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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
author bgruening
date Wed, 15 May 2019 07:42:07 -0400
parents 86e1e2874460
children efbec977a47d
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1 <tool id="sklearn_model_validation" name="Model Validation" version="@VERSION@">
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2 <description>evaluates estimator performance by cross-validation</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 python "$sklearn_model_validation_script" '$inputs'
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12 ]]>
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13 </command>
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14 <configfiles>
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15 <inputs name="inputs" />
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16 <configfile name="sklearn_model_validation_script">
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17 <![CDATA[
17
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18 import imblearn
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19 import json
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20 import numpy as np
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21 import pandas as pd
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22 import pickle
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23 import pprint
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24 import skrebate
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25 import sys
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26 import warnings
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27 import xgboost
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28 from mlxtend import classifier, regressor
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29 from sklearn import (
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30 cluster, compose, decomposition, ensemble, feature_extraction,
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31 feature_selection, gaussian_process, kernel_approximation, metrics,
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32 model_selection, naive_bayes, neighbors, pipeline, preprocessing,
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33 svm, linear_model, tree, discriminant_analysis)
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34
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35 sys.path.insert(0, '$__tool_directory__')
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36 from utils import SafeEval, get_cv, get_scoring, load_model, read_columns
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38 N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
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40 warnings.filterwarnings('ignore')
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42 safe_eval = SafeEval()
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43
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44 input_json_path = sys.argv[1]
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45 with open(input_json_path, 'r') as param_handler:
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46 params = json.load(param_handler)
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47
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48 #if $model_validation_functions.options.cv_selector.selected_cv\
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49 in ['GroupKFold', 'GroupShuffleSplit', 'LeaveOneGroupOut', 'LeavePGroupsOut']:
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50 params['model_validation_functions']['options']['cv_selector']['groups_selector']['infile_g'] =\
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51 '$model_validation_functions.options.cv_selector.groups_selector.infile_g'
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52 #end if
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53
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54 input_type = params['input_options']['selected_input']
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55 if input_type == 'tabular':
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56 header = 'infer' if params['input_options']['header1'] else None
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57 column_option = params['input_options']['column_selector_options_1']['selected_column_selector_option']
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58 if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']:
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59 c = params['input_options']['column_selector_options_1']['col1']
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60 else:
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61 c = None
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62 X = read_columns(
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63 '$input_options.infile1',
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64 c = c,
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65 c_option = column_option,
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66 sep='\t',
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67 header=header,
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68 parse_dates=True).astype(float)
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69 else:
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70 X = mmread('$input_options.infile1')
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71
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72 header = 'infer' if params['input_options']['header2'] else None
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73 column_option = params['input_options']['column_selector_options_2']['selected_column_selector_option2']
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74 if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']:
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75 c = params['input_options']['column_selector_options_2']['col2']
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76 else:
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77 c = None
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78 y = read_columns(
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79 '$input_options.infile2',
3
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80 c = c,
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81 c_option = column_option,
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82 sep='\t',
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83 header=header,
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84 parse_dates=True)
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85 y = y.ravel()
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86
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87 ## handle options
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88 options = params['model_validation_functions']['options']
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89 splitter, groups = get_cv( options.pop('cv_selector') )
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90 options['cv'] = splitter
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91 options['groups'] = groups
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92 options['n_jobs'] = N_JOBS
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93 if 'scoring' in options:
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94 primary_scoring = options['scoring']['primary_scoring']
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95 options['scoring'] = get_scoring(options['scoring'])
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96 if 'pre_dispatch' in options and options['pre_dispatch'] == '':
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97 options['pre_dispatch'] = None
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98
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99 ## load pipeline
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100 with open('$infile_pipeline', 'rb') as pipeline_handler:
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101 pipeline = load_model(pipeline_handler)
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102
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103 ## Set up validator, run pipeline through validator and return results.
