Datasets:
py_ast

	---
	pretty_name: PyAst
	annotations_creators:
	- machine-generated
	language_creators:
	- found
	language:
	- code
	license:
	- bsd-2-clause
	- mit
	multilinguality:
	- monolingual
	size_categories:
	- 100K<n<1M
	source_datasets:
	- original
	task_categories:
	- text2text-generation
	- text-generation
	- fill-mask
	task_ids: []
	paperswithcode_id: null
	tags:
	- code-modeling
	- code-generation
	dataset_info:
	features:
	- name: ast
	sequence:
	- name: type
	dtype: string
	- name: value
	dtype: string
	- name: children
	sequence: int32
	config_name: ast
	splits:
	- name: train
	num_bytes: 1870790180
	num_examples: 100000
	- name: test
	num_bytes: 907514993
	num_examples: 50000
	download_size: 526642289
	dataset_size: 2778305173
	---
	# Dataset Card for [py_ast]

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- homepage: [py150](https://www.sri.inf.ethz.ch/py150)
	- Paper: [Probabilistic Model for Code with Decision Trees](https://www.semanticscholar.org/paper/Probabilistic-model-for-code-with-decision-trees-Raychev-Bielik/62e176977d439aac2e2d7eca834a7a99016dfcaf)
	- Leaderboard:
	- Point of Contact:

	### Dataset Summary

	The dataset consists of parsed ASTs that were used to train and evaluate the DeepSyn tool.
	The Python programs are collected from GitHub repositories
	by removing duplicate files, removing project forks (copy of another existing repository),
	keeping only programs that parse and have at most 30'000 nodes in the AST and
	we aim to remove obfuscated files

	### Supported Tasks and Leaderboards

	Code Representation, Unsupervised Learning
	### Languages

	Python
	## Dataset Structure

	### Data Instances
	A typical datapoint contains an AST of a python program, parsed.
	The main key is `ast` wherein every program's AST is stored.
	Each children would have,
	`type` which will formulate the type of the node.
	`children` which enumerates if a given node has children(non-empty list).
	`value`, if the given node has any hardcoded value(else "N/A").
	An example would be,
	'''
	[ {"type":"Module","children":[1,4]},{"type":"Assign","children":[2,3]},{"type":"NameStore","value":"x"},{"type":"Num","value":"7"}, {"type":"Print","children":[5]}, {"type":"BinOpAdd","children":[6,7]}, {"type":"NameLoad","value":"x"}, {"type":"Num","value":"1"} ]
	'''
	### Data Fields
	- `ast`: a list of dictionaries, wherein every dictionary is a node in the Abstract Syntax Tree.
	- `type`: explains the type of the node.
	- `children`: list of nodes which are children under the given
	- `value`: hardcoded value, if the node holds an hardcoded value.

	### Data Splits

	The data is split into a training and test set.
	The final split sizes are as follows:

	\| \| train \| validation \|
	\|------------------\|--------:\|------------:\|
	\| py_ast examples \| 100000 \| 50000 \|

	## Dataset Creation
	[More Information Needed]
	### Curation Rationale

	[More Information Needed]

	### Source Data

	#### Initial Data Collection and Normalization

	[More Information Needed]

	#### Who are the source language producers?

	[More Information Needed]

	### Annotations

	#### Annotation process

	[More Information Needed]

	#### Who are the annotators?

	[More Information Needed]

	### Personal and Sensitive Information

	[More Information Needed]

	## Considerations for Using the Data

	### Social Impact of Dataset

	[More Information Needed]

	### Discussion of Biases

	[More Information Needed]

	### Other Known Limitations

	[More Information Needed]

	## Additional Information

	### Dataset Curators
	Raychev, V., Bielik, P., and Vechev, M
	### Licensing Information
	MIT, BSD and Apache
	### Citation Information
	@InProceedings{OOPSLA ’16, ACM,
	title = {Probabilistic Model for Code with Decision Trees.},
	authors={Raychev, V., Bielik, P., and Vechev, M.},
	year={2016}
	}

	```
	@inproceedings{10.1145/2983990.2984041,
	author = {Raychev, Veselin and Bielik, Pavol and Vechev, Martin},
	title = {Probabilistic Model for Code with Decision Trees},
	year = {2016},
	isbn = {9781450344449},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/2983990.2984041},
	doi = {10.1145/2983990.2984041},
	booktitle = {Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications},
	pages = {731–747},
	numpages = {17},
	keywords = {Code Completion, Decision Trees, Probabilistic Models of Code},
	location = {Amsterdam, Netherlands},
	series = {OOPSLA 2016}
	}
	```

	### Contributions

	Thanks to [@reshinthadithyan](https://github.com/reshinthadithyan) for adding this dataset.