annotate scpred_train_model.xml @ 7:a7269f8f45aa draft default tip

"planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/ commit a1ad1ddd9b8e4db5bb82c3accae8311e0e488b19"

<tool id="scpred_train_model" name="Scpred train" version="@TOOL_VERSION@+galaxy0" profile="@PROFILE@">
    <description>Train classification model</description>
    <macros>
        <import>scpred_macros.xml</import>
    </macros>
    <expand macro="requirements" />
    <command detect_errors="exit_code"><![CDATA[
        scpred_train_model.R --input-object "${input_object}" --model "${model}" --output-path "${output_obj_path}" --iter-num "${iter_num}" --num-cores \${GALAXY_SLOTS:-1} --tune-length "${tune_length}" --metric "${metric}" --preprocess "${preprocess}" --return-data "${return_data}" --save-predictions "${save_predictions}" 
        
        #if $train_id
        --train-id '${train_id}' 
        #end if
        #if $resample_method
        --resample-method '${resample_method}' 
        #end if
        #if $reclassify
        --reclassify "${reclassify}" 
        #end if
        
        ]]></command>
    <inputs>

        <param type="data" name="input_object" label="Input SCE object" format="rdata" help="Input SCE object in .rds format"/>
        <param type="text" name="train_id" optional="true" label="Dataset ID" help="ID of the training dataset" />
        <param type="text" name="model" label="Model type to train" value="svmRadial" help="Model type used for training. Must be one of the models supported by Caret package. Default: svmRadial" />
        <param type="text" name="resample_method" optional="true" value="cv" label="Resample Method" help="Resampling method used for model fit evaluation" />
        <param type="integer" name="iter_num" optional="true" value="5" label="Number of Iterations" help="Number of resampling iterations. Default: 5"  />
        <param type="integer" name="tune_length" optional="true" value="3" label="Tune Length" help="An integer denoting the amount of granularity in the tuning parameter grid"  />
        <param type="text" name="metric" optional="true" value="ROC,PR,Accuracy,Kappa" label="Performance Metric" help="Performance metric to be used to select best model" />
        <param type="text" name="preprocess" optional="true" value="center,scale" label="Pre-processing Method" help="A string vector that defines a pre-processing of the predictor data. Enter values as comma-separated string. Current possibilities are 'BoxCox', 'YeoJohnson', 'expoTrans', 'center', 'scale', 'range', 'knnImpute', 'bagImpute', 'medianImpute', 'pca', 'ica' and 'spatialSign'. The default is 'center' and 'scale'." />
        <param type="boolean" checked="false" name="return_data" label="Return Data"  help="If TRUE, training data is returned within scPred object. Default: False"/>
        <param type="text" name="save_predictions" optional="true" value="final" label="Save Predictions" help="Specifies the set of hold-out predictions for each resample that should be returned. Values can be either 'all', 'final' or 'none'." />
        <param type="text" name="reclassify" optional="true" label="Cells to Reclassify" help="Cell types to reclassify using a different model" />

    </inputs>
    <outputs>
        <data name="output_obj_path" format="rdata" />
    </outputs>
     <tests>
        <test>
            <param name="input_object" value="scPred_feat_space.rds" />
            <param name="train_id" value="E-ENAD-16" />
            <output name="output_obj_path" file="scPred_trained.rds" compare="sim_size" delta="10000000"/>
        </test>
    </tests>
     <help><![CDATA[
    @HELP@
    
    @VERSION_HISTORY@
    ]]></help>
    <expand macro="citations" />
</tool>