I am looking at a couple of complex models that seem to need a lot of computational power. I am currently using the R package "glmmTMB" to account for spatio-temporal autocorrelation and random effects. In theory, glmmTMB should be able to run much faster using parallelization: https://cran.r-project.org/web/packages/glmmTMB/vignettes/parallel.html
If your OS supports OpenMP parallelization and R was installed using
OpenMP, glmmTMB will automatically pick up the OpenMP flags from R’s
Makevars and compile the C++ model with OpenMP support. If the flag is
not available, then the model will be compiled with serial
optimization only.
Instead of running these models on my personal maschine, I decided to set up a virtual maschine in a HPC environment. How can I install R using OpenMP on Ubuntu 20.04? I couldn't find anything on this topic.
There are some options to access R libraries in Spark:
directly using sparkr
using language bindings like rpy2 or rscala
using standalone service like opencpu
It looks like SparkR is quite limited, OpenCPU requires keeping additional service and bindings can have stability issue. Is there something else specific to Spark architecture which make using any solution not easy.
Do you have any experience with integrating R and Spark you can share?
The main language for the project seems like an important factor.
If pyspark is a good way to use Spark for you (meaning that you are accessing Spark from Python) accessing R through rpy2 should not make much difference from using any other Python library with a C-extension.
There exist reports of users doing so (although with occasional questions such as How can I partition pyspark RDDs holding R functions or Can I connect an external (R) process to each pyspark worker during setup)
If R is your main language, helping the SparkR authors with feedback or contributions where you feel there are limitation would be way to go.
If your main language is Scala, rscala should be your first try.
While the combo pyspark + rpy2 would seem the most "established" (as in "uses the oldest and probably most-tried codebase"), this does not necessarily mean that it is the best solution (and young packages can evolve quickly). I'd assess first what is the preferred language for the project and try options from there.
I'm trying to use the google-analytics framework to create predictive analysis tools. For example I would like to cluster my webpage visitors, etc.
In general, is there any list of machine learning algorithms implemented by this framework? for example: regression, clustering, classification, feature selection, etc.
Thank you for any help
Depending upon your language of choice, you might want to export your Google Analytics Metrics to flat files or a database and then start experimenting with ML models. Two popular languages with stable ML Implementations are Python and R. R's caret package includes tools for building a predictive model pipeline. Python's scikit-learn also contains implementations of all major classes of ML algorithms.
When you say GA framework I'll assume you're referring to the set of Google Analytics APIs listed here. The framework by itself doesn't provide machine learning capabilities. It merely provides access to the processed and aggregated GA data stored in Google's servers. You can use the API and feed the data to a machine learning application/system/program that does all of the stuff you mentioned.
There doesn't seem to be too many options for deploying predictive models in production which is surprising given the explosion in Big Data.
I understand that the open-source PMML can be used to export models as an XML specification. This can then be used for in-database scoring/prediction. However it seems that to make this work you need to use the PMML plugin by Zementis which means the solution is not truly open source. Is there an easier open way to map PMML to SQL for scoring?
Another option would be to use JSON instead of XML to output model predictions. But in this case, where would the R model sit? I'm assuming it would always need to be mapped to SQL...unless the R model could sit on the same server as the data and then run against that incoming data using an R script?
Any other options out there?
The following is a list of the alternatives that I have found so far to deploy an R model in production. Please note that the workflow to use these products varies significantly between each other, but they are all somehow oriented to facilitate the process of exposing a trained R model as a service:
openCPU
AzureML
DeployR
yhat (already mentioned by #Ramnath)
Domino
Sense.io
The answer really depends on what your production environment is.
If your "big data" are on Hadoop, you can try this relatively new open source PMML "scoring engine" called Pattern.
Otherwise you have no choice (short of writing custom model-specific code) but to run R on your server. You would use save to save your fitted models in .RData files and then load and run corresponding predict on the server. (That is bound to be slow but you can always try and throw more hardware at it.)
How you do that really depends on your platform. Usually there is a way to add "custom" functions written in R. The term is UDF (user-defined function). In Hadoop you can add such functions to Pig (e.g. https://github.com/cd-wood/pigaddons) or you can use RHadoop to write simple map-reduce code that would load the model and call predict in R. If your data are in Hive, you can use Hive TRANSFORM to call external R script.
There are also vendor-specific ways to add functions written in R to various SQL databases. Again look for UDF in the documentation. For instance, PostgreSQL has PL/R.
You can create RESTful APIs for your R scripts using plumber (https://github.com/trestletech/plumber).
I wrote a blog post about it (http://www.knowru.com/blog/how-create-restful-api-for-machine-learning-credit-model-in-r/) using deploying credit models as an example.
In general, I do not recommend PMML because the packages you used might not support translation to PMML.
A common practice is scoring a new/updated dataset in R and moving only the results (IDs, scores, probabilities, other necessary fields) into the production environment/data warehouse.
I know this has its limitations (infrequent refreshes, reliance upon IT, data set size/computing power restrictions) and may not be the cutting edge answer many (of your bosses) are looking for; but for many use-cases this works well (and is cost friendly!).
It’s been a few years since the question was originally asked.
For rapid prototyping I would argue the easiest approach currently is to use the Jupyter Kernel Gateway. This allows you to add REST endpoints to any cell in your Jupyter notebook. This works for both R and Python, depending on the kernel you’re using.
This means you can easily call any R or Python code through a web interface. When used in conjunction with Docker it lends itself to a microservices approach to deploying and scaling your application.
Here’s an article that takes you from start to finish to quickly set up your Jupyter Notebook with the Jupyter Kernel Gateway.
Learn to Build Machine Learning Services, Prototype Real Applications, and Deploy your Work to Users
For moving solutions to production the leading approach in 2019 is to use Kubeflow. Kubeflow was created and is maintained by Google, and makes "scaling machine learning (ML) models and deploying them to production as simple as possible."
From their website:
You adapt the configuration to choose the platforms and services that you want to use for each stage of the ML workflow: data preparation, model training, prediction serving, and service management.
You can choose to deploy your workloads locally or to a cloud environment.
Elise from Yhat here.
Like #Ramnath and #leo9r mentioned, our software allows you to put any R (or Python, for that matter) model directly into production via REST API endpoints.
We handle real-time or batch, as well as all of the model testing and versioning + systems management associated with the process.
This case study we co-authored with VIA SMS might be useful if you're thinking about how to get R models into production (their data sci team was recoding into PHP prior to using Yhat).
Cheers!
I do a lot of computational intelligence research. I have used Matlab almost exclusively as my programming medium for a decade or so. I am now trying to move to OSS. I have settled on R as my new environment.
After a long search for neural net software, the only Matlab-comparable OSS packages are Stuttgart NN and FANN (this can be debated another time =). The former doesn't appear to be maintained so I'd like to go with the latter. So my question is:
Does anyone have experience using R and FANN?
FANN has C++ bindings and R seems to have a couple of packages for a C++ interface, but since I'm a R newbie I need an idea of where exactly to start. Any guidance or recommendations would be appreciated.
Cheers.
I do not know anything abuot FANN but I can assure you that R has an actively maintained interface to the Stuttgart Neural Net Simulator (SNNS) library via the
RSNNS package --- as RSNNS happens to employ the
Rcpp package for interfacing R and C++ which I am involved in.