© 2010 IBM Corporation Business Analytics software Extending and Customizing IBM SPSS Statistics with R, Python, and.NET Jon Peck Senior Software Engineer,

© 2010 IBM Corporation

Business Analytics software

Extending and Customizing IBM SPSS Statistics with R, Python, and .NET

Jon Peck

Senior Software Engineer, IBM

[email protected]

November, 2010

mailto:[email protected]



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IBM SPSS Statistics IBM ® SPSS ® Statistics has an extensive command language (syntax) for data acquisition,

manipulation, and statistical and graphical procedures

Programmability and scripting dramatically extend these built-in capabilities

Allow custom user interfaces and output to be produced

Converting large SASapplications is likelyto require the use ofprogrammability



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Agenda Programmability introduction

Four examples– Automating repetitive work:

applySyntaxToFiles

– Integrating programs and scripting:

SPSSINC MODIFY TABLES

– Adding a procedure from R:

SPSSINC QUANTILE REGRESSION

– Adding a procedure in Python:

SPSSINC TURF



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Programmability increases your power, flexibility, and productivity

Generalization–React flexibly to metadata, results, and the environment–Benefit: Write fewer similar jobs

Automation–Embed program logic in jobs–Benefit: Less manual work

Extension–Tap existing R or Python statistical modules–Add your own or extend standard procedures and transformations– Benefit: More capabilities

Integration–Connect IBM SPSS Statistics inputs and outputs to other agents– Benefit: Make IBM SPSS Statistics part of a larger production process

More productivity and more fun



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IBM SPSS Statistics embeds three programming languages

Plug-ins let you extend capabilities using–Python–R–.NET languages (Windows only)

Free plug-in downloads

SPSS Developer Central web site provides articles, SPSS-written modules, plug-ins and user contributions

–New SPSS Community on IBM myDeveloperWorks

http://www.spss.com/devcentral



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GET FILE="c:/data/important.sav".

BEGIN PROGRAM PYTHON.import spssprint "Hello, IBM"END PROGRAM.DESCRIPTIVES ....

Python or R program code goes in the normal Statistics syntax window

My first Python program



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A program in the input stream can communicate with IBM SPSS Statistics and control it and use Python or R facilities and modules (internal mode)

spss.Submit("GET FILE='c:/data/cars.sav'.")

A Python or .NET application can embed IBM SPSS Statistics inside itself (external mode)

–User interface does not appear

There is a lower level C API available in an SDK

Programmability combines SPSS Statistics with Python, R, or .NET



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Programmability functionality is fully integrated into IBM SPSS StatisticsPrograms run in the regular syntax stream

Users can define IBM SPSS Statistics syntax for program and scripts via Extension mechanism.

Users can create dialog boxes and menus using the Custom Dialog Builder.

–Not just for extensions or programs

Python and R output appears in the Viewer–plain text–pivot tables–charts



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Python and R Programmability API's cover these areas

State information of Statistics

Get/Set variable dictionary information

Get/Set data

Get Viewer output (via xmlworkspace)

Create tables/charts/text objects in Viewer

Run Statistics commands (Python only)



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Python and VB scripting API's cover user interface and output

Programmability is a backend (SPSS Processor) domain

Scripting is mainly a frontend (user interface, including output) domain

Managing output Viewer and objects– tables: formatting, pivoting, editing, …– objects: visibility, order, titles, outline

text,…

General user interface control

Almost anything you can do via the user interface

Not available for R



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Statistics, graphs, and data management via Statistics

Two pages of VB.NET code

.NET plug-in embeds Statistics inside another programExample: Statistical Explorer



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Python and R are open source software

Programmability plug-ins are an optional installation– They are free (but require a Statistics license)– They make possible tapping the work of the Python and R communities– Python and R have license agreements– IBM Non-warrenty license agreement– For R, GPL license



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Extension commands eliminate need for user to learn Python or R

Extension mechanism lets you define IBM SPSS Statistics-style syntax for programs

IBM SPSS Statistics takes care of validation and parsing

Passes user input to a program in an easy-to-digest form

Automatically loaded when IBM SPSS Statistics starts–Look to the user like built in commands

Easy to distribute to others



Extension Name Description

PLS Partial least squares (P)

PROPOR Confidence intervals for proportions (P)

SPSSINC APRIORI Association rules (R)

SPSSINC BREUSCH PAGAN Residual heteroscedasticity tests (R)

SPSSINC HETCOR Polychoric and polyserial correlation (P+R)

SPSSINC MFP GLM Fractional polynomial generalized linear models (R)

SPSSINC QQPLOT2 Empirical Q-Q plots (R)

SPSSINC QUANTREG Quantile regression (R)

SPSSINC RAKE Adjust weights to control totals (P)

SPSSINC RANFOR & SPSSINC RANPRED

Random forests (R)

SPSSINC RASCH Rasch models (R)

