mlaut is a modelling and workflow toolbox in python, written with the aim of simplifying large scale benchmarking of machine learning strategies, e.g., validation, evaluation and comparison with respect to predictive/task-specific performance or runtime. Key features are:
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Automation of the most common workflows for benchmarking modelling strategies on multiple datasets including statistical post-hoc analyses, with user-friendly default settings.
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Unified interface with support for scikit-learn strategies, keras deep neural network architectures, including easy user extensibility to (partially or completely) custom strategies.
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Higher-level meta-data interface for strategies, allowing easy specification of scikit-learn pipelines and keras deep network architectures, with user-friendly (sensible) default configurations.
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Easy setting up and loading of data set collections for local use (e.g., data frames from local memory, UCI repository, openML, Delgado study, PMLB).
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Back-end agnostic, automated local file system management of datasets, fitted models, predictions, and results, with the ability to easily resume crashed benchmark experiments with long running times.
<<<<<< Documentation available on alan-turing-institute.github.io/mlaut >>>>>>
An example with the basic usage of mlaut can be found in the following Jupyter Notebook
Please check the examples directory for more advanced use cases.
Requires Python 3.6 or greater.
pip install mlaut