https://github.com/cthoyt/pykeen
🤖 A Python library for learning and evaluating knowledge graph embeddings
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🤖 A Python library for learning and evaluating knowledge graph embeddings
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- Host: GitHub
- Owner: cthoyt
- License: mit
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- Homepage: https://pykeen.readthedocs.io
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PyKEEN
PyKEEN (Python KnowlEdge EmbeddiNgs) is a Python package designed to train and evaluate knowledge graph embedding models (incorporating multi-modal information).
## Installation   The latest stable version of PyKEEN can be downloaded and installed from [PyPI](https://pypi.org/project/pykeen) with: ```bash $ pip install pykeen ``` The latest version of PyKEEN can be installed directly from the source on [GitHub](https://github.com/pykeen/pykeen) with: ```bash 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](https://pykeen.readthedocs.io/en/latest/installation.html). ## Quickstart [](https://pykeen.readthedocs.io/en/latest/?badge=latest) This example shows how to train a model on a dataset and test on another dataset. 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](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.TransE.html#pykeen.models.TransE) model on the [Nations](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Nations.html#pykeen.datasets.Nations) dataset. By default, the training loop uses the [stochastic local closed world assumption (sLCWA)](https://pykeen.readthedocs.io/en/latest/reference/training.html#pykeen.training.SLCWATrainingLoop) training approach and evaluates with [rank-based evaluation](https://pykeen.readthedocs.io/en/latest/reference/evaluation/rank_based.html#pykeen.evaluation.RankBasedEvaluator). ```python from pykeen.pipeline import pipeline result = pipeline( model='TransE', dataset='nations', ) ``` The results are returned in an instance of the [PipelineResult](https://pykeen.readthedocs.io/en/latest/reference/pipeline.html#pykeen.pipeline.PipelineResult) dataclass that has attributes for the trained model, the training loop, the evaluation, and more. See the tutorials on [understanding the evaluation](https://pykeen.readthedocs.io/en/latest/tutorial/understanding_evaluation.html) and [making novel link predictions](https://pykeen.readthedocs.io/en/latest/tutorial/making_predictions.html). 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, datasets, training modes, evaluators, and metrics implemented in ``pykeen``. ### Datasets (21) | Name | Reference | Description | |---------------|-------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------| | ckg | [`pykeen.datasets.CKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.CKG.html) | The Clinical Knowledge Graph (CKG) dataset from [santos2020]_. | | codexlarge | [`pykeen.datasets.CoDExLarge`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.CoDExLarge.html) | The CoDEx large dataset. | | codexmedium | [`pykeen.datasets.CoDExMedium`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.CoDExMedium.html) | The CoDEx medium dataset. | | codexsmall | [`pykeen.datasets.CoDExSmall`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.CoDExSmall.html) | The CoDEx small dataset. | | conceptnet | [`pykeen.datasets.ConceptNet`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.ConceptNet.html) | The ConceptNet dataset from [speer2017]_. | | drkg | [`pykeen.datasets.DRKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.DRKG.html) | The DRKG dataset. | | fb15k | [`pykeen.datasets.FB15k`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.FB15k.html) | The FB15k dataset. | | fb15k237 | [`pykeen.datasets.FB15k237`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.FB15k237.html) | The FB15k-237 dataset. | | hetionet | [`pykeen.datasets.Hetionet`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Hetionet.html) | The Hetionet dataset is a large biological network. | | kinships | [`pykeen.datasets.Kinships`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Kinships.html) | The Kinships dataset. | | nations | [`pykeen.datasets.Nations`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.Nations.html) | The Nations dataset. | | ogbbiokg | [`pykeen.datasets.OGBBioKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.OGBBioKG.html) | The OGB BioKG dataset. | | ogbwikikg | [`pykeen.datasets.OGBWikiKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.OGBWikiKG.html) | The OGB WikiKG dataset. | | openbiolink | [`pykeen.datasets.OpenBioLink`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.OpenBioLink.html) | The