SPSSINC ROBUST REGR Robust regression (R)

SPSSINC TOBIT REGR Tobit regression (R)

SPSSINC TURF TURF analysis (P)

Some statistical extensions on Dev Central

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Extension Name Description

FUZZY Case-control exact and approximate matching (P)

GATHERMD Gather data file metadata (P)

HIDECOLS Hide pivot table columns (P)

SCRIPTEX SCRIPT commands with parameters (P)

SETSMACRO Syntax for using variable sets (P)

SPSSINC ANON Anonomize data (P)

SPSSINC COMPARE DATASETS Compare two sav files (P)

SPSSINC CREATE DUMMIES Create dummy variables for categories (P)

SPSSINC GETURI DATA Read data from the Internet (P)

SPSSINC MERGE TABLES Merge two pivot tables (P)

SPSSINC MODIFY OUTPUT Set Viewer outline titling and styling (P)

SPSSINC MODIFY TABLES Set pivot table cell and label styling (P)

SPSSINC TRANS Apply Python functions to cases (P)

SPSSINC TRANSLATE Translate Viewer output (P)

TEXT Create block of text in Viewer (P)15

Some non-statistical extensions on Dev Central



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–Write Python or R functions to implement the functionality or tap existing packages• Use input API's to get data to Python or R• Use output API's to create pivot tables

Can each be a single line of code

–For extensions,• Define the syntax in an xml file• Use tools in extension.py (Python) or spsspkg (R) to receive

parsed output and pass to implementing function• New in v18: R version of extension.py

–Use the Custom Dialog Builder to create the interface• The CDB is not just for extensions

–Test and document!–Package and distribute–Contributions to Developer Central are welcome

Documentation is at SPSS Developer Central

You can create and share your own additions to IBM SPSS Statistics




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Example: SPSSINC BREUSCH PAGAN– implemented using an R package

SPSSINC_BREUSCH_PAGAN.xml specifies the syntax to the Statistics parser

The R mapping code in SPSSINC_BREUSCH_PAGAN.R respecifies the syntax and invokes the executing routine with parsed parameters

– overlaps with xml syntax definition but provides additional features

SPSSINC BREUSCH PAGAN DEPENDENT = salary ENTER = educ jobcat /OPTIONS MISSING=LISTWISE /SAVE RESIDUALSDATASET=resids COEFSDATASET=coefs.

Extension commands: validation and mapping from syntax to Python or R function parameters is handled for you



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An XML file defines the syntax to the SPSS Universal Parser



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Python or, in this case, R code gets the parsed syntax, which is turned into function arguments



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Expand the audience by creating IBM SPSS Statistics syntax and dialog boxes



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Example I

Generalize and automate work

You have syntax files and need to process datasets not known in advance every day

applySyntaxToFiles function applies a syntax file to each file in input specification



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Apply standard processing to an unknown set of files

Produce processed data and reports

Use programmability to automate routine processes



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begin program.import spss, spssaux3spssaux3.applySyntaxToFiles(inputspec="c:/temp/parts/*.sav", syntax = "c:/myjobs/dailychecks.sps", outputdatadir = "c:/temp/processed", outputfiledir = "c:/temp/processed", logfile ="c:/temp/processed/report.txt")end program.

dailychecks.sps could apply data cleaning rules, modify data, and create reports

Could be run daily through Production Mode or C&DS job scheduler or used interactively

Extended version available as SPSSINC PROCESS FILES

Use a program to drive processing



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Example II

Automate dynamic or static formatting of tables

Use integrated scripting for better table presentation



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• TableLooks provide static formatting for entire areas of a table– data cells– row and column layers

• You want tables with formatting beyond tableLooks• Many users copy tables to Excel and manually format

them • Basic and Python Scripting provide programmatic way to

do formatting• SPSSINC MODIFY TABLES provides syntax for

extensive formatting– Eliminates need to know scripting– Uses Extension mechanism for programs and Python scripting

SPSSINC MODIFY TABLES extension command manipulates table formatting and structure



SPSSINC MODIFY TABLES SUBTYPE='Crosstabulation' DIMENSION=ROWS SELECT='Std. Residual'/STYLES TEXTSTYLE=BOLD BACKGROUNDCOLOR=255 0 0APPLYTO='abs(x) >2'.

Use dynamic highlighting to make crosstab table easier to read

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Dialog created withCustom Dialog Builder

Generates extension command syntax

Easy to distribute

Custom dialog boxes are easy to create



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SPSSINC MODIFY TABLES subtype='variables in the equation' SELECT="B" "Sig."/STYLES TEXTCOLOR = 0 0 255 BACKGROUNDCOLOR=0 255 0.

