idmtools-calibra 3.0.0

Calibration tool for IDM-Tools

emodpy

idmtools-calibra

Table of Contents

• User Installation
  • Pre-requisites
• Development Environment Setup
  • First Time Setup
  • Development Tips
  • Documentation
    • Build mkdocs locally
    • Build and publish document from Github Actions
  • Resume Supports Parameters
  • Examples as Reference

User Installation

pip install idmtools-calibra 

Pre-requisites

• Python 3.10, 3.11, 3.12, 3.13, 31.4 x64

Development Environment Setup

When setting up your environment for the first time, you can use the following instructions

First Time Setup

1. Clone the repository:

   > git clone https://github.com/InstituteforDiseaseModeling/idmtools_calibra.git
   

2. Create a virtualenv. On Windows, please use venv to create the environment python -m venv idmtools_calibra On Unix(Mac/Linux) you can use venv or virtualenv
3. Activate the virtualenv
4. Then run python ./.dev_scripts/bootstrap.py. This will install all the tools.

Development Tips

There is a Makefile file available for most common development tasks. Here is a list of commands

clean       -   Clean up temproary files
lint        -   Lint package and tests
test        -   Run All tests
docs        -   Build mkdocs documentation
bump-patch  -   Bump patch version
bump-minor  -   Bump minor version
bump-major  -   Bump major version

Documentation

Documentation available at https://institutefordiseasemodeling.github.io/idmtools-calibra/.

Build mkdocs locally

Create and activate a venv. Navigate to the root directory of the repo.

cd docs
pip install -r requirments.txt
mkdocs build  # build docs
mkdocs serve  # view docs from  http://127.0.0.1:8000/idmtools_calibra/
mkdocs gh-deploy # deploy to github (need to be on the specific branch)

Build and publish document from Github Actions

Go to Github Actions -> Run GHA job: Deploy MkDocs via GitHub Pages API

Resume Supports Parameters

CalibManager's run_calibration method supports the following parameters for resume action, for example:

run_calibration(resume=True, iteration=2, iter_step='analyze')

• resume: bool, default=False, required for resume Note: resume=True is required for resume, otherwise all the parameters will be ignored
• iteration: int, default=None, from which iteration to resume Note: in None case, calibra will detect and take the last iteration from last run
• iter_step: str, default=None, from which calibration step to resume Note: in None case, calibra will detect and take the valid step of the iteration
• loop: bool, default=True, if like to continue to next iteration Note: by default, resume will continue to run next iteration
• max_iterations, int, default=None, user can override the max_iterations defined in calib_manager Note: in None case, calibra will take max_iterations defined in calib_manager
• backup: bool, default=False, if like to backup Calibration.json Note: by default, resume will not make a backup of Calibration.json
• dry_run: bool, default=False, if like to really execute resume action Note: by default (dry_run= False), resume will go ahead to execute resume action

iter_step supports the following options:

• commission: will start to run a new iteration
• analyze: analyze the output data from given iteration
• plot: just plot for given iteration
• next_point: go directly to next iteration

Examples as Reference

The examples/ directory contains canonical usage patterns:

• examples/solar/ — Simplest: fits a linear model (y = mx + c) to data using OptimTool + RMSE
• examples/sir/ — SIR epidemiological model with multiple output configuration options
• examples/emod_sir/ — EMOD sir disease model calibrations using COMPS platform When implementing new features or debugging, the solar example (solar_optimtool_linear_model.py) is the clearest end-to-end reference.