Revolution R Enterprise 6.0 launched!

Just got the email-more software is good news!

Revolution R Enterprise 6.0 for 32-bit and 64-bit Windows and 64-bit Red Hat Enterprise Linux (RHEL 5.x and RHEL 6.x) features an updated release of the RevoScaleR package that provides fast, scalable data management and data analysis: the same code scales from data frames to local, high-performance .xdf files to data distributed across a Windows HPC Server cluster or IBM Platform Computing LSF cluster.  RevoScaleR also allows distribution of the execution of essentially any R function across cores and nodes, delivering the results back to the user.

Detailed information on what’s new in 6.0 and known issues:
http://www.revolutionanalytics.com/doc/README_RevoEnt_Windows_6.0.0.pdf

and from the manual-lots of function goodies for Big Data

 

  • IBM Platform LSF Cluster support [Linux only]. The new RevoScaleR function, RxLsfCluster, allows you to create a distributed compute context for the Platform LSF workload manager.
  •  Azure Burst support added for Microsoft HPC Server [Windows only]. The new RevoScaleR function, RxAzureBurst, allows you to create a distributed compute context to have computations performed in the cloud using Azure Burst
  • The rxExec function allows distributed execution of essentially any R function across cores and nodes, delivering the results back to the user.
  • functions RxLocalParallel and RxLocalSeq allow you to create compute context objects for local parallel and local sequential computation, respectively.
  • RxForeachDoPar allows you to create a compute context using the currently registered foreach parallel backend (doParallel, doSNOW, doMC, etc.). To execute rxExec calls, simply register the parallel backend as usual, then set your compute context as follows: rxSetComputeContext(RxForeachDoPar())
  • rxSetComputeContext and rxGetComputeContext simplify management of compute contexts.
  • rxGlm, provides a fast, scalable, distributable implementation of generalized linear models. This expands the list of full-featured high performance analytics functions already available: summary statistics (rxSummary), cubes and cross tabs (rxCube,rxCrossTabs), linear models (rxLinMod), covariance and correlation matrices (rxCovCor),
    binomial logistic regression (rxLogit), and k-means clustering (rxKmeans)example: a Tweedie family with 1 million observations and 78 estimated coefficients (categorical data)
    took 17 seconds with rxGlm compared with 377 seconds for glm on a quadcore laptop

     

    and easier working with R’s big brother SAS language

     

    RevoScaleR high-performance analysis functions will now conveniently work directly with a variety of external data sources (delimited and fixed format text files, SAS files, SPSS files, and ODBC data connections). New functions are provided to create data source objects to represent these data sources (RxTextData, RxOdbcData, RxSasData, and RxSpssData), which in turn can be specified for the ‘data’ argument for these RevoScaleR analysis functions: rxHistogramrxSummary, rxCube, rxCrossTabs, rxLinMod, rxCovCor, rxLogit, and rxGlm.


    example, 

    you can analyze a SAS file directly as follows:


    # Create a SAS data source with information about variables and # rows to read in each chunk

    sasDataFile <- file.path(rxGetOption(“sampleDataDir”),”claims.sas7bdat”)
    sasDS <- RxSasData(sasDataFile, stringsAsFactors = TRUE,colClasses = c(RowNum = “integer”),rowsPerRead = 50)

    # Compute and draw a histogram directly from the SAS file
    rxHistogram( ~cost|type, data = sasDS)
    # Compute summary statistics
    rxSummary(~., data = sasDS)
    # Estimate a linear model
    linModObj <- rxLinMod(cost~age + car_age + type, data = sasDS)
    summary(linModObj)
    # Import a subset into a data frame for further inspection
    subData <- rxImport(inData = sasDS, rowSelection = cost > 400,
    varsToKeep = c(“cost”, “age”, “type”))
    subData

 

The installation instructions and instructions for getting started with Revolution R Enterprise & RevoDeployR for Windows: http://www.revolutionanalytics.com/downloads/instructions/windows.php

Interview Alvaro Tejada Galindo, SAP Labs Montreal, Using SAP Hana with #Rstats

Here is a brief interview with Alvaro Tejada Galindo aka Blag who is a developer working with SAP Hana and R at SAP Labs, Montreal. SAP Hana is SAP’s latest offering in BI , it’s also a database and a computing environment , and using R and HANA together on the cloud can give major productivity gains in terms of both speed and analytical ability, as per preliminary use cases.

