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Torch-GRU4Rec
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PyTorch implementation GRU4Rec model
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Torch-GRU4Rec
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# Torch-GRU4REC - PyTorch implementation of the algorithm of [Session-based Recommendations with Recurrent Neural Networks(ICLR 2016)](https://arxiv.org/pdf/1511.06939.pdf). With the extensions introduced in [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations(CIKM 2018)](https://arxiv.org/abs/1706.03847) - This code is based on [pyGRU4REC](https://github.com/yhs-968/pyGRU4REC) and [original Theano code written by the authors of the GRU4REC paper](https://github.com/hidasib/GRU4Rec) - This Version supports TOP1, BPR, TOP1-max, BPR-max, Negative Sampling, Cross-Entropy Losses and other parameters of [original Theano code](https://github.com/hidasib/GRU4Rec). - Model achieves the same performance reported in [original Theano code example](https://github.com/hidasib/GRU4Rec/blob/master/examples/rsc15/run_rsc15.py) for the Recsys Challenge 2015 (RSC15) dataset. ## Requirements - PyTorch 1.3.1 - Python 3.7 - pandas 0.24.2 - numpy 1.16.2 - jupyter 4.4.0 ## Usage ### Dataset - Download RecSys Challenge 2015 Dataset from [HERE](https://2015.recsyschallenge.com/) - Extract Data and locate to `PATH_TO_ORIGINAL_DATA` directory. Default `( /Data )` ### Data Preprocessing For data preprocessing and data cleaning run `Preprocessing.py` or see ***Data Preprocessing*** section in `Run_GRU4Rec.ipynb.` - The training set (`yoochoose-clicks.dat`) itself is divided into training, validation and testing sets where the testing is the last day sessions. - After preprocessing, `rsc15Train.csv`, `rsc15Valid.csv`, `rsc15Test.csv` obtain that stores in `PATH_TO_PROCESSED_DATA` directory. Default `( /Data/Cleaned )` - for use only 1/N data before preprocessing change N parameter. ### Training And Testing Using Jupyter Notebook See ``Run_GRU4Rec.ipnyb` that contains: 1. **Loads Data** - Load `rsc15Train.csv` , `rsc15Valid.csv` and create Dataset object 2. **Model Parameters** - Set model parameters. in this section parameters set to default like [original Theano code](https://github.com/hidasib/GRU4Rec). - The following list of parameters - ```inputSize``` GRU Input Size = Number of Items In Dataset <br> ```outputSize``` GRU output Size = Number of Items In Dataset <br> ```hiddenSize``` Number of Neurons per GRU Layers (Default = 100) <br> ```nLayers``` Number of GRU Layers (Default = 1) <br> ```batchSize``` Mini Batch Size (Default = 32) <br> ```negative``` Use Negative Sampling In Training Process Or Not (Default = True) <br> ```embeddingDim``` Size Of The Embedding Used, `embeddingDim <= 0` Means Not To Use Embedding (Default = -1) <br> ```dropoutHidden``` Dropout at each hidden layer (Default = 0.0)<br> ```dropoutEmbed``` Dropout Of The Input Units, Applicable Only If Embeddings Are Used (Default: 0.0) ```sigma``` "Width" Of Initialization. Either The standard Deviation Or The Min/Max Of The Initializations Interval (With Normal And Uniform Initializations Respectively). 0 Means Adaptive Normalization (Sigma Depends On The Size Of The Weight Matrix) (Default: 0.0) <br> ```initAsNormal``` False: Initializations From Uniform Distribution On [-sigma,sigma]. True: Initializations From Normal Distribution On (0,sigma). (default: False) <br> ```cuda``` Use GPU Or Not <br> ```finalAct``` Activation Function (Default = Elu-1.0) <br> ```lossType``` Type of loss function TOP1 / BPR / TOP1-max / BPR-max / Cross-Entropy/ NLL (Default: BPR-max) <br> ```optimizerType``` Optimizer (Default = Adagrad)<br> ```lr``` Learning rate (Default = 0.1).<br> ```weightDecay``` Weight decay (Default = 0.0)<br> ```momentum``` Momentum Value (Default = 0.0) <br> ```bpreg``` Score Regularization Coefficient For The BPR-max Loss Function (Default: 1.0)<br> ```nEpochs``` Number of epochs (Default = 10)<br> ```timeSort``` Whether To Ensure The Order Of Sessions Is Chronological (Default: True)<br> ```trainRandomOrder``` Whether To Randomize The Order Of Sessions In Each Epoch (Default: False) <br> ```sampleAlpha``` The Probability Of An Item Used As An Additional Negative Sample (Default: 0.75)<br> ```trainNSample``` Number Of Additional Negative Samples To Be Used In Training Mini Batch Generator (Default: 2048) <br> ```validNSample``` Number Of Additional Negative Samples To Be Used In Validation Mini Batch Generator (Default: 2048) <br> ```sampleStore``` Number Of by Precomputing Batch Of Negative Samples (Default: 10000000)<br> ```topN``` Value of K used durig Recall@K and MRR@K Evaluation (Default = 20)<br> 3. **BPR-max, no embedding** - train and evaluate the model with best parameters for BPR-max,no embedding 4. **BPR-max, constrained embedding** - train and evaluate the model with best parameters for BPR-max, constrained embedding 5. **Cross-entropy** - train and evaluate the model with best parameters for Log Softmax and Negative Likelihood Loss(Cross Entropy Loss) 6. **Testing** - Load `rsc15Test.csv` and create test Dataset object - Load trained Model and testing
