Tag: R

Which software do we buy? -It depends

Software (novel)
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Often I am asked by clients, friends and industry colleagues on the suitability or unsuitability of particular software for analytical needs.  My answer is mostly-

It depends on-

1) Cost of Type 1 error in purchase decision versus Type 2 error in Purchase Decision. (forgive me if I mix up Type 1 with Type 2 error- I do have some weird childhood learning disabilities which crop up now and then)

Here I define Type 1 error as paying more for a software when there were equivalent functionalities available at lower price, or buying components you do need , like SPSS Trends (when only SPSS Base is required) or SAS ETS, when only SAS/Stat would do.

The first kind is of course due to the presence of free tools with GUI like R, R Commander and Deducer (Rattle does have a 500$ commercial version).

The emergence of software vendors like WPS (for SAS language aficionados) which offer similar functionality as Base SAS, as well as the increasing convergence of business analytics (read predictive analytics), business intelligence (read reporting) has led to somewhat brand clutter in which all softwares promise to do everything at all different prices- though they all have specific strengths and weakness. To add to this, there are comparatively fewer business analytics independent analysts than say independent business intelligence analysts.

2) Type 2 Error- In this case the opportunity cost of delayed projects, business models , or lower accuracy – consequences of buying a lower priced software which had lesser functionality than you required.

To compound the magnitude of error 2, you are probably in some kind of vendor lock-in, your software budget is over because of buying too much or inappropriate software and hardware, and still you could do with some added help in business analytics. The fear of making a business critical error is a substantial reason why open source software have to work harder at proving them competent. This is because writing great software is not enough, we need great marketing to sell it, and great customer support to sustain it.

As Business Decisions are decisions made in the constraints of time, information and money- I will try to create a software purchase matrix based on my knowledge of known softwares (and unknown strengths and weakness), pricing (versus budgets), and ranges of data handling. I will add in basically an optimum approach based on known constraints, and add in flexibility for unknown operational constraints.

I will restrain this matrix to analytics software, though you could certainly extend it to other classes of enterprise software including big data databases, infrastructure and computing.

Noted Assumptions- 1) I am vendor neutral and do not suffer from subjective bias or affection for particular software (based on conferences, books, relationships,consulting etc)

2) All software have bugs so all need customer support.

3) All software have particular advantages , strengths and weakness in terms of functionality.

4) Cost includes total cost of ownership and opportunity cost of business analytics enabled decision.

5) All software marketing people will praise their own software- sometimes over-selling and mis-selling product bundles.

Software compared are SPSS, KXEN, R,SAS, WPS, Revolution R, SQL Server,  and various flavors and sub components within this. Optimized approach will include parallel programming, cloud computing, hardware costs, and dependent software costs.

To be continued-

 

 

 

 

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Going Deap : Algols in Python

Logo of PyPy
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Here is an important new step in Python- the established statistical programming language (used to be really pushed by SPSS in pre-IBM days and the rPy package integrates R and Python).

Well the news  ( http://www.kdnuggets.com/2010/10/eap-evolutionary-algorithms-in-python.html ) is the release of Distributed Evolutionary Algorithms in Python. If your understanding of modeling means running regression and iterating it- you may need to read some more.  If you have felt frustrated at lack of parallelization in statistical software as well as your own hardware constraints- well go DEAP (and for corporate types the licensing is

http://www.gnu.org/licenses/lgpl.html ).

http://code.google.com/p/deap/

DEAP

DEAP is intended to be an easy to use distributed evolutionary algorithm library in the Python language. Its two main components are modular and can be used separately. The first module is a Distributed Task Manager (DTM), which is intended to run on cluster of computers. The second part is the Evolutionary Algorithms in Python (EAP) framework.

DTM

DTM is a distributed task manager that is able to spread workload over a buch of computers using a TCP or a MPI connection.

DTM include the following features:

 

EAP

Features

EAP includes the following features:

  • Genetic algorithm using any imaginable representation
    • List, Array, Set, Dictionary, Tree, …
  • Genetic programing using prefix trees
    • Loosely typed, Strongly typed
    • Automatically defined functions (new v0.6)
  • Evolution strategies (including CMA-ES)
  • Multi-objective optimisation (NSGA-II, SPEA-II)
  • Parallelization of the evaluations (and maybe more) (requires python2.6 and preferably python2.7) (new v0.6)
  • Genealogy of an evolution (that is compatible with NetworkX) (new v0.6)
  • Hall of Fame of the best individuals that lived in the population (new v0.5)
  • Milestones that take snapshot of a system regularly (new v0.5)

 

Documentation

See the eap user’s guide for EAP 0.6 documentation.

