Programmability in SPSS 14, SPSS
15 and SPSS 16
The Revolution Continues
Jon Peck
Technical Advisor
SPSS
Copyright (c) SPSS Inc, 2007
Agenda

Recap of SPSS 14 Python programmability

Developer Central

New features in SPSS 15 programmability


Writing first-class procedures
Updating the data
New features in SPSS 16 programmability

Interacting with the user

Q&A

Conclusion
Copyright (c) SPSS Inc, 2007

Quotations from SPSS Users
"Because of programmability, SPSS 14 is the most important
release since I started using SPSS fifteen years ago."

"I think I am going to like using Python."

"Python and SPSS 14 and later are, IMHO, GREAT!"

"By the way, Python is a great addition to SPSS."

From InfoWorld (April 19, 2007)

"Of all the tools fueling the dynamic-language trend in the enterprise,
general-purpose dynamic languages such as Python and Ruby present
the greatest upside for enhancing developer productivity."
Copyright (c) SPSS Inc, 2007

The Combination of SPSS and
Python
SPSS provides a powerful engine for statistical
and graphical methods and for data
management.

Python® provides a powerful, elegant, and
easy-to-learn language for controlling and
responding to this engine.

Together they provide a comprehensive system
for serious applications of analytical methods to
data.
Copyright (c) SPSS Inc, 2007

Programmability Features in
SPSS 14, 15, and 16

SPSS 14.0 provided






SPSS 15 adds
Read and write case data
Create new variables directly rather than generating syntax
Create pivot tables and text blocks via backend API's
Easier setup
SPSS 16 will add



EXTENSION command for user procedures with SPSS syntax
Dataset features for complex data management
Ability to use R procedures within SPSS through R Plug-In
Copyright (c) SPSS Inc, 2007





Programmability
Multiple datasets
Variable and File Attributes
Programmability read-access to case data
Ability to control SPSS from a Python program
Programmability Advantages
Makes possible easy jobs that respond to datasets, output,
environment

Allows greater generality, more automation

Makes jobs more robust

Allows extending the capabilities of SPSS

Enables better organized and more maintainable code

Facilitates staff specialization

Increases productivity

More fun
Copyright (c) SPSS Inc, 2007

Programmability Overview

Python extends SPSS via





Runs in "back-end" syntax context (like macro)


Two modes



SaxBasic scripting runs in "front-end" context
Traditional SPSS syntax window
Drive SPSS from Python (external mode)
Optional install (licensed with SPSS Base)
Copyright (c) SPSS Inc, 2007

General programming language
Access to variable dictionary, case data, and output
Access to standard and third-party modules
SPSS Developer Central modules
Module structure for building libraries of code
Legal Notice

Copyright (c) SPSS Inc, 2007
SPSS is not the owner or licensor of the Python
software. Any user of Python must agree to the
terms of the Python license agreement located
on the Python web site. SPSS is not making any
statement about the quality of the Python
program. SPSS fully disclaims all liability
associated with your use of the Python program.
The SPSS Programmability
Software Development Kit

Supports implementing various programming
languages


Requires a programmer to implement a new language
VB.NET Plug-In available on Developer Central
Works only in external mode
Copyright (c) SPSS Inc, 2007

How Programmability Works
Python interpreter embedded within SPSS

SPSS runs in traditional way until BEGIN PROGRAM
command is found

Python collects commands until END PROGRAM command
is found; then runs the program

Python can communicate with SPSS through API's (calls to
functions)


Includes running SPSS syntax inside Python program
Includes creating macro values for later use in syntax

Python can access SPSS output and data

OMS is a key tool
Copyright (c) SPSS Inc, 2007

Example:
Summarize Categorical Variables
BEGIN PROGRAM.
import spss, spssaux
spssaux.GetSPSSInstallDir("SPSSDIR")
spssaux.OpenDataFile("SPSSDIR/employee data.sav")
DESC !catVars.
Run
Copyright (c) SPSS Inc, 2007
# find categorical variables
catVars = spssaux.VariableDict(variableLevel=['nominal', 'ordinal'])
if catVars:
spss.Submit("FREQ " + " ".join(catVars.variables))
# create a macro listing categorical variables
spss.SetMacroValue("!catVars", " ".join(catVars.variables))
END PROGRAM.
Programmability Inside or Outside
SPSS

Two modes of operation

SPSS Drives mode (inside): traditional syntax context



BEGIN PROGRAM …program… END PROGRAM
Program in 14, 15, or 16 is in Python or, new in 16, in R
X Drives mode (outside): eXternal program drives SPSS

Python interpreter (or VB.NET)
No SPSS Viewer, Data Editor, or SPSS user interface



Output sent as text to the application – can be suppressed
Has performance advantages
Build programs with an IDE

