Programmability in spss 14

Programmability in SPSS 14:
A Radical Increase in Power
A Platform for Statistical Applications
Jon K. Peck
Technical Advisor
SPSS Inc.
peck@spss.com
May, 2006
Copyright (c) SPSS Inc, 2006

1. External Programming Language (BEGIN PROGRAM)
2. Multiple Datasets
3. XML Workspace and OMS Enhancements
4. Dataset and Variable Attributes
5. Drive SPSS Processor Externally
Working together, they dramatically increase
the power of SPSS.
SPSS becomes a platform that enables you to
build statistical/data manipulation applications.
GPL provides new programming power for graphics.
The Five Big ThingsThe Five Big Things

 Many datasets open at once
 One is active at a time (set by syntax or UI)
 DATASET ACTIVATE command
 Each dataset has a Data Editor window
 Copy, paste, and merge between windows
 Write tabular results to a dataset using Output
Management System
 Retrieve via Programmability
 No longer necessary to organize jobs linearly
Multiple DatasetsMultiple Datasets

XML WorkspaceXML Workspace
 Store dictionary and selected results in workspace
 Write results to workspace as XML with Output
Management System (OMS)
 Retrieve selected contents from workspace via
external programming language
 Persists for entire session

OMS Output: XML or DatasetOMS Output: XML or Dataset
 Write tabular results to Datasets with OMS
 Main dataset remains active
 Prior to SPSS 14, write to SAV file, close active, and open to use
results
 Tables can be accessed via workspace or as datasets
 XML workspace and XPath accessors are very general
 Accessed via programmability functions
 Dataset output more familiar to SPSS users
 Accessed via programmability functions or traditional SPSS syntax
 Use with DATASET ACTIVATE command

AttributesAttributes
 Extended metadata for files and variables
 VARIABLE ATTRIBUTE, DATAFILE ATTRIBUTE
 Keep facts and notes about data permanently with
the data. E.g., validation rules, source, usage,
question text, formula
 Two kinds: User defined and SPSS defined
 Saved with the data in the SAV file
 Can be used in program logic

ProgrammabilityProgrammability
 Integrates external programming language into SPSS syntax
 BEGIN PROGRAM … END PROGRAM
 set of functions to communicate with SPSS
 SPSS has integrated the Python language
 SDK enabling other languages available
 New: VB.NET available soon
 External processes can drive SPSS Processor
 VB.NET works only in this mode
 SPSS Developer Central has SDK, Python Integration Plug-In,
and many extension modules
 Available for all SPSS 14 platforms

The Python LanguageThe Python Language
 Free, portable, elegant, object oriented, versatile,
widely supported, easy to learn,…
 Download from Python.org.
 Version 2.4.1 or later required
 Python tutorial
 Python user discussion list
 The Cheeseshop: Third-party modules

Legal NoticeLegal Notice
 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.

Programmability Enables…Programmability Enables…
 Generalized jobs by controlling logic based on
 Variable Dictionary
 Procedure output (XML or datasets)
 Case data (requires SPSS 14.0.1)
 Environment
 Enhanced data management
 Manipulation of output
 Computations not built in to SPSS
 Use of intelligent Python IDE driving SPSS (14.0.1)
 statement completion, syntax checking, and debugging
 External Control of SPSS Processor

Programmability MakesProgrammability Makes
Obsolete…Obsolete…
 SPSS Macro
 except as a shorthand for lists or constants
 Learning Python is much easier than learning Macro
 SaxBasic
 except for autoscripts
 but autoscripts become less important
 These have not gone away.
 The SPSS transformation language continues to be
important.

