PROSE Public Benchmark Suite

This repository contains the Microsoft PROSE public benchmark suite.

This suite contains benchmarks drawn from three classes of tasks:

• Transformation.Text: string-to-string transformation
• Split.Text: text-to-table transformation
• Extraction.Text: substring extraction from semi-structured text

See below for more detailed descriptions of each class.

Where did this data come from?

A subset of the benchmarks were derived from publicly redistributable data sources. The source and license for each such benchmark are detailed in LICENSE.

The remainder of the benchmarks were synthetically generated by the PROSE team to conform to patterns that might be observed in real-world systems and databases. For example, numbers were randomly generated to simulate social security numbers. No personal data was used to generate this data. There is a small chance that synthetically generated data could inadvertently match one or more attributes of a real person. If you have any concerns that synthetically generated data matches attributes of a real person, please contact us at prose-contact@microsoft.com with details. We are offering the data under a permissive license, but to help address any concerns of this nature at the source, we encourage you not to redistribute the data.

File Structure

• Transformation.Text
  • <benchmark dir>
    • meta.json
    • spec.json
  • <benchmark dir>
    • meta.json
    • spec.json
  • ...
• Split.Text
  • <benchmark dir>
    • meta.json
    • input.txt
    • output.json
  • <benchmark dir>
    • meta.json
    • input.txt
    • output.json
  • ...
• Extraction.Text
  • <benchmark dir>
    • meta.json
    • input.txt
    • output.json
    • <ancestor>.<descendant>.spec.json
  • <benchmark dir>
    • meta.json
    • input.txt
    • output.json
    • <ancestor>.<descendant>.spec.json
  • ...

All files are encoded in UTF-8 without BOM.

Metadata

Each benchmark directory contains a distinguished meta.json file that annotates that benchmark with descriptive metadata.

The Features field denotes a list of classifications describing the transformations required to solve the task. At this time, only benchmarks in Transformation.Text have these annotations.

• Casing: converting character cases between capitals and non-capitals
• Concatenation: concatenating strings
• Conditional: conditioning transformations on predicates
• DateTimeRange: computing date/time ranges
• DateTimeRounding: rounding dates/times
• DateTime: manipulating dates/times
• Multicolumn: transforming inputs consisting of multiple strings
• Numeric: manipulating numbers
• NumericRange: computing numeric ranges
• NumericRounding: rounding numbers
• Substring: extracting substrings

In the case that the benchmark was generated synthetically, meta.json will contain field Synthetic with value true otherwise false.

In addition to the meta.json file, each benchmark has the following structure:

Transformation.Text

A Transformation.Text benchmark consists of a single JSON file spec.json containing input-output pairs. Example:

{
  "Examples": [
    {
      "Input": [
        "/libero/enim7.png"
      ],
      "Output": "enim7"
    },
    {
      "Input": [
        "/"
      ],
      "Output": "root"
    },
    {
      "Input": [
        "/libero/enim9.png"
      ],
      "Output": "enim9"
    }
  ]
}

Split.Text

A Split.Text benchmark consists of two files:

1. A text file input.txt containing the raw string from which to extract a table. Example:

   (58.326261139, 89.99508561)
   (65.889370802, 72.93175018)
   
   

2. A JSON file output.json describing the tabular structure of (1). Note that nonconforming rows are represented as lists populated with null in each element, so that every constituent list in Rows contains the same number of elements. Example:

   {
     "Rows": [
       [
         "(",
         "58.326261139",
         ", ",
         "89.99508561",
         ")"
       ],
       [
         "(",
         "65.889370802",
         ", ",
         "72.93175018",
         ")"
       ],
       [
         null,
         null,
         null,
         null,
         null
       ]
     ]
   }
   

Extraction.Text

An Extraction.Text benchmark consists of three or more files:

1. A text file input.txt containing the raw string from which extraction is to be performed. Example:

   Header0
       Subheading0a
   	Subheading0aa
   	Subheading0ba
       Subheading0b
   	Subheading0ba
   	Subheading0ca
   Header1
       Subheading1a
   	Subheading1aa
   	Subheading1ab
   Header2
       Subheading2a
   	Subheading2aa
   	Subheading2ab
       Subheading2b
   	Subheading2ba
   	Subheading2bb
   Header3
       Subheading3a
       Subheading3b
   	Subheading3ba
       Subheading3c
   	Subheading3ca
   	Subheading3cb
   

