Mercurial > repos > bgruening > sklearn_train_test_eval

annotate search_model_validation.py @ 0:68aaa903052a draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
author bgruening
date Fri, 09 Aug 2019 07:09:06 -0400
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children cc49634df38f
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68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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1 import argparse
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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2 import collections
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3 import imblearn
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4 import joblib
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5 import json
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6 import numpy as np
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7 import pandas as pd
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8 import pickle
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9 import skrebate
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10 import sklearn
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11 import sys
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12 import xgboost
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13 import warnings
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14 from imblearn import under_sampling, over_sampling, combine
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15 from scipy.io import mmread
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16 from mlxtend import classifier, regressor
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17 from sklearn.base import clone
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18 from sklearn import (cluster, compose, decomposition, ensemble,
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19 feature_extraction, feature_selection,
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20 gaussian_process, kernel_approximation, metrics,
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21 model_selection, naive_bayes, neighbors,
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22 pipeline, preprocessing, svm, linear_model,
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23 tree, discriminant_analysis)
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24 from sklearn.exceptions import FitFailedWarning
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25 from sklearn.model_selection._validation import _score, cross_validate
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26 from sklearn.model_selection import _search, _validation
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27
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28 from galaxy_ml.utils import (SafeEval, get_cv, get_scoring, load_model,
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29 read_columns, try_get_attr, get_module)
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31
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32 _fit_and_score = try_get_attr('galaxy_ml.model_validations', '_fit_and_score')
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33 setattr(_search, '_fit_and_score', _fit_and_score)
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34 setattr(_validation, '_fit_and_score', _fit_and_score)
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36 N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
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37 CACHE_DIR = './cached'
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38 NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', '_path',
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39 'nthread', 'callbacks')
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40 ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', 'ReduceLROnPlateau',
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41 'CSVLogger', 'None')
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42
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44 def _eval_search_params(params_builder):
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45 search_params = {}
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46
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47 for p in params_builder['param_set']:
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48 search_list = p['sp_list'].strip()
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49 if search_list == '':
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50 continue
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51
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52 param_name = p['sp_name']
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53 if param_name.lower().endswith(NON_SEARCHABLE):
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54 print("Warning: `%s` is not eligible for search and was "
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55 "omitted!" % param_name)
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56 continue
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57
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58 if not search_list.startswith(':'):
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59 safe_eval = SafeEval(load_scipy=True, load_numpy=True)
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60 ev = safe_eval(search_list)
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61 search_params[param_name] = ev
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62 else:
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63 # Have `:` before search list, asks for estimator evaluatio
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64 safe_eval_es = SafeEval(load_estimators=True)
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65 search_list = search_list[1:].strip()
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66 # TODO maybe add regular express check
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67 ev = safe_eval_es(search_list)
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68 preprocessings = (
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69 preprocessing.StandardScaler(), preprocessing.Binarizer(),
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70 preprocessing.MaxAbsScaler(),
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71 preprocessing.Normalizer(), preprocessing.MinMaxScaler(),
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72 preprocessing.PolynomialFeatures(),
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73 preprocessing.RobustScaler(), feature_selection.SelectKBest(),
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74 feature_selection.GenericUnivariateSelect(),
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75 feature_selection.SelectPercentile(),
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76 feature_selection.SelectFpr(), feature_selection.SelectFdr(),
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77 feature_selection.SelectFwe(),
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78 feature_selection.VarianceThreshold(),
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79 decomposition.FactorAnalysis(random_state=0),
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80 decomposition.FastICA(random_state=0),
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81 decomposition.IncrementalPCA(),
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82 decomposition.KernelPCA(random_state=0, n_jobs=N_JOBS),
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83 decomposition.LatentDirichletAllocation(
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84 random_state=0, n_jobs=N_JOBS),
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85 decomposition.MiniBatchDictionaryLearning(
