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hopwise documentation
hopwise documentation

Get Started

  • Install Hopwise
  • Distributed Training

User Guide

  • Usage
    • Use run_hopwise
    • Use Modules
    • Parameter Tuning
    • Running New Dataset
    • Running Different Models
    • Load Pre-trained Embedding
    • Save and load data and model
    • Case study
    • Use Tensorboard
    • Use Weights & Biases
    • Clarifications on some practical issues
    • Significance Test
  • Config Introduction
    • Environment Settings
    • Data settings
    • Training Settings
    • Evaluation Settings
    • Parameters Configuration
  • Data Module Introduction
    • Dataset Download
    • Data Flow
    • Atomic Files
    • Interaction
    • Label of data
  • Tasks & Models
    • Context-aware Recommendation
      • LR
      • FM
      • NFM
      • DeepFM
      • xDeepFM
      • AFM
      • FFM
      • FwFM
      • FNN
      • PNN
      • DSSM
      • WideDeep
      • DIN
      • DIEN
      • DCN
      • DCN V2
      • AutoInt
      • XGBOOST(External algorithm library)
      • LIGHTGBM(External algorithm library)
      • KD_DAGFM
      • FiGNN
      • EulerNet
    • General Recommendation
      • AsymKNN
      • Pop
      • ItemKNN
      • BPR
      • NeuMF
      • ConvNCF
      • DMF
      • FISM
      • NAIS
      • SpectralCF
      • GCMC
      • NGCF
      • LightGCN
      • DGCF
      • LINE
      • MultiVAE
      • MultiDAE
      • MacridVAE
      • CDAE
      • ENMF
      • NNCF
      • RaCT
      • RecVAE
      • EASE
      • SLIMElastic
      • SGL
      • ADMMSLIM
      • NCEPLRec
      • SimpleX
      • NCL
      • Random
      • DiffRec
      • LDiffRec
    • Knowledge-based Recommendation
      • CKE
      • CFKG
      • KTUP
      • KGAT
      • KGIN
      • RippleNet
      • MCCLK
      • MKR
      • KGCN
      • KGNNLS
      • KGLRR
      • PGPR
      • CAFE
      • TPRec
    • Knowledge Graph Embeddings
      • TransE
      • TransH
      • TransR
      • TransD
      • TorusE
      • RotatE
      • TuckER
      • DistMult
      • Analogy
      • HolE
      • RESCAL
      • ComplEx
      • ConvE
      • ConvKB
    • Path Reasoning Recommendation
      • KGGLM
      • PEARLM
      • PLM
    • Sequential Recommendation
      • FPMC
      • GRU4Rec
      • NARM
      • STAMP
      • Caser
      • NextItNet
      • TransRec
      • SASRec
      • BERT4Rec
      • SRGNN
      • GCSAN
      • GRU4RecF
      • SASRecF
      • FDSA
      • S3Rec
      • GRU4RecKG
      • KSR
      • FOSSIL
      • SHAN
      • RepeatNet
      • HGN
      • HRM
      • NPE
      • LightSANs
      • SINE
      • CORE
      • FEARec
      • SASRecCPR
      • GRU4RecCPR
  • Training & Evaluation Introduction
  • Hyperparameters Tuning Introduction

