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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
author bgruening
date Mon, 16 Dec 2019 05:28:32 -0500
parents 887e0aaa482e
children 9b017b0da56e
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1 <tool id="sklearn_model_validation" name="Model Validation" version="@VERSION@">
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2 <description>includes cross_validate, cross_val_predict, learning_curve, and more</description>
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3 <macros>
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4 <import>main_macros.xml</import>
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5 </macros>
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6 <expand macro="python_requirements"/>
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7 <expand macro="macro_stdio"/>
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8 <version_command>echo "@VERSION@"</version_command>
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9 <command>
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10 <![CDATA[
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11 export HDF5_USE_FILE_LOCKING='FALSE';
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12 python "$sklearn_model_validation_script" '$inputs'
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13 ]]>
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14 </command>
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15 <configfiles>
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16 <inputs name="inputs" />
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17 <configfile name="sklearn_model_validation_script">
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18 <![CDATA[
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19 import imblearn
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20 import joblib
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21 import json
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22 import numpy as np
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23 import os
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24 import pandas as pd
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25 import pickle
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26 import pprint
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27 import skrebate
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28 import sys
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29 import warnings
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30 import xgboost
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31 from mlxtend import classifier, regressor
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32 from sklearn import (
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33 cluster, compose, decomposition, ensemble, feature_extraction,
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34 feature_selection, gaussian_process, kernel_approximation, metrics,
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35 model_selection, naive_bayes, neighbors, pipeline, preprocessing,
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36 svm, linear_model, tree, discriminant_analysis)
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37 from sklearn.model_selection import _validation
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38
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39 from galaxy_ml.utils import (SafeEval, get_cv, get_scoring, load_model,
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40 read_columns, get_module)
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41 from galaxy_ml.model_validations import _fit_and_score
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44 setattr(_validation, '_fit_and_score', _fit_and_score)
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46 N_JOBS = int(os.environ.get('GALAXY_SLOTS', 1))
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47 CACHE_DIR = os.path.join(os.getcwd(), 'cached')
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48 del os
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49 ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', 'ReduceLROnPlateau',
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50 'CSVLogger', 'None')
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52 warnings.filterwarnings('ignore')
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54 safe_eval = SafeEval()
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56 input_json_path = sys.argv[1]
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57 with open(input_json_path, 'r') as param_handler:
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58 params = json.load(param_handler)
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60 ## load estimator
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61 with open('$infile_estimator', 'rb') as estimator_handler:
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62 estimator = load_model(estimator_handler)
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64 estimator_params = estimator.get_params()
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65
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66 ## check estimator hyperparameters
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67 memory = joblib.Memory(location=CACHE_DIR, verbose=0)
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68 # cache iraps_core fits could increase search speed significantly
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69 if estimator.__class__.__name__ == 'IRAPSClassifier':
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70 estimator.set_params(memory=memory)
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71 else:
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72 # For iraps buried in pipeline
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73 for p, v in estimator_params.items():
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74 if p.endswith('memory'):
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75 # for case of `__irapsclassifier__memory`
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76 if len(p) > 8 and p[:-8].endswith('irapsclassifier'):
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77 # cache iraps_core fits could increase search
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78 # speed significantly
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79 new_params = {p: memory}
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80 estimator.set_params(**new_params)
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81 # security reason, we don't want memory being
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82 # modified unexpectedly
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83 elif v:
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84 new_params = {p, None}
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85 estimator.set_params(**new_params)
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86 # For now, 1 CPU is suggested for iprasclassifier
