Mercurial > repos > bgruening > sklearn_generalized_linear

annotate search_model_validation.py @ 24:b628de0d101f draft

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author bgruening
date Wed, 15 May 2019 07:40:56 -0400
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b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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diff changeset
1 import argparse
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2 import collections
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3 import imblearn
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4 import json
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5 import numpy as np
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6 import pandas
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7 import pickle
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8 import skrebate
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9 import sklearn
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10 import sys
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11 import xgboost
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12 import warnings
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13 import iraps_classifier
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14 import model_validations
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15 import preprocessors
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16 import feature_selectors
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17 from imblearn import under_sampling, over_sampling, combine
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18 from scipy.io import mmread
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19 from mlxtend import classifier, regressor
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20 from sklearn import (cluster, compose, decomposition, ensemble,
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21 feature_extraction, feature_selection,
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22 gaussian_process, kernel_approximation, metrics,
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23 model_selection, naive_bayes, neighbors,
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24 pipeline, preprocessing, svm, linear_model,
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25 tree, discriminant_analysis)
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26 from sklearn.exceptions import FitFailedWarning
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27 from sklearn.externals import joblib
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28 from sklearn.model_selection._validation import _score
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29
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30 from utils import (SafeEval, get_cv, get_scoring, get_X_y,
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31 load_model, read_columns)
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32 from model_validations import train_test_split
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34
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35 N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
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36 CACHE_DIR = './cached'
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37 NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', 'steps',
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38 'nthread', 'verbose')
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40
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41 def _eval_search_params(params_builder):
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42 search_params = {}
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44 for p in params_builder['param_set']:
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45 search_list = p['sp_list'].strip()
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46 if search_list == '':
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47 continue
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49 param_name = p['sp_name']
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50 if param_name.lower().endswith(NON_SEARCHABLE):
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51 print("Warning: `%s` is not eligible for search and was "
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52 "omitted!" % param_name)
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53 continue
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54
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55 if not search_list.startswith(':'):
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56 safe_eval = SafeEval(load_scipy=True, load_numpy=True)
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57 ev = safe_eval(search_list)
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58 search_params[param_name] = ev
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59 else:
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60 # Have `:` before search list, asks for estimator evaluatio
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61 safe_eval_es = SafeEval(load_estimators=True)
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62 search_list = search_list[1:].strip()
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63 # TODO maybe add regular express check
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64 ev = safe_eval_es(search_list)
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65 preprocessors = (
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66 preprocessing.StandardScaler(), preprocessing.Binarizer(),
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67 preprocessing.Imputer(), preprocessing.MaxAbsScaler(),
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68 preprocessing.Normalizer(), preprocessing.MinMaxScaler(),
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69 preprocessing.PolynomialFeatures(),
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70 preprocessing.RobustScaler(), feature_selection.SelectKBest(),
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71 feature_selection.GenericUnivariateSelect(),
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72 feature_selection.SelectPercentile(),
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73 feature_selection.SelectFpr(), feature_selection.SelectFdr(),
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74 feature_selection.SelectFwe(),
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75 feature_selection.VarianceThreshold(),
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76 decomposition.FactorAnalysis(random_state=0),
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77 decomposition.FastICA(random_state=0),
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78 decomposition.IncrementalPCA(),
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79 decomposition.KernelPCA(random_state=0, n_jobs=N_JOBS),
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80 decomposition.LatentDirichletAllocation(
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81 random_state=0, n_jobs=N_JOBS),
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82 decomposition.MiniBatchDictionaryLearning(
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83 random_state=0, n_jobs=N_JOBS),
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84 decomposition.MiniBatchSparsePCA(
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85 random_state=0, n_jobs=N_JOBS),
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86 decomposition.NMF(random_state=0),
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87 decomposition.PCA(random_state=0),
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88 decomposition.SparsePCA(random_state=0, n_jobs=N_JOBS),
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89 decomposition.TruncatedSVD(random_state=0),
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90 kernel_approximation.Nystroem(random_state=0),
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91 kernel_approximation.RBFSampler(random_state=0),
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92 kernel_approximation.AdditiveChi2Sampler(),
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93 kernel_approximation.SkewedChi2Sampler(random_state=0),
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94 cluster.FeatureAgglomeration(),