Requirement

The most basic features of EAP requires Python2.5 (we simply do not offer support for 2.4). In order to use multiprocessing you will need Python2.6 and to be able to combine the toolbox and the multiprocessing module Python2.7 is needed for its support to pickle partial functions.

Projects using EAP

If you want your project listed here, simply send us a link and a brief description and we’ll be glad to add it.

and from the wordpress.com blog (funny how people like code.google.com but not blogger.google.com anymore) at http://deapdev.wordpress.com/

EAP is part of the DEAP project, that also includes some facilities for the automatic distribution and parallelization of tasks over a cluster of computers. The D part of DEAP, called DTM, is under intense development and currently available as an alpha version. DTM currently provides two and a half ways to distribute workload on a cluster or LAN of workstations, based on MPI and TCP communication managers.

This public release (version 0.6) is more complete and simpler than ever. It includes Genetic Algorithms using any imaginable representation, Genetic Programming with strongly and loosely typed trees in addition to automatically defined functions, Evolution Strategies (including Covariance Matrix Adaptation), multiobjective optimization techniques (NSGA-II and SPEA2), easy parallelization of algorithms and much more like milestones, genealogy, etc.

We are impatient to hear your feedback and comments on that system at .

Best,

François-Michel De Rainville
Félix-Antoine Fortin
Marc-André Gardner
Christian Gagné
Marc Parizeau

Laboratoire de vision et systèmes numériques
Département de génie électrique et génie informatique
Université Laval
Quebec City (Quebec), Canada

and if you are new to Python -sigh here are some statistical things (read ad-van-cED analytics using Python) by a slideshare from Visual numerics (pre Rogue Wave acquisition)

Also see,

http://code.google.com/p/deap/wiki/SimpleExample

 

 

 

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India to make own DoS -citing cyber security

After writing code for the whole world, Indian DoD (Department of Defense) has decided to start making it’s own Operating System citing cyber security. Presumably they know all about embedded code in chips, sneak kill code routines in dependent packages in operating system, and would not be using Linus Trovald’s original kernel (maybe the website was hacked to insert a small call k function 😉

as the ancient Chinese said- May you live in interesting times. Still cyber wars are better than real wars- and StuxNet virus is but a case study why countries can kill enemy plans without indulging in last century tactics.

Source-Manick Sorcar, The great Indian magician

http://www.manicksorcar.com/cartoon33.jpg

http://timesofindia.indiatimes.com/tech/news/software-services/Security-threat-DRDO-to-make-own-OS/articleshow/6719375.cms

BANGALORE: India would develop its own futuristic computer operating system to thwart attempts of cyber attacks and data theft and things of that nature, a top defence scientist said.

Dr V K Saraswat, Scientific Adviser to the Defence Minister, said the DRDO has just set up a software development  centre each here and in Delhi, with the mandate develop such a system. This “national effort” would be spearheaded by the  Defence Research and Development Organisation (DRDO) in partnership with software companies in and around Bangalore,  Hyderabad and Delhi as also academic institutions like Indian Institute of Science Bangalore and IIT Chennai, among others.

“There are many gaps in our software areas; particularly we don’t have our own operating system,” said  Saraswat, also Director General of DRDO and Secretary, Defence R & D. India currently uses operating systems developed by western countries.

Read more: Security threat: DRDO to make own OS – The Times of India http://timesofindia.indiatimes.com/tech/news/software-services/Security-threat-DRDO-to-make-own-OS/articleshow/6719375.cms#ixzz1227Y3oHg

 

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Top ten RRReasons R is bad for you ?

This is the original symbol of the Perl progra...
Image via Wikipedia

R stands for programming language based out of www.r-project.org

R is bad for you because –

1) It is slower with bigger datasets than SPSS language and SAS language .If you use bigger datasets, then you should either consider more hardware , or try and wait for some of the ODBC connect packages.

2) It needs more time to learn than SAS language .Much more time to learn how to do much more.

3) R programmers are lesser paid than SAS programmers.They prefer it that way.It equates the satisfaction of creating a package in development with a world wide community with the satisfaction of using a package and earning much more money per hour.

