comparison qiime2__longitudinal__feature_volatility.xml @ 3:956dbe6f39bd draft

planemo upload for repository https://github.com/qiime2/galaxy-tools/tree/main/tools/suite_qiime2__longitudinal commit 389df0134cd0763dcf02aac6e623fc15f8861c1e

2:ba818c170b06

<?xml version='1.0' encoding='utf-8'?>
<!--
Copyright (c) 2023, 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: 2023.5.0)
for:
    qiime2 (version: 2023.5.1)
-->
<tool name="qiime2 longitudinal feature-volatility" id="qiime2__longitudinal__feature_volatility" version="2023.5.0+q2galaxy.2023.5.0.2" profile="22.05" license="BSD-3-Clause">
    <description>Feature volatility analysis</description>
    <requirements>
        <container type="docker">quay.io/qiime2/core:2023.5</container>
    </requirements>
    <version_command>q2galaxy version longitudinal</version_command>
    <command detect_errors="exit_code">q2galaxy run longitudinal feature_volatility '$inputs'</command>
    <configfiles>
        <inputs name="inputs" data_style="paths"/>
    </configfiles>
    <inputs>
        <param name="table" type="data" format="qza" label="table: FeatureTable[Frequency]" 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[Frequency]"/>

                    </param>
                </when>
            </conditional>
            <param name="cv" type="integer" min="1" value="5" label="cv: Int % Range(1, None)" help="[default: 5]  Number of k-fold cross-validations to perform."/>
            <param name="random_state" type="integer" optional="true" label="random_state: Int" help="[optional]  Seed used by random number generator."/>
            <param name="n_jobs" type="integer" value="1" label="n_jobs: Int" help="[default: 1]  Number of jobs to run in parallel."/>
            <param name="n_estimators" type="integer" min="1" value="100" label="n_estimators: Int % Range(1, None)" help="[default: 100]  Number of trees to grow for estimation. More trees will improve predictive accuracy up to a threshold level, but will also increase time and memory requirements. This parameter only affects ensemble estimators, such as Random Forest, AdaBoost, ExtraTrees, and GradientBoosting."/>
            <param name="estimator" type="select" label="estimator: Str % Choices('RandomForestRegressor', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor', 'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR')">
                <option value="RandomForestRegressor" selected="true">RandomForestRegressor</option>
                <option value="ExtraTreesRegressor">ExtraTreesRegressor</option>
                <option value="GradientBoostingRegressor">GradientBoostingRegressor</option>

3:956dbe6f39bd

<?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: 2024.2.1)
for:
    qiime2 (version: 2024.2.0)
-->
<tool name="qiime2 longitudinal feature-volatility" id="qiime2__longitudinal__feature_volatility" version="2024.2.0+q2galaxy.2024.2.1" profile="22.05" license="BSD-3-Clause">
    <description>Feature volatility analysis</description>
    <requirements>
        <container type="docker">quay.io/qiime2/amplicon:2024.2</container>
    </requirements>
    <version_command>q2galaxy version longitudinal</version_command>
    <command detect_errors="exit_code">q2galaxy run longitudinal feature_volatility '$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]" 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[Frequency]"/>

                    </param>
                </when>
            </conditional>
            <param name="cv" type="integer" min="1" value="5" label="cv: Int % Range(1, None)" help="[default: 5]  Number of k-fold cross-validations to perform."/>
            <param name="random_state" type="integer" optional="true" label="random_state: Int" help="[optional]  Seed used by random number generator."/>
            <param name="n_estimators" type="integer" min="1" value="100" label="n_estimators: Int % Range(1, None)" help="[default: 100]  Number of trees to grow for estimation. More trees will improve predictive accuracy up to a threshold level, but will also increase time and memory requirements. This parameter only affects ensemble estimators, such as Random Forest, AdaBoost, ExtraTrees, and GradientBoosting."/>
            <param name="estimator" type="select" label="estimator: Str % Choices('RandomForestRegressor', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor', 'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR')">
                <option value="RandomForestRegressor" selected="true">RandomForestRegressor</option>
                <option value="ExtraTreesRegressor">ExtraTreesRegressor</option>
                <option value="GradientBoostingRegressor">GradientBoostingRegressor</option>