comparison simple_model_fit.xml @ 12:f903c8cf1455 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb

11:fc70bf5c3b58

<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>

    </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" />

                <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" />

- sparse


**Output**

- fitted model, a pickled python object in zip format.
- optional hdf5 file containing weights for deep learning models.

        ]]>
    </help>
    <expand macro="sklearn_citation">

12:f903c8cf1455

<tool id="sklearn_model_fit" name="Fit a Pipeline, Ensemble" version="@VERSION@" profile="@PROFILE@">
    <description>or other models using a labeled dataset</description>
    <macros>
        <import>main_macros.xml</import>
        <import>keras_macros.xml</import>
    </macros>

    </command>
    <configfiles>
        <inputs name="inputs" />
    </configfiles>
    <inputs>
        <param name="infile_estimator" type="data" format="h5mlm" 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" />

                <expand macro="sparse_target" />
            </when>
        </conditional>
    </inputs>
    <outputs>
        <data format="h5mlm" 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="h5mlm" />
            <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" ftype="h5mlm"/>
            <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" />

- sparse


**Output**

- fitted model, h5mlm model.
- optional hdf5 file containing weights for deep learning models.

        ]]>
    </help>
    <expand macro="sklearn_citation">