Category: Analytics

Analytics 2012 Conference

A nice conference from the grand old institution of Analytics,  SAS  Institute’s annual analytic pow-wow.

I especially like some of the trainings- and wonder if they could be stored as e-learning modules for students/academics to review

in SAS’s extensive and generous Online Education Program.

Sunday Morning Workshop

SAS Sentiment Analysis Studio: Introduction to Building Models

This course provides an introduction to SAS Sentiment Analysis Studio. It is designed for system designers, developers, analytical consultants and managers who want to understand techniques and approaches for identifying sentiment in textual documents.
View outline
Sunday, Oct. 7, 8:30a.m.-12p.m. – $250

Sunday Afternoon Workshops

Business Analytics Consulting Workshops

This workshop is designed for the analyst, statistician, or executive who wants to discuss best-practice approaches to solving specific business problems, in the context of analytics. The two-hour workshop will be customized to discuss your specific analytical needs and will be designed as a one-on-one session for you, including up to five individuals within your company sharing your analytical goal. This workshop is specifically geared for an expert tasked with solving a critical business problem who needs consultation for developing the analytical approach required. The workshop can be customized to meet your needs, from a deep-dive into modeling methods to a strategic plan for analytic initiatives. In addition to the two hours at the conference location, this workshop includes some advanced consulting time over the phone, making it a valuable investment at a bargain price.
View outline
Sunday, Oct. 7; 1-3 p.m. or 3:30-5:30 p.m. – $200

Demand-Driven Forecasting: Sensing Demand Signals, Shaping and Predicting Demand

This half-day lecture teaches students how to integrate demand-driven forecasting into the consensus forecasting process and how to make the current demand forecasting process more demand-driven.
View outline
Sunday, Oct. 7; 1-5 p.m.

Forecast Value Added Analysis

Forecast Value Added (FVA) is the change in a forecasting performance metric (such as MAPE or bias) that can be attributed to a particular step or participant in the forecasting process. FVA analysis is used to identify those process activities that are failing to make the forecast any better (or might even be making it worse). This course provides step-by-step guidelines for conducting FVA analysis – to identify and eliminate the waste, inefficiency, and worst practices from your forecasting process. The result can be better forecasts, with fewer resources and less management time spent on forecasting.
View outline
Sunday, Oct. 7; 1-5 p.m.

SAS Enterprise Content Categorization: An Introduction

This course gives an introduction to methods of unstructured data analysis, document classification and document content identification. The course also uses examples as the basis for constructing parse expressions and resulting entities.
View outline
Sunday, Oct. 7; 1-5 p.m.

 

 
You can see more on this yourself at –

http://www.sas.com/events/analytics/us/

 

 

 

 

 

 

 

 

 

 

 

Webscraping using iMacros

The noted Diamonds dataset in the ggplot2 package of R is actually culled from the website http://www.diamondse.info/diamond-prices.asp

However it has ~55000 diamonds, while the whole Diamonds search engine has almost ten times that number. Using iMacros – a Google Chrome Plugin, we can scrape that data (or almost any data). The iMacros chrome plugin is available at  https://chrome.google.com/webstore/detail/cplklnmnlbnpmjogncfgfijoopmnlemp while notes on coding are at http://wiki.imacros.net

Imacros makes coding as easy as recording macro and the code is automatcially generated for whatever actions you do. You can set parameters to extract only specific parts of the website, and code can be run into a loop (of 9999 times!)

Here is the iMacros code-Note you need to navigate to the web site http://www.diamondse.info/diamond-prices.asp before running it

VERSION BUILD=5100505 RECORDER=CR
FRAME F=1
SET !EXTRACT_TEST_POPUP NO
SET !ERRORIGNORE YES
TAG POS=6 TYPE=TABLE ATTR=TXT:* EXTRACT=TXT
TAG POS=1 TYPE=DIV ATTR=CLASS:paginate_enabled_next
SAVEAS TYPE=EXTRACT FOLDER=* FILE=test+3

 

 

 

 

 

 

 

 

 

and voila- all the diamonds you need to analyze!

The returning data can be read using the standard delimiter data munging in the language of SAS or R.

More on IMacros from

https://chrome.google.com/webstore/detail/cplklnmnlbnpmjogncfgfijoopmnlemp/details

Description

Automate your web browser. Record and replay repetitious work

If you encounter any problems with iMacros for Chrome, please let us know in our Chrome user forum at http://forum.iopus.com/viewforum.php?f=21

Our forum is also the best place for new feature suggestions :-)
----

iMacros was designed to automate the most repetitious tasks on the web. If there’s an activity you have to do repeatedly, just record it in iMacros. The next time you need to do it, the entire macro will run at the click of a button! With iMacros, you can quickly and easily fill out web forms, remember passwords, create a webmail notifier, and more. You can keep the macros on your computer for your own use, use them within bookmark sync / Xmarks or share them with others by embedding them on your homepage, blog, company Intranet or any social bookmarking service as bookmarklet. The uses are limited only by your imagination!

Popular uses are as web macro recorder, form filler on steroids and highly-secure password manager (256-bit AES encryption).