OpenBioLink dataset. | | openbiolinkf1 | [`pykeen.datasets.OpenBioLinkF1`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.OpenBioLinkF1.html) | The PyKEEN First Filtered OpenBioLink 2020 Dataset. | | openbiolinkf2 | [`pykeen.datasets.OpenBioLinkF2`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.OpenBioLinkF2.html) | The PyKEEN Second Filtered OpenBioLink 2020 Dataset. | | openbiolinklq | [`pykeen.datasets.OpenBioLinkLQ`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.OpenBioLinkLQ.html) | The low-quality variant of the OpenBioLink dataset. | | umls | [`pykeen.datasets.UMLS`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.UMLS.html) | The UMLS dataset. | | wn18 | [`pykeen.datasets.WN18`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.WN18.html) | The WN18 dataset. | | wn18rr | [`pykeen.datasets.WN18RR`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.WN18RR.html) | The WN18-RR dataset. | | yago310 | [`pykeen.datasets.YAGO310`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.YAGO310.html) | The YAGO3-10 dataset is a subset of YAGO3 that only contains entities with at least 10 relations. | ### Models (23) | Name | Reference | Citation | |---------------------|---------------------------------------------------------------------------------------------------------------------------|------------------------------| | ComplEx | [`pykeen.models.ComplEx`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.ComplEx.html) | Trouillon *et al.*, 2016 | | ComplExLiteral | [`pykeen.models.ComplExLiteral`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.ComplExLiteral.html) | Agustinus *et al.*, 2018 | | ConvE | [`pykeen.models.ConvE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.ConvE.html) | Dettmers *et al.*, 2018 | | ConvKB | [`pykeen.models.ConvKB`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.ConvKB.html) | Nguyen *et al.*, 2018 | | DistMult | [`pykeen.models.DistMult`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.DistMult.html) | Yang *et al.*, 2014 | | DistMultLiteral | [`pykeen.models.DistMultLiteral`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.DistMultLiteral.html) | Agustinus *et al.*, 2018 | | ERMLP | [`pykeen.models.ERMLP`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.ERMLP.html) | Dong *et al.*, 2014 | | ERMLPE | [`pykeen.models.ERMLPE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.ERMLPE.html) | Sharifzadeh *et al.*, 2019 | | HolE | [`pykeen.models.HolE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.HolE.html) | Nickel *et al.*, 2016 | | KG2E | [`pykeen.models.KG2E`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.KG2E.html) | He *et al.*, 2015 | | NTN | [`pykeen.models.NTN`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.NTN.html) | Socher *et al.*, 2013 | | ProjE | [`pykeen.models.ProjE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.ProjE.html) | Shi *et al.*, 2017 | | RESCAL | [`pykeen.models.RESCAL`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.RESCAL.html) | Nickel *et al.*, 2011 | | RGCN | [`pykeen.models.RGCN`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.RGCN.html) | Schlichtkrull *et al.*, 2018 | | RotatE | [`pykeen.models.RotatE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.RotatE.html) | Sun *et al.*, 2019 | | SimplE | [`pykeen.models.SimplE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.SimplE.html) | Kazemi *et al.*, 2018 | | StructuredEmbedding | [`pykeen.models.StructuredEmbedding`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.StructuredEmbedding.html) | Bordes *et al.*, 2011 | | TransD | [`pykeen.models.TransD`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.TransD.html) | Ji *et al.*, 2015 | | TransE | [`pykeen.models.TransE`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.TransE.html) | Bordes *et al.*, 2013 | | TransH | [`pykeen.models.TransH`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.TransH.html) | Wang *et al.*, 2014 | | TransR | [`pykeen.models.TransR`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.TransR.html) | Lin *et al.*, 2015 | | TuckER | [`pykeen.models.TuckER`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.TuckER.html) | Balazevic *et al.*, 2019 | | UnstructuredModel | [`pykeen.models.UnstructuredModel`](https://pykeen.readthedocs.io/en/latest/api/pykeen.models.UnstructuredModel.html) | Bordes *et al.