Mercurial > repos > bgruening > sklearn_pairwise_metrics

annotate stacking_ensembles.py @ 24:86020dbc8ef7 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
author bgruening
date Tue, 14 May 2019 18:08:50 -0400
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children e84c0db80565
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24
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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1 import argparse
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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2 import json
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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3 import pandas as pd
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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4 import pickle
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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5 import xgboost
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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6 import warnings
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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7 from sklearn import (cluster, compose, decomposition, ensemble,
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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8 feature_extraction, feature_selection,
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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9 gaussian_process, kernel_approximation, metrics,
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10 model_selection, naive_bayes, neighbors,
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11 pipeline, preprocessing, svm, linear_model,
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12 tree, discriminant_analysis)
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13 from sklearn.model_selection._split import check_cv
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14 from feature_selectors import (DyRFE, DyRFECV,
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15 MyPipeline, MyimbPipeline)
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16 from iraps_classifier import (IRAPSCore, IRAPSClassifier,
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17 BinarizeTargetClassifier,
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18 BinarizeTargetRegressor)
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19 from preprocessors import Z_RandomOverSampler
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20 from utils import load_model, get_cv, get_estimator, get_search_params
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22 from mlxtend.regressor import StackingCVRegressor, StackingRegressor
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23 from mlxtend.classifier import StackingCVClassifier, StackingClassifier
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26 warnings.filterwarnings('ignore')
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28 N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
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31 def main(inputs_path, output_obj, base_paths=None, meta_path=None,
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32 outfile_params=None):
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33 """
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34 Parameter
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35 ---------
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36 inputs_path : str
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37 File path for Galaxy parameters
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38
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39 output_obj : str
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40 File path for ensemble estimator ouput
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41
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42 base_paths : str
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43 File path or paths concatenated by comma.
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44
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45 meta_path : str
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46 File path
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47
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48 outfile_params : str
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49 File path for params output
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50 """
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51 with open(inputs_path, 'r') as param_handler:
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52 params = json.load(param_handler)
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53
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54 base_estimators = []
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55 for idx, base_file in enumerate(base_paths.split(',')):
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56 if base_file and base_file != 'None':
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57 with open(base_file, 'rb') as handler:
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58 model = load_model(handler)
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59 else:
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60 estimator_json = (params['base_est_builder'][idx]
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61 ['estimator_selector'])
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62 model = get_estimator(estimator_json)
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63 base_estimators.append(model)
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64
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65 if meta_path:
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66 with open(meta_path, 'rb') as f:
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67 meta_estimator = load_model(f)
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68 else:
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69 estimator_json = params['meta_estimator']['estimator_selector']
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70 meta_estimator = get_estimator(estimator_json)
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71
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72 options = params['algo_selection']['options']
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73
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74 cv_selector = options.pop('cv_selector', None)
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75 if cv_selector:
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76 splitter, groups = get_cv(cv_selector)
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77 options['cv'] = splitter
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78 # set n_jobs
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79 options['n_jobs'] = N_JOBS
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80
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81 if params['algo_selection']['estimator_type'] == 'StackingCVClassifier':
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82 ensemble_estimator = StackingCVClassifier(
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83 classifiers=base_estimators,
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84 meta_classifier=meta_estimator,
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85 **options)
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86
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87 elif params['algo_selection']['estimator_type'] == 'StackingClassifier':
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88 ensemble_estimator = StackingClassifier(
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89 classifiers=base_estimators,
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90 meta_classifier=meta_estimator,
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91 **options)
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92
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93 elif params['algo_selection']['estimator_type'] == 'StackingCVRegressor':
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94 ensemble_estimator = StackingCVRegressor(
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95 regressors=base_estimators,
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96 meta_regressor=meta_estimator,
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97 **options)
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98
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99 else:
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100 ensemble_estimator = StackingRegressor(
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101 regressors=base_estimators,
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102 meta_regressor=meta_estimator,
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103 **options)
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104
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105 print(ensemble_estimator)
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106 for base_est in base_estimators:
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107 print(base_est)
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108
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109 with open(output_obj, 'wb') as out_handler:
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110 pickle.dump(ensemble_estimator, out_handler, pickle.HIGHEST_PROTOCOL)
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111
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112 if params['get_params'] and outfile_params:
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113 results = get_search_params(ensemble_estimator)
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114 df = pd.DataFrame(results, columns=['', 'Parameter', 'Value'])
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115 df.to_csv(outfile_params, sep='\t', index=False)
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116
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117
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118 if __name__ == '__main__':
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119 aparser = argparse.ArgumentParser()
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120 aparser.add_argument("-b", "--bases", dest="bases")
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121 aparser.add_argument("-m", "--meta", dest="meta")
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122 aparser.add_argument("-i", "--inputs", dest="inputs")
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123 aparser.add_argument("-o", "--outfile", dest="outfile")
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124 aparser.add_argument("-p", "--outfile_params", dest="outfile_params")
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125 args = aparser.parse_args()
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126
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127 main(args.inputs, args.outfile, base_paths=args.bases,
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128 meta_path=args.meta, outfile_params=args.outfile_params)