Pipelines and Primitives for Machine Learning and Data Science.
- Free software: MIT license
- Development Status: Pre-Alpha
- Documentation: https://HDI-Project.github.io/MLBlocks
- Homepage: https://github.com/HDI-Project/MLBlocks
MLBlocks is a simple framework for composing end-to-end tunable Machine Learning Pipelines by seamlessly combining tools from any python library with a simple, common and uniform interface.
Features include:
- Build Machine Learning Pipelines combining any Machine Learning Library in Python.
- Access a repository with hundreds of primitives and pipelines ready to be used with little to no python code to write, carefully curated by Machine Learning and Domain experts.
- Extract machine-readable information about which hyperparameters can be tuned and within which ranges, allowing automated integration with Hyperparameter Optimization tools like BTB.
- Complex multi-branch pipelines and DAG configurations, with unlimited number of inputs and outputs per primitive.
- Easy save and load Pipelines using JSON Annotations.
MLBlocks has been developed and tested on Python 3.5 and 3.6
Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid interfering with other software installed in the system where MLBlocks is run.
The easiest and recommended way to install MLBlocks is using pip:
pip install mlblocksThis will pull and install the latest stable release from PyPi.
If you want to install from source or contribute to the project please read the Contributing Guide.
In order to be usable, MLBlocks requires a compatible primitives library.
The official library, required in order to follow the following MLBlocks tutorial, is MLPrimitives, which you can install with this command:
pip install mlprimitivesBelow there is a short example about how to use MLBlocks to solve the Adult Census Dataset classification problem using a pipeline which combines primitives from MLPrimitives, scikit-learn and xgboost.
from mlblocks import MLPipeline
from mlprimitives.datasets import load_dataset
dataset = load_dataset('census')
X_train, X_test, y_train, y_test = dataset.get_splits(1)
primitives = [
'mlprimitives.custom.preprocessing.ClassEncoder',
'mlprimitives.custom.feature_extraction.CategoricalEncoder',
'sklearn.impute.SimpleImputer',
'xgboost.XGBClassifier',
'mlprimitives.custom.preprocessing.ClassDecoder'
]
pipeline = MLPipeline(primitives)
pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)
dataset.score(y_test, predictions)If you want to learn more about how to tune the pipeline hyperparameters, save and load the pipelines using JSON annotations or build complex multi-branched pipelines, please check our documentation site.
Also do not forget to have a look at the notebook tutorials!
If you use MLBlocks for your research, please consider citing our related papers.
For the current design of MLBlocks and its usage within the larger Machine Learning Bazaar project at the MIT Data To AI Lab, please see:
Micah J. Smith, Carles Sala, James Max Kanter, and Kalyan Veeramachaneni. "The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development." arXiv Preprint 1905.08942. 2019.
@article{smith2019mlbazaar,
author = {Smith, Micah J. and Sala, Carles and Kanter, James Max and Veeramachaneni, Kalyan},
title = {The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development},
journal = {arXiv e-prints},
year = {2019},
eid = {arXiv:1905.08942},
pages = {arXiv:1905.08942},
archivePrefix = {arXiv},
eprint = {1905.08942},
}For the first MLBlocks version from 2015, designed for only multi table, multi entity temporal data, please refer to Bryan Collazo’s thesis:
- Machine learning blocks. Bryan Collazo. Masters thesis, MIT EECS, 2015.
With recent availability of a multitude of libraries and tools, we decided it was time to integrate them and expand the library to address other data types: images, text, graph, time series and integrate with deep learning libraries.