Visualizing Bigger Data in R using Tabplot

The amazing tabplot package creates the tableplot feature for visualizing huge chunks of data. This is a great example of creative data visualization that is resource lite and extremely fast in a first look at the data. (note- The tabplot package is being used and table plot function is being used . The TABLEPLOT package is different and is NOT being used here).

library(ggplot2)
data(diamonds)
library(tabplot)
tableplot(diamonds)
system.time(tableplot(diamonds))

visualizing a 50000 row by 10 variable dataset in 0.7 s is fast !!

click on screenshot to see it

and some say R is slow 😉

 

Note I used a free Windows Amazon EC2 Instance for it-

See screenshot for hardware configuration

 

the best thing is there is a handy GTK GUI for this package. You can check it out at

 

 

New RCommander with ggplot #rstats

 

My favorite GUI (or one of them) R Commander has a relatively new plugin called KMGGplot2. Until now Deducer was the only GUI with ggplot features , but the much lighter and more popular R Commander has been a long champion in people wanting to pick up R quickly.

 

http://cran.r-project.org/web/packages/RcmdrPlugin.KMggplot2/

RcmdrPlugin.KMggplot2: Rcmdr Plug-In for Kaplan-Meier Plot and Other Plots by Using the ggplot2 Package

 

As you can see by the screenshot- it makes ggplot even easier for people (like R  newbies and experienced folks alike)

 

This package is an R Commander plug-in for Kaplan-Meier plot and other plots by using the ggplot2 package.

Version: 0.1-0
Depends: R (≥ 2.15.0), stats, methods, grid, Rcmdr (≥ 1.8-4), ggplot2 (≥ 0.9.1)
Imports: tcltk2 (≥ 1.2-3), RColorBrewer (≥ 1.0-5), scales (≥ 0.2.1), survival (≥ 2.36-14)
Published: 2012-05-18
Author: Triad sou. and Kengo NAGASHIMA
Maintainer: Triad sou. <triadsou at gmail.com>
License: GPL-2
CRAN checks: RcmdrPlugin.KMggplot2 results

 

----------------------------------------------------------------
NEWS file for the RcmdrPlugin.KMggplot2 package
----------------------------------------------------------------

----------------------------------------------------------------

Changes in version 0.1-0 (2012-05-18)

 o Restructuring implementation approach for efficient
   maintenance.
 o Added options() for storing package specific options (e.g.,
   font size, font family, ...).
 o Added a theme: theme_simple().
 o Added a theme element: theme_rect2().
 o Added a list box for facet_xx() functions in some menus
   (Thanks to Professor Murtaza Haider).
 o Kaplan-Meier plot: added confidence intervals.
 o Box plot: added violin plots.
 o Bar chart for discrete variables: deleted dynamite plots.
 o Bar chart for discrete variables: added stacked bar charts.
 o Scatter plot matrix: added univariate plots at diagonal
   positions (ggplot2::plotmatrix).
 o Deleted the dummy data for histograms, which is large in
   size.

----------------------------------------------------------------

Changes in version 0.0-4 (2011-07-28)

 o Fixed "scale_y_continuous(formatter = "percent")" to
   "scale_y_continuous(labels = percent)" for ggplot2
   (>= 0.9.0).
 o Fixed "legend = FALSE" to "show_guide = FALSE" for
   ggplot2 (>= 0.9.0).
 o Fixed the DESCRIPTION file for ggplot2 (>= 0.9.0) dependency.

----------------------------------------------------------------

Changes in version 0.0-3 (2011-07-28; FIRST RELEASE VERSION)

 o Kaplan-Meier plot: Show no. at risk table on outside.
 o Histogram: Color coding.
 o Histogram: Density estimation.
 o Q-Q plot: Create plots based on a maximum likelihood estimate
   for the parameters of the selected theoretical distribution.
 o Q-Q plot: Create plots based on a user-specified theoretical
   distribution.
 o Box plot / Errorbar plot: Box plot.
 o Box plot / Errorbar plot: Mean plus/minus S.D.
 o Box plot / Errorbar plot: Mean plus/minus S.D. (Bar plot).
 o Box plot / Errorbar plot: 95 percent Confidence interval
   (t distribution).
 o Box plot / Errorbar plot: 95 percent Confidence interval
   (bootstrap).
 o Scatter plot: Fitting a linear regression.
 o Scatter plot: Smoothing with LOESS for small datasets or GAM
   with a cubic regression basis for large data.
 o Scatter plot matrix: Fitting a linear regression.
 o Scatter plot matrix: Smoothing with LOESS for small datasets
   or GAM with a cubic regression basis for large data.
 o Line chart: Normal line chart.
 o Line chart: Line char with a step function.
 o Line chart: Area plot.
 o Pie chart: Pie chart.
 o Bar chart for discrete variables: Bar chart for discrete
   variables.
 o Contour plot: Color coding.
 o Contour plot: Heat map.
 o Distribution plot: Normal distribution.
 o Distribution plot: t distribution.
 o Distribution plot: Chi-square distribution.
 o Distribution plot: F distribution.
 o Distribution plot: Exponential distribution.
 o Distribution plot: Uniform distribution.
 o Distribution plot: Beta distribution.
 o Distribution plot: Cauchy distribution.
 o Distribution plot: Logistic distribution.
 o Distribution plot: Log-normal distribution.
 o Distribution plot: Gamma distribution.
 o Distribution plot: Weibull distribution.
 o Distribution plot: Binomial distribution.
 o Distribution plot: Poisson distribution.
 o Distribution plot: Geometric distribution.
 o Distribution plot: Hypergeometric distribution.
 o Distribution plot: Negative binomial distribution.

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!

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.