Tag: model

R on Windows HPC Server

From HPC Wire, the newsletter/site for all HPC news-

Source- Link

PALO ALTO, Calif., Sept. 20 — Revolution Analytics, the leading commercial provider of software and support for the popular open source R statistics language, today announced it will deliver Revolution R Enterprise for Microsoft Windows HPC Server 2008 R2, released today, enabling users to analyze very large data sets in high-performance computing environments.

R is a powerful open source statistics language and the modern system for predictive analytics. Revolution Analytics recently introduced RevoScaleR, new “Big Data” analysis capabilities, to its R distribution, Revolution R Enterprise. RevoScaleR solves the performance and capacity limitations of the R language by with parallelized algorithms that stream data across multiple cores on a laptop, workstation or server. Users can now process, visualize and model terabyte-class data sets at top speeds — without the need for specialized hardware.

“Revolution Analytics is pleased to support Microsoft’s Technical Computing initiative, whose efforts will benefit scientists, engineers and data analysts,” said David Champagne, CTO at Revolution. “We believe the engineering we have done for Revolution R Enterprise, in particular our work on big-data statistics and multicore computing, along with Microsoft’s HPC platform for technical computing, makes an ideal combination for high-performance large scale statistical computing.”

“Processing and analyzing this ‘big data’ is essential to better prediction and decision making,” said Bill Hamilton, director of technical computing at Microsoft Corp. “Revolution R Enterprise for Windows HPC Server 2008 R2 gives customers an extremely powerful tool that handles analysis of very large data and high workloads.”

To learn more about Revolution R Enterprise and its Big Data capabilities, download thewhite paper. Revolution Analytics also has an on-demand webcast, “High-performance analytics with Revolution R and Windows HPC Server,” available online.

AND from Microsoft’s website

http://www.microsoft.com/hpc/en/us/solutions/hpc-for-life-sciences.aspx

REvolution R Enterprise »

REvolution Computing

REvolution R Enterprise is designed for both novice and experienced R users looking for a production-grade R distribution to perform mission critical predictive analytics tasks right from the desktop and scale across multiprocessor environments. Featuring RPE™ REvolution’s R Productivity Environment for Windows.

Of course R Enterprise is available on Linux but on Red Hat Enterprise Linux- it would be nice to see Amazom Machine Images as well as Ubuntu versions as well.

An Amazon Machine Image (AMI) is a special type of virtual appliance which is used to instantiate (create) a virtual machine within the Amazon Elastic Compute Cloud. It serves as the basic unit of deployment for services delivered using EC2.[1]

Like all virtual appliances, the main component of an AMI is a read-only filesystem image which includes an operating system (e.g., Linux, UNIX, or Windows) and any additional software required to deliver a service or a portion of it.[2]

The AMI filesystem is compressed, encrypted, signed, split into a series of 10MB chunks and uploaded into Amazon S3 for storage. An XML manifest file stores information about the AMI, including name, version, architecture, default kernel id, decryption key and digests for all of the filesystem chunks.

An AMI does not include a kernel image, only a pointer to the default kernel id, which can be chosen from an approved list of safe kernels maintained by Amazon and its partners (e.g., RedHat, Canonical, Microsoft). Users may choose kernels other than the default when booting an AMI.[3]

[edit]Types of images

  • Public: an AMI image that can be used by any one.
  • Paid: a for-pay AMI image that is registered with Amazon DevPay and can be used by any one who subscribes for it. DevPay allows developers to mark-up Amazon’s usage fees and optionally add monthly subscription fees.

Matlab-Mathematica-R and GPU Computing

Matlab announced they have a parallel computing toolbox- specially to enable GPU computing as well

http://www.mathworks.com/products/parallel-computing/

Parallel Computing Toolbox™ lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—let you parallelize MATLAB® applications without CUDA or MPI programming. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel.

MATLAB GPU Support

The toolbox provides eight workers (MATLAB computational engines) to execute applications locally on a multicore desktop. Without changing the code, you can run the same application on a computer cluster or a grid computing service (using MATLAB Distributed Computing Server™). You can run parallel applications interactively or in batch.

