Easy and powerful template for ML projects

Prerequisites

pip install -r requirements.txt

Getting Started

The following command creates a structure specified in template.json for your ML projects

python build.py -dir project_example/ -template_file  template.json

An example of structure that can be defined in the template.json

{
  "num_algorithm": 2,
  "num_training_testing_validation": 2,
  "directory_training_suffix": "training",
  "datasets": [
    "dataset_1"
  ],
  "main_directories": [
    "config_pipelines",
    "data_experiments",
    "@framework",
    "@jupyter",
    "@luigi_pipeline"
  ],
  "sub_directories": [
    {
      "father": "config_pipelines",
      "dirs": [
        "directory_algoritm_suffix*num_algorithm",
        "data_generation",
        "data_processing",
        "metrics"
      ]
    }
  ]
}

• @ in "@framework" is used to specify if the folder is a python module
• num_algorithm is used to specify how many algorithms you would like to test
• "directory_algoritm_suffix*num_algorithm" is used to generate multiple folders where the * suffix(directory_algoritm_suffix) and the number (num_algorithm) are specified in the template too.

Luigi Pipeline for experiments

In the folder pipeline_example, there is an dummy example of how to use Luigi pipeline for evaluating a KMeans algorithm.

More info on the amazing framework Luigi ( or Gigino from friends in Naples) can be found here: https://github.com/spotify/luigi

The main idea is to define the experiments using excel as follows:

experiment	diminstance	clusters	n_features	random_state	file_dataframe	file_label_true	k	file_label_predicted	file_metrics
1	100@data_generation	10@data_generation	5@data_generation	0@data_generation	data_experiments/data_generation/file_dataframe.csv@file	data_experiments/data_generation/file_label_true.csv@file	10@kmeansalgo0	data_experiments/algorithm0/file_label_predicted_algorithm0_1.csv@file	data_experiments/metrics/metrics_algorithm_1.csv@file
2	100@data_generation	10@data_generation	5@data_generation	0@data_generation	data_experiments/data_generation/file_dataframe.csv@file	data_experiments/data_generation/file_label_true.csv@file	20@kmeansalgo0	data_experiments/algorithm0/file_label_predicted_algorithm0_2.csv@file	data_experiments/metrics/metrics_algorithm_2.csv@file
3	100@data_generation	10@data_generation	5@data_generation	0@data_generation	data_experiments/data_generation/file_dataframe.csv@file	data_experiments/data_generation/file_label_true.csv@file	30@kmeansalgo0	data_experiments/algorithm0/file_label_predicted_algorithm0_3.csv@file	data_experiments/metrics/metrics_algorithm_3.csv@file

Each row is an experiment Each column is an attribute of the configuration file @ is used to defined the key of the dictorany in the configuration file. For example :

experiment	k
1	10@kmeansalgo0

becomes in a configuration file :

[kmeansalgo0]
k = 30

The extraction of the configuration file from the excel file is done using the python script update_config_files.py

The bash file exp_cluster.sh is used to run the pipeline:

This is used to create the configuration file using the data defined in the experiment 1

python scripts/update_config_files.py -excel_file experimental_settings/experiments_metafile.xlsx -sheet exp_cluster -experiment 1 -conf_file config_pipelines/data_generation/evaluation_pipeline.conf

Then the pipeline is lunched using the configuration file created above:

luigi --module luigi_pipeline.evaluation_pipeline   GenerateData  --conf config_pipelines/data_generation/evaluation_pipeline.conf  --local-scheduler --no-lock

Authors

Antonio Penta