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1 #!/usr/bin/env python
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2
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3 import os,sys,math,pickle
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4 from lefse import *
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5
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6 def read_params(args):
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7 parser = argparse.ArgumentParser(description='LEfSe 1.0')
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8 parser.add_argument('input_file', metavar='INPUT_FILE', type=str, help="the input file")
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9 parser.add_argument('output_file', metavar='OUTPUT_FILE', type=str,
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10 help="the output file containing the data for the visualization module")
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11 parser.add_argument('-o',dest="out_text_file", metavar='str', type=str, default="",
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12 help="set the file for exporting the result (only concise textual form)")
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13 parser.add_argument('-a',dest="anova_alpha", metavar='float', type=float, default=0.05,
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14 help="set the alpha value for the Anova test (default 0.05)")
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15 parser.add_argument('-w',dest="wilcoxon_alpha", metavar='float', type=float, default=0.05,
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16 help="set the alpha value for the Wilcoxon test (default 0.05)")
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17 parser.add_argument('-l',dest="lda_abs_th", metavar='float', type=float, default=2.0,
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18 help="set the threshold on the absolute value of the logarithmic LDA score (default 2.0)")
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19 parser.add_argument('--nlogs',dest="nlogs", metavar='int', type=int, default=3,
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20 help="max log ingluence of LDA coeff")
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21 parser.add_argument('--verbose',dest="verbose", metavar='int', choices=[0,1], type=int, default=0,
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22 help="verbose execution (default 0)")
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23 parser.add_argument('--wilc',dest="wilc", metavar='int', choices=[0,1], type=int, default=1,
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24 help="wheter to perform the Wicoxon step (default 1)")
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25 parser.add_argument('-r',dest="rank_tec", metavar='str', choices=['lda','svm'], type=str, default='lda',
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26 help="select LDA or SVM for effect size (default LDA)")
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27 parser.add_argument('--svm_norm',dest="svm_norm", metavar='int', choices=[0,1], type=int, default=1,
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28 help="whether to normalize the data in [0,1] for SVM feature waiting (default 1 strongly suggested)")
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29 parser.add_argument('-b',dest="n_boots", metavar='int', type=int, default=30,
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30 help="set the number of bootstrap iteration for LDA (default 30)")
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31 parser.add_argument('-e',dest="only_same_subcl", metavar='int', type=int, default=0,
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32 help="set whether perform the wilcoxon test only among the subclasses with the same name (default 0)")
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33 parser.add_argument('-c',dest="curv", metavar='int', type=int, default=0,
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34 help="set whether perform the wilcoxon test ing the Curtis's approach [BETA VERSION] (default 0)")
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35 parser.add_argument('-f',dest="f_boots", metavar='float', type=float, default=0.67,
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36 help="set the subsampling fraction value for each bootstrap iteration (default 0.66666)")
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37 parser.add_argument('-s',dest="strict", choices=[0,1,2], type=int, default=0,
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38 help="set the multiple testing correction options. 0 no correction (more strict, default), 1 correction for independent comparisons, 2 correction for independent comparison")
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39 # parser.add_argument('-m',dest="m_boots", type=int, default=5,
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40 # help="minimum cardinality of classes in each bootstrapping iteration (default 5)")
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41 parser.add_argument('--min_c',dest="min_c", metavar='int', type=int, default=10,
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42 help="minimum number of samples per subclass for performing wilcoxon test (default 10)")
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43 parser.add_argument('-t',dest="title", metavar='str', type=str, default="",
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44 help="set the title of the analysis (default input file without extension)")
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45 parser.add_argument('-y',dest="multiclass_strat", choices=[0,1], type=int, default=0,
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46 help="(for multiclass tasks) set whether the test is performed in a one-against-one ( 1 - more strict!) or in a one-against-all setting ( 0 - less strict) (default 0)")
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47 args = parser.parse_args()
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48
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49 params = vars(args)
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50 if params['title'] == "": params['title'] = params['input_file'].split("/")[-1].split('.')[0]
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51 return params
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52
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53
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54
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55 if __name__ == '__main__':
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56 init()
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57 params = read_params(sys.argv)
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58 feats,cls,class_sl,subclass_sl,class_hierarchy = load_data(params['input_file'])
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59 kord,cls_means = get_class_means(class_sl,feats)
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60 wilcoxon_res = {}
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61 kw_n_ok = 0
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62 nf = 0
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63 for feat_name,feat_values in feats.items():
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64 if params['verbose']:
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65 print "Testing feature",str(nf),": ",feat_name,
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66 nf += 1
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67 kw_ok,pv = test_kw_r(cls,feat_values,params['anova_alpha'],sorted(cls.keys()))
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68 if not kw_ok:
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69 if params['verbose']: print "\tkw ko"
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70 del feats[feat_name]
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71 wilcoxon_res[feat_name] = "-"
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72 continue
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73 if params['verbose']: print "\tkw ok\t",
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74
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75 if not params['wilc']: continue
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76 kw_n_ok += 1
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77 res_wilcoxon_rep = test_rep_wilcoxon_r(subclass_sl,class_hierarchy,feat_values,params['wilcoxon_alpha'],params['multiclass_strat'],params['strict'],feat_name,params['min_c'],params['only_same_subcl'],params['curv'])
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78 wilcoxon_res[feat_name] = str(pv) if res_wilcoxon_rep else "-"
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79 if not res_wilcoxon_rep:
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80 if params['verbose']: print "wilc ko"
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81 del feats[feat_name]
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82 elif params['verbose']: print "wilc ok\t"
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83
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84 if len(feats) > 0:
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85 print "Number of significantly discriminative features:", len(feats), "(", kw_n_ok, ") before internal wilcoxon"
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86 if params['lda_abs_th'] < 0.0:
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87 lda_res,lda_res_th = dict([(k,0.0) for k,v in feats.items()]), dict([(k,v) for k,v in feats.items()])
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88 else:
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89 if params['rank_tec'] == 'lda': lda_res,lda_res_th = test_lda_r(cls,feats,class_sl,params['n_boots'],params['f_boots'],params['lda_abs_th'],0.0000000001,params['nlogs'])
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90 elif params['rank_tec'] == 'svm': lda_res,lda_res_th = test_svm(cls,feats,class_sl,params['n_boots'],params['f_boots'],params['lda_abs_th'],0.0,params['svm_norm'])
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91 else: lda_res,lda_res_th = dict([(k,0.0) for k,v in feats.items()]), dict([(k,v) for k,v in feats.items()])
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92 else:
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93 print "Number of significantly discriminative features:", len(feats), "(", kw_n_ok, ") before internal wilcoxon"
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94 print "No features with significant differences between the two classes"
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95 lda_res,lda_res_th = {},{}
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96 outres = {}
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97 outres['lda_res_th'] = lda_res_th
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98 outres['lda_res'] = lda_res
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99 outres['cls_means'] = cls_means
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100 outres['cls_means_kord'] = kord
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101 outres['wilcox_res'] = wilcoxon_res
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102 print "Number of discriminative features with abs LDA score >",params['lda_abs_th'],":",len(lda_res_th)
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103 save_res(outres,params["output_file"])
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