0.3.4 - 2019-11-01

  • Ability to return intermediate context - Issue #110 by @csala

  • Support for static or class methods - Issue #107 by @csala

0.3.3 - 2019-09-09

  • Improved intermediate outputs management - Issue #105 by @csala

0.3.2 - 2019-08-12

  • Allow passing fit and produce arguments as init_params - Issue #96 by @csala

  • Support optional fit and produce args and arg defaults - Issue #95 by @csala

  • Isolate primitives from their hyperparameters dictionary - Issue #94 by @csala

  • Add functions to explore the available primitives and pipelines - Issue #90 by @csala

  • Add primitive caching - Issue #22 by @csala

0.3.1 - Pipelines Discovery

  • Support flat hyperparameter dictionaries - Issue #92 by @csala

  • Load pipelines by name and register them as entry_points - Issue #88 by @csala

  • Implement partial re-fit -Issue #61 by @csala

  • Move argument parsing to MLBlock - Issue #86 by @csala

  • Allow getting intermediate outputs - Issue #58 by @csala

0.3.0 - New Primitives Discovery

  • New primitives discovery system based on entry_points.

  • Conditional Hyperparameters filtering in MLBlock initialization.

  • Improved logging and exception reporting.

0.2.4 - 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.

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.


  • First release on PyPI.