Even if to be run in traditional mode
Copyright (c) SPSS Inc, 2007

PythonWin IDE Controlling SPSS
(eXternal Mode)
Copyright (c) SPSS Inc, 2007
Python Resources

Be productive quickly

Get more return as you learn more

Python.org

Python Tutorial

Cheeseshop
over 2200 packages as of April 11, 2007

SPSS Developer Central

SPSS Programming and Data Management, 4th ed, 2006.
Copyright (c) SPSS Inc, 2007

Python Books

Dive Into Python book or PDF

Practical Python by Magnus Lie Hetland

Extensive examples and discussion of Python
Python Cookbook, 2nd ed by Martelli, Ravenscroft, &
Ascher

Python in a Nutshell, 2nd ed by Martelli, O'Reilly


Very clear, comprehensive reference material
wxPython in Action by Rappin and Dunn

Explains user interface building with wxPython
Copyright (c) SPSS Inc, 2007

Cheeseshop: scipy

scipy 0.5.2 Scientific Algorithms Library for Python

Scipy.org
scipy is an open source library of scientific tools for
Python. scipy gathers a variety of high level science and
engineering modules together as a single package. scipy
provides modules for statistics, optimization, integration,
linear algebra, Fourier transforms, signal and image
processing, genetic algorithms, ODE solvers, special
functions, and more. scipy requires and supplements
NumPy, which provides a multidimensional array object
and other basic functionality.

Python is becoming a major language for scientific
computing
Copyright (c) SPSS Inc, 2007

SPSS Developer Central
SPSS Developer Central is the web home for
developing SPSS applications

Python, .NET, R Integration Plug-Ins

Supplementary modules by SPSS and others

Articles on programmability and graphics

Forums for asking questions and exchanging
information

Programmability Extension SDK

Get Python itself from Python.org or CD



SPSS 14, 15 use 2.4. (2.4.3)
SPSS 16 will use 2.5
Not limited to programmability


GPL graphics
User-contributed code
Key Supplementary
Modules
spssaux
spssdata
New for SPSS 15
trans
extendedTransforms
rake
pls
enhanced tables.py
Copyright (c) SPSS Inc, 2007

Example: Manipulating Output:
Merging Tables

tables.py module on Developer Central can merge two
tables into one.



E.g., Ctables significance tests into main tables
Merge or replace cells with cells from a different table
Flexibly define the join
tables.py can also censor cells, e.g., blank statistics
based on small counts.

Merge example: data on importance of education
qualifications for immigration by region of Europe

CTABLES /TABLE qfimeduBin BY Region
/TITLES TITLE='Qualifications for Immigration'
/COMPARETEST TYPE=PROP
Copyright (c) SPSS Inc, 2007

Ctables Output
Copyright (c) SPSS Inc, 2007
Program to Merge
BEGIN PROGRAM.
import spss, tables
cmd=r"""CTABLES /TABLE qfimeduBin BY Region
/TITLES TITLE='Qualifications for Immigration'
/COMPARETEST TYPE=PROP"""
tables.mergeLatest(cmd, autofit=False)
END PROGRAM.
Runs Ctables and merges test table into main table

Using default merge behavior

"If it really is this simple this will generate a lot of
excitement for us."

"This is really fantastic."
Copyright (c) SPSS Inc, 2007

Merged Output
Qualifications for Immigration
Comparisons of Column Proportions
Qualification for
immigration:
good educational
qualifications
0
1
3
4
5
1361
BD
2940
D
3543
3585
C
1931
C
Southern
Count
(D)
533
Results are bas ed on two-sided tests with significance level 0.05. For each
significant pair, the key of the category with the smaller column proportion
appears under the category with the larger column proportion.
574
1555
2038
2229
AC
1299
AC
Copyright (c) SPSS Inc, 2007
2
Western
Count
(A)
974
Region of Europe
Eastern
Northern
Count
Count
(B)
(C)
376
1024
AB D
336
1282
AB D
974
2720
AB D
1130
2989
B
1288
2540
C
823
876
AC
Approaches to Creating
New Procedures

You can extend SPSS capabilities by building new procedures


Or use ones that others have built
Combine SPSS procedures and transformations with Python
logic

Poisson regression (SPSS 14) example using iterated CNLR
 New raking procedure built over GENLOG
Calculate data aggregates in SPSS and pass to algorithm
coded in Python


Raking procedure starts with AGGREGATE; uses GENLOG
Acquire case data and compute in Python


Use Python standard modules and third-party additions
Partial Least Squares Regression (pls module)
Copyright (c) SPSS Inc, 2007

GENLIN
in SPSS 15
Adapt Existing Code Libraries

Common to adapt existing libraries or code for
use as Python extension modules


Python tools and API's to assist

Chap 25 in Python in a Nutshell
Tutorial on extending and embedding the Python
interpreter
Call R programs with SPSS 16
Copyright (c) SPSS Inc, 2007


