healthcareai-py v1.0

https://github.com/HealthCatalyst/healthcareai-py/releases/tag/v1.0

We are stoked to release this new version. It is a near complete refactor, with a much simpler API, better quality code, 10x more test coverage, more database support, and many other great things. Please see the new example files and docs for details, as the API has entirely changed.
 

Note this is a major release and most of the API has changed. Please consult the documentation for full details

Added

• SupervisedModelTrainer class: the main simple API
• AdvancedSupervisedModelTrainer class: the main advanced user API
• TrainedSupervisedModel class: the object returned after training that is easy to .save(). It has many convenience functions to get metrics, graphs, and contains the trained model, a feature model and the data preparation pipeline which makes deployment simple.
• SupervisedModelTrainer.ensemble()
• Lasso, KNN
• AzureBlogStorageHelper class to make storing text & pickled objects simple.
• table_archiver() utility function makes it easy to create a timestamped history from any table.
• Five new combinations of prediction dataframes to make deployment easy and flexible.
• Database connection helpers for MSSQL, MySQL, SQLite
• file I/O utilities for pickling objects or JSON
• many new dataframe filters and transformers that are used in the SupervisedModelTrainer or can be used individually.
• feature scaling transformer
• Architecture diagram and document
• Examples split into training and prediction
• Randomized hyperparameter search by default on SupervisedModelTrainer

Changed

• load_diabetes(): a built in sample dataset rather than manually digging around for the .csv file.
• Better PR/ROC plots w/ ideal cutoffs marked
• Better metrics
• Google style docstrings on most functions
• Much more helpful error messages
• Lots of code simplification and decoupling
• 126 new tests (up from 11)

Fixed

• Many, many things (https://github.com/HealthCatalyst/healthcareai-py/issues/163)

Removed

• DevelopTrainedSupervisedModel entire class
• DeploySupervisedModel entire class
• model_eval.clfreport()
• model_eval.findtopthreefactors()
• filters.remove_datetime_columns()