Emulated-Inc/python-execution-prediction-training-pool

Python execution prediction training pool

Short Python functions, a concrete call of each one, and the value that call really returns, from five public sources read at the pinned revisions named below and laid out twice. Train on either layer or on both.

pool.jsonl

Every source rewritten into one shape, 38154 rows, one JSON object per line, with these fields.

Field	What it holds
id	a row identifier unique within this file
code	the Python source that defines the function
entry_point	the name of the function the call names
input	the argument text, so the call is entry_point(input)
output	the repr of the value that call returns
source	the name of the source directory the row came from
source_repo, source_revision	the dataset and the revision it was read at
source_file, source_id	the file and the row's own name in that set: its id or task and position for PyX, the execution trace set and MBPP, the function's URL for CodeSearchNet, and repository, path and identifier for The Vault
provenance_class	how the row came to exist
licence	the licence the row is under: the source's term, or for The Vault the repository's own licence as the source publishes it

Every output here was computed by running the function, not copied from its source. Each candidate was run twice in a separate process under a two second deadline, and a row survives only when both runs return the same value, the value's repr parses back to an equal value, and that text is at most 200 characters. Rows whose function returns None were dropped. Rows are deduplicated across sources on the function text together with its entry point and its input, keeping the first source in the order of the sections below.

sources/

The same data untouched, 604602 rows, one directory per source, holding the files at the paths, in the formats and with the columns its own repository publishes. Nothing here was renamed, reshaped, reordered or deduplicated, and no output here was computed by this builder. Use this layer if you want a field the rewritten one drops, such as the written reasoning in the first source or the documentation strings in the two code corpora, or if you would rather choose the calls yourself.

The sources

sources/pyx

Python functions written by a language model from short problem statements, each paired with a concrete call of the function. Two of every three rows carry no call this builder can read and contribute to the raw layer only.

From semcoder/PyX at revision 7f328668db983ff1d52deec102f65c4ca117e094, files pyx.jsonl. 93158 rows here, and 30571 rows of pool.jsonl were built from them. Provenance class model-generated, licence mit.

Worth knowing. The functions and the calls were produced by a model, so a function may be odd or wrong in the way a model is wrong. That does not matter for this shape of data: the output recorded here is what the function really returns, whatever the function meant to do, which is also true of the graded material.

sources/execution_trace

Self-contained Python programs written by people for this purpose, each with a call and the output it produces, chosen to exercise control flow, collections, exceptions and standard library behaviour.

From databounty-io/python-execution-trace-output-prediction-cmskdimp at revision 88c74bd6525825f46bec4d68b2a9ed7f11fa0cf9, files data/items.jsonl, manifest.json. 996 rows here, and 935 rows of pool.jsonl were built from them. Provenance class human, licence cc- by-4.0.

Worth knowing. One thousand rows, the smallest source here and the one closest in intent to what the rows are for. Its own card names the twenty six people who wrote it.

sources/mbpp

Short Python programming problems written by crowdworkers, each with a reference solution and three assertions. Every assertion of the form call equals value is one row here, so one problem contributes up to three.

From google-research-datasets/mbpp at revision 4bb6404fdc6cacfda99d4ac4205087b89d32030c, files full/train-00000-of-00001.parquet, full/validation-00000-of-00001.parquet, full/prompt-00000-of-00001.parquet. 474 rows here, and 1391 rows of pool.jsonl were built from them. Provenance class human, licence cc-by-4.0.

Worth knowing. The train, validation and prompt splits only. The test split is left out, since it is the held-out half of its own release and is widely used as one.

sources/code_search_net

Python functions with documentation, collected from open source repositories on GitHub. The calls are not published with them and are drawn here.

From code-search-net/code_search_net at revision bd0cf261e357a3eb5c8fba490d23ec1a1cd59555, files python/train-00000-of-00001.parquet, python/validation-00000-of-00001.parquet, python/test-00000-of-00001.parquet. 457339 rows here, and 4685 rows of pool.jsonl were built from them. Provenance class collected, licence unknown.

Worth knowing. The licence tag on the repository is literally other. Its collectors kept only repositories whose licence permits redistributing parts of the project, but the corpus does not record which licence each function came under, so every row here carries the licence unknown and names its repository instead.

sources/the_vault

Python functions with documentation, collected from permissively licensed repositories. The calls are not published with them and are drawn here.

From Fsoft-AIC/the-vault-function at revision 505c679056e49a2a269b64777ee7c496d22e1440, files data/validation/python-00000-of-00001.parquet, data/test/python-00000-of-00001.parquet. 52635 rows here, and 572 rows of pool.jsonl were built from them. Provenance class collected, licence mit.

Worth knowing. The validation and test splits only, which are two complete files. Its train configurations run to fifteen gigabytes of Python and yield the same kind of row as the other corpus here, so they would add size rather than variety.

Provenance and licences

By provenance class the curated layer holds 5257 rows collected, 2326 rows human, 30571 rows model-generated. human means a person wrote the function and the call, collected means the function was taken from a public repository and the call was drawn here from a fixed catalogue of values, and model-generated means a language model wrote both. Every output, in every class, was computed by running the function.

The pool as a whole is offered under cc-by-4.0, which is the most restrictive term its sources compose to. PyX is mit, the execution trace set and MBPP are cc-by-4.0, The Vault publishes the licence of each function's repository with the row and every one of its rows here is under a permissive term (mit, apache-2.0, the BSD family and a few others, none copyleft), and CodeSearchNet carries the tag other with no per-row licence recorded, so its rows carry the licence unknown and name their repository instead. Each row carries its own, so a subset under a single licence can be selected.

Filtering

Rows whose function duplicated or closely matched a function in a held-out set were removed before publication, from both layers alike, by a check on the normalised function text (0 rows) followed by a word-level 8-gram overlap check (576 rows). That held-out set is not distributed here. Nothing else was filtered out of the raw layer.