Metadata-Version: 2.1
Name: fastemc
Version: 0.0.3
Summary: FastEMC is a method for dimensionality reduction.
Home-page: https://github.com/rowland-208/fastemc
Author: James Rowland
Author-email: rowland.208@gmail.com
License: UNKNOWN
Description: # FastEMC
        ### 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:
        
        [1] 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)
        
        [2] Rowland, J.R. FastEMC. 2019. (https://github.com/rowland-208/fastemc)
Platform: UNKNOWN
Requires-Python: >=2.7
Description-Content-Type: text/markdown
