Diagrams

This section contains visual representations of the genetic algorithm implementation and model architecture within the Ensemble Genetic Algorithm project.

Data Pipeline and Genetic Algorithm

main.py Command-Line Workflow

!main.py Workflow

  • Source: assets/main_py_workflow.mmd

  • Description: Illustrates the end-to-end workflow when running an experiment from the command line using main.py, including the main grid search loop and optional evaluation and plotting steps.

GA Example Usage, Data Grid and GA Grid Permutations, System Flow (example_usage.ipynb)

!GA System Flow

  • Source: assets/example_usage_permutations.mmd

  • Description: Illustrates the genetic algorithm search over grid parameters, as demonstrated in the example usage notebook.

GA Data Flow

!GA Data Flow

  • Source: assets/ga_data_diagram.mmd

  • Description: Illustrates the flow of data through the genetic algorithm pipeline, from input to ensemble generation.

Model Class Structure

!Model Class Structure

  • Source: assets/model_classes.mmd

  • Description: Shows the inheritance hierarchy and relationships between the different model classes used in the project.

Genetic Algorithm Components

Weighting System

!GA Weighting System

  • Source: assets/ga_weighting.mmd

  • Description: Demonstrates the weighting mechanism applied to individual base learners within an ensemble.

Parameter Space Grid

!Grid Parameter Space

  • Source: assets/grid_param_space_ga.mmd

  • Description: Visualizes how the genetic algorithm explores the parameter space, including feature and hyperparameter grids.

Model Generation Workflows

SVC Model Generation

!SVC Model Generation

  • Source: assets/svc_model_gen.mmd

  • Description: Flow diagram detailing the process for generating Support Vector Classifier (SVC) models as base learners.

PyTorch Model Generation

!PyTorch Model Generation

  • Source: assets/torch_model_gen.mmd

  • Description: Flow diagram illustrating the generation process for PyTorch neural network models, including aspects of neural architecture search.

XGBoost Model Generation

!XGBoost Model Generation

  • Source: assets/xgb_model_gen.mmd

  • Description: Flow diagram outlining the generation process for XGBoost models.

Diagram Format

All diagrams are available in both Mermaid source format (.mmd) and rendered formats (.png/.svg). The Mermaid source files can be edited and re-rendered as needed for documentation updates, ensuring the diagrams remain current with the project’s development.