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annotate stacking_ensembles.xml @ 3:0a1812986bc3 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
author bgruening
date Wed, 09 Aug 2023 11:10:37 +0000
parents 38c4f8a98038
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0a1812986bc3 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
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1 <tool id="stacking_ensemble_models" name="Stacking Ensembles" version="@VERSION@" profile="@PROFILE@">
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2 <description>builds stacking, voting ensemble models with numerous base options</description>
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3 <macros>
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4 <import>main_macros.xml</import>
3
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5 <macro name="stacking_ensemble_inputs">
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6 <section name="options" title="Advanced Options" expanded="false">
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7 <yield />
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8 <param argument="use_features_in_secondary" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" />
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9 <param argument="store_train_meta_features" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" />
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10 </section>
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11 </macro>
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12 <macro name="stacking_base_estimator">
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13 <conditional name="estimator_selector">
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14 <param name="selected_module" type="select" label="Choose the module that contains target estimator:" >
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15 <expand macro="estimator_module_options">
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16 <option value="custom_estimator">Load a custom estimator</option>
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17 </expand>
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18 </param>
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19 <expand macro="estimator_suboptions">
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20 <when value="custom_estimator">
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21 <param name="c_estimator" type="data" format="h5mlm" label="Choose the dataset containing the custom estimator or pipeline" />
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22 </when>
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23 </expand>
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24 </conditional>
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25 </macro>
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26 <macro name="stacking_voting_weights">
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27 <section name="options" title="Advanced Options" expanded="false">
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28 <param argument="weights" type="text" value="[]" optional="true" help="Sequence of weights (float or int). Uses uniform weights if None (`[]`).">
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29 <sanitizer>
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30 <valid initial="default">
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31 <add value="[" />
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32 <add value="]" />
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33 </valid>
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34 </sanitizer>
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35 </param>
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36 <yield />
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37 </section>
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38 </macro>
0
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39 </macros>
3
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40 <expand macro="python_requirements" />
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41 <expand macro="macro_stdio" />
1
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42 <version_command>echo "$ENSEMBLE_VERSION"</version_command>
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43 <command>
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44 <![CDATA[
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45 #for $i, $base in enumerate($base_est_builder)
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46 #if $i == 0
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47 #if $base.estimator_selector.selected_module == 'custom_estimator'
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48 bases='${base.estimator_selector.c_estimator}';
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49 #else
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50 bases='None';
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51 #end if
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52 #elif $base.estimator_selector.selected_module == 'custom_estimator'
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53 bases="\$bases,${base.estimator_selector.c_estimator}";
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54 #else
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55 bases="\$bases,None";
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56 #end if
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57 #end for
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58 python '$__tool_directory__/stacking_ensembles.py'
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59 --inputs '$inputs'
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60 --outfile '$outfile'
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61 --bases "\$bases"
1
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62 #if $algo_selection.estimator_type not in ('sklearn.ensemble_VotingClassifier', 'sklearn.ensemble_VotingRegressor')
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63 #if $algo_selection.meta_estimator.estimator_selector.selected_module == 'custom_estimator'
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64 --meta '${algo_selection.meta_estimator.estimator_selector.c_estimator}'
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65 #end if
0
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66 #end if
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67 ]]>
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68 </command>
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69 <configfiles>
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70 <inputs name="inputs" />
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71 </configfiles>
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72 <inputs>
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73 <conditional name="algo_selection">
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74 <param name="estimator_type" type="select" label="Choose the stacking ensemble type">
1
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75 <option value="sklearn.ensemble_VotingClassifier" selected="true">sklearn.ensemble -- VotingClassifier</option>
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76 <option value="sklearn.ensemble_VotingRegressor">sklearn.ensemble -- VotingRegressor</option>
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77 <option value="mlxtend.classifier_StackingCVClassifier">mlxtend.classifier -- StackingCVClassifier</option>
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78 <option value="mlxtend.classifier_StackingClassifier">mlxtend.classifier -- StackingClassifier</option>
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79 <option value="mlxtend.regressor_StackingCVRegressor">mlxtend.regressor -- StackingCVRegressor</option>
