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        "description": "# FastEMC\n### Fast Exponential Monte Carlo\n\nFastEMC is a method for dimensionality reduction.\nFastEMC was designed for datasets with a small number of samples,\nand a large number of features.\nThis version of FastEMC can only handle numerical features,\nand binary classification of samples.\nFastEMC can be installed using pip\n```\n$ pip install fastemc\n```\nIf pip fails on windows try installing scikit-learn manually using conda,\nthen install fastemc using pip.\nYou can interact with FastEMC directly using the python module\n```\n>>> import fastemc\n>>> scores, clusters = fastemc.run(features, labels, **kwargs)\n```\nor through the command line\n```\n$ python -m fastemc --features features.csv --labels labels.csv\n```\nThe features.csv and labels.csv files can be generated using pandas, e.g.,\n```\n>>> labels.to_csv(\"labels.csv\")\n>>> features.to_csv(\"features.csv\")\n```\nwhere labels and features are pandas dataframes with the same index.\n\nFastEMC outputs a list of feature clusters.\nThe size of each cluster and the number of clusters to collect are optional parameters.\nEach cluster is also given a score.\nThe score is based on k-fold cross-validation of a logistic regression classifier using only features in the cluster.\n\n\nWhen using FastEMC in published works, please cite the original manuscript \nand the author of the software:\n\n[1] Stackhouse, C.T.; Rowland, J.R.; Shevin, R.S.; Singh, R.; Gillespie, G.Y.; Willey, C.D. A Novel\nAssay for Profiling GBM Cancer Model Heterogeneity and Drug Screening. Cells 2019, 8, 702. (https://www.ncbi.nlm.nih.gov/pubmed/31336733)\n\n[2] Rowland, J.R. FastEMC. 2019. (https://github.com/rowland-208/fastemc)\n\n",
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