# HG changeset patch # User bgruening # Date 1601582816 0 # Node ID 02d2be90dedbecf7c48f07d55952947209663985 # Parent d2afc87db26bc57d691f4b01320c4488606cab8c "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2afb24f3c81d625312186750a714d702363012b5" diff -r d2afc87db26b -r 02d2be90dedb pca.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/pca.py Thu Oct 01 20:06:56 2020 +0000 @@ -0,0 +1,99 @@ +import argparse +import numpy as np +from sklearn.decomposition import PCA, IncrementalPCA, KernelPCA +from galaxy_ml.utils import read_columns + +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 + cols = [] + 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() diff -r d2afc87db26b -r 02d2be90dedb test-data/pca_classical_header_names_output.dat --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/pca_classical_header_names_output.dat Thu Oct 01 20:06:56 2020 +0000 @@ -0,0 +1,300 @@ +-3.9779 1.8818 2.6506 -1.1628 -0.0983 +-2.3700 0.6756 -1.6186 -1.4164 1.0327 +-3.0925 2.4577 -1.8726 -0.4041 1.5016 +-4.0151 2.3686 0.3256 -0.0685 -0.2168 +-4.7065 3.1394 2.3137 0.1907 -1.6303 +-4.9613 4.6941 1.2908 1.0298 -0.8817 +-4.8364 5.2936 0.8793 1.3212 0.0849 +-5.0224 2.7565 4.6351 -0.5397 -1.3489 +-2.9068 1.6461 -1.9066 -0.5100 -0.6820 +-4.7713 4.0171 2.1659 0.5434 -1.4630 +-2.7521 -1.9309 -0.9117 -3.5728 1.7161 +-3.7407 1.3845 0.0141 -1.5357 1.3068 +-4.3751 1.0682 2.3906 -1.5298 0.9064 +-2.8532 0.2430 -0.8937 -1.7428 -1.1825 +-2.4091 -0.1393 -0.0986 -2.0343 1.5498 +-3.8699 2.7278 0.9261 -0.4924 0.7933 +-4.6180 3.2535 2.4650 -0.0468 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