Architecture

  • U Configuration
  • Create Datasets
  • Create Samplers
  • Create DataLoaders
  • Create Trainers
  • Add Models to hopwise
  • Create Metrics
  • API Reference
    • hopwise
      • hopwise.__main__
      • hopwise.cli
      • hopwise.config
        • hopwise.config.configurator
      • hopwise.data
        • hopwise.data.dataloader
          • hopwise.data.dataloader.abstract_dataloader
          • hopwise.data.dataloader.general_dataloader
          • hopwise.data.dataloader.knowledge_dataloader
          • hopwise.data.dataloader.user_dataloader
        • hopwise.data.dataset
          • hopwise.data.dataset.customized_dataset
          • hopwise.data.dataset.dataset
          • hopwise.data.dataset.decisiontree_dataset
          • hopwise.data.dataset.kg_dataset
          • hopwise.data.dataset.kg_path_dataset
          • hopwise.data.dataset.kg_seq_dataset
          • hopwise.data.dataset.sequential_dataset
        • hopwise.data.interaction
        • hopwise.data.transform
        • hopwise.data.utils
      • hopwise.evaluator
        • hopwise.evaluator.base_metric
        • hopwise.evaluator.collector
        • hopwise.evaluator.evaluator
        • hopwise.evaluator.metrics
        • hopwise.evaluator.register
        • hopwise.evaluator.utils
      • hopwise.model
        • hopwise.model.abstract_recommender
        • hopwise.model.context_aware_recommender
          • hopwise.model.context_aware_recommender.afm
          • hopwise.model.context_aware_recommender.autoint
          • hopwise.model.context_aware_recommender.dcn
          • hopwise.model.context_aware_recommender.dcnv2
          • hopwise.model.context_aware_recommender.deepfm
          • hopwise.model.context_aware_recommender.dssm
          • hopwise.model.context_aware_recommender.eulernet
          • hopwise.model.context_aware_recommender.ffm
          • hopwise.model.context_aware_recommender.fignn
          • hopwise.model.context_aware_recommender.fm
          • hopwise.model.context_aware_recommender.fnn
          • hopwise.model.context_aware_recommender.fwfm
          • hopwise.model.context_aware_recommender.kd_dagfm
          • hopwise.model.context_aware_recommender.lr
          • hopwise.model.context_aware_recommender.nfm
          • hopwise.model.context_aware_recommender.pnn
          • hopwise.model.context_aware_recommender.widedeep
          • hopwise.model.context_aware_recommender.xdeepfm
        • hopwise.model.exlib_recommender
          • hopwise.model.exlib_recommender.lightgbm
          • hopwise.model.exlib_recommender.xgboost
        • hopwise.model.general_recommender
          • hopwise.model.general_recommender.admmslim
          • hopwise.model.general_recommender.bpr
          • hopwise.model.general_recommender.cdae
          • hopwise.model.general_recommender.convncf
          • hopwise.model.general_recommender.dgcf
          • hopwise.model.general_recommender.diffrec
          • hopwise.model.general_recommender.dmf
          • hopwise.model.general_recommender.ease
          • hopwise.model.general_recommender.enmf
          • hopwise.model.general_recommender.fism
          • hopwise.model.general_recommender.gcmc
          • hopwise.model.general_recommender.itemknn
          • hopwise.model.general_recommender.ldiffrec
          • hopwise.model.general_recommender.lightgcn
          • hopwise.model.general_recommender.line
          • hopwise.model.general_recommender.macridvae
          • hopwise.model.general_recommender.multidae
          • hopwise.model.general_recommender.multivae
          • hopwise.model.general_recommender.nais
          • hopwise.model.general_recommender.nceplrec
          • hopwise.model.general_recommender.ncl
          • hopwise.model.general_recommender.neumf
          • hopwise.model.general_recommender.ngcf
          • hopwise.model.general_recommender.nncf
          • hopwise.model.general_recommender.pop
          • hopwise.model.general_recommender.ract
          • hopwise.model.general_recommender.random
          • hopwise.model.general_recommender.recvae
          • hopwise.model.general_recommender.sgl
          • hopwise.model.general_recommender.simplex
          • hopwise.model.general_recommender.slimelastic
          • hopwise.model.general_recommender.spectralcf
        • hopwise.model.init
        • hopwise.model.knowledge_aware_recommender
          • hopwise.model.knowledge_aware_recommender.cafe
          • hopwise.model.knowledge_aware_recommender.cfkg
          • hopwise.model.knowledge_aware_recommender.cke
          • hopwise.model.knowledge_aware_recommender.kgat
          • hopwise.model.knowledge_aware_recommender.kgcn
          • hopwise.model.knowledge_aware_recommender.kgin
          • hopwise.model.knowledge_aware_recommender.kglrr
          • hopwise.model.knowledge_aware_recommender.kgnnls
          • hopwise.model.knowledge_aware_recommender.ktup
