Package: deeptables
DeepTablesWe Are Hiring！Dear folks, we are opening several precious positions based in Beijing both for professionals and interns avid in AutoML/NAS, please send your resume/cv toyangjian@zetyun.com. (Application deadline: TBD.)DeepTables: Deep-learning Toolkit for Tabular dataDeepTables(DT) is a easy-to-use toolkit that enables deep learning to unleash great power on tabular data.OverviewMLP (also known as Fully-connected neural networks) have been shown inefficient in learning distribution representation. The "add" operations of the perceptron layer have been proven poor performance to exploring multiplicative feature interactions. In most cases, manual feature engineering is necessary and this work requires extensive domain knowledge and very cumbersome. How learning feature interactions efficiently in neural networks becomes the most important problem.Various models have been proposed to CTR prediction and continue to outperform existing state-of-the-art approaches to the late years. Well-known examples include FM, DeepFM, Wide&Deep, DCN, PNN, etc. These models can also provide good performance on tabular data under reasonable utilization.DT aims to utilize the latest research findings to provide users with an end-to-end toolkit on tabular data.DT has been designed with these key goals in mind:Easy to use, non-experts can also use.Provide good performance out of the box.Flexible architecture and easy expansion by user.TutorialsPlease refer to the official docs athttps://deeptables.readthedocs.io/en/latest/.Quick StartExamplesModelConfigModelsLayersAutoMLInstallationpipis recommended to install DeepTables:pipinstalltensorflowdeeptablesNote:Tensorflow is required by DeepTables, install it before running DeepTables.GPUSetup (Optional)To use DeepTables with GPU devices, installtensorflow-gpuinstead oftensorflow.pipinstalltensorflow-gpudeeptablesVerify the installation:python-c"from deeptables.utils.quicktest import test; test()"Optional dependenciesFollowing libraries are not hard dependencies and are not automatically installed when you install DeepTables. To use all functionalities of DT, these optional dependencies must be installed.pipinstallshapExample：A simple binary classification exampleimportnumpyasnpfromdeeptables.modelsimportdeeptable,deepnetsfromdeeptables.datasetsimportdsutilsfromsklearn.model_selectionimporttrain_test_split#loading datadf=dsutils.load_bank()df_train,df_test=train_test_split(df,test_size=0.2,random_state=42)y=df_train.pop('y')y_test=df_test.pop('y')#trainingconfig=deeptable.ModelConfig(nets=deepnets.DeepFM)dt=deeptable.DeepTable(config=config)model,history=dt.fit(df_train,y,epochs=10)#evaluationresult=dt.evaluate(df_test,y_test,batch_size=512,verbose=0)print(result)#scoringpreds=dt.predict(df_test)A solution using DeepTables to win the 1st place in Kaggle Categorical Feature Encoding Challenge IIClick hereCitationIf you use DeepTables in your research, please cite us as follows:Jian Yang, Xuefeng Li, Haifeng Wu.DeepTables: A Deep Learning Python Package for Tabular Data.https://github.com/DataCanvasIO/DeepTables, 2022. Version 0.2.x.BibTex:@misc{deeptables,
  author={Jian Yang, Xuefeng Li, Haifeng Wu},
  title={{DeepTables}: { A Deep Learning Python Package for Tabular Data}},
  howpublished={https://github.com/DataCanvasIO/DeepTables},
  note={Version 0.2.x},
  year={2022}
}DataCanvasDeepTables is an open source project created byDataCanvas.