Use static formatting to call out parts of a table



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SPSSINC MODIFY TABLES SUBTYPE="Custom Table" SELECT = "Total" DIMENSION=ROWS/STYLES BACKGROUNDCOLOR=255 255 88 TEXTSTYLE = BOLD

Format CTABLES totals to call them out



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SPSSINC MODIFY TABLES SUBTYPE='Report' SELECT="<<ALL>>"/STYLES APPLYTO=DATACELLS TEXTCOLOR=255 255 255 TEXTSTYLE=BOLDCUSTOMFUNCTION="customstylefunctions.washColumnsBlue".

def washColumnsBlue(obj, i, j, numrows, numcols, section, more): mincolor=150. maxcolor=255. increment = (maxcolor - mincolor)/(numcols-1) colorvalue = round(mincolor + increment * j) obj.SetBackgroundColorAt(i,j, RGB((mincolor, mincolor, colorvalue)))

Use custom functions for special effects



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It is possible to get carried away with this



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Example III

Extend IBM SPSS Statistics by tapping the work of the R and Python communities

Add R procedures seamlessly to IBM SPSS Statistics



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R

R is a programming language for statistics–leading edge statistics–many contributed statistics and graphics packages–free

R is not so easy to learn–Documentation by experts for experts–Feels like a complex programming language – because it is–Syntax is a lot like C

–Error in optim(rho, f, control = control, hessian = TRUE, method = “BFGS”) :initial value in ‘vmmin’ is not finite

• Good for programmers(?); bad for users

R holds data in memory

R for SAS and SPSS Users, Bob Muenchen, Addison-Wesley, 2008



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R procedures can be accessed from IBM SPSS Statistics using the R plug-inThe R plug-in makes it easy to use R packages

–IBM SPSS Statistics datasets and Viewer output can be processed by R using plug-in

–Graphical, text, and table output appear in the Viewer• Pivot tables can be created with R code

–New IBM SPSS Statistics datasets can be created from R–R communicates with IBM SPSS Statistics via API's in plug-in

–Integration requires writing a little R wrapper code–IBM SPSS Statistics can provide

• dialog box interface• IBM SPSS Statistics-style syntax• pivot table output

Plug-in is downloadable from Developer Central



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Quantile regression models conditional quantilesOrdinary regression models conditional mean

Median regression is 50th quantile

Estimating quantiles is useful with varying spread, asymmetries, outliers

Areas of application include–empirical finance

• value at risk• mutual fund investment styles• credit scoring

–school quality–demand analysis–others



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SPSS QUANTILE REGRESSION extension embeds R quantreg package



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Pivot tables and plots appear in the Viewer



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New datasets appear in Data Editor windows



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Example IV

Extend IBM SPSS Statistics by adding procedures in Python

TURF analysis



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TURF Analysis is popular in market research

Total Unduplicated Reach and Frequency (TURF)

Find the highest coverage of positive responses for a small number of questions

Example: How do you reach the largest audience by advertising on a few kinds of sports?

• football, cricket, basketball, cycling, ...

Example: What ice cream flavors should you offer in your shops that have three dispensing machines?

Example: What phone features should you promote?–multi-line, voicemail, paging, internet ...

Simple FREQUENCIES does not account for overlap



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Must compute all possible set unions of positive responses (up to a maximum number of variables).

Each set is a list of case ID’s with positive response on a question.

This problem is computationally explosive

Calculations for best 10 combinations of variables

Variables Set Union Calculations

3 4

6 57

12 4070

24 4,540,361

48 8,682,997,422

Is a scripting language like Python too slow?

TURF calculations are demanding



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Extension command SPSSINC TURF is implemented in Python

Provides–Dialog box interface–IBM SPSS Statistics style syntax–The computations–Pivot table output

Fewer than 300 lines of Python code–Plus dialog box definition–Plus extension command syntax definition

Executes requests involving a few million set comparisons in a few minutes

Initial version written in two days



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Telcosurvey(9 variables1000 cases)

dialogcreatedwithCustomDialogBuilder

Analysis of phone data



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PivottablecreatedfromPythoncode

Best singles are conference calling, call forwarding,and call waiting

Results show the combination of features – best reach



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Calculations completed in a few seconds

The best three are not the top three one at a time



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Python and R integrationUnification of programs and scriptsCustom Dialog BuilderExtensionsSPSS Developer Central is your friend

Where we have been today





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Questions

?

?



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Programmability increases your power, flexibility, and productivity with IBM SPSS Statistics

Generalization and automation

–applySyntaxToFiles

–SPSS MODIFY TABLES

Extension

–SPSSINC QUANTREG using R

–SPSSINC TURF using Python

–Many new extension commands available

Integration

–applySyntaxToFiles as part of a process

And it's still more fun



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Jon K Peck, Ph. D.Senior Software EngineerIBM SPSS

[email protected]: insideout.spss.com

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© 2010 IBM Corporation Business Analytics software Extending and Customizing IBM SPSS Statistics with R, Python, and.NET Jon Peck Senior Software Engineer,