First commit with the whole implementation.

Author: iYiYaHa

.gitignore

@@ -102,3 +102,7 @@ venv.bak/
 
 # mypy
 .mypy_cache/
+
+# IRGAN output
+runs/
+runs_bak/

IRGAN-SGD-dns-gen.ipynb

@@ -1 +1,49 @@
-# PyTorch-IRGAN
+# PyTorch-IRGAN
+
+
+## Description
+
+This project contains a pytorch version implementation about the item recommendation part of [IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models](https://arxiv.org/abs/1705.10513). The official implementation can be found at https://github.com/geek-ai/irgan. If you have any problems on this implementation, please open an issue.
+
+
+## Requirements
+
+Please refer to requirements.txt
+
+## Project Structure 
+
+.
+├── config.py (Configurations about IRGAN and BPR.)
+├── data (Data Files)
+│   ├── movielens-100k-test.txt
+│   └── movielens-100k-train.txt
+├── data_utils.py (Utilities about dataset.)
+├── evaluation (Evaluation metrics and tools.)
+│   ├── __init__.py
+│   ├── rank_metrics.py
+│   └── rec_evaluator.py
+├── exp_notebooks (Notebooks containing experiments for comparison. dns means using pre-trained models with dynamic negative sampling. 
+│    │         gen means pre-training generator while dis means pre-training discriminator. SGD and Adam are optimizers adopted.)
+│   ├── BPR.ipynb
+│   ├── IRGAN-Adam-dns-gen-dis.ipynb
+│   ├── IRGAN-Adam-dns-gen.ipynb
+│   ├── IRGAN-Adam-without-pretrained-model.ipynb
+│   ├── IRGAN-dns-gen-Adam-G-SGD-D.ipynb
+│   ├── IRGAN-SGD-dns-gen-dis.ipynb
+│   ├── IRGAN-SGD-dns-gen-static-negative-sampling.ipynb
+│   ├── IRGAN-SGD-without-pretrained-model.ipynb
+│   ├── Pretrain-Discriminator-Dynamic-Negative-Sampling-Adam.ipynb
+│   ├── Pretrain-Discriminator-Dynamic-Negative-Sampling.ipynb
+│   └── Pretrain-Discriminator-Static-Negative-Sampling.ipynb
+├── IRGAN-SGD-dns-gen.ipynb(IRGAN with the SGD optimizer and a pre-trained model with dynamic negative sampling for generator.)
+├── model.py (Model definition.)
+├── pretrained_models (Pre-trained models)
+│   ├── pretrained_model_dns.pkl
+│   └── pretrained_model_sns.pkl
+├── readme.md
+
+## How to run
+1. Execute **conda create --name <env_name> --file requirements.txt** to create an virtual environment and install required packages.
+2. Run a jupyter notebook server by **jupyter notebook**.
+3. Open IRGAN-SGD-dns-gen.ipynb in a browser and run all cells. 
+   The output of loss and other evaluation metrics can be observed with tensorboard.(Other notebooks from the *exp_notebooks* directory can be moved out to its upper-level directory and run). 

README.md

@@ -0,0 +1,31 @@
+"""Configuration files
+
+"""
+
+class Config():
+    def __init__(self):
+        self.epochs = 200
+        self.epochs_g = 50
+        self.epochs_d = 100
+        self.batch_size = 64
+        self.eta_G = 1e-3   # Learning Rate for generator
+        self.eta_D = 1e-3   # Learning Rate for discriminator
+        self.dir_path = "./data/"
+        self.emb_dim = 5
+        self.weight_decay = 1e-5
+        self.weight_decay_g = 1e-5
+        self.weight_decay_d = 1e-5
+        self.patience = 300
+        self.device = "cuda:0"
+        
+class BprConfig():
+    def __init__(self):
+        self.epochs = 200
+        self.batch_size = 64
+        self.eta = 1e-3   
+        self.dir_path = "./data/"
+        self.emb_dim = 5
+        self.device = "cuda:0"
+        self.weight_decay = 1e-5
+bpr_config = BprConfig()
+irgan_config = Config()