          • hopwise.model.knowledge_aware_recommender.mcclk
          • hopwise.model.knowledge_aware_recommender.mkr
          • hopwise.model.knowledge_aware_recommender.pgpr
          • hopwise.model.knowledge_aware_recommender.ripplenet
          • hopwise.model.knowledge_aware_recommender.tprec
        • hopwise.model.knowledge_graph_embedding_recommender
          • hopwise.model.knowledge_graph_embedding_recommender.analogy
          • hopwise.model.knowledge_graph_embedding_recommender.complex
          • hopwise.model.knowledge_graph_embedding_recommender.conve
          • hopwise.model.knowledge_graph_embedding_recommender.convkb
          • hopwise.model.knowledge_graph_embedding_recommender.distmult
          • hopwise.model.knowledge_graph_embedding_recommender.hole
          • hopwise.model.knowledge_graph_embedding_recommender.rescal
          • hopwise.model.knowledge_graph_embedding_recommender.rotate
          • hopwise.model.knowledge_graph_embedding_recommender.toruse
          • hopwise.model.knowledge_graph_embedding_recommender.transd
          • hopwise.model.knowledge_graph_embedding_recommender.transe
          • hopwise.model.knowledge_graph_embedding_recommender.transh
          • hopwise.model.knowledge_graph_embedding_recommender.transr
          • hopwise.model.knowledge_graph_embedding_recommender.tucker
        • hopwise.model.layers
        • hopwise.model.logits_processor
        • hopwise.model.loss
        • hopwise.model.path_language_modeling_recommender
          • hopwise.model.path_language_modeling_recommender.kgglm
          • hopwise.model.path_language_modeling_recommender.pearlm
          • hopwise.model.path_language_modeling_recommender.pearlmgpt2
          • hopwise.model.path_language_modeling_recommender.pearlmllama2
          • hopwise.model.path_language_modeling_recommender.pearlmllama3
          • hopwise.model.path_language_modeling_recommender.plm
        • hopwise.model.ranker
        • hopwise.model.sequential_recommender
          • hopwise.model.sequential_recommender.bert4rec
          • hopwise.model.sequential_recommender.caser
          • hopwise.model.sequential_recommender.core
          • hopwise.model.sequential_recommender.dien
          • hopwise.model.sequential_recommender.din
          • hopwise.model.sequential_recommender.fdsa
          • hopwise.model.sequential_recommender.fearec
          • hopwise.model.sequential_recommender.fossil
          • hopwise.model.sequential_recommender.fpmc
          • hopwise.model.sequential_recommender.gcsan
          • hopwise.model.sequential_recommender.gru4rec
          • hopwise.model.sequential_recommender.gru4reccpr
          • hopwise.model.sequential_recommender.gru4recf
          • hopwise.model.sequential_recommender.gru4reckg
          • hopwise.model.sequential_recommender.hgn
          • hopwise.model.sequential_recommender.hrm
          • hopwise.model.sequential_recommender.ksr
          • hopwise.model.sequential_recommender.lightsans
          • hopwise.model.sequential_recommender.narm
          • hopwise.model.sequential_recommender.nextitnet
          • hopwise.model.sequential_recommender.npe
          • hopwise.model.sequential_recommender.repeatnet
          • hopwise.model.sequential_recommender.s3rec
          • hopwise.model.sequential_recommender.sasrec
          • hopwise.model.sequential_recommender.sasreccpr
          • hopwise.model.sequential_recommender.sasrecf
          • hopwise.model.sequential_recommender.shan
          • hopwise.model.sequential_recommender.sine
          • hopwise.model.sequential_recommender.srgnn
          • hopwise.model.sequential_recommender.stamp
          • hopwise.model.sequential_recommender.transrec
      • hopwise.quick_start
        • hopwise.quick_start.quick_start
      • hopwise.sampler
        • hopwise.sampler.sampler
      • hopwise.trainer
        • hopwise.trainer.hf_path_trainer
        • hopwise.trainer.hyper_tuning
        • hopwise.trainer.trainer
      • hopwise.utils
        • hopwise.utils.argument_list
        • hopwise.utils.case_study
        • hopwise.utils.enum_type
        • hopwise.utils.logger
        • hopwise.utils.url
        • hopwise.utils.utils
        • hopwise.utils.wandblogger
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Usage¶

In order to help users learn the depth usage of hopwise, we write the following usage docs to give a detailed introduction about hopwise’s features.

  • Use run_hopwise
  • Use Modules
  • Parameter Tuning
  • Running New Dataset
  • Running Different Models
  • Load Pre-trained Embedding
  • Save and load data and model
  • Case study
  • Use Tensorboard
  • Use Weights & Biases
  • Clarifications on some practical issues
  • Significance Test
Next
Use run_hopwise
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Distributed Training
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