.grafo.mork.0.18.source-code.serializers.yml Maven / Gradle / Ivy

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serializers:
  solution-json:
    # Enable default JSON serializer for solutions. Exports the best solution of each algorithm to JSON.
    # If you want to export solutions to a custom format, extend SolutionSerializer,
    # NOTE: the default JSON solution serializer is automatically disabled when the SolutionSerializer class is extended.
    enabled: false

    # Should we serialize:
    # - all solutions for each iterations (all)
    # - best solution for each algorithm instance pair (best_per_alg_instance)
    # - best solution for each instance only (best_per_instance)
    frequency: best_per_instance

    # Path where solutions will be exported (Only if a solution serializer is enabled, see below)
    folder: 'solutions'

    # If enabled, pretty print JSON (indentation + new lines)
    pretty: true

    # Filename format, replacements are applied as follows
    # yyyy: replaced with current year, ex 2020
    # MM, dd, HH, mm, ss: replaced by month, day, hour, minute and seconds
    # any letters [a-zA-Z] can be part of the filename as long as they are between single quotes
    # Always prepends ExperimentName, InstanceName and algorithm name to prevent name collisions
    format: "'.json'"

  csv:
    # Export results to CSV, set to false to skip serializing results to CSV
    enabled: false

    # Can use commas, semicolons, \t (tabs) or any other character to separate columns when exporting the results to CSV
    # Note: Use only a single character
    separator: ','

    # Results folder
    folder: 'results'


    # Filename format, replacements are applied as follows
    # yyyy: replaced with current year, ex 2020
    # MM, dd, HH, mm, ss: replaced by month, day, hour, minute and seconds
    # any letters [a-zA-Z] can be part of the filename as long as they are between single quotes
    format: "'Results'_yyyy-MM-dd_HH-mm-ss.'csv'"

    # When should results be exported to disk?
    # experiment_end: Export results when experiment ends (only once)
    # per_instance: Each time an instance completes all algorithms
    frequency: experiment_end

  xlsx:
    # Enable XLSX results serializer, set to false to skip serializing results to Excel 2007+
    enabled: true

    # Defines how to handle calculated values when serializing to Excel 2007+
    # Possible values: java, excel, auto.
    calculationMode: auto

    # When generating the pivot table, should algorithms be in rows or columns?
    # True: Instances per row, algorithms in columns
    # False: Algorithms in rows, instances in columns
    algorithmsInColumns: true

    # Results folder
    folder: 'results'

    # Filename format, replacements are applied as follows
    # yyyy: replaced with current year, ex 2020
    # MM, dd, HH, mm, ss: replaced by month, day, hour, minute and seconds
    # letters [a-zA-Z] can be part of the filename as long as they are between single quotes
    format: "'Results'_yyyy-MM-dd_HH-mm-ss.'xlsx'"

    # When should results be exported to disk?
    # experiment_end: Export results when experiment ends (only once)
    # per_instance: Each time an instance completes all algorithms
    frequency: experiment_end

    # Create a sheet detailing instance properties
    instance-sheet-enabled: true

    # Show best (min or max) score in pivot table
    bestScoreEnabled: true

    # Show average score in pivot table
    avgScoreEnabled: false

    # Show standard deviation of solution score in pivot table. Uses Excel STD_DEVP function
    stdScoreEnabled: false

    # Show variance of score in pivot table. Uses Excel VARP function
    varScoreEnabled: false

    # Show average time in seconds per iteration in pivot table.
    avgTimeEnabled: false

    # Show total time in seconds for a given (algorithm, instance) in pivot table.
    totalTimeEnabled: true

    # Show average time to the best solution in seconds in pivot table.
    avgTTBEnabled: false

    # Show total time to the best solution in seconds in pivot table.
    totalTTBEnabled: false

    # Show number of times a given algorithm reaches the best known solution.
    sumBestKnownEnabled: false

    # Show 1 if a given algorithm reaches the best solution for an instance, 0 otherwise.
    hasBestKnownEnabled: true

    # Show average percentage deviation to best known solution in pivot table.
    avgDevToBestKnownEnabled: false

    # Show minimum percentage deviation to best known solution in pivot table.
    minDevToBestKnownEnabled: true

    # Show generated grand total in pivot table
    rowGrandTotal: false
    columnGrandTotal: false