Mercurial > repos > bgruening > sklearn_feature_selection

annotate stacking_ensembles.py @ 33:b0b584350b7e draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f031d8ddfb73cec24572648666ac44ee47f08aad
author bgruening
date Thu, 11 Aug 2022 08:04:32 +0000
parents 75e9f6fb6536
children b9d86fc6359d
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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 pickle
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5 import sys
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6 import warnings
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8 import mlxtend.classifier
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9 import mlxtend.regressor
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10 import pandas as pd
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11 from galaxy_ml.utils import (get_cv, get_estimator, get_search_params,
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12 load_model)
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14 warnings.filterwarnings("ignore")
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16 N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1))
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19 def main(inputs_path, output_obj, base_paths=None, meta_path=None, outfile_params=None):
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20 """
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21 Parameter
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22 ---------
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23 inputs_path : str
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24 File path for Galaxy parameters
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26 output_obj : str
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27 File path for ensemble estimator ouput
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29 base_paths : str
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30 File path or paths concatenated by comma.
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32 meta_path : str
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33 File path
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35 outfile_params : str
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36 File path for params output
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37 """
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38 with open(inputs_path, "r") as param_handler:
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39 params = json.load(param_handler)
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40
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41 estimator_type = params["algo_selection"]["estimator_type"]
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42 # get base estimators
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43 base_estimators = []
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44 for idx, base_file in enumerate(base_paths.split(",")):
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45 if base_file and base_file != "None":
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46 with open(base_file, "rb") as handler:
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47 model = load_model(handler)
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48 else:
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49 estimator_json = params["base_est_builder"][idx]["estimator_selector"]
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50 model = get_estimator(estimator_json)
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52 if estimator_type.startswith("sklearn"):
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53 named = model.__class__.__name__.lower()
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54 named = "base_%d_%s" % (idx, named)
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55 base_estimators.append((named, model))
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56 else:
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57 base_estimators.append(model)
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59 # get meta estimator, if applicable
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60 if estimator_type.startswith("mlxtend"):
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61 if meta_path:
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62 with open(meta_path, "rb") as f:
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63 meta_estimator = load_model(f)
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64 else:
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65 estimator_json = params["algo_selection"]["meta_estimator"][
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66 "estimator_selector"
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67 ]
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68 meta_estimator = get_estimator(estimator_json)
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70 options = params["algo_selection"]["options"]
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72 cv_selector = options.pop("cv_selector", None)
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73 if cv_selector:
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74 splitter, _groups = get_cv(cv_selector)
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75 options["cv"] = splitter
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76 # set n_jobs
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77 options["n_jobs"] = N_JOBS
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79 weights = options.pop("weights", None)
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80 if weights:
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81 weights = ast.literal_eval(weights)
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82 if weights:
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83 options["weights"] = weights
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85 mod_and_name = estimator_type.split("_")
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86 mod = sys.modules[mod_and_name[0]]
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87 klass = getattr(mod, mod_and_name[1])
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88
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89 if estimator_type.startswith("sklearn"):
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90 options["n_jobs"] = N_JOBS
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91 ensemble_estimator = klass(base_estimators, **options)
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92
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93 elif mod == mlxtend.classifier:
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94 ensemble_estimator = klass(
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95 classifiers=base_estimators, meta_classifier=meta_estimator, **options
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96 )
18
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97
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98 else:
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99 ensemble_estimator = klass(
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100 regressors=base_estimators, meta_regressor=meta_estimator, **options
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101 )
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102
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103 print(ensemble_estimator)
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104 for base_est in base_estimators:
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105 print(base_est)
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106
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107 with open(output_obj, "wb") as out_handler:
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108 pickle.dump(ensemble_estimator, out_handler, pickle.HIGHEST_PROTOCOL)
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109
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110 if params["get_params"] and outfile_params:
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111 results = get_search_params(ensemble_estimator)
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112 df = pd.DataFrame(results, columns=["", "Parameter", "Value"])
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113 df.to_csv(outfile_params, sep="\t", index=False)
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114
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115
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116 if __name__ == "__main__":
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117 aparser = argparse.ArgumentParser()
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118 aparser.add_argument("-b", "--bases", dest="bases")
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119 aparser.add_argument("-m", "--meta", dest="meta")
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120 aparser.add_argument("-i", "--inputs", dest="inputs")
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121 aparser.add_argument("-o", "--outfile", dest="outfile")
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122 aparser.add_argument("-p", "--outfile_params", dest="outfile_params")
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123 args = aparser.parse_args()
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124
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125 main(
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126 args.inputs,
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127 args.outfile,
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128 base_paths=args.bases,
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129 meta_path=args.meta,
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130 outfile_params=args.outfile_params,
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131 )