Programmability in spss 14, 15 and 16

Programmability in SPSS 14, SPSS
15 and SPSS 16
The Revolution Continues
Jon Peck
Technical Advisor
SPSS
Copyright (c) SPSS Inc, 2007

 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)SPSSInc,2007
Agenda

 "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."
Quotations from SPSS Users

 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.
The Combination of SPSS and
Python

 SPSS 14.0 provided
 Programmability
 Multiple datasets
 Variable and File Attributes
 Programmability read-access to case data
 Ability to control SPSS from a Python program
 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
Programmability Features in
SPSS 14, 15, and 16

 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
Programmability Advantages

 Python extends SPSS via
 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
 Runs in "back-end" syntax context (like macro)
 SaxBasic scripting runs in "front-end" context
 Two modes
 Traditional SPSS syntax window
 Drive SPSS from Python (external mode)
 Optional install (licensed with SPSS Base)
Programmability Overview

 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.
Legal Notice

 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
The SPSS Programmability
Software Development Kit

 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
How Programmability Works

BEGIN PROGRAM.
import spss, spssaux
spssaux.GetSPSSInstallDir("SPSSDIR")
spssaux.OpenDataFile("SPSSDIR/employee data.sav")
# 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.
DESC !catVars. Run
Example:
Summarize Categorical Variables

 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
Programmability Inside or Outside
SPSS

PythonWin IDE Controlling SPSS
(eXternal Mode)

 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.
Python Resources

 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
Python Books

 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
Cheeseshop: scipy

 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
SPSS Developer Central

 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
Example: Manipulating Output:
Merging Tables

Ctables Output

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."
Program to Merge

Qualifications for Immigration
Comparisons of Column Proportions
974 376 1024
A B D
533
1361
B D
336 1282
A B D
574
2940
D
974 2720
A B D
1555
3543 1130 2989
B
2038
3585
C
1288
C
2540 2229
A C
1931
C
823
A C
876 1299
A C
0
1
2
3
4
5
Qualification for
immigration:
good educational
qualifications
Count
(A)
Western
Count
(B)
Eastern
Count
(C)
Northern
Count
(D)
Southern
Region of Europe
Results are based 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.
Merged Output

 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
GENLIN
in SPSS 15
 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)
Approaches to Creating
New Procedures

 Common to adapt existing libraries or code for
use as Python extension modules
 C, C++, VB, Fortran,...
 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
Adapt Existing Code Libraries

 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.
Partial Least Squares Regression

 Strategy
 Fetches data from SPSS
 Uses scipy matrix operations to compute results
 Third-party module from Cheeseshop
 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
The pls Module for SPSS 15

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
pls.plsproc("sales", """curb_wgt engine_s fuel_cap horsepow
length mpg price resale type wheelbas width""",
yhat="predsales")
end program.
 plsproc defaults to five factors
pls Example: REGRESSION vs
PLS

 PLS with 5 factors
almost equals
regression with 11
variables
Results

 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
SPSS 16 User Procedures

 "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
Raking Sample Weights

 Strategy: combine SPSS procedures with Python logic
 rake.py (from SPSS Developer Central)
 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
rake.rake("age sex",
[{0: 1140, 1:1140}, {0: 104.6, 1:2175.4}],
finalweight="finalwt")
Raking Module

 SPSS 14 programmability can wrap SPSS syntax in
Python logic, e.g., generate COMPUTE commands
on the fly
 Useful when definitions can be expressed in SPSS syntax
 SPSS 15 programmability can
 Generate new variables directly
 Add new cases directly
 Create new datasets from scratch
 SPSS 16 has additional dataset capabilities
Extending SPSS Transformations

 trans module facilitates plugging in Python code to
iterate over cases
 Runs as an SPSS procedure
 Passes the data
 Adds variables to the SPSS variable dictionary
 Can apply any calculation casewise
 Use with
 Standard Python functions (e.g., math module)
 Any user-written functions or appropriate classes
 Functions in extendedTransforms module
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
trans and extendedTransforms
Modules

 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
Python Regular Expressions

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