Release Overview

v1.0.6

Minor Bug Fixes

• Update sinlge assay hangling. The MAMSI Struct Search tool is now able to handle a single assay only.
• Update harmonising of structural clusters.

Further, this version of MAMSI changed handling of external files from depracted pkg_resources to importlib_resoureces. Unit tests for structural search added

v1.0.5

Minor Bug Fixes

• Update randint high in .montecarlo_cv() from 4_294_967_296 to 2_147_483_647 to comply with systems where Windows defaults to stricter 32-bit behaviour.

Release History

v1.0.4

New Features

• Parallelised .kfold_cv()
• Parallelised .montecarlo_cv()
• Parallelised .estimate_lv()
• Parallelised .mb_vip_permtest()

v1.0.3

This version of the package was used for data analysis reported in a manuscript entitled 'MAMSI: Integration of multi-assay liquid chromatography – mass spectrometry metabolomics data using multi-view machine learning', that has been submitted to ACS Analytical Chemistry journal on 4th March 2025 (date of this release).

There were previous releases of the package published on PyPI: https://pypi.org/project/mamsi/.

New Features

• k-fold cross-validation implemented as a method .kfold_cv() that can be used for model performance evaluation. This method includes GroupKFold option.
• Monte Carlo cross-validaton (MCCV), also nown as 'random sampling cross-validation' implemented as a method .montecarlo_cv() that can be used for model performance evaluation.
• .estimate_lv() method now allows to choose between k-fold CV and MC-CV using parameter method

Bug Fixes and Behavioural Changes

• Plot title for .block_importance() fixed.
• For regression analysis, MSE metric changed to RMSE
• For .estimate_lv() method, parameter y_continuous=False was replaced with classification=True

v1.0.2

New Features

• New method 'MamsiPls.block_importance()': Calculate the block importance for each block in the multiblock PLS model and plot the results.

Minor Bug Fixes and Behaviour Changes

• Behavioural changes for MamsiPls.mb_vip(): The MB-VIP plot is now printed by default, scores are not returned by default. New default arguments (plot=True, get_scores=False).
• Argument changes for MamsiPls.estimate_lv(): Old Arguments (no_folds, n_components) changed to (n_slplits, max_components) respectively.
• Plots: 'Verdana' is no longer the default font. The default font changed to Matplotlib default 'DejaVu Sans'.
• Updates to MamsiStructSearch class to comply with future warnings - Pandas 3.0.

v1.0.1

Minor Bugs Update

• Fixes instances where flattened correlation clusters were misaligned to structural clusters.
• Readme licence badge links directly to GitHub licence file (URL).