Mercurial > repos > bgruening > keras_model_builder

annotate stacking_ensembles.py @ 1:90da8d8aa664 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ba6a47bdf76bbf4cb276206ac1a8cbf61332fd16"
author bgruening
date Fri, 13 Sep 2019 12:01:32 -0400
parents ac8bef635fcb
children 25d4cbb56e1a
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ac8bef635fcb planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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1 import argparse
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2 import ast
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3 import json
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4 import mlxtend.regressor
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5 import mlxtend.classifier
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6 import pandas as pd
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7 import pickle
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8 import sklearn
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9 import sys
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10 import warnings
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11 from sklearn import ensemble
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12
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13 from galaxy_ml.utils import (load_model, get_cv, get_estimator,
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14 get_search_params)
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17 warnings.filterwarnings('ignore')
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19 N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
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22 def main(inputs_path, output_obj, base_paths=None, meta_path=None,
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23 outfile_params=None):
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24 """
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25 Parameter
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26 ---------
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27 inputs_path : str
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28 File path for Galaxy parameters
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30 output_obj : str
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31 File path for ensemble estimator ouput
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33 base_paths : str
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34 File path or paths concatenated by comma.
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36 meta_path : str
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37 File path
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38
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39 outfile_params : str
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40 File path for params output
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41 """
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42 with open(inputs_path, 'r') as param_handler:
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43 params = json.load(param_handler)
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44
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45 estimator_type = params['algo_selection']['estimator_type']
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46 # get base estimators
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47 base_estimators = []
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48 for idx, base_file in enumerate(base_paths.split(',')):
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49 if base_file and base_file != 'None':
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50 with open(base_file, 'rb') as handler:
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51 model = load_model(handler)
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52 else:
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53 estimator_json = (params['base_est_builder'][idx]
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54 ['estimator_selector'])
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55 model = get_estimator(estimator_json)
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56
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57 if estimator_type.startswith('sklearn'):
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58 named = model.__class__.__name__.lower()
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59 named = 'base_%d_%s' % (idx, named)
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60 base_estimators.append((named, model))
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61 else:
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62 base_estimators.append(model)
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63
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64 # get meta estimator, if applicable
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65 if estimator_type.startswith('mlxtend'):
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66 if meta_path:
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67 with open(meta_path, 'rb') as f:
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68 meta_estimator = load_model(f)
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69 else:
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70 estimator_json = (params['algo_selection']
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71 ['meta_estimator']['estimator_selector'])
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72 meta_estimator = get_estimator(estimator_json)
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73
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74 options = params['algo_selection']['options']
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75
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76 cv_selector = options.pop('cv_selector', None)
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77 if cv_selector:
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78 splitter, groups = get_cv(cv_selector)
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79 options['cv'] = splitter
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80 # set n_jobs
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81 options['n_jobs'] = N_JOBS
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82
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83 weights = options.pop('weights', None)
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84 if weights:
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85 options['weights'] = ast.literal_eval(weights)
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86
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87 mod_and_name = estimator_type.split('_')
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88 mod = sys.modules[mod_and_name[0]]
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89 klass = getattr(mod, mod_and_name[1])
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90
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91 if estimator_type.startswith('sklearn'):
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92 options['n_jobs'] = N_JOBS
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93 ensemble_estimator = klass(base_estimators, **options)
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94
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95 elif mod == mlxtend.classifier:
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96 ensemble_estimator = klass(
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97 classifiers=base_estimators,
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98 meta_classifier=meta_estimator,
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99 **options)
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100
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101 else:
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102 ensemble_estimator = klass(
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103 regressors=base_estimators,
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104 meta_regressor=meta_estimator,
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105 **options)
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106
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107 print(ensemble_estimator)
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108 for base_est in base_estimators:
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109 print(base_est)
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110
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111 with open(output_obj, 'wb') as out_handler:
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112 pickle.dump(ensemble_estimator, out_handler, pickle.HIGHEST_PROTOCOL)
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113
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114 if params['get_params'] and outfile_params:
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115 results = get_search_params(ensemble_estimator)
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116 df = pd.DataFrame(results, columns=['', 'Parameter', 'Value'])
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117 df.to_csv(outfile_params, sep='\t', index=False)
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118
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119
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120 if __name__ == '__main__':
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121 aparser = argparse.ArgumentParser()
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122 aparser.add_argument("-b", "--bases", dest="bases")
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123 aparser.add_argument("-m", "--meta", dest="meta")
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124 aparser.add_argument("-i", "--inputs", dest="inputs")
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125 aparser.add_argument("-o", "--outfile", dest="outfile")
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126 aparser.add_argument("-p", "--outfile_params", dest="outfile_params")
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127 args = aparser.parse_args()
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128
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129 main(args.inputs, args.outfile, base_paths=args.bases,
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130 meta_path=args.meta, outfile_params=args.outfile_params)