Package: gbiz-torch
Introduction to gbiz_torchA comprehensive toolkit packagedesigned to help you accurately predict key metrics such as Click-Through Rates (CTR), Conversion Rates (CVR), uplift, and pricing strategies. Built with state-of-the-art algorithms and user-friendly interfaces, our package streamlines the process of forecasting and decision-making, allowing you to make data-driven choices with confidence. Whether you're looking to optimize your marketing campaigns, boost sales conversions, or fine-tune your pricing model, our package provides the insights you need to succeed in today's competitive market.TutorialYou can learn to use the package by referring to the examples in the directory./exampleMore solution examples will be released soon~Useful Eval MatrixThe following eval matrix has been implemented:#Eval MatrixExplanationNote1AUCArea Under the ROC CurveFor Classification2Confusion_MatrixConfusion Matrix is a performance measurement for classificationFor Classification3ACC_F1_scoreAccuracy, Macro-F1 and Weighted-F1For Classification4Top_K_Acctop_k_accuracy_scoreFor Classification5Multi_Class_RPMulti Class precision, recall and F-betaFor Classification6r2_scoreR2_scoreFor Classification7MAEMean Absolute ErrorFor Regression8MSEMean Square ErrorFor Regression9MAPEMean Absolute Percentage ErrorFor Regression10tsnet-distributed stochastic neighbor embeddingFor Manifold11sp_embspectral decomposition to the corresponding graph laplacianFor ManifoldUniversal Layers in Commercial AlgorithmsThe models currently implemented in recommendation algorithms:#Model NamemodelNote1Wide and DeepWndModelTraditional recommendations2DNNDNNModelTraditional recommendations3DeepFMDeepFMModelTraditional recommendations4Deep and CrossDCNModelTraditional recommendations5NFMNFMModelTraditional recommendations6TowerTowerModelTraditional recommendations7FLENFLENModelTraditional recommendations8FibinetFiBiNetModelTraditional recommendations9InterHAtInterHAtModelTraditional recommendations10CANCANModelTraditional recommendations11MaskNetMaskNetModelTraditional recommendations12ContextNetContextNetModelTraditional recommendations13EDCNEDCNModelTraditional recommendations14BertSeqBert4RecModelSequence recommendation15GRU4RecGRU4RecModelSequence recommendation16DINDINModelSequence recommendation17DCAPDCAPModelSequence recommendation18FBASFBASModelSequence recommendation19ESMMESMMModelMulti objective recommendation20MMoEGeneralMMoEModelMulti objective recommendation21Hard SharingHardSharingModelMulti objective recommendation22Cross SharingCrossSharingModelMulti objective recommendation23Cross StitchCrossStitchModelMulti objective recommendation24PLEPLEModelMulti objective recommendationUniversal Layers in Commercial AlgorithmsIn the consolidated algorithms, the following Layer networks have been implemented, which can be conveniently called by higher-level models, or users can directly call the Layer layers to assemble their own models.#Graph-based LayerNote1HOMOGNNLayerGeneral GNN layers for Homogeneity  Graph (GCNConv, GATConv, SAGEConv, TransformerConv, ARMAConv)2HETEGNNLayerGeneral GNN layers for heterogeneous Graph (HGTConv,HANConv)#LayerNote1DNNLayerDNN Net2FMLayerFM Net in DeepFM, NFM3CrossLayerCross Net in Deep and Cross4CINLayerCIN Net in XDeepFM5MultiHeadAttentionLayermulti head attention in Bert6SelfAttentionLayerscaled dot self attention in Bert7LayerNormLayer Normalization in Bert8PositionWiseFeedForwardLayerPosition wise feed forward in Bert9TransformerLayerTransformer(including multi head attention and LayerNorm) in Bert10TransformerEncoderMulti-Transformer in Bert11AutoIntLayerSimilar with TransformerLayer12FuseLayerLocal Activation Unit in DIN13SENETLayerSqueeze and Excitation Layer14FieldWiseBiInteractionLayerFM and MF layer in FLEN15CrossStitchLayerCross-stitch Networks for Multi-task Learning16GeneralMMoELayerModeling Task Relationships in Multi-task Learning with   Multi-gate Mixture-of-Experts17DiceDice activation function18PositionEncodingLayerPositional Encoding Layer in Transformer19CGCGatingNetworkLayertask and expert Net in PLE20BiLinearInteractionLayerLast feature net in Fibinet21CoActionLayerco-action unit layer in CAN22MaskBlockLayerMaskBlockLayer in MaskNetCitationIf you find this code useful in your research, please cite it using the following BibTeX:@software{Wang_gbiz_torch_A_comprehensive_2023,author={Wang, Haowen},doi={10.5281/zenodo.10222799},month=nov,title={{gbiz_torch: A comprehensive toolkit for predicting key metrics in e-commercial fields}},url={https://github.com/whw199833/gbiz_torch},version={2.0.4},year={2023}}or following APA:Wang, H. (2023). gbiz_torch: A comprehensive toolkit for predicting key metrics in e-commercial fields (Version 2.0.4) [Computer software]. https://doi.org/10.5281/zenodo.10222799ContactIf you have some questions or some advice, or want to contribute to this repo, do not hesitate to contact me:mail:wanghw@zju.edu.cnwechat: whw199833