Create Predicted Eventlog

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This page contains instructions how to install and configure eventlog prediction. The prediction creates a new model that contains the source model data and the predictions. Prediction is able to create case attributes for the generated cases and event attribute values for the predicted events. To distinguish the real and predicted events and cases, there are following attributes:

  • Event attribute Predicted indicates whether the event is from the source data (false) or whether is a predicted (true).
  • Case attribute Generated indicates whether the case existed in the source data (false) or whether the prediction generated it as a new case.

Prerequisites

Following prerequisites need to be fulfilled to run the eventlog prediction:

  • QPR ProcessAnalyzer 2024.2 or later in use
  • Snowflake connection is configured
  • Source models are stored to Snowflake

Prediction installation to Snowflake

Follow these steps to install the eventlog prediction to Snowflake:

  1. Go to Snowflake UI, and create a Snowflake-managed stage with name PREDICTION to the same schema that is configured to QPR ProcessAnalyzer (in the Snowflake connection string).
  2. Open the created stage and upload the predict.pyz file to the stage (ask the file from your QPR representative).
  3. Create the following procedure to the same schema:
CREATE OR REPLACE PROCEDURE QPRPA_SP_PREDICTION("CONFIGURATION" OBJECT)
RETURNS OBJECT
LANGUAGE PYTHON
STRICT
RUNTIME_VERSION = '3.8'
PACKAGES = ('nltk','numpy','pandas==1.5.3','scikit-learn','snowflake-snowpark-python','tensorflow','dill','prophet','holidays==0.18','python-kubernetes','docker-py')
HANDLER = 'main'
EXECUTE AS OWNER
AS '
import sys
def main(session, parameters_in: dict) -> dict:
	session.file.get(''@decision_intelligence/predict.pyz'', ''/tmp'')
	sys.path.append(''/tmp/predict.pyz'')
	import predict
	return predict.main(session, parameters_in)
';

Prediction setup to QPR ProcessAnalyzer

1. Go to QPR ProcessAnalyzer and create the following expression script with name ML library: 

let predictionProcedureName = "qprpa_sp_prediction";

function RunFunctionWithParallelLogging(logTable, callbackFunc) 
{
  let state = #{};

  logTable.Truncate();

  _system.Parallel.Run([
    () => {
      try {
        state.Set("Result", callbackFunc());
      }
      finally {
        state.Set("Finished", true);
      }
    },
    () => {
      let readRows = 0;
      while (true) {
        try {
          let finished = state.TryGetValue("Finished");
          if (finished == true) {
            WriteLog(`Work finished. Log written to table ${logTable.Name} (id: ${logTable.Id})`);
            break;
          }

          let rowsDf = logTable.Synchronize().SqlDataFrame.Skip(readRows).Collect();
          readRows = readRows + rowsDf.NRows;
          rowsDf.Rows.{
            let row = _;
            WriteLog(`${row[0]} \t${row[1]}`);
          }
        }
        catch (ex) {
          if (ex.InnerExceptions?[0]?.Type == "UnauthorizedAccess") {
            WriteLog(`Log data table has disappeared. The generated log will not be complete!`);
            break;
          }
          WriteLog(`Log has not yet been created.`);
        }
      }
      Sleep(5000);
    }
  ]);
  return state.TryGetValue("Result");
}

function Train(params, logTable)
{
  function TrainCallback()
  {
    WriteLog("Starting training...");

    let connection = logTable.Project.CreateSnowflakeConnection();
    let finalParams = params.Clone();
    finalParams.Set("_typed", params["_typed"].Extend(#{
      "log_table": logTable
    }));
    let result = connection.CallStoredProcedure(predictionProcedureName, #{"configuration": finalParams});

    WriteLog("Finished training...")
    return result;
  }
  return RunFunctionWithParallelLogging(logTable, TrainCallback);
}

function Generate(params, logTable) 
{
  function GenerateCallback()
  {
    WriteLog("Starting generation...")