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80 <option value="mlxtend.regressor_StackingRegressor">mlxtend.regressor -- StackingRegressor</option>
0
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81 </param>
1
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82 <when value="sklearn.ensemble_VotingClassifier">
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83 <expand macro="stacking_voting_weights">
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84 <param argument="voting" type="select" help="If ‘hard’, uses predicted class labels for majority rule voting. Else if ‘soft’, predicts the class label based on the argmax of the sums of the predicted probabilities, which is recommended for an ensemble of well-calibrated classifiers.">
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85 <option value="hard" selected="true">hard</option>
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86 <option value="soft">soft</option>
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87 </param>
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88 <param argument="flatten_transform" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" help="" />
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89 </expand>
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90 </when>
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91 <when value="sklearn.ensemble_VotingRegressor">
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92 <expand macro="stacking_voting_weights" />
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93 </when>
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94 <when value="mlxtend.classifier_StackingCVClassifier">
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95 <expand macro="stacking_ensemble_inputs">
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96 <expand macro="cv_reduced" />
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97 <param argument="use_probas" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" />
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98 <param argument="drop_last_proba" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" />
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99 </expand>
1
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100 <section name="meta_estimator" title="Meta Estimator" expanded="true">
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101 <expand macro="stacking_base_estimator" />
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102 </section>
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103 </when>
1
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104 <when value="mlxtend.classifier_StackingClassifier">
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105 <expand macro="stacking_ensemble_inputs">
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106 <param argument="use_probas" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" />
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107 <param argument="average_probas" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" />
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108 <param argument="drop_last_proba" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" />
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109 </expand>
1
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110 <section name="meta_estimator" title="Meta Estimator" expanded="true">
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111 <expand macro="stacking_base_estimator" />
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112 </section>
0
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113 </when>
1
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114 <when value="mlxtend.regressor_StackingCVRegressor">
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115 <expand macro="stacking_ensemble_inputs">
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116 <expand macro="cv_reduced" />
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117 <!--TODO support group splitters. Hint: `groups` is a fit_param-->
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118 <expand macro="shuffle" label="shuffle" />
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119 <expand macro="random_state" default_value="" help_text="Integer number. The seed of the pseudo random number generator to use when shuffling the data." />
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120 <param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" />
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121 </expand>
1
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122 <section name="meta_estimator" title="Meta Estimator" expanded="true">
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123 <expand macro="stacking_base_estimator" />
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124 </section>
0
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125 </when>
1
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126 <when value="mlxtend.regressor_StackingRegressor">
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127 <expand macro="stacking_ensemble_inputs">
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128 <param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" />
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129 </expand>
1
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130 <section name="meta_estimator" title="Meta Estimator" expanded="true">
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131 <expand macro="stacking_base_estimator" />
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132 </section>
0
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133 </when>
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134 </conditional>
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135 <repeat name="base_est_builder" min="1" max="20" title="Base Estimator">
3
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136 <expand macro="stacking_base_estimator" />
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137 </repeat>
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138 </inputs>
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139 <outputs>
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140 <data format="h5mlm" name="outfile" label="${algo_selection.estimator_type} on ${on_string}" />
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141 </outputs>
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142 <tests>
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143 <test>
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144 <conditional name="algo_selection">
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145 <param name="estimator_type" value="sklearn.ensemble_VotingClassifier" />
1
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146 <section name="options">
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147 <param name="weights" value="[1, 2]" />
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148 </section>
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149 </conditional>
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150 <repeat name="base_est_builder">
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151 <conditional name="estimator_selector">
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152 <param name="selected_module" value="svm" />
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153 <param name="selected_estimator" value="SVC" />
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154 </conditional>
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155 </repeat>
c1b0c8232816 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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156 <repeat name="base_est_builder">
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157 <conditional name="estimator_selector">
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158 <param name="selected_module" value="xgboost" />
