--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/qiime2/qiime_sample-classifier_fit-regressor.xml	Wed Jul 17 03:05:17 2019 -0400
@@ -0,0 +1,161 @@
+<?xml version="1.0" ?>
+<tool id="qiime_sample-classifier_fit-regressor" name="qiime sample-classifier fit-regressor" version="2019.4">
+	<description> - Fit a supervised learning regressor.</description>
+	<requirements>
+		<requirement type="package" version="2019.4">qiime2</requirement>
+	</requirements>
+	<command><![CDATA[
+qiime