    let connection = logTable.Project.CreateSnowflakeConnection();

    let typedParams = params["_typed"];
    let resultEventsTable = typedParams["target_event_data_table"];
    let resultCasesTable = typedParams.TryGetValue("target_case_data_table");
    
    if (params["overwrite"]) {
      resultEventsTable.Truncate();
      resultCasesTable?.Truncate();
    }

    let finalParams = params.Clone();
    finalParams.Set("_typed", typedParams.Extend(#{
      "log_table": logTable
    }));

    let result = connection.CallStoredProcedure(predictionProcedureName, #{"configuration": finalParams});

    resultEventsTable.Synchronize();
    resultCasesTable?.Synchronize();
    
    WriteLog("Finished generation...")
    return result;
  }

  return RunFunctionWithParallelLogging(logTable, GenerateCallback);
}

function GetSampledEvents(sourceModel, sampledCaseCount, filter)
{
  if (IsNull(sampledCaseCount) || sampledCaseCount < 1)
    return sourceModel.EventsDataTable.SqlDataFrame;
  let sampleFilterRule = sampledCaseCount == null 
    ? null
    : #{
        "Type":"IncludeCases",
        "Items":[#{
          "Type": "SqlExpressionValue",
          "Configuration": #{
            "Root": `Cases.WithColumn("_Random", Rand()).OrderByColumns(["_Random"], [true]).Head(${sampledCaseCount})`
          }
        }]
      };
  let filterRules = filter?["Items"];
  if (!IsNullTop(filterRules)) {
    if (!IsNullTop(sampleFilterRule))
      filterRules = Concat(filterRules, sampleFilterRule);
  }
  else {
    if (!IsNullTop(sampleFilterRule))
      filterRules = [sampleFilterRule];
    else
      filterRules = [];
  }

  let finalFilter = #{
    "Items":filterRules
  };
  sourceModel.CacheTableSqlDataFrame(finalFilter);
}

function TrainMLModelForModel(modelName, sourceModel, targetProject, trainingConfiguration, recreatePredictionModel, trainingDataFilter, logTable, trainingCaseSampleSize)
{
  let sanitizedModelName = `${sourceModel.Id}-${modelName}`; 

  let originalCaseCount = sourceModel.EventsDataTable.SqlDataFrame.SelectDistinct([sourceModel.EventsDataTable.ColumnMappings["CaseId"]]).NRows;
  let trainEventDataSdf = GetSampledEvents(sourceModel, trainingCaseSampleSize, trainingDataFilter);
  let actualTrainingCaseSampleSize = trainEventDataSdf.SelectDistinct([trainEventDataSdf.ColumnMappings["CaseId"]]).NRows;
  WriteLog(`Starting to train a prediction model using ${ToString(100*actualTrainingCaseSampleSize/originalCaseCount, "F")}% of the original cases (${actualTrainingCaseSampleSize}/${originalCaseCount}) found in source model ${sourceModel.Name}.`)

  let trainCaseDataSdf = sourceModel.CasesDataTable?.SqlDataFrame;

  let columnMappings = trainEventDataSdf.ColumnMappings;
  if (trainCaseDataSdf != null) {
    columnMappings = columnMappings.Extend(#{"Case_CaseId": trainCaseDataSdf.ColumnMappings["CaseId"]});
  }

  trainingConfiguration = trainingConfiguration.Extend(#{ 
    "_typed": #{
      "event_data": trainEventDataSdf,  
      "project": targetProject
    },
    "model_name": sanitizedModelName,
    "column_mappings": columnMappings,
    "overwrite_model": recreatePredictionModel,
    "case_start_time_probability_multiplier": originalCaseCount / actualTrainingCaseSampleSize
  });

  if (trainCaseDataSdf != null) {
    trainingConfiguration["_typed"].Set("case_data", trainCaseDataSdf);
  }

