Reverse Dependencies of optuna
The following projects have a declared dependency on optuna:
- pysiml — SiML - a Simulation ML library
- pytelligence — pycaret clone aimed for simplicity and production ready code
- python-EasyGraph — Easy Graph
- python-vivid — Support Tools for Machine Learning VIVIDLY
- pytorch-forecasting — Forecasting timeseries with PyTorch - dataloaders, normalizers, metrics and models
- pytorch-forecasting-unofficial-hotfix — Forecasting timeseries with PyTorch - dataloaders, normalizers, metrics and models
- pytorch-frame — Tabular Deep Learning Library for PyTorch
- pytorch-tao — A toolbox for a specific Machine Learning training project
- qsarKit — A Python package that offers robust predictive modeling using QSAR for evaluating the transfer of environmental contaminants in breast milk. It integrates multiple predictive models, provides synthetic data generation via GANs, and is tailored for researchers and health professionals.
- qsprpred — A cheminformatics package for training and testing QSAR/QSPR models
- quantbullet — Toolkit for swift quant analysis
- QuickClus — UMAP + HDBSCAN for numeric and/or categorical variables
- raccoon-cluster — Scale-adaptive clustering in Python
- rapidflow — rapidFlow - A framework to perform micro experimentation fast with easy scaling.
- rapidgbm — RapidGBM is a powerful Python package designed to streamline the process of tuning LightGBM models using the optimization framework Optuna.
- raptor-functions — no summary
- rdsmproj — Set of tools for use in research of rare disease related text.
- redshells — Tasks which are defined using gokart.TaskOnKart. The tasks can be used with data pipeline library "luigi".
- renge — Infer gene regulatory networks from time-series single-cell CRISPR data.
- replay-rec — RecSys Library
- resampy — Efficient signal resampling
- retriv — retriv: A Python Search Engine for Humans.
- rl-algo-impls — Implementations of reinforcement learning algorithms
- rl-zoo3 — A Training Framework for Stable Baselines3 Reinforcement Learning Agents
- rlberry — An easy-to-use reinforcement learning library for research and education
- roicat — A library for classifying and tracking ROIs.
- route-distances — Models for calculating distances between synthesis routes
- rrt-ml — Rapidly exploring random trees with machine learning
- runningz — fe_master_runningz
- sapientml-core — A SapientML plugin of SapientMLGenerator
- scikit-tune — A friendly way to tune scikit-learn pipelines.
- scioptim — collection and wrapper for different optimizer
- scorepyo — This is the scorepyo repository.
- Seance — A Wrapper around MLForecast.
- sec-certs — A tool for data scraping and analysis of security certificates from Common Criteria and FIPS 140-2/3 frameworks
- sequence-model-train — time series model for training sequence dataset
- serval-ml-commons — SerVal Machine learning commons is a tools box that ease the development of ML experiments at SerVal.
- setfit — Efficient few-shot learning with Sentence Transformers
- siatune — no summary
- simulai-toolkit — A Python package with data-driven pipelines for physics-informed machine learning
- siste-test — HyperFetch. A tool to optimize and fetch hyperparameters for your reinforcement learning application.
- skforecast — Forecasting time series with scikit-learn regressors. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM, XGBoost, Ranger...).
- slg-nimrod — minimal deep learning framework
- SLP — Speech, Language and Multimodal Processing models and utilities in PyTorch
- snapper-ml — A framework for reproducible machine learning
- social-net-img-classifier — no summary
- sparsify — Easy-to-use UI for automatically sparsifying neural networks and creating sparsification recipes for better inference performance and a smaller footprint
- sparsify-nightly — Easy-to-use UI for automatically sparsifying neural networks and creating sparsification recipes for better inference performance and a smaller footprint
- spock-config — Spock is a framework designed to help manage complex parameter configurations for Python applications
- sportslabkit — A Python package for sports analytics.
- sprt-tandem — SPRT-TANDEM for sequential density ratio estimation to simultaneously optimize both speed and accuracy of early-classification.
- SRRec — Short Reads Rectification
- stockdatamanager — A comprehensive library for financial analysis
- stonkgs — Sophisticated Transformers for Biomedical Text and Knowledge Graph Data
- swarmist — A DSL for building metaheuristics
- synthcity — Synthetic data generator and evaluator!
- synthdata — Generate synthetic data to fill, balance and expand datasets
- tabular-augmentation — Implementing easy-to-use methods for classical and novel tabular data augmentation and synthesis.
- tabular-ml — This library wraps popular tabular regression/classification model enabling rapid evaluation and optimization.
- tabular-ml-toolkit — A helper library to jumpstart your machine learning project based on tabular or structured data.
- tango-mlflow — MLflow integration for ai2-tango
- teddynote — datasets and tutorial package made and maintained by TeddyNote
- temporai — TemporAI: ML-centric Toolkit for Medical Time Series
- testgailbot002 — GailBot API
- testgailbotapi — GailBot Test API
- testgailbotapi001 — GailBot Test API
- TezzAutoML — Just another AutoML library, but better and faster.
- tftk — Machine Learning Toolkit using TensorFlow
- tfts — Deep learning time series with TensorFlow
- tfyolo — Series yolo detection in TensorFlow
- thegolem — Framework for Graph Optimization and Learning by Evolutionary Methods
- TIdeS-ML — Tool for ORF-calling and ORF-classification using ML approaches
- time-interpret — Model interpretability library for PyTorch with a focus on time series.
- TimeMurmur — Time series forecasting at scale with LightGBM
- TiRank — A comprehensive analysis tool for transfering phenotype of bulk transcritomic data to single cell or spatial transcriptomic data.
- tknlp — no summary
- tmnt — Topic modeling neural toolkit
- tmu — Your project description
- torchal — A codebase for active learning built on top of pycls.
- torchapp — A wrapper for fastai projects to create easy command-line inferfaces and manage hyper-parameter tuning.
- torchtemplates — A package to create pytorch projects quickly
- total-points-model — Train an AFL Total Points model
- tpcp — Pipeline and Dataset helpers for complex algorithm evaluation.
- tpk — TempportalPredictionsKit: toolset for timeseries data predictions
- TPOT2 — Tree-based Pipeline Optimization Tool
- tradeforce — Tradeforce is a comprehensive Python trading framework designed for high-performance backtesting, hyperparameter optimization, and live trading.
- trainme — tune with optuna and model
- transformers — State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow
- transformers-rlfh — RLFH with transformers
- transfusion — Transformers 🤝 diffusion
- truesight — Truesight is a python package for time series prediction using deep learning and statistical models.
- tsme — A package that provides estimation methods for differential equations of dynamical systems based on timeseries data.
- tune — An abstraction layer for hyper parameter tuning
- tune-easy — tune-easy: A hyperparameter tuning tool, extremely easy to use.
- tuneta — Optimize financial technical indicators for machine learning
- tuneup — Global optimizer comparison and combination
- tuning — 调参工具
- twinbooster — Python package for TwinBooster: Synergising Chemical Structures and Bioassay Descriptions for Enhanced Molecular Property Prediction in Drug Discovery
- utils-for-ds — common used functions for Data Scientist
- vimms — A framework to develop, test and optimise fragmentation strategies in LC-MS metabolomics.