Mercurial > repos > bgruening > sklearn_data_preprocess

annotate search_model_validation.py @ 23:d6b8103c909c draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
author bgruening
date Sun, 30 Dec 2018 01:58:00 -0500
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children 9e43ee712723
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d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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diff changeset
1 import imblearn
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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2 import json
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3 import numpy as np
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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4 import os
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5 import pandas
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6 import pickle
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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7 import skrebate
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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8 import sklearn
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9 import sys
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10 import xgboost
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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11 import warnings
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12 from imblearn import under_sampling, over_sampling, combine
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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13 from imblearn.pipeline import Pipeline as imbPipeline
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14 from sklearn import (cluster, compose, decomposition, ensemble, feature_extraction,
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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15 feature_selection, gaussian_process, kernel_approximation, metrics,
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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16 model_selection, naive_bayes, neighbors, pipeline, preprocessing,
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17 svm, linear_model, tree, discriminant_analysis)
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18 from sklearn.exceptions import FitFailedWarning
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19 from sklearn.externals import joblib
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20 from utils import get_cv, get_scoring, get_X_y, load_model, read_columns, SafeEval
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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21
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23 N_JOBS = int(os.environ.get('GALAXY_SLOTS', 1))
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26 def get_search_params(params_builder):
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27 search_params = {}
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28 safe_eval = SafeEval(load_scipy=True, load_numpy=True)
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29 safe_eval_es = SafeEval(load_estimators=True)
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30
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31 for p in params_builder['param_set']:
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32 search_p = p['search_param_selector']['search_p']
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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33 if search_p.strip() == '':
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34 continue
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35 param_type = p['search_param_selector']['selected_param_type']
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36
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37 lst = search_p.split(':')
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38 assert (len(lst) == 2), "Error, make sure there is one and only one colon in search parameter input."
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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39 literal = lst[1].strip()
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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40 param_name = lst[0].strip()
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41 if param_name:
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42 if param_name.lower() == 'n_jobs':
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43 sys.exit("Parameter `%s` is invalid for search." %param_name)
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44 elif not param_name.endswith('-'):
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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45 ev = safe_eval(literal)
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46 if param_type == 'final_estimator_p':
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47 search_params['estimator__' + param_name] = ev
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48 else:
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49 search_params['preprocessing_' + param_type[5:6] + '__' + param_name] = ev
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50 else:
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51 # only for estimator eval, add `-` to the end of param
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52 #TODO maybe add regular express check
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53 ev = safe_eval_es(literal)
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54 for obj in ev:
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55 if 'n_jobs' in obj.get_params():
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56 obj.set_params( n_jobs=N_JOBS )
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57 if param_type == 'final_estimator_p':
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58 search_params['estimator__' + param_name[:-1]] = ev
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59 else:
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60 search_params['preprocessing_' + param_type[5:6] + '__' + param_name[:-1]] = ev
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61 elif param_type != 'final_estimator_p':
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62 #TODO regular express check ?
