A package for training and evaluating multimodal knowledge graph embeddings

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  • License: MIT License (MIT)
  • Author: Mehdi Ali
  • Maintainer: Mehdi Ali
  • Tags Knowledge Graph Embeddings, Machine Learning, Data Mining, Linked Data
  • Requires: Python >=3.6
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Project description

PyKEEN

Travis CI AppVeyor License DOI Optuna integrated

PyKEEN (Python KnowlEdge EmbeddiNgs) is a Python package designed to train and evaluate knowledge graph embedding models (incorporating multi-modal information).

Installation • Quickstart • Datasets • Models • Support • Citation

Installation PyPI - Python Version PyPI

The latest stable version of PyKEEN can be downloaded and installed from PyPI with:

$ pip install pykeen

The latest version of PyKEEN can be installed directly from the source on GitHub with:

pip install git+https://github.com/pykeen/pykeen.git

More information about installation (e.g., development mode, Windows installation, extras) can be found in the installation documentation.

Quickstart Documentation Status

This example shows how to train a model on a data set and test on another data set.

The fastest way to get up and running is to use the pipeline function. It provides a high-level entry into the extensible functionality of this package. The following example shows how to train and evaluate the TransE model on the Nations dataset. By default, the training loop uses the stochastic local closed world assumption (sLCWA) training approach and evaluates with rank-based evaluation.

from pykeen.pipeline import pipeline

result = pipeline(
    model='TransE',
    dataset='nations',
)

The results are returned in an instance of the PipelineResult dataclass that has attributes for the trained model, the training loop, the evaluation, and more. See the tutorials on understanding the evaluation and making novel link predictions.

PyKEEN is extensible such that:

  • Each model has the same API, so anything from pykeen.models can be dropped in
  • Each training loop has the same API, so pykeen.training.LCWATrainingLoop can be dropped in
  • Triples factories can be generated by the user with from pykeen.triples.TriplesFactory

The full documentation can be found at https://pykeen.readthedocs.io.

Implementation

Below are the models, data sets, training modes, evaluators, and metrics implemented in pykeen.

Datasets (13)

Name Reference Description
fb15k pykeen.datasets.FB15k The FB15k data set.
fb15k237 pykeen.datasets.FB15k237 The FB15k-237 data set.
hetionet pykeen.datasets.Hetionet The Hetionet dataset is a large biological network.
kinships pykeen.datasets.Kinships The Kinships data set.
nations pykeen.datasets.Nations The Nations data set.
openbiolink pykeen.datasets.OpenBioLink The OpenBioLink dataset.
openbiolinkf1 pykeen.datasets.OpenBioLinkF1 The PyKEEN First Filtered OpenBioLink 2020 Dataset.
openbiolinkf2 pykeen.datasets.OpenBioLinkF2 The PyKEEN Second Filtered OpenBioLink 2020 Dataset.
openbiolinklq pykeen.datasets.OpenBioLinkLQ The low-quality variant of the OpenBioLink dataset.
umls pykeen.datasets.UMLS The UMLS data set.
wn18 pykeen.datasets.WN18 The WN18 data set.
wn18rr pykeen.datasets.WN18RR The WN18-RR data set.
yago310 pykeen.datasets.YAGO310 The YAGO3-10 data set is a subset of YAGO3 that only contains entities with at least 10 relations.

Models (23)

Name Reference Citation
ComplEx pykeen.models.ComplEx Trouillon et al., 2016
ComplExLiteral pykeen.models.ComplExLiteral Agustinus et al., 2018
ConvE pykeen.models.ConvE Dettmers et al., 2018
ConvKB pykeen.models.ConvKB Nguyen et al., 2018
DistMult pykeen.models.DistMult Yang et al., 2014
DistMultLiteral pykeen.models.DistMultLiteral Agustinus et al., 2018
ERMLP pykeen.models.ERMLP Dong et al., 2014
ERMLPE pykeen.models.ERMLPE Sharifzadeh et al., 2019
HolE pykeen.models.HolE Nickel et al., 2016
KG2E pykeen.models.KG2E He et al., 2015
NTN pykeen.models.NTN Socher et al., 2013
ProjE pykeen.models.ProjE Shi et al., 2017
RESCAL pykeen.models.RESCAL Nickel et al., 2011
RGCN pykeen.models.RGCN Schlichtkrull et al., 2018
RotatE pykeen.models.RotatE Sun et al., 2019
SimplE pykeen.models.SimplE Kazemi et al., 2018
StructuredEmbedding pykeen.models.StructuredEmbedding Bordes et al., 2011
TransD pykeen.models.TransD Ji et al., 2015
TransE pykeen.models.TransE Bordes et al., 2013
TransH pykeen.models.TransH Wang et al., 2014
TransR pykeen.models.TransR Lin et al., 2015
TuckER pykeen.models.TuckER Balazevic et al., 2019
UnstructuredModel pykeen.models.UnstructuredModel Bordes et al., 2014

Losses (7)

Name Reference Description
bceaftersigmoid pykeen.losses.BCEAfterSigmoidLoss A loss function which uses the numerically unstable version of explicit Sigmoid + BCE.
bcewithlogits pykeen.losses.BCEWithLogitsLoss A wrapper around the numeric stable version of the PyTorch binary cross entropy loss.
crossentropy pykeen.losses.CrossEntropyLoss Evaluate cross entropy after softmax output.
marginranking pykeen.losses.MarginRankingLoss A wrapper around the PyTorch margin ranking loss.
mse pykeen.losses.MSELoss A wrapper around the PyTorch mean square error loss.
nssa pykeen.losses.NSSALoss An implementation of the self-adversarial negative sampling loss function proposed by [sun2019]_.
softplus pykeen.losses.SoftplusLoss A loss function for the softplus.

