Creating and managing custom nodes
The Custom Dialog Builder for Extensions creates nodes to use inside SPSS® Modeler streams.
Using the Custom Dialog Builder for Extensions you can:
- Create a custom node dialog for executing a node that is implemented in R, or in Apache Spark (via Python). See Building the script template for more information.
- Open a file containing the specification for a custom node dialog--perhaps created by another user--and add the dialog to your installation of IBM® SPSS Modeler, optionally making your own modifications.
- Save the specification for a custom node dialog so that other users can add it to their installations of IBM SPSS Modeler.
- Create custom nodes and write Python for Spark scripts to read data from wherever your data source is, and write data out to any data format supported by Apache Spark. See Importing and exporting data using Python for Spark for more information.
- Create custom nodes and write R scripts to read data from wherever your data source is, and write data out to any data format supported by R. See Importing and exporting data using R for more information.
In the Custom Dialog Builder for Extensions, you create or modify custom node dialogs within extensions. When you open the Custom Dialog Builder for Extensions, a new extension that contains an empty custom node dialog is created. When you save or install custom node dialogs from the Custom Dialog Builder for Extensions, they are saved or installed as part of an extension.
- You cannot create your own version of a node dialog for a standard IBM SPSS Modeler node.
- Scripting is not supported for nodes that are created with Custom Dialog Builder, including Custom Dialog Builder R nodes and Custom Dialog Builder Python nodes.
How to start the Custom Dialog Builder for Extensions
From the menus, choose
- Python nodes depend on the Spark environment.
- Python scripts must use the Spark API because data will be presented in the form of a Spark DataFrame.
- Old nodes created in version 17.1 will still only run against IBM SPSS Analytic Server (the data originates from an IBM SPSS Analytic Server source node and has not been extracted to IBM SPSS Modeler Server). New Python and Custom Dialog Builder nodes created in version 18.0 or later can run against IBM SPSS Modeler Server.
- When installing Python, make sure all users have permission to access the Python installation.
- If you want to use the Machine Learning Library (MLlib), you must install a version of Python that includes NumPy. Then you must configure the IBM SPSS Modeler Server (or the local server in IBM SPSS Modeler Client) to use your Python installation. For details, see Scripting with Python for Spark.