Using The findAll() Function Most SPSS Modeler scripts include code that locates an existing node e.g.: stream = modeler.script.stream() typenode = stream.findByType("type", None) However, some scripts need to search for all nodes – maybe by node type but also matching some other criteria. The Modeler scripting API documentation (PDF) mentions a findAll() function: d.findAll(filter, recursive):...
New extensions for SPSS Modeler using PySpark and MLlib algorithms. Now available on GitHub and the Extension Hub in Modeler 18: Gradient-Boosted Trees, K-Means Clustering, and Multinomial Naive Bayes.
Today we are releasing a Modeler version 18. There a quite a number of important changes and improvements in this version. We have four groupings of changes – Big Data Algorithms in Modeler, changes that continue Extend and Embrace the Value of Open Source, Platform Flexibility and other changes.
In this article we'll look at the code used in a Modeler extension node which allow modeler streams to leverage Spark's Collaborative Filtering algorithm to build a simple recommender system.
Check out this example of Python scripting in terms of SQL optimization. When retrieving data from a database, one may want to limit data to a certain amount of dates. Learn how to select data relative to the current date when the stream is executed.
The core principle of IBM SPSS Modeler has long been being able to do complex data analysis and sophisticated model building all without programming. Still, there are many times when users want a way to run Modeler streams without having to click buttons or move nodes. As a result, Modeler has had...
Learn how to convert SPSS Statistics data into Python objects, with an example converting to a Panda's dataframe. With only a few lines of Python, you can easily convert a Statistics dataset into a Panda’s dataframe, Numpy array, or NetworkX object.
In this article, we use SPSS Modeler scripting to generate a stream to scan a named data set for categories in a particular field, present the available categories to the user, and generate a histogram of a numeric field for the category chosen by the user.
IBM SPSS Modeler supports Python scripting using Jython, a Java[tm] implementation of the Python language. Modeler versions 16 and 17 use Jython 2.5.1 which includes a number of useful and popular modules. Learn more on this article
Do you love Python and IBM SPSS Statistics? Read this post to see a demonstration of how easy it is to develop a Python script that extends functionality in SPSS Statistics.