SAS Model Studio is a user-friendly low code/no code application that provides an intuitive interface that enables data scientists and analysts to build and deploy machine learning models. It supports various products such as SAS Visual Data Mining and Machine Learning, SAS Visual Text Analytics, and SAS Visual Forecasting, making it a versatile tool for data scientists and analysts.   Although SAS Model Studio simplifies the process of building and deploying machine learning models, like any software, it can still encounter issues that may be difficult to resolve independently. In this article, we concentrate on the SAS Visual Data Mining and Machine Learning (VDMML) product and guide on collecting pertinent information and logs to send to SAS Technical Support. Additionally, we explore how the hmeq data set can help to recreate problems and swiftly address any issues.   Getting started with troubleshooting SAS VDMML issues   When troubleshooting SAS VDMML issues with SAS Technical Support, the first step is to collect basic information such as the site number and product version. See Contact SAS Technical Support to know how to collect them. Once you have this information, the next step is to enable debug reporting to generate logs for troubleshooting.   Enabling Debug Reporting in SAS VDMML   The Logging window lets you control how much information is generated in the logs.  It can be either for a project (via the Settings icon -> Project settings)   or for all your projects (via the User icon -> Settings.)   To avoid generating too much content in the logs, unless instructed otherwise, only check "Enable debug reporting" and then "Add time information and headers".  Refer to Enable Debug Reporting in the User's Guide for more information.     Collecting relevant information and logs   To troubleshoot issues with SAS Technical Support effectively, it's important to collect relevant information and logs to identify the root cause of the problem. Follow these steps: Determine if the issue is new or existing to identify potential causes. Provide a screenshot of the issue, for example, a popup window containing the error message or a pipeline containing the failing node, to help visualize the problem and improve communication. Collect information such as date, time, job, and user to identify the specific problem area. Provide a step-by-step guide on how to reproduce the issue. Identify recent changes to the environment that may be related to the issue.  Changes to the system, server, or hardware can sometimes cause unexpected issues, so it's essential to check for any recent changes that may have triggered the problem. If logs are available, collect and review the appropriate logs to gather more information about the issue. For example, the Project Data Advisor log can provide information about issues with creating a project, while the node log can provide information about issues with running a node. Notify SAS Technical Support If logs are not available. To find the logs of a SAS VDMML project, open the project, click the Settings button in the upper right corner of the window, and then click Project logs.         The types of logs required will vary depending on the issue at hand. Here are some examples of when and where to find different logs. Troubleshooting issues with creating a project: If a project fails to be created, and the Data tab does not appear on the project page, then the Project Data Advisor log can provide insight into the creation of the data source's metadata such as variable names, labels, type, role, and level assignments.   Troubleshooting issues with running the Data node: If the Data tab (that contains metadata information) has been created, but the Data node is running indefinitely or fails to run, then the Project Partitioning log can provide more information about the creation of (train, validate, and test) data partitions.   Troubleshooting issues with running a node: If a project is created successfully, but a node fails to run, then the node log can provide information about the SAS code that runs behind the scenes.  Right-click the node and select Log from the pull-down menu to get the node's log.   Additional logs might be available from Project logs: Log for Project Retraining, Log for Project Batch Retraining, Log for Initiating Project Batch Retraining, and Log for Scoring and Assessing. These logs can provide more information about specific operations related to each log. If a retrain job was run via running Batch API code, then the Project Batch Retraining and the Project Retraining logs are generated.   If the project data source is replaced, then the Initiating Project Batch Retraining log is generated.   After scoring holdout data, you might find multiple logs prefixed by ’Log for Scoring and Assessing’.  For instance, let's assume there are two pipelines in your project where the winning model from the first pipeline is the Gradient Boosting node and the winning model from the second pipeline is the Logistic Regression node.  The following three logs are generated:       Reproducing SAS VDMML issues with sample data   The sample home equity (hmeq) data set is an excellent data source for testing SAS VDMML issues. This data set includes information about 5,960 fictitious mortgages, each representing an applicant for a home equity loan. The binary target BAD indicates whether an applicant in the training data eventually defaulted or was ever seriously delinquent.  When you can reproduce the issue using the sample hmeq data, it suggests the issue is less likely data-dependent but more likely relevant to the environment. When you cannot reproduce the issue using the sample hmeq data, additional logs from the Viya services and CAS server might be required to diagnose the problem further. You can download the hmeq data from the Example Data Sets for the SAS® Viya® Platform page.   In summary, SAS Model Studio is a low-code/no-code application that simplifies the process of building and deploying machine learning models, including SAS Visual Data Mining and Machine Learning (VDMML). However, issues may still arise, and collecting relevant information and logs is essential to troubleshoot them effectively. To do so, one should determine if the issue is new or existing, provide a screenshot of the problem, collect information such as date and time, provide a step-by-step guide on how to reproduce the issue, identify recent changes to the environment that may be related to the issue, and gather the appropriate logs. The sample home equity (hmeq) dataset is an excellent resource for testing SAS VDMML issues. ... View more

