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comparison stacking_ensembles.xml @ 0:fcc5eaaec401 draft

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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 07:25:29 -0400
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-1:000000000000 0:fcc5eaaec401
1 <tool id="sklearn_stacking_ensemble_models" name="Stacking Ensemble Models" version="0.1.0">
2 <description>builds a strong model by stacking multiple algorithms</description>
3 <macros>
4 <import>main_macros.xml</import>
5 </macros>
6 <expand macro="python_requirements"/>
7 <expand macro="macro_stdio"/>
8 <version_command>echo "$version"</version_command>
9 <command>
10 <![CDATA[
11 #for $i, $base in enumerate($base_est_builder)
12 #if $i == 0
13 #if $base.estimator_selector.selected_module == 'custom_estimator'
14 bases='${base.estimator_selector.c_estimator}';
15 #else
16 bases='None';
17 #end if
18 #elif $base.estimator_selector.selected_module == 'custom_estimator'
19 bases="\$bases,${base.estimator_selector.c_estimator}";
20 #else
21 bases="\$bases,None";
22 #end if
23 #end for
24 python '$__tool_directory__/stacking_ensembles.py'
25 --inputs '$inputs'
26 --outfile '$outfile'
27 --bases "\$bases"
28 #if $meta_estimator.estimator_selector.selected_module == 'custom_estimator'
29 --meta '${meta_estimator.estimator_selector.c_estimator}'
30 #end if
31 #if $get_params
32 --outfile_params '$outfile_params'
33 #end if
34 ]]>
35 </command>
36 <configfiles>
37 <inputs name="inputs" />
38 </configfiles>
39 <inputs>
40 <conditional name="algo_selection">
41 <param name="estimator_type" type="select" label="Choose the stacking ensemble type">
42 <option value="StackingCVClassifier" selected="true">classification -- StackingCVClassifier</option>
43 <option value="StackingClassifier">classification -- StackingClassifier</option>
44 <option value="StackingCVRegressor">regression -- StackingCVRegressor</option>
45 <option value="StackingRegressor">regression -- StackingRegressor</option>
46 </param>
47 <when value="StackingCVClassifier">
48 <expand macro="stacking_ensemble_inputs">
49 <expand macro="cv_reduced"/>
50 <expand macro="shuffle" label="shuffle"/>
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."/>
52 <param argument="use_probas" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false"/>
53 </expand>
54 </when>
55 <when value="StackingClassifier">
56 <expand macro="stacking_ensemble_inputs">
57 <param argument="use_probas" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false"/>
58 <param argument="average_probas" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false"/>
59 </expand>
60 </when>
61 <when value="StackingCVRegressor">
62 <expand macro="stacking_ensemble_inputs">
63 <expand macro="cv_reduced"/>
64 <!--TODO support group splitters. Hint: `groups` is a fit_param-->
65 <expand macro="shuffle" label="shuffle"/>
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."/>
67 <param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true"/>
68 </expand>
69 </when>
70 <when value="StackingRegressor">
71 <expand macro="stacking_ensemble_inputs">
72 <param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true"/>
73 </expand>
74 </when>
75 </conditional>
76 <repeat name="base_est_builder" min="1" max="20" title="Base Estimator">
77 <expand macro="stacking_base_estimator"/>
78 <!--param name="base_estimator" type="data" format="zip,json" label="Select the dataset containing base estimator" help="One estimator at a time."/-->
79 </repeat>
80 <!--param name="meta_estimator" type="data" format="zip,json" label="Select the dataset containing the Meta estimator"/-->
81 <section name="meta_estimator" title="Meta Estimator" expanded="true">
82 <expand macro="stacking_base_estimator"/>
83 </section>
84 <param name="get_params" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Output parameters for searchCV?"
85 help="Optional. Tunable parameters could be obtained through `estimator_attributes` tool."/>
86 </inputs>
87 <outputs>
88 <data format="zip" name="outfile" label="${algo_selection.estimator_type} on ${on_string}"/>
89 <data format="tabular" name="outfile_params" label="get_params for ${algo_selection.estimator_type}">
90 <filter>get_params</filter>
91 </data>
92 </outputs>
93 <tests>
94 <test>
95 <conditional name="algo_selection">
96 <param name="estimator_type" value="StackingCVRegressor"/>
97 </conditional>
98 <repeat name="base_est_builder">
99 <conditional name="estimator_selector">
100 <param name="selected_module" value="custom_estimator"/>
101 <param name="c_estimator" value="RandomForestRegressor01.zip" ftype="zip"/>
102 </conditional>
103 </repeat>
104 <repeat name="base_est_builder">
105 <conditional name="estimator_selector">
106 <param name="selected_module" value="custom_estimator"/>
107 <param name="c_estimator" value="XGBRegressor01.zip" ftype="zip"/>
108 </conditional>
109 </repeat>
110 <section name="meta_estimator">
111 <conditional name="estimator_selector">
112 <param name="selected_module" value="custom_estimator"/>
113 <param name="c_estimator" value="LinearRegression01.zip" ftype="zip"/>
114 </conditional>
115 </section>
116 <param name="get_params" value="false"/>
117 <output name="outfile" file="StackingCVRegressor01.zip" compare="sim_size" delta="5"/>
118 </test>
119 <test>
120 <conditional name="algo_selection">
121 <param name="estimator_type" value="StackingCVRegressor"/>
122 </conditional>
123 <repeat name="base_est_builder">
124 <conditional name="estimator_selector">
125 <param name="selected_module" value="custom_estimator"/>
126 <param name="c_estimator" value="RandomForestRegressor01.zip" ftype="zip"/>
127 </conditional>
128 </repeat>
129 <repeat name="base_est_builder">
130 <conditional name="estimator_selector">
131 <param name="selected_module" value="xgboost"/>
132 <param name="selected_estimator" value="XGBRegressor"/>
133 </conditional>
134 </repeat>
135 <section name="meta_estimator">
136 <conditional name="estimator_selector">
137 <param name="selected_module" value="svm"/>
138 <param name="selected_estimator" value="SVR"/>
139 </conditional>
140 </section>
141 <param name="get_params" value="false"/>
142 <output name="outfile" file="StackingCVRegressor02.zip" compare="sim_size" delta="5"/>
143 </test>
144 </tests>
145 <help>
146 <![CDATA[
147 This tool wrapps Stacking Regression, also called Super Learning, in which different base algorithms train
148 on the original dataset and predict results respectively, a second level of `metalearner` fits on the previous
149 prediction results to ensemble a strong learner.
150 Refer to `http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html#introduction`_.
151
152 .. _`http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html#introduction`:
153 http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html#introduction
154
155 ]]>
156 </help>
157 <expand macro="sklearn_citation">
158 <expand macro="skrebate_citation"/>
159 <expand macro="xgboost_citation"/>
160 <expand macro="imblearn_citation"/>
161 <citation type="bibtex">
162 @article{raschkas_2018_mlxtend,
163 author = {Sebastian Raschka},
164 title = {MLxtend: Providing machine learning and data science
165 utilities and extensions to Python’s
166 scientific computing stack},
167 journal = {The Journal of Open Source Software},
168 volume = {3},
169 number = {24},
170 month = apr,
171 year = 2018,
172 publisher = {The Open Journal},
173 doi = {10.21105/joss.00638},
174 url = {http://joss.theoj.org/papers/10.21105/joss.00638}
175 }
176 </citation>
177 </expand>
178 </tool>