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86 random_state=0, n_jobs=N_JOBS),
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87 decomposition.MiniBatchSparsePCA(
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88 random_state=0, n_jobs=N_JOBS),
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89 decomposition.NMF(random_state=0),
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90 decomposition.PCA(random_state=0),
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91 decomposition.SparsePCA(random_state=0, n_jobs=N_JOBS),
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92 decomposition.TruncatedSVD(random_state=0),
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93 kernel_approximation.Nystroem(random_state=0),
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94 kernel_approximation.RBFSampler(random_state=0),
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95 kernel_approximation.AdditiveChi2Sampler(),
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96 kernel_approximation.SkewedChi2Sampler(random_state=0),
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97 cluster.FeatureAgglomeration(),
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98 skrebate.ReliefF(n_jobs=N_JOBS),
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99 skrebate.SURF(n_jobs=N_JOBS),
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100 skrebate.SURFstar(n_jobs=N_JOBS),
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101 skrebate.MultiSURF(n_jobs=N_JOBS),
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102 skrebate.MultiSURFstar(n_jobs=N_JOBS),
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103 imblearn.under_sampling.ClusterCentroids(
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104 random_state=0, n_jobs=N_JOBS),
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105 imblearn.under_sampling.CondensedNearestNeighbour(
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106 random_state=0, n_jobs=N_JOBS),
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107 imblearn.under_sampling.EditedNearestNeighbours(
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108 random_state=0, n_jobs=N_JOBS),
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109 imblearn.under_sampling.RepeatedEditedNearestNeighbours(
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110 random_state=0, n_jobs=N_JOBS),
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111 imblearn.under_sampling.AllKNN(random_state=0, n_jobs=N_JOBS),
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112 imblearn.under_sampling.InstanceHardnessThreshold(
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113 random_state=0, n_jobs=N_JOBS),
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114 imblearn.under_sampling.NearMiss(
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115 random_state=0, n_jobs=N_JOBS),
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116 imblearn.under_sampling.NeighbourhoodCleaningRule(
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117 random_state=0, n_jobs=N_JOBS),
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118 imblearn.under_sampling.OneSidedSelection(
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119 random_state=0, n_jobs=N_JOBS),
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120 imblearn.under_sampling.RandomUnderSampler(
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121 random_state=0),
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122 imblearn.under_sampling.TomekLinks(
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123 random_state=0, n_jobs=N_JOBS),
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124 imblearn.over_sampling.ADASYN(random_state=0, n_jobs=N_JOBS),
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125 imblearn.over_sampling.RandomOverSampler(random_state=0),
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126 imblearn.over_sampling.SMOTE(random_state=0, n_jobs=N_JOBS),
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127 imblearn.over_sampling.SVMSMOTE(random_state=0, n_jobs=N_JOBS),
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128 imblearn.over_sampling.BorderlineSMOTE(
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129 random_state=0, n_jobs=N_JOBS),
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130 imblearn.over_sampling.SMOTENC(
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131 categorical_features=[], random_state=0, n_jobs=N_JOBS),
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132 imblearn.combine.SMOTEENN(random_state=0),
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133 imblearn.combine.SMOTETomek(random_state=0))
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134 newlist = []
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135 for obj in ev:
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136 if obj is None:
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137 newlist.append(None)
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138 elif obj == 'all_0':
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139 newlist.extend(preprocessings[0:35])
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140 elif obj == 'sk_prep_all': # no KernalCenter()
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141 newlist.extend(preprocessings[0:7])
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142 elif obj == 'fs_all':
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143 newlist.extend(preprocessings[7:14])
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144 elif obj == 'decomp_all':
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145 newlist.extend(preprocessings[14:25])
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146 elif obj == 'k_appr_all':
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147 newlist.extend(preprocessings[25:29])
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148 elif obj == 'reb_all':
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149 newlist.extend(preprocessings[30:35])
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150 elif obj == 'imb_all':
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151 newlist.extend(preprocessings[35:54])
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152 elif type(obj) is int and -1 < obj < len(preprocessings):
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153 newlist.append(preprocessings[obj])
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154 elif hasattr(obj, 'get_params'): # user uploaded object
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155 if 'n_jobs' in obj.get_params():
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156 newlist.append(obj.set_params(n_jobs=N_JOBS))
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157 else:
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158 newlist.append(obj)
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159 else:
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160 sys.exit("Unsupported estimator type: %r" % (obj))
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161
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162 search_params[param_name] = newlist
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163
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164 return search_params
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165
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166
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167 def main(inputs, infile_estimator, infile1, infile2,
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168 outfile_result, outfile_object=None,
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169 outfile_weights=None, groups=None,
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170 ref_seq=None, intervals=None, targets=None,