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104
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105 validator = params['model_validation_functions']['selected_function']
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106 validator = getattr(model_selection, validator)
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107
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108 selected_function = params['model_validation_functions']['selected_function']
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109
2
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110 if selected_function == 'cross_validate':
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111 res = validator(pipeline, X, y, **options)
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112 rval = pd.DataFrame(res)
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113 col_rename = {}
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114 for col in rval.columns:
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115 if col.endswith('_primary'):
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116 col_rename[col] = col[:-7] + primary_scoring
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117 rval.rename(inplace=True, columns=col_rename)
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118 elif selected_function == 'cross_val_predict':
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119 predicted = validator(pipeline, X, y, **options)
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120 if len(predicted.shape) == 1:
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121 rval = pd.DataFrame(predicted, columns=['Predicted'])
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122 else:
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123 rval = pd.DataFrame(predicted)
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124 elif selected_function == 'learning_curve':
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125 try:
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126 train_sizes = safe_eval(options['train_sizes'])
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127 except:
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128 sys.exit("Unsupported train_sizes input! Supports int/float in tuple and array-like structure.")
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129 if type(train_sizes) is tuple:
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130 train_sizes = np.linspace(*train_sizes)
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131 options['train_sizes'] = train_sizes
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132 train_sizes_abs, train_scores, test_scores = validator(pipeline, X, y, **options)
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133 rval = pd.DataFrame(dict(
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134 train_sizes_abs = train_sizes_abs,
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135 mean_train_scores = np.mean(train_scores, axis=1),
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136 std_train_scores = np.std(train_scores, axis=1),
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137 mean_test_scores = np.mean(test_scores, axis=1),
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138 std_test_scores = np.std(test_scores, axis=1)))
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139 rval = rval[['train_sizes_abs', 'mean_train_scores', 'std_train_scores',
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140 'mean_test_scores', 'std_test_scores']]
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141 elif selected_function == 'permutation_test_score':
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142 score, permutation_scores, pvalue = validator(pipeline, X, y, **options)
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143 permutation_scores_df = pd.DataFrame(dict(
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144 permutation_scores = permutation_scores))
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145 score_df = pd.DataFrame(dict(
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146 score = [score],
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147 pvalue = [pvalue]))
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148 rval = pd.concat([score_df[['score', 'pvalue']], permutation_scores_df], axis=1)
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149
17
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150 rval.to_csv(path_or_buf='$outfile', sep='\t', header=True, index=False)
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151
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152 ]]>
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153 </configfile>
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154 </configfiles>
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155 <inputs>
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156 <param name="infile_pipeline" type="data" format="zip" label="Choose the dataset containing model/pipeline object"/>
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157 <conditional name="model_validation_functions">
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158 <param name="selected_function" type="select" label="Select a model validation function">
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159 <option value="cross_validate">cross_validate - Evaluate metric(s) by cross-validation and also record fit/score times</option>
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160 <option value="cross_val_predict">cross_val_predict - Generate cross-validated estimates for each input data point</option>
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161 <option value="learning_curve">learning_curve - Learning curve</option>
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162 <option value="permutation_test_score">permutation_test_score - Evaluate the significance of a cross-validated score with permutations</option>
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163 <option value="validation_curve">validation_curve - Use grid search with one parameter instead</option>
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164 </param>
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165 <when value="cross_validate">
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166 <section name="options" title="Other Options" expanded="false">
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167 <expand macro="scoring_selection"/>
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168 <expand macro="model_validation_common_options"/>
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169 <!--param argument="return_train_score" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true" help="Whether to include train scores."/> -->
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170 <!--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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171 <!--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."/> -->
0
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172 <!--fit_params-->
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173 <expand macro="pre_dispatch"/>
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174 </section>
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175 </when>
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176 <when value="cross_val_predict">
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177 <section name="options" title="Other Options" expanded="false">
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178 <expand macro="model_validation_common_options" />
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179 <!--fit_params-->
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180 <expand macro="pre_dispatch" value="2*n_jobs’" help="Controls the number of jobs that get dispatched during parallel execution"/>
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181 <param argument="method" type="select" label="Invokes the passed method name of the passed estimator">
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182 <option value="predict" selected="true">predict</option>
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183 <option value="predict_proba">predict_proba</option>
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184 </param>
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185 </section>
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186 </when>
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187 <when value="learning_curve">
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188 <section name="options" title="Other Options" expanded="false">
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189 <expand macro="scoring_selection"/>
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190 <expand macro="model_validation_common_options"/>
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191 <param argument="train_sizes" type="text" value="(0.1, 1.0, 5)" label="train_sizes"
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192 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.]">
cf9aa11b91c8 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
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193 <sanitizer>
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194 <valid initial="default">
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195 <add value="["/>
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196 <add value="]"/>
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197 </valid>
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198 </sanitizer>
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199 </param>
