Scikit-Learn Integrated MLTSA module

These are the functions that are integrated with sklearn for MLTSA and training of ML models.

models.SKL_Train(clf, X, Y)

Wrapper to train any machine learning model/classifier from the Scikit-Learn suite which uses fit() to train and predict() to predict the outcome values.

Parameters

  • clf –

  • X –

  • Y –

Returns

MLTSA_sk.MLTSA(data, ans, model, drop_mode='Average', data_mode='Normal')

Function to apply the Machine Learning Transition State Analysis to a given training dataset/answers and trained model. It calculates the Gloabl Means and re-calculates accuracy for predicting each outcome.

Parameters

  • data (list) – Training data used for training the ML model. Must have shape (samples, features)

  • ans (list) – Outcomes for each sample on “data”. Shape must be (samples)

  • model –

  • drop_mode –

  • data_mode –

Returns

MLTSA_sk.MLTSA_Plot(FR, dataset_og, pots, errorbar=True)

Wrapper for plotting the results from the Accuracy Drop procedure

Parameters

  • FR – Values from the feature reduction

  • dataset_og – Original dataset object class used for generating the data

  • pots – Original potentials object class used for generating the data

  • errorbar – Flag for including or not including the errobars in case of using replicas.

Returns