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(C)ase studies for (A)uto(ML). Performs data cleaning and a variety of feature transformations for automated machine learning case studies.

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CAML

Case study for AutoML (CAML). Performs data cleaning and a variety of feature transformations for machine learning case studies. Uses the AutoML software TPOT, based in Scikit-Learn, for machine learning. Includes specialized options for spectral input data.

Installation

Once the source files are downloaded, the following commands can be used to download the required packages.

$ cd CAML
$ conda install numpy scipy scikit-learn pandas joblib pytorch
$ conda install -c anaconda pyyaml 
$ pip install tpot

Running Optimus

Once installed, from the home directory, the code can be run with:

$ caml.py --help

Example

First unzip the csv file and ensure the path in the simple_input.yaml file matches where you want to store it. An example can be run by using simple-input.yaml as the request file

$ caml.py simple-input.yaml

Results will be output to a folder marked request with a datetime ID number.

License

CAML is released under the MIT license; see LICENSE for details.

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(C)ase studies for (A)uto(ML). Performs data cleaning and a variety of feature transformations for automated machine learning case studies.

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