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comparison qiime2__sample_classifier__regress_samples.xml @ 2:09981e91ac53 draft

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planemo upload for repository https://github.com/qiime2/galaxy-tools/tree/main/tools/suite_qiime2__sample_classifier commit 65e4952f33eb335528e8553150e9097e5ea8f556
author q2d2
date Thu, 08 Jun 2023 19:51:42 +0000
parents 7d56c6806c36
children fa6055719fa7
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1:7d56c6806c36 2:09981e91ac53
4 4
5 Distributed under the terms of the Modified BSD License. (SPDX: BSD-3-Clause) 5 Distributed under the terms of the Modified BSD License. (SPDX: BSD-3-Clause)
6 --> 6 -->
7 <!-- 7 <!--
8 This tool was automatically generated by: 8 This tool was automatically generated by:
9 q2galaxy (version: 2022.11.1) 9 q2galaxy (version: 2023.5.0)
10 for: 10 for:
11 qiime2 (version: 2022.11.1) 11 qiime2 (version: 2023.5.1)
12 --> 12 -->
13 <tool name="qiime2 sample-classifier regress-samples" id="qiime2__sample_classifier__regress_samples" version="2022.11.1+q2galaxy.2022.11.1.2" profile="22.05" license="BSD-3-Clause"> 13 <tool name="qiime2 sample-classifier regress-samples" id="qiime2__sample_classifier__regress_samples" version="2023.5.0+q2galaxy.2023.5.0.2" profile="22.05" license="BSD-3-Clause">
14 <description>Train and test a cross-validated supervised learning regressor.</description> 14 <description>Train and test a cross-validated supervised learning regressor.</description>
15 <requirements> 15 <requirements>
16 <container type="docker">quay.io/qiime2/core:2022.11</container> 16 <container type="docker">quay.io/qiime2/core:2023.5</container>
17 </requirements> 17 </requirements>
18 <version_command>q2galaxy version sample_classifier</version_command> 18 <version_command>q2galaxy version sample_classifier</version_command>
19 <command detect_errors="exit_code">q2galaxy run sample_classifier regress_samples '$inputs'</command> 19 <command detect_errors="exit_code">q2galaxy run sample_classifier regress_samples '$inputs'</command>
20 <configfiles> 20 <configfiles>
21 <inputs name="inputs" data_style="paths"/> 21 <inputs name="inputs" data_style="paths"/>
50 <param name="step" type="float" min="1e-06" max="0.999999" value="0.05" label="step: Float % Range(0.0, 1.0, inclusive_start=False)" help="[default: 0.05] If optimize_feature_selection is True, step is the percentage of features to remove at each iteration."/> 50 <param name="step" type="float" min="1e-06" max="0.999999" value="0.05" label="step: Float % Range(0.0, 1.0, inclusive_start=False)" help="[default: 0.05] If optimize_feature_selection is True, step is the percentage of features to remove at each iteration."/>
51 <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."/> 51 <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."/>
52 <param name="random_state" type="integer" optional="true" label="random_state: Int" help="[optional] Seed used by random number generator."/> 52 <param name="random_state" type="integer" optional="true" label="random_state: Int" help="[optional] Seed used by random number generator."/>
53 <param name="n_jobs" type="integer" value="1" label="n_jobs: Int" help="[default: 1] Number of jobs to run in parallel."/> 53 <param name="n_jobs" type="integer" value="1" label="n_jobs: Int" help="[default: 1] Number of jobs to run in parallel."/>
54 <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."/> 54 <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."/>
55 <param name="estimator" type="select" label="estimator: Str % Choices('RandomForestRegressor', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor', 'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR')"> 55 <param name="estimator" type="select" label="estimator: Str % Choices('RandomForestRegressor', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor[DecisionTree]', 'AdaBoostRegressor[ExtraTrees]', 'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR')">
56 <option value="RandomForestRegressor" selected="true">RandomForestRegressor</option> 56 <option value="RandomForestRegressor" selected="true">RandomForestRegressor</option>
57 <option value="ExtraTreesRegressor">ExtraTreesRegressor</option> 57 <option value="ExtraTreesRegressor">ExtraTreesRegressor</option>
58 <option value="GradientBoostingRegressor">GradientBoostingRegressor</option> 58 <option value="GradientBoostingRegressor">GradientBoostingRegressor</option>
59 <option value="AdaBoostRegressor">AdaBoostRegressor</option> 59 <option value="AdaBoostRegressor__ob__DecisionTree__cb__">AdaBoostRegressor[DecisionTree]</option>
60 <option value="AdaBoostRegressor__ob__ExtraTrees__cb__">AdaBoostRegressor[ExtraTrees]</option>
60 <option value="ElasticNet">ElasticNet</option> 61 <option value="ElasticNet">ElasticNet</option>
61 <option value="Ridge">Ridge</option> 62 <option value="Ridge">Ridge</option>
62 <option value="Lasso">Lasso</option> 63 <option value="Lasso">Lasso</option>
63 <option value="KNeighborsRegressor">KNeighborsRegressor</option> 64 <option value="KNeighborsRegressor">KNeighborsRegressor</option>
64 <option value="LinearSVR">LinearSVR</option> 65 <option value="LinearSVR">LinearSVR</option>