Library for the generation of regression models.

The main script of the library is run.py:

usage: run.py [-h] -c CONFIGURATION_FILE [-d] [-s SEED] [-o OUTPUT] [-j J]
              [-g] [-t] [-l]

Perform exploration of regression techniques

optional arguments:
  -h, --help            show this help message and exit
  -c CONFIGURATION_FILE, --configuration-file CONFIGURATION_FILE
                        The configuration file for the infrastructure
  -d, --debug           Enable debug messages
  -s SEED, --seed SEED  The seed
  -o OUTPUT, --output OUTPUT
                        The output where all the models will be stored
  -j J                  The number of processes to be used
  -g, --generate-plots  Generate plots
  -t, --self-check      Predict the input data with the generate regressor
  -l, --details         Print results of the single experiments

Example of configuration files can be found under example_configurations directory. See also the README.md file there.

To run your first example job with this library, please issue the following command in your terminal:

python3 run.py -c example_configurations/simplest_example_1.ini -o output_example

This will extract the experiment configuration from the simplest_example_1.ini file and write any output file into the output_example folder. If the -o argument is missing, the default name output will be used for the output folder. Please note that if the output folder already exists, it will not be overwritten, and the execution will stop right away.

Results will be summarized in the results.txt file, as well as printed to screen during the execution of the experiment.

This library also has a predicting module, in which you can use an output regressor in the form of a Pickle file to make predictions about new, previously-unseen data. It is run via the predict.py file. First of all, run the library to create a regression model similarly to what was indicated in the first part of the tutorial section:

python3 run.py -c example_configurations/faas_test.ini -o output_test

Then, you can apply the obtained regressor in the form of the LRRidge.pickle file by running:

python3 predict.py -c example_configurations/faas_predict.ini -r output_test/LRRidge.pickle -o output_test_predict

For more information, please refer to the predict.py file itself and to the README.md for configuration files.

This section shows how to create and use the Docker container image for this library. It is not strictly needed, but it ensures an environment in which dependencies have the correct version, and in which it is guaranteed that the library works correctly. This Docker image can be built from the Dockerfile at the root folder of this repository by issuing the command line instruction

sudo docker build -t amllibrary .

To run a container and mount a volume which includes the root folder of this repository, please use

sudo docker run --name aml --rm -v $(pwd):/aMLlibrary -it amllibrary

which defaults to a bash terminal unless a specific command is appended to the line. In this terminal, you may run the same commands as in a regular terminal, including the ones from the Tutorial section.

This library is integrated with the Hyperopt package for hyperparameter tuning via Bayesian Optimization. For more information, please refer to the README.md for configuration files.