  Train(trainingConfiguration, logTable);
}

function GenerateNewModel(modelName, sourceModel, targetProject, generationConfiguration, incompleteCasesFilter, logTable)
{
  let sanitizedModelName = `${sourceModel.Id}-${modelName}`; 
  let eventsTableName = `${modelName} - events`;
  let casesTableName = `${modelName} - cases`;
  let m = targetProject.ModelByName(modelName);
  let eventsTable = null;
  let casesTable = null;  
  if (m != null)
  {
    eventsTable = m.EventsDataTable;
    casesTable = m.CasesDataTable;
  }
  else {
    let eventsTableConfiguration = #{
      "Name": eventsTableName,
      "Type": "Snowflake"
    };
    eventsTable = targetProject.DataTableByName(eventsTableName) ?? targetProject.CreateDataTable(eventsTableConfiguration).Synchronize();
    if (sourceModel.CasesDataTable != null) {
      let casesTableConfiguration = #{
        "Name": casesTableName,
        "Type": "Snowflake"
      };
      casesTable = targetProject.DataTableByName(casesTableName) ?? targetProject.CreateDataTable(casesTableConfiguration).Synchronize();
    }
  }

  eventsTable.Truncate();
  casesTable?.Truncate();

  let generationParams, result;

  if (generationConfiguration.TryGetValue("cases_to_generate") != 0) {
    WriteLog(`Generating new cases with new events...`);

    generationParams = generationConfiguration.Extend(#{ 
      "_typed": #{
        "target_event_data_table": eventsTable,
        "project": targetProject
      },
      "model_name": sanitizedModelName,
      "overwrite": false
    });
    if (casesTable != null) {
      generationConfiguration["_typed"].Set("target_case_data_table", casesTable);
    }

    result = Generate(generationParams, logTable);

    WriteLog(`Generation results:\r\n${result}`);

    result = ParseJson(result);
    if (result["result"] != "success")
      throw result["exception"]
  }

  if (incompleteCasesFilter != null) {
    WriteLog(`Generating new events for running cases...`);
    let incompleteCasesSqlDataFrame = sourceModel.EventsDataTable.SqlDataFrame.ApplyFilter(incompleteCasesFilter, sourceModel.CasesDataTable?.SqlDataFrame);

    generationParams = generationConfiguration.Extend(#{ 
      "_typed": #{
        "event_data": incompleteCasesSqlDataFrame,  
        "target_event_data_table": eventsTable,
        "project": targetProject
      },
      "model_name": sanitizedModelName,
      "overwrite": false,
      "temperature": 0
    });
    if (casesTable != null) {
      generationConfiguration["_typed"].Set("case_data", sourceModel.CasesDataTable.SqlDataFrame);
    }

    result = Generate(generationParams, logTable);
    WriteLog(`Generation results:\r\n${result}`);

    result = ParseJson(result);
    if (result["result"] != "success")
      throw result["exception"]
  }

  if (!("Predicted".In(eventsTable.ColumnTypes.Name)))
    eventsTable
      .AddColumn("Predicted", "Boolean");

  eventsTable
    .DeleteRows(
      Column(result["column_mappings"]["events"]["EventType"]) == "__FINISH__"
    )
    .UpdateRows(
      1==1,
      "Predicted", true
    );

  if (casesTable != null) {
    if (!("Generated".In(casesTable.ColumnTypes.Name)))
      casesTable
        .AddColumn("Generated", "Boolean");

    casesTable
      .UpdateRows(
        1==1,
        "Generated", true
      );
  } 

  WriteLog(`Appending original model data...`);
  eventsTable.Import(sourceModel.EventsDataTable.SqlDataFrame, #{"Append": true});
  if (casesTable != null)
    casesTable.Import(sourceModel.CasesDataTable.SqlDataFrame, #{"Append": true});

  eventsTable
    .UpdateRows(
      Column("Predicted") == null,
      "Predicted", false
    );

  casesTable
    ?.UpdateRows(
      Column("Generated") == null,
      "Generated", false
    );

  if (m != null)
    return m;