4) It forces you to learn the exact details of what you are doing due to its object oriented structure. Thus you either get no answer or get an exact answer. Your customer pays you by the hour not by the correct answers.

5) You can not push a couple of buttons or refer to a list of top ten most commonly used commands to finish the project.

6) It is free. And open for all. It is socialism expressed in code. Some of the packages are built by university professors. It is free.Free is bad. Who pays for the mortgage of the software programmers if all softwares were free ? Who pays for the Friday picnics. Who pays for the Good Night cruises?

7) It is free. Your organization will not commend you for saving them money- they will question why you did not recommend this before. And why did you approve all those packages that expire in 2011.R is fReeeeee. Customers feel good while spending money.The more software budgets you approve the more your salary is. R thReatens all that.

8) It is impossible to install a package you do not need or want. There is no one calling you on the phone to consider one more package or solution. R can make you lonely.

9) R uses mostly Command line. Command line is from the Seventies. Or the Eighties. The GUI’s RCmdr and Rattle are there but still…..

10) R forces you to learn new stuff by the month. You prefer to only earn by the month. Till the day your job got offshored…

Written by a R user in English language

( which fortunately was not copyrighted otherwise we would be paying Britain for each word)

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Ajay- The above post was reprinted by personal request. It was written on Jan 2009- and may not be truly valid now. It is meant to be taken in good humor-not so seriously.

BI Software

Here is the brand new release from Jaspersoft at a groovy price of 9000$. Somebody stop these guys!

It’s a great company to watch for buyouts as well- given their expertise in REPORTING and clientele- especially for anyone looking to im prove thier standing in both open source world and reporting software branding.

From AOL owned Arrogantion’s site http://www.crunchbase.com/company/jaspersoft

 

Total $24.5M
Series D, 8/07 1
Scale Venture Partners
SAP Ventures
Doll Capital Management
Partech International
Morgenthaler Ventures		 $12M
Unattributed, 12/08 2
Adams Street Partners
Red Hat
Morgenthaler Ventures
Doll Capital Management
Partech International

 

 

The news-

Announcing JasperReports Server Professional

More Resources

Webinar: Introducing JasperReports Server Professional

Thursday October 14

In this live webinar, learn how a new solution from Jaspersoft combines the world’s favorite reporting server with powerful, mature report server functionality—for about 80% less.

  • Date: Thu, Oct 14
  • Time: 10:00 AM PDT
  • Duration: 60 minutes

The World’s Most Powerful and Affordable Reporting Server

Limited Time Introductory Offer: Starting from $9,000 (restrictions apply)

JasperReports Server is the recommended product for organizations requiring an affordable reporting solution for interactive, operational, and production-based reporting. Deployed as a standalone reporting server or integrated inside another application, JasperReports Server is a flexible, powerful, interactive reporting environment for small or large enterprises.

Powered by the world’s most popular reporting tools in JasperReports and iReport, developers and users can take advantage of more interactivity, security, and scheduling of their reports.

Key Benefits:

  • Affordable: Unlimited reports for unlimited users starting at $9,000
  • Powerful: Report scheduling and distribution to 1,000s of users on a single server
  • Flexible: Web service architecture simplifies application integration
  • Secure: Centralized repository authenticates report access
  • Interactive: Easy to interact, self-serve parameterized-based reports
  • Visual appeal: Flash-based charts and maps engage users and enhance applications
  • Open: Access to any data source including relational, XML, Hibernate, EJB, POJO, and custom

or just watch the 5 min video-

 

Speaking of videos -here is a great video on BI from good ol Tennessee-a great 27 min tutorial on BI for newbies

 

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Dataists shake up R community with a rocking contest

Flipboard
Image by Johan Larsson via Flickr

Newly created Dataists are creating waves on Hacker News and beyond with their innovative contest- A Recommendation Engine for R Packages.

Not only is the contest useful, it is likely to teach R Users some data hacking skills, as well as the basics of creating a GitHub Project.

Read more here-http://www.dataists.com/2010/10/using-data-tools-to-find-data-tools-the-yo-dawg-of-data-hacking/

For that reason, we’ve settled on the more manageable question, “which packages are most often installed by normal R users?”

