BigData {LaplacesDemon}  R Documentation 
Big Data
Description
This function enables Bayesian inference with data that is too large for computer memory (RAM) with the simplest method: reading in batches of data (where each batch is a section of rows), applying a function to the batch, and combining the results.
Usage
BigData(file, nrow, ncol, size=1, Method="add", CPUs=1, Type="PSOCK",
FUN, ...)
Arguments
file 
This required argument accepts a path and filename that must refer to a .csv file, and that must contain only a numeric matrix without a header, row names, or column names. 
nrow 
This required argument accepts a scalar integer that indicates the number of rows in the big data matrix. 
ncol 
This required argument accepts a scalar integer that indicates the number of columns in the big data matrix. 
size 
This argument accepts a scalar integer that specifies the number of rows of each batch. The last batch is not required to have the same number of rows as the other batches. The largest possible size, and therefore the fewest number of batches, should be preferred. 
Method 
This argument accepts a scalar string, defaults to
"add", and alternatively accepts "rbind". When

CPUs 
This argument accepts an integer that specifies the number
of central processing units (CPUs) of the multicore computer or
computer cluster. This argument defaults to 
Type 
This argument specifies the type of parallel processing to
perform, accepting either 
FUN 
This required argument accepts a userspecified function that will be performed on each batch. The first argument in the function must be the data. 
... 
Additional arguments are used within the userspecified function. Additional arguments often refer to parameters. 
Details
Big data is defined loosely here as data that is too large for
computer memory (RAM). The BigData
function uses the
splitapplycombine strategy with a big data set. The unmanageable
big data set is split into smaller, manageable pieces (batches),
a function is applied to each batch, and results are combined.
Each iteration, the BigData
function opens a connection to a
big data set and keeps the connection open while the scan
function reads in each batch of data (elsewhere, batches are often
referred to chunks). A userspecified function is applied to each
batch of data, the results are combined together, the connection is
closed, and the results are returned.
As an introductory example, suppose a statistician updates a linear
regression model, but the design matrix \textbf{X}
is too
large for computer memory. Suppose the design matrix has 100 million
rows, and the statistician specifies size=1e6
. The statistician
combines dependent variable \textbf{y}
with design matrix
\textbf{X}
. Each iteration in IterativeQuadrature
,
LaplaceApproximation
, LaplacesDemon
,
PMC
, or VariationalBayes
, the
BigData
function sequentially reads in one million rows of the
combined data \textbf{X}
, calculates expectation vector
\mu
, and finally returns the sum of the loglikelihood. The sum
of the loglikelihood is added together for all batches, and returned.
There are many limitations with this function.
This function is not fast, in the sense that the entire big data set is processed in batches, each iteration. With iterative methods, this may perform well, albeit slowly.
There are many functions that cannot be performed on batches, though most models in the Examples vignette may easily be updated with big data.
Large matrices of samples are unaddressed, only the data.
Although many (but not all) models may be estimated, many additional
functions in this package will not work when applied after the model
has updated. Instead, a batch or random sample of data (see the
read.matrix
function for sampling from big data) should
be used in the usual way, in the Data
argument, and the
Model
function coded in the usual way without the
BigData
function.
Parallel processing may be performed when the user specifies
CPUs
to be greater than one, implying that the specified number
of CPUs exists and is available. Parallelization may be performed on a
multicore computer or a computer cluster. Either a Simple Network of
Workstations (SNOW) or Message Passing Interface (MPI) is used. Each
call to BigData
establishes and closes the parallelization,
which is costly, and unfortunately results in copious output to the
console. With small data sets, parallel processing may be slower, due
to computer network communication. With larger data sets, the user
should experience a faster runtime.
There have been several alternative approaches suggested for big data.
Huang and Gelman (2005) propose that the user creates batches by sampling from big data, updating a separate Bayesian model on each batch, and combining the results into a consensus posterior. This manyminimodel approach may be faster when feasible, because multiple models may be updated in parallel, say one per CPU. Such results will work with all functions in this package. With the manyminimodel approach, several methods are proposed for combining posterior samples from batchlevel models, such as by using a normal approximation, updating from prior to posterior sequentially (the posterior from the last batch becomes the prior of the next batch), sample from the full posterior via importance sampling from the batched posteriors, and more.
Scott et al. (2013) propose a method that they call Consensus Monte Carlo, which consists of breaking the data down into chunks, calling each chunk a shard, and use a manyminimodel approach as well, but propose their own method of weighting the posteriors back together.