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171 fasta_path=None):
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172 """
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173 Parameter
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174 ---------
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175 inputs : str
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176 File path to galaxy tool parameter
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177
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178 infile_estimator : str
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179 File path to estimator
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180
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181 infile1 : str
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182 File path to dataset containing features
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183
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184 infile2 : str
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185 File path to dataset containing target values
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186
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187 outfile_result : str
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188 File path to save the results, either cv_results or test result
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189
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190 outfile_object : str, optional
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191 File path to save searchCV object
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192
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193 outfile_weights : str, optional
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194 File path to save model weights
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195
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196 groups : str
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197 File path to dataset containing groups labels
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198
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199 ref_seq : str
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200 File path to dataset containing genome sequence file
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201
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202 intervals : str
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203 File path to dataset containing interval file
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204
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205 targets : str
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206 File path to dataset compressed target bed file
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207
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208 fasta_path : str
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209 File path to dataset containing fasta file
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210 """
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211 warnings.simplefilter('ignore')
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212
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213 with open(inputs, 'r') as param_handler:
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214 params = json.load(param_handler)
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215
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216 params_builder = params['search_schemes']['search_params_builder']
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217
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218 with open(infile_estimator, 'rb') as estimator_handler:
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219 estimator = load_model(estimator_handler)
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220 estimator_params = estimator.get_params()
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221
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222 # store read dataframe object
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223 loaded_df = {}
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224
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225 input_type = params['input_options']['selected_input']
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226 # tabular input
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227 if input_type == 'tabular':
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228 header = 'infer' if params['input_options']['header1'] else None
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229 column_option = (params['input_options']['column_selector_options_1']
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230 ['selected_column_selector_option'])
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231 if column_option in ['by_index_number', 'all_but_by_index_number',
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232 'by_header_name', 'all_but_by_header_name']:
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233 c = params['input_options']['column_selector_options_1']['col1']
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234 else:
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235 c = None
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236
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237 df_key = infile1 + repr(header)
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238 df = pd.read_csv(infile1, sep='\t', header=header,
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239 parse_dates=True)
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240 loaded_df[df_key] = df
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241
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242 X = read_columns(df, c=c, c_option=column_option).astype(float)
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243 # sparse input
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244 elif input_type == 'sparse':
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245 X = mmread(open(infile1, 'r'))
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246
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247 # fasta_file input
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248 elif input_type == 'seq_fasta':
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249 pyfaidx = get_module('pyfaidx')
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250 sequences = pyfaidx.Fasta(fasta_path)
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251 n_seqs = len(sequences.keys())
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252 X = np.arange(n_seqs)[:, np.newaxis]
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253 for param in estimator_params.keys():
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254 if param.endswith('fasta_path'):
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255 estimator.set_params(
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256 **{param: fasta_path})
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257 break
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258 else:
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259 raise ValueError(
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260 "The selected estimator doesn't support "
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261 "fasta file input! Please consider using "
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262 "KerasGBatchClassifier with "
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263 "FastaDNABatchGenerator/FastaProteinBatchGenerator "
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264 "or having GenomeOneHotEncoder/ProteinOneHotEncoder "
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265 "in pipeline!")
