This function provides a MLE based network search in the space of node orders by Simulated Annealing. For a given sample from an unknown categorical network, it returns a list of catNetwork objects, with complexity up to some maximal value, that best fit the data.
cnSearchSA(data, perturbations,
maxParentSet=0, parentSizes=NULL,
maxComplexity=0, nodeCats=NULL,
parentsPool=NULL, fixedParents=NULL,
edgeProb=NULL, dirProb=NULL,
selectMode = "BIC",
tempStart=1, tempCoolFact=0.9, tempCheckOrders=10, maxIter=100,
orderShuffles=1, stopDiff=0,
numThreads=2, priorSearch=NULL, echo=FALSE)A catNetworkEvaluate object.
a matrix in row-nodes format or a data.frame in column-nodes format
a binary matrix with the dimensions of data. A value 1 designates the node in the corresponding sample as perturbed
an integer, maximal number of parents for all nodes
an integer vector, maximal number of parents per node
an integer, maximal network complexity for the search
a list of node categories
a list of parent sets to choose from
a list of fixed parent sets
a square matrix of length the number of nodes specifying prior edge probabilities
a square matrix of length the number of nodes specifying prior directional probabilities
a character, optimization network selection criterion such as "AIC" and "BIC"
a numeric value, the initial temperature for the annealing
a numeric value, the temperature multiplicative decreasing factor
an integer, the number of iteration, orders to be searched, with constant temperature
an integer, the total number of iterations, thus orders, to be processed
a numeric, the number of shuffles for generating new candidate orders from the last accepted
a numeric value, stopping epsilon criterion
an integer value, the number of parallel threads
a catNetworkEvaluate object from a previous search
a logical that sets on/off some functional progress and debug information
N. Balov, P. Salzman
The function implements a Simulated Annealing version of the Metropolis algorithm by constructing a Markov chain in the space of node orders. Given a currently selected order, the algorithm tries to improve its likelihood score by exploring its neighborhood. The order score is defined as the likelihood of the selected according to selectMode network from the set of estimated networks compatible with that order.
The data can be a matrix of character categories with rows specifying the node-variables and columns assumed to be independent samples from an unknown network, or
a data.frame with columns specifying the nodes and rows being the samples.
The number of categories for each node is obtained from the data. It is the user responsibility to make sure the data can be categorized reasonably. If the data is numerical it will be forcibly coerced to integer one, which however may result to NA entries or too many node categories per some nodes, and in either case to the function failure. Use cnDiscretize to convert numeric data into categorical.
If given, the nodeCats is used as a list of categories. In that case, nodeCats should include the node categories presented in the data.
The function returns a list of networks, one for any possible complexity within the specified range. Stochastic optimization, based on the criterion of maximizing the likelihood, is carried on the network with complexity closest to, but not above, maxComplexity.
If maxComplexity is not specified, thus the function is called with the default zero value, then maxComplexity is set to be the complexity of a network with all nodes having the maximum, maxParentSet, the number of parents.
The selectMode parameter sets the selection criterion for the network upon which the
maximum likelihood optimization is carried on. "BIC" is the default choice, while any value
different from "AIC" and "BIC" results in the maximum complexity criterion to be used,
the one which selects the network with complexity given by maxComplexity.
The parameters tempStart, tempCoolFact and tempCheckOrders control the Simulated Annealing schedule.
tempStart is the starting temperature of the annealing process.
tempCoolFact is the cooling factor from one temperature step to another.
It is a number between 0 and 1, inclusively;
For example, if tempStart is the temperature in the first step,
tempStart*tempCoolFact will be temperature in the second.
tempCheckOrders is the number of proposals, that is, the candidate orders from the current order neighborhood, to be checked before decreasing the temperature.
If for example maxIter is 40 and tempCheckOrders is 4,
then 10 temperature decreasing steps will be eventually performed.
The orderShuffles parameters controls the extend of the current order neighborhood. A value of zero indicates that random orders should be used as proposals. For positive orderShuffles's, a candidate order is obtained from the current one by performing orderShuffles number of times the following operation: a random position is picked up at random (uniformly) and it is exchanged with the position right up next to it. If orderShuffles is negative, then the operation is: two positions are picked up at random and their values are exchanged.
maxIter is the maximum length of the Markov chain.
orderShuffles is a number that controls the extent of the order neighborhoods.
Each new proposed order is obtained from the last accepted one by
orderShuffles switches of two node indices.
stopDiff is a stopping criterion. If at a current temperature,
after tempCheckOrders orders being checked, no likelihood improvement of level at least stopDiff is found, then the SA stops and the function exists.
Setting this parameter to zero guarantees exhausting all of the maximum allowed
maxIter order searches.
The function speeds up the Markov Chain by implementing a pre-computing buffer. It runs numThreads number of parallel threads each of which process a proposed order. If we have more than one acceptance in the batch, the first one is taken as next order selection. The performance boost is more apparent when the Markov chain has a low acceptance rate, in which case the chain can run up to numThreads-times faster.
priorSearch is a result from previous search. This parameters allows a new search to be initiated
from the best order found so far. Thus a chain of searches can be constructed with varying parameters
providing greater adaptability and user control.
See the vignettes for more details on the algorithm.
cnSearchOrder
cnet <- cnRandomCatnet(numnodes=6, maxParents=2, numCategories=2)
psamples <- cnSamples(object=cnet, numsamples=100)
nets <- cnSearchSA(data=psamples, perturbations=NULL,
maxParentSet=1, maxComplexity=16)
cc <- cnComplexity(object=cnet)
cnFind(object=nets, complexity=cc)
Run the code above in your browser using DataLab