Mercurial > repos > bgruening > sklearn_stacking_ensemble_models

annotate stacking_ensembles.xml @ 2:e18d9b17c322 draft

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author bgruening
date Fri, 09 Aug 2019 13:52:50 -0400
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children 8b5b653ba1ed
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e18d9b17c322 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c035d399196b3bef9982db4f8e47331411dbb20e
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1 <tool id="sklearn_stacking_ensemble_models" name="Stacking Ensemble Models" version="@ENSEMBLE_VERSION@">
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2 <description>builds a strong model by stacking multiple algorithms</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
0
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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>
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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>
2
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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>
2
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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>
2
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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>
0
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82 </when>
2
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83 <when value="mlxtend.regressor_StackingCVRegressor">
0
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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."/>
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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">
0
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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?"
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110 help="Optional. Tunable parameters could be obtained through `estimator_attributes` tool."/>
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111 </inputs>
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112 <outputs>
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113 <data format="zip" name="outfile" label="${algo_selection.estimator_type} on ${on_string}"/>
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114 <data format="tabular" name="outfile_params" label="get_params for ${algo_selection.estimator_type}">
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115 <filter>get_params</filter>
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116 </data>
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117 </outputs>
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118 <tests>
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119 <test>
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120 <conditional name="algo_selection">
2
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121 <param name="estimator_type" value="sklearn.ensemble_VotingClassifier"/>
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122 <section name="options">
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123 <param name="weights" value="[1, 2]"/>
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124 </section>
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125 </conditional>
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126 <repeat name="base_est_builder">
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127 <conditional name="estimator_selector">
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128 <param name="selected_module" value="svm"/>
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129 <param name="selected_estimator" value="SVC"/>
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130 </conditional>
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131 </repeat>
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132 <repeat name="base_est_builder">
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133 <conditional name="estimator_selector">
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134 <param name="selected_module" value="xgboost"/>
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135 <param name="selected_estimator" value="XGBClassifier"/>
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136 </conditional>
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137 </repeat>
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138 <param name="get_params" value="false"/>
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139 <output name="outfile" file="StackingVoting03.zip" compare="sim_size" delta="5"/>
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140 </test>
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141 <test>
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142 <conditional name="algo_selection">
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143 <param name="estimator_type" value="mlxtend.regressor_StackingCVRegressor"/>
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144 <section name="meta_estimator">
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145 <conditional name="estimator_selector">
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146 <param name="selected_module" value="custom_estimator"/>
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147 <param name="c_estimator" value="LinearRegression01.zip" ftype="zip"/>
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148 </conditional>
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149 </section>
0
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150 </conditional>
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151 <repeat name="base_est_builder">
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152 <conditional name="estimator_selector">
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153 <param name="selected_module" value="custom_estimator"/>
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154 <param name="c_estimator" value="RandomForestRegressor01.zip" ftype="zip"/>
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155 </conditional>
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156 </repeat>
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157 <repeat name="base_est_builder">
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158 <conditional name="estimator_selector">
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159 <param name="selected_module" value="custom_estimator"/>
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160 <param name="c_estimator" value="XGBRegressor01.zip" ftype="zip"/>
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161 </conditional>
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162 </repeat>
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163 <param name="get_params" value="false"/>
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164 <output name="outfile" file="StackingCVRegressor01.zip" compare="sim_size" delta="5"/>
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165 </test>
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166 <test>
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167 <conditional name="algo_selection">
2
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168 <param name="estimator_type" value="mlxtend.regressor_StackingRegressor"/>
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169 <section name="meta_estimator">
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170 <conditional name="estimator_selector">
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171 <param name="selected_module" value="svm"/>
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172 <param name="selected_estimator" value="SVR"/>
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173 </conditional>
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174 </section>
0
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175 </conditional>
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176 <repeat name="base_est_builder">
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177 <conditional name="estimator_selector">
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178 <param name="selected_module" value="custom_estimator"/>
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179 <param name="c_estimator" value="RandomForestRegressor01.zip" ftype="zip"/>
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180 </conditional>
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181 </repeat>
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182 <repeat name="base_est_builder">
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183 <conditional name="estimator_selector">
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184 <param name="selected_module" value="xgboost"/>
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185 <param name="selected_estimator" value="XGBRegressor"/>
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186 </conditional>
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187 </repeat>
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188 <param name="get_params" value="false"/>
2
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189 <output name="outfile" file="StackingRegressor02.zip" compare="sim_size" delta="5"/>
0
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190 </test>
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191 </tests>
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192 <help>
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193 <![CDATA[
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194 This tool wrapps Stacking Regression, also called Super Learning, in which different base algorithms train
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195 on the original dataset and predict results respectively, a second level of `metalearner` fits on the previous
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196 prediction results to ensemble a strong learner.
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197 Refer to `http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html#introduction`_.
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198
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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 http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html#introduction
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201
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202 ]]>
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203 </help>
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204 <expand macro="sklearn_citation">
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205 <expand macro="skrebate_citation"/>
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206 <expand macro="xgboost_citation"/>
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207 <expand macro="imblearn_citation"/>
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208 <citation type="bibtex">
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209 @article{raschkas_2018_mlxtend,
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210 author = {Sebastian Raschka},
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211 title = {MLxtend: Providing machine learning and data science
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212 utilities and extensions to Python’s
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213 scientific computing stack},
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214 journal = {The Journal of Open Source Software},
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215 volume = {3},
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216 number = {24},
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217 month = apr,
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218 year = 2018,
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219 publisher = {The Open Journal},
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220 doi = {10.21105/joss.00638},
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221 url = {http://joss.theoj.org/papers/10.21105/joss.00638}
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222 }
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223 </citation>
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224 </expand>
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225 </tool>