# Article-GADES This repository represents generating and benchmarking the results of the [GADES](https://github.com/lab-medvedeva/GADES-main) package for Distance Matrix Calculation ## Installation ```shell git lfs install git clone https://github.com/lab-medvedeva/Article-GADES.git cd Article-GADES ``` Put the Real datasets in the MEX format to the folder `Datasets/Real`. ### Running benchmark using Docker Deployment ```shell docker run --gpus all \ -v $PWD/Datasets:/workspace/Article-GADES/Datasets \ -v $PWD/results:/workspace/Article-GADES/results \ akhtyamovpavel/article-gades ``` ## Step 01. Generation of the datasets ### Step 01.1. Generated Dense Datasets ```shell cd ./scripts/MatricesGeneration ./generate_dense.sh ../../Datasets/ ``` ### Step 01.2. Generated Sparse Datasets ```shell cd ./scripts/MatricesGeneration ./generate_sparse.sh ../../Datasets/ ``` ## Step 02. Benchmarking ### Step 02.1. Generated Dense Datasets ```shell cd ./scripts/Benchmarking ./run_benchmark_generated_dense.sh ../../ ./run_benchmark_python_dense.sh ../../ ``` ### Step 02.2. Generated Sparse Datasets ```shell cd ./scripts/Benchmarking/ ./run_benchmark_generated_sparse.sh ../../ ``` ### Step 02.3 Real Datasets ```shell cd ./Scripts/Benchmarking/ ./run_benchmark_real_python.sh ../../results/RealDatasets// ./run_benchmark_real_R.sh ../../results/RealDatasets// ``` Example: ```shell ./run_benchmark.sh ../../Datasets/Real/HLCA_marrow.mtx ../results/RealDatasets/HLCA_marrow/ ``` ### Step 02.4. Ablation Study for the Batch Size Usage ### Step 02.5. Ablation Study for the Memory Usage ``` cd ./Scripts/Benchmarking ./run_benchmark_real_python_memory_usage.sh ../../results/RealDatasetsBatchSizeFixedMemory//500/ ``` Example: ``` ./run_benchmark_real_python_memory_usage.sh ../../Datasets/CellLines.mtx ../../results/RealDatasetsBatchSizeFixedMemory/CellLines/500/ ``` ## Step 03. Drawing charts We split reproducibility notebooks into two parts: * Aggregation over datasets * Plotting charts ### Aggregation 1. For Generated Dense datasets you could use the [GeneratedDatasetsCollector](/reproducibility/00-GeneratedDatasetsCollector.ipynb) notebook. 2. For Generated Sparse datasets you could use the [GeneratedSparseCollector](/reproducibility/00-GeneratedSparseCollector.ipynb) notebook. ### Analyzing datasets 1. Generated datasets analyzed in the [GeneratedDatasetAnalysis](/reproducibility/GeneratedDatasetAnalysis.ipynb) notebook. 2. Real datasets analyzed in the [RealDatasetAnalysis](/reproducibility/RealDatasetsAnalysis.ipynb) notebook. 3. Analysis of ablation study could be found in the [reproducibility](/reproducibility/03-AblationStudy.ipynb) notebook.