Mercurial > repos > bgruening > sklearn_feature_selection

annotate stacking_ensembles.py @ 39:46b43ee6d367 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5eca9041ce0154eded5aec07195502d5eb3cdd4f
author bgruening
date Fri, 03 Nov 2023 22:33:22 +0000
parents 61edd9e5c17f
children
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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ec25331946b8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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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 sys
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5 import warnings
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6 from distutils.version import LooseVersion as Version
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8 import mlxtend.classifier
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9 import mlxtend.regressor
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10 from galaxy_ml import __version__ as galaxy_ml_version
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11 from galaxy_ml.model_persist import dump_model_to_h5, load_model_from_h5
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12 from galaxy_ml.utils import get_cv, get_estimator
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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):
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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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34 """
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35 with open(inputs_path, "r") as param_handler:
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36 params = json.load(param_handler)
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37
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38 estimator_type = params["algo_selection"]["estimator_type"]
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39 # get base estimators
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40 base_estimators = []
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41 for idx, base_file in enumerate(base_paths.split(",")):
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42 if base_file and base_file != "None":
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43 model = load_model_from_h5(base_file)
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44 else:
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45 estimator_json = params["base_est_builder"][idx]["estimator_selector"]
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46 model = get_estimator(estimator_json)
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48 if estimator_type.startswith("sklearn"):
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49 named = model.__class__.__name__.lower()
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50 named = "base_%d_%s" % (idx, named)
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51 base_estimators.append((named, model))
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52 else:
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53 base_estimators.append(model)
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54
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55 # get meta estimator, if applicable
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56 if estimator_type.startswith("mlxtend"):
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57 if meta_path:
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58 meta_estimator = load_model_from_h5(meta_path)
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59 else:
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60 estimator_json = params["algo_selection"]["meta_estimator"][
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61 "estimator_selector"
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62 ]
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63 meta_estimator = get_estimator(estimator_json)
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64
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65 options = params["algo_selection"]["options"]
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67 cv_selector = options.pop("cv_selector", None)
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68 if cv_selector:
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69 if Version(galaxy_ml_version) < Version("0.8.3"):
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70 cv_selector.pop("n_stratification_bins", None)
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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(
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92 classifiers=base_estimators, meta_classifier=meta_estimator, **options
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93 )
18
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94
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95 else:
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96 ensemble_estimator = klass(
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97 regressors=base_estimators, meta_regressor=meta_estimator, **options
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98 )
18
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99
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100 print(ensemble_estimator)
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101 for base_est in base_estimators:
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102 print(base_est)
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103
35
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104 dump_model_to_h5(ensemble_estimator, output_obj)
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105
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106
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107 if __name__ == "__main__":
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108 aparser = argparse.ArgumentParser()
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109 aparser.add_argument("-b", "--bases", dest="bases")
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110 aparser.add_argument("-m", "--meta", dest="meta")
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111 aparser.add_argument("-i", "--inputs", dest="inputs")
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112 aparser.add_argument("-o", "--outfile", dest="outfile")
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113 args = aparser.parse_args()
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114
35
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115 main(args.inputs, args.outfile, base_paths=args.bases, meta_path=args.meta)