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Change Log

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

Unreleased

Added

  • Percentage of nulls reported via console.
  • new top-level load_csv() function makes it easier for users by avoiding any pandas knowledge.
  • SupervisedModelTrainer now warns users about columns/features with high and low cardinality.
  • 9 new sample healthcare data sets from the UCI Machine Learning Repository.

Changed

  • Catalyst db validation test now makes it's own safely named database and table, runs the test then cleans it up. This eliminated the need for .mdf fixtures on appveyor.
  • validate_catalyst_prediction_sam_connection made much more robust with tests.
  • Conda environment files cleaned up substantially, speeding up builds.
  • Release preparation notes moved out of README and into a separate doc.
  • Decorators for supervised_model_trainer is now simplified, and debug option is no longer an option.

Fixed

  • Feature importance plots now have a configurable limit to the amount of features they show with a default of 15.
  • Dataframe column filter handles None gracefully. For example, if no grain column is specified.
  • Getting started section of README vastly improved.

Deprecated

Removed

[1.0] - 2017-08-15

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

Removed

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

[0.1.8] - 2017-02-16

Added

  • Added changelog
  • Changed all docs to markdown
  • added setup.cfg and some functions in setup.py for PyPI
  • mkdocs.yml for readthedocs hosting
  • Conda environment files

Changed

  • Lots of documentation, especially around installation on the various platforms.
  • Removed doc templates and css now that docs will live on readthedocs

Fixed

  • example jupyter notebook

0.1.7 - 2016-11-01

Added

This release encompasses basic healthcare ML functionality:

  • Model comparison between random forest and logistic regression algorithms
  • Model deployment to SQL Server, providing top-three most important features
  • Imputation (column mean for numeric and column mode for categorical)
  • Hyperparameter tuning, using mtry and number of trees for random forest
  • Plots
    • ROC
    • Random forest feature ranking
  • Model performance evaluated via AU_ROC and AU_PR
  • First release after setting up the following infrastructure:
    • AppveyorCI
    • Sphinx
    • Nose unit testing
    • Docker