comparison simple_model_fit.xml @ 6:26decbf4bdb8 draft

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

5:1c25f305db70

<tool id="sklearn_model_fit" name="Fit a Pipeline, Ensemble" version="@VERSION@">
    <description>or other models using a labeled dataset</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__/simple_model_fit.py'

    </command>
    <configfiles>
        <inputs name="inputs" />
    </configfiles>
    <inputs>
        <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator"/>
        <param name="is_deep_learning" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Is the estimator a deep learning model?"/>
        <conditional name="input_options">
            <expand macro="data_input_options"/>
            <when value="tabular">
                <expand macro="samples_tabular" label1="Choose the training dataset containing features" multiple1="true" multiple2="false"/>
            </when>
            <when value="sparse">
                <expand macro="sparse_target"/>
            </when>
        </conditional>
    </inputs>
    <outputs>
        <data format="zip" name="out_object" label="Fitted model (skeleton) on $(on_string)"/>
        <data format="h5" name="out_weights" label="Weights trained on ${on_string}">
            <filter>is_deep_learning</filter>
        </data>
    </outputs>
    <tests>
        <test>
            <param name="infile_estimator" value="pipeline05" ftype="zip"/>
            <param name="infile1" value="regression_X.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.tabular" ftype="tabular"/>
            <param name="header2" value="true"/>
            <param name="col2" value="1"/>
            <output name="out_object" file="model_fit01" compare="sim_size" delta="50"/>
        </test>
        <test>
            <param name="infile_estimator" value="keras_model04"/>
            <param name="is_deep_learning" value="true"/>
            <param name="infile1" value="regression_X.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.tabular" ftype="tabular"/>
            <param name="header2" value="true"/>
            <param name="col2" value="1"/>
            <output name="out_object" file="model_fit02" compare="sim_size" delta="10"/>
            <output name="out_weights" file="model_fit02.h5" compare="sim_size" delta="10"/>
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**

- optional hdf5 file containing weights for deep learning models.

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

6:26decbf4bdb8

<tool id="sklearn_model_fit" name="Fit a Pipeline, Ensemble" version="@VERSION@" profile="20.05">
    <description>or other models using a labeled dataset</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__/simple_model_fit.py'

    </command>
    <configfiles>
        <inputs name="inputs" />
    </configfiles>
    <inputs>
        <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator" />
        <param name="is_deep_learning" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Is the estimator a deep learning model?" />
        <conditional name="input_options">
            <expand macro="data_input_options" />
            <when value="tabular">
                <expand macro="samples_tabular" label1="Choose the training dataset containing features" multiple1="true" multiple2="false" />
            </when>
            <when value="sparse">
                <expand macro="sparse_target" />
            </when>
        </conditional>
    </inputs>
    <outputs>
        <data format="zip" name="out_object" label="Fitted model (skeleton) on $(on_string)" />
        <data format="h5" name="out_weights" label="Weights trained on ${on_string}">
            <filter>is_deep_learning</filter>
        </data>
    </outputs>
    <tests>
        <test>
            <param name="infile_estimator" value="pipeline05" ftype="zip" />
            <param name="infile1" value="regression_X.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.tabular" ftype="tabular" />
            <param name="header2" value="true" />
            <param name="col2" value="1" />
            <output name="out_object" file="model_fit01" compare="sim_size" delta="50" />
        </test>
        <test>
            <param name="infile_estimator" value="keras_model04" />
            <param name="is_deep_learning" value="true" />
            <param name="infile1" value="regression_X.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.tabular" ftype="tabular" />
            <param name="header2" value="true" />
            <param name="col2" value="1" />
            <output name="out_object" file="model_fit02" compare="sim_size" delta="50" />
            <output name="out_weights" file="model_fit02.h5" compare="sim_size" delta="50" />
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**

- optional hdf5 file containing weights for deep learning models.

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