| Previous changeset 2:e72fb1ecbadc (2023-06-08) Next changeset 4:84156d6e1291 (2024-06-03) |
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Commit message:
planemo upload for repository https://github.com/qiime2/galaxy-tools/tree/main/tools/suite_qiime2__sample_classifier commit 389df0134cd0763dcf02aac6e623fc15f8861c1e |
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modified:
qiime2__sample_classifier__classify_samples.xml |
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| diff -r e72fb1ecbadc -r 096916b40a5a qiime2__sample_classifier__classify_samples.xml --- a/qiime2__sample_classifier__classify_samples.xml Thu Jun 08 19:52:08 2023 +0000 +++ b/qiime2__sample_classifier__classify_samples.xml Thu Apr 25 21:26:22 2024 +0000 |
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| @@ -1,31 +1,34 @@ <?xml version='1.0' encoding='utf-8'?> <!-- -Copyright (c) 2023, QIIME 2 development team. +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: 2023.5.0) + q2galaxy (version: 2024.2.1) for: - qiime2 (version: 2023.5.1) + qiime2 (version: 2024.2.0) --> -<tool name="qiime2 sample-classifier classify-samples" id="qiime2__sample_classifier__classify_samples" version="2023.5.0+q2galaxy.2023.5.0.2" profile="22.05" license="BSD-3-Clause"> +<tool name="qiime2 sample-classifier classify-samples" id="qiime2__sample_classifier__classify_samples" version="2024.2.0+q2galaxy.2024.2.1" profile="22.05" license="BSD-3-Clause"> <description>Train and test a cross-validated supervised learning classifier.</description> <requirements> - <container type="docker">quay.io/qiime2/core:2023.5</container> + <container type="docker">quay.io/qiime2/amplicon:2024.2</container> </requirements> <version_command>q2galaxy version sample_classifier</version_command> <command detect_errors="exit_code">q2galaxy run sample_classifier classify_samples '$inputs'</command> <configfiles> - <inputs name="inputs" data_style="paths"/> + <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."> + <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[RelativeFrequency]"/> + <filter type="add_value" value="FeatureTable[PresenceAbsence]"/> </options> - <validator type="expression" message="Incompatible type">hasattr(value.metadata, "semantic_type") and value.metadata.semantic_type in ['FeatureTable[Frequency]']</validator> + <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> <conditional name="metadata"> <param name="type" type="select" label="metadata: MetadataColumn[Categorical]" help="[required] Categorical metadata column to use as prediction target."> @@ -50,7 +53,6 @@ <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."/> <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('RandomForestClassifier', 'ExtraTreesClassifier', 'GradientBoostingClassifier', 'AdaBoostClassifier[DecisionTree]', 'AdaBoostClassifier[ExtraTrees]', 'KNeighborsClassifier', 'LinearSVC', 'SVC')"> <option value="RandomForestClassifier" selected="true">RandomForestClassifier</option> |