Mercurial > repos > bgruening > sklearn_train_test_eval

annotate pca.py @ 8:e03a58b31c12 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2afb24f3c81d625312186750a714d702363012b5"
author bgruening
date Fri, 02 Oct 2020 08:43:15 +0000
parents
children ead7adad8d0e
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e03a58b31c12 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2afb24f3c81d625312186750a714d702363012b5"
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1 import argparse
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2 import numpy as np
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3 from sklearn.decomposition import PCA, IncrementalPCA, KernelPCA
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4 from galaxy_ml.utils import read_columns
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5
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6 def main():
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7 parser = argparse.ArgumentParser(description='RDKit screen')
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8 parser.add_argument('-i', '--infile',
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9 help="Input file")
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10 parser.add_argument('--header', action='store_true', help="Include the header row or skip it")
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11 parser.add_argument('-c', '--columns', type=str.lower, default='all', choices=['by_index_number', 'all_but_by_index_number',\
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12 'by_header_name', 'all_but_by_header_name', 'all_columns'],
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13 help="Choose to select all columns, or exclude/include some")
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14 parser.add_argument('-ci', '--column_indices', type=str.lower,
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15 help="Choose to select all columns, or exclude/include some")
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16 parser.add_argument('-n', '--number', nargs='?', type=int, default=None,\
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17 help="Number of components to keep. If not set, all components are kept")
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18 parser.add_argument('--whiten', action='store_true', help="Whiten the components")
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19 parser.add_argument('-t', '--pca_type', type=str.lower, default='classical', choices=['classical', 'incremental', 'kernel'],
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20 help="Choose which flavour of PCA to use")
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21 parser.add_argument('-s', '--svd_solver', type=str.lower, default='auto', choices=['auto', 'full', 'arpack', 'randomized'],
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22 help="Choose the type of svd solver.")
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23 parser.add_argument('-b', '--batch_size', nargs='?', type=int, default=None,\
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24 help="The number of samples to use for each batch")
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25 parser.add_argument('-k', '--kernel', type=str.lower, default='linear',\
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26 choices=['linear', 'poly', 'rbf', 'sigmoid', 'cosine', 'precomputed'],
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27 help="Choose the type of kernel.")
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28 parser.add_argument('-g', '--gamma', nargs='?', type=float, default=None,
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29 help='Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other kernels')
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30 parser.add_argument('-tol', '--tolerance', type=float, default=0.0,
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31 help='Convergence tolerance for arpack. If 0, optimal value will be chosen by arpack')
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32 parser.add_argument('-mi', '--max_iter', nargs='?', type=int, default=None,\
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33 help="Maximum number of iterations for arpack")
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34 parser.add_argument('-d', '--degree', type=int, default=3,\
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35 help="Degree for poly kernels. Ignored by other kernels")
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36 parser.add_argument('-cf', '--coef0', type=float, default=1.0,
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37 help='Independent term in poly and sigmoid kernels')
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38 parser.add_argument('-e', '--eigen_solver', type=str.lower, default='auto', choices=['auto', 'dense', 'arpack'],
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39 help="Choose the type of eigen solver.")
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40 parser.add_argument('-o', '--outfile',
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41 help="Base name for output file (no extension).")
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42 args = parser.parse_args()
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43
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44 usecols = None
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45 cols = []
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46 pca_params = {}
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47
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48 if args.columns == 'by_index_number' or args.columns == 'all_but_by_index_number':
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49 usecols = [int(i) for i in args.column_indices.split(',')]
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50 elif args.columns == 'by_header_name' or args.columns == 'all_but_by_header_name':
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51 usecols = args.column_indices
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52
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53 header = 'infer' if args.header else None
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54
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55 pca_input = read_columns(
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56 f=args.infile,
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57 c=usecols,
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58 c_option=args.columns,
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59 sep='\t',
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60 header=header,
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61 parse_dates=True,
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62 encoding=None,
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63 index_col=None)
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64
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65 pca_params.update({'n_components': args.number})
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66
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67 if args.pca_type == 'classical':
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68 pca_params.update({'svd_solver': args.svd_solver, 'whiten': args.whiten})
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69 if args.svd_solver == 'arpack':
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70 pca_params.update({'tol': args.tolerance})
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71 pca = PCA()
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72
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73 elif args.pca_type == 'incremental':
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74 pca_params.update({'batch_size': args.batch_size, 'whiten': args.whiten})
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75 pca = IncrementalPCA()
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76
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77 elif args.pca_type == 'kernel':
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78 pca_params.update({'kernel': args.kernel, 'eigen_solver': args.eigen_solver, 'gamma': args.gamma})
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79
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80 if args.kernel == 'poly':
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81 pca_params.update({'degree': args.degree, 'coef0': args.coef0})
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82 elif args.kernel == 'sigmoid':
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83 pca_params.update({'coef0': args.coef0})
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84 elif args.kernel == 'precomputed':
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85 pca_input = np.dot(pca_input, pca_input.T)
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86
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87 if args.eigen_solver == 'arpack':
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88 pca_params.update({'tol': args.tolerance, 'max_iter': args.max_iter})
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89
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90 pca = KernelPCA()
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91
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92 print(pca_params)
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93 pca.set_params(**pca_params)
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94 pca_output = pca.fit_transform(pca_input)
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95 np.savetxt(fname=args.outfile, X=pca_output, fmt='%.4f', delimiter='\t')
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96
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97
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98 if __name__ == "__main__":
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99 main()