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63 ev = safe_eval_es(literal)
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64 preprocessors = [preprocessing.StandardScaler(), preprocessing.Binarizer(), preprocessing.Imputer(),
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65 preprocessing.MaxAbsScaler(), preprocessing.Normalizer(), preprocessing.MinMaxScaler(),
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66 preprocessing.PolynomialFeatures(),preprocessing.RobustScaler(),
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67 feature_selection.SelectKBest(), feature_selection.GenericUnivariateSelect(),
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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68 feature_selection.SelectPercentile(), feature_selection.SelectFpr(), feature_selection.SelectFdr(),
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69 feature_selection.SelectFwe(), feature_selection.VarianceThreshold(),
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70 decomposition.FactorAnalysis(random_state=0), decomposition.FastICA(random_state=0), decomposition.IncrementalPCA(),
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71 decomposition.KernelPCA(random_state=0, n_jobs=N_JOBS), decomposition.LatentDirichletAllocation(random_state=0, n_jobs=N_JOBS),
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72 decomposition.MiniBatchDictionaryLearning(random_state=0, n_jobs=N_JOBS),
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73 decomposition.MiniBatchSparsePCA(random_state=0, n_jobs=N_JOBS), decomposition.NMF(random_state=0),
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74 decomposition.PCA(random_state=0), decomposition.SparsePCA(random_state=0, n_jobs=N_JOBS),
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75 decomposition.TruncatedSVD(random_state=0),
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76 kernel_approximation.Nystroem(random_state=0), kernel_approximation.RBFSampler(random_state=0),
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77 kernel_approximation.AdditiveChi2Sampler(), kernel_approximation.SkewedChi2Sampler(random_state=0),
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78 cluster.FeatureAgglomeration(),
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79 skrebate.ReliefF(n_jobs=N_JOBS), skrebate.SURF(n_jobs=N_JOBS), skrebate.SURFstar(n_jobs=N_JOBS),
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80 skrebate.MultiSURF(n_jobs=N_JOBS), skrebate.MultiSURFstar(n_jobs=N_JOBS),
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81 imblearn.under_sampling.ClusterCentroids(random_state=0, n_jobs=N_JOBS),
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82 imblearn.under_sampling.CondensedNearestNeighbour(random_state=0, n_jobs=N_JOBS),
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83 imblearn.under_sampling.EditedNearestNeighbours(random_state=0, n_jobs=N_JOBS),
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84 imblearn.under_sampling.RepeatedEditedNearestNeighbours(random_state=0, n_jobs=N_JOBS),
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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85 imblearn.under_sampling.AllKNN(random_state=0, n_jobs=N_JOBS),
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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86 imblearn.under_sampling.InstanceHardnessThreshold(random_state=0, n_jobs=N_JOBS),
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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87 imblearn.under_sampling.NearMiss(random_state=0, n_jobs=N_JOBS),
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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88 imblearn.under_sampling.NeighbourhoodCleaningRule(random_state=0, n_jobs=N_JOBS),
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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89 imblearn.under_sampling.OneSidedSelection(random_state=0, n_jobs=N_JOBS),
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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90 imblearn.under_sampling.RandomUnderSampler(random_state=0),
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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91 imblearn.under_sampling.TomekLinks(random_state=0, n_jobs=N_JOBS),
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92 imblearn.over_sampling.ADASYN(random_state=0, n_jobs=N_JOBS),
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93 imblearn.over_sampling.RandomOverSampler(random_state=0),
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94 imblearn.over_sampling.SMOTE(random_state=0, n_jobs=N_JOBS),
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95 imblearn.over_sampling.SVMSMOTE(random_state=0, n_jobs=N_JOBS),
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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96 imblearn.over_sampling.BorderlineSMOTE(random_state=0, n_jobs=N_JOBS),
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97 imblearn.over_sampling.SMOTENC(categorical_features=[], random_state=0, n_jobs=N_JOBS),
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98 imblearn.combine.SMOTEENN(random_state=0), imblearn.combine.SMOTETomek(random_state=0)]
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99 newlist = []
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100 for obj in ev:
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101 if obj is None:
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102 newlist.append(None)
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103 elif obj == 'all_0':
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104 newlist.extend(preprocessors[0:36])
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105 elif obj == 'sk_prep_all': # no KernalCenter()
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106 newlist.extend(preprocessors[0:8])
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107 elif obj == 'fs_all':
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108 newlist.extend(preprocessors[8:15])
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109 elif obj == 'decomp_all':
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110 newlist.extend(preprocessors[15:26])
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111 elif obj == 'k_appr_all':
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112 newlist.extend(preprocessors[26:30])
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113 elif obj == 'reb_all':
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114 newlist.extend(preprocessors[31:36])
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115 elif obj == 'imb_all':
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116 newlist.extend(preprocessors[36:55])
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117 elif type(obj) is int and -1 < obj < len(preprocessors):
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118 newlist.append(preprocessors[obj])
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119 elif hasattr(obj, 'get_params'): # user object
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120 if 'n_jobs' in obj.get_params():
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121 newlist.append( obj.set_params(n_jobs=N_JOBS) )
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122 else:
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123 newlist.append(obj)
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124 else:
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125 sys.exit("Unsupported preprocessor type: %r" %(obj))
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126 search_params['preprocessing_' + param_type[5:6]] = newlist
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127 else:
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128 sys.exit("Parameter name of the final estimator can't be skipped!")
