FastEMC is a method for dimensionality reduction.
Fast Exponential Monte Carlo
FastEMC is a method for dimensionality reduction. FastEMC was designed for datasets with a small number of samples, and a large number of features. This version of FastEMC can only handle numerical features, and binary classification of samples. FastEMC can be installed using pip
$ pip install fastemc
If pip fails on windows try installing scikit-learn manually using conda, then install fastemc using pip. You can interact with FastEMC directly using the python module
>>> import fastemc >>> scores, clusters = fastemc.run(features, labels, **kwargs)
or through the command line
$ python -m fastemc --features features.csv --labels labels.csv
The features.csv and labels.csv files can be generated using pandas, e.g.,
>>> labels.to_csv("labels.csv") >>> features.to_csv("features.csv")
where labels and features are pandas dataframes with the same index.
FastEMC outputs a list of feature clusters. The size of each cluster and the number of clusters to collect are optional parameters. Each cluster is also given a score. The score is based on k-fold cross-validation of a logistic regression classifier using only features in the cluster.
When using FastEMC in published works, please cite the original manuscript and the author of the software:
 Stackhouse, C.T.; Rowland, J.R.; Shevin, R.S.; Singh, R.; Gillespie, G.Y.; Willey, C.D. A Novel Assay for Profiling GBM Cancer Model Heterogeneity and Drug Screening. Cells 2019, 8, 702. (https://www.ncbi.nlm.nih.gov/pubmed/31336733)
 Rowland, J.R. FastEMC. 2019. (https://github.com/rowland-208/fastemc)
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