LightGBM
pyvirtualcam
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LightGBM | pyvirtualcam | |
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11 | 6 | |
16,043 | 434 | |
1.0% | - | |
9.2 | 6.3 | |
7 days ago | 3 months ago | |
C++ | C++ | |
MIT License | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
LightGBM
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SIRUS.jl: Interpretable Machine Learning via Rule Extraction
SIRUS.jl is a pure Julia implementation of the SIRUS algorithm by Bénard et al. (2021). The algorithm is a rule-based machine learning model meaning that it is fully interpretable. The algorithm does this by firstly fitting a random forests and then converting this forest to rules. Furthermore, the algorithm is stable and achieves a predictive performance that is comparable to LightGBM, a state-of-the-art gradient boosting model created by Microsoft. Interpretability, stability, and predictive performance are described in more detail below.
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[D] RAM speeds for tabular machine learning algorithms
Hey, thanks everybody for your answers. I've asked around in the XGBoost and LightGBM repos and some folks there also agreed that memory speed will be a bottleneck yes.
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[P] LightGBM but lighter in another language?
LightBGM seems to have C API support, and C++ example in the main repo
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Use whatever is best for the problem, but still
LGBM doesn't do RF well, but it's easy to manually bag single LGBM trees.
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What's New with AWS: Amazon SageMaker built-in algorithms now provides four new Tabular Data Modeling Algorithms
LightGBM is a popular and high-performance open-source implementation of the Gradient Boosting Decision Tree (GBDT). To learn how to use this algorithm, please see example notebooks for Classification and Regression.
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Search YouTube from the terminal written in python
Microsoft lightGBM. https://github.com/microsoft/LightGBM
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LightGBM VS CXXGraph - a user suggested alternative
2 projects | 28 Feb 2022
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Writing the fastest GBDT libary in Rust
Here are our benchmarks on training time comparing Tangram's Gradient Boosted Decision Tree Library to LightGBM, XGBoost, CatBoost, and sklearn.
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Workstation Management With Nix Flakes: Build a Cmake C++ Package
{ inputs = { nixpkgs = { url = "github:nixos/nixpkgs/nixos-unstable"; }; flake-utils = { url = "github:numtide/flake-utils"; }; }; outputs = { nixpkgs, flake-utils, ... }: flake-utils.lib.eachDefaultSystem (system: let pkgs = import nixpkgs { inherit system; }; lightgbm-cli = (with pkgs; stdenv.mkDerivation { pname = "lightgbm-cli"; version = "3.3.1"; src = fetchgit { url = "https://github.com/microsoft/LightGBM"; rev = "v3.3.1"; sha256 = "pBrsey0RpxxvlwSKrOJEBQp7Hd9Yzr5w5OdUuyFpgF8="; fetchSubmodules = true; }; nativeBuildInputs = [ clang cmake ]; buildPhase = "make -j $NIX_BUILD_CORES"; installPhase = '' mkdir -p $out/bin mv $TMP/LightGBM/lightgbm $out/bin ''; } ); in rec { defaultApp = flake-utils.lib.mkApp { drv = defaultPackage; }; defaultPackage = lightgbm-cli; devShell = pkgs.mkShell { buildInputs = with pkgs; [ lightgbm-cli ]; }; } ); }
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Is it possible to clean memory after using a package that has a memory leak in my python script?
I'm working on the AutoML python package (Github repo). In my package, I'm using many different algorithms. One of the algorithms is LightGBM. The algorithm after the training doesn't release the memory, even if del is called and gc.collect() after. I created the issue on LightGBM GitHub -> link. Because of this leak, memory consumption is growing very fast during algorithm training.
pyvirtualcam
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What are some Python scripts have u made for fun and daily life?
There is this project. You could probably also use openCV.
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How to edit frames from camera input and output them into meeting app?
The parent it seems you’re missing right now is the virtual cam. This is the repo that the OP linked. There’s another package too, pyvirtualcam. Both require you to install the actual virtual camera binary to your computer where the package simply routes your edited frames to the camera.
- OBS Studio 27.1.1
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A library for python virtual microphone input
Just like we have pyfakecam do we have a similar library for real-time audio? Actually, I need to manipulate the audio input from the microphone and send it to the webrtc in form of microphone input. pyfakecam works seamlessly with opencv and I can easily manipulate live camera input and create a virtual camera for it. But same is not possible for audio input.
What are some alternatives?
tensorflow - An Open Source Machine Learning Framework for Everyone
v4l2loopback - v4l2-loopback device
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
webcamoid - Webcamoid is a full featured and multiplatform webcam suite.
GPBoost - Combining tree-boosting with Gaussian process and mixed effects models
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
yggdrasil-decision-forests - A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
virtualvideo - virtualvideo allows you to write simple programs that feed images to a v4l2loopback device
amazon-sagemaker-examples - Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠Amazon SageMaker.
obs-websocket - Remote-control of OBS Studio through WebSocket
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
obs-studio - OBS Studio - Free and open source software for live streaming and screen recording