Mercurial > repos > bgruening > sklearn_fitted_model_eval

annotate fitted_model_eval.xml @ 2:153c7a85f117 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
author bgruening
date Mon, 16 Dec 2019 09:41:59 +0000
parents 4fc9e02801f9
children 981c7b89af13
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1 <tool id="sklearn_fitted_model_eval" name="Evaluate a Fitted Model" version="@VERSION@">
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2 <description>using a new batch of labeled data</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 '$__tool_directory__/fitted_model_eval.py'
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14 --inputs '$inputs'
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15 --infile_estimator '$infile_estimator'
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16 --outfile_eval '$outfile_eval'
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17 --infile_weights '$infile_weights'
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18 --infile1 '$input_options.infile1'
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19 --infile2 '$input_options.infile2'
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20 ]]>
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21 </command>
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22 <configfiles>
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23 <inputs name="inputs" />
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24 </configfiles>
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25 <inputs>
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26 <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/>
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27 <param name="infile_weights" type="data" format="h5" optional="true" label="Choose the dataset containing weights for the estimator above" help="Optional. For deep learning only."/>
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28 <expand macro="scoring_selection"/>
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29 <conditional name="input_options">
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30 <expand macro="data_input_options"/>
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31 <when value="tabular">
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32 <expand macro="samples_tabular" label1="Dataset containing features:" multiple1="true" multiple2="false"/>
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33 </when>
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34 <when value="sparse">
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35 <expand macro="sparse_target"/>
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36 </when>
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37 </conditional>
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38 </inputs>
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39 <outputs>
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40 <data format="tabular" name="outfile_eval"/>
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41 </outputs>
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42 <tests>
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43 <test>
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44 <param name="infile_estimator" value="searchCV01" ftype="zip"/>
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45 <conditional name="scoring">
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46 <param name="primary_scoring" value="r2"/>
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47 </conditional>
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48 <param name="infile1" value="train_test_split_test01.tabular" ftype="tabular"/>
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49 <param name="header1" value="true" />
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50 <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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51 <param name="infile2" value="regression_y_split_test01.tabular" ftype="tabular"/>
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52 <param name="header2" value="true"/>
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53 <param name="col2" value="1"/>
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54 <output name="outfile_eval" file="fitted_model_eval01.tabular"/>
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55 </test>
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56 </tests>
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57 <help>
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58 <![CDATA[
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59 **What it does**
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60
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61 Given a fitted estimator and a labeled dataset, this tool outputs the performances of the fitted estimator on the labeled dataset with selected scorers.
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62
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63 For the estimator, this tool supports fitted sklearn estimators (pickled) and trained deep learning models (model skeleton + weights). For input datasets, it supports the following:
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64
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65 - tabular
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66
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67 - sparse
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68
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69
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70 **Output**
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71
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72 A tabular file containing performance scores,
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73 e.g.:
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74
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75 ======== ======== =========
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76 accuracy f1_macro precision
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77 ======== ======== =========
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78 0.8613 0.6759 0.7928
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79 ======== ======== =========
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80
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81 ]]>
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82 </help>
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83 <expand macro="sklearn_citation">
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84 <expand macro="keras_citation"/>
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85 <expand macro="selene_citation"/>
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86 </expand>
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87 </tool>