comparison search_model_validation.xml @ 19:cb5635e30842 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"

18:e5a5ad882f1f

<tool id="sklearn_searchcv" name="Hyperparameter Search" version="@VERSION@">
    <description>performs hyperparameter optimization using various SearchCVs</description>
    <macros>
        <import>main_macros.xml</import>
         <macro name="search_cv_estimator">
            <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/>

        </macro>
    </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' &&
        #end if
        python '$__tool_directory__/search_model_validation.py'
            --inputs '$inputs'
            --estimator '$search_schemes.infile_estimator'
            #if $input_options.selected_input == 'seq_fasta'
            --fasta_path '$input_options.fasta_path'
            #elif $input_options.selected_input == 'refseq_and_interval'
            --ref_seq '$input_options.ref_genome_file'
            --interval '$input_options.interval_file'
            --targets "`pwd`/${target_file.element_identifier}.gz"
            #else
            --infile1 '$input_options.infile1'
            #end if
            --infile2 '$input_options.infile2'
            #if $save != 'save_no_fit'
            --outfile_result "`pwd`/tmp_outfile_result"
            #end if
            #if $save == 'save_estimator'
            --outfile_object '$outfile_object'
            #end if
            #if $save == 'save_no_fit'
            --outfile_object '$outfile_object_no_fit'
            #end if
            #if $search_schemes.is_deep_learning == 'booltrue' and $save == 'save_estimator' and $outer_split.split_mode == 'nested_cv'
            --outfile_weights '$outfile_weights'
            #end if
            #if $search_schemes.options.cv_selector.selected_cv in ['GroupKFold', 'GroupShuffleSplit', 'LeaveOneGroupOut', 'LeavePGroupsOut']
            --groups '$search_schemes.options.cv_selector.groups_selector.infile_g'
            #end if
            #if $save != 'save_no_fit'
            >'$outfile_result' && cp '$outfile_result' "`pwd`/../tool_stdout"
            && cp "`pwd`/tmp_outfile_result" '$outfile_result';
            #end if

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

19:cb5635e30842

<tool id="sklearn_searchcv" name="Hyperparameter Search" version="@VERSION@" profile="20.05">
    <description>performs hyperparameter optimization using various SearchCVs</description>
    <macros>
        <import>main_macros.xml</import>
         <macro name="search_cv_estimator">
            <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/>

        </macro>
    </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' &&
        #end if
        python '$__tool_directory__/search_model_validation.py'
            --inputs '$inputs'
            --estimator '$search_schemes.infile_estimator'
            #if $input_options.selected_input == 'seq_fasta'
                --fasta_path '$input_options.fasta_path'
            #elif $input_options.selected_input == 'refseq_and_interval'
                --ref_seq '$input_options.ref_genome_file'
                --interval '$input_options.interval_file'
                --targets "`pwd`/${target_file.element_identifier}.gz"
            #else
                --infile1 '$input_options.infile1'
            #end if
                --infile2 '$input_options.infile2'
            #if $save != 'save_no_fit'
                --outfile_result '$outfile_result'
            #end if
            #if $save == 'save_estimator'
                --outfile_object '$outfile_object'
            #end if
            #if $save == 'save_no_fit'
                --outfile_object '$outfile_object_no_fit'
            #end if
            #if $search_schemes.is_deep_learning == 'booltrue' and $save == 'save_estimator' and $outer_split.split_mode == 'nested_cv'
                --outfile_weights '$outfile_weights'
            #end if
            #if $search_schemes.options.cv_selector.selected_cv in ['GroupKFold', 'GroupShuffleSplit', 'LeaveOneGroupOut', 'LeavePGroupsOut']
                --groups '$search_schemes.options.cv_selector.groups_selector.infile_g'
            #end if
    ]]>
    </command>
    <configfiles>
        <inputs name="inputs" />
    </configfiles>
    <inputs>