Changelog

All notable changes to this project will be documented in this file.

[1.3.3] - 2026-04-22

Added

  • LightGBM Integration: Added LightGBM algorithm support to the model screener functionality

  • Classifier AutoML: Completed classifier AutoML capabilities for expanded model screening

  • Pradhan et al. 2026: Added reproducible workflow and datasets for the Inverse Design paper by Pradhan et al. 2026

Changed

  • RDKit Fingerprints: Upgraded the RDKFP implementation to utilize RDKit’s modern rdFingerprintGenerator set of fingerprint generators

  • Descriptor Calculation Logic: AutoML’s RDKit descriptor calculation now relies on RDKDesc() wrapper class in FOSS descriptor module

  • FOSS Descriptors/Tests: Modified FOSS descriptors functionality logic and expanded related tests

  • Documentation Setup: Transitioned the repository’s Markdown changelog into docs/changelog.rst to support unified ReadTheDocs rendering, and updated the main index accordingly

  • Tutorial Notebooks: Updated feature representation documentation and notebooks corresponding to new RDKFP code outputs

  • Progress Tracking via tqdm: Implemented a progress bar for RDKit descriptor calculations and genetic algorithm using tqdm

Fixed/Miscellaneous

  • Modernized Packaging: Added pyproject.toml for standard PEP 517/518 builds, eliminating setup.py and –use-pep517 flag during install

  • Pandas 3.0 Compatibility: Fixed some incompatibilities with pandas 3.0 found in CI/CD testing; in theory this should make ChemML compatible with pandas 3.0, but further testing is needed

  • TensorFlow 2.x Compatibility: Patched backwards compatibility errors in Neural Fingerprint graph convolutional layers

  • Python 3.8 Support: Ensured dependencies, test module configurations, and setups maintain Python 3.8 backwards compatibility

  • Documentation: Updated documentation index page to keep up-to-date with README.md

Commits Included:

  1. 5eb23ef - updating feature rep docs with new RDKFP code

  2. 6e3fe21 - patching TF2.x error in neural fingerprints

  3. c5dc859 - updating RTD page, moving changelog to docs

  4. e725bed - Updating RDKFP to use modern rdFingerprintGenerator

  5. 8e6d60c - Adding LightGBM to modelscreener

  6. 2ae5a02 - Using foss_descriptors’s RDKit descriptor function

  7. 28ee6bd - adding progress bar for rdkit descriptor calculation

  8. 85bc354 - completing classifier AutoML

  9. 0c4782f - Confirming pip install fix (closed pyproject.toml dev)

  10. a526f01 - fixing pandas 3.0 issues on CI/CD

  11. 6bf11de - Modifying foss descriptors and tests

  12. 8ca2e1f - adding pyproject.toml and 3.8 backwards compatibility

  13. 35fe0cf - patched CI/CD to fix nitinmad issue

  14. 6205026 - Updated the GeneticAlgorithm to be abel view as progress bar using tqdm

  15. 368d2ec - Added tqdm

  16. fcca36b - Add reproducible workflow and datasets for Pradhan et al. 2026 Inverse Design paper

[1.3.2] - 2025-12-05

Added

  • MLP Model Enhancement: Added get_params() method to the MLP class for scikit-learn compatibility with model screening tools

  • GitHub Actions Workflow: Created comprehensive CI/CD pipeline (test.yml) for cross-platform testing on Ubuntu, macOS, and Windows with native coverage reporting

  • Mixed Precision Workaround: Added mixed precision policy initialization in LorentzLorenz to prevent ml_dtypes.float4_e2m1fn compatibility errors with TensorFlow 2.19+

  • Keras 3 Compatibility: Updated Adam optimizer imports and instantiations to use Keras 3 standard (removed deprecated .legacy module and decay parameter)

  • Dictionary-based Metrics: Implemented Keras 3-compliant multi-output model metrics using output name mapping to prevent duplicate metric naming errors

  • GitHub Native Coverage Reporting: Added artifact uploads for coverage XML reports from each test run on different platforms

  • GitHub Actions Badge: Added workflow status badge to README for visibility into test status

Changed

  • Loss Specification: Updated LorentzLorenz model compilation to use list of losses for each output instead of single loss string, matching Keras 3 requirements

