Emgraph (Embedding graphs) is a Python library for graph representation learning.
It provides a simple API for design, train, and evaluate graph embedding models. You can use the base models to easily develop your own model.
Embedding wordnet11 graph using TransE model:
from sklearn.metrics import brier_score_loss, log_loss from scipy.special import expit from emgraph.datasets import BaseDataset, DatasetType from emgraph.models import TransE def train_transe(data): model = TransE(batches_count=64, seed=0, epochs=20, k=100, eta=20, optimizer='adam', optimizer_params={'lr': 0.0001}, loss='pairwise', verbose=True, large_graphs=False) model.fit(data['train']) scores = model.predict(data['test']) return scores if __name__ == '__main__': wn11_dataset = BaseDataset.load_dataset(DatasetType.WN11) scores = train_transe(data=wn11_dataset) print("Scores: ", scores) print("Brier score loss:", brier_score_loss(wn11_dataset['test_labels'], expit(scores)))Evaluating ComplEx model after training:
import numpy as np from emgraph.datasets import BaseDataset, DatasetType from emgraph.models import ComplEx from emgraph.evaluation import evaluate_performance def complex_performance(data): model = ComplEx(batches_count=10, seed=0, epochs=20, k=150, eta=1, loss='nll', optimizer='adam') model.fit(np.concatenate((data['train'], data['valid']))) filter_triples = np.concatenate((data['train'], data['valid'], data['test'])) ranks = evaluate_performance(data['test'][:5], model=model, filter_triples=filter_triples, corrupt_side='s+o', use_default_protocol=False) return ranks if __name__ == '__main__': wn18_dataset = BaseDataset.load_dataset(DatasetType.WN18) ranks = complex_performance(data=wn18_dataset) print("ranks {}".format(ranks))Embedding wordnet11 graph using DistMult model:
from sklearn.metrics import brier_score_loss, log_loss from scipy.special import expit from emgraph.datasets import BaseDataset, DatasetType from emgraph.models import DistMult def train_dist_mult(data): model = DistMult(batches_count=1, seed=555, epochs=20, k=10, loss='pairwise', loss_params={'margin': 5}) model.fit(data['train']) scores = model.predict(data['test']) return scores if __name__ == '__main__': wn11_dataset = BaseDataset.load_dataset(DatasetType.WN11) scores = train_dist_mult(data=wn11_dataset) print("Scores: ", scores) print("Brier score loss:", brier_score_loss(wn11_dataset['test_labels'], expit(scores)))The Emgraph project welcomes your expertise and enthusiasm!
Ways to contribute to Emgraph:
- Writing code
- Review pull requests
- Develop tutorials, presentations, and other educational materials
- Translate documentation and readme contents
If you happened to encounter any issue in the codes, please report it here. A better way is to fork the repository on Github and/or create a pull request.
- Support CPU/GPU
- Vectorized operations
- Preprocessors
- Dataset loader
- Standard API
- Documentation
- Test driven development
This repository is a transformation of the AmpliGraph library for TensorFlow 2, with a modular architecture implementation. It also draws inspiration from PyKEEN and Spectral. Credit is extended to these exceptional projects.
Copyright © 2019-2024 Emgraph Developers Soran Ghaderi (soran.gdr.cs@gmail.com) follow me
Taleb Zarhesh (taleb.zarhesh@gmail.com) follow me