Parallel Computing with MATLAB on Amazon Elastic Compute Cloud (EC2)

Also a video of using Mathematica and GPU

Also R has many packages for GPU computing

Parallel computing: GPUs

from http://cran.r-project.org/web/views/HighPerformanceComputing.html

  • The gputools package by Buckner provides several common data-mining algorithms which are implemented using a mixture of nVidia‘s CUDA langauge and cublas library. Given a computer with an nVidia GPU these functions may be substantially more efficient than native R routines. The rpud package provides an optimised distance metric for NVidia-based GPUs.
  • The cudaBayesreg package by da Silva implements the rhierLinearModel from the bayesm package using nVidia’s CUDA langauge and tools to provide high-performance statistical analysis of fMRI voxels.
  • The rgpu package (see below for link) aims to speed up bioinformatics analysis by using the GPU.
  • The magma package provides an interface to the hybrid GPU/CPU library Magma (see below for link).
  • The gcbd package implements a benchmarking framework for BLAS and GPUs (using gputools).

I tried to search for SAS and GPU and SPSS and GPU but got nothing. Maybe they would do well to atleast test these alternative hardwares-

Also see Matlab on GPU comparison for the product Jacket vs Parallel Computing Toolbox

http://www.accelereyes.com/products/compare

Hearst DataMining Challenge

Check out the Hearst Data Mining Challenge- a new competition-sponsored by DMA, Hearst Magazine, and EXL

THE HEARST CHALLENGE STARTS ON OCTOBER 14TH

CHALLENGE

DESCRIPTION

Over the years, the magazine publishing industry has made significant strides in improving subscription based circulation by developing analytic frameworks that better predict customer response to acquisition and renewal offers. The objective of this contest is to apply the same analytic discipline and effectively predict newsstand locations “response”. Specifically the objective is to predict the number of copies to be placed in each newsstand location to optimize the overall contribution of the newsstand location typically referred to as draw.

Data for the competition is provided by CMG and Experian.

and

RULES

HOW TO ENTER: Beginning October 14th, 2010 at 12:01 AM (ET) throughDecember 3rd, 2010 at 11:59 PM (ET) go to the Hearst Challenge website located at http://www.HearstChallenge.com (the “Site”) and complete and submit the entry form pursuant to the onscreen instructions. Entrants will be provided a historical sample of newsstand location draw, sales and associated location level data to help develop their predictive algorithm. Hearst will in turn hold back two distinct sets of draw/sales data, one to be used as a validation set by the contestant and one to be used as a final contest evaluation set. Entrants may not include any other external variables for the challenge. Additional details will be provided with the data. Entrants will be able to track their performance against the validation set throughout the course of the challenge via a leader tracking board to be made available on the Site. Entries must include the following documentation:

  • Data file with id variables and expected sales values by store and publication
  • The final model/ algorithm code used to score the final data set
  • Any supporting documentation that pertains to the development of the submitted model/algorithm including variable creation. Variables that were used in the model need to be traced through from input to coefficient / node (if using a tree based methodology).

Check out http://www.hearstchallenge.com/index.php for further details.

Rattle Re-Introduced

Latest version of Rattle just went online-

Here is the change log- Dr Graham Williams is also coming out with a book on using Rattle- the R GUI devoted to data mining.

Source-http://cran.r-project.org/web/packages/rattle/index.html

rattle (2.5.42) unstable; urgency=low

  * Update rattle.info() to recursively identify all dependencies,
 report
    their version number and any updates available from CRAN and generate
    command to update packages that have updates available. See
    ?rattle.info for the options.

  * Fix bug causing R Dataset option of the Evaluate window to always
    revert to the first named dataset.

  * Fix bug in transforms where weights were not being handled in
    refreshing of the Data tab.

  * Fix a bug in box plots when trying to label outliers when there aren't
    any.

 -- Graham Williams <Graham.Williams@togaware.com>  Sun, 
19 Sep 2010 05:01:51 +1000

rattle (2.5.41) unstable; urgency=low

  * Use GtkBuilder for Export dialog.

  * Test use of glade vs GtkBuilder on multiple platforms.

  * Rename rattle.info to rattle.version.

  * Add weight column to data tab.

  * Support weights for nnet, multinom, survival.