This last question could potentially be answered in a variety of ways. Our current approach uses a convenience sample of installation data that we’ve collected from volunteers in the R community, who kindly agreed to send us a list of the packages they have on their systems. We’ve anonymized this data and compiled a set of metadata-based predictors that allow us to predict the installation probabilities quite well. We’re releasing all of our current work, including the data we have and all of the code we’ve used so far for our exploratory analyses. The contest itself will go live on Kaggle on Sunday and will end four months from Sunday on February 10, 2011. The rules, prizes and official data sets are all described below.

Rules and Prizes

To win the contest, you need to predict the probability that a user U has a package P installed on their system for every pair, (U, P). We’ll assess your performance using ROC methods, which will be evaluated against a held out test data set. The winning team will receive 3 UseR! books of their choosing. In order to win the contest, you’ll have to provide your analysis code to us by creating a fork of our GitHub repository. You’ll also be required to provide a written description of your approach. We’re asking for so much openness from the winning team because we want this contest to serve as a stepping stone for the R community. We’re also hoping that enterprising data hackers will extend the lessons learned through this contest to other programming languages.

Extract from-http://www.dataists.com/2010/10/using-data-tools-to-find-data-tools-the-yo-dawg-of-data-hacking/

Read the full article there

Revolution R for Linux

Screenshot of the Redhat Enterprise Linux Desktop
Image via Wikipedia

New software just released from the guys in California (@RevolutionR) so if you are a Linux user and have academic credentials you can download it for free  (@Cmastication doesnt), you can test it to see what the big fuss is all about (also see http://www.revolutionanalytics.com/why-revolution-r/benchmarks.php) –

Revolution Analytics has just released Revolution R Enterprise 4.0.1 for Red Hat Enterprise Linux, a significant step forward in enterprise data analytics. Revolution R Enterprise 4.0.1 is built on R 2.11.1, the latest release of the open-source environment for data analysis and graphics. Also available is the initial release of our deployment server solution, RevoDeployR 1.0, designed to help you deliver R analytics via the Web. And coming soon to Linux: RevoScaleR, a new package for fast and efficient multi-core processing of large data sets.

As a registered user of the Academic version of Revolution R Enterprise for Linux, you can take advantage of these improvements by downloading and installing Revolution R Enterprise 4.0.1 today. You can install Revolution R Enterprise 4.0.1 side-by-side with your existing Revolution R Enterprise installations; there is no need to uninstall previous versions.

Download Information

The following information is all you will need to download and install the Academic Edition.

Supported Platforms:

Revolution R Enterprise Academic edition and RevoDeployR are supported on Red Hat® Enterprise Linux® 5.4 or greater (64-bit processors).

Approximately 300MB free disk space is required for a full install of Revolution R Enterprise. We recommend at least 1GB of RAM to use Revolution R Enterprise.

For the full list of system requirements for RevoDeployR, refer to the RevoDeployR™ Installation Guide for Red Hat® Enterprise Linux®.

Download Links:

You will first need to download the Revolution R Enterprise installer.

Installation Instructions for Revolution R Enterprise Academic Edition

After downloading the installer, do the following to install the software:

  • Log in as root if you have not already.
  • Change directory to the directory containing the downloaded installer.
  • Unpack the installer using the following command:
    tar -xzf Revo-Ent-4.0.1-RHEL5-desktop.tar.gz
  • Change directory to the RevolutionR_4.0.1 directory created.
  • Run the installer by typing ./install.py and following the on-screen prompts.

Getting Started with the Revolution R Enterprise

After you have installed the software, launch Revolution R Enterprise by typing Revo64 at the shell prompt.

Documentation is available in the form of PDF documents installed as part of the Revolution R Enterprise distribution. Type Revo.home(“doc”) at the R prompt to locate the directory containing the manuals Getting Started with Revolution R (RevoMan.pdf) and the ParallelR User’s Guide(parRman.pdf).

Installation Instructions for RevoDeployR (and RServe)

After downloading the RevoDeployR distribution, use the following steps to install the software:

Note: These instructions are for an automatic install.  For more details or for manual install instructions, refer to RevoDeployR_Installation_Instructions_for_RedHat.pdf.

  1. Log into the operating system as root.
    su –
  2. Change directory to the directory containing the downloaded distribution for RevoDeployR and RServe.
  3. Unzip the contents of the RevoDeployR tar file. At prompt, type:
    tar -xzf deployrRedHat.tar.gz
  4. Change directories. At the prompt, type:
    cd installFiles
  5. Launch the automated installation script and follow the on-screen prompts. At the prompt, type:
    ./installRedHat.sh
    Note: Red Hat installs MySQL without a password.

