Mercurial > repos > florianbegusch > qiime2_suite
diff qiime2/qiime_sample-classifier_fit-classifier.xml @ 14:a0a8d77a991c draft
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author | florianbegusch |
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date | Thu, 03 Sep 2020 09:51:29 +0000 |
parents | f190567fe3f6 |
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--- a/qiime2/qiime_sample-classifier_fit-classifier.xml Thu Sep 03 09:46:00 2020 +0000 +++ b/qiime2/qiime_sample-classifier_fit-classifier.xml Thu Sep 03 09:51:29 2020 +0000 @@ -1,40 +1,62 @@ <?xml version="1.0" ?> -<tool id="qiime_sample-classifier_fit-classifier" name="qiime sample-classifier fit-classifier" version="2019.7"> - <description> - Fit a supervised learning classifier.</description> - <requirements> - <requirement type="package" version="2019.7">qiime2</requirement> - </requirements> - <command><![CDATA[ +<tool id="qiime_sample-classifier_fit-classifier" name="qiime sample-classifier fit-classifier" + version="2020.8"> + <description>Fit a supervised learning classifier.</description> + <requirements> + <requirement type="package" version="2020.8">qiime2</requirement> + </requirements> + <command><![CDATA[ qiime sample-classifier fit-classifier --i-table=$itable ---m-metadata-column="$mmetadatacolumn" - -#if str($pstep): - --p-step=$pstep -#end if +# if $input_files_mmetadatafile: + # def list_dict_to_string(list_dict): + # set $file_list = list_dict[0]['additional_input'].__getattr__('file_name') + # for d in list_dict[1:]: + # set $file_list = $file_list + ' --m-metadata-file=' + d['additional_input'].__getattr__('file_name') + # end for + # return $file_list + # end def +--m-metadata-file=$list_dict_to_string($input_files_mmetadatafile) +# end if -#if str($pcv): - --p-cv=$pcv +#if '__ob__' in str($mmetadatacolumn): + #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('__ob__', '[') + #set $mmetadatacolumn = $mmetadatacolumn_temp +#end if +#if '__cb__' in str($mmetadatacolumn): + #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('__cb__', ']') + #set $mmetadatacolumn = $mmetadatacolumn_temp #end if - -#if str($prandomstate): - --p-random-state="$prandomstate" +#if 'X' in str($mmetadatacolumn): + #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('X', '\\') + #set $mmetadatacolumn = $mmetadatacolumn_temp +#end if +#if '__sq__' in str($mmetadatacolumn): + #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('__sq__', "'") + #set $mmetadatacolumn = $mmetadatacolumn_temp +#end if +#if '__db__' in str($mmetadatacolumn): + #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('__db__', '"') + #set $mmetadatacolumn = $mmetadatacolumn_temp #end if -#set $pnjobs = '${GALAXY_SLOTS:-4}' - -#if str($pnjobs): - --p-n-jobs="$pnjobs" -#end if +--m-metadata-column=$mmetadatacolumn -#if str($pnestimators): - --p-n-estimators=$pnestimators +--p-step=$pstep + +--p-cv=$pcv + +#if str($prandomstate): + --p-random-state=$prandomstate #end if +--p-n-jobs=$pnjobs + +--p-n-estimators=$pnestimators #if str($pestimator) != 'None': - --p-estimator=$pestimator +--p-estimator=$pestimator #end if #if $poptimizefeatureselection: @@ -46,58 +68,61 @@ #end if #if str($pmissingsamples) != 'None': - --p-missing-samples=$pmissingsamples +--p-missing-samples=$pmissingsamples #end if - - -#if $metadatafile: - --m-metadata-file=$metadatafile -#end if - - - --o-sample-estimator=osampleestimator + --o-feature-importance=ofeatureimportance + +#if str($examples) != 'None': +--examples=$examples +#end if + ; -cp osampleestimator.qza $osampleestimator; cp ofeatureimportance.qza $ofeatureimportance - ]]></command> - <inputs> - <param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[Frequency] Feature table containing all features that should be used for target prediction. [required]" name="itable" optional="False" type="data"/> - <param label="--m-metadata-column: COLUMN MetadataColumn[Categorical] Numeric metadata column to use as prediction target. [required]" name="mmetadatacolumn" optional="False" type="text"/> - <param label="--p-step: PROPORTION Range(0.0, 1.0, inclusive_start=False) If optimize-feature-selection is True, step is the percentage of features to remove at each iteration. [default: 0.05]" name="pstep" optional="True" type="float" value="0.05" min="0" max="1" exclusive_start="True"/> - <param label="--p-cv: INTEGER Number of k-fold cross-validations to perform. Range(1, None) [default: 5]" name="pcv" optional="True" type="integer" value="5" min="1"/> - <param label="--p-random-state: INTEGER Seed used by random number generator. [optional]" name="prandomstate" optional="True" type="integer"/> - <param label="--p-n-estimators: INTEGER Range(1, None) Number of trees to grow for estimation. More trees will improve predictive accuracy up to a threshold level, but will also increase time and memory requirements. This parameter only affects ensemble estimators, such as Random Forest, AdaBoost, ExtraTrees, and GradientBoosting. [default: 100]" name="pnestimators" optional="True" type="integer" value="100" min="1"/> - <param label="--p-estimator: " name="pestimator" optional="True" type="select"> - <option selected="True" value="None">Selection is Optional</option> - <option value="RandomForestClassifier">RandomForestClassifier</option> - <option value="ExtraTreesClassifier">ExtraTreesClassifier</option> - <option value="GradientBoostingClassifier">GradientBoostingClassifier</option> - <option value="AdaBoostClassifier">AdaBoostClassifier</option> - <option value="KNeighborsClassifier">KNeighborsClassifier</option> - <option