Ajay- Describe how you got involved with databases and R language.
Blag-  I used to work as an ABAP Consultant for 11 years, but also been involved with programming since the last 13 years, so I was in touch with SQLServer, Oracle, MySQL and SQLite. When I joined SAP, I heard that SAP HANA was going to use an statistical programming language called “R”. The next day I started my “R” learning.

Ajay- What made the R language a fit for SAP HANA. Did you consider other languages? What is your view on Julia/Python/SPSS/SAS/Matlab languages

Blag- I think “R” is a must for SAP HANA. As the fastest database in the market, we needed a language that could help us shape the data in the best possible way. “R” filled that purpose very well. Right now, “R” is not the only language as “L” can be used as well (http://wiki.tcl.tk/17068) …not forgetting “SQLScript” which is our own version of SQL (http://goo.gl/x3bwh) . I have to admit that I tried Julia, but couldn’t manage to make it work. Regarding Python, it’s an interesting question as I’m going to blog about Python and SAP HANA soon. About Matlab, SPSS and SAS I haven’t used them, so I got nothing to say there.

Ajay- What is your view on some of the limitations of R that can be overcome with using it with SAP HANA.

Blag-  I think mostly the ability of SAP HANA to work with big data. Again, SAP HANA and “R” can work very nicely together and achieve things that weren’t possible before.

Ajay-  Have you considered other vendors of R including working with RStudio, Revolution Analytics, and even Oracle R Enterprise.

Blag-  I’m not really part of the SAP HANA or the R groups inside SAP, so I can’t really comment on that. I can only say that I use RStudio every time I need to do something with R. Regarding Oracle…I don’t think so…but they can use any of our products whenever they want.

Ajay- Do you have a case study on an actual usage of R with SAP HANA that led to great results.

Blag-   Right now the use of “R” and SAP HANA is very preliminary, I don’t think many people has start working on it…but as an example that it works, you can check this awesome blog entry from my friend Jitender Aswani “Big Data, R and HANA: Analyze 200 Million Data Points and Later Visualize Using Google Maps “ (http://allthingsr.blogspot.com/#!/2012/04/big-data-r-and-hana-analyze-200-million.html)

Ajay- Does your group in SAP plan to give to the R ecosystem by attending conferences like UseR 2012, sponsoring meets, or package development etc

Blag- My group is in charge of everything developers, so sure, we’re planning to get more in touch with R developers and their ecosystem. Not sure how we’re going to deal with it, but at least I’m going to get myself involved in the Montreal R Group.

 

About-

http://scn.sap.com/people/alvaro.tejadagalindo3

Name: Alvaro Tejada Galindo
Email: a.tejada.galindo@sap.com
Profession: Development
Company: SAP Canada Labs-Montreal
Town/City: Montreal
Country: Canada
Instant Messaging Type: Twitter
Instant Messaging ID: Blag
Personal URL: http://blagrants.blogspot.com
Professional Blog URL: http://www.sdn.sap.com/irj/scn/weblogs?blog=/pub/u/252210910
My Relation to SAP: employee
Short Bio: Development Expert for the Technology Innovation and Developer Experience team.Used to be an ABAP Consultant for the last 11 years. Addicted to programming since 1997.

http://www.sap.com/solutions/technology/in-memory-computing-platform/hana/overview/index.epx

and from

http://en.wikipedia.org/wiki/SAP_HANA

SAP HANA is SAP AG’s implementation of in-memory database technology. There are four components within the software group:[1]

  • SAP HANA DB (or HANA DB) refers to the database technology itself,
  • SAP HANA Studio refers to the suite of tools provided by SAP for modeling,
  • SAP HANA Appliance refers to HANA DB as delivered on partner certified hardware (see below) as anappliance. It also includes the modeling tools from HANA Studio as well replication and data transformation tools to move data into HANA DB,[2]
  • SAP HANA Application Cloud refers to the cloud based infrastructure for delivery of applications (typically existing SAP applications rewritten to run on HANA).