DemonstrationDemonstration
Code and supporting modules can be downloaded
from SPSS Developer Central
examples are on the CD

Initialization for ExamplesInitialization for Examples
 * SPSS Directions, May 2006.
* In preparation for the examples, specify where SPSS
standard data files reside.
BEGIN PROGRAM.
import spss, spssaux
spssaux.GetSPSSInstallDir("SPSSDIR")
END PROGRAM.
 This program creates a File Handle pointing to the SPSS installation
directory, where the sample files are installed

* EXAMPLE 0: My first program.
BEGIN PROGRAM.
import spss
print "Hello, world!"
END PROGRAM.
 Inside BEGIN PROGRAM, you write Python code.
 import spss connects program to SPSS.
 Import needed once per session.
 Output goes to Viewer log items.
 Executed when END PROGRAM reached.
RunRun Copyright (c) SPSS Inc, 2006
Example 0: Hello, worldExample 0: Hello, world

*Run an SPSS command from a program; create file handle.
BEGIN PROGRAM.
spss.Submit("SHOW ALL.")
spssaux.GetSPSSInstallDir("SPSSDIR")
END PROGRAM.
 Submit, in module spss is called to run one or more SPSS
commands within BEGIN PROGRAM.
 One of many functions (API's) that interacts with SPSS.
 GetSPSSInstallDir, in the spssaux module, creates a FILE
HANDLE to that directory
Example 1: Run SPSS CommandExample 1: Run SPSS Command

* Print useful information in the Viewer and then get help
on an API.
BEGIN PROGRAM.
spss.Submit("GET FILE='SPSSDIR/employee data.sav'.")
varcount = spss.GetVariableCount()
casecount = spss.GetCaseCount()
print "The number of variables is " + str(varcount) + "
and the number of cases is " + str(casecount)
print help(spss.GetVariableCount)
END PROGRAM.
 There are API's in the spss module to get variable dictionary
information.
 help function prints short API documentation in Viewer.
Example 2: Some API'sExample 2: Some API's

Example 3a: Data-DirectedExample 3a: Data-Directed
AnalysisAnalysis
* Summarize variables according to measurement level.
BEGIN PROGRAM.
spssaux.OpenDataFile("SPSSDIR/employee data.sav")
# make variable dictionaries by measurement level
catVars = spssaux.VariableDict(variableLevel=['nominal',
'ordinal'])
scaleVars = spssaux.VariableDict(variableLevel=['scale'])
print "Categorical Variablesn"
for var in catVars:
print var, var.VariableName, "t", "var.VariableLabel"
Continued

# summarize variables based on measurement level
if catVars:
spss.Submit("FREQ " + " ".join(catVars.variables))
if scaleVars:
spss.Submit("DESC "+" ".join(scaleVars.variables))
# create a macro listing scale variables
spss.SetMacroValue("!scaleVars", "
".join(scaleVars.variables))
END PROGRAM.
DESC !scaleVars.
" ".join(['x', 'y', 'z']) produces
'x y z'
Example 3a (continued)Example 3a (continued)

* Handle an error. Use another standard Python module.
BEGIN PROGRAM.
import sys
try:
spss.Submit("foo.")
except:
print "That command did not work! ", sys.exc_info()[0]
END PROGRAM.
 Errors generate exceptions
 Makes it easy to check whether a long syntax job worked
 Hundreds of standard modules and many others available from
SPSS and third parties
Example 5: Handling ErrorsExample 5: Handling Errors

* Create set of dummy variables for a categorical
variable and a macro name for them.
BEGIN PROGRAM.
import spss, spssaux, spssaux2
mydict = spssaux.VariableDict()
spssaux2.CreateBasisVariables(mydict.["educ"],
"EducDummy", macroname = "!EducBasis")
spss.Submit("REGRESSION /STATISTICS=COEF /DEP=salary"
+ "/ENTER=jobtime prevexp !EducBasis.")
END PROGRAM.
 Discovers educ values from the data and generates
appropriate transformation commands.
 Creates macro !EducBasis
Example 8: Create Basis VariablesExample 8: Create Basis Variables

* Automatically add cases from all SAV files in a directory.
BEGIN PROGRAM.
import glob
savlist = glob.glob("c:/temp/parts/*.sav")
if savlist:
cmd = ["ADD FILES "] +
["/FILE='" + fn + "'" for fn in savlist] +
[".", "EXECUTE."]
spss.Submit(cmd)
print "Files merged:n", "n".join(savlist)
else:
print "No files found to merge"
END PROGRAM.
 The glob module resolves file-system wildcards
 If savlist tests whether there are any matching files.
Example 9: Merge DirectoryExample 9: Merge Directory
ContentsContents