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266
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267 elif input_type == 'refseq_and_interval':
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268 path_params = {
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269 'data_batch_generator__ref_genome_path': ref_seq,
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270 'data_batch_generator__intervals_path': intervals,
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271 'data_batch_generator__target_path': targets
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272 }
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273 estimator.set_params(**path_params)
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274 n_intervals = sum(1 for line in open(intervals))
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275 X = np.arange(n_intervals)[:, np.newaxis]
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276
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277 # Get target y
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278 header = 'infer' if params['input_options']['header2'] else None
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279 column_option = (params['input_options']['column_selector_options_2']
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280 ['selected_column_selector_option2'])
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281 if column_option in ['by_index_number', 'all_but_by_index_number',
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282 'by_header_name', 'all_but_by_header_name']:
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283 c = params['input_options']['column_selector_options_2']['col2']
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284 else:
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285 c = None
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286
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287 df_key = infile2 + repr(header)
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288 if df_key in loaded_df:
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289 infile2 = loaded_df[df_key]
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290 else:
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291 infile2 = pd.read_csv(infile2, sep='\t',
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292 header=header, parse_dates=True)
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293 loaded_df[df_key] = infile2
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294
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295 y = read_columns(
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296 infile2,
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297 c=c,
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298 c_option=column_option,
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299 sep='\t',
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300 header=header,
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301 parse_dates=True)
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302 if len(y.shape) == 2 and y.shape[1] == 1:
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303 y = y.ravel()
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304 if input_type == 'refseq_and_interval':
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305 estimator.set_params(
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306 data_batch_generator__features=y.ravel().tolist())
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307 y = None
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308 # end y
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309
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310 optimizer = params['search_schemes']['selected_search_scheme']
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311 optimizer = getattr(model_selection, optimizer)
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312
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313 # handle gridsearchcv options
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314 options = params['search_schemes']['options']
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315
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316 if groups:
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317 header = 'infer' if (options['cv_selector']['groups_selector']
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318 ['header_g']) else None
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319 column_option = (options['cv_selector']['groups_selector']
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320 ['column_selector_options_g']
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321 ['selected_column_selector_option_g'])
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322 if column_option in ['by_index_number', 'all_but_by_index_number',
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323 'by_header_name', 'all_but_by_header_name']:
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324 c = (options['cv_selector']['groups_selector']
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325 ['column_selector_options_g']['col_g'])
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326 else:
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327 c = None
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328
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329 df_key = groups + repr(header)
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330 if df_key in loaded_df:
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331 groups = loaded_df[df_key]
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332
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333 groups = read_columns(
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334 groups,
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335 c=c,
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336 c_option=column_option,
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337 sep='\t',
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338 header=header,
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339 parse_dates=True)
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340 groups = groups.ravel()
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341 options['cv_selector']['groups_selector'] = groups
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342
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343 splitter, groups = get_cv(options.pop('cv_selector'))
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344 options['cv'] = splitter
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345 options['n_jobs'] = N_JOBS
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346 primary_scoring = options['scoring']['primary_scoring']
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347 options['scoring'] = get_scoring(options['scoring'])
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348 if options['error_score']:
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349 options['error_score'] = 'raise'
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350 else:
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351 options['error_score'] = np.NaN
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352 if options['refit'] and isinstance(options['scoring'], dict):
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353 options['refit'] = primary_scoring
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354 if 'pre_dispatch' in options and options['pre_dispatch'] == '':
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355 options['pre_dispatch'] = None
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diff changeset
356
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
357 # del loaded_df
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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358 del loaded_df
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
359
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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360 # handle memory
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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361 memory = joblib.Memory(location=CACHE_DIR, verbose=0)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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362 # cache iraps_core fits could increase search speed significantly
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363 if estimator.__class__.__name__ == 'IRAPSClassifier':
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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364 estimator.set_params(memory=memory)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
365 else:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
366 # For iraps buried in pipeline
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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367 for p, v in estimator_params.items():
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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368 if p.endswith('memory'):
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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369 # for case of `__irapsclassifier__memory`
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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370 if len(p) > 8 and p[:-8].endswith('irapsclassifier'):
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
371 # cache iraps_core fits could increase search
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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372 # speed significantly
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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373 new_params = {p: memory}
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374 estimator.set_params(**new_params)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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375 # security reason, we don't want memory being
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376 # modified unexpectedly
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377 elif v:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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378 new_params = {p, None}
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379 estimator.set_params(**new_params)
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380 # For now, 1 CPU is suggested for iprasclassifier
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381 elif p.endswith('n_jobs'):
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382 new_params = {p: 1}
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383 estimator.set_params(**new_params)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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384 # for security reason, types of callbacks are limited
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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385 elif p.endswith('callbacks'):
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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386 for cb in v:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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387 cb_type = cb['callback_selection']['callback_type']
