kerasformula: A High-Level R Interface for Neural Nets

Building Neural Networks in R for Political Research

Pete Mohanty 8/14/2018

Political scientists are increasingly interested in machine learning approaches such as neural networks. Neural networks offer predictive accuracy in spite of complex data generating processes and may also aid researchers interested in examining the scope conditions of inferential claims. Until recently, the programming requirements for neural networks have been much steeper for neural networks than for statistical techniques like regression (perhaps not unlike the early days of Bayesian Markov Chain Monte Carlo) and many of the best techniques were limited to Python. This workshop introduces the theory behind neural networks and shows how to build them in R using the library kerasformula. The workshop will provide political examples such as Twitter data and Congressional forecasting. These examples will also serve to highlight the comparative strengths and weaknesses of neural networks in comparison with classical statistical approaches. The library kerasformula is a high-level interface for Keras and Tensorflow in R that allows researchers to fit a model in as little as one line of code and which allows for a high degree of customization (shape and depth of the network, loss and activation function, etc.). The workshop will be conducted in an ‘active learning’ paradigm whereby mini-lectures will alternate with hands-on coding activities. Participants will be encouraged to bring a sample of their own data and to build a working prototype by the end of the day. Some familiarity with R and RStudio is assumed but participants need not be advanced coders.

Data

Participants should have a sample of their own data in a data.frame which is clean enough to run a regression on. Alternatively, code will also be provided to quickly construct such a data.frame (similar to the data used in the slides).

Software

This course requires that that the R library kerasformula (version 1.5.1 or higher) be installed, as well as it's depedencies. How much fuss that is depends a bit on your computer (whether it's Windows or Mac, what you've already installed, and so on). Please note, due to various compability issues, (legacy) Python 2.7 is recommended, not (current) Python 3.x.

-- The Cloud (fastest, simplest install). In your web browser, go to https://rstudio.cloud and make a free account and then click to start a new project and open RStudio in your browser. Proceed with Mac Desktop instructions

-- Mac Desktop

Open R or RStudio and enter the following into the Console:

install.packages("tm")
install.packages("kerasformula")
library(kerasformula)
install_keras()                     # run only once

install_keras() is run only once on each computer (including if you use https://rstudio.cloud). install_keras() also provides high performance computing options (GPU) which will be briefly discusssed in the course but

-- Windows users If you have not already installed Python 2.7, please do so from here. Then proceed with Mac instructions.

-- Confirming if all has gone well, you can now fit a neural net like so:

hello_world <- kms(mpg ~ weight + cyl, mtcars)

-- Troubleshooting If that did not work, it could be that one or another dependency failed to install. In particular, check to see whether the R libraries tensorflow, keras, and reticulate are installed; install individually as need be. If everything installed but you are seeing a lengthy error message in Python (complaining in part about None or NoneType), R is probably attempting to access Tensorflow via Python 3.x. Assuming it's installed, load the library reticulate and provide the path to your copy of Python 2.7 to the use_python() function (documentation).