R is integrated in HANA DB via TCP/IP. HANA uses SQL-SHM, a shared memory-based data exchange to incorporate R’s vertical data structure. HANA also introduces R scripts equivalent to native database operations like join or aggregation.[20] HANA developers can write R scripts in SQL and the types are automatically converted in HANA. R scripts can be invoked with HANA tables as both input and output in the SQLScript. R environments need to be deployed to use R within SQLScript

More blog posts on using SAP and R together

Dealing with R and HANA

http://scn.sap.com/community/in-memory-business-data-management/blog/2011/11/28/dealing-with-r-and-hana
R meets HANA

http://scn.sap.com/community/in-memory-business-data-management/blog/2012/01/29/r-meets-hana

HANA meets R

http://scn.sap.com/community/in-memory-business-data-management/blog/2012/01/26/hana-meets-r
When SAP HANA met R – First kiss

http://scn.sap.com/community/developer-center/hana/blog/2012/05/21/when-sap-hana-met-r–first-kiss

 

Using RODBC with SAP HANA DB-

SAP HANA: My experiences on using SAP HANA with R

http://scn.sap.com/community/in-memory-business-data-management/blog/2012/02/21/sap-hana-my-experiences-on-using-sap-hana-with-r

and of course the blog that started it all-

Jitender Aswani’s http://allthingsr.blogspot.in/

 

 

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

 

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: SourceDatasetModel and Prediction. You cancreatereadupdate, and delete resources using the respective standard HTTP methods: POSTGET,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

send email by R

For automated report delivery I have often used send email options in BASE SAS. For R, for scheduling tasks and sending me automated mails on completion of tasks I have two R options and 1 Windows OS scheduling option. Note red font denotes the parameters that should be changed. Anything else should NOT be changed.

Option 1-

Use the mail package at

http://cran.r-project.org/web/packages/mail/mail.pdf

> library(mail)

Attaching package: ‘mail’

The following object(s) are masked from ‘package:sendmailR’:

sendmail

>
> sendmail(“ohri2007@gmail.com“, subject=”Notification from R“,message=“Calculation finished!”, password=”rmail”)
[1] “Message was sent to ohri2007@gmail.com! You have 19 messages left.”

Disadvantage- Only 20 email messages by IP address per day. (but thats ok!)

Option 2-

use sendmailR package at http://cran.r-project.org/web/packages/sendmailR/sendmailR.pdf

install.packages()
library(sendmailR)
from <- sprintf(“<sendmailR@%s>”, Sys.info()[4])
to <- “<ohri2007@gmail.com>”
subject <- “Hello from R
body <- list(“It works!”, mime_part(iris))
sendmail(from, to, subject, body,control=list(smtpServer=”ASPMX.L.GOOGLE.COM”))

 

 

BiocInstaller version 1.2.1, ?biocLite for help
> install.packages(“sendmailR”)
Installing package(s) into ‘/home/ubuntu/R/library’
(as ‘lib’ is unspecified)
also installing the dependency ‘base64’

trying URL ‘http://cran.at.r-project.org/src/contrib/base64_1.1.tar.gz&#8217;
Content type ‘application/x-gzip’ length 61109 bytes (59 Kb)
opened URL
==================================================
downloaded 59 Kb

trying URL ‘http://cran.at.r-project.org/src/contrib/sendmailR_1.1-1.tar.gz&#8217;
Content type ‘application/x-gzip’ length 6399 bytes
opened URL
==================================================
downloaded 6399 bytes

