BigML meets R #rstats

I am just checking the nice new R package created by BigML.com co-founder Justin Donaldson. The name of the new package is bigml, which can confuse a bit since there do exist many big suffix named packages in R (including biglm)

The bigml package is available at CRAN http://cran.r-project.org/web/packages/bigml/index.html

I just tweaked the code given at http://blog.bigml.com/2012/05/10/r-you-ready-for-bigml/ to include the ssl authentication code at http://www.brocktibert.com/blog/2012/01/19/358/

so it goes

> library(bigml)
Loading required package: RJSONIO
Loading required package: RCurl
Loading required package: bitops
Loading required package: plyr
> setCredentials(“bigml_username”,”API_key”)

# download the file needed for authentication
download.file(url="http://curl.haxx.se/ca/cacert.pem", destfile="cacert.pem")

# set the curl options
curl <- getCurlHandle()
options(RCurlOptions = list(capath = system.file("CurlSSL", "cacert.pem",
package = "RCurl"),
ssl.verifypeer = FALSE))
curlSetOpt(.opts = list(proxy = 'proxyserver:port'), curl = curl)
> iris.model = quickModel(iris, objective_field = ‘Species’)

Of course there are lots of goodies added here , so read the post yourself at http://blog.bigml.com/2012/05/10/r-you-ready-for-bigml/

Incidentally , the author of this R package (bigml) Justin Donalsdon who goes by name sudojudo at http://twitter.com/#!/sudojudo has also recently authored two other R packages including tsne at  http://cran.r-project.org/web/packages/tsne/index.html (tsne: T-distributed Stochastic Neighbor Embedding for R (t-SNE) -A “pure R” implementation of the t-SNE algorithm) and a GUI toolbar http://cran.r-project.org/web/packages/sculpt3d/index.html (sculpt3d is a GTK+ toolbar that allows for more interactive control of a dataset inside the RGL plot window. Controls for simple brushing, highlighting, labeling, and mouseMode changes are provided by point-and-click rather than through the R terminal interface)

This along with the fact the their recently released python bindings for bigml.com was one of the top news at Hacker News- shows bigML.com is going for some traction in bringing cloud computing, better software interfaces and data mining together!

Interview BigML.com

Here is an interview with Charlie Parker, head of large scale online algorithms at http://bigml.com

Ajay-  Describe your own personal background in scientific computing, and how you came to be involved with machine learning, cloud computing and BigML.com

Charlie- I am a machine learning Ph.D. from Oregon State University. Francisco Martin (our founder and CEO), Adam Ashenfelter (the lead developer on the tree algorithm), and myself were all studying machine learning at OSU around the same time. We all went our separate ways after that.

Francisco started Strands and turned it into a 100+ million dollar company building recommender systems. Adam worked for CleverSet, a probabilistic modeling company that was eventually sold to Cisco, I believe. I worked for several years in the research labs at Eastman Kodak on data mining, text analysis, and computer vision.

When Francisco left Strands to start BigML, he brought in Justin Donaldson who is a brilliant visualization guy from Indiana, and an ex-Googler named Jose Ortega who is responsible for most of our data infrastructure. They pulled in Adam and I a few months later. We also have Poul Petersen, a former Strands employee, who manages our herd of servers. He is a wizard and makes everyone else’s life much easier.

Ajay- You use clojure for the back end of BigML.com .Are there any other languages and packages you are considering? What makes clojure such a good fit for cloud computing ?

Charlie- Clojure is a great language because it offers you all of the benefits of Java (extensive libraries, cross-platform compatibility, easy integration with things like Hadoop, etc.) but has the syntactical elegance of a functional language. This makes our code base small and easy to read as well as powerful.

We’ve had occasional issues with speed, but that just means writing the occasional function or library in Java. As we build towards processing data at the Terabyte level, we’re hoping to create a framework that is language-agnostic to some extent. So if we have some great machine learning code in C, for example, we’ll use Clojure to tie everything together, but the code that does the heavy lifting will still be in C. For the API and Web layers, we use Python and Django, and Justin is a huge fan of HaXe for our visualizations.

 Ajay- Current support is for Decision Trees. When can we see SVM, K Means Clustering and Logit Regression?

