On Thu, 15 Jan 2015, Christian Sigg wrote:
Given a large data.frame, a function trains a series of models by looping over
two steps:
1. Create a model-specific subset of the complete training data
2. Train a model on the subset data
The function returns a list of trained models which are later used for
prediction on test data.
Due to how models and closures work in R, each model contains a lot of data
that is not necessary for prediction. The space requirements of all models
combined can become prohibitive if the number of samples in the training data
and the number of models is sufficiently large:
1. A trained linear model (and other models that follow the same conventions)
contains the training data itself and related quantities (such as residuals).
While this is convenient for some kinds of analysis, it negates the space
saving effect of compacting the training data into the model parameters.
2. Any function created in the loop contains the training data in its enclosing
environment. For example, a linearising transform defined as
linearise <- function(x) {
x^gamma
}
(where gamma is derived from the training data) does not only contain `gamma`
but other objects in its enclosing environment as well (e.g. intermediate
computations in the loop). If `linearise` is returned with the model, those
objects are also returned implicitly.
The first point can be dealt with by removing those components of the model which
are not necessary for prediction (e.g. model$residuals <- NULL). For the second
point, more work and care is needed to clean up all enclosing environments of
created functions (not only `linearise` but also model$terms etc.).
I have read that V8's garbage collector avoids this problem by distinguishing
between local and context variables
https://stackoverflow.com/questions/5326300/garbage-collection-with-node-js
Can something similar be done in R? Is there a programming technique that is less tedious
than "manual" cleanup of all enclosing environments?
R's semantics do not permit this sort of optimization in general --
there may be something we could do if users could provide annotations
that allow the semantics to be relaxed; that sort of thing is being
considered but won't be available anytime soon.
The approach I use in situations like this is to write top-level
functions that create closures. So for your example, replace
linearise <- function(x) {
x^gamma
}
with
makeLinearize(x, gamma),
where makeLinearize is defined at top level as
makeLinearize <- function(x, gamma) {
function(x) {
x^gamma
}
}
Best,
luke
Thanks,
Christian
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--
Luke Tierney
Ralph E. Wareham Professor of Mathematical Sciences
University of Iowa Phone: 319-335-3386
Department of Statistics and Fax: 319-335-3017
Actuarial Science
241 Schaeffer Hall email: luke-tier...@uiowa.edu
Iowa City, IA 52242 WWW: http://www.stat.uiowa.edu
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