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87 elif p.endswith('n_jobs'):
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88 new_params = {p: 1}
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89 estimator.set_params(**new_params)
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90 # for security reason, types of callback are limited
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91 elif p.endswith('callbacks'):
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92 for cb in v:
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93 cb_type = cb['callback_selection']['callback_type']
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94 if cb_type not in ALLOWED_CALLBACKS:
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95 raise ValueError(
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96 "Prohibited callback type: %s!" % cb_type)
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97
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98 ## store read dataframe object
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99 loaded_df = {}
17
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100
19
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101 #if $input_options.selected_input == 'tabular'
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102 header = 'infer' if params['input_options']['header1'] else None
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103 column_option = params['input_options']['column_selector_options_1']['selected_column_selector_option']
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104 if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']:
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105 c = params['input_options']['column_selector_options_1']['col1']
0
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106 else:
19
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107 c = None
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108 infile1 = '$input_options.infile1'
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109 df_key = infile1 + repr(header)
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110 df = pd.read_csv(infile1, sep='\t', header=header, parse_dates=True)
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111 loaded_df[df_key] = df
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112 X = read_columns(df, c=c, c_option=column_option).astype(float)
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113
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114 #elif $input_options.selected_input == 'sparse':
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115 X = mmread('$input_options.infile1')
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116
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117 #elif $input_options.selected_input == 'seq_fasta'
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118 fasta_path = '$input_options.fasta_path'
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119 pyfaidx = get_module('pyfaidx')
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120 sequences = pyfaidx.Fasta(fasta_path)
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121 n_seqs = len(sequences.keys())
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122 X = np.arange(n_seqs)[:, np.newaxis]
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123 for param in estimator_params.keys():
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124 if param.endswith('fasta_path'):
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125 estimator.set_params(
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126 **{param: fasta_path})
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127 break
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128 else:
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129 raise ValueError(
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130 "The selected estimator doesn't support "
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131 "fasta file input! Please consider using "
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132 "KerasGBatchClassifier with "
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133 "FastaDNABatchGenerator/FastaProteinBatchGenerator "
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134 "or having GenomeOneHotEncoder/ProteinOneHotEncoder "
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135 "in pipeline!")
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136 #elif $input_options.selected_input == 'refseq_and_interval'
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137 ref_seq = '$input_options.ref_genome_file'
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138 intervals = '$input_options.interval_file'
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139 targets = __import__('os').path.join(__import__('os').getcwd(),
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140 '${target_file.element_identifier}.gz')
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141 path_params = {
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142 'data_batch_generator__ref_genome_path': ref_seq,
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143 'data_batch_generator__intervals_path': intervals,
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144 'data_batch_generator__target_path': targets
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145 }
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146 estimator.set_params(**path_params)
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147 n_intervals = sum(1 for line in open(intervals))
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148 X = np.arange(n_intervals)[:, np.newaxis]
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149 #end if
0
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150
16
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151 header = 'infer' if params['input_options']['header2'] else None
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152 column_option = params['input_options']['column_selector_options_2']['selected_column_selector_option2']
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153 if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']:
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154 c = params['input_options']['column_selector_options_2']['col2']
3
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155 else:
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156 c = None
19
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157 infile2 = '$input_options.infile2'
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158 df_key = infile2 + repr(header)
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159 if df_key in loaded_df:
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160 infile2 = loaded_df[df_key]
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161 else:
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162 infile2 = pd.read_csv(infile2, sep='\t', header=header, parse_dates=True)
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163 loaded_df[df_key] = infile2
0
