Mercurial > repos > bgruening > stacking_ensemble_models

annotate stacking_ensembles.xml @ 0:8e93241d5d28 draft default tip

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author bgruening
date Tue, 14 May 2019 18:04:46 -0400
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8e93241d5d28 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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1 <tool id="stacking_ensemble_models" name="Stacking Ensemble Models" version="0.1.0">
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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 "$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 $meta_estimator.estimator_selector.selected_module == 'custom_estimator'
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29 --meta '${meta_estimator.estimator_selector.c_estimator}'
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30 #end if
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31 #if $get_params
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32 --outfile_params '$outfile_params'
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33 #end if
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34 ]]>
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35 </command>
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36 <configfiles>
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37 <inputs name="inputs" />
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38 </configfiles>
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39 <inputs>
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40 <conditional name="algo_selection">
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41 <param name="estimator_type" type="select" label="Choose the stacking ensemble type">
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42 <option value="StackingCVClassifier" selected="true">classification -- StackingCVClassifier</option>
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43 <option value="StackingClassifier">classification -- StackingClassifier</option>
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44 <option value="StackingCVRegressor">regression -- StackingCVRegressor</option>
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45 <option value="StackingRegressor">regression -- StackingRegressor</option>
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46 </param>
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47 <when value="StackingCVClassifier">
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48 <expand macro="stacking_ensemble_inputs">
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49 <expand macro="cv_reduced"/>
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50 <expand macro="shuffle" label="shuffle"/>
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51 <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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52 <param argument="use_probas" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false"/>
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53 </expand>
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54 </when>
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55 <when value="StackingClassifier">
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56 <expand macro="stacking_ensemble_inputs">
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57 <param argument="use_probas" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false"/>
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58 <param argument="average_probas" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false"/>
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59 </expand>
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60 </when>
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61 <when value="StackingCVRegressor">
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62 <expand macro="stacking_ensemble_inputs">
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63 <expand macro="cv_reduced"/>
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64 <!--TODO support group splitters. Hint: `groups` is a fit_param-->
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65 <expand macro="shuffle" label="shuffle"/>
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66 <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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67 <param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true"/>
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68 </expand>
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69 </when>
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70 <when value="StackingRegressor">
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71 <expand macro="stacking_ensemble_inputs">
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72 <param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true"/>
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73 </expand>
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74 </when>
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75 </conditional>
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76 <repeat name="base_est_builder" min="1" max="20" title="Base Estimator">
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77 <expand macro="stacking_base_estimator"/>
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78 <!--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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79 </repeat>
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80 <!--param name="meta_estimator" type="data" format="zip,json" label="Select the dataset containing the Meta estimator"/-->
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81 <section name="meta_estimator" title="Meta Estimator" expanded="true">
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82 <expand macro="stacking_base_estimator"/>
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83 </section>
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84 <param name="get_params" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Output parameters for searchCV?"
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85 help="Optional. Tunable parameters could be obtained through `estimator_attributes` tool."/>
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86 </inputs>
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87 <outputs>
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88 <data format="zip" name="outfile" label="${algo_selection.estimator_type} on ${on_string}"/>
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89 <data format="tabular" name="outfile_params" label="get_params for ${algo_selection.estimator_type}">
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90 <filter>get_params</filter>
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91 </data>
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92 </outputs>
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93 <tests>
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94 <test>
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95 <conditional name="algo_selection">
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96 <param name="estimator_type" value="StackingCVRegressor"/>
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97 </conditional>
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98 <repeat name="base_est_builder">
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99 <conditional name="estimator_selector">
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100 <param name="selected_module" value="custom_estimator"/>
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101 <param name="c_estimator" value="RandomForestRegressor01.zip" ftype="zip"/>
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102 </conditional>
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103 </repeat>
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104 <repeat name="base_est_builder">
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105 <conditional name="estimator_selector">
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106 <param name="selected_module" value="custom_estimator"/>
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107 <param name="c_estimator" value="XGBRegressor01.zip" ftype="zip"/>
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108 </conditional>
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109 </repeat>
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110 <section name="meta_estimator">
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111 <conditional name="estimator_selector">
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112 <param name="selected_module" value="custom_estimator"/>
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113 <param name="c_estimator" value="LinearRegression01.zip" ftype="zip"/>
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114 </conditional>
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115 </section>
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116 <param name="get_params" value="false"/>
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117 <output name="outfile" file="StackingCVRegressor01.zip" compare="sim_size" delta="5"/>
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118 </test>
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119 <test>
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120 <conditional name="algo_selection">
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121 <param name="estimator_type" value="StackingCVRegressor"/>
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122 </conditional>
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123 <repeat name="base_est_builder">
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124 <conditional name="estimator_selector">
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125 <param name="selected_module" value="custom_estimator"/>
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126 <param name="c_estimator" value="RandomForestRegressor01.zip" ftype="zip"/>
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127 </conditional>
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128 </repeat>
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129 <repeat name="base_est_builder">
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130 <conditional name="estimator_selector">
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131 <param name="selected_module" value="xgboost"/>
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132 <param name="selected_estimator" value="XGBRegressor"/>
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133 </conditional>
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134 </repeat>
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135 <section name="meta_estimator">
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136 <conditional name="estimator_selector">
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137 <param name="selected_module" value="svm"/>
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138 <param name="selected_estimator" value="SVR"/>
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139 </conditional>
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140 </section>
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141 <param name="get_params" value="false"/>
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142 <output name="outfile" file="StackingCVRegressor02.zip" compare="sim_size" delta="5"/>
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143 </test>
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144 </tests>
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145 <help>
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146 <![CDATA[
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147 This tool wrapps Stacking Regression, also called Super Learning, in which different base algorithms train
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148 on the original dataset and predict results respectively, a second level of `metalearner` fits on the previous
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149 prediction results to ensemble a strong learner.
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150 Refer to `http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html#introduction`_.
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151
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152 .. _`http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html#introduction`:
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153 http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html#introduction
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154
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155 ]]>
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156 </help>
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157 <expand macro="sklearn_citation">
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158 <expand macro="skrebate_citation"/>
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159 <expand macro="xgboost_citation"/>
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160 <expand macro="imblearn_citation"/>
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161 <citation type="bibtex">
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162 @article{raschkas_2018_mlxtend,
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163 author = {Sebastian Raschka},
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164 title = {MLxtend: Providing machine learning and data science
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165 utilities and extensions to Python’s
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166 scientific computing stack},
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167 journal = {The Journal of Open Source Software},
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168 volume = {3},
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169 number = {24},
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170 month = apr,
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171 year = 2018,
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172 publisher = {The Open Journal},
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173 doi = {10.21105/joss.00638},
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174 url = {http://joss.theoj.org/papers/10.21105/joss.00638}
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175 }
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176 </citation>
8e93241d5d28 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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177 </expand>
8e93241d5d28 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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178 </tool>