annotate qiime2__sample_classifier__predict_classification.xml @ 8:15c9178c2b50 draft default tip

planemo upload for repository https://github.com/qiime2/galaxy-tools/tree/main/tools/suite_qiime2__sample_classifier commit 64ed09f1f1c680ad8373d261bd6be43a4f8a8d5b

<?xml version='1.0' encoding='utf-8'?>
<!--
Copyright (c) 2025, 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: 2025.10.0)
for:
    qiime2 (version: 2025.10.0)
-->
<tool name="qiime2 sample-classifier predict-classification" id="qiime2__sample_classifier__predict_classification" version="2025.10.0+q2galaxy.2025.10.0" profile="22.05" license="BSD-3-Clause">
    <description>Use trained classifier to predict target values for new samples.</description>
    <xrefs>
        <xref type="bio.tools">qiime2</xref>
    </xrefs>
    <requirements>
        <container type="docker">quay.io/qiime2/amplicon:2025.10</container>
    </requirements>
    <version_command>q2galaxy version sample_classifier</version_command>
    <command detect_errors="exit_code">q2galaxy run sample_classifier predict_classification '$inputs'</command>
    <configfiles>
        <inputs name="inputs" data_style="staging_path_and_source_path"/>
    </configfiles>
    <inputs>
        <param name="table" type="data" format="qza" label="table: FeatureTable[Frequency | RelativeFrequency | PresenceAbsence | Composition]" help="[required]  Feature table containing all features that should be used for target prediction.">
            <options options_filter_attribute="metadata.semantic_type">
                <filter type="add_value" value="FeatureTable[Composition]"/>
                <filter type="add_value" value="FeatureTable[Frequency]"/>
                <filter type="add_value" value="FeatureTable[PresenceAbsence]"/>
                <filter type="add_value" value="FeatureTable[RelativeFrequency]"/>
            </options>
            <validator type="expression" message="Incompatible type">hasattr(value.metadata, "semantic_type") and value.metadata.semantic_type in ['FeatureTable[Composition]', 'FeatureTable[Frequency]', 'FeatureTable[PresenceAbsence]', 'FeatureTable[RelativeFrequency]']</validator>
        </param>
        <param name="sample_estimator" type="data" format="qza" label="sample_estimator: SampleEstimator[Classifier]" help="[required]  Sample classifier trained with fit_classifier.">
            <options options_filter_attribute="metadata.semantic_type">
                <filter type="add_value" value="SampleEstimator[Classifier]"/>
            </options>
            <validator type="expression" message="Incompatible type">hasattr(value.metadata, "semantic_type") and value.metadata.semantic_type in ['SampleEstimator[Classifier]']</validator>
        </param>
    </inputs>
    <outputs>
        <data name="predictions" format="qza" label="${tool.name} on ${on_string}: predictions.qza" from_work_dir="predictions.qza"/>
        <data name="probabilities" format="qza" label="${tool.name} on ${on_string}: probabilities.qza" from_work_dir="probabilities.qza"/>
    </outputs>
    <tests/>
    <help>
QIIME 2: sample-classifier predict-classification
=================================================
Use trained classifier to predict target values for new samples.


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