Mercurial > repos > q2d2 > qiime2__sample_classifier__fit_regressor

--- a/qiime2__sample_classifier__fit_regressor.xml	Thu Jun 08 19:52:01 2023 +0000
+++ b/qiime2__sample_classifier__fit_regressor.xml	Thu Apr 25 21:26:12 2024 +0000
@@ -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 fit-regressor" id="qiime2__sample_classifier__fit_regressor" version="2023.5.0+q2galaxy.2023.5.0.2" profile="22.05" license="BSD-3-Clause">
+<tool name="qiime2 sample-classifier fit-regressor" id="qiime2__sample_classifier__fit_regressor" version="2024.2.0+q2galaxy.2024.2.1" profile="22.05" license="BSD-3-Clause">
     <description>Fit a supervised learning regressor.</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 fit_regressor '$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[Numeric]" help="[required]  Numeric metadata column to use as prediction target.">
@@ -49,7 +52,6 @@
             <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('RandomForestRegressor', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor[DecisionTree]', 'AdaBoostRegressor[ExtraTrees]', 'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR')">
                 <option value="RandomForestRegressor" selected="true">RandomForestRegressor</option>