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159 <param name="selected_estimator" value="XGBClassifier" />
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160 </conditional>
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161 </repeat>
3
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162 <output name="outfile" file="StackingVoting03.h5mlm" compare="sim_size" delta="5" />
1
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163 </test>
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164 <test>
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165 <conditional name="algo_selection">
3
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166 <param name="estimator_type" value="mlxtend.regressor_StackingCVRegressor" />
1
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167 <section name="meta_estimator">
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168 <conditional name="estimator_selector">
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169 <param name="selected_module" value="custom_estimator" />
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170 <param name="c_estimator" value="LinearRegression01.h5mlm" ftype="h5mlm" />
1
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171 </conditional>
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172 </section>
0
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173 </conditional>
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174 <repeat name="base_est_builder">
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175 <conditional name="estimator_selector">
3
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176 <param name="selected_module" value="custom_estimator" />
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177 <param name="c_estimator" value="RandomForestRegressor01.h5mlm" ftype="h5mlm" />
0
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178 </conditional>
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179 </repeat>
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180 <repeat name="base_est_builder">
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181 <conditional name="estimator_selector">
3
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182 <param name="selected_module" value="custom_estimator" />
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183 <param name="c_estimator" value="XGBRegressor01.h5mlm" ftype="h5mlm" />
0
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184 </conditional>
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185 </repeat>
3
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186 <output name="outfile" file="StackingCVRegressor01.h5mlm" compare="sim_size" delta="5" />
0
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187 </test>
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188 <test>
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189 <conditional name="algo_selection">
3
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190 <param name="estimator_type" value="mlxtend.regressor_StackingRegressor" />
1
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191 <section name="meta_estimator">
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192 <conditional name="estimator_selector">
3
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193 <param name="selected_module" value="svm" />
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194 <param name="selected_estimator" value="SVR" />
1
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195 </conditional>
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196 </section>
0
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197 </conditional>
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198 <repeat name="base_est_builder">
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199 <conditional name="estimator_selector">
3
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200 <param name="selected_module" value="custom_estimator" />
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201 <param name="c_estimator" value="RandomForestRegressor01.h5mlm" ftype="h5mlm" />
0
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202 </conditional>
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203 </repeat>
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204 <repeat name="base_est_builder">
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205 <conditional name="estimator_selector">
3
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206 <param name="selected_module" value="xgboost" />
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207 <param name="selected_estimator" value="XGBRegressor" />
0
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208 </conditional>
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209 </repeat>
3
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210 <output name="outfile" file="StackingRegressor02.h5mlm" compare="sim_size" delta="5" />
0
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211 </test>
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212 </tests>
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213 <help>
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214 <![CDATA[
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215 This tool wrapps Stacking Regression, also called Super Learning, in which different base algorithms train
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216 on the original dataset and predict results respectively, a second level of `metalearner` fits on the previous
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217 prediction results to ensemble a strong learner.
3
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218 Refer to https://h2o-release.s3.amazonaws.com/h2o/rel-ueno/2/docs-website/h2o-docs/data-science/stacked-ensembles.html.
0
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219
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220
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221 ]]>
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222 </help>
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223 <expand macro="sklearn_citation">
3
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224 <expand macro="skrebate_citation" />
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225 <expand macro="xgboost_citation" />
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226 <expand macro="imblearn_citation" />
0
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227 <citation type="bibtex">
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228 @article{raschkas_2018_mlxtend,
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229 author = {Sebastian Raschka},
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230 title = {MLxtend: Providing machine learning and data science
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231 utilities and extensions to Python’s
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232 scientific computing stack},
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233 journal = {The Journal of Open Source Software},
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234 volume = {3},
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235 number = {24},
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236 month = apr,
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237 year = 2018,
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238 publisher = {The Open Journal},
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239 doi = {10.21105/joss.00638},
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240 url = {http://joss.theoj.org/papers/10.21105/joss.00638}
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241 }
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242 </citation>
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243 </expand>
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244 </tool>