Charlie- Right now we’re focused on perfecting our infrastructure and giving you new ways to put data in the system, but expect to see more algorithms appearing in the next few months. We want to make sure they are as beautiful and easy to use as the trees are. Without giving too much away, the first new thing we will probably introduce is an ensemble method of some sort (such as Boosting or Bagging). Clustering is a little further away but we’ll get there soon!

Ajay- How can we use the BigML.com API using R and Python.

Charlie- We have a public github repo for the language bindings. https://github.com/bigmlcom/io Right now, there there are only bash scripts but that should change very soon. The python bindings should be there in a matter of days, and the R bindings in probably a week or two. Clojure and Java bindings should follow shortly after that. We’ll have a blog post about it each time we release a new language binding. http://blog.bigml.com/

Ajay-  How can we predict large numbers of observations using a Model  that has been built and pruned (model scoring)?

Charlie- We are in the process of refactoring our backend right now for better support for batch prediction and model evaluation. This is something that is probably only a few weeks away. Keep your eye on our blog for updates!

Ajay-  How can we export models built in BigML.com for scoring data locally.

Charlie- This is as simple as a call to our API. https://bigml.com/developers/models The call gives you a JSON object representing the tree that is roughly equivalent to a PMML-style representation.

About-

You can read about Charlie Parker at http://www.linkedin.com/pub/charles-parker/11/85b/4b5 and the rest of the BigML team at

https://bigml.com/team

 

Protected: Converting SAS language code to Java

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Avengers Review

Avengers is the big ticket block buster which heralds summer just like the groundhog denotes spring. An ensemble cast (of superheroes and okay actors) , it stars Hulk (angry green man aka Dr Bruce Banner /Mark Ruffalo) ,Iron Man (genius billionaire philanthropist playboy aka Tony Stark / Robert Downey Jr), Thor (an Australian looking Chris H), Loki (God of Mischief played  by German looking Tom Hiddleston ), Captain America  and Scarlet Johnassen and Jeremy “Hurt Locker” Renner and Samuel L Jackson. You know somethings is gotta give if the A List stars(?) in the cast is going to be longer than a plot summary.

Well Loki the bad guys strikes a deal with some other bad Guys of funnily named world called Assguard (parallel universe!) and tries to find a cube (which is all energy powerful like the Transformers 1 Cube)  and in return gets an Army from the dark side (who look just  like Cybertrons and Lords of the Rings orcs combined).

The Avengers after much dilly dallying, trying to emote, create bromances, tension buildup, in the end decide to give you what you came looking for- a visual feast of credible looking CGI to counter the bad guys. The scene stealer is the Hulk. He is kind of cute for a big green guy, if you dont know what I mean, see the movie!

This is American cinema at its most profoundly intellectual since the Die Hard series. and its quite entertaining, especially if you are a geeky comic book fan-boy (like me).

Summer is here and so are the super-heroes!! Unleash the popcorn.

 

Software Review- Google Drive versus Dropbox

Here are some notes from reviewing Google Drive  https://drive.google.com/ vs Dropbox https://www.dropbox.com/.

1) Google Drive gives more free space upfront  than Dropbox.5GB versus 2GB

2) Dropbox has a referral system 500 mb per referral while there is no referral system for Google Drive

3) The sync facility with Google Docs makes Google Drive especially useful for prior users of Google Docs.

4) API access to Google Drive is only for Chrome apps which is intriguing!

https://developers.google.com/drive/apps_overview

Apps will not have any API access to files unless users have first installed the app in Chrome Web Store.

You can use the Dropbox API much more easily –

See the platforms at

https://www.dropbox.com/developers/start/core

Choose your platform:

iOS Android Python Ruby

But-

(though I wonder if you set the R working directory to the local shared drive for Google Drive it should sync up as well but of course be slower –http://scrogster.wordpress.com/2011/01/29/using-dropbox-with-r-2/)

5) Google Drive icon is ugly (seriously, dude!) , but the features in the Windows app is just the same as the Dropbox App. Too similar 😉

 

6) Upgrade space is much more cheaper to Google Drive than Dropbox ( by Google Drive prices being exactly  a quarter of prices on Dropbox and max storage being 16 times as much). This will affect power storage users. I expect to see some slowdown in Dropbox new business unless G Drive has outage (like Gmail) . Existing users at Dropbox probably wont shift for the small dollar amount- though it is quite easy to do so.