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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388 if cb_type not in ALLOWED_CALLBACKS:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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389 raise ValueError(
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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390 "Prohibited callback type: %s!" % cb_type)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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391
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392 param_grid = _eval_search_params(params_builder)
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393 searcher = optimizer(estimator, param_grid, **options)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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394
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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395 # do nested split
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396 split_mode = params['outer_split'].pop('split_mode')
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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397 # nested CV, outer cv using cross_validate
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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398 if split_mode == 'nested_cv':
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399 outer_cv, _ = get_cv(params['outer_split']['cv_selector'])
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400
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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401 if options['error_score'] == 'raise':
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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402 rval = cross_validate(
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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403 searcher, X, y, scoring=options['scoring'],
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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404 cv=outer_cv, n_jobs=N_JOBS, verbose=0,
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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405 error_score=options['error_score'])
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
406 else:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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407 warnings.simplefilter('always', FitFailedWarning)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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408 with warnings.catch_warnings(record=True) as w:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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409 try:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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410 rval = cross_validate(
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
411 searcher, X, y,
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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412 scoring=options['scoring'],
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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413 cv=outer_cv, n_jobs=N_JOBS,
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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414 verbose=0,
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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415 error_score=options['error_score'])
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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416 except ValueError:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
417 pass
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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418 for warning in w:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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419 print(repr(warning.message))
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420
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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421 keys = list(rval.keys())
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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422 for k in keys:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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423 if k.startswith('test'):
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
424 rval['mean_' + k] = np.mean(rval[k])
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
425 rval['std_' + k] = np.std(rval[k])
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
426 if k.endswith('time'):
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diff changeset
427 rval.pop(k)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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428 rval = pd.DataFrame(rval)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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parents:
diff changeset
429 rval = rval[sorted(rval.columns)]
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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parents:
diff changeset
430 rval.to_csv(path_or_buf=outfile_result, sep='\t',
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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431 header=True, index=False)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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432 else:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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433 if split_mode == 'train_test_split':
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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parents:
diff changeset
434 train_test_split = try_get_attr(
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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parents:
diff changeset
435 'galaxy_ml.model_validations', 'train_test_split')
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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parents:
diff changeset
436 # make sure refit is choosen
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
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437 # this could be True for sklearn models, but not the case for
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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438 # deep learning models
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
439 if not options['refit'] and \
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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440 not all(hasattr(estimator, attr)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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parents:
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441 for attr in ('config', 'model_type')):
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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442 warnings.warn("Refit is change to `True` for nested "
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
443 "validation!")
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
444 setattr(searcher, 'refit', True)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
445 split_options = params['outer_split']
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
446
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
447 # splits
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
448 if split_options['shuffle'] == 'stratified':
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
449 split_options['labels'] = y
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
450 X, X_test, y, y_test = train_test_split(X, y, **split_options)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
451 elif split_options['shuffle'] == 'group':
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
452 if groups is None:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
453 raise ValueError("No group based CV option was "
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
454 "choosen for group shuffle!")
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
455 split_options['labels'] = groups
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
456 if y is None:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
457 X, X_test, groups, _ =\
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
458 train_test_split(X, groups, **split_options)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
459 else:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
460 X, X_test, y, y_test, groups, _ =\
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
461 train_test_split(X, y, groups, **split_options)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
462 else:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
463 if split_options['shuffle'] == 'None':
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
464 split_options['shuffle'] = None
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
465 X, X_test, y, y_test =\
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
466 train_test_split(X, y, **split_options)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
467 # end train_test_split
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
468
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
469 # shared by both train_test_split and non-split
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
470 if options['error_score'] == 'raise':
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
471 searcher.fit(X, y, groups=groups)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
472 else:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
473 warnings.simplefilter('always', FitFailedWarning)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
474 with warnings.catch_warnings(record=True) as w:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
475 try:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
476 searcher.fit(X, y, groups=groups)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
477 except ValueError:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
478 pass
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
479 for warning in w:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
480 print(repr(warning.message))
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
481
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
482 # no outer split
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
483 if split_mode == 'no':
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
484 # save results
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
485 cv_results = pd.DataFrame(searcher.cv_results_)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
486 cv_results = cv_results[sorted(cv_results.columns)]
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
487 cv_results.to_csv(path_or_buf=outfile_result, sep='\t',
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
488 header=True, index=False)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
489
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
490 # train_test_split, output test result using best_estimator_
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
491 # or rebuild the trained estimator using weights if applicable.