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95 skrebate.ReliefF(n_jobs=N_JOBS),
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96 skrebate.SURF(n_jobs=N_JOBS),
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97 skrebate.SURFstar(n_jobs=N_JOBS),
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98 skrebate.MultiSURF(n_jobs=N_JOBS),
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99 skrebate.MultiSURFstar(n_jobs=N_JOBS),
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100 imblearn.under_sampling.ClusterCentroids(
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101 random_state=0, n_jobs=N_JOBS),
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102 imblearn.under_sampling.CondensedNearestNeighbour(
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103 random_state=0, n_jobs=N_JOBS),
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104 imblearn.under_sampling.EditedNearestNeighbours(
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105 random_state=0, n_jobs=N_JOBS),
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106 imblearn.under_sampling.RepeatedEditedNearestNeighbours(
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107 random_state=0, n_jobs=N_JOBS),
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108 imblearn.under_sampling.AllKNN(random_state=0, n_jobs=N_JOBS),
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109 imblearn.under_sampling.InstanceHardnessThreshold(
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110 random_state=0, n_jobs=N_JOBS),
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111 imblearn.under_sampling.NearMiss(
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112 random_state=0, n_jobs=N_JOBS),
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113 imblearn.under_sampling.NeighbourhoodCleaningRule(
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114 random_state=0, n_jobs=N_JOBS),
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115 imblearn.under_sampling.OneSidedSelection(
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116 random_state=0, n_jobs=N_JOBS),
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117 imblearn.under_sampling.RandomUnderSampler(
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118 random_state=0),
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119 imblearn.under_sampling.TomekLinks(
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120 random_state=0, n_jobs=N_JOBS),
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121 imblearn.over_sampling.ADASYN(random_state=0, n_jobs=N_JOBS),
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122 imblearn.over_sampling.RandomOverSampler(random_state=0),
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123 imblearn.over_sampling.SMOTE(random_state=0, n_jobs=N_JOBS),
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124 imblearn.over_sampling.SVMSMOTE(random_state=0, n_jobs=N_JOBS),
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125 imblearn.over_sampling.BorderlineSMOTE(
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126 random_state=0, n_jobs=N_JOBS),
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127 imblearn.over_sampling.SMOTENC(
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128 categorical_features=[], random_state=0, n_jobs=N_JOBS),
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129 imblearn.combine.SMOTEENN(random_state=0),
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130 imblearn.combine.SMOTETomek(random_state=0))
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131 newlist = []
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132 for obj in ev:
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133 if obj is None:
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134 newlist.append(None)
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135 elif obj == 'all_0':
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136 newlist.extend(preprocessors[0:36])
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137 elif obj == 'sk_prep_all': # no KernalCenter()
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138 newlist.extend(preprocessors[0:8])
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139 elif obj == 'fs_all':
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140 newlist.extend(preprocessors[8:15])
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141 elif obj == 'decomp_all':
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142 newlist.extend(preprocessors[15:26])
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143 elif obj == 'k_appr_all':
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144 newlist.extend(preprocessors[26:30])
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145 elif obj == 'reb_all':
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146 newlist.extend(preprocessors[31:36])
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147 elif obj == 'imb_all':
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148 newlist.extend(preprocessors[36:55])
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149 elif type(obj) is int and -1 < obj < len(preprocessors):
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150 newlist.append(preprocessors[obj])
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151 elif hasattr(obj, 'get_params'): # user uploaded object
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152 if 'n_jobs' in obj.get_params():
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153 newlist.append(obj.set_params(n_jobs=N_JOBS))
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154 else:
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155 newlist.append(obj)
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156 else:
24
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157 sys.exit("Unsupported estimator type: %r" % (obj))
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158
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159 search_params[param_name] = newlist
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160
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161 return search_params
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162
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163
24
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164 def main(inputs, infile_estimator, infile1, infile2,
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165 outfile_result, outfile_object=None, groups=None):
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166 """
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167 Parameter
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168 ---------
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169 inputs : str
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170 File path to galaxy tool parameter
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171
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172 infile_estimator : str
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173 File path to estimator
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174
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175 infile1 : str
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176 File path to dataset containing features
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177
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178 infile2 : str
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179 File path to dataset containing target values
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180
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181 outfile_result : str
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182 File path to save the results, either cv_results or test result
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183
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184 outfile_object : str, optional
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185 File path to save searchCV object
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186
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187 groups : str
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188 File path to dataset containing groups labels
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189 """
23
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190
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191 warnings.simplefilter('ignore')
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192
24