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164 y = read_columns(
19
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165 infile2,
3
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166 c = c,
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167 c_option = column_option,
0
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168 sep='\t',
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169 header=header,
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170 parse_dates=True)
19
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171 if len(y.shape) == 2 and y.shape[1] == 1:
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172 y = y.ravel()
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173 #if $input_options.selected_input == 'refseq_and_interval'
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174 estimator.set_params(
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175 data_batch_generator__features=y.ravel().tolist())
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176 y = None
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177 #end if
0
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178
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179 ## handle options
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180 options = params['model_validation_functions']['options']
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181
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182 #if $model_validation_functions.options.cv_selector.selected_cv\
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183 in ['GroupKFold', 'GroupShuffleSplit', 'LeaveOneGroupOut', 'LeavePGroupsOut']:
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184 infile_g = '$model_validation_functions.options.cv_selector.groups_selector.infile_g'
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185 header = 'infer' if options['cv_selector']['groups_selector']['header_g'] else None
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186 column_option = (options['cv_selector']['groups_selector']['column_selector_options_g']
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187 ['selected_column_selector_option_g'])
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188 if column_option in ['by_index_number', 'all_but_by_index_number',
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189 'by_header_name', 'all_but_by_header_name']:
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190 c = (options['cv_selector']['groups_selector']['column_selector_options_g']['col_g'])
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191 else:
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192 c = None
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193 df_key = infile_g + repr(header)
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194 if df_key in loaded_df:
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195 infile_g = loaded_df[df_key]
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196 groups = read_columns(infile_g, c=c, c_option=column_option,
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197 sep='\t', header=header, parse_dates=True)
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198 groups = groups.ravel()
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199 options['cv_selector']['groups_selector'] = groups
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200 #end if
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201
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202 ## del loaded_df
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203 del loaded_df
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204
16
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205 splitter, groups = get_cv( options.pop('cv_selector') )
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206 options['cv'] = splitter
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207 options['groups'] = groups
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208 options['n_jobs'] = N_JOBS
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209 if 'scoring' in options:
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210 primary_scoring = options['scoring']['primary_scoring']
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211 options['scoring'] = get_scoring(options['scoring'])
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212 if 'pre_dispatch' in options and options['pre_dispatch'] == '':
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213 options['pre_dispatch'] = None
0
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214
19
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215 ## Set up validator, run estimator through validator and return results.
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216
16
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217 validator = params['model_validation_functions']['selected_function']
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218 validator = getattr(_validation, validator)
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219
16
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220 selected_function = params['model_validation_functions']['selected_function']
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221
2
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222 if selected_function == 'cross_validate':
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223 res = validator(estimator, X, y, **options)
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224 stat = {}
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225 for k, v in res.items():
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226 if k.startswith('test'):
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227 stat['mean_' + k] = np.mean(v)
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228 stat['std_' + k] = np.std(v)
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229 res.update(stat)
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230 rval = pd.DataFrame(res)
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231 rval = rval[sorted(rval.columns)]
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232 elif selected_function == 'cross_val_predict':
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233 predicted = validator(estimator, X, y, **options)
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234 if len(predicted.shape) == 1:
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235 rval = pd.DataFrame(predicted, columns=['Predicted'])
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236 else:
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237 rval = pd.DataFrame(predicted)
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238 elif selected_function == 'learning_curve':
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239 try:
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240 train_sizes = safe_eval(options['train_sizes'])
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241 except:
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242 sys.exit("Unsupported train_sizes input! Supports int/float in tuple and array-like structure.")
