generate synthetic scores to train prediction models

Generate a table with synthetic scores (0.0 - 10.0), where a number of rows and column names for the table are provided by the user.
Use case: to create a table of possible scores then assign scores and use it to do the scoring, using several operators.

  • learn from the table (createScoreCombiner) then apply using combineScores operator
  • approximateLabelLook operator
  • regular join

Operator Usage in Easy Mode

  1. Click + on the parent node.
  2. Enter the Generate Scores operator in the search field and select the operator from the Results to open the operator form.
  3. In the Table drop-down, enter or select the table containing the data to run this operator on.
  4. In the Number of Rows field, enter the number of rows to generate the scores.
  5. In the Column names, click Add More to add the list of column names to generate.
  6. Click Run to view the result.
  7. Click Save to add the operator to the playbook.
  8. Click Cancel to discard the operator form.

Usage Details

generateScores(table, numberOfRows, arrayOfColumnNames)

table: input table, not important, the operators requires a parent step, output is not correlated to input
numberOfRows: number of rows to produce
arrayOfColumnNames: column names example, ["score1", "score2", "score3"]


generateScores(lsof, 20, ["score1", "score2", "score3", "score4"])
--should create 20 rows and 4 columns in the arguments + lhub_score with no scores table where each row is distinct

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