  * Add weights information to PMML as a PMML Extension.

  * Ensure GtkFrame is available as a data type whilst waiting for 
updated
    RGtk2.

  * Bug fix to packageIsAvailable not reruning any result.

  * Replace destroy with withdraw for plot window as the former has
    started crashing R.

  * Improve Log formatting for various model build commands.

  * Be sure to include the car package for Anova for multinom models.

  * Release pmml 1.2.24: Bug fix glm binomial regression - note as
    classification model.

 -- Graham Williams <Graham.Williams@togaware.com>  Wed, 15 Sep 2010 
14:56:09 +1000
And a video I did of exploring various Rattle options using Camtasia,
 a very useful software for screen capture and video tutorials
from http://www.techsmith.com/download/camtasiatrial.asp
Updated- my video skils being quite bad- I replaced it with another video. 
However Camtasia is the best screen capture video tool


Also , an update Analyticdroid is on hold for now.
see- for more details http://rattle.togaware.com/

Making NeW R

Tal G in his excellent blog piece talks of “Why R Developers  should not be paid” http://www.r-statistics.com/2010/09/open-source-and-money-why-r-developers-shouldnt-be-paid/

His argument of love is not very original though it was first made by these four guys

I am going to argue that “some” R developers should be paid, while the main focus should be volunteers code. These R developers should be paid as per usage of their packages.

Let me expand.

Imagine the following conversation between Ross Ihaka, Norman Nie and Peter Dalgaard.

Norman- Hey Guys, Can you give me some code- I got this new startup.

Ross Ihaka and Peter Dalgaard- Sure dude. Here is 100,000 lines of code, 2000 packages and 2 decades of effort.

Norman- Thanks guys.

Ross Ihaka- Hey, What you gonna do with this code.

Norman- I will better it. Sell it. Finally beat Jim Goodnight and his **** Proc GLM and **** Proc Reg.

Ross- Okay, but what will you give us? Will you give us some code back of what you improve?

Norman – Uh, let me explain this open core …

Peter D- Well how about some royalty?

Norman- Sure, we will throw parties at all conferences, snacks you know at user groups.

Ross – Hmm. That does not sound fair. (walks away in a huff muttering)-He takes our code, sells it and wont share the code

Peter D- Doesnt sound fair. I am back to reading Hamlet, the great Dane, and writing the next edition of my book. I am glad I wrote a book- Ross didnt even write that.

Norman-Uh Oh. (picks his phone)- Hey David Smith, We need to write some blog articles pronto – these open source guys ,man…

———–I think that sums what has been going on in the dynamics of R recently. If Ross Ihaka and R Gentleman had adopted an open core strategy- meaning you can create packages to R but not share the original where would we all be?

At this point if he is reading this, David Smith , long suffering veteran of open source  flameouts is rolling his eyes while Tal G is wondering if he will publish this on R Bloggers and if so when or something.

Lets bring in another R veteran-  Hadley Wickham who wrote a book on R and also created ggplot. Thats the best quality, most often used graphics package.

In terms of economic utilty to end user- the ggplot package may be as useful if not more as the foreach package developed by Revolution Computing/Analytics.

Now http://cran.r-project.org/web/packages/foreach/index.html says that foreach is licensed under http://www.apache.org/licenses/LICENSE-2.0

However lets come to open core licensing ( read it here http://alampitt.typepad.com/lampitt_or_leave_it/2008/08/open-core-licen.html ) which is where the debate is- Revolution takes code- enhances it (in my opinion) substantially with new formats XDF for better efficieny, web services API, and soon coming next year a GUI (thanks in advance , Dr Nie and guys)

and sells this advanced R code to businesses happy to pay ( they are currently paying much more to DR Goodnight and HIS guys)

Why would any sane customer buy it from Revolution- if he could download exactly the same thing from http://r-project.org

Hence the business need for Revolution Analytics to have an enhanced R- as they are using a product based software model not software as a service model.

If Revolution gives away source code of these new enhanced codes to R core team- how will R core team protect the above mentioned intelectual property- given they have 2 decades experience of giving away free code , and back and forth on just code.

Now Revolution also has a marketing budget- and thats how they sponsor some R Core events, conferences, after conference snacks.

