comparison fitted_model_eval.xml @ 5:981c7b89af13 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"

4:7e0e2c85eba7

<tool id="sklearn_fitted_model_eval" name="Evaluate a Fitted Model" version="@VERSION@">
    <description>using a new batch of labeled data</description>
    <macros>
        <import>main_macros.xml</import>
        <import>keras_macros.xml</import>
    </macros>
    <expand macro="python_requirements"/>
    <expand macro="macro_stdio"/>
    <version_command>echo "@VERSION@"</version_command>
    <command>
        <![CDATA[
        export HDF5_USE_FILE_LOCKING='FALSE';
        python '$__tool_directory__/fitted_model_eval.py'

    </command>
    <configfiles>
        <inputs name="inputs" />
    </configfiles>
    <inputs>
        <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/>
        <param name="infile_weights" type="data" format="h5" optional="true" label="Choose the dataset containing weights for the estimator above" help="Optional. For deep learning only."/>
        <expand macro="scoring_selection"/>
        <conditional name="input_options">
            <expand macro="data_input_options"/>
            <when value="tabular">
                <expand macro="samples_tabular" label1="Dataset containing features:" multiple1="true" multiple2="false"/>
            </when>
            <when value="sparse">
                <expand macro="sparse_target"/>
            </when>
    </conditional>
    </inputs>
    <outputs>
        <data format="tabular" name="outfile_eval"/>
    </outputs>
    <tests>
        <test>
            <param name="infile_estimator" value="searchCV01" ftype="zip"/>
            <conditional name="scoring">
                <param name="primary_scoring" value="r2"/>
            </conditional>
            <param name="infile1" value="train_test_split_test01.tabular" ftype="tabular"/>
            <param name="header1" value="true" />
            <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
            <param name="infile2" value="regression_y_split_test01.tabular" ftype="tabular"/>
            <param name="header2" value="true"/>
            <param name="col2" value="1"/>
            <output name="outfile_eval" file="fitted_model_eval01.tabular"/>
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**

======== ======== =========

        ]]>
    </help>
    <expand macro="sklearn_citation">
        <expand macro="keras_citation"/>
        <expand macro="selene_citation"/>
    </expand>
</tool>

5:981c7b89af13

<tool id="sklearn_fitted_model_eval" name="Evaluate a Fitted Model" version="@VERSION@" profile="20.05">
    <description>using a new batch of labeled data</description>
    <macros>
        <import>main_macros.xml</import>
        <import>keras_macros.xml</import>
    </macros>
    <expand macro="python_requirements" />
    <expand macro="macro_stdio" />
    <version_command>echo "@VERSION@"</version_command>
    <command>
        <![CDATA[
        export HDF5_USE_FILE_LOCKING='FALSE';
        python '$__tool_directory__/fitted_model_eval.py'

    </command>
    <configfiles>
        <inputs name="inputs" />
    </configfiles>
    <inputs>
        <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object" />
        <param name="infile_weights" type="data" format="h5" optional="true" label="Choose the dataset containing weights for the estimator above" help="Optional. For deep learning only." />
        <expand macro="scoring_selection" />
        <conditional name="input_options">
            <expand macro="data_input_options" />
            <when value="tabular">
                <expand macro="samples_tabular" label1="Dataset containing features:" multiple1="true" multiple2="false" />
            </when>
            <when value="sparse">
                <expand macro="sparse_target" />
            </when>
        </conditional>
    </inputs>
    <outputs>
        <data format="tabular" name="outfile_eval" />
    </outputs>
    <tests>
        <test>
            <param name="infile_estimator" value="searchCV01" ftype="zip" />
            <conditional name="scoring">
                <param name="primary_scoring" value="r2" />
            </conditional>
            <param name="infile1" value="train_test_split_test01.tabular" ftype="tabular" />
            <param name="header1" value="true" />
            <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17" />
            <param name="infile2" value="regression_y_split_test01.tabular" ftype="tabular" />
            <param name="header2" value="true" />
            <param name="col2" value="1" />
            <output name="outfile_eval" file="fitted_model_eval01.tabular" />
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**

======== ======== =========

        ]]>
    </help>
    <expand macro="sklearn_citation">
        <expand macro="keras_citation" />
        <expand macro="selene_citation" />
    </expand>
</tool>