 

Install Google Drive on your local workstation and cut and paste your Dropbox local folder to the Google Drive local folder!!

7) Dropbox deserves credit for being first (like Hotmail and AOL) but Google Drive is almost better in all respects!

Google Drive

Free
5 GB of Drive (0% used)
10 GB of Gmail (48% used)
1 GB of Picasa (0% used)

Upgrade:

25 GB
2,49 $ / Month
+25 GB for Drive and Picasa
Bonus: Your Gmail storage will be upgraded to 25 GB.
Choose this plan

100 GB
4,99 $ / Month
+100 GB for Drive and Picasa
Bonus: Your Gmail storage will be upgraded to 25 GB.
Choose this plan

 Need more storage?

Up to 16 TB available

Dropbox–

Current account type

Large DropboxDropbox Badge greenFree
Free
Up to 18 GB (2 GB + 500 MB per referral)
 Account info 

Other account types

Large DropboxDropbox Badge orange50 GB +
Pro 50
+1 GB per referral, up to +32 GB
 $9.99/month or $99.00/year Upgrade to Pro 50
Large DropboxDropbox Badge purple100 GB +
Pro 100
+1 GB per referral, up to +32 GB
 $19.99/month or $199.00/year Upgrade to Pro 100
Triple DropboxDropbox For Teams Badge1 TB +
Teams
Plans starting at 1 TB
 Large shared quota, centralized admin and billing, and more!

 

 

 

Software Review- BigML.com – Machine Learning meets the Cloud

I had a chance to dekko the new startup BigML https://bigml.com/ and was suitably impressed by the briefing and my own puttering around the site. Here is my review-

1) The website is very intutively designed- You can create a dataset from an uploaded file in one click and you can create a Decision Tree model in one click as well. I wish other cloud computing websites like  Google Prediction API make design so intutive and easy to understand. Also unlike Google Prediction API, the models are not black box models, but have a description which can be understood.

2) It includes some well known data sources for people trying it out. They were kind enough to offer 5 invite codes for readers of Decisionstats ( if you want to check it yourself, use the codes below the post, note they are one time only , so the first five get the invites.

BigML is still invite only but plan to get into open release soon.

3) Data Sources can only be by uploading files (csv) but they plan to change this hopefully to get data from buckets (s3? or Google?) and from URLs.

4) The one click operation to convert data source into a dataset shows a histogram (distribution) of individual variables.The back end is clojure , because the team explained it made the easiest sense and fit with Java. The good news (?) is you would never see the clojure code at the back end. You can read about it from http://clojure.org/

As cloud computing takes off (someday) I expect clojure popularity to take off as well.

Clojure is a dynamic programming language that targets the Java Virtual Machine (and the CLR, and JavaScript). It is designed to be a general-purpose language, combining the approachability and interactive development of a scripting language with an efficient and robust infrastructure for multithreaded programming. Clojure is a compiled language – it compiles directly to JVM bytecode, yet remains completely dynamic. Every feature supported by Clojure is supported at runtime. Clojure provides easy access to the Java frameworks, with optional type hints and type inference, to ensure that calls to Java can avoid reflection.

Clojure is a dialect of Lisp

 

5) As of now decision trees is the only distributed algol, but they expect to roll out other machine learning stuff soon. Hopefully this includes regression (as logit and linear) and k means clustering. The trees are created and pruned in real time which gives a slightly animated (and impressive effect). and yes model building is an one click operation.

The real time -live pruning is really impressive and I wonder why /how it can ever be replicated in other software based on desktop, because of the sheer interactive nature.

 

Making the model is just half the work. Creating predictions and scoring the model is what is really the money-earner. It is one click and customization is quite intuitive. It is not quite PMML compliant yet so I hope some Zemanta like functionality can be added so huge amounts of models can be applied to predictions or score data in real time.

