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# Reproducibility Guide
## Overview
This part of repo contains the implementation and experiments. This guide will help you reproduce the results using Docker or manual installation.
---
## Docker Setup (Recommended)
### 1. Build Docker Image
```bash
docker build -t yambda-image .
```
### 2. Run Container with GPU Support
```bash
docker run --gpus all \
--runtime=nvidia \
-it \
-v </absolute/path/to/local/data>:/yambda/data \
yambda-image
```
---
## Data Organization
Create following structure in mounted data directory:
```bash
data/
├── flat/
│ └── 50m/
│ ├── likes.parquet
│ ├── listens.parquet
│ └── ...
└── sequential/
└── 50m/
├── likes.parquet
├── listens.parquet
└── ...
```
Note:
Sequential data is only needed for sasrec. You can build it from flat using scripts/transform2sequential.py or download
---
## Running Experiments
### General Usage
```bash
# For example random_rec
cd models/random_rec/
# Show help for main script
python main.py --help
# Basic execution
python main.py
```
### Specific Methods
#### BPR/ALS
```bash
cd models/bpr_als
python main.py --model bpr
python main.py --model als
```
#### SASRec
```bash
cd models/sasrec
# Training
python train.py --exp_name exp1
# Evaluation
python eval.py --exp_name exp1
```
---
## Manual Installation (Not Recommedned)
### 1. Install Core Dependencies
```bash
pip install torch torchvision torchaudio
```
### 2. Install Implicit (CUDA 11.8 required)
Implicit works only with cuda<12. See reasons [here](https://github.com/NVIDIA/nvidia-docker/issues/700#issuecomment-381073278)
```bash
CUDACXX=/usr/local/cuda-11.8/bin/nvcc \
pip install implicit
```
### 3. Install SANSA
```bash
sudo apt-get install libsuitesparse-dev
git clone https://github.com/glami/sansa.git
cd sansa && \
SUITESPARSE_INCLUDE_DIR=/usr/include/suitesparse \
SUITESPARSE_LIBRARY_DIR=/usr/lib \
pip install .
```
### 4. Install Project Package
```bash
pip install .
```
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