view qiime2__sample_classifier__predict_regression.xml @ 5:e0913981ccc4 draft default tip

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<?xml version='1.0' encoding='utf-8'?>
<!--
Copyright (c) 2024, QIIME 2 development team.

Distributed under the terms of the Modified BSD License. (SPDX: BSD-3-Clause)
-->
<!--
This tool was automatically generated by:
    q2galaxy (version: 2024.10.0)
for:
    qiime2 (version: 2024.10.1)
-->
<tool name="qiime2 sample-classifier predict-regression" id="qiime2__sample_classifier__predict_regression" version="2024.10.0+q2galaxy.2024.10.0" profile="22.05" license="BSD-3-Clause">
    <description>Use trained regressor to predict target values for new samples.</description>
    <xrefs>
        <xref type="bio.tools">qiime2</xref>
    </xrefs>
    <requirements>
        <container type="docker">quay.io/qiime2/amplicon:2024.10</container>
    </requirements>
    <version_command>q2galaxy version sample_classifier</version_command>
    <command detect_errors="exit_code">q2galaxy run sample_classifier predict_regression '$inputs'</command>
    <configfiles>
        <inputs name="inputs" data_style="staging_path_and_source_path"/>
    </configfiles>
    <inputs>
        <param name="table" type="data" format="qza" label="table: FeatureTable[Frequency | RelativeFrequency | PresenceAbsence | Composition]" help="[required]  Feature table containing all features that should be used for target prediction.">
            <options options_filter_attribute="metadata.semantic_type">
                <filter type="add_value" value="FeatureTable[RelativeFrequency]"/>
                <filter type="add_value" value="FeatureTable[Frequency]"/>
                <filter type="add_value" value="FeatureTable[PresenceAbsence]"/>
                <filter type="add_value" value="FeatureTable[Composition]"/>
            </options>
            <validator type="expression" message="Incompatible type">hasattr(value.metadata, "semantic_type") and value.metadata.semantic_type in ['FeatureTable[Composition]', 'FeatureTable[Frequency]', 'FeatureTable[PresenceAbsence]', 'FeatureTable[RelativeFrequency]']</validator>
        </param>
        <param name="sample_estimator" type="data" format="qza" label="sample_estimator: SampleEstimator[Regressor]" help="[required]  Sample regressor trained with fit_regressor.">
            <options options_filter_attribute="metadata.semantic_type">
                <filter type="add_value" value="SampleEstimator[Regressor]"/>
            </options>
            <validator type="expression" message="Incompatible type">hasattr(value.metadata, "semantic_type") and value.metadata.semantic_type in ['SampleEstimator[Regressor]']</validator>
        </param>
    </inputs>
    <outputs>
        <data name="predictions" format="qza" label="${tool.name} on ${on_string}: predictions.qza" from_work_dir="predictions.qza"/>
    </outputs>
    <tests/>
    <help>
QIIME 2: sample-classifier predict-regression
=============================================
Use trained regressor to predict target values for new samples.


Outputs:
--------
:predictions.qza: Predicted target values for each input sample.

|  

Description:
------------
Use trained estimator to predict target values for new samples. These will typically be unseen samples, e.g., test data (derived manually or from split_table) or samples with unknown values, but can theoretically be any samples present in a feature table that contain overlapping features with the feature table used to train the estimator.


|  

</help>
    <citations>
        <citation type="doi">10.21105/joss.00934</citation>
        <citation type="bibtex">@article{cite2,
 author = {Pedregosa, Fabian and Varoquaux, Gaël and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David and Brucher, Matthieu and Perrot, Matthieu and Duchesnay, Édouard},
 journal = {Journal of machine learning research},
 number = {Oct},
 pages = {2825--2830},
 title = {Scikit-learn: Machine learning in Python},
 volume = {12},
 year = {2011}
}
</citation>
        <citation type="doi">10.1038/s41587-019-0209-9</citation>
    </citations>
</tool>