2. A JSON file output.json specifying the tree structure of (1). Example:

   {
     "Property": "root",
     "Start": 0,
     "End": 363,
     "Children": [
       {
         "Property": "HeaderStruct",
         "Start": 0,
         "End": 102,
         "Children": [
           {
             "Property": "Header",
             "Start": 0,
             "End": 7,
             "Value": "Header0"
           },
           {
             "Property": "SubHeaderStruct",
             "Start": 12,
             "End": 59,
             "Children": [
               {
                 "Property": "SubHeader",
                 "Start": 12,
                 "End": 24,
                 "Value": "Subheading0a"
               },
               {
                 "Property": "SubSubHeaderStruct",
                 "Start": 26,
                 "End": 41,
                 "Children": [
                   {
                     "Property": "SubSubHeader",
                     "Start": 26,
                     "End": 39,
                     "Value": "Subheading0aa"
                   }
                 ]
               },
               {
                 "Property": "SubSubHeaderStruct",
                 "Start": 41,
                 "End": 59,
                 "Children": [
                   {
                     "Property": "SubSubHeader",
                     "Start": 41,
                     "End": 54,
                     "Value": "Subheading0ba"
                   }
                 ]
               }
             ]
           },
   ...
   

   Each node has a Property field containing a descriptive label. Each node also has Start and End indices denoting the node's corresponding character extent from input.txt. Each leaf node additionally has a Value field with corresponding substring, while each non-leaf node instead has a Children field containing a list of its subnodes. The distinguished Property value root is reserved for the root node that covers the entire input string.

3. One or more JSON files with the naming scheme <ancestor>.<descendant>.spec.json. Each contains examples for extracting strings with Property <descendant> from strings with Property <ancestor>.

   Each .spec.json file denotes one of two possible kinds of extractions. If Kind is Sequence, then the extraction is of type string -> list of string, and the task is to extract a list of substrings from the input string. An instance of Microsoft.ProgramSynthesis.Extraction.Text.SequenceProgram in the PROSE SDK can be one solution to such a task. Example:

   {
     "Kind": "Sequence",
     "Examples": [
       {
         "Input": [
           {
             "Start": 0,
             "End": 363,
             "Value": "Header0\n    Subheading0a\n\tSubheading0aa\n\tSubheading0ba\n    Subheading0b\n\tSubheading0ba\n\tSubheading0ca\nHeader1\n    Subheading1a\n\tSubheading1aa\n\tSubheading1ab\nHeader2\n    Subheading2a\n\tSubheading2aa\n\tSubheading2ab\n    Subheading2b\n\tSubheading2ba\n\tSubheading2bb\nHeader3\n    Subheading3a\n    Subheading3b\n\tSubheading3ba\n    Subheading3c\n\tSubheading3ca\n\tSubheading3cb\n"
           }
         ],
         "Output": [
           [
             {
               "Start": 0,
               "End": 7,
               "Value": "Header0"
             },
             {
               "Start": 102,
               "End": 109,
               "Value": "Header1"
             },
             {
               "Start": 157,
               "End": 164,
               "Value": "Header2"
             },
             {
               "Start": 259,
               "End": 266,
               "Value": "Header3"
             }
           ]
         ]
       }
     ]
   }
   

   In this case, each input string in Input has a corresponding list of output strings in Output to be extracted from it.

   If Kind is Field, then the extraction is of type string -> string, and the task is to extract a substring from the input string. An instance of Microsoft.ProgramSynthesis.Extraction.Text.RegionProgram in the PROSE SDK can be one solution to such a task. Example:

   {
     "Kind": "Field",
     "Examples": [
       {
         "Input": [
           {
             "Start": 0,
             "End": 102,
             "Value": "Header0\n    Subheading0a\n\tSubheading0aa\n\tSubheading0ba\n    Subheading0b\n\tSubheading0ba\n\tSubheading0ca\n"
           },
           {
             "Start": 102,
             "End": 157,
             "Value": "Header1\n    Subheading1a\n\tSubheading1aa\n\tSubheading1ab\n"
           },
           {
             "Start": 157,
             "End": 259,
             "Value": "Header2\n    Subheading2a\n\tSubheading2aa\n\tSubheading2ab\n    Subheading2b\n\tSubheading2ba\n\tSubheading2bb\n"
           },
           {
             "Start": 259,
             "End": 363,
             "Value": "Header3\n    Subheading3a\n    Subheading3b\n\tSubheading3ba\n    Subheading3c\n\tSubheading3ca\n\tSubheading3cb\n"
           }
         ],
         "Output": [
           {
             "Start": 0,
             "End": 7,
             "Value": "Header0"
           },
           {
             "Start": 102,
             "End": 109,
             "Value": "Header1"
           },
           {
             "Start": 157,
             "End": 164,
             "Value": "Header2"
           },
           {
             "Start": 259,
             "End": 266,
             "Value": "Header3"
           }
         ]
       }
     ]
   }
   

   In this case, Input is a list of strings, and its corresponding Output is a list of substrings, one for each respective input string.

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