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193 with open(inputs, 'r') as param_handler:
23
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194 params = json.load(param_handler)
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195 if groups:
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196 (params['search_schemes']['options']['cv_selector']
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197 ['groups_selector']['infile_g']) = groups
23
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198
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199 params_builder = params['search_schemes']['search_params_builder']
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200
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201 input_type = params['input_options']['selected_input']
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202 if input_type == 'tabular':
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203 header = 'infer' if params['input_options']['header1'] else None
24
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204 column_option = (params['input_options']['column_selector_options_1']
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205 ['selected_column_selector_option'])
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206 if column_option in ['by_index_number', 'all_but_by_index_number',
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207 'by_header_name', 'all_but_by_header_name']:
23
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208 c = params['input_options']['column_selector_options_1']['col1']
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209 else:
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210 c = None
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211 X = read_columns(
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212 infile1,
24
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213 c=c,
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214 c_option=column_option,
23
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215 sep='\t',
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216 header=header,
24
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217 parse_dates=True).astype(float)
23
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218 else:
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219 X = mmread(open(infile1, 'r'))
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220
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221 header = 'infer' if params['input_options']['header2'] else None
24
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222 column_option = (params['input_options']['column_selector_options_2']
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223 ['selected_column_selector_option2'])
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224 if column_option in ['by_index_number', 'all_but_by_index_number',
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225 'by_header_name', 'all_but_by_header_name']:
23
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226 c = params['input_options']['column_selector_options_2']['col2']
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227 else:
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228 c = None
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229 y = read_columns(
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230 infile2,
24
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231 c=c,
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232 c_option=column_option,
23
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233 sep='\t',
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234 header=header,
24
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235 parse_dates=True)
23
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236 y = y.ravel()
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237
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238 optimizer = params['search_schemes']['selected_search_scheme']
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239 optimizer = getattr(model_selection, optimizer)
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240
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241 options = params['search_schemes']['options']
24
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242
23
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243 splitter, groups = get_cv(options.pop('cv_selector'))
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244 options['cv'] = splitter
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245 options['n_jobs'] = N_JOBS
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246 primary_scoring = options['scoring']['primary_scoring']
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247 options['scoring'] = get_scoring(options['scoring'])
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248 if options['error_score']:
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249 options['error_score'] = 'raise'
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250 else:
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251 options['error_score'] = np.NaN
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252 if options['refit'] and isinstance(options['scoring'], dict):
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253 options['refit'] = primary_scoring
23
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254 if 'pre_dispatch' in options and options['pre_dispatch'] == '':
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255 options['pre_dispatch'] = None
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256
24
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257 with open(infile_estimator, 'rb') as estimator_handler:
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258 estimator = load_model(estimator_handler)
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259
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260 memory = joblib.Memory(location=CACHE_DIR, verbose=0)
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261 # cache iraps_core fits could increase search speed significantly
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262 if estimator.__class__.__name__ == 'IRAPSClassifier':
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263 estimator.set_params(memory=memory)
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264 else:
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265 for p, v in estimator.get_params().items():
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266 if p.endswith('memory'):
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267 if len(p) > 8 and p[:-8].endswith('irapsclassifier'):
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268 # cache iraps_core fits could increase search
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269 # speed significantly
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270 new_params = {p: memory}
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271 estimator.set_params(**new_params)
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272 elif v:
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273 new_params = {p, None}
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274 estimator.set_params(**new_params)
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275 elif p.endswith('n_jobs'):
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276 new_params = {p: 1}
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277 estimator.set_params(**new_params)
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278
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279 param_grid = _eval_search_params(params_builder)
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280 searcher = optimizer(estimator, param_grid, **options)
23
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281
24
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282 # do train_test_split
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283 do_train_test_split = params['train_test_split'].pop('do_split')
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284 if do_train_test_split == 'yes':
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285 # make sure refit is choosen
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286 if not options['refit']:
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287 raise ValueError("Refit must be `True` for shuffle splitting!")
