0.3.4 - 2019-11-01¶
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¶
0.3.0 - New Primitives Discovery¶
New primitives discovery system based on
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.
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
yarrays and be compatible with
Add XGBClassifier and XGBRegressor primitives.
keras.applicationspretrained 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.
0.1.5 - Collaborative Filtering Pipelines¶
Add LightFM primitive.
0.1.4 - Image pipelines improved¶
Fix bug with MLHyperparam types and Keras.
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
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.