Getting Started with RevoDeployR

After installing RevoDeployR, you will be directed to the RevoDeployR landing page. The landing page has links to documentation, the RevoDeployR management console, the API Explorer development tool, and sample code.

Support

For help installing this Academic Edition, please email support@revolutionanalytics.com

Also interestingly some benchmarks on Revolution R vs R.

http://www.revolutionanalytics.com/why-revolution-r/benchmarks.php

R-25 Benchmarks

The simple R-benchmark-25.R test script is a quick-running survey of general R performance. The Community-developed test consists of three sets of small benchmarks, referred to in the script as Matrix Calculation, Matrix Functions, and Program Control.

R-25 Matrix Calculation R-25 Matrix Functions R-Matrix Program Control
R-25 Benchmarks Base R 2.9.2 Revolution R (1-core) Revolution R (4-core) Speedup (4 core)
Matrix Calculation 34 sec 6.6 sec 4.4 sec 7.7x
Matrix Functions 20 sec 4.4 sec 2.1 sec 9.5x
Program Control 4.7 sec 4 sec 4.2 sec Not Appreciable

Speedup = Slower time / Faster Time – 1   Test descriptions available at http://r.research.att.com/benchmarks

Additional Benchmarks

Revolution Analytics has created its own tests to simulate common real-world computations.  Their descriptions are explained below.

Matrix Multiply Cholesky Factorization
Singular Value Decomposition Principal Component Analysis Linear Discriminant Analysis
Linear Algebra Computation Base R 2.9.2 Revolution R (1-core) Revolution R (4-core) Speedup (4 core)
Matrix Multiply 243 sec 22 sec 5.9 sec 41x
Cholesky Factorization 23 sec 3.8 sec 1.1 sec 21x
Singular Value Decomposition 62 sec 13 sec 4.9 sec 12.6x
Principal Components Analysis 237 sec 41 sec 15.6 sec 15.2x
Linear Discriminant Analysis 142 sec 49 sec 32.0 sec 4.4x

Speedup = Slower time / Faster Time – 1

Matrix Multiply

This routine creates a random uniform 10,000 x 5,000 matrix A, and then times the computation of the matrix product transpose(A) * A.

set.seed (1)
m <- 10000
n <-  5000
A <- matrix (runif (m*n),m,n)
system.time (B <- crossprod(A))

The system will respond with a message in this format:

User   system elapsed
37.22    0.40   9.68

The “elapsed” times indicate total wall-clock time to run the timed code.

The table above reflects the elapsed time for this and the other benchmark tests. The test system was an INTEL® Xeon® 8-core CPU (model X55600) at 2.5 GHz with 18 GB system RAM running Windows Server 2008 operating system. For the Revolution R benchmarks, the computations were limited to 1 core and 4 cores by calling setMKLthreads(1) and setMKLthreads(4) respectively. Note that Revolution R performs very well even in single-threaded tests: this is a result of the optimized algorithms in the Intel MKL library linked to Revolution R. The slight greater than linear speedup may be due to the greater total cache available to all CPU cores, or simply better OS CPU scheduling–no attempt was made to pin execution threads to physical cores. Consult Revolution R’s documentation to learn how to run benchmarks that use less cores than your hardware offers.

Cholesky Factorization

The Cholesky matrix factorization may be used to compute the solution of linear systems of equations with a symmetric positive definite coefficient matrix, to compute correlated sets of pseudo-random numbers, and other tasks. We re-use the matrix B computed in the example above:

system.time (C <- chol(B))

Singular Value Decomposition with Applications

The Singular Value Decomposition (SVD) is a numerically-stable and very useful matrix decompisition. The SVD is often used to compute Principal Components and Linear Discriminant Analysis.

# Singular Value Deomposition
m <- 10000
n <- 2000
A <- matrix (runif (m*n),m,n)
system.time (S <- svd (A,nu=0,nv=0))

# Principal Components Analysis
m <- 10000
n <- 2000
A <- matrix (runif (m*n),m,n)
system.time (P <- prcomp(A))

# Linear Discriminant Analysis
require (‘MASS’)
g <- 5
k <- round (m/2)
A <- data.frame (A, fac=sample (LETTERS[1:g],m,replace=TRUE))
train <- sample(1:m, k)
system.time (L <- lda(fac ~., data=A, prior=rep(1,g)/g, subset=train))

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