C, C++, VB, Fortran,...
Partial Least Squares Regression
Regression with large number of predictors (even k > N)

Similar to Principal Components but considers dependent
variable simultaneously

Calculates principal components of (y, X) then use regression
on the scores instead of original data

Equivalent to ordinary regression when number of factors
equals number of predictors and one y variable

For more information see An Optimization Perspective on
Kernel Partial Least Squares Regression.pdf.
Copyright (c) SPSS Inc, 2007

The pls Module for SPSS 15

Strategy


Fetches data from SPSS
Uses scipy matrix operations to compute results


Writes pivot tables to SPSS Viewer


Subject to OMS
SPSS 14 viewer module created pivot table using OLE
automation
SPSS 15 has direct pivot table API's
Saves predicted values to active dataset
Copyright (c) SPSS Inc, 2007


Third-party module from Cheeseshop
pls Example: REGRESSION vs
PLS
GET FILE="c:/spss15/tutorial/sample_files/car_sales.sav".
REGRESSION /STATISTICS COEFF R /DEPENDENT sales
/METHOD=ENTER curb_wgt engine_s fuel_cap horsepow
length mpg price resale type wheelbas width .
begin program.
import spss, pls

plsproc defaults to five factors
Copyright (c) SPSS Inc, 2007
pls.plsproc("sales", """curb_wgt engine_s fuel_cap horsepow
length mpg price resale type wheelbas width""",
yhat="predsales")
end program.
Results
PLS with 5 factors
almost equals
regression with 11
variables
Copyright (c) SPSS Inc, 2007

SPSS 16 User Procedures
User procedures can be written in Python but specified using SPSS
traditional syntax

User never writes or sees Python code

Used as if a built-in SPSS command

EXTENSION command defines command to SPSS via simple XML file

Python module called with syntax already checked and processed by
SPSS

More general PLS module


PLS y1 y2 y3 BY fac1 fac2 WITH z1 z2 z3
/CRITERIA LATENTFACTORS=2.
Dialog box interface tools in SPSS 17

In the meantime, use wxPython or
something similar
Copyright (c) SPSS Inc, 2007

Raking Sample Weights

"Raking" adjusts sample weights to control totals in n
dimensions

Example: data classified by age and sex with known
population totals or proportions

Calculated by fitting a main effects loglinear model


Various adjustments required
Not a complete solution to reweighting
Not directly available in SPSS
Copyright (c) SPSS Inc, 2007

Raking Module

Strategy: combine SPSS procedures with Python logic

rake.py (from SPSS Developer Central)





rake.rake("age sex",
[{0: 1140, 1:1140}, {0: 104.6, 1:2175.4}],
finalweight="finalwt")
Copyright (c) SPSS Inc, 2007

Aggregates data via AGGREGATE to new dataset
Creates new variable with control totals
Applies GENLOG, saving predicted counts
Adjusts predicted counts
Matches back into original dataset
 Does not use MATCH FILES or require a SORT command
Written in one (long) day
Extending SPSS Transformations

SPSS 14 programmability can wrap SPSS syntax in
Python logic, e.g., generate COMPUTE commands
on the fly

SPSS 15 programmability can

Generate new variables directly
 Add new cases directly
 Create new datasets from scratch

SPSS 16 has additional dataset capabilities
Copyright (c) SPSS Inc, 2007

Useful when definitions can be expressed in SPSS syntax
trans and extendedTransforms
Modules

trans module facilitates plugging in Python code to
iterate over cases

Runs as an SPSS procedure


Use with



Standard Python functions (e.g., math module)
Any user-written functions or appropriate classes
Functions in extendedTransforms module
Copyright (c) SPSS Inc, 2007
Passes the data
 Adds variables to the SPSS variable dictionary
 Can apply any calculation casewise
trans and extendedTransforms
Modules
trans strategy
 Pass case data through Python code writing
result back to SPSS in new variables

extendedTransforms collection of 12 functions to
apply to SPSS variables, including

Regular expression search/replace
 soundex and nysiis functions for phonetic equivalence
 Date/time conversions based on patterns
Copyright (c) SPSS Inc, 2007

Python Regular Expressions

Pattern matching in text strings

If you use SPSS index or replace, you need these

Standardize string data (Mr, Mr., Herr, Senor,...)

Extract data from loosely structured text
"simvastatin-- PO 80mg TAB" -> "simvastatin", "80"

Patterns can be simple strings (as with SPSS index) or
complex patterns

Pick out variable names with common parts

Can greatly simplify code
Copyright (c) SPSS Inc, 2007

Write to Me!
Copyright (c) SPSS Inc, 2007