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288 split_options = params['train_test_split']
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289
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290 # splits
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291 if split_options['shuffle'] == 'stratified':
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292 split_options['labels'] = y
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293 X, X_test, y, y_test = train_test_split(X, y, **split_options)
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294 elif split_options['shuffle'] == 'group':
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295 if not groups:
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296 raise ValueError("No group based CV option was "
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297 "choosen for group shuffle!")
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298 split_options['labels'] = groups
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299 X, X_test, y, y_test, groups, _ =\
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300 train_test_split(X, y, **split_options)
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301 else:
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302 if split_options['shuffle'] == 'None':
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303 split_options['shuffle'] = None
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304 X, X_test, y, y_test =\
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305 train_test_split(X, y, **split_options)
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306 # end train_test_split
23
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307
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308 if options['error_score'] == 'raise':
24
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309 searcher.fit(X, y, groups=groups)
23
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310 else:
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311 warnings.simplefilter('always', FitFailedWarning)
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312 with warnings.catch_warnings(record=True) as w:
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313 try:
24
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314 searcher.fit(X, y, groups=groups)
23
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315 except ValueError:
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316 pass
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317 for warning in w:
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318 print(repr(warning.message))
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319
24
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320 if do_train_test_split == 'no':
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321 # save results
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322 cv_results = pandas.DataFrame(searcher.cv_results_)
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323 cv_results = cv_results[sorted(cv_results.columns)]
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324 cv_results.to_csv(path_or_buf=outfile_result, sep='\t',
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325 header=True, index=False)
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326
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327 # output test result using best_estimator_
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328 else:
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329 best_estimator_ = searcher.best_estimator_
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330 if isinstance(options['scoring'], collections.Mapping):
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331 is_multimetric = True
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332 else:
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333 is_multimetric = False
23
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334
24
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335 test_score = _score(best_estimator_, X_test,
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336 y_test, options['scoring'],
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337 is_multimetric=is_multimetric)
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338 if not is_multimetric:
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339 test_score = {primary_scoring: test_score}
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340 for key, value in test_score.items():
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341 test_score[key] = [value]
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342 result_df = pandas.DataFrame(test_score)
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343 result_df.to_csv(path_or_buf=outfile_result, sep='\t',
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344 header=True, index=False)
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345
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346 memory.clear(warn=False)
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347
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348 if outfile_object:
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349 with open(outfile_object, 'wb') as output_handler:
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350 pickle.dump(searcher, output_handler, pickle.HIGHEST_PROTOCOL)
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351
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352
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353 if __name__ == '__main__':
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354 aparser = argparse.ArgumentParser()
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355 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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356 aparser.add_argument("-e", "--estimator", dest="infile_estimator")
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357 aparser.add_argument("-X", "--infile1", dest="infile1")
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358 aparser.add_argument("-y", "--infile2", dest="infile2")
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359 aparser.add_argument("-r", "--outfile_result", dest="outfile_result")
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360 aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
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361 aparser.add_argument("-g", "--groups", dest="groups")
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents: 23
diff changeset
362 args = aparser.parse_args()
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents: 23
diff changeset
363
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents: 23
diff changeset
364 main(args.inputs, args.infile_estimator, args.infile1, args.infile2,
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents: 23
diff changeset
365 args.outfile_result, outfile_object=args.outfile_object,
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents: 23
diff changeset
366 groups=args.groups)