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Changelog

0.6.1 - 2023-09-26

  • Add python 3.11 to MLBlocks - Issue #143 by @sarahmish

0.6.0 - 2023-04-14

  • Support python 3.9 and 3.10 - Issue #141 by @sarahmish

0.5.0 - 2023-01-22

  • Update numpy dependency and isolate tests - Issue #139 by @sarahmish

0.4.1 - 2021-10-08

  • Update NumPy dependency - Issue #136 by @sarahmish
  • Support dynamic inputs and outputs - Issue #134 by @pvk-developer

0.4.0 - 2021-01-09

  • Stop pipeline fitting after the last block - Issue #131 by @sarahmish
  • Add memory debug and profiling - Issue #130 by @pvk-developer
  • Update Python support - Issue #129 by @csala
  • Get execution time for each block - Issue #127 by @sarahmish
  • Allow loading a primitive or pipeline directly from the JSON path - Issue #114 by @csala
  • Pipeline Diagrams - Issue #113 by @erica-chiu
  • Get Pipeline Inputs - Issue #112 by @erica-chiu

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.

0.1.0

  • First release on PyPI.