How would people decide if they are being too generous or too stingy in their contribution (compared to the formidable generosity of SAS Institute to its employees, stakeholders and even third party analysts).

Would it not be better- IF Revolution can shift that aspect of relationship to its Research and Development budget than it’s marketing budget- come with some sort of incentive for “SOME” developers – even researchers need grants and assistantships, scholarships, make a transparent royalty formula say 17.5 % of the NEW R sales goes to R PACKAGE Developers pool, which in turn examines usage rate of packages and need/merit before allocation- that would require Revolution to evolve from a startup to a more sophisticated corporate and R Core can use this the same way as John M Chambers software award/scholarship

Dont pay all developers- it would be an insult to many of them – say Prof Harrell creator of HMisc to accept – but can Revolution expand its dev base (and prospect for future employees) by even sponsoring some R Scholarships.

And I am sure that if Revolution opens up some more code to the community- they would the rest of the world and it’s help useful. If it cant trust people like R Gentleman with some source code – well he is a board member.

——————————————————————————————–

Now to sum up some technical discussions on NeW R

1)  An accepted way of benchmarking efficiencies.

2) Code review and incorporation of efficiencies.

3) Multi threading- Multi core usage are trends to be incorporated.

4) GUIs like R Commander E Plugins for other packages, and Rattle for Data Mining to have focussed (or Deducer). This may involve hiring User Interface Designers (like from Apple 😉  who will work for love AND money ( Even the Beatles charge royalty for that song)

5) More support to cloud computing initiatives like Biocep and Elastic R – or Amazon AMI for using cloud computers- note efficiency arguements dont matter if you just use a Chrome Browser and pay 2 cents a hour for an Amazon Instance. Probably R core needs more direct involvement of Google (Cloud OS makers) and Amazon as well as even Salesforce.com (for creating Force.com Apps). Note even more corporates here need to be involved as cloud computing doesnot have any free and open source infrastructure (YET)

_______________________________________________________

Debates will come and go. This is an interesting intellectual debate and someday the liitle guys will win the Revolution-

From Hugh M of Gaping Void-

http://www.gapingvoid.com/Moveable_Type/archives/cat_microsoft_blue_monster_series.html

HOW DOES A SOFTWARE COMPANY MAKE MONEY, IF ALL

SOFTWARE IS FREE?

“If something goes wrong with Microsoft, I can phone Microsoft up and have it fixed. With Open Source, I have to rely on the community.”

And the community, as much as we may love it, is unpredictable. It might care about your problem and want to fix it, then again, it may not. Anyone who has ever witnessed something online go “viral”, good or bad, will know what I’m talking about.

and especially-

http://gapingvoid.com/2007/04/16/how-well-does-open-source-currently-meet-the-needs-of-shareholders-and-ceos/

Source-http://gapingvoidgallery.com/

Kind of sums up why the open core licensing is all about.

Oracle Open World/ RODM package

From the press release, here comes Oracle Open World. They really have an excellent rock concert in that as well.

.NET and Windows @ Oracle Develop and Oracle OpenWorld 2010

Oracle Develop will again feature a .NET track for Oracle developers. Oracle Develop is suited for all levels of .NET developers, from beginner to advanced. It covers introductory Oracle .NET material, new features, deep dive application tuning, and includes three hours of hands-on labs apply what you learned from the sessions.

To register, go to Oracle Develop registration site.

Oracle OpenWorld will include several sessions on using the Oracle Database on Windows and .NET.

Session schedules and locations for Windows and .NET sessions at Oracle Develop and OpenWorld are now available.

Download: 32-bit ODAC 11.2.0.1.2 for Visual Studio 2010 and .NET Framework 4

With ODAC 11.2.0.1.2, developers can connect to Oracle Database versions 9.2 and higher from Visual Studio 2010 and .NET Framework 4. ODAC components support the full framework, as well as the new .NET Framework Client Profile.

Statement of Direction: Oracle Database and Microsoft Entity Framework

Learn about Oracle’s beta and production plans to support Microsoft Entity Framework with Oracle Database.

