morgoth 1.0

Interpretable and reliable multivariate random forest for simultaneous classification and regression

MORGOTH

This is the implementation of our novel random forest (RF)-based approach for Multivariate classificatiOn and Regression increasinG trustwOrTHiness (MORGOTH). A detailed description and application of the model can be found in our pre-print `Increasing trustworthiness of machine learning-based drug sensitivity prediction with a multivariate random forest approach'. MORGOTH can be used to simultaneously perform classification and regression using a novel objective function during the training, which is a linear combination of classification and regression error. Moreover, it offers the possibility to perform conformal prediction (CP), which can be used to obtain reliable classification and regression results. A more detailed explanation of CP and the framework we use can be found in our article 'Reliable anti-cancer drug sensitivity prediction and prioritization'. Additionally, MORGOTH provides a graph representation of the random forest to address model interpretability, and a cluster analysis of the leaves to measure the dissimilarity of new inputs from the training data to account for its reliability.

For issues and questions, please contact Lisa-Marie Rolli (lisa-marie.rolli[at]uni-saarland.de) or Kerstin Lenhof (research[at]klenhof.de).

Installation

You can install our morgoth package using pip:

pip install git+https://github.com/volkamerlab/MORGOTH.git

used python3 libraries: fireducks pandas numpy typing math bisect operator copy sklearn time scipy collections multiprocessing functools re

Usage

An exemplary use is running our provided main as a module, which you can call after downloading the Example_Data folder from our GitHub.

python3 -m morgoth Example_Data/example_Json_config.json

Note that the directory tree should be kept and the path to the output folder should be edited in the file Example_Data/example_JSON_config.json. The prediction results for classification will be found in <output_dir>/<analysis_name>_ClassificationResultsFile1.txt and the regression results are stored in <output_dir>/<analysis_name>_<1-error_rate>_RegressionResultsFile1.txt. If if the field swap_test_calibration in the config file is set to 'True' there will be one additional file per task, respectively, where the '1' in the file name is replaced by a '2'. If a distance measure is given in the config, <output_dir>/<analysis_name>_SilhouetteScoresTrainSamples_<distance>.txt and <output_dir>/<analysis_name>_SilhouetteScoresTestSamples_<distance>.txt will contain the silhouette scores for the training and test samples, respectively. If draw_graph is set to True, the files <output_dir>/<analysis_name>_<sample_name>.dot contain the sample specific graphs and <output_dir>/<analysis_name>__graph_whole_forest.dot and <output_dir>/<analysis_name>__graph_average_whole_forest.dot contain the graph for the whole test set with either the raw count across all samples as edge weight or averaged by the number of test samples, respectively.