BiocInstaller version 1.2.1, ?biocLite for help
* installing *source* package ‘base64’ …
** package ‘base64’ successfully unpacked and MD5 sums checked
** libs
gcc -std=gnu99 -I/usr/local/lib64/R/include -I/usr/local/include -fpic -g -O2 -c base64.c -o base64.o
gcc -std=gnu99 -shared -L/usr/local/lib64 -o base64.so base64.o -L/usr/local/lib64/R/lib -lR
installing to /home/ubuntu/R/library/base64/libs
** R
** preparing package for lazy loading
** help
*** installing help indices
** building package indices …
** testing if installed package can be loaded
BiocInstaller version 1.2.1, ?biocLite for help

* DONE (base64)
BiocInstaller version 1.2.1, ?biocLite for help
* installing *source* package ‘sendmailR’ …
** package ‘sendmailR’ successfully unpacked and MD5 sums checked
** R
** preparing package for lazy loading
** help
*** installing help indices
** building package indices …
** testing if installed package can be loaded
BiocInstaller version 1.2.1, ?biocLite for help

* DONE (sendmailR)

The downloaded packages are in
‘/tmp/RtmpsM222s/downloaded_packages’
> library(sendmailR)
Loading required package: base64
> from <- sprintf(“<sendmailR@%s>”, Sys.info()[4])
> to <- “<ohri2007@gmail.com>”
> subject <- “Hello from R”
> body <- list(“It works!”, mime_part(iris))
> sendmail(from, to, subject, body,
+ control=list(smtpServer=”ASPMX.L.GOOGLE.COM”))
$code
[1] “221”

$msg
[1] “2.0.0 closing connection ff2si17226764qab.40”

Disadvantage-This worked when I used the Amazon Cloud using the BioConductor AMI (for free 2 hours) at http://www.bioconductor.org/help/cloud/

It did NOT work when I tried it use it from my Windows 7 Home Premium PC from my Indian ISP (!!) .

It gave me this error

or in wait_for(250) :
SMTP Error: 5.7.1 [180.215.172.252] The IP you’re using to send mail is not authorized

 

PAUSE–

ps Why do this (send email by R)?

Note you can add either of the two programs of the end of the code that you want to be notified automatically. (like daily tasks)

This is mostly done for repeated business analytics tasks (like reports and analysis that need to be run at specific periods of time)

pps- What else can I do with this?

Can be modified to include sms or tweets  or even blog by email by modifying the   “to”  location appropriately.

3) Using Windows Task Scheduler to run R codes automatically (either the above)

or just sending an email

got to Start>  All Programs > Accessories >System Tools > Task Scheduler ( or by default C:Windowssystem32taskschd.msc)

Create a basic task

Now you can use this to run your daily/or scheduled R code  or you can send yourself email as well.

and modify the parameters- note the SMTP server (you can use the ones for google in example 2 at ASPMX.L.GOOGLE.COM)

and check if it works!

 

Related

 Geeky Things , Bro

Configuring IIS on your Windows 7 Home Edition-

note path to do this is-

Control Panel>All Control Panel Items> Program and Features>Turn Windows features on or off> Internet Information Services

and

http://stackoverflow.com/questions/709635/sending-mail-from-batch-file

 

Using R for Cloud Computing – made very easy and free by BioConductor

I really liked the no hassles way Biocnoductor has put a cloud AMI loaded with RStudio to help people learn R, and even try using R from within a browser in the cloud.

Not only is the tutorial very easy to use- they also give away 2 hours for free computing!!!

Check it out-

Step 1

Step 2

Step 3

and wow! I am using Google Chrome to run R ..and its awesome!

Interesting- check out two hours for free — all you need is a browser and internet connection

http://www.bioconductor.org/help/cloud/