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Batch scoring configuration and usage

You can use H2O eScorer to perform batch scoring (database scoring). The batch scoring process can be summarized as follows:

  • Read the data from the source database
  • Make predictions on the data
  • Write the results back to the database

For more in-depth information on database scoring in H2O eScorer, download the H2O eScorer Database Scoring PDF.

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Batch scoring tasks require JDBC drivers to connect to the data source to ingest data and record predictions. The following is a comprehensive list of the JDBC drivers and their supported versions for the most common batch scoring use cases, AWS S3 and Snowflake.

AWS S3

  • mojo2-runtime.jar
  • aws-java-sdk-core-1.12.311.jar
  • aws-java-sdk-s3-1.12.311.jar
  • jackson-annotations-2.12.6.jar
  • httpclient-4.5.13.jar
  • httpcore-4.4.15.jar
  • jackson-databind-2.12.6.1.jar
  • jackson-core-2.12.6.jar
  • commons-io-2.4.jar

Snowflake

  • mojo2-runtime.jar
  • ibatis-core-3.0.jar (Optional - Required only if you want to do the post-processing)
  • 1.4.2-1.0.jar (Optional - Required only if you want to run MLI)
  • aws-java-sdk-core-1.12.311.jar
  • snowflake-jdbc-3.9.2.jar

The following sections describe how to auto-generate and use a Properties file to configure your Batch Scorer.

Auto-generate a properties file​

The H2O eScorer Wave app lets you automatically generate deployment code for you model with the AutoGen feature.

To generate the model deployment code, click Deploy -> AutoGen and specify a Model and Deployment Type. To confirm your selection, click the Generate button. In this example, a batch scorer properties file called ManagedCloud_Database_Scorer.properties is generated to score the riskmodel.mojo model.

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In this example, riskmodel.mojo had already been deployed to the eScorer and made available in the model registry.

batch_scorer_properties.png

What do the properties mean?​

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Notice how the model features are auto-generated for the riskmodel.mojo file.

  1. JDBC connection string for the database:

    SQLConnectionString=jdbc:<vendor>://<database-ip>:<database-port>/<instance-name>

  2. A description for the model:

    Description = A H2O.ai Machine Learning model

  3. Use Environment Variables to set values for the parameters. For example, SQLUSER='$USER' sets the SQLUSER parameter with the value from the environment variable.

    EnvVariables = true

  4. Name and path of the Model. Do not change for H2O eScorer.

    ModelName = '$MODELNAME'

  5. SELECT SQL statement to get the data that will be scored:

    SQLSelect = SELECT <add-table-key-or-unique-field>, loan_amnt, term, int_rate, installment, emp_length, annual_inc, verification_status, addr_state, dti, inq_last_6mths, revol_bal, revol_util, total_acc FROM <add-table-name>.<add-table-name>

  6. Unique key of the table

    SQLKey = <add-table-key-or-unique-field>

  7. Prediction class to return. One of: 0, 1, __blank__ (All)

    SQLPrediction =

  8. SQL statement to write the results back to the database

    SQLWriteBatch=insert into <results-table> ("<SQLKey>", "bad_loan_0", "bad_loan_1")  values(?, ?, ? )

  9. SQL friendly model target field. For ex: removed spaces and dot predictions

    SQLWriteFriendlyNames = false

  10. Skip features with empty string

    IgnoreEmptyStrings = true

  11. Ignore logic checks (enable with caution)

    IgnoreChecks = False

  12. Ignore SQL statement logic checks (enable with caution)

    IgnoreSQLChecks = False

  13. Change NULL in SQL results to NA. This depends on the model training and should match it.

    SetNullAs = NA
    NullCharacters="NA|NULL"

  14. Return Shapley contributions with the Predictions. (SQLWriteBatch must be set)

    ShapPredictContrib = false
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