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200 <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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201 <expand macro="pre_dispatch"/>
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202 <expand macro="shuffle" checked="false" label="shuffle" help="Whether to shuffle training data before taking prefixes"/>
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203 <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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204 </section>
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205 </when>
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206 <when value="permutation_test_score">
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207 <section name="options" title="Other Options" expanded="false">
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208 <expand macro="scoring_selection"/>
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209 <expand macro="model_validation_common_options"/>
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210 <param name="n_permutations" type="integer" value="100" optional="true" label="n_permutations" help="Number of times to permute y"/>
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211 <expand macro="random_state"/>
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212 </section>
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213 </when>
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214 <when value="validation_curve"/>
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215 </conditional>
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216 <expand macro="sl_mixed_input"/>
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217 </inputs>
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218 <outputs>
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219 <data format="tabular" name="outfile"/>
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220 </outputs>
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221 <tests>
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222 <test>
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223 <param name="infile_pipeline" value="pipeline02"/>
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224 <param name="selected_function" value="cross_validate"/>
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225 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
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226 <param name="col1" value="1,2,3,4,5"/>
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227 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
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228 <param name="col2" value="6"/>
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229 <output name="outfile">
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230 <assert_contents>
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231 <has_n_columns n="4"/>
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232 <has_text text="0.9999961390418067"/>
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233 <has_text text="0.9944541531269271"/>
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234 <has_text text="0.9999193322454393"/>
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235 </assert_contents>
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236 </output>
0
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237 </test>
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238 <test>
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239 <param name="infile_pipeline" value="pipeline02"/>
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240 <param name="selected_function" value="cross_val_predict"/>
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241 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
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242 <param name="col1" value="1,2,3,4,5"/>
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243 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
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244 <param name="col2" value="6"/>
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245 <output name="outfile" file="mv_result02.tabular" lines_diff="4"/>
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246 </test>
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247 <test>
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248 <param name="infile_pipeline" value="pipeline05"/>
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249 <param name="selected_function" value="learning_curve"/>
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250 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
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251 <param name="header1" value="true" />
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252 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
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253 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
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254 <param name="header2" value="true" />
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255 <param name="col2" value="1"/>
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256 <output name="outfile" file="mv_result03.tabular"/>
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257 </test>
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258 <test>
17
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259 <param name="infile_pipeline" value="pipeline05"/>
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260 <param name="selected_function" value="permutation_test_score"/>
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261 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
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262 <param name="col1" value="1,2,3,4,5"/>
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263 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
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264 <param name="col2" value="6"/>
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265 <output name="outfile">
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266 <assert_contents>
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267 <has_n_columns n="3"/>
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268 <has_text text="0.25697059258228816"/>
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269 </assert_contents>
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270 </output>
0
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271 </test>
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272 <test>
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273 <param name="infile_pipeline" value="pipeline05"/>
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274 <param name="selected_function" value="cross_val_predict"/>
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275 <section name="groups_selector">
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276 <param name="infile_groups" value="regression_y.tabular" ftype="tabular"/>
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277 <param name="header_g" value="true"/>
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278 <param name="selected_column_selector_option_g" value="by_index_number"/>
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279 <param name="col_g" value="1"/>
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280 </section>
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281 <param name="selected_cv" value="GroupKFold"/>
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282 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
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283 <param name="header1" value="true"/>
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284 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
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285 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
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286 <param name="header2" value="true"/>
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287 <param name="col2" value="1"/>
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288 <output name="outfile" file="mv_result05.tabular"/>
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289 </test>
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290 </tests>
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291 <help>
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292 <![CDATA[
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293 **What it does**
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294 This tool includes model validation functions to evaluate estimator performance in the cross-validation approach. This tool is based on
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295 sklearn.model_selection package.
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296 For information about model validation functions and their parameter settings please refer to `Scikit-learn model_selection`_.
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297
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298 .. _`Scikit-learn model_selection`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection
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299 ]]>
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300 </help>
13
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301 <expand macro="sklearn_citation">
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302 <expand macro="skrebate_citation"/>
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303 <expand macro="xgboost_citation"/>
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304 </expand>
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305 </tool>