Name: sgd
Owner: Airoldi Lab
Description: An R package for large scale estimation with stochastic gradient descent
Created: 2014-12-05 23:10:59.0
Updated: 2017-10-20 04:19:09.0
Pushed: 2017-11-12 04:23:47.0
Size: 2126
Language: C++
GitHub Committers
User | Most Recent Commit | # Commits |
---|
Other Committers
User | Most Recent Commit | # Commits |
---|
sgd is an R package for large scale estimation. It features many stochastic gradient methods, built-in models, visualization tools, automated hyperparameter tuning, model checking, interval estimation, and convergence diagnostics.
At the core of the package is the function
formula, data, model, model.control, sgd.control)
It estimates parameters for a given data set and model using stochastic gradient
descent. The optional arguments model.control
and sgd.control
specify
attributes about the model and stochastic gradient method. Taking advantage of
the bigmemory package, sgd also operates on data sets which are too large to fit
in RAM as well as streaming data.
Example of large-scale linear regression:
ary(sgd)
mensions
1e5 # number of data points
1e2 # number of features
nerate data.
matrix(rnorm(N*d), ncol=d)
a <- rep(5, d+1)
<- rnorm(N)
cbind(1, X) %*% theta + eps
<- data.frame(y=y, x=X)
theta <- sgd(y ~ ., data=dat, model="lm")
Any loss function may be specified. For convenience the following are built-in:
The following stochastic gradient methods exist:
Check out the vignette in vignettes/
or examples in demo/
.
In R, the equivalent commands are vignette(package="sgd")
and
demo(package="sgd")
.
To install the latest version from CRAN:
all.packages("sgd")
To install the latest development version from Github:
stall.packages("devtools")
ools::install_github("airoldilab/sgd")
sgd is written by Dustin Tran and Panos Toulis, and is under active development. Please feel free to contribute by submitting any issues or requests?or by solving any current issues!
We thank all other members of the Airoldi Lab (led by Prof. Edo Airoldi) for their feedback and contributions.
icle{tran2015stochastic,
thor = {Tran, Dustin and Toulis, Panos and Airoldi, Edoardo M},
tle = {Stochastic gradient descent methods for estimation with large data sets},
urnal = {arXiv preprint arXiv:1509.06459},
ar = {2015}