  WriteLog(`Creating model...`);
  let eventColumnMappings = result["column_mappings"]["events"];
  let timestampMapping = eventColumnMappings["TimeStamp"];
  eventColumnMappings.Remove("TimeStamp");
  eventColumnMappings.Set("Timestamp", timestampMapping);

  let modelConfiguration = #{
    "DataSource": #{
      "Events":#{
        "DataSourceType": "datatable",
        "DataTableName": eventsTableName,
        "Columns": eventColumnMappings
      }
    }
  };

  if (casesTable != null) {
    modelConfiguration["DataSource"].Set("Cases", #{
      "DataSourceType": "datatable",
      "DataTableName": casesTableName,
      "Columns": result["column_mappings"]["cases"]
    });
  }

  targetProject.CreateModel(#{"Name": modelName, "Configuration": modelConfiguration});
}

function GeneratePredictionModel(configuration)
{
  let modelName = configuration["Name"];
  let sourceModel = configuration["SourceModel"];
  let targetProject = configuration["TargetProject"];
  let trainingConfiguration = configuration["TrainingConfiguration"];
  let generationConfiguration = configuration["GenerationConfiguration"];
  let trainingDataFilter = configuration["TrainingDataFilter"];
  let incompleteCasesFilter = configuration["IncompleteCasesFilter"];
  let recreatePredictionModel = configuration["RecreatePredictionModel"];
  let trainingCaseSampleSize = configuration["TrainingCaseSampleSize"];

  let result = null;
  let logTableName = `_MLLog:${Now}`;
  let logTable = targetProject.CreateDataTable(#{"Name": logTableName, "Type": "Snowflake"});
  try {
    WriteLog(`Training a prediction model based on model:\r\n${sourceModel.Name} (id=${sourceModel.Id})`);
    result = TrainMLModelForModel(modelName, sourceModel, targetProject, trainingConfiguration, recreatePredictionModel, trainingDataFilter, logTable, trainingCaseSampleSize);
    WriteLog(`Model training completed. Training result:\r\n${result}`);

    if (generationConfiguration != null) {
      WriteLog(`Generating a new model named ${modelName} based on trained model ${modelName}):\r\n${result}`);
      let newModel = GenerateNewModel(modelName, sourceModel, targetProject, generationConfiguration, incompleteCasesFilter, logTable);

      WriteLog(`Model ${newModel.Name} (id=${newModel.Id}) generated into project ${targetProject.Name}:\r\n${result}`);
    }
  }
  finally {
    targetProject.DataTableByName(logTableName)?.DeletePermanently();
  }
}

return #{
  "GeneratePredictionModel": GeneratePredictionModel,
  "RunFunctionWithParallelLogging": RunFunctionWithParallelLogging,
  "Train": Train,
  "Generate": Generate
};
  1. Create the following expression script (e.g., with name Create prediction model):
let lib = First(Project.Scripts.Where(Name == "ML library")).Run(#{});
let targetProject = Project;
lib.GeneratePredictionModel(#{
  "Name": "My prediction model",
  "SourceModel": ModelById(1),
  "TargetProject": targetProject,
  "TrainingConfiguration": #{},
  "GenerationConfiguration": #{"cases_to_generate": 1000},
  "TrainingDataFilter": null,
  "IncompleteCasesFilter": null,
  "RecreatePredictionModel": true,
  "TrainingCaseSampleSize": 1000
});
  1. Configure prediction as instructed in the next chapter.

Configure prediction

The prediction has the following settings (defined in the script to create the prediction model):

  • Name: Name of the QPR ProcessAnalyzer model that is created to the target project. The model will contain the source model content and the predictions.
  • SourceModel: Source model.
  • TargetProject: Target project to create the model into.
  • TrainingConfiguration: Training parameters.
  • GenerationConfiguration: Event generation parameters.
  • TrainingDataFilter: Optional filter to select specific cases to training the prediction model.
  • IncompleteCasesFilter: Optional filter to select which for cases the prediction is made. To improve performance of the prediction, it's recommended to include only the incomplete cases for which new events might appear, and skip the completed cases for which new events are not expected anymore.
  • RecreatePredictionModel: Should a prediction model be overwritten if one already exists for this source model and target model name combination.
  • TrainingCaseSampleSize: Maximum number of cases to use from the source model (random sampled).
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