* Run regression; get selected statistics, but do not display the
regular Regression output. Use OMS and Xpath wrapper functions.
BEGIN PROGRAM.
spssaux.OpenDataFile("SPSSDIR/CARS.SAV")
try:
handle, failcode = spssaux.CreateXMLOutput(
"REGRESSION /DEPENDENT accel /METHOD=ENTER weight horse
year.", visible=False)
horseCoef = spssaux.GetValuesFromXMLWorkspace(
handle, "Coefficients", rowCategory="Horsepower",
colCategory="B",cellAttrib="number")
print "The effect of horsepower on acceleration is: ",
horseCoef
Rsq = spssaux.GetValuesFromXMLWorkspace(
handle, "Model Summary", colCategory="R Square",
cellAttrib="text")
print "The R square is: ", Rsq
spss.DeleteXPathHandle(handle)
except:
print "*** Regression command failed. No results available."
raise
END PROGRAM. RunRun Copyright (c) SPSS Inc, 2006
Example 10: Use Parts of Output -Example 10: Use Parts of Output -
XMLXML

BEGIN PROGRAM.
import spss, Transform
spssaux.OpenDataFile('SPSSDIR/employee data.sav')
newvar = Transform.Compute(varname="average_increase",
varlabel="Salary increase per month of experience
if at least a year",
varmeaslvl="Scale",
varmissval=[999,998,997],
varformat="F8.4")
newvar.expression = "(salary-salbegin)/jobtime"
newvar.condition = "jobtime > 12"
newvar.retransformable=True
newvar.generate() # Get exception if compute fails
Transform.timestamp("average_increase")
spss.Submit("DISPLAY DICT /VAR=average_increase.")
spss.Submit("DESC average_increase.")
END PROGRAM.
Example 11: Transformations inExample 11: Transformations in
Python SyntaxPython Syntax

BEGIN PROGRAM.
import spss, Transform
try:
Transform.retransform("average_increase")
Transform.timestamp("average_increase")
except:
print "Could not update average_increase."
else:
spss.Submit("display dictionary"+
"/variable=average_increase.")
END PROGRAM.
 Transformation saved using Attributes
Example 11A: Repeat TransformExample 11A: Repeat Transform

BEGIN PROGRAM.
import spss, viewer
spss.Submit("DESCRIPTIVES ALL")
spssapp = viewer.spssapp()
try:
actualName = spssapp.SaveDesignatedOutput(
"c:/temp/myoutput.spo")
except:
print "Save failed. Name:", actualName
else:
spssapp.ExportDesignatedOutput(
"c:/temp/myoutput.doc", format="Word")
spssapp.CloseDesignatedOutput()
END PROGRAM.
Example 12: Controlling theExample 12: Controlling the
Viewer Using AutomationViewer Using Automation

BEGIN PROGRAM.
from poisson_regression import *
spssaux.OpenDataFile(
'SPSSDIR/Tutorial/Sample_Files/autoaccidents.sav')
poisson_regression("accident", covariates=["age"],
factors=["gender"])
END PROGRAM.
 Poisson regression module built from SPSS CNLR and
transformations commands.
 PROGRAMS can get case data and use other Python
modules or code on it.
Example 13: A New ProcedureExample 13: A New Procedure
Poisson RegressionPoisson Regression

* Mean salary by education level.
BEGIN PROGRAM.
import spssdata
data = spssdata.Spssdata(indexes=('salary', 'educ'))
Counts ={}; Salaries={}
for case in data:
cat = int(case.educ)
Counts[cat] = Counts.get(cat, 0) + 1
Salaries[cat] = Salaries.get(cat,0) + case.salary
print "educ mean salaryn"
for cat in sorted(Counts):
print " %2d $%6.0f" % (cat,
Salaries[cat]/Counts[cat])
del data
END PROGRAM.
Example 14: Using Case DataExample 14: Using Case Data

