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A versatile machine learning ensemble platform designed to streamline the process of running experiments with various classifiers, preprocessing methods, feature selection techniques, and model selection strategies.

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ML-Tools

Prerequisites

  • Python 3.x

Installation

Clone the repository and install the dependencies,

git clone https://github.com/6ixGODD/ML-EnsembleHub.git
cd ML-EnsembleHub
pip install -r requirements.txt

Usage

Execute the script with command line options,

python main.py  --data <path_to_data> 
                --cfg <path_to_config> 
                --save-dir <path_to_save_dir> 
                --name <name_of_experiment> 
                --save 
                --plot

in english:

  • --data - specify the path to the data
  • --cfg - specify the path to the config
  • --save-dir - set the directory to save the results
  • --name - define the name of the experiment
  • --save - enable result saving (metrics, models)
  • --plot - enable plot

Configuration

YAML configure the experiment:

shuffle: <bool>
random_state: <int>

preprocessing:
    method: <method_name>/null
    <method_name>:
        <param_name>: <param_value>

classifiers:
    method: 
      - <method1_name>
      - <method2_name>
      - ...
    <method1_name>:
        <param_name>: <param_value>
    <method2_name>:
        <param_name>: <param_value>
    ...

feature_selection:
    method: <method_name>/null
    <method_name>:
        <param_name>: <param_value>

model_selection:
    method: <method_name>
    <method_name>:
        <param_name>: <param_value>
  • shuffle - shuffle the data
  • random_state - random state
  • preprocessing - preprocessing method / disable(null)
  • classifiers - list of classifiers
  • feature_selection - feature selection method / disable(null)
  • model_selection - model selection method

DataSet

The data must be in the following format:

label feature1 feature2 ...
0/1 value1 value2 ...
... ... ... ...

Example

Execute the script with command line options,

python main.py  --data data/credit.csv --cfg configs/credit.yml --save-dir output --name credit --save --plot

Result

  • metrics:output/<name_of_experiment>/metrics/metrics.csv
  • plots:output/<name_of_experiment>/plots/
  • models:output/<name_of_experiment>/models/
  • log:output/<name_of_experiment>/log.txt

:)

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A versatile machine learning ensemble platform designed to streamline the process of running experiments with various classifiers, preprocessing methods, feature selection techniques, and model selection strategies.

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