This is a Python package to apply the MLTSA approach for relevant CV identification on Molecular Dynamics data using both Sklearn and TensorFlow modules.It also includes both a suite of 1D Potential Analytical model feature generation module for light testing and a suite of different 2D potential shapes (Spiral, Z-shaped) generation as well as the posterior feature generation by 1D projections of the 2D data. In this package you will find:
- Data Generation Module (MLTSA_datasets) : Contains files with the easy to call 1D/2D/MD examples to generate data or play around with it as tests for the approach.
- Scikit-Learn-based ML models and Feature Reduction module (MLTSA_sklearn) : Contains the Scikit-Learn integrated functions to apply MLTSA on data.
- TensorFlow-based ML models and Feature Reduction module (MLTSA_tensorflow): Contains the set of functions and different models built on TensorFlow to apply MLTSA on data.
- Example OneD
- Example TwoD
- Example Train
- Example MLTSA
To use MLTSA, first install it using pip:
(.venv) $ pip install MLTSA