
The posterior marginal probability of an edge \((i,j)\) is defined as
\[\pi_{ij} = \sum_{\boldsymbol A}A_{ij}P(\boldsymbol A|\boldsymbol D)\]where \(P(\boldsymbol A|\boldsymbol D)\) is the posterior probability given the data.
- collect_marginal_multigraph(g=None)#
Collect marginal latent multigraph during MCMC runs.
- Parameters:
- g
Graph
(optional, default:None
) Previous marginal multigraph.
- g
- Returns:
- g
Graph
New marginal graph, with internal edge
EdgePropertyMap
"w"
and"wcount"
, containing the edge multiplicities and their respective counts.
- g
Notes
The mean posterior marginal multiplicity distribution of a multi-edge \((i,j)\) is defined as
\[\pi_{ij}(w) = \sum_{\boldsymbol G}\delta_{w,G_{ij}}P(\boldsymbol G|\boldsymbol D)\]where \(P(\boldsymbol G|\boldsymbol D)\) is the posterior probability of a multigraph \(\boldsymbol G\) given the data.
- copy(**kwargs)#
Return a copy of the state.
- entropy(latent_edges=True, density=True, **kwargs)#
Return the entropy, i.e. negative log-likelihood.
- get_block_state()#
Return the underlying block state, which can be either
BlockState
orNestedBlockState
.
- get_edge_prob(u, v, x, entropy_args={}, epsilon=1e-08)#
Return conditional posterior log-probability of edge \((u,v)\).
- get_edges_prob(elist, entropy_args={}, epsilon=1e-08)#
Return conditional posterior log-probability of an edge list, with shape \((E,2)\).
- get_graph()#
Return the current inferred graph.
- get_x()#
Return edge couplings.
- mcmc_sweep(r=0.5, p=0.1, pstep=0.1, h=0.1, hstep=1, xstep=0.1, multiflip=True, **kwargs)#
Perform sweeps of a Metropolis-Hastings acceptance-rejection sampling MCMC to sample network partitions and latent edges. The parameter
r
controls the probability with which edge move will be attempted, instead of partition moves. The parameterh
controls the relative probability with which moves for the parametersr_v
will be attempted, andhstep
is the size of the step. The parameterp
controls the relative probability with which moves for the parametersglobal_beta
andr
will be attempted, andpstep
is the size of the step. The paramterxstep
determines the size of the attempted steps for the edge coupling parameters.The remaining keyword parameters will be passed to
mcmc_sweep()
ormultiflip_mcmc_sweep()
, ifmultiflip=True
.
- multiflip_mcmc_sweep(**kwargs)#
Alias for
mcmc_sweep()
withmultiflip=True
.
- set_params(params)#
Sets the model parameters via the dictionary
params
.
- set_state(g, w)#
- virtual_add_edge(u, v, entropy_args={})#
- virtual_remove_edge(u, v, entropy_args={})#