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?
Thanks,
Christian
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