Mercurial > repos > bgruening > keras_model_builder

annotate stacking_ensembles.py @ 8:449a757be9c9 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
author bgruening
date Tue, 13 Apr 2021 18:29:36 +0000
parents 4bd0bd2add7f
children e3b420d0b71a
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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 sys
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9 import warnings
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10 from galaxy_ml.utils import load_model, get_cv, get_estimator, get_search_params
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13 warnings.filterwarnings("ignore")
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15 N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1))
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18 def main(inputs_path, output_obj, base_paths=None, meta_path=None, outfile_params=None):
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19 """
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20 Parameter
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21 ---------
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22 inputs_path : str
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23 File path for Galaxy parameters
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25 output_obj : str
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26 File path for ensemble estimator ouput
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28 base_paths : str
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29 File path or paths concatenated by comma.
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31 meta_path : str
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32 File path
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34 outfile_params : str
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35 File path for params output
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36 """
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37 with open(inputs_path, "r") as param_handler:
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38 params = json.load(param_handler)
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39
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40 estimator_type = params["algo_selection"]["estimator_type"]
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41 # get base estimators
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42 base_estimators = []
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43 for idx, base_file in enumerate(base_paths.split(",")):
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44 if base_file and base_file != "None":
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45 with open(base_file, "rb") as handler:
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46 model = load_model(handler)
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47 else:
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48 estimator_json = params["base_est_builder"][idx]["estimator_selector"]
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49 model = get_estimator(estimator_json)
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50
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51 if estimator_type.startswith("sklearn"):
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52 named = model.__class__.__name__.lower()
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53 named = "base_%d_%s" % (idx, named)
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54 base_estimators.append((named, model))
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55 else:
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56 base_estimators.append(model)
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57
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58 # get meta estimator, if applicable
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59 if estimator_type.startswith("mlxtend"):
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60 if meta_path:
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61 with open(meta_path, "rb") as f:
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62 meta_estimator = load_model(f)
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63 else:
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64 estimator_json = params["algo_selection"]["meta_estimator"]["estimator_selector"]
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65 meta_estimator = get_estimator(estimator_json)
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67 options = params["algo_selection"]["options"]
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69 cv_selector = options.pop("cv_selector", None)
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70 if cv_selector:
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71 splitter, _groups = get_cv(cv_selector)
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72 options["cv"] = splitter
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73 # set n_jobs
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74 options["n_jobs"] = N_JOBS
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76 weights = options.pop("weights", None)
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77 if weights:
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78 weights = ast.literal_eval(weights)
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79 if weights:
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80 options["weights"] = weights
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81
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82 mod_and_name = estimator_type.split("_")
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83 mod = sys.modules[mod_and_name[0]]
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84 klass = getattr(mod, mod_and_name[1])
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85
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86 if estimator_type.startswith("sklearn"):
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87 options["n_jobs"] = N_JOBS
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88 ensemble_estimator = klass(base_estimators, **options)
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89
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90 elif mod == mlxtend.classifier:
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91 ensemble_estimator = klass(classifiers=base_estimators, meta_classifier=meta_estimator, **options)
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92
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93 else:
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94 ensemble_estimator = klass(regressors=base_estimators, meta_regressor=meta_estimator, **options)
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95
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96 print(ensemble_estimator)
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97 for base_est in base_estimators:
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98 print(base_est)
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99
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100 with open(output_obj, "wb") as out_handler:
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101 pickle.dump(ensemble_estimator, out_handler, pickle.HIGHEST_PROTOCOL)
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102
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103 if params["get_params"] and outfile_params:
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104 results = get_search_params(ensemble_estimator)
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105 df = pd.DataFrame(results, columns=["", "Parameter", "Value"])
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106 df.to_csv(outfile_params, sep="\t", index=False)
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107
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108
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109 if __name__ == "__main__":
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110 aparser = argparse.ArgumentParser()
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111 aparser.add_argument("-b", "--bases", dest="bases")
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112 aparser.add_argument("-m", "--meta", dest="meta")
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113 aparser.add_argument("-i", "--inputs", dest="inputs")
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114 aparser.add_argument("-o", "--outfile", dest="outfile")
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115 aparser.add_argument("-p", "--outfile_params", dest="outfile_params")
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116 args = aparser.parse_args()
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117
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118 main(
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119 args.inputs,
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120 args.outfile,
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121 base_paths=args.bases,
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122 meta_path=args.meta,
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123 outfile_params=args.outfile_params,
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124 )