BEGIN PROGRAM.
# <accumulate Counts and Salaries as in Example 14>
desViewer = viewer.spssapp().GetDesignatedOutput()
rowcats = []; cells = []
for cat in sorted(Counts):
rowcats.append(int(cat))
cells.append(Salaries[cat]/Counts[cat])
ptable = viewer.PivotTable("a Python table",
tabletitle="Effect of Education on Salary",
caption="Data from employee data.sav",
rowdim="Years of Education",
rowlabels=rowcats,
collabels=["Mean Salary"],
cells = cells,
tablelook="c:/data/goodlook.tlo")
ptable.insert(desViewer)
END PROGRAM.
Example 14a: Output As a PivotExample 14a: Output As a Pivot
TableTable

get file='c:/spss14/cars.sav'.
DATASET NAME maindata.
DATASET DECLARE regcoef.
DATASET DECLARE regfit.
OMS /IF SUBTYPE=["coefficients"]
/DESTINATION FORMAT = sav OUTFILE=regcoef.
OMS /IF SUBTYPE=["Model Summary"]
/DESTINATION FORMAT = sav OUTFILE=regfit.
REGRESSION /DEPENDENT accel /METHOD=ENTER
weight horse year.
OMSEND.
Use OMS directly to figure out what to retrieve programmatically
Exploring OMS Dataset OutputExploring OMS Dataset Output

BEGIN PROGRAM.
import spss, spssaux, spssdata
try:
coefhandle, rsqhandle, failcode =
spssaux.CreateDatasetOutput(
"REGRESSION /DEPENDENT accel /METHOD=ENTER
weight horse year.",
subtype=["coefficients", "Model Summary"])
cursor = spssdata.Spssdata(indexes=["Var2",
"B"], dataset=coefhandle)
for case in cursor:
if case.Var2.startswith("Horsepower"):
print "The effect of horsepower on
acceleration is: ", case.B
cursor.close()
Example 10a: Use Bits of Output -Example 10a: Use Bits of Output -
DatasetsDatasets

cursor =spssdata.Spssdata(indexes=["RSquare"],
dataset=rsqhandle)
row = cursor.fetchone()
print "The R Squared is: ", row.RSquare
cursor.close()
except:
print "*** Regression command failed. No
results available."
raise
spssdata.Dataset("maindata").activate()
spssdata.Dataset(coefhandle).close()
spssdata.Dataset(rsqhandle).close()
END PROGRAM.
Example 10a: Use Bits of Output –Example 10a: Use Bits of Output –
Datasets (continued)Datasets (continued)

 Variable Dictionary access
 Procedures selected based on variable properties
 Actions based on environment
 Automatic construction of transformations
 Error handling
 Variables that remember their formulas
 Management of the SPSS Viewer
 New statistical procedure
 Access to case data
What We SawWhat We Saw

 SPSS Processor (backend) can be embedded and
controlled by Python or other processes
 Build applications using SPSS functionality
invisibly
 Application supplies user interface
 No SPSS Viewer
 Allows use of Python IDE to build programs
 Pythonwin or many others
Externally Controlling SPSSExternally Controlling SPSS

PythonWin IDE Controlling SPSSPythonWin IDE Controlling SPSS

 Extend SPSS functionality
 Write more general and flexible jobs
 Handle errors
 React to results and metadata
 Implement new features
 Write simpler, clearer, more efficient code
 Greater productivity
 Automate repetitive tasks
 Build SPSS functionality into other applications
What Are the ProgrammabilityWhat Are the Programmability
Benefits?Benefits?

 SPSS 14 (14.0.1 for data access and IDE)
 Python (visit Python.org)
 Installation
 Tutorial
 Many other resources
 SPSS® Programming and Data Management, 3rd Edition: A Guide for SPSS®
and SAS® Users new
 SPSS Developer Central
 Python Plug-In (14.0.1 version covers 14.0.2)
 Example modules
 Dive Into Python (diveintopython.org) book or PDF
 Practical Python by Magnus Lie Hetland
 Python Cookbook, 2nd
ed by Martelli, Ravenscroft, & Ascher
Python and
Plug-In
On the CD in
SPSS 15
Getting StartedGetting Started

 Five power features of SPSS 14
 Examples of programmability using Python
 How to get started: materials and resources
RecapRecap

?
?
?
?
QuestionsQuestions

Working together these new features give you a
dramatically more powerful SPSS.
SPSS becomes a platform that enables you to
build your own statistical applications.
1. Programmability
2. Multiple datasets
3. XML Workspace and OMS enhancements
4. Attributes
5. External driver application
In ClosingIn Closing

Jon Peck can now be reached at:
peck@us.ibm.com
ContactContact

Programmability in spss 14

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