Skip to content

Latest commit

 

History

History
109 lines (79 loc) · 3.45 KB

HISTORY.md

File metadata and controls

109 lines (79 loc) · 3.45 KB
Raw

Changelog

0.2.3 - Demo Datasets

  • Add new methods to Dataset class.
  • Add documentation for the datasets module.

0.2.2 - MLPipeline Load/Save

  • Implement save and load methods for MLPipelines
  • Add more datasets

0.2.1 - New Documentation

  • Add mlblocks.datasets module with demo data download functions.
  • Extensive documentation, including multiple pipeline examples.

0.2.0 - New MLBlocks API

A new MLBlocks API and Primitive format.

This is a summary of the changes:

  • Primitives JSONs and Python code has been moved to a different repository, called MLPrimitives
  • Optional usage of multiple JSON primitive folders.
  • JSON format has been changed to allow more flexibility and features:
    • input and output arguments, as well as argument types, can be specified for each method
    • both classes and function as primitives are supported
    • multitype and conditional hyperparameters fully supported
    • data modalities and primitive classifiers introduced
    • metadata such as documentation, description and author fields added
  • Parsers are removed, and now the MLBlock class is responsible for loading and reading the JSON primitive.
  • Multiple blocks of the same primitive are supported within the same pipeline.
  • Arbitrary inputs and outputs for both pipelines and blocks are allowed.
  • Shared variables during pipeline execution, usable by multiple blocks.

0.1.9 - Bugfix Release

  • Disable some NetworkX functions for incompatibilities with some types of graphs.

0.1.8 - New primitives and some improvements

  • Improve the NetworkX primitives.
  • Add String Vectorization and Datetime Featurization primitives.
  • Refactor some Keras primitives to work with single dimension y arrays and be compatible with pickle.
  • Add XGBClassifier and XGBRegressor primitives.
  • Add some keras.applications pretrained networks as preprocessing primitives.
  • Add helper class to allow function primitives.

0.1.7 - Nested hyperparams dicts

  • Support passing hyperparams as nested dicts.

0.1.6 - Text and Graph Pipelines

  • Add LSTM classifier and regressor primitives.
  • Add OneHotEncoder and MultiLabelEncoder primitives.
  • Add several NetworkX graph featurization primitives.
  • Add community.best_partition primitive.

0.1.5 - Collaborative Filtering Pipelines

  • Add LightFM primitive.

0.1.4 - Image pipelines improved

  • Allow passing init_params on MLPipeline creation.
  • Fix bug with MLHyperparam types and Keras.
  • Rename produce_params as predict_params.
  • Add SingleCNN Classifier and Regressor primitives.
  • Simplify and improve Trivial Predictor

0.1.3 - Multi Table pipelines improved

  • Improve RandomForest primitive ranges
  • Improve DFS primitive
  • Add Tree Based Feature Selection primitives
  • Fix bugs in TrivialPredictor
  • Improved documentation

0.1.2 - Bugfix release

  • Fix bug in TrivialMedianPredictor
  • Fix bug in OneHotLabelEncoder

0.1.1 - Single Table pipelines improved

  • New project structure and primitives for integration into MIT-TA2.
  • MIT-TA2 default pipelines and single table pipelines fully working.

0.1.0

  • First release on PyPI.