graph_tool.inference.MixedMeasuredBlockState#
- class graph_tool.inference.MixedMeasuredBlockState(g, n, x, n_default=1, x_default=0, fn_params={'alpha': 1, 'beta': 10}, fp_params={'mu': 1, 'nu': 10}, aE=nan, nested=True, state_args={}, bstate=None, self_loops=False, **kwargs)[source]#
Bases:
UncertainBaseState
Inference state of a measured graph with heterogeneous errors, using the stochastic block model as a prior.
- Parameters:
- g
Graph
Measured graph.
- n
EdgePropertyMap
Edge property map of type
int
, containing the total number of measurements for each edge.- x
EdgePropertyMap
Edge property map of type
int
, containing the number of positive measurements for each edge.- n_default
int
(optional, default:1
) Total number of measurements for each non-edge.
- x_default
int
(optional, default:1
) Total number of positive measurements for each non-edge.
- fn_params
dict
(optional, default:dict(alpha=1, beta=10)
) Beta distribution hyperparameters for the probability of missing edges (false negatives).
- fp_params
dict
(optional, default:dict(mu=1, nu=10)
) Beta distribution hyperparameters for the probability of spurious edges (false positives).
- aE
float
(optional, default:NaN
) Expected total number of edges used in prior. If
NaN
, a flat prior will be used instead.- nested
boolean
(optional, default:True
) If
True
, aNestedBlockState
will be used, otherwiseBlockState
.- state_args
dict
(optional, default:{}
) Arguments to be passed to
NestedBlockState
orBlockState
.- bstate
NestedBlockState
orBlockState
(optional, default:None
) If passed, this will be used to initialize the block state directly.
- self_loopsbool (optional, default:
False
) If
True
, it is assumed that the uncertain graph can contain self-loops.
- g
References
[peixoto-reconstructing-2018]Tiago P. Peixoto, “Reconstructing networks with unknown and heterogeneous errors”, Phys. Rev. X 8 041011 (2018). DOI: 10.1103/PhysRevX.8.041011 [sci-hub, @tor], arXiv: 1806.07956
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[, 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.
mcmc_sweep
([r, h, hstep, multiflip])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_hparams
(alpha, beta, mu, nu)Set edge and non-edge hyperparameters.
set_state
(g, w)sync_q
()transform
(na, xa)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.
- 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, 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.
- mcmc_sweep(r=0.5, h=0.1, hstep=1, multiflip=True, **kwargs)[source]#
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 hyperparamters moves will be attempted, andhstep
is the size of the step.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_state(g, w)#
- virtual_add_edge(u, v, entropy_args={})#
- virtual_remove_edge(u, v, entropy_args={})#