How To Fix RSANA_UMM_MLF_API007 - Inference input is not a table with flat structure.

SAP Error Message - Details

• Message type: E = Error

• Message class: RSANA_UMM_MLF_API -

• Message number: 007

• Message text: Inference input is not a table with flat structure.

Self-Explanatory Message

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Smart SAP Assistant

• What is the cause and solution for SAP error message RSANA_UMM_MLF_API007 - Inference input is not a table with flat structure. ?

  The SAP error message RSANA_UMM_MLF_API007 indicates that the inference input provided to a machine learning function is not in the expected format. Specifically, it suggests that the input data is not a table with a flat structure, which is required for the machine learning model to process the data correctly.
  Cause:
  
  Input Data Structure: The input data might be in a nested or hierarchical format rather than a flat table. Machine learning models typically require a flat structure where each row represents a single observation and each column represents a feature.
  Data Type Issues: The data types of the columns may not be compatible with the model's expectations. For example, categorical variables may need to be encoded properly.
  Missing Columns: The input data may be missing required columns that the model expects.
  Incorrect Data Source: The data source being used may not be the correct one, leading to an unexpected structure.
  
  Solution:
  
  
  Check Input Data Structure: Ensure that the input data is in a flat table format. Each row should represent a single instance, and each column should represent a feature.
  
  You can use tools like SAP Data Intelligence or SAP HANA to transform the data into the required format.
  
  
  
  Data Preparation: 
  
  Flatten any nested structures if necessary.
  Ensure that all required columns are present and correctly named.
  Convert categorical variables into a suitable format (e.g., one-hot encoding).
  
  
  
  Validate Data Types: Check that the data types of each column match the expected types for the model. For example, numerical features should be of a numeric type, and categorical features should be properly encoded.
  
  
  Use Data Profiling Tools: Utilize SAP tools to profile your data and identify any discrepancies in the structure or data types.
  
  
  Test with Sample Data: If possible, test the model with a small sample of data that is known to be in the correct format to ensure that the model works as expected.
  
  
  Related Information:
  
  SAP Documentation: Refer to the official SAP documentation for the specific machine learning function you are using. It often contains details about the expected input format and data requirements.
  SAP Community: Engage with the SAP Community forums to see if others have encountered similar issues and what solutions they found.
  Data Transformation Tools: Familiarize yourself with SAP tools like SAP Data Hub or SAP Data Intelligence, which can assist in transforming and preparing data for machine learning tasks.
  
  By ensuring that your input data meets the required flat structure and data type specifications, you should be able to resolve the error and successfully run your machine learning inference.