Mercurial > repos > immuneml > immuneml_tools

annotate immuneml_train_ml_model.xml @ 17:b63bd4447c24 draft

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"planemo upload commit baf8a7b46ce256e676d2c14b18ab58ee6f5b3cca"
author immuneml
date Fri, 04 Feb 2022 14:25:21 +0000
parents cd57c1c66f8b
children 051d349fdc8c
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1 <tool id="immuneml_train_ml_model" name="Train machine learning models" version="@VERSION@.0">
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2 <description></description>
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3 <macros>
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4 <import>prod_macros.xml</import>
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5 </macros>
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6 <expand macro="requirements" />
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7 <command><![CDATA[
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8
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9 #if $iml_input
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10 cp -r ${iml_input.extra_files_path}/result/* . &&
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11 (mv repertoires/* . &>/dev/null || :) &&
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12 rm -rf repertoires &&
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13 #end if
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14
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15 #set $input_orig_names = []
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16 #if $data_input
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17 #for $input in $data_input
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18 #set input_orig_names += [str($input.element_identifier)]
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19 ([ -e ./"$input.element_identifier" ] && echo "File '$input.element_identifier' already exists in the input folder, skipping." || ln -s $input "$input.element_identifier") &&
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20 #end for#
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21 #end if
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22
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23 cp "$yaml_input" yaml_copy &&
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24 immune-ml ./yaml_copy ${html_outfile.files_path} --tool GalaxyTrainMLModel &&
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25 mv ${html_outfile.files_path}/index.html ${html_outfile} &&
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26 mv ${html_outfile.files_path}/exported_models/*.zip ${optimal_model} &&
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27 mv ${html_outfile.files_path}/immuneML_output.zip $archive
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28 ]]>
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29 </command>
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30 <inputs>
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31 <param name="iml_input" type="data" format="immuneml_receptors" label="immuneML dataset" optional="true" help="This field accepts an ImmuneML dataset, as created by the Create Dataset tool."/>
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32 <param name="yaml_input" type="data" format="txt" label="YAML specification" multiple="false"/>
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33 <param name="data_input" type="data" multiple="true" label="Additional files" optional="true" help="This field should include individual repertoire files, metadata files, receptor data and others."/>
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34 </inputs>
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35 <outputs>
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36 <data format="zip" name="optimal_model" label="optimal_ml_settings.zip"/>
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37 <data format="zip" name="archive" label="Archive: ML model training"/>
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38 <data format="html" name="html_outfile" label="Summary: ML model training"/>
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39 </outputs>
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40
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41
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42 <help>
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43 <![CDATA[
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44
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45 This tool can be used to run hyperparameter optimization over several different ML settings,
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46 which include ML models and their parameters, encodings and preprocessing steps. Nested cross-validation is used to identify the optimal combination of ML settings.
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47
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48 This is a YAML-based Galaxy tool, if you prefer a button-based interface that assumes less ML knowledge,
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49 see `Train immune receptor classifiers (easy interface) <root?tool_id=immuneml_train_classifiers>`_ and
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50 `Train immune repertoire classifiers (easy interface) <root?tool_id=novice_immuneml_interface>`_.
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51
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52 For more details on how to train ML models in Galaxy, see `the documentation <https://docs.immuneml.uio.no/latest/galaxy/galaxy_train_ml_models.html>`_.
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53
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54 **Tool output**
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55
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56 This Galaxy tool will produce the following history elements:
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57
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58 - Summary: ML model training: a HTML page that allows you to browse through all results, including prediction accuracies on
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59 the various data splits and report results.
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60
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61 - Archive: ML model training: a .zip file containing the complete output folder as it was produced by immuneML. This folder
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62 contains the output of the TrainMLModel instruction including all trained models and their predictions, and report results.
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63 Furthermore, the folder contains the complete YAML specification file for the immuneML run, the HTML output and a log file.
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64
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65 - optimal_ml_settings.zip: a .zip file containing the raw files for the optimal trained ML settings (ML model, encoding, and
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66 optionally preprocessing steps). This .zip file can subsequently be used as an input when `applying previously trained ML models to a new AIRR dataset in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_apply_ml_models.html>`_.
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67
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68 ]]>
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69
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70 </help>
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71
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72 </tool>