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243 if type(train_sizes) is tuple:
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244 train_sizes = np.linspace(*train_sizes)
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245 options['train_sizes'] = train_sizes
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246 train_sizes_abs, train_scores, test_scores = validator(estimator, X, y, **options)
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247 rval = pd.DataFrame(dict(
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248 train_sizes_abs = train_sizes_abs,
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249 mean_train_scores = np.mean(train_scores, axis=1),
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250 std_train_scores = np.std(train_scores, axis=1),
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251 mean_test_scores = np.mean(test_scores, axis=1),
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252 std_test_scores = np.std(test_scores, axis=1)))
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253 rval = rval[['train_sizes_abs', 'mean_train_scores', 'std_train_scores',
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254 'mean_test_scores', 'std_test_scores']]
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255 elif selected_function == 'permutation_test_score':
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256 score, permutation_scores, pvalue = validator(estimator, X, y, **options)
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257 permutation_scores_df = pd.DataFrame(dict(
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258 permutation_scores = permutation_scores))
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259 score_df = pd.DataFrame(dict(
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260 score = [score],
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261 pvalue = [pvalue]))
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262 rval = pd.concat([score_df[['score', 'pvalue']], permutation_scores_df], axis=1)
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263
17
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264 rval.to_csv(path_or_buf='$outfile', sep='\t', header=True, index=False)
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265
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266 ]]>
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267 </configfile>
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268 </configfiles>
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269 <inputs>
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270 <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing model/pipeline object"/>
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271 <conditional name="model_validation_functions">
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272 <param name="selected_function" type="select" label="Select a model validation function">
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273 <option value="cross_validate">cross_validate - Evaluate metric(s) by cross-validation and also record fit/score times</option>
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274 <option value="cross_val_predict">cross_val_predict - Generate cross-validated estimates for each input data point</option>
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275 <option value="learning_curve">learning_curve - Learning curve</option>
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276 <option value="permutation_test_score">permutation_test_score - Evaluate the significance of a cross-validated score with permutations</option>
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277 <option value="validation_curve">validation_curve - Use grid search with one parameter instead</option>
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278 </param>
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279 <when value="cross_validate">
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280 <section name="options" title="Other Options" expanded="false">
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281 <expand macro="scoring_selection"/>
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282 <expand macro="model_validation_common_options"/>
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283 <param argument="return_train_score" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" help="Whether to include train scores."/>
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284 <!--param argument="return_estimator" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" help="Whether to return the estimators fitted on each split."/> -->
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285 <!--param argument="error_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Raise fit error:" help="If false, the metric score is assigned to NaN if an error occurs in estimator fitting and FitFailedWarning is raised."/> -->
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286 <!--fit_params-->
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287 <expand macro="pre_dispatch"/>
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288 </section>
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289 </when>
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290 <when value="cross_val_predict">
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291 <section name="options" title="Other Options" expanded="false">
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292 <expand macro="model_validation_common_options" />
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293 <!--fit_params-->
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294 <expand macro="pre_dispatch" value="2*n_jobs’" help="Controls the number of jobs that get dispatched during parallel execution"/>
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295 <param argument="method" type="select" label="Invokes the passed method name of the passed estimator">
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296 <option value="predict" selected="true">predict</option>
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297 <option value="predict_proba">predict_proba</option>
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298 </param>
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299 </section>
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300 </when>
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301 <when value="learning_curve">
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302 <section name="options" title="Other Options" expanded="false">
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303 <expand macro="scoring_selection"/>
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304 <expand macro="model_validation_common_options"/>
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305 <param argument="train_sizes" type="text" value="(0.1, 1.0, 5)" label="train_sizes"
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306 help="Relative or absolute numbers of training examples that will be used to generate the learning curve. Supports 1) tuple, to be evaled by np.linspace, e.g. (0.1, 1.0, 5); 2) array-like, e.g. [0.1 , 0.325, 0.55 , 0.775, 1.]">
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307 <sanitizer>
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308 <valid initial="default">
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309 <add value="["/>
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310 <add value="]"/>
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311 </valid>
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312 </sanitizer>
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313 </param>
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314 <param argument="exploit_incremental_learning" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" help="Whether to apply incremental learning to speed up fitting of the estimator if supported"/>
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315 <expand macro="pre_dispatch"/>
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316 <expand macro="shuffle" checked="false" label="shuffle" help="Whether to shuffle training data before taking prefixes"/>
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317 <expand macro="random_state" help_text="If int, the seed used by the random number generator. Used when `shuffle` is True"/>
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318 </section>
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319 </when>
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320 <when value="permutation_test_score">
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321 <section name="options" title="Other Options" expanded="false">
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322 <expand macro="scoring_selection"/>
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323 <expand macro="model_validation_common_options"/>
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324 <param name="n_permutations" type="integer" value="100" optional="true" label="n_permutations" help="Number of times to permute y"/>
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325 <expand macro="random_state"/>
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326 </section>
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327 </when>
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328 <when value="validation_curve"/>
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329 </conditional>
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330 <expand macro="sl_mixed_input_plus_sequence"/>
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331 </inputs>
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332 <outputs>
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333 <data format="tabular" name="outfile"/>
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334 </outputs>
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335 <tests>
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336 <test>
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337 <param name="infile_estimator" value="pipeline02"/>