SAS Contextual Analysis is a web-based text analytics application that uses contextual analysis to provide a comprehensive solution to the challenge of identifying and categorizing key textual data.    As an analyst, your textual data can be provided to you in different formats. For example, it could be text-based documents stored within a directory in your network, prepared as a SAS data set, or recorded a spreadsheet.     SAS Contextual Analysis is able to work with most of the data formats; however, sometimes additional steps are required in order to bring in your data.  In this article, we will first discuss how SAS Contextual Analysis reads in textual data.  Then we will discuss two commonly seen hurdles and how to work around them.   How SAS Contextual Analysis reads your data When you define the data source in your project, there are basically two options to identify your data:     Option 1: A SAS data set.  To enable this method, you will check the “Select variables from within a data set” as shown below:     The context of the Text variable is either the actual data or the file locations of the text documents (by checking the Text variable contains a file reference box).  The latter enables you to reference directly to text documents that are stored in network directories.   There is also an advantage of using the file reference approach while working with large documents.   A SAS character variable’s length cannot exceed 32,767 character bytes.  So the maximum length of text that can be stored and analyzed is around 32K unless you use the file reference approach where SAS Contextual Analysis will train on the entire content of each document.   Consider creating a variable that contains the file location of each document while you preparing your data, if your documents are large.  Also be aware that these documents are required to be text files, that is, with .txt file extension.   Option 2: A document collection. To enable this method, you will check the Use a file in a directory button as shown below.        The input documents can be in text-based file formats such as MS Office, OpenDocument (OpenOffice), PDF, XML, HTML, and others.  You can browse to the directory that contains your corpus.  Documents contained in the subfolders are also imported and analyzed.   This input option allows you to read documents with various data formats, which SAS Contextual Analysis normalizes into text files to be processed later on.   Common challenges when you work with your input data   Challenge 1:  The SAS data sets must be registered in metadata   A SAS data set must be registered before it can be shown in your SAS Contextual Analysis data library during the data source creation.  This also includes the synonym list, stop list, and start list. This task is most likely performed by your SAS administrator. The steps for registering data are given in the section “Quick Start Steps”  of the SAS Contextual Analysis 14.2: Administrator's Guide.     What this means is you must have a metadata permission to register your SAS data sets into SAS Metadata.  As a SAS Contextual Analysis user, you probably do not have the required permission. It becomes a challenge when you have many input tables to work on, and your SAS administrator has to to register each table for you once it is ready to be analyzed.    One way to work around is to have your SAS administrator (for example,“sasadm”) perform the following two steps just once so you can register the table yourself.     Step 1: create a metadata folder under “/SharedData” for you (for example “sasdemo”) as shown below.      Step2: “sasadm” grants required metadata rights to “sasdemo” on its own metadata folder “meta_dir_sasdemo” under “/SharedData”.   Once the step above is performed, “sasdemo” will log on to SAS Management Console and create its own library (for instance, "demolib") under the “meta_dir_sasdemo” metadata folder as shown in the following two screen shots:       The “sasdemo” user now can register its own data set (for instance, "COMMENTS") in SAS Management Console without having to ask its SAS Administrator.         Once the table "COMMENTS" is registered, "sasdemo” is able to find the table in the personal library during the data source creation:       Challenge 2:  My text data is stored in an Excel file   If your text data is stored as a Microsoft Excel file, SAS Contextual Analysis imports the entire content of a worksheet and converts it to a single text document.  This behavior is probably not what you've expected -- you want to extract the text data in each cell to a separate document.    To work around, you can either create a SAS data set from the Excel file or create a folder containing the collection of the documents (extracted from the text column in your Excel file).  We will focus on the first approach by using File->Import Data in SAS Enterprise Guide as shown in the following steps.   Step1: select the output library for the SAS data set to be created in.  Here we use the same library "demolib" that was created in the example shown in the first challenge.  Once the SAS data set is created, the only step left for you is to register the data set into your Metadata library.     Step 2: Use File -> Import Data to import your Excel file:     Open your Excel file.     The first window asks you the information about the source of the Excel file and the corresponding output data set to be created.      Next, depending on how your data is stored in your worksheet, you will adjust the setting accordingly.     Next, verify the attributes of your text variable and modify it if necessary:     Lastly, examine your text column to see if the content is as expected.     Once the SAS data set is created, you can register it using the workaround provided in the first challenge. ... 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