Interactive dimensionality reduction for large datasets

Related tags

Deep Learningblossom
Overview

BlosSOM 🌼

BlosSOM is a graphical environment for running semi-supervised dimensionality reduction with EmbedSOM. You can use it to explore multidimensional datasets, and produce great-looking 2-dimensional visualizations.

WARNING: BlosSOM is still under development, some stuff may not work right, but things will magically improve without notice. Feel free to open an issue if something looks wrong.

screenshot

BlosSOM was developed at the MFF UK Prague, in cooperation with IOCB Prague.

MFF logoIOCB logo

Overview

BlosSOM creates a landmark-based model of the dataset, and dynamically projects all dataset point to your screen (using EmbedSOM). Several other algorithms and tools are provided to manage the landmarks; a quick overview follows:

  • High-dimensional landmark positioning:
    • Self-organizing maps
    • k-Means
  • 2D landmark positioning
    • k-NN graph generation (only adds edges, not vertices)
    • force-based graph layouting
    • dynamic t-SNE
  • Dimensionality reduction
    • EmbedSOM
    • CUDA EmbedSOM (with roughly 500x speedup, enabling smooth display of a few millions of points)
  • Manual landmark position optimization
  • Visualization settings (colors, transparencies, cluster coloring, ...)
  • Dataset transformations and dimension scaling
  • Import from matrix-like data files
    • FCS3.0 (Flow Cytometry Standard files)
    • TSV (Tab-separated CSV)
  • Export of the data for plotting

Compiling and running BlosSOM

You will need cmake build system and SDL2.

For CUDA EmbedSOM to work, you need the NVIDIA CUDA toolkit. Append -DBUILD_CUDA=1 to cmake options to enable the CUDA version.

Windows (Visual Studio 2019)

Dependencies

The project requires SDL2 as an external dependency:

  1. install vcpkg tool and remember your vcpkg directory
  2. install SDL: vcpkg install SDL2:x64-windows

Compilation

git submodule init
git submodule update

mkdir build
cd build

# You need to fix the path to vcpkg in the following command:
cmake .. -G "Visual Studio 16 2019" -A x64 -DCMAKE_BUILD_TYPE="Release" -DCMAKE_INSTALL_PREFIX=./inst -DCMAKE_TOOLCHAIN_FILE=your-vcpkg-clone-directory/scripts/buildsystems/vcpkg.cmake

cmake --build . --config Release
cmake --install . --config Release

Running

Open Visual Studio solution BlosSOM.sln, set blossom as startup project, set configuration to Release and run the project.

Linux (and possibly other unix-like systems)

Dependencies

The project requires SDL2 as an external dependency. Install libsdl2-dev (on Debian-based systems) or SDL2-devel (on Red Hat-based systems), or similar (depending on the Linux distribution). You should be able to install cmake package the same way.

Compilation

git submodule init
git submodule update

mkdir build
cd build
cmake .. -DCMAKE_INSTALL_PREFIX=./inst    # or any other directory
make install                              # use -j option to speed up the build

Running

./inst/bin/blossom

Documentation

Quickstart

  1. Click on the "plus" button on the bottom right side of the window
  2. Choose Open file (the first button from the top) and open a file from the demo_data/ directory
  3. You can now add and delete landmarks using ctrl+mouse click, and drag them around.
  4. Use the tools and settings available under the "plus" button to optimize the landmark positions and get a better visualization.

See the HOWTO for more details and hints.

Performance and CUDA

If you pass -DBUILD_CUDA=1 to the cmake commands, you will get extra executable called blossom_cuda (or blossom_cuda.exe, on Windows).

The 2 versions of BlosSOM executable differ mainly in the performance of EmbedSOM projection, which is more than 100× faster on GPUs than on CPUs. If the dataset gets large, only a fixed-size slice of the dataset gets processed each frame (e.g., at most 1000 points in case of CPU) to keep the framerate in a usable range. The defaults in BlosSOM should work smoothly for many use-cases (defaulting at 1k points per frame on CPU and 50k points per frame on GPU).

If required (e.g., if you have a really fast GPU), you may modify the constants in the corresponding source files, around the call sites of clean_range(), which is the function that manages the round-robin refreshing of the data. Functionality that dynamically chooses the best data-crunching rate is being implemented and should be available soon.

License

BlosSOM is licensed under GPLv3 or later. Several small libraries bundled in the repository are licensed with MIT-style licenses.

Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback This is our Pytorch implementation for the paper: Yinwei Wei,

17 Jun 10, 2022
Python wrapper of LSODA (solving ODEs) which can be called from within numba functions.

numbalsoda numbalsoda is a python wrapper to the LSODA method in ODEPACK, which is for solving ordinary differential equation initial value problems.

Nick Wogan 52 Jan 09, 2023
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

Realtime Unsupervised Depth Estimation from an Image This is the caffe implementation of our paper "Unsupervised CNN for single view depth estimation:

Ravi Garg 227 Nov 28, 2022
Official implementation of the NeurIPS'21 paper 'Conditional Generation Using Polynomial Expansions'.

Conditional Generation Using Polynomial Expansions Official implementation of the conditional image generation experiments as described on the NeurIPS

Grigoris 4 Aug 07, 2022
Sdf sparse conv - Deep Learning on SDF for Classifying Brain Biomarkers

Deep Learning on SDF for Classifying Brain Biomarkers To reproduce the results f

1 Jan 25, 2022
Predict halo masses from simulations via graph neural networks

HaloGraphNet Predict halo masses from simulations via Graph Neural Networks. Given a dark matter halo and its galaxies, creates a graph with informati

Pablo Villanueva Domingo 20 Nov 15, 2022
PyTorch-based framework for Deep Hedging

PFHedge: Deep Hedging in PyTorch PFHedge is a PyTorch-based framework for Deep Hedging. PFHedge Documentation Neural Network Architecture for Efficien

139 Dec 30, 2022
HyperLib: Deep learning in the Hyperbolic space

HyperLib: Deep learning in the Hyperbolic space Background This library implements common Neural Network components in the hypberbolic space (using th

105 Dec 25, 2022
Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera.

Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera. This project prepares training and t

305 Dec 16, 2022
The Official Repository for "Generalized OOD Detection: A Survey"

Generalized Out-of-Distribution Detection: A Survey 1. Overview This repository is with our survey paper: Title: Generalized Out-of-Distribution Detec

Jingkang Yang 338 Jan 03, 2023
A GridMixup augmentation, inspired by GridMask and CutMix

GridMixup A GridMixup augmentation, inspired by GridMask and CutMix Easy install pip install git+https://github.com/IlyaDobrynin/GridMixup.git Overvie

IlyaDo 42 Dec 28, 2022
Code for the CVPR 2021 paper "Triple-cooperative Video Shadow Detection"

Triple-cooperative Video Shadow Detection Code and dataset for the CVPR 2021 paper "Triple-cooperative Video Shadow Detection"[arXiv link] [official l

Zhihao Chen 24 Oct 04, 2022
TCube generates rich and fluent narratives that describes the characteristics, trends, and anomalies of any time-series data (domain-agnostic) using the transfer learning capabilities of PLMs.

TCube: Domain-Agnostic Neural Time series Narration This repository contains the code for the paper: "TCube: Domain-Agnostic Neural Time series Narrat

Mandar Sharma 7 Oct 31, 2021
PyTorch Implementation of PIXOR: Real-time 3D Object Detection from Point Clouds

PIXOR: Real-time 3D Object Detection from Point Clouds This is a custom implementation of the paper from Uber ATG using PyTorch 1.0. It represents the

Philip Huang 270 Dec 14, 2022
Code for the paper "Generative design of breakwaters usign deep convolutional neural network as a surrogate model"

Generative design of breakwaters usign deep convolutional neural network as a surrogate model This repository contains the code for the paper "Generat

2 Apr 10, 2022
An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

AlphaZero-Gomoku This is an implementation of the AlphaZero algorithm for playing the simple board game Gomoku (also called Gobang or Five in a Row) f

Junxiao Song 2.8k Dec 26, 2022
a curated list of docker-compose files prepared for testing data engineering tools, databases and open source libraries.

data-services A repository for storing various Data Engineering docker-compose files in one place. How to use it ? Set the required settings in .env f

BigData.IR 525 Dec 03, 2022
Code for KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs

KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs Check out the paper on arXiv: https://arxiv.org/abs/2103.13744 This repo cont

Christian Reiser 373 Dec 20, 2022
Implementation of Vaswani, Ashish, et al. "Attention is all you need."

Attention Is All You Need Paper Implementation This is my from-scratch implementation of the original transformer architecture from the following pape

Brando Koch 195 Dec 30, 2022
Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations

TopClus The source code used for Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations, published in WWW 2022. Requ

Yu Meng 63 Dec 18, 2022