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An ML Pipeline to be executed by 'mlpiper' tool

Project description

README

This is an example of a machine-learning pipeline that is implemented by the 'mlpiper' platform.

This specific example is no more than a simple sklearn implementation, without using the built-in sklearn Pipeline structure.

The pipeline description is provided by the pipeline/pipeline.json file.

Setup

  1. Download the tarball source code and extract it

  2. Open a terminal and change directory to the extracted folder

  3. Create virtual env, whose name is 'mlpiper', or anything else (If you've already created it, use workon to activate it):

    • > mkvirtualenv --python=3 mlpiper
  4. Install the requirements (Skip if already installed):

    • > pip install -r requirements.txt

Run Example

(Note: Please make sure the environment was setup properly. Refer to 'Setup' section above for more information)

  1. Browse to the extracted folder

  2. Run the run_pipeline.sh script:

    • > run_pipeline.sh

Results

...
Log Loss: 0.6571923425816221
ROC AUC Score: 0.6350086940964011
...
real	0m2.199s
user	0m2.528s
sys		0m0.295s

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Files for mlpiper, version 1.0.1
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