Mercurial > repos > q2d2 > qiime2__sample_classifier__fit_classifier
comparison qiime2__sample_classifier__fit_classifier.xml @ 2:f31f3c575d7e draft
planemo upload for repository https://github.com/qiime2/galaxy-tools/tree/main/tools/suite_qiime2__sample_classifier commit 65e4952f33eb335528e8553150e9097e5ea8f556
author | q2d2 |
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date | Thu, 08 Jun 2023 19:51:55 +0000 |
parents | 71cd01b7a74f |
children | 431ccbdd8582 |
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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 fit-classifier" id="qiime2__sample_classifier__fit_classifier" version="2022.11.1+q2galaxy.2022.11.1.2" profile="22.05" license="BSD-3-Clause"> | 13 <tool name="qiime2 sample-classifier fit-classifier" id="qiime2__sample_classifier__fit_classifier" version="2023.5.0+q2galaxy.2023.5.0.2" profile="22.05" license="BSD-3-Clause"> |
14 <description>Fit a supervised learning classifier.</description> | 14 <description>Fit a supervised learning classifier.</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 fit_classifier '$inputs'</command> | 19 <command detect_errors="exit_code">q2galaxy run sample_classifier fit_classifier '$inputs'</command> |
20 <configfiles> | 20 <configfiles> |
21 <inputs name="inputs" data_style="paths"/> | 21 <inputs name="inputs" data_style="paths"/> |
49 <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."/> | 49 <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="cv" type="integer" min="1" value="5" label="cv: Int % Range(1, None)" help="[default: 5] Number of k-fold cross-validations to perform."/> | 50 <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="random_state" type="integer" optional="true" label="random_state: Int" help="[optional] Seed used by random number generator."/> | 51 <param name="random_state" type="integer" optional="true" label="random_state: Int" help="[optional] Seed used by random number generator."/> |
52 <param name="n_jobs" type="integer" value="1" label="n_jobs: Int" help="[default: 1] Number of jobs to run in parallel."/> | 52 <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_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."/> | 53 <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="estimator" type="select" label="estimator: Str % Choices('RandomForestClassifier', 'ExtraTreesClassifier', 'GradientBoostingClassifier', 'AdaBoostClassifier', 'KNeighborsClassifier', 'LinearSVC', 'SVC')"> | 54 <param name="estimator" type="select" label="estimator: Str % Choices('RandomForestClassifier', 'ExtraTreesClassifier', 'GradientBoostingClassifier', 'AdaBoostClassifier[DecisionTree]', 'AdaBoostClassifier[ExtraTrees]', 'KNeighborsClassifier', 'LinearSVC', 'SVC')"> |
55 <option value="RandomForestClassifier" selected="true">RandomForestClassifier</option> | 55 <option value="RandomForestClassifier" selected="true">RandomForestClassifier</option> |
56 <option value="ExtraTreesClassifier">ExtraTreesClassifier</option> | 56 <option value="ExtraTreesClassifier">ExtraTreesClassifier</option> |
57 <option value="GradientBoostingClassifier">GradientBoostingClassifier</option> | 57 <option value="GradientBoostingClassifier">GradientBoostingClassifier</option> |
58 <option value="AdaBoostClassifier">AdaBoostClassifier</option> | 58 <option value="AdaBoostClassifier__ob__DecisionTree__cb__">AdaBoostClassifier[DecisionTree]</option> |
59 <option value="AdaBoostClassifier__ob__ExtraTrees__cb__">AdaBoostClassifier[ExtraTrees]</option> | |
59 <option value="KNeighborsClassifier">KNeighborsClassifier</option> | 60 <option value="KNeighborsClassifier">KNeighborsClassifier</option> |
60 <option value="LinearSVC">LinearSVC</option> | 61 <option value="LinearSVC">LinearSVC</option> |
61 <option value="SVC">SVC</option> | 62 <option value="SVC">SVC</option> |
62 </param> | 63 </param> |
63 <param name="optimize_feature_selection" type="boolean" truevalue="__q2galaxy__::literal::True" falsevalue="__q2galaxy__::literal::False" label="optimize_feature_selection: Bool" help="[default: No] Automatically optimize input feature selection using recursive feature elimination."/> | 64 <param name="optimize_feature_selection" type="boolean" truevalue="__q2galaxy__::literal::True" falsevalue="__q2galaxy__::literal::False" label="optimize_feature_selection: Bool" help="[default: No] Automatically optimize input feature selection using recursive feature elimination."/> |