v0.3.0

• New primitives discovery system based on entry_points.
• Conditional Hyperparameters filtering in MLBlock initialization.
• Improved logging and exception reporting.

New Datasets and Unit Tests

• Add a new multi-table dataset.
• Add Unit Tests up to 50% coverage.
• Improve documentation.
• Fix minor bug in newsgroups dataset.

v0.2.3 - Demo Datasets

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

v0.2.2 - 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.

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.

Bugfix release

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

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.

Nested hyperparams dicts

• Support passing hyperparams as nested dicts.

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.