Metadata-Version: 2.1
Name: ogb
Version: 1.2.0
Summary: Open Graph Benchmark
Home-page: https://github.com/snap-stanford/ogb
Author: OGB Team
Author-email: ogb@cs.stanford.edu
License: MIT
Description: <p align="center">
          <img width="40%" src="https://snap-stanford.github.io/ogb-web/assets/img/OGB_rectangle.png" />
        </p>
        
        --------------------------------------------------------------------------------
        
        [![PyPI](https://img.shields.io/pypi/v/ogb)](https://pypi.org/project/ogb/)
        [![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/snap-stanford/ogb/blob/master/LICENSE)
        
        ## Overview
        
        The Open Graph Benchmark (OGB) is a collection of benchmark datasets, data loaders, and evaluators for graph machine learning. Datasets cover a variety of graph machine learning tasks and real-world applications.
        The OGB data loaders are fully compatible with popular graph deep learning frameworks, including [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/) and [Deep Graph Library (DGL)](https://www.dgl.ai/). They provide automatic dataset downloading, standardized dataset splits, and unified performance evaluation.
        
        <p align="center">
          <img width="80%" src="https://snap-stanford.github.io/ogb-web/assets/img/ogb_overview.png" />
        </p>
        
        OGB aims to provide graph datasets that cover important graph machine learning tasks, diverse dataset scale, and rich domains.
        
        **Graph ML Tasks:** We cover three fundamental graph machine learning tasks: prediction at the level of nodes, links, and graphs.
        
        **Diverse scale:** Small-scale graph datasets can be processed within a single GPU, while medium- and large-scale graphs might require multiple GPUs or clever sampling/partition techniques.
        
        **Rich domains:** Graph datasets come from diverse domains ranging from scientific ones to social/information networks, and also include heterogeneous knowledge graphs. 
        
        <p align="center">
          <img width="70%" src="https://snap-stanford.github.io/ogb-web/assets/img/dataset_overview.png" />
        </p>
        
        OGB is an on-going effort, and we are planning to increase our coverage in the future.
        
        ## Installation
        You can install OGB using Python's package manager `pip`.
        **If you have previously installed ogb, please make sure you update the version to 1.2.0.**
        
        #### Requirements
         - Python>=3.5
         - PyTorch>=1.2
         - DGL>=0.4.1 or torch-geometric>=1.3.1
         - Numpy>=1.16.0
         - pandas>=0.24.0
         - urllib3>=1.24.0
         - scikit-learn>=0.20.0
        
         **Note:** `torch-geometric>=1.5.0` is recommended to run our [example code](https://github.com/snap-stanford/ogb/tree/master/examples).
        
        #### Pip install
        The recommended way to install OGB is using Python's package manager pip:
        ```bash
        pip install ogb
        ```
        
        ```bash
        python -c "import ogb; print(ogb.__version__)"
        # This should print "1.2.0". Otherwise, please update the version by
        pip install -U ogb
        ```
        
        
        #### From source
        You can also install OGB from source. This is recommended if you want to contribute to OGB.
        ```bash
        git clone https://github.com/snap-stanford/ogb
        cd ogb
        python setup.py install
        ```
        
        ## Example
        We highlight two key features of OGB, namely, (1) easy-to-use data loaders, and (2) standardized evaluators.
        #### (1) Data loaders
        We prepare easy-to-use PyTorch Geometric and DGL data loaders. We handle dataset downloading as well as standardized dataset splitting.
        Below, on PyTorch Geometric, we see that a few lines of code is sufficient to prepare and split the dataset! Needless to say, you can enjoy the same convenience for DGL!
        ```python
        from ogb.graphproppred import PygGraphPropPredDataset
        from torch_geometric.data import DataLoader
        
        dataset = PygGraphPropPredDataset(name = "ogbg-molhiv")
        
        split_idx = dataset.get_idx_split() 
        train_loader = DataLoader(dataset[split_idx["train"]], batch_size=32, shuffle=True)
        valid_loader = DataLoader(dataset[split_idx["valid"]], batch_size=32, shuffle=False)
        test_loader = DataLoader(dataset[split_idx["test"]], batch_size=32, shuffle=False)
        ```
        
        #### (2) Evaluators
        We also prepare standardized evaluators for easy evaluation and comparison of different methods. The evaluator takes `input_dict` (a dictionary whose format is specified in `evaluator.expected_input_format`) as input, and returns a dictionary storing the performance metric appropriate for the given dataset.
        The standardized evaluation protocol allows researchers to reliably compare their methods.
        ```python
        from ogb.graphproppred import Evaluator
        
        evaluator = Evaluator(name = "ogbg-molhiv")
        # You can learn the input and output format specification of the evaluator as follows.
        # print(evaluator.expected_input_format) 
        # print(evaluator.expected_output_format) 
        input_dict = {"y_true": y_true, "y_pred": y_pred}
        result_dict = evaluator.eval(input_dict) # E.g., {"rocauc": 0.7321}
        ```
        
        ## Citing OGB
        If you use OGB datasets in your work, please cite our paper (Bibtex below).
        ```
        @article{hu2020ogb,
          title={Open Graph Benchmark: Datasets for Machine Learning on Graphs},
          author={Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec},
          journal={arXiv preprint arXiv:2005.00687},
          year={2020}
        }
        ```
        
Keywords: pytorch,graph machine learning,graph representation learning,graph neural networks
Platform: UNKNOWN
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
