comparison fitted_model_eval.xml @ 12:8e7622bf46e3 draft

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

11:c2e2aa55dd5c

<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>

        export HDF5_USE_FILE_LOCKING='FALSE';
        python '$__tool_directory__/fitted_model_eval.py'
            --inputs '$inputs'
            --infile_estimator '$infile_estimator'
            --outfile_eval '$outfile_eval'
            --infile_weights '$infile_weights'
            --infile1 '$input_options.infile1'
            --infile2 '$input_options.infile2'
        ]]>
    </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" />

        <![CDATA[
**What it does**

Given a fitted estimator and a labeled dataset, this tool outputs the performances of the fitted estimator on the labeled dataset with selected scorers.

For the estimator, this tool supports fitted sklearn estimators (pickled) and trained deep learning models (model skeleton + weights). For input datasets, it supports the following:

- tabular

- sparse

12:8e7622bf46e3

<tool id="sklearn_fitted_model_eval" name="Evaluate a Fitted Model" version="@VERSION@" profile="@PROFILE@">
    <description>using a new batch of labeled data</description>
    <macros>
        <import>main_macros.xml</import>
        <import>keras_macros.xml</import>
    </macros>

        export HDF5_USE_FILE_LOCKING='FALSE';
        python '$__tool_directory__/fitted_model_eval.py'
            --inputs '$inputs'
            --infile_estimator '$infile_estimator'
            --outfile_eval '$outfile_eval'
            --infile1 '$input_options.infile1'
            --infile2 '$input_options.infile2'
        ]]>
    </command>
    <configfiles>
        <inputs name="inputs" />
    </configfiles>
    <inputs>
        <param name="infile_estimator" type="data" format="h5mlm" label="Choose the dataset containing pipeline/estimator object" />
        <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="searchCV03" ftype="h5mlm" />
            <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" />

        <![CDATA[
**What it does**

Given a fitted estimator and a labeled dataset, this tool outputs the performances of the fitted estimator on the labeled dataset with selected scorers.

For the estimator, this tool supports fitted sklearn estimators and trained deep learning models. For input datasets, it supports the following:

- tabular

- sparse