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338 <param name="selected_function" value="cross_validate"/>
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339 <param name="return_train_score" value="True"/>
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340 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
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341 <param name="col1" value="1,2,3,4,5"/>
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342 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
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343 <param name="col2" value="6"/>
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344 <output name="outfile">
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345 <assert_contents>
19
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346 <has_n_columns n="6"/>
17
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347 <has_text text="0.9999961390418067"/>
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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348 <has_text text="0.9944541531269271"/>
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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349 <has_text text="0.9999193322454393"/>
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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350 </assert_contents>
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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351 </output>
0
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352 </test>
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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353 <test>
19
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354 <param name="infile_estimator" value="pipeline02"/>
17
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355 <param name="selected_function" value="cross_val_predict"/>
0
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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356 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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357 <param name="col1" value="1,2,3,4,5"/>
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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358 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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359 <param name="col2" value="6"/>
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887e0aaa482e "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 02087ce2966cf8b4aac9197a41171e7f986c11d1-dirty"
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360 <output name="outfile" file="mv_result02.tabular" lines_diff="14"/>
0
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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361 </test>
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362 <test>
19
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363 <param name="infile_estimator" value="pipeline05"/>
0
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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364 <param name="selected_function" value="learning_curve"/>
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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365 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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366 <param name="header1" value="true" />
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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367 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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368 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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369 <param name="header2" value="true" />
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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370 <param name="col2" value="1"/>
17
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371 <output name="outfile" file="mv_result03.tabular"/>
0
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372 </test>
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373 <test>
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374 <param name="infile_estimator" value="pipeline05"/>
0
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375 <param name="selected_function" value="permutation_test_score"/>
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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376 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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377 <param name="col1" value="1,2,3,4,5"/>
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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378 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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379 <param name="col2" value="6"/>
17
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380 <output name="outfile">
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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381 <assert_contents>
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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382 <has_n_columns n="3"/>
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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383 <has_text text="0.25697059258228816"/>
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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384 </assert_contents>
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385 </output>
0
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386 </test>
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387 <test>
19
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388 <param name="infile_estimator" value="pipeline05"/>
17
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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389 <param name="selected_function" value="cross_val_predict"/>
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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390 <section name="groups_selector">
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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391 <param name="infile_groups" value="regression_y.tabular" ftype="tabular"/>
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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392 <param name="header_g" value="true"/>
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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393 <param name="selected_column_selector_option_g" value="by_index_number"/>
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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394 <param name="col_g" value="1"/>
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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395 </section>
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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396 <param name="selected_cv" value="GroupKFold"/>
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397 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
17
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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398 <param name="header1" value="true"/>
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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399 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
0
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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400 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
17
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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401 <param name="header2" value="true"/>
0
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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402 <param name="col2" value="1"/>
17
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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403 <output name="outfile" file="mv_result05.tabular"/>
0
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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404 </test>
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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405 </tests>
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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406 <help>
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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407 <![CDATA[
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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408 **What it does**
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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409 This tool includes model validation functions to evaluate estimator performance in the cross-validation approach. This tool is based on
333507faecab planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2e1e78576b38110cf5b1f2ed83b08b9c3a6cbfee
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410 sklearn.model_selection package.
9
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411 For information about model validation functions and their parameter settings please refer to `Scikit-learn model_selection`_.
0
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412
9
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413 .. _`Scikit-learn model_selection`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection
0
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414 ]]>
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415 </help>
13
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416 <expand macro="sklearn_citation">
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417 <expand macro="skrebate_citation"/>
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418 <expand macro="xgboost_citation"/>
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419 </expand>
0
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420 </tool>