  • Metrics Configuration: Changed from list-based metrics to dictionary-based metrics for multi-output models to ensure unique metric names in Keras 3

  • AutoML Multi-core Support: Enhanced model_screener.py with improved multi-core processing capabilities

  • Model Screener: Updated test_hyp() compatibility for better parameter tracking and reporting

  • CI/CD Infrastructure: Migrated from Travis CI to GitHub Actions with improved coverage reporting

  • Version Tracking: Updated README to reference GitHub releases instead of PyPI for latest version

  • Version Number: Bumped to 1.3.2 to reflect Keras 3 compatibility and infrastructure improvements

Removed

  • Travis CI Configuration: Removed outdated .travis.yml file (superseded by GitHub Actions)

  • Codecov Integration: Removed external Codecov service dependency in favor of GitHub-native coverage artifact uploads

  • PyPI Badge: Replaced with GitHub releases badge as repo is ahead of PyPI

Fixed

  • Keras 3/TensorFlow 2.19 Compatibility: Fixed “Found two metrics with the same name” error by implementing proper output naming and dictionary-based metrics

  • Adam Optimizer: Removed incompatible decay parameter for Keras 3 Adam optimizer initialization

  • Mixed Precision Issues: Resolved ml_dtypes.float4_e2m1fn AttributeError by setting global mixed precision policy to float32

  • PyTorch Installation: Added OS-specific PyTorch installation in GitHub Actions workflow

  • OpenBabel Import: Fixed openbabel import failures in GitHub Actions by adding openbabel-wheel pip installation alongside conda installation

Technical Details

Commits Included:

  1. b83abd9 - patch to published models - Added get_params() method to MLP class - Fixed LorentzLorenz model metrics for Keras 3 compatibility - Updated notebook documentation

  2. dba73a6 - backwards compatibility fixes - Updated Adam optimizer imports (removed .legacy) - Fixed mixed precision initialization in LorentzLorenz - Updated test imports for consistency

  3. 5739610 - AutoML multi-core update - Enhanced model_screener.py with improved parallelization - Updated space.py for better genetic algorithm integration - Modified MLP to support model screening via get_params() - Updated test cases for AutoML screening

  4. 8bbe071 - Updated readme and setup for local install - Updated README installation instructions - Modified setup.py for Python 3.12 compatibility

  5. d167ce8 - code CI/CD updated to GitHub Actions - Migrated from Travis CI to GitHub Actions workflow - Added .github/workflows/test.yml for cross-platform testing - Configured conda-forge dependencies with Mambaforge - Added OS-specific PyTorch installation for CI environments

  6. 400b260 - CI/CD patches - Added pytest and coverage configuration - Skipped test_Dragon in CI due to software availability - Configured matplotlib backend for CI environments - Added coverage XML artifact uploads

  7. 29e3715 - macOS CI/CD patch for tkinter - Added tk to conda-forge dependencies for macOS compatibility - Enhanced matplotlib configuration for non-GUI backend - Added MPLBACKEND environment variable for CI

  8. 5571532 - removing extra import to avoid CI/CD issues - Removed unused from turtle import back import from explain.py - Fixed ModuleNotFoundError for _tkinter on macOS CI

Dependencies Updated

  • TensorFlow/Keras: Now compatible with Keras 3 and TensorFlow 2.19+

  • PyTorch: Added proper CPU-only installation for CI environments

  • System Libraries: Added openbabel-wheel for proper pip installation alongside conda openbabel

Testing

  • All tests pass on Ubuntu, macOS, and Windows with Python 3.12

  • Cross-platform CI validation implemented via GitHub Actions

  • Coverage reporting integrated with Codecov

Migration Guide for Users

If you’re upgrading from the previous version, note these breaking changes:

  1. Adam Optimizer Parameters: The decay parameter is no longer supported in Adam. Use learning_rate scheduling instead.

  2. Keras 3 Models: Multi-output models now require dictionary-based metrics configuration:

    metrics_dict = {
    'output_name': ['metric1', 'metric2'],
    ...
}
model.compile(metrics=metrics_dict)

  3. Mixed Precision: Mixed precision is now disabled by default to ensure compatibility. Enable it explicitly if needed.

—

For more information on each change, see the individual commit messages or the pull request discussions.