Mercurial > repos > bgruening > sklearn_build_pipeline
view pca.py @ 24:b1eda492f063 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f031d8ddfb73cec24572648666ac44ee47f08aad
author | bgruening |
---|---|
date | Thu, 11 Aug 2022 09:20:25 +0000 |
parents | 4de3d598c116 |
children |
line wrap: on
line source
import argparse import numpy as np from galaxy_ml.utils import read_columns from sklearn.decomposition import IncrementalPCA, KernelPCA, PCA def main(): parser = argparse.ArgumentParser(description="RDKit screen") parser.add_argument("-i", "--infile", help="Input file") parser.add_argument( "--header", action="store_true", help="Include the header row or skip it" ) parser.add_argument( "-c", "--columns", type=str.lower, default="all", choices=[ "by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name", "all_columns", ], help="Choose to select all columns, or exclude/include some", ) parser.add_argument( "-ci", "--column_indices", type=str.lower, help="Choose to select all columns, or exclude/include some", ) parser.add_argument( "-n", "--number", nargs="?", type=int, default=None, help="Number of components to keep. If not set, all components are kept", ) parser.add_argument("--whiten", action="store_true", help="Whiten the components") parser.add_argument( "-t", "--pca_type", type=str.lower, default="classical", choices=["classical", "incremental", "kernel"], help="Choose which flavour of PCA to use", ) parser.add_argument( "-s", "--svd_solver", type=str.lower, default="auto", choices=["auto", "full", "arpack", "randomized"], help="Choose the type of svd solver.", ) parser.add_argument( "-b", "--batch_size", nargs="?", type=int, default=None, help="The number of samples to use for each batch", ) parser.add_argument( "-k", "--kernel", type=str.lower, default="linear", choices=["linear", "poly", "rbf", "sigmoid", "cosine", "precomputed"], help="Choose the type of kernel.", ) parser.add_argument( "-g", "--gamma", nargs="?", type=float, default=None, help="Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other kernels", ) parser.add_argument( "-tol", "--tolerance", type=float, default=0.0, help="Convergence tolerance for arpack. If 0, optimal value will be chosen by arpack", ) parser.add_argument( "-mi", "--max_iter", nargs="?", type=int, default=None, help="Maximum number of iterations for arpack", ) parser.add_argument( "-d", "--degree", type=int, default=3, help="Degree for poly kernels. Ignored by other kernels", ) parser.add_argument( "-cf", "--coef0", type=float, default=1.0, help="Independent term in poly and sigmoid kernels", ) parser.add_argument( "-e", "--eigen_solver", type=str.lower, default="auto", choices=["auto", "dense", "arpack"], help="Choose the type of eigen solver.", ) parser.add_argument( "-o", "--outfile", help="Base name for output file (no extension)." ) args = parser.parse_args() usecols = None pca_params = {} if args.columns == "by_index_number" or args.columns == "all_but_by_index_number": usecols = [int(i) for i in args.column_indices.split(",")] elif args.columns == "by_header_name" or args.columns == "all_but_by_header_name": usecols = args.column_indices header = "infer" if args.header else None pca_input = read_columns( f=args.infile, c=usecols, c_option=args.columns, sep="\t", header=header, parse_dates=True, encoding=None, index_col=None, ) pca_params.update({"n_components": args.number}) if args.pca_type == "classical": pca_params.update({"svd_solver": args.svd_solver, "whiten": args.whiten}) if args.svd_solver == "arpack": pca_params.update({"tol": args.tolerance}) pca = PCA() elif args.pca_type == "incremental": pca_params.update({"batch_size": args.batch_size, "whiten": args.whiten}) pca = IncrementalPCA() elif args.pca_type == "kernel": pca_params.update( { "kernel": args.kernel, "eigen_solver": args.eigen_solver, "gamma": args.gamma, } ) if args.kernel == "poly": pca_params.update({"degree": args.degree, "coef0": args.coef0}) elif args.kernel == "sigmoid": pca_params.update({"coef0": args.coef0}) elif args.kernel == "precomputed": pca_input = np.dot(pca_input, pca_input.T) if args.eigen_solver == "arpack": pca_params.update({"tol": args.tolerance, "max_iter": args.max_iter}) pca = KernelPCA() print(pca_params) pca.set_params(**pca_params) pca_output = pca.fit_transform(pca_input) np.savetxt(fname=args.outfile, X=pca_output, fmt="%.4f", delimiter="\t") if __name__ == "__main__": main()