comparison keras_train_and_eval.xml @ 14:818f9b69d8a0 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 80417bf0158a9b596e485dd66408f738f405145a

13:0af678661e20

    </macros>
    <expand macro="python_requirements" />
    <expand macro="macro_stdio" />
    <version_command>echo "@VERSION@"</version_command>
    <command>
        <![CDATA[
        export HDF5_USE_FILE_LOCKING='FALSE';
        #if $input_options.selected_input == 'refseq_and_interval'
        bgzip -c '$input_options.target_file' > '${target_file.element_identifier}.gz' &&
        tabix -p bed '${target_file.element_identifier}.gz' &&
        cp '$input_options.ref_genome_file' '${ref_genome_file.element_identifier}' &&

            #else
            --infile1 '$input_options.infile1'
            #end if
            --infile2 '$input_options.infile2'
            --outfile_result '$outfile_result'
            #if $save and 'save_estimator' in str($save)
            --outfile_object '$outfile_object'
            #end if
            #if $save and 'save_prediction' in str($save)
            --outfile_y_true '$outfile_y_true'
            --outfile_y_preds '$outfile_y_preds'
            #end if
            #if $experiment_schemes.test_split.split_algos.shuffle == 'group'
            --groups '$experiment_schemes.test_split.split_algos.groups_selector.infile_g'
            #end if

        ]]>
    </command>
    <configfiles>
        <inputs name="inputs" />
    </configfiles>

        </conditional>
        <expand macro="sl_mixed_input_plus_sequence" />
        <param name="save" type="select" multiple='true' display="checkboxes" label="Save the fitted model" optional="true" help="Evaluation scores will be output by default.">
            <option value="save_estimator" selected="true">Fitted estimator</option>
            <option value="save_prediction">True labels and prediction results from evaluation for downstream analysis</option>
        </param>
    </inputs>
    <outputs>
        <data format="tabular" name="outfile_result" />
        <data format="h5mlm" name="outfile_object" label="Fitted estimator or estimator skeleton on ${on_string}">
            <filter>str(save) and 'save_estimator' in str(save)</filter>
        </data>
        <data format="tabular" name="outfile_y_true" label="True labels/target values on ${on_string}">
            <filter>str(save) and 'save_prediction' in str(save)</filter>

14:818f9b69d8a0

    </macros>
    <expand macro="python_requirements" />
    <expand macro="macro_stdio" />
    <version_command>echo "@VERSION@"</version_command>
    <command>
        <![CDATA[        
        export HDF5_USE_FILE_LOCKING='FALSE';
        #if $input_options.selected_input == 'refseq_and_interval'
        bgzip -c '$input_options.target_file' > '${target_file.element_identifier}.gz' &&
        tabix -p bed '${target_file.element_identifier}.gz' &&
        cp '$input_options.ref_genome_file' '${ref_genome_file.element_identifier}' &&

            #else
            --infile1 '$input_options.infile1'
            #end if
            --infile2 '$input_options.infile2'
            --outfile_result '$outfile_result'
            #if $save and 'save_csvlogger' in str($save)
            --outfile_history '$outfile_history'
            #end if
            #if $save and 'save_estimator' in str($save)
            --outfile_object '$outfile_object'
            #end if
            #if $save and 'save_prediction' in str($save)
            --outfile_y_true '$outfile_y_true'
            --outfile_y_preds '$outfile_y_preds'
            #end if
            #if $experiment_schemes.test_split.split_algos.shuffle == 'group'
            --groups '$experiment_schemes.test_split.split_algos.groups_selector.infile_g'
            #end if
        ]]>
    </command>
    <configfiles>
        <inputs name="inputs" />
    </configfiles>

        </conditional>
        <expand macro="sl_mixed_input_plus_sequence" />
        <param name="save" type="select" multiple='true' display="checkboxes" label="Save the fitted model" optional="true" help="Evaluation scores will be output by default.">
            <option value="save_estimator" selected="true">Fitted estimator</option>
            <option value="save_prediction">True labels and prediction results from evaluation for downstream analysis</option>
            <option value="save_csvlogger">Display CSVLogger if selected as a callback in the Keras model builder tool</option>
        </param>
    </inputs>
    <outputs>
        <data format="tabular" name="outfile_result" />
         <data format="tabular" name="outfile_history" label="Deep learning training history log on ${on_string}">
            <filter>str(save) and 'save_csvlogger' in str(save)</filter>
        </data>
        <data format="h5mlm" name="outfile_object" label="Fitted estimator or estimator skeleton on ${on_string}">
            <filter>str(save) and 'save_estimator' in str(save)</filter>
        </data>
        <data format="tabular" name="outfile_y_true" label="True labels/target values on ${on_string}">
            <filter>str(save) and 'save_prediction' in str(save)</filter>