Datasets:
license: mit
task_categories:
- graph-ml
Dataset Card for ogbg-code2
Table of Contents
Dataset Description
- Homepage
- Repository::
- Paper:: Open Graph Benchmark: Datasets for Machine Learning on Graphs (see citation)
- Leaderboard:: OGB leaderboard and Papers with code leaderboard
Dataset Summary
The ogbg-code2 dataset contains Abstract Syntax Trees (ASTs) obtained from 450 thousands Python method definitions, from GitHub CodeSearchNet. "Methods are extracted from a total of 13,587 different repositories across the most popular projects on GitHub.", by teams at Stanford, to be a part of the Open Graph Benchmark. See their website or paper for dataset postprocessing.
Supported Tasks and Leaderboards
"The task is to predict the sub-tokens forming the method name, given the Python method body represented by AST and its node features. This task is often referred to as “code summarization”, because the model is trained to find succinct and precise description for a complete logical unit."
The score is the F1 score of sub-token prediction.
External Use
PyGeometric
To load in PyGeometric, do the following:
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
graphs_dataset = load_dataset("graphs-datasets/ogbg-code2")
# For the train set (replace by valid or test as needed)
graph_list = [Data(graph) for graph in graphs_dataset["train"]]
graphs_pygeometric = DataLoader(graph_list)
Dataset Structure
Data Properties
| property | value |
|---|---|
| scale | medium |
| #graphs | 452,741 |
| average #nodes | 125.2 |
| average #edges | 124.2 |
| average node degree | 2.0 |
| average cluster coefficient | 0.0 |
| MaxSCC ratio | 1.000 |
| graph diameter | 13.5 |
Data Fields
Each row of a given file is a graph, with:
edge_index(list: 2 x #edges): pairs of nodes constituting edgesedge_feat(list: #edges x #edge-features): features of edgesnode_feat(list: #nodes x #node-features): the nodes features, embeddednode_feat_expanded(list: #nodes x #node-features): the nodes features, as codenode_is_attributed(list: 1 x #nodes): ?node_dfs_order(list: #nodes x #1): the nodes order in the abstract tree, if parsed using a depth first searchnode_depth(list: #nodes x #1): the nodes depth in the abstract treey(list: 1 x #tokens): contains the tokens to predict as method namenum_nodes(int): number of nodes of the graphptr(list: 2): index of first and last node of the graphbatch(list: 1 x #nodes): ?
Data Splits
This data comes from the PyGeometric version of the dataset provided by OGB, and follows the provided data splits. This information can be found back using
from ogb.graphproppred import PygGraphPropPredDataset
dataset = PygGraphPropPredDataset(name = 'ogbg-code2')
split_idx = dataset.get_idx_split()
train = dataset[split_idx['train']] # valid, test
More information (node_feat_expanded) has been added through the typeidx2type and attridx2attr csv files of the repo.
Additional Information
Licensing Information
The dataset has been released under MIT license license.
Citation Information
@inproceedings{hu-etal-2020-open,
author = {Weihua Hu and
Matthias Fey and
Marinka Zitnik and
Yuxiao Dong and
Hongyu Ren and
Bowen Liu and
Michele Catasta and
Jure Leskovec},
editor = {Hugo Larochelle and
Marc Aurelio Ranzato and
Raia Hadsell and
Maria{-}Florina Balcan and
Hsuan{-}Tien Lin},
title = {Open Graph Benchmark: Datasets for Machine Learning on Graphs},
booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference
on Neural Information Processing Systems 2020, NeurIPS 2020, December
6-12, 2020, virtual},
year = {2020},
url = {https://proceedings.neurips.cc/paper/2020/hash/fb60d411a5c5b72b2e7d3527cfc84fd0-Abstract.html},
}
Contributions
Thanks to @clefourrier for adding this dataset.