Also see http://www.oracle.com/technetwork/articles/datawarehouse/saternos-r-161569.html

for

Data Mining Using the RDOM Package

By Casimir Saternos

Some excerpts-

Open R and enter the following command.

> library(RODM)

This command loads the RODM library and as well the dependent RODBC package. The next step is to make a database connection.

> DB <- RODM_open_dbms_connection(dsn="orcl", uid="dm", pwd="dm")

Subsequent commands use the DB object (an instance of the RODBC class) to connect to the database. The DNS specified in the command is the name you used earlier for the Data Source Name during the ODBC connection configuration. You can view the actual R code being executed by the command by simply typing the function name (without parentheses).

> RODM_open_dbms_connection

And say making a Model in Oracle and R-

> numrows <- length(orange_data[,1])
> orange_data.rows <- length(orange_data[,1])
> orange_data.id <- matrix(seq(1, orange_data.rows),  nrow=orange_data.rows, ncol=1, dimnames= list(NULL, c(“CASE_ID”)))
> orange_data <- cbind(orange_data.id, orange_data)

This adjustment to the data frame then needs to be propagated to the database. You can confirm the change using the sqlColumns function, as listed earlier.

> RODM_create_dbms_table(DB, "orange_data")
> sqlColumns(DB, 'orange_data')$COLUMN_NAME

> glm <- RODM_create_glm_model(
database = DB,
data_table_name = “orange_data”,
case_id_column_name = “CASE_ID”,
target_column_name = “circumference”,
model_name = “GLM_MODEL”,
mining_function = “regression”)

Information about this model can then be obtained by analyzing value returned from the model and stored in the variable named glm.

> glm$model.model_settings
> glm$glm.globals
> $glm.coefficients

Once you have a model, you can apply the model to a new set of data. To begin, create or retrieve sample data in the same format as the training data.

> query<-('select 999 case_id, 1 tree, 120 age, 
32 circumference from dual')

> orange_test<-sqlQuery(DB, query)
> RODM_create_dbms_table(DB, "orange_test")
and 
Finally, the model can be applied to the new data set and the results analyzed.


results <- RODM_apply_model(database = DB, 
data_table_name = "orange_test",
model_name = "GLM_MODEL",
supplemental_cols = "circumference")

When your session is complete, you can clean up objects that were created (if you like) and you should close the database connection:

> RODM_drop_model(database=DB,'GLM_MODEL')
> RODM_drop_dbms_table(DB, "orange_test")
> RODM_drop_dbms_table(DB, "orange_data")
> RODM_close_dbms_connection(DB)

See the full article at http://www.oracle.com/technetwork/articles/datawarehouse/saternos-r-161569.html

AsterData releases nCluster 4.6

From the press release

Aster Data nCluster 4.6, which includes a column data store, making Aster Data nCluster 4.6 the first platform with a unified SQL-MapReduce analytic framework on a hybrid row and column massively parallel processing (MPP) database management system (DBMS). The unified SQL-MapReduce analytic framework and Aster Data’s suite of 1000+ MapReduce-ready analytic functions, delivers a substantial breakthrough in richer, high performance analytics on large data volumes where data can be stored in either a row or column format.

With Aster Data nCluster 4.6, customers can choose the data format best suited to their needs and benefit from the power of Aster Data’s SQL-MapReduce analytic capabilities, providing maximum query performance by leveraging row-only, column-only, or hybrid storage strategies. Aster Data makes selection of the appropriate storage strategy easy with the new Data Model Express tool that determines the optimal data model based on a customer’s query workloads.  Both row and column stores in Aster Data nCluster 4.6 benefit from platform-level services including Online Precision Scaling™ on commodity hardware, dynamic workload management, and always-on availability, all of which now operate on both row and column stores. All 1000+ MapReduce-ready analytic functions released previously through Aster Data Analytic Foundation — a powerful suite of pre-built MapReduce analytic software building blocks — now run on a hybrid row and column architecture.  Aster Data nCluster 4.6 also includes new pre-built analytic functions, including decision trees and histograms. For custom analytic application development, the Aster Data IDE, Aster Data Developer Express, also fully and seamlessly supports the hybrid row and column store in Aster DatanCluster 4.6.

More advanced analytics infrastructure.