Reverse Dependencies of optuna

The following projects have a declared dependency on optuna:

• ablator — Model Ablation Tool-Kit
• ablator-ken-test — Model Ablation Tool-Kit
• ablator-ken-test2 — Model Ablation Tool-Kit
• ablator-ken-test3 — Model Ablation Tool-Kit
• aclose — ACLOSE- Automatic Clustering and Labeling Of Semantic Embeddings
• adapter-transformers — A friendly fork of HuggingFace's Transformers, adding Adapters to PyTorch language models
• adelecv — no summary
• advanced-data-processing — An advanced data processing pipeline for machine learning workflows
• aequitas — no summary
• aideml — Autonomous AI for Data Science and Machine Learning
• airi-test-task — This library contains the code used to run a test job to AIRI.
• allennlp-optuna — AllenNLP integration for hyperparameter optimization
• alphaml — Build a CLETE Binary Classification Model
• AMLpp — Wrapper for ml library
• Amplo — Fully automated end to end machine learning pipeline
• anai-opensource — Automated ML
• annif — Automated subject indexing and classification tool
• ANTIPASTI — Deep Learning model that predicts the binding affinity of antibodies from their three-dimensional structure.
• ape-core — Ape: your AI prompt engineer
• apropos-ai — Learning algorithms for production language model programs
• AQMLator — A package for auto quantum machine learning-izing your experiments!
• AquaAgent — An autonomous and interactive AI agent for intelligent water quality analysis and reporting.
• aspect-based-sentiment-analysis — Aspect Based Sentiment Analysis: Transformer & Interpretability (TensorFlow)
• astromodule — Astronomy Tools
• AstroSubtractor — Machine learning classifier
• atlantic — Atlantic: Automated Preprocessing Framework for Supervised Machine Learning
• atom-ml — A Python package for fast exploration of machine learning pipelines
• auf — no summary
• auto-clustering — Automatic Clustering selection with Ray Tune
• auto-ds — Auto Data Science Toolkit
• auto-prep — AutoML with enhanced preprocessing and explainability.
• auto-synthetic-data-platform — Google EMEA gTech Ads Data Science Team's solution to create privacy-safe synthetic data out of real data. The solution is a wrapper around the synthcity package (https://github.com/vanderschaarlab/synthcity) simplifying the process of model tuning.
• autocare-dlt — Autocare Tx Model
• autointent — A tool for automatically configuring a text classification pipeline for intent prediction.
• autolgb — no summary
• autolgbm — autolgbm: tuning lightgbm with optuna
• automl-alex — State-of-the art Automated Machine Learning python library for Tabular Data
• automl-infrastructure — AutoML Infrastructure.
• automl-self-improvement — Autonomous AI model optimization framework with meta-learning and hybrid tuning
• automlkiller — Auto machine learning, deep learning library in Python.
• autopeptideml — AutoML system for building trustworthy peptide bioactivity predictors
• autopilotml — A package for automating machine learning tasks
• autoprognosis — A system for automating the design of predictive modeling pipelines tailored for clinical prognosis.
• autoprototype — This is a module for Hyper-parameter tuning and rapid prototyping
• autorad — Radiomics-related modules for extraction and experimenting
• autoresevaluator — no summary
• autospectra — Automated spectroscopic modelling
• autotonne — Auto machine learning, deep learning library in Python.
• autotrain-advanced — no summary
• autotransformers — a Python package for automatic training and benchmarking of Language Models.
• autotuneml — A package for automated machine learning on tabular data
• autotuner — Automated hyper-parameter tuning for scikit-learn estimators using optuna.
• autovf — autovf: tuning xgboost with optuna
• autoxgb — autoxgb: tuning xgboost with optuna
• autoxgb-aucpr-bc — xgbauto: tuning xgboost with optuna, autoxgb with aucpr for binary classification
• autoxgbAUC — xgbauto: tuning xgboost with optuna, autoxgb with aucpr for binary classification
• baguetter — Baguetter is a flexible and efficient search engine library implemented in Python. It supports sparse (traditional), dense (semantic), and hybrid retrieval methods.
• baseline-optimal — no summary
• bbrl-algos — BBRL algos, a library of reinforcement learning algorithms
• beam-ds — Beam Datascience package
• benchq — "BenchQ platform for resource estimation"
• bigdl-chronos — Scalable time series analysis using AutoML
• bigdl-chronos-spark2 — Scalable time series analysis using AutoML
• bigdl-chronos-spark3 — Scalable time series analysis using AutoML
• bio-corgi — Classifier for ORganelle Genomes Inter alia
• biodem — Dual-extraction method for phenotypic prediction and functional gene mining
• biofit — BioFit: Bioinformatics Machine Learning Framework
• biopytorch — PyTorch implementation of Hebbian "Bio-Learning" convolutional layers
• blml1 — blml1
• blobcity — Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.
• bluecast — A lightweight and fast automl framework
• bnpm — A library of useful modules for data analysis.
• boyd-ml-utils — machine learning utilities by boy-dad
• bucky-covid — The Bucky model is a spatial SEIR model for simulating COVID-19 at the county level.
• budget-optimizer — Budget optimizer for nested MMMs
• bvpTune — Library for fine tuning the numerical settings of boundary value problem solvers
• cabrnet — Generic library for prototype-based classifiers
• calisim — A toolbox for the calibration and evaluation of simulation models.
• Caml — Extensions & abstractions of advanced econometric techniques leveraging machine learning.
• canswim — "Developer toolkit for CANSLIM investment style practitioners"
• catalyst — Catalyst. Accelerated deep learning R&D with PyTorch.
• catalyst-pdm — Catalyst fork compatible with PDM
• catasta — _Catasta_ is a Python library designed to simplify the process of Machine Learning model experimentation. Optimization, training, evaluation and inference all in one place!
• catboost-extensions — Extensions for catboost models
• cehrgpt — CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines
• cellmaps-vnn — The Cell Maps VNN Tool enables creation, training, and usage of an interpretable neural network-based models that predict cell response to a drug.
• CELLULAR-CL — A package for generating an embedding space from scRNA-Seq. This space can be used for cell type annotation, novel cell type detection, cell type representations, and visualization.
• chem-mrl — SMILES-based Matryoshka Representation Learning Embedding Model
• chemprobe — A package for chemprobe
• chemprop — Molecular Property Prediction with Message Passing Neural Networks
• cherrypick — Some tools to help the process of feature selection
• clarinpl-embeddings — no summary
• class-resolver — Lookup and instantiate classes with style.
• classifier-toolkit — no summary
• classWeightLearn — Optuna Class Weight Cost-Sensitive Learning
• cleanrl — High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features
• clep — A Hybrid Data and Knowledge Driven Framework for Generating Patient Representations
• climaticai — climaticai is a library that builds, optimizes, and evaluates machine learning pipelines
• cmonge — Extension of the Monge Gap to learn conditional optimal transport maps
• cody-adapter-transformers — A friendly fork of HuggingFace's Transformers, adding Adapters to PyTorch language models