value="LinearSVC">LinearSVC</option> - <option value="SVC">SVC</option> - </param> - <param label="--p-optimize-feature-selection: --p-no-optimize-feature-selection Automatically optimize input feature selection using recursive feature elimination. [default: False]" name="poptimizefeatureselection" selected="False" type="boolean"/> - <param label="--p-parameter-tuning: --p-no-parameter-tuning Automatically tune hyperparameters using random grid search. [default: False]" name="pparametertuning" selected="False" type="boolean"/> - <param label="--p-missing-samples: " name="pmissingsamples" optional="True" type="select"> - <option selected="True" value="None">Selection is Optional</option> - <option value="error">error</option> - <option value="ignore">ignore</option> - </param> - <param label="--m-metadata-file METADATA" name="metadatafile" type="data" format="tabular,qza,no_unzip.zip" /> + ]]></command> + <inputs> + <param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[Frequency] Feature table containing all features that should be used for target prediction. [required]" name="itable" optional="False" type="data" /> + <repeat name="input_files_mmetadatafile" optional="True" title="--m-metadata-file"> + <param format="tabular,qza,no_unzip.zip" label="--m-metadata-file: METADATA" name="additional_input" optional="True" type="data" /> + </repeat> + <param label="--m-metadata-column: COLUMN MetadataColumn[Categorical] Numeric metadata column to use as prediction target. [required]" name="mmetadatacolumn" optional="False" type="text" /> + <param exclude_min="True" label="--p-step: PROPORTION Range(0.0, 1.0, inclusive_start=False) If optimize-feature-selection is True, step is the percentage of features to remove at each iteration. [default: 0.05]" max="1.0" min="0.0" name="pstep" optional="True" type="float" value="0.05" /> + <param label="--p-cv: INTEGER Number of k-fold cross-validations to perform. Range(1, None) [default: 5]" min="1" name="pcv" optional="True" type="integer" value="5" /> + <param label="--p-random-state: INTEGER Seed used by random number generator. [optional]" name="prandomstate" optional="False" type="text" /> + <param label="--p-n-estimators: INTEGER Range(1, None) Number of trees to grow for estimation. More trees will improve predictive accuracy up to a threshold level, but will also increase time and memory requirements. This parameter only affects ensemble estimators, such as Random Forest, AdaBoost, ExtraTrees, and GradientBoosting. [default: 100]" min="1" name="pnestimators" optional="True" type="integer" value="100" /> + <param label="--p-estimator: " name="pestimator" optional="True" type="select"> + <option selected="True" value="None">Selection is Optional</option> + <option value="RandomForestClassifier">RandomForestClassifier</option> + <option value="ExtraTreesClassifier">ExtraTreesClassifier</option> + <option value="GradientBoostingClassifier">GradientBoostingClassifier</option> + <option value="AdaBoostClassifier">AdaBoostClassifier</option> + <option value="KNeighborsClassifier">KNeighborsClassifier</option> + <option value="LinearSVC">LinearSVC</option> + <option value="SVC">SVC</option> + </param> + <param label="--p-optimize-feature-selection: --p-optimize-feature-selection: / --p-no-optimize-feature-selection Automatically optimize input feature selection using recursive feature elimination. [default: False]" name="poptimizefeatureselection" selected="False" type="boolean" /> + <param label="--p-parameter-tuning: --p-parameter-tuning: / --p-no-parameter-tuning Automatically tune hyperparameters using random grid search. [default: False]" name="pparametertuning" selected="False" type="boolean" /> + <param label="--p-missing-samples: " name="pmissingsamples" optional="True" type="select"> + <option selected="True" value="None">Selection is Optional</option> + <option value="error">error</option> + <option value="ignore">ignore</option> + </param> + <param label="--examples: Show usage examples and exit." name="examples" optional="False" type="data" /> + + </inputs> - </inputs> - <outputs> - <data format="qza" label="${tool.name} on ${on_string}: sampleestimator.qza" name="osampleestimator"/> - <data format="qza" label="${tool.name} on ${on_string}: featureimportance.qza" name="ofeatureimportance"/> - </outputs> - <help><![CDATA[ + <outputs> + <data format="qza" label="${tool.name} on ${on_string}: sampleestimator.qza" name="osampleestimator" /> + <data format="qza" label="${tool.name} on ${on_string}: featureimportance.qza" name="ofeatureimportance" /> + + </outputs> + + <help><![CDATA[ Fit a supervised learning classifier. -##################################### +############################################################### Fit a supervised learning classifier. Outputs the fit estimator (for prediction of test samples and/or unknown samples) and the relative @@ -119,6 +144,8 @@ Number of k-fold cross-validations to perform. random_state : Int, optional Seed used by random number generator. +n_jobs : Int, optional + Number of jobs to run in parallel. n_estimators : Int % Range(1, None), optional Number of trees to grow for estimation. More trees will improve predictive accuracy up to a threshold level, but will also increase @@ -144,9 +171,9 @@ Trained sample classifier. feature_importance : FeatureData[Importance] Importance of each input feature to model accuracy. - ]]></help> -<macros> + ]]></help> + <macros> <import>qiime_citation.xml</import> -</macros> -<expand macro="qiime_citation"/> -</tool> + </macros> + <expand macro="qiime_citation"/> +</tool> \ No newline at end of file