| Next changeset 1:fba2a1dc2e30 (2023-01-13) |
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planemo upload for repository https://github.com/qiime2/galaxy-tools/tree/main/tools/suite_qiime2__sample_classifier commit 9023cfd83495a517fbcbb6f91d5b01a6f1afcda1 |
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qiime2__sample_classifier__regress_samples_ncv.xml test-data/.gitkeep |
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| diff -r 000000000000 -r 896b0ff9a810 qiime2__sample_classifier__regress_samples_ncv.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/qiime2__sample_classifier__regress_samples_ncv.xml Mon Aug 29 20:23:38 2022 +0000 |
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| @@ -0,0 +1,113 @@ +<?xml version='1.0' encoding='utf-8'?> +<!-- +Copyright (c) 2022, 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: 2022.8.1) +for: + qiime2 (version: 2022.8.1) +--> +<tool name="qiime2 sample-classifier regress-samples-ncv" id="qiime2__sample_classifier__regress_samples_ncv" version="2022.8.0+q2galaxy.2022.8.1.2" profile="22.05" license="BSD-3-Clause"> + <description>Nested cross-validated supervised learning regressor.</description> + <requirements> + <container type="docker">quay.io/qiime2/core:2022.8</container> + </requirements> + <version_command>q2galaxy version sample_classifier</version_command> + <command detect_errors="aggressive">q2galaxy run sample_classifier regress_samples_ncv '$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]"/> + </options> + <validator type="expression" message="Incompatible type">hasattr(value.metadata, "semantic_type") and value.metadata.semantic_type in ['FeatureTable[Frequency]']</validator> + </param> + <conditional name="metadata"> + <param name="type" type="select" label="metadata: MetadataColumn[Numeric]" help="[required] Numeric metadata column to use as prediction target."> + <option value="tsv" selected="true">Metadata from TSV</option> + <option value="qza">Metadata from Artifact</option> + </param> + <when value="tsv"> + <param name="source" type="data" format="tabular,qiime2.tabular" label="Metadata Source"/> + <param name="column" type="data_column" label="Column Name" data_ref="source" use_header_names="true"> + <validator type="expression" message="The first column cannot be selected (they are IDs).">value != "1"</validator> + </param> + </when> + <when value="qza"> + <param name="source" type="data" format="qza" label="Metadata Source"/> + <param name="column" type="text" label="Column Name"> + <validator type="empty_field"/> + </param> + </when> + </conditional> + <section name="__q2galaxy__GUI__section__extra_opts__" title="Click here for additional options"> + <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> + <option value="AdaBoostRegressor">AdaBoostRegressor</option> + <option value="ElasticNet">ElasticNet</option> + <option value="Ridge">Ridge</option> + <option value="Lasso">Lasso</option> + <option value="KNeighborsRegressor">KNeighborsRegressor</option> + <option value="LinearSVR">LinearSVR</option> + <option value="SVR">SVR</option> + </param> + <param name="stratify" type="boolean" truevalue="__q2galaxy__::literal::True" falsevalue="__q2galaxy__::literal::False" label="stratify: Bool" help="[default: No] Evenly stratify training and test data among metadata categories. If True, all values in column must match at least two samples."/> + <param name="parameter_tuning" type="boolean" truevalue="__q2galaxy__::literal::True" falsevalue="__q2galaxy__::literal::False" label="parameter_tuning: Bool" help="[default: No] Automatically tune hyperparameters using random grid search."/> + <param name="missing_samples" type="select" label="missing_samples: Str % Choices('error', 'ignore')" display="radio"> + <option value="error" selected="true">error</option> + <option value="ignore">ignore</option> + </param> + </section> + </inputs> + <outputs> + <data name="predictions" format="qza" label="${tool.name} on ${on_string}: predictions.qza" from_work_dir="predictions.qza"/> + <data name="feature_importance" format="qza" label="${tool.name} on ${on_string}: feature_importance.qza" from_work_dir="feature_importance.qza"/> + </outputs> + <tests/> + <help> +QIIME 2: sample-classifier regress-samples-ncv +============================================== +Nested cross-validated supervised learning regressor. + + +Outputs: +-------- +:predictions.qza: Predicted target values for each input sample. +:feature_importance.qza: Importance of each input feature to model accuracy. + +| + +Description: +------------ +Predicts a continuous sample metadata column using a supervised learning regressor. Uses nested stratified k-fold cross validation for automated hyperparameter optimization and sample prediction. Outputs predicted values for each input sample, and relative importance of each feature for model accuracy. + + +| + +</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> |