annotate stacking_ensembles.xml @ 15:b94babda32e4 draft default tip

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f031d8ddfb73cec24572648666ac44ee47f08aad
author bgruening
date Thu, 11 Aug 2022 09:11:27 +0000
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1 <tool id="sklearn_stacking_ensemble_models" name="Stacking Ensembles" version="@VERSION@" profile="20.05">
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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>
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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 "$ENSEMBLE_VERSION"</version_command>
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9 <command>
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10 <![CDATA[
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11 #for $i, $base in enumerate($base_est_builder)
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12 #if $i == 0
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13 #if $base.estimator_selector.selected_module == 'custom_estimator'
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14 bases='${base.estimator_selector.c_estimator}';
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15 #else
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16 bases='None';
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17 #end if
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18 #elif $base.estimator_selector.selected_module == 'custom_estimator'
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19 bases="\$bases,${base.estimator_selector.c_estimator}";
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20 #else
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21 bases="\$bases,None";
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22 #end if
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23 #end for
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24 python '$__tool_directory__/stacking_ensembles.py'
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25 --inputs '$inputs'
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26 --outfile '$outfile'
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27 --bases "\$bases"
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28 #if $algo_selection.estimator_type not in ('sklearn.ensemble_VotingClassifier', 'sklearn.ensemble_VotingRegressor')
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29 #if $algo_selection.meta_estimator.estimator_selector.selected_module == 'custom_estimator'
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30 --meta '${algo_selection.meta_estimator.estimator_selector.c_estimator}'
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31 #end if
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32 #end if
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33 #if $get_params
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34 --outfile_params '$outfile_params'
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35 #end if
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36 ]]>
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37 </command>
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38 <configfiles>
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39 <inputs name="inputs" />
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40 </configfiles>
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41 <inputs>
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42 <conditional name="algo_selection">
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43 <param name="estimator_type" type="select" label="Choose the stacking ensemble type">
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44 <option value="sklearn.ensemble_VotingClassifier" selected="true">sklearn.ensemble -- VotingClassifier</option>
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45 <option value="sklearn.ensemble_VotingRegressor">sklearn.ensemble -- VotingRegressor</option>
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46 <option value="mlxtend.classifier_StackingCVClassifier">mlxtend.classifier -- StackingCVClassifier</option>
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47 <option value="mlxtend.classifier_StackingClassifier">mlxtend.classifier -- StackingClassifier</option>
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48 <option value="mlxtend.regressor_StackingCVRegressor">mlxtend.regressor -- StackingCVRegressor</option>
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49 <option value="mlxtend.regressor_StackingRegressor">mlxtend.regressor -- StackingRegressor</option>
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50 </param>
2
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51 <when value="sklearn.ensemble_VotingClassifier">
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52 <expand macro="stacking_voting_weights">
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53 <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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54 <option value="hard" selected="true">hard</option>
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55 <option value="soft">soft</option>
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56 </param>
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57 <param argument="flatten_transform" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" help="" />
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58 </expand>
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59 </when>
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60 <when value="sklearn.ensemble_VotingRegressor">
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61 <expand macro="stacking_voting_weights" />
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62 </when>
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63 <when value="mlxtend.classifier_StackingCVClassifier">
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64 <expand macro="stacking_ensemble_inputs">
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65 <expand macro="cv_reduced" />
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66 <expand macro="shuffle" label="shuffle" />
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67 <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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68 <param argument="use_probas" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" />
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69 </expand>
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70 <section name="meta_estimator" title="Meta Estimator" expanded="true">
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71 <expand macro="stacking_base_estimator" />
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72 </section>
0
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73 </when>
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74 <when value="mlxtend.classifier_StackingClassifier">
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75 <expand macro="stacking_ensemble_inputs">
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76 <param argument="use_probas" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" />
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77 <param argument="average_probas" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" />
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78 </expand>
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79 <section name="meta_estimator" title="Meta Estimator" expanded="true">
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80 <expand macro="stacking_base_estimator" />
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81 </section>
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82 </when>
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83 <when value="mlxtend.regressor_StackingCVRegressor">
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84 <expand macro="stacking_ensemble_inputs">
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85 <expand macro="cv_reduced" />
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86 <!--TODO support group splitters. Hint: `groups` is a fit_param-->
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87 <expand macro="shuffle" label="shuffle" />
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88 <expand macro="random_state" default_value="" help_text="Integer number. The seed of the pseudo random number generator to use when shuffling the data." />
ac40a2fe5750 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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89 <param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" />
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90 </expand>
2
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91 <section name="meta_estimator" title="Meta Estimator" expanded="true">
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92 <expand macro="stacking_base_estimator" />
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93 </section>
0
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94 </when>
2
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95 <when value="mlxtend.regressor_StackingRegressor">
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96 <expand macro="stacking_ensemble_inputs">
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97 <param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" />
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98 </expand>
2
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99 <section name="meta_estimator" title="Meta Estimator" expanded="true">
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100 <expand macro="stacking_base_estimator" />
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101 </section>
0
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102 </when>
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103 </conditional>
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104 <repeat name="base_est_builder" min="1" max="20" title="Base Estimator">
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105 <expand macro="stacking_base_estimator" />
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106 <!--param name="base_estimator" type="data" format="zip,json" label="Select the dataset containing base estimator" help="One estimator at a time."/-->
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107 </repeat>
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108 <!--param name="meta_estimator" type="data" format="zip,json" label="Select the dataset containing the Meta estimator"/-->
