Metadata-Version: 2.1
Name: pykeen
Version: 1.3.0
Summary: A package for training and evaluating multimodal knowledge graph embeddings
Home-page: https://github.com/pykeen/pykeen
Author: Mehdi Ali
Author-email: mehdi.ali@cs.uni-bonn.de
Maintainer: Mehdi Ali
Maintainer-email: mehdi.ali@cs.uni-bonn.de
License: MIT
Download-URL: https://github.com/pykeen/pykeen/releases
Project-URL: Bug Tracker, https://github.com/pykeen/pykeen/issues
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          <img src="docs/source/logo.png" height="150">
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        <h1 align="center">
          PyKEEN
        </h1>
        
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        <p align="center">
            <b>PyKEEN</b> (<b>P</b>ython <b>K</b>nowl<b>E</b>dge <b>E</b>mbeddi<b>N</b>gs) is a Python package designed to
            train and evaluate knowledge graph embedding models (incorporating multi-modal information).
        </p>
        
        <p align="center">
          <a href="#installation">Installation</a> •
          <a href="#quickstart">Quickstart</a> •
          <a href="#datasets-23">Datasets</a> •
          <a href="#models-23">Models</a> •
          <a href="#supporters">Support</a> •
          <a href="#citation">Citation</a>
        </p>
        
        ## Installation ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pykeen) ![PyPI](https://img.shields.io/pypi/v/pykeen)
        
        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 [![Documentation Status](https://readthedocs.org/projects/pykeen/badge/?version=latest)](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 (23)
        
        | 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]_.                                                         |
        | cskg          | [`pykeen.datasets.CSKG`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.CSKG.html)                   | The CSKG dataset.                                                                                 |
        | dbpedia50     | [`pykeen.datasets.DBpedia50`](https://pykeen.readthedocs.io/en/latest/api/pykeen.datasets.DBpedia50.html)         | The DBpedia50 dataset.                                                                            |
        | 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 module for the numerically unstable version of explicit Sigmoid + BCE loss.                     |
        | bcewithlogits   | [`pykeen.losses.BCEWithLogitsLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.BCEWithLogitsLoss.html)     | A module for the binary cross entropy loss.                                                       |
        | crossentropy    | [`pykeen.losses.CrossEntropyLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.CrossEntropyLoss.html)       | A module for the cross entopy loss that evaluates the cross entropy after softmax output.         |
        | marginranking   | [`pykeen.losses.MarginRankingLoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.MarginRankingLoss.html)     | A module for the margin ranking loss.                                                             |
        | mse             | [`pykeen.losses.MSELoss`](https://pykeen.readthedocs.io/en/latest/api/pykeen.losses.MSELoss.html)                         | A module for the 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 module for the softplus loss.                                                                   |
        
        ### 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 (5)
        
        | Name    | Reference                                                                                                                       | Description                            |
        |---------|---------------------------------------------------------------------------------------------------------------------------------|----------------------------------------|
        | csv     | [`pykeen.trackers.CSVResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.CSVResultTracker.html)         | Tracking results to a CSV file.        |
        | json    | [`pykeen.trackers.JSONResultTracker`](https://pykeen.readthedocs.io/en/latest/api/pykeen.trackers.JSONResultTracker.html)       | Tracking results to a JSON lines file. |
        | 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 Biosciences](https://www.envedabio.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-Universität München](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}
        }
        ```
        
Keywords: Knowledge Graph Embeddings,Machine Learning,Data Mining,Linked Data
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: templating
Provides-Extra: plotting
Provides-Extra: mlflow
Provides-Extra: wandb
Provides-Extra: neptune
Provides-Extra: docs
