The Open Graph Benchmark (OGB) is an expansive collection of datasets, data loaders, and evaluators designed specifically for graph machine learning. It has extensive resources covering various tasks and real-world applications to help you thrive in the graph ML landscape. In this article, we will walk you through the installation, usage, and troubleshooting of OGB.

Overview of OGB

OGB aims to provide datasets that encompass essential graph machine learning tasks, diverse dataset scales, and rich domains. Here’s a snapshot of what it offers:

• Graph ML Tasks: Covers tasks like node, link, and graph prediction.
• Diverse Scale: Handles small-scale datasets efficiently on a single GPU, while medium and large-scale might require advanced techniques or multiple GPUs.
• Rich Domains: Includes datasets from multiple domains such as scientific and social information networks along with heterogeneous knowledge graphs.

Installing OGB

OGB can be smoothly installed using Python’s package manager, pip. Follow these steps to ensure a successful installation.

Requirements

• Python = 3.6
• PyTorch = 1.6
• DGL = 0.5.0 or torch-geometric = 2.0.2
• NumPy = 1.16.0
• pandas = 0.24.0
• urllib3 = 1.24.0
• scikit-learn = 0.20.0
• outdated = 0.2.0

Pip Installation

The easiest way to install OGB is via pip. Run the following command:

pip install ogb

Once installed, you can verify the installation with:

python -c "import ogb; print(ogb.__version__)"

This should display version 1.3.6. If not, please update it with:

pip install -U ogb

From Source Installation

If you plan to contribute to OGB, you might want to install it from source:

git clone https://github.com/snap-stanford/ogbcd ogb
pip install -e .

Using OGB

Now that you have installed OGB, let’s look at some key functionalities:

Data Loaders

OGB provides easy-to-use data loaders for both PyTorch Geometric and DGL frameworks. Think of data loaders as a well-organized library where all your needed books are easily available and categorized! The code below demonstrates how simple it is to prepare your datasets:

from ogb.graphproppred import PygGraphPropPredDataset
from torch_geometric.loader import DataLoader

# Preparing and loading the dataset
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)

As you can see, just a few lines result in a fully loaded dataset!

Evaluators

Evaluators are tools that allow you to assess your models. Imagine an evaluator as a referee in a sports game who clarifies the score based on the set rules. Here’s how you can set it up:

from ogb.graphproppred import Evaluator

evaluator = Evaluator(name='ogbg-molhiv')

input_dict = {'y_true': y_true, 'y_pred': y_pred}
result_dict = evaluator.eval(input_dict)  # e.g., rocauc: 0.7321

This gives you reliable results for comparing the performance of different methods!

Troubleshooting

If you encounter any issues during installation or usage, here are some troubleshooting suggestions:

• Ensure your Python version and package requirements match those listed above.
• If you face installation errors, consider running the pip install command in a virtual environment.
• For version conflicts, use pip list to check your installed packages and their versions.
• If the data loaders are not working as expected, double-check the dataset names and paths you are using.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.