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109 <param name="get_params" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Output parameters for searchCV?" help="Optional. Tunable parameters could be obtained through `estimator_attributes` tool." />
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110 </inputs>
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111 <outputs>
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112 <data format="zip" name="outfile" label="${algo_selection.estimator_type} on ${on_string}" />
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113 <data format="tabular" name="outfile_params" label="get_params for ${algo_selection.estimator_type}">
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114 <filter>get_params</filter>
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115 </data>
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116 </outputs>
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117 <tests>
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118 <test>
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119 <conditional name="algo_selection">
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120 <param name="estimator_type" value="sklearn.ensemble_VotingClassifier" />
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121 <section name="options">
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122 <param name="weights" value="[1, 2]" />
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123 </section>
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124 </conditional>
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125 <repeat name="base_est_builder">
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126 <conditional name="estimator_selector">
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127 <param name="selected_module" value="svm" />
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128 <param name="selected_estimator" value="SVC" />
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129 </conditional>
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130 </repeat>
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131 <repeat name="base_est_builder">
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132 <conditional name="estimator_selector">
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133 <param name="selected_module" value="xgboost" />
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134 <param name="selected_estimator" value="XGBClassifier" />
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135 </conditional>
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136 </repeat>
10
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137 <param name="get_params" value="false" />
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138 <output name="outfile" file="StackingVoting03.zip" compare="sim_size" delta="5" />
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139 </test>
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140 <test>
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141 <conditional name="algo_selection">
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142 <param name="estimator_type" value="mlxtend.regressor_StackingCVRegressor" />
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143 <section name="meta_estimator">
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144 <conditional name="estimator_selector">
10
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145 <param name="selected_module" value="custom_estimator" />
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146 <param name="c_estimator" value="LinearRegression01.zip" ftype="zip" />
2
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147 </conditional>
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148 </section>
0
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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">
10
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152 <param name="selected_module" value="custom_estimator" />
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153 <param name="c_estimator" value="RandomForestRegressor01.zip" ftype="zip" />
0
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154 </conditional>
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155 </repeat>
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156 <repeat name="base_est_builder">
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157 <conditional name="estimator_selector">
10
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158 <param name="selected_module" value="custom_estimator" />
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159 <param name="c_estimator" value="XGBRegressor01.zip" ftype="zip" />
0
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160 </conditional>
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161 </repeat>
10
ac40a2fe5750 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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162 <param name="get_params" value="false" />
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163 <output name="outfile" file="StackingCVRegressor01.zip" compare="sim_size" delta="5" />
0
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164 </test>
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165 <test>
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166 <conditional name="algo_selection">
10
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167 <param name="estimator_type" value="mlxtend.regressor_StackingRegressor" />
2
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diff changeset
168 <section name="meta_estimator">
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169 <conditional name="estimator_selector">
10
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170 <param name="selected_module" value="svm" />
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171 <param name="selected_estimator" value="SVR" />
2
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172 </conditional>
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173 </section>
0
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174 </conditional>
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175 <repeat name="base_est_builder">
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176 <conditional name="estimator_selector">
10
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177 <param name="selected_module" value="custom_estimator" />
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178 <param name="c_estimator" value="RandomForestRegressor01.zip" ftype="zip" />
0
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179 </conditional>
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180 </repeat>
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181 <repeat name="base_est_builder">
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182 <conditional name="estimator_selector">
10
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183 <param name="selected_module" value="xgboost" />
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184 <param name="selected_estimator" value="XGBRegressor" />
0
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185 </conditional>
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186 </repeat>
10
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187 <param name="get_params" value="false" />
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188 <output name="outfile" file="StackingRegressor02.zip" compare="sim_size" delta="5" />
0
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189 </test>
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190 </tests>
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191 <help>
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192 <![CDATA[
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193 This tool wrapps Stacking Regression, also called Super Learning, in which different base algorithms train
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194 on the original dataset and predict results respectively, a second level of `metalearner` fits on the previous
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195 prediction results to ensemble a strong learner.
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196 Refer to `http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html#introduction`_.
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197
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198 .. _`http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html#introduction`:
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199 http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html#introduction
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200
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201 ]]>
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202 </help>
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203 <expand macro="sklearn_citation">
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204 <expand macro="skrebate_citation" />
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205 <expand macro="xgboost_citation" />
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206 <expand macro="imblearn_citation" />
0
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207 <citation type="bibtex">
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208 @article{raschkas_2018_mlxtend,
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209 author = {Sebastian Raschka},
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210 title = {MLxtend: Providing machine learning and data science
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211 utilities and extensions to Python’s
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212 scientific computing stack},
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213 journal = {The Journal of Open Source Software},
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214 volume = {3},
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215 number = {24},
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216 month = apr,
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217 year = 2018,
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218 publisher = {The Open Journal},
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219 doi = {10.21105/joss.00638},
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220 url = {http://joss.theoj.org/papers/10.21105/joss.00638}
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221 }
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222 </citation>
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223 </expand>
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224 </tool>