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129
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130 return search_params
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131
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132
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133 if __name__ == '__main__':
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134
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135 warnings.simplefilter('ignore')
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136
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137 input_json_path = sys.argv[1]
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138 with open(input_json_path, 'r') as param_handler:
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139 params = json.load(param_handler)
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140
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141 infile_pipeline = sys.argv[2]
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142 infile1 = sys.argv[3]
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143 infile2 = sys.argv[4]
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144 outfile_result = sys.argv[5]
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145 if len(sys.argv) > 6:
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146 outfile_estimator = sys.argv[6]
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147 else:
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148 outfile_estimator = None
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149
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150 params_builder = params['search_schemes']['search_params_builder']
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151
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152 input_type = params['input_options']['selected_input']
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153 if input_type == 'tabular':
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154 header = 'infer' if params['input_options']['header1'] else None
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155 column_option = params['input_options']['column_selector_options_1']['selected_column_selector_option']
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156 if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']:
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157 c = params['input_options']['column_selector_options_1']['col1']
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158 else:
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159 c = None
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160 X = read_columns(
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161 infile1,
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162 c = c,
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163 c_option = column_option,
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164 sep='\t',
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165 header=header,
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166 parse_dates=True
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167 )
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168 else:
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169 X = mmread(open(infile1, 'r'))
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170
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171 header = 'infer' if params['input_options']['header2'] else None
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172 column_option = params['input_options']['column_selector_options_2']['selected_column_selector_option2']
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173 if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']:
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174 c = params['input_options']['column_selector_options_2']['col2']
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175 else:
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176 c = None
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177 y = read_columns(
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178 infile2,
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179 c = c,
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180 c_option = column_option,
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181 sep='\t',
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182 header=header,
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183 parse_dates=True
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184 )
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185 y = y.ravel()
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186
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187 optimizer = params['search_schemes']['selected_search_scheme']
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188 optimizer = getattr(model_selection, optimizer)
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189
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190 options = params['search_schemes']['options']
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191 splitter, groups = get_cv(options.pop('cv_selector'))
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192 if groups is None:
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193 options['cv'] = splitter
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194 elif groups == '':
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195 options['cv'] = list( splitter.split(X, y, groups=None) )
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196 else:
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197 options['cv'] = list( splitter.split(X, y, groups=groups) )
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198 options['n_jobs'] = N_JOBS
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199 primary_scoring = options['scoring']['primary_scoring']
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200 options['scoring'] = get_scoring(options['scoring'])
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201 if options['error_score']:
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202 options['error_score'] = 'raise'
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203 else:
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204 options['error_score'] = np.NaN
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205 if options['refit'] and isinstance(options['scoring'], dict):
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206 options['refit'] = 'primary'
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207 if 'pre_dispatch' in options and options['pre_dispatch'] == '':
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208 options['pre_dispatch'] = None
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209
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210 with open(infile_pipeline, 'rb') as pipeline_handler:
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211 pipeline = load_model(pipeline_handler)
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212
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213 search_params = get_search_params(params_builder)
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214 searcher = optimizer(pipeline, search_params, **options)
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215
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216 if options['error_score'] == 'raise':
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217 searcher.fit(X, y)
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218 else:
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219 warnings.simplefilter('always', FitFailedWarning)
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220 with warnings.catch_warnings(record=True) as w:
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221 try:
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222 searcher.fit(X, y)
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223 except ValueError:
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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224 pass
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225 for warning in w:
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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226 print(repr(warning.message))
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227
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228 cv_result = pandas.DataFrame(searcher.cv_results_)
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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229 cv_result.rename(inplace=True, columns={'mean_test_primary': 'mean_test_'+primary_scoring, 'rank_test_primary': 'rank_test_'+primary_scoring})
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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230 cv_result.to_csv(path_or_buf=outfile_result, sep='\t', header=True, index=False)
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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231
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232 if outfile_estimator:
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233 with open(outfile_estimator, 'wb') as output_handler:
d6b8103c909c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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234 pickle.dump(searcher.best_estimator_, output_handler, pickle.HIGHEST_PROTOCOL)