annotate simple_model_fit.xml @ 7:5dc03d8e137e draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
author bgruening
date Tue, 13 Apr 2021 22:46:15 +0000
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1 <tool id="sklearn_model_fit" name="Fit a Pipeline, Ensemble" version="@VERSION@" profile="20.05">
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2 <description>or other models using a labeled dataset</description>
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3 <macros>
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4 <import>main_macros.xml</import>
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5 <import>keras_macros.xml</import>
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6 </macros>
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7 <expand macro="python_requirements" />
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8 <expand macro="macro_stdio" />
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9 <version_command>echo "@VERSION@"</version_command>
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10 <command>
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11 <![CDATA[
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12 export HDF5_USE_FILE_LOCKING='FALSE';
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13 python '$__tool_directory__/simple_model_fit.py'
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14 --inputs '$inputs'
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15 --infile_estimator '$infile_estimator'
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16 --infile1 '$input_options.infile1'
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17 --infile2 '$input_options.infile2'
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18 --out_object '$out_object'
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19 #if $is_deep_learning == 'booltrue'
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20 --out_weights '$out_weights'
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21 #end if
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22 ]]>
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23 </command>
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24 <configfiles>
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25 <inputs name="inputs" />
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26 </configfiles>
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27 <inputs>
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28 <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator" />
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29 <param name="is_deep_learning" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Is the estimator a deep learning model?" />
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30 <conditional name="input_options">
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31 <expand macro="data_input_options" />
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32 <when value="tabular">
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33 <expand macro="samples_tabular" label1="Choose the training dataset containing features" multiple1="true" multiple2="false" />
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34 </when>
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35 <when value="sparse">
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36 <expand macro="sparse_target" />
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37 </when>
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38 </conditional>
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39 </inputs>
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40 <outputs>
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41 <data format="zip" name="out_object" label="Fitted model (skeleton) on $(on_string)" />
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42 <data format="h5" name="out_weights" label="Weights trained on ${on_string}">
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43 <filter>is_deep_learning</filter>
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44 </data>
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45 </outputs>
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46 <tests>
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47 <test>
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48 <param name="infile_estimator" value="pipeline05" ftype="zip" />
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49 <param name="infile1" value="regression_X.tabular" ftype="tabular" />
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50 <param name="header1" value="true" />
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51 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17" />
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52 <param name="infile2" value="regression_y.tabular" ftype="tabular" />
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53 <param name="header2" value="true" />
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54 <param name="col2" value="1" />
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55 <output name="out_object" file="model_fit01" compare="sim_size" delta="50" />
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56 </test>
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57 <test>
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58 <param name="infile_estimator" value="keras_model04" />
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59 <param name="is_deep_learning" value="true" />
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60 <param name="infile1" value="regression_X.tabular" ftype="tabular" />
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61 <param name="header1" value="true" />
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62 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17" />
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63 <param name="infile2" value="regression_y.tabular" ftype="tabular" />
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64 <param name="header2" value="true" />
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65 <param name="col2" value="1" />
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66 <output name="out_object" file="model_fit02" compare="sim_size" delta="50" />
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67 <output name="out_weights" file="model_fit02.h5" compare="sim_size" delta="50" />
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68 </test>
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69 </tests>
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70 <help>
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71 <![CDATA[
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72 **What it does**
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73
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74 This tools takes a pre-built model to simply fit on the provided labeled dataset by calling scikit-learn API `fit`. The model could be built in `build_pipeline` tool, `stacking_ensemble` tool or other places. Limited deep learning model is also supported.
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75
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76 The supported labeled dataset are the following:
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77
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78 - tabular
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79
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80 - sparse
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81
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82
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83 **Output**
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84
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85 - fitted model, a pickled python object in zip format.
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86 - optional hdf5 file containing weights for deep learning models.
734c66aa945a "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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87
734c66aa945a "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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88 ]]>
734c66aa945a "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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89 </help>
734c66aa945a "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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90 <expand macro="sklearn_citation">
6
26decbf4bdb8 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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91 <expand macro="keras_citation" />
26decbf4bdb8 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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92 <expand macro="selene_citation" />
0
734c66aa945a "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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93 </expand>
734c66aa945a "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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94 </tool>