CXXGraph
LightGBM
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CXXGraph | LightGBM | |
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84 | 11 | |
381 | 15,962 | |
- | 0.9% | |
8.5 | 9.2 | |
9 days ago | 3 days ago | |
C++ | C++ | |
GNU Affero General Public License v3.0 | MIT License |
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.
CXXGraph
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Hacktoberfest is ON CXXGraph
Actual Web Site
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Revolutionizing Data Processing with CXXGraph: A Comprehensive Guide to Graph Data Structures in C++
CXXGraph is a C++ library for graph data structures that provides an easy-to-use interface for creating and processing direct and nondirect graphs. CXXGraph provides a simplified interface for creating and manipulating graphs. Using CXXGraph, developers can create graphs, add and remove edges, and perform various graph processing algorithms.
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CXXGraph Library : Header-Only C++ Library for Graph Representation and Algorithms
If you have 5 minutes to get in touch, click on the project or write to me at [email protected]
- 2 Weeks of Hacktoberfest, How is it going??
- Hacktoberfest is started, give your contribution!
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Hacktobefest 2022: My Repo
View on GitHub
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GraphScope VS CXXGraph - a user suggested alternative
2 projects | 17 Mar 2022
A Fast Graph library in C++
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libgrape-lite VS CXXGraph - a user suggested alternative
2 projects | 17 Mar 2022
A good library for graph algorithms
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euler VS CXXGraph - a user suggested alternative
2 projects | 17 Mar 2022
Good alternative for algorithms
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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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.
What are some alternatives?
tensorflow - An Open Source Machine Learning Framework for Everyone
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.
GPBoost - Combining tree-boosting with Gaussian process and mixed effects models
amazon-sagemaker-examples - Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
yggdrasil-decision-forests - A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
decision-tree-classifier - Decision Tree Classifier and Boosted Random Forest
pyvirtualcam - 🎥 Send frames to a virtual camera from Python
sirix - SirixDB is an an embeddable, bitemporal, append-only database system and event store, storing immutable lightweight snapshots. It keeps the full history of each resource. Every commit stores a space-efficient snapshot through structural sharing. It is log-structured and never overwrites data. SirixDB uses a novel page-level versioning approach.
rstan - RStan, the R interface to Stan