Regularizers (5)

Name Reference Description
combined pykeen.regularizers.CombinedRegularizer A convex combination of regularizers.
lp pykeen.regularizers.LpRegularizer A simple L_p norm based regularizer.
no pykeen.regularizers.NoRegularizer A regularizer which does not perform any regularization.
powersum pykeen.regularizers.PowerSumRegularizer A simple x^p based regularizer.
transh pykeen.regularizers.TransHRegularizer A regularizer for the soft constraints in TransH.

Optimizers (6)

Name Reference Description
adadelta torch.optim.Adadelta Implements Adadelta algorithm.
adagrad torch.optim.Adagrad Implements Adagrad algorithm.
adam torch.optim.Adam Implements Adam algorithm.
adamax torch.optim.Adamax Implements Adamax algorithm (a variant of Adam based on infinity norm).
adamw torch.optim.AdamW Implements AdamW algorithm.
sgd torch.optim.SGD Implements stochastic gradient descent (optionally with momentum).

Training Loops (2)

Name Reference Description
lcwa pykeen.training.LCWATrainingLoop A training loop that uses the local closed world assumption training approach.
slcwa pykeen.training.SLCWATrainingLoop A training loop that uses the stochastic local closed world assumption training approach.

Negative Samplers (2)

Name Reference Description
basic pykeen.sampling.BasicNegativeSampler A basic negative sampler.
bernoulli pykeen.sampling.BernoulliNegativeSampler An implementation of the bernoulli negative sampling approach proposed by [wang2014]_.

Stoppers (2)

Name Reference Description
early pykeen.stoppers.EarlyStopper A harness for early stopping.
nop pykeen.stoppers.NopStopper A stopper that does nothing.

Evaluators (2)

Name Reference Description
rankbased pykeen.evaluation.RankBasedEvaluator A rank-based evaluator for KGE models.
sklearn pykeen.evaluation.SklearnEvaluator An evaluator that uses a Scikit-learn metric.

Metrics (6)

Metric Description Evaluator Reference
Adjusted Mean Rank The mean over all chance-adjusted ranks: mean_i (2r_i / (num_entities+1)). Lower is better. rankbased pykeen.evaluation.RankBasedMetricResults
Average Precision Score The area under the precision-recall curve, between [0.0, 1.0]. Higher is better. sklearn pykeen.evaluation.SklearnMetricResults
Hits At K The hits at k for different values of k, i.e. the relative frequency of ranks not larger than k. Higher is better. rankbased pykeen.evaluation.RankBasedMetricResults
Mean Rank The mean over all ranks: mean_i r_i. Lower is better. rankbased pykeen.evaluation.RankBasedMetricResults
Mean Reciprocal Rank The mean over all reciprocal ranks: mean_i (1/r_i). Higher is better. rankbased pykeen.evaluation.RankBasedMetricResults
Roc Auc Score The area under the ROC curve between [0.0, 1.0]. Higher is better. sklearn pykeen.evaluation.SklearnMetricResults

Trackers (2)

Name Reference Description
mlflow pykeen.trackers.MLFlowResultTracker A tracker for MLFlow.
wandb pykeen.trackers.WANDBResultTracker A tracker for Weights and Biases.

Hyper-parameter Optimization

Samplers (3)

Name Reference Description
grid optuna.samplers.GridSampler Sampler using grid search.
random optuna.samplers.RandomSampler Sampler using random sampling.
tpe optuna.samplers.TPESampler Sampler using TPE (Tree-structured Parzen Estimator) algorithm.

Experimentation

Reproduction

PyKEEN includes a set of curated experimental settings for reproducing past landmark experiments. They can be accessed and run like:

pykeen experiments reproduce tucker balazevic2019 fb15k

Where the three arguments are the model name, the reference, and the data set. The output directory can be optionally set with -d.

Ablation

PyKEEN includes the ability to specify ablation studies using the hyper-parameter optimization module. They can be run like:

pykeen experiments ablation ~/path/to/config.json

Contributing

Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See CONTRIBUTING.md for more information on getting involved.

Acknowledgements

Supporters

This project has been supported by several organizations (in alphabetical order):

Logo

The PyKEEN logo was designed by Carina Steinborn.

Citation

If you have found PyKEEN useful in your work, please consider citing our article:

@article{ali2020pykeen,
  title={PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Emebddings},
  author={Ali, Mehdi and Berrendorf, Max and Hoyt, Charles Tapley and Vermue, Laurent and Sharifzadeh, Sahand and Tresp, Volker and Lehmann, Jens},
  journal={arXiv preprint arXiv:2007.14175},
  year={2020}
}

Project details

Verified details

These details have been verified by PyPI
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Avatar for cthoyt from gravatar.com cthoyt Avatar for ddomingof from gravatar.com ddomingof Avatar for mali90 from gravatar.com mali90

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Project links
Meta
  • License: MIT License (MIT)
  • Author: Mehdi Ali
  • Maintainer: Mehdi Ali
  • Tags Knowledge Graph Embeddings, Machine Learning, Data Mining, Linked Data
  • Requires: Python >=3.6
Classifiers

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