psb2 1.1.2

Utilities for sampling the datasets of PSB2.

PSB2 - Python Sampling Library

A Python library for fetching and sampling training and test data for experimenting with the program synthesis dataset PSB2. The library will automatically download datasets to the given location, and will cache them to avoid repeated downloads.

Installation

Easily installed using pip:

pip install psb2

Usage

There is one constant and one function available in this library. psb2.PROBLEMS is the list of all problems in the PSB2 benchmark suite as strings, and psb2.PROBLEMS_PSB1 is similarly the problems from PSB1:

>>> import psb2
>>> psb2.PROBLEMS
['basement', 'bouncing-balls', 'bowling', 'camel-case', 'coin-sums', 'cut-vector', 'dice-game', 'find-pair', 'fizz-buzz', 'fuel-cost', 'gcd', 'indices-of-substring', 'leaders', 'luhn', 'mastermind', 'middle-character', 'paired-digits', 'shopping-list', 'snow-day', 'solve-boolean', 'spin-words', 'square-digits', 'substitution-cipher', 'twitter', 'vector-distance']
>>> psb2.PROBLEMS_PSB1
['checksum', 'collatz-numbers', 'compare-string-lengths', 'count-odds', 'digits', 'double-letters', 'even-squares', 'for-loop-index', 'grade', 'last-index-of-zero', 'median', 'mirror-image', 'negative-to-zero', 'number-io', 'pig-latin', 'replace-space-with-newline', 'scrabble-score', 'small-or-large', 'smallest', 'string-differences', 'string-lengths-backwards', 'sum-of-squares', 'super-anagrams', 'syllables', 'vector-average', 'vectors-summed', 'wallis-pi', 'word-stats', 'x-word-lines']

The fetch_examples function downloads (if necessary) and samples training and test data for a specific problem in PSB2:

>>> import psb2
>>> (train_data, test_data) = psb2.fetch_examples("path/to/PSB2/datasets/", "fizz-buzz", 200, 2000)
>>> train_data
[{'input1': 1, 'output1': '1'},
 {'input1': 2, 'output1': '2'},
 {'input1': 3, 'output1': 'Fizz'},
 {'input1': 4, 'output1': '4'},
 ...
 {'input1': 405919, 'output1': '405919'},
 {'input1': 405789, 'output1': 'Fizz'}]

Or, if you'd like your test cases in a different format, you can supply an optional format argument:

>>> import psb2
>>> (train_data, test_data) = psb2.fetch_examples("path/to/PSB2/datasets/", "fizz-buzz", 200, 2000, format='lists')
>>> train_data
[([1], ['1']),
 ([2], ['2']),
 ([3], ['Fizz']),
 ([4], ['4']),
 ...
 ([405919], ['405919']),
 ([405789], ['Fizz'])

>>> import psb2
>>> (train_data, test_data) = psb2.fetch_examples("path/to/PSB2/datasets/", "fizz-buzz", 200, 2000, format='competitive')
>>> train_data
[(['1'], ['1']),
 (['2'], ['2']),
 (['3'], ['Fizz']),
 (['4'], ['4']),
 ...
 (['405919'], ['405919']),
 (['405789'], ['Fizz'])

Each example in the returned train_data and test_data lists is a map containing one key for each input and each output. train_data includes all defined edge cases for a problem, as well as enough randomly generated cases to fill the training set (200 in the example above). test_data will sample n_test cases from the randomly generated cases.

Citation

If you use these datasets in a publication, please cite the paper PSB2: The Second Program Synthesis Benchmark Suite and include a link to this repository.

BibTeX entry for paper:

@InProceedings{Helmuth:2021:GECCO,
  author =	"Thomas Helmuth and Peter Kelly",
  title =	"{PSB2}: The Second Program Synthesis Benchmark Suite",
  booktitle =	"2021 Genetic and Evolutionary Computation Conference",
  series = {GECCO '21},
  year = 	"2021",
  isbn13 = {978-1-4503-8350-9},
  address = {Lille, France},
  size = {10 pages},
  doi = {10.1145/3449639.3459285},
  publisher = {ACM},
  publisher_address = {New York, NY, USA},
  month = {10-14} # jul,
  doi-url = {https://doi.org/10.1145/3449639.3459285},
  URL = {https://dl.acm.org/doi/10.1145/3449639.3459285},
}

License

Copyright © 2021-present Thomas Helmuth

This program and the accompanying materials are made available under the terms of the Eclipse Public License 2.0 which is available at http://www.eclipse.org/legal/epl-2.0.