*, 2014 | ### Losses (7) | Name | Reference | Description | |-----------------|---------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------| | bceaftersigmoid | [`pykeen.losses.BCEAfterSigmoidLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.BCEAfterSigmoidLoss.html) | A loss function which uses the numerically unstable version of explicit Sigmoid + BCE. | | bcewithlogits | [`pykeen.losses.BCEWithLogitsLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.BCEWithLogitsLoss.html) | A wrapper around the numeric stable version of the PyTorch binary cross entropy loss. | | crossentropy | [`pykeen.losses.CrossEntropyLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.CrossEntropyLoss.html) | Evaluate cross entropy after softmax output. | | marginranking | [`pykeen.losses.MarginRankingLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.MarginRankingLoss.html) | A wrapper around the PyTorch margin ranking loss. | | mse | [`pykeen.losses.MSELoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.MSELoss.html) | A wrapper around the PyTorch mean square error loss. | | nssa | [`pykeen.losses.NSSALoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.NSSALoss.html) | An implementation of the self-adversarial negative sampling loss function proposed by [sun2019]_. | | softplus | [`pykeen.losses.SoftplusLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.SoftplusLoss.html) | A loss function for the softplus. | ### Regularizers (5) | Name | Reference | Description | |----------|---------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------| | combined | [`pykeen.regularizers.CombinedRegularizer`](https://pykeen.readthedocs.io/en/latest/api/pykeen.regularizers.CombinedRegularizer.html) | A convex combination of regularizers. | | lp | [`pykeen.regularizers.LpRegularizer`](https://pykeen.readthedocs.io/en/latest/api/pykeen.regularizers.LpRegularizer.html) | A simple L_p norm based regularizer. | | no | [`pykeen.regularizers.NoRegularizer`](https://pykeen.readthedocs.io/en/latest/api/pykeen.regularizers.NoRegularizer.html) | A regularizer which does not perform any regularization. | | powersum | [`pykeen.regularizers.PowerSumRegularizer`](https://pykeen.readthedocs.io/en/latest/api/pykeen.regularizers.PowerSumRegularizer.html) | A simple x^p based regularizer. | | transh | [`pykeen.regularizers.TransHRegularizer`](https://pykeen.readthedocs.io/en/latest/api/pykeen.regularizers.TransHRegularizer.html) | A regularizer for the soft constraints in TransH. | ### Optimizers (6) | Name | Reference | Description | |----------|-------------------------------------------------------------------------------------------|-------------------------------------------------------------------------| | adadelta | [`torch.optim.Adadelta`](https://pytorch.org/docs/stable/optim.html#torch.optim.Adadelta) | Implements Adadelta algorithm. | | adagrad | [`torch.optim.Adagrad`](https://pytorch.org/docs/stable/optim.html#torch.optim.Adagrad) | Implements Adagrad algorithm. | | adam | [`torch.optim.Adam`](https://pytorch.org/docs/stable/optim.html#torch.optim.Adam) | Implements Adam algorithm. | | adamax | [`torch.optim.Adamax`](https://pytorch.org/docs/stable/optim.html#torch.optim.Adamax) | Implements Adamax algorithm (a variant of Adam based on infinity norm). | | adamw | [`torch.optim.AdamW`](https://pytorch.org/docs/stable/optim.html#torch.optim.AdamW) | Implements AdamW algorithm. | | sgd | [`torch.optim.SGD`](https://pytorch.org/docs/stable/optim.html#torch.optim.SGD) | Implements stochastic gradient descent (optionally with momentum). | ### Training Loops (2) | Name | Reference | Description | |--------|------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------| | lcwa | [`pykeen.training.LCWATrainingLoop`](https://pykeen.readthedocs.io/en/latest/reference/training.html#pykeen.training.LCWATrainingLoop) | A training loop that uses the local closed world assumption training approach. | | slcwa | [`pykeen.training.SLCWATrainingLoop`](https://pykeen.readthedocs.io/en/latest/reference/training.html#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`](https://pykeen.readthedocs.io/en/latest/api/pykeen.sampling.BasicNegativeSampler.html) | A basic negative sampler. | | bernoulli | [`pykeen.sampling.BernoulliNegativeSampler`](https://pykeen.readthedocs.io/en/latest/api/pykeen.sampling.BernoulliNegativeSampler.html) | An implementation of the Bernoulli negative sampling approach proposed by [wang2014]_. | ### Stoppers (2) | Name | Reference | Description | |--------|--------------------------------------------------------------------------------------------------------------------------------|-------------------------------| | early | [`pykeen.stoppers.EarlyStopper`](https://pykeen.readthedocs.io/en/latest/reference/stoppers.html#pykeen.stoppers.EarlyStopper) | A harness for early stopping. | | nop | [`pykeen.stoppers.NopStopper`](https://pykeen.readthedocs.io/en/latest/reference/stoppers.html#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 (3) | Name | Reference | Description | |---------|---------------------------------------------------------------------------------------------------------------------------------|-----------------------------------| | mlflow | [`pykeen.trackers.MLFlowResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.MLFlowResultTracker.html) | A tracker for MLflow. | | neptune | [`pykeen.trackers.NeptuneResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.NeptuneResultTracker.html) | A tracker for Neptune.ai. | | wandb | [`pykeen.trackers.WANDBResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.WANDBResultTracker.html) | A tracker for Weights and Biases. | ## Hyper-parameter Optimization ### Samplers (3) | Name | Reference | Description | |--------|-----------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------| | grid | [`optuna.samplers.GridSampler`](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.samplers.GridSampler.html) | Sampler using grid search. | | random | [`optuna.samplers.RandomSampler`](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.samplers.RandomSampler.html) | Sampler using random sampling. | | tpe | [`optuna.samplers.TPESampler`](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.samplers.TPESampler.html) | Sampler using TPE (Tree-structured Parzen Estimator) algorithm. | Any sampler class extending the [optuna.samplers.BaseSampler](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.samplers.BaseSampler.html#optuna.samplers.BaseSampler), such as their sampler implementing the [CMA-ES](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.samplers.CmaEsSampler.html#optuna.samplers.CmaEsSampler) algorithm, can also be used. ## Experimentation ### Reproduction PyKEEN includes a set of curated experimental settings for reproducing past landmark experiments. They can be accessed and run like: ```bash pykeen experiments reproduce tucker balazevic2019 fb15k ``` Where the three arguments are the model name, the reference, and the dataset. 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: ```bash pykeen experiments ablation ~/path/to/config.json ``` ### Large-scale Reproducibility and Benchmarking Study We used PyKEEN to perform a large-scale reproducibility and benchmarking study which are described in [our article](https://arxiv.org/abs/2006.13365): ```bibtex @article{ali2020benchmarking, title={Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework}, author={Ali, Mehdi and Berrendorf, Max and Hoyt, Charles Tapley and Vermue, Laurent and Galkin, Mikhail and Sharifzadeh, Sahand and Fischer, Asja and Tresp, Volker and Lehmann, Jens}, journal={arXiv preprint arXiv:2006.13365}, year={2020} } ``` We have made all code, experimental configurations, results, and analyses that lead to our interpretations available at https://github.com/pykeen/benchmarking. ## Contributing Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See [CONTRIBUTING.md](/CONTRIBUTING.md) for more information on getting involved. ## Acknowledgements ### Supporters This project has been supported by several organizations (in alphabetical order): - [Bayer](https://www.bayer.com/) - [Enveda Therapeutics](https://envedatherapeutics.com/) - [Fraunhofer Institute for Algorithms and Scientific Computing](https://www.scai.fraunhofer.de) - [Fraunhofer Institute for Intelligent Analysis and Information Systems](https://www.iais.fraunhofer.de) - [Fraunhofer Center for Machine Learning](https://www.cit.fraunhofer.de/de/zentren/maschinelles-lernen.html) - [Ludwig-Maximilians-Universitt Mnchen](https://www.en.uni-muenchen.de/index.html) - [Munich Center for Machine Learning (MCML)](https://mcml.ai/) - [Siemens](https://new.siemens.com/global/en.html) - [Smart Data Analytics Research Group (University of Bonn & Fraunhofer IAIS)](https://sda.tech) - [Technical University of Denmark - DTU Compute - Section for Cognitive Systems](https://www.compute.dtu.dk/english/research/research-sections/cogsys) - [Technical University of Denmark - DTU Compute - Section for Statistics and Data Analysis](https://www.compute.dtu.dk/english/research/research-sections/stat) - [University of Bonn](https://www.uni-bonn.de/) ### Logo The PyKEEN logo was designed by Carina Steinborn. ## Citation If you have found PyKEEN useful in your work, please consider citing [our article](https://arxiv.org/abs/2007.14175): ```bibtex @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} } ```
Owner
- Name: Charles Tapley Hoyt
- Login: cthoyt
- Kind: user
- Location: Bonn, Germany
- Company: RWTH Aachen University
- Website: https://cthoyt.com
- Repositories: 489
- Profile: https://github.com/cthoyt