Package: optimalflow
OptimalFlowAuthor:Tony DongOptimalFlowis an Omni-ensemble Automated Machine Learning toolkit, which is based onPipeline Cluster Traversal Experimentapproach, to help data scientists building optimal models in easy way, and automate Supervised Learning workflow with simple codes.In the latest version(0.1.10), it added a "no-code" Web App, based on flask framework, as OptimalFlow's GUI. Users could build Automated Machine Learning workflow all by clicks, without any coding at all! (Read more detailshttps://optimal-flow.readthedocs.io/en/latest/webapp.html)Comparing other popular "AutoML or Automated Machine Learning" APIs,OptimalFlowis designed as an omni-ensembled ML workflow optimizer with higher-level API targeting to avoid manual repetitive train-along-evaluate experiments in general pipeline building.To achieve that,OptimalFlowappliesPipeline Cluster Traversal Experimentsalgorithm to assemble all cross-matching pipelines covering major tasks of Machine Learning workflow, and apply traversal-experiment to search the optimal baseline model.Besides, by modularizing all key pipeline components in reuseable packages, it allows all components to be custom tunable along with high scalability.The core concept inOptimalFlowisPipeline Cluster Traversal Experiments, which is a theory, first raised by Tony Dong during Genpact 2020 GVector Conference, to optimize and automate Machine Learning Workflow using ensemble pipelines algorithm.Comparing other automated or classic machine learning workflow's repetitive experiments using single pipeline,Pipeline Cluster Traversal Experimentsis more powerful, with larger coverage scope, to find the best model without manual intervention, and also more flexible with elasticity to cope with unseen data due to its ensemble designs in each component.In summary,OptimalFlowshares a few useful properties for data scientists:Easy & less coding- High-level APIs to implementPipeline Cluster Traversal Experiments, and each ML component is highly automated and modularized;Well ensembled- Each key component is ensemble of popular algorithms w/ optimal hyperparameters tuning included;Omni-Coverage- UsingPipeline Cluster Traversal Experiments, to cross-experiment with combined permutated input datasets, feature selection, and model selection;Scalable- Each module could add new algorithms easily due to its ensemble and reuseable coding design;Adaptable-Pipeline Cluster Traversal Experimentsmakes it easier to adapt unseen datasets with the right pipeline;Custom Modify Welcomed- Support custom settings to add/remove algorithms or modify hyperparameters for elastic requirements.Documentation:https://Optimal-Flow.readthedocs.io/InstallationpipinstallOptimalFlowCore Modules:autoPP for feature preprocessingautoFS for classification/regression features selectionautoCV for classification/regression model selection and evaluationautoPipe forPipeline Cluster Traversal ExperimentsautoViz for pipeline cluster visualization. Current available: Model retrieval diagramautoFlow for logging & tracking.Notebook Demo:An End-to-End OptimalFlow Automated Machine Learning Tutorial with Real ProjectsPart 1:https://towardsdatascience.com/end-to-end-optimalflow-automated-machine-learning-tutorial-with-real-projects-formula-e-laps-8b57073a7b50Part 2:https://towardsdatascience.com/end-to-end-optimalflow-automated-machine-learning-tutorial-with-real-projects-formula-e-laps-31d810539102Other Stories:Ensemble Feature Selection in Machine Learning using OptimalFlow - Easy Way with Simple Code to Select top Features:https://towardsdatascience.com/ensemble-feature-selection-in-machine-learning-by-optimalflow-49f6ee0d52ebEnsemble Model Selection & Evaluation in Machine Learning using OptimalFlow - Easy Way with Simple Code to Select the Optimal Model:https://towardsdatascience.com/ensemble-model-selection-evaluation-in-machine-learning-by-optimalflow-9e5126308f12Build No-code Automated Machine Learning Model with OptimalFlow Web App:https://towardsdatascience.com/build-no-code-automated-machine-learning-model-with-optimalflow-web-app-8acaad8262b1Support OptimalFlowIf you like OptimalFlow, please consider starring or forking it on GitHub and spreading the word!Please, Avoid Selling this Work as YoursVoice from the Author: I am glad if you findOptimalFlowuseful and helpful. Feel free to add it to your project and let more people know how good it is. But please avoid simply changing the name and selling it as your work. That's not why I'm sharing the source code, at all. All copyrights reserved by Tony Dong following MIT license.License:MITUpdates History:Updates on 9/29/2020Added SearchinSpace settings page in Web App. Users could custom set estimators/regressors' parameters for optimal tuning outputs.Modified some layouts of existing pages in Web App.Updates on 9/16/2020Created a Web App based on flask framework as OptimalFlow's GUI, to build PCTE Automated Machine Learning by simply clicks without any coding at all!Web App included PCTE workflow bulder, LogsViewer, Visualization, Documentation sections.Fix the filename issues in autoViz module, and remove auto_open function when generating new html format plots.Updates on 8/31/2020Modify autoPP's default_parameters: Remove "None" in  "scaler", modify "sparsity" : [0.50], modify "cols" : [100]Modify autoViz clf_table_report()'s coloring settingsFix bugs in autoViz reg_table_report()'s gradient coloring functionUpdates on 8/28/2020Remove evaluate_model() function's round() bugs in coping with classification problemMove out SVM based algorithm from fastClassifier & fastRegressor's default estimators settingsMove out SVM based algorithm from autoFS class's default selectors settingsUpdates on 8/26/2020Fix evaluate_model() function's bugs in coping with regression problemAdd reg_table_report() function to create dynamic table report for regression problem in autoVizUpdates on 8/24/2020Fix evaluate_model() function's precision_score issue when running modelmulti-class classification problemsAdd custom_selectors args for customized algorithm settings with autoFS's 2 classes(dynaFS_reg, dynaFS_clf)Updates on 8/20/2020Add Dynamic Table for Pipeline Cluster Model Evaluation Report in autoViz moduleAdd custom_estimators args for customized algorithm settings with autoCV's 4 classes(dynaClassifier,dynaRegressor,fastClassifier, and fastRegressor)Updates on 8/14/2020AddfastClassifier, andfastRegressorclass which are both random parameter search basedModify the display settings when using dynaClassifier in non in_pipeline modeUpdates on 8/10/2020Stable 0.1.0 version release on PypiUpdates on 8/7/2020Add estimators: HuberRegressor, RidgeCV, LassoCV, SGDRegressor, and HistGradientBoostingRegressorModify parameters.json, and reset_parameters.json for the added estimatorsAdd autoViz for classification problem model retrieval diagram