Balakrishnan and Madigan (2006) introduced a Sequential Monte Carlo (SMC) sampler, a refinement of an earlier proposal, that was designed for big data. It makes one pass through the massive data set, after an initial MCMC estimation on a small sample. Each particle is updated for each record, resulting in numerous evaluations per record.
Welling and Teh (2011) proposed a new class of MCMC sampler in which
only a random sample of big data is used each iteration. The
stochastic gradient Langevin dynamics (SGLD) algorithm is available
in the LaplacesDemon
function.
An important alternative to consider is using the ff
package,
where "ff" stands for fast access file. The ff
package has been
tested successfully with updating a model in LaplacesDemon
.
Once the big data set, say \textbf{X}
, is an object of
class ff_matrix
, simply include it in the list of data as
usual, and modify the Model
specification function
appropriately. For example, change mu < tcrossprod(X, t(beta))
to mu < tcrossprod(X[], t(beta))
. The ff
package is
not included as a dependency in the LaplacesDemon
package, so
it must be installed and activated.
Value
The BigData
function returns output that is the result of
performing a userspecified function on batches of big data. Output is
a matrix, and may have one or more column vectors.
Author(s)
Statisticat, LLC software@bayesianinference.com
References
Balakrishnan, S. and Madigan, D. (2006). "A OnePass Sequential Monte Carlo Method for Bayesian Analysis of Massive Datasets". Bayesian Analysis, 1(2), p. 345–362.
Huang, Z. and Gelman, A. (2005) "Sampling for Bayesian Computation with Large Datasets". SSRN eLibrary.
Scott, S.L., Blocker, A.W. and Bonassi, F.V. (2013). "Bayes and Big Data: The Consensus Monte Carlo Algorithm". In Bayes 250.
Welling, M. and Teh, Y.W. (2011). "Bayesian Learning via Stochastic Gradient Langevin Dynamics". Proceedings of the 28th International Conference on Machine Learning (ICML), p. 681–688.
See Also
IterativeQuadrature
,
LaplaceApproximation
,
LaplacesDemon
,
LaplacesDemon.RAM
,
PMC
,
PMC.RAM
,
read.matrix
, and
VariationalBayes
.
Examples
### Below is an example of a linear regression model specification
### function in which BigData reads in a batch of 1,000 records of
### Data$N records from a data set that is too large to fully open
### in memory. The example simulates on 10,000 records, which is
### not big data; it's just a toy example. The data set is file X.csv,
### and the first column of matrix X is the dependent variable y. The
### user supplies a function to BigData along with parameters beta and
### sigma. When each batch of 1,000 records is read in,
### mu = XB is calculated, and then the LL is calculated as
### y ~ N(mu, sigma^2). These results are added together from all
### batches, and returned as LL.
library(LaplacesDemon)
N < 10000
J < 10 #Number of predictors, including the intercept
X < matrix(1,N,J)
for (j in 2:J) {X[,j] < rnorm(N,runif(1,3,3),runif(1,0.1,1))}
beta.orig < runif(J,3,3)
e < rnorm(N,0,0.1)
y < as.vector(tcrossprod(beta.orig, X) + e)
mon.names < c("LP","sigma")
parm.names < as.parm.names(list(beta=rep(0,J), log.sigma=0))
PGF < function(Data) return(c(rnormv(Data$J,0,0.01),
log(rhalfcauchy(1,1))))
MyData < list(J=J, PGF=PGF, N=N, mon.names=mon.names,
parm.names=parm.names) #Notice that X and y are not included here
filename < tempfile("X.csv")
write.table(cbind(y,X), filename, sep=",", row.names=FALSE,
col.names=FALSE)
Model < function(parm, Data)
{
### Parameters
beta < parm[1:Data$J]
sigma < exp(parm[Data$J+1])
### Log(Prior Densities)
beta.prior < sum(dnormv(beta, 0, 1000, log=TRUE))
sigma.prior < dhalfcauchy(sigma, 25, log=TRUE)
### LogLikelihood
LL < BigData(file=filename, nrow=Data$N, ncol=Data$J+1, size=1000,
Method="add", CPUs=1, Type="PSOCK",
FUN=function(x, beta, sigma) sum(dnorm(x[,1], tcrossprod(x[,1],
t(beta)), sigma, log=TRUE)), beta, sigma)
### LogPosterior
LP < LL + beta.prior + sigma.prior
Modelout < list(LP=LP, Dev=2*LL, Monitor=c(LP,sigma),
yhat=0,#rnorm(length(mu), mu, sigma),
parm=parm)
return(Modelout)
}
### From here, the user may update the model as usual.