Serializing Non-R Objects and R Objects Together

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When working with objects in R that are pointers to lower-level constructs, such as those used by popular machine learning libraries like LightGBM, saving and loading these objects can be a challenge. The standard solution often involves using separate savers and load functions specific to the library, which can lead to cluttered file systems and inconvenient workflows. In this article, we’ll explore an alternative approach that uses R’s built-in serialization functions to bind R objects and non-R objects together into a single file.

Understanding Serialization in R

Serialization is the process of converting an object into a byte stream that can be written to disk or sent over a network. R provides several functions for serializing objects, including saveRDS, writeBin, and readBin. These functions allow you to save and load R objects, as well as non-R objects like binary data.

The Problem with Saving Non-R Objects

When trying to serialize an object that is not a native R object, such as a LightGBM model, we encounter the issue of how to represent this object in a way that can be written to disk. The saveRDS function, which is designed for serializing R objects, will fail when encountering a non-R object.

Using readBin to Serialize Non-R Objects

One potential solution is to use the readBin function to serialize the non-R object as binary data. This approach involves creating a temporary file and writing the non-R object’s binary representation to it using writeBin. We can then read this binary data back into R using readBin.

Here’s an example of how we might use readBin to serialize a LightGBM model:

tf <- tempfile()
lgb.save(bst, file = tf)
bst <- 100:150 # fake data
writeBin(bst, file = tf) # poor man's lgb.save :-)

In this example, we create a temporary file tf and write the LightGBM model’s binary representation to it using writeBin. We then create some fake data and write it to the same file.

Binding R Objects and Non-R Objects Together

Now that we have a way to serialize non-R objects as binary data, we can bind R objects and non-R objects together into a single file. We’ll use saveRDS to serialize an R object (in this case, a list containing the serialized LightGBM model) along with the non-R object’s binary data.

Here’s the updated code:

results <- list(bst = rawbst, metadata = 'other stuff')
saveRDS(results, file = 'so_post_temp')

In this example, we create an R object results that contains two components: a serialized LightGBM model (rawbst) and some metadata. We then use saveRDS to serialize the entire object to disk.

Loading and Rehydrating the Serialized Object

To load the serialized object back into R, we’ll use readBin to deserialize the non-R object’s binary data and readRDS to deserialize the R object.

tf2 <- tempfile()
results <- readRDS('so_post_temp')
writeBin(results$bst, tf2)
bst <- lgb.load(tf2)
file.remove(tf2)

In this example, we create a temporary file tf2 and use readRDS to deserialize the R object from disk. We then write the deserialized LightGBM model’s binary data back to the same file using writeBin. Finally, we load the deserialized model into R using lgb.load.

Conclusion

Serializing non-R objects and binding them together with R objects is a powerful technique for working with complex machine learning workflows. By leveraging R’s built-in serialization functions, such as saveRDS, writeBin, and readBin, we can create flexible and efficient workflows that scale to large datasets.

While this approach may require some additional effort to get right, the benefits of binding R objects and non-R objects together are well worth it. With this technique, you’ll be able to streamline your workflow, reduce clutter, and focus on what matters most: building robust and scalable machine learning models.

Last modified on 2023-09-01