view fitted_model_eval.xml @ 2:cf54bae8ad42 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"

<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 '$__tool_directory__/fitted_model_eval.py'
            --inputs '$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" />
            <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**

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


**Output**

A tabular file containing performance scores,
e.g.:

======== ======== =========
accuracy f1_macro precision
======== ======== =========
 0.8613   0.6759   0.7928
======== ======== =========

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