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
492 else:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
493 scorer_ = searcher.scorer_
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
494 if isinstance(scorer_, collections.Mapping):
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
495 is_multimetric = True
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
496 else:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
497 is_multimetric = False
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
498
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
499 best_estimator_ = getattr(searcher, 'best_estimator_', None)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
500 if not best_estimator_:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
501 raise ValueError("GridSearchCV object has no "
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
502 "`best_estimator_` when `refit`=False!")
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
503
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
504 if best_estimator_.__class__.__name__ == 'KerasGBatchClassifier' \
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
505 and hasattr(estimator.data_batch_generator, 'target_path'):
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
506 test_score = best_estimator_.evaluate(
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
507 X_test, scorer=scorer_, is_multimetric=is_multimetric)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
508 else:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
509 test_score = _score(best_estimator_, X_test,
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
510 y_test, scorer_,
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
511 is_multimetric=is_multimetric)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
512
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
513 if not is_multimetric:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
514 test_score = {primary_scoring: test_score}
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
515 for key, value in test_score.items():
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
516 test_score[key] = [value]
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
517 result_df = pd.DataFrame(test_score)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
518 result_df.to_csv(path_or_buf=outfile_result, sep='\t',
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
519 header=True, index=False)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
520
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
521 memory.clear(warn=False)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
522
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
523 if outfile_object:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
524 best_estimator_ = getattr(searcher, 'best_estimator_', None)
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
525 if not best_estimator_:
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
526 warnings.warn("GridSearchCV object has no attribute "
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
527 "'best_estimator_', because either it's "
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
528 "nested gridsearch or `refit` is False!")
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
529 return
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
530
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
531 main_est = best_estimator_
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
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532 if isinstance(best_estimator_, pipeline.Pipeline):
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533 main_est = best_estimator_.steps[-1][-1]
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534
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535 if hasattr(main_est, 'model_') \
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536 and hasattr(main_est, 'save_weights'):
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537 if outfile_weights:
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538 main_est.save_weights(outfile_weights)
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539 del main_est.model_
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540 del main_est.fit_params
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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541 del main_est.model_class_
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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542 del main_est.validation_data
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543 if getattr(main_est, 'data_generator_', None):
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544 del main_est.data_generator_
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545 del main_est.data_batch_generator
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546
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547 with open(outfile_object, 'wb') as output_handler:
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548 pickle.dump(best_estimator_, output_handler,
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549 pickle.HIGHEST_PROTOCOL)
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550
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551
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552 if __name__ == '__main__':
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553 aparser = argparse.ArgumentParser()
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554 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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555 aparser.add_argument("-e", "--estimator", dest="infile_estimator")
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556 aparser.add_argument("-X", "--infile1", dest="infile1")
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557 aparser.add_argument("-y", "--infile2", dest="infile2")
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558 aparser.add_argument("-O", "--outfile_result", dest="outfile_result")
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559 aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
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560 aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights")
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561 aparser.add_argument("-g", "--groups", dest="groups")
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562 aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
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563 aparser.add_argument("-b", "--intervals", dest="intervals")
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564 aparser.add_argument("-t", "--targets", dest="targets")
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565 aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
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566 args = aparser.parse_args()
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567
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568 main(args.inputs, args.infile_estimator, args.infile1, args.infile2,
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569 args.outfile_result, outfile_object=args.outfile_object,
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570 outfile_weights=args.outfile_weights, groups=args.groups,
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571 ref_seq=args.ref_seq, intervals=args.intervals,
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572 targets=args.targets, fasta_path=args.fasta_path)