 

If you are a developer/data hacker, you should check out this section too- it is quite impressive that the designers of BigML have planned for API access so early.

https://bigml.com/developers

BigML.io gives you:

  • Secure programmatic access to all your BigML resources.
  • Fully white-box access to your datasets and models.
  • Asynchronous creation of datasets and models.
  • Near real-time predictions.

 

Note: For your convenience, some of the snippets below include your real username and API key.

Please keep them secret.

REST API

BigML.io conforms to the design principles of Representational State Transfer (REST). BigML.io is enterely HTTP-based.

BigML.io gives you access to four basic resources: Source, Dataset, Model and Prediction. You cancreate, read, update, and delete resources using the respective standard HTTP methods: POST, GET,PUT and DELETE.

All communication with BigML.io is JSON formatted except for source creation. Source creation is handled with a HTTP PUT using the “multipart/form-data” content-type

HTTPS

All access to BigML.io must be performed over HTTPS

and https://bigml.com/developers/quick_start ( In think an R package which uses JSON ,RCurl  would further help in enhancing ease of usage).

 

Summary-

Overall a welcome addition to make software in the real of cloud computing and statistical computation/business analytics both easy to use and easy to deploy with fail safe mechanisms built in.

Check out https://bigml.com/ for yourself to see.

The invite codes are here -one time use only- first five get the invites- so click and try your luck, machine learning on the cloud.

If you dont get an invite (or it is already used, just leave your email there and wait a couple of days to get approval)

  1. https://bigml.com/accounts/register/?code=E1FE7
  2. https://bigml.com/accounts/register/?code=09991
  3. https://bigml.com/accounts/register/?code=5367D
  4. https://bigml.com/accounts/register/?code=76EEF
  5. https://bigml.com/accounts/register/?code=742FD

Oracle R Updated!

Interesting message from https://blogs.oracle.com/R/ the latest R blog

 

_——–_

Oracle just released the latest update to Oracle R Enterprise, version 1.1. This release includes the Oracle R Distribution (based on open source R, version 2.13.2), an improved server installation, and much more.  The key new features include:

  • Extended Server Support: New support for Windows 32 and 64-bit server components, as well as continuing support for Linux 64-bit server components
  • Improved Installation: Linux 64-bit server installation now provides robust status updates and prerequisite checks
  • Performance Improvements: Improved performance for embedded R script execution calculations

In addition, the updated ROracle package, which is used with Oracle R Enterprise, now reads date data by conversion to character strings.

We encourage you download Oracle software for evaluation from the Oracle Technology Network. See these links for R-related software: Oracle R Distribution, Oracle R Enterprise, ROracle, Oracle R Connector for Hadoop.  As always, we welcome comments and questions on the Oracle R Forum.

 

 

Oracle R Distribution 2-13.2 Update Available

Oracle has released an update to the Oracle R Distribution, an Oracle-supported distribution of open source R. Oracle R Distribution 2-13.2 now contains the ability to dynamically link the following libraries on both Windows and Linux:

  • The Intel Math Kernel Library (MKL) on Intel chips
  • The AMD Core Math Library (ACML) on AMD chips

 

To take advantage of the performance enhancements provided by Intel MKL or AMD ACML in Oracle R Distribution, simply add the MKL or ACML shared library directory to the LD_LIBRARY_PATH system environment variable. This automatically enables MKL or ACML to make use of all available processors, vastly speeding up linear algebra computations and eliminating the need to recompile R.  Even on a single core, the optimized algorithms in the Intel MKL libraries are faster than using R’s standard BLAS library.

Open-source R is linked to NetLib’s BLAS libraries, but they are not multi-threaded and only use one core. While R’s internal BLAS are efficient for most computations, it’s possible to recompile R to link to a different, multi-threaded BLAS library to improve performance on eligible calculations. Compiling and linking to R yourself can be involved, but for many, the significantly improved calculation speed justifies the effort. Oracle R Distribution notably simplifies the process of using external math libraries by enabling R to auto-load MKL orACML. For R commands that don’t link to BLAS code, taking advantage of database parallelism usingembedded R execution in Oracle R Enterprise is the route to improved performance.

For more information about rebuilding R with different BLAS libraries, see the linear algebra section in the R Installation and Administration manual. As always, the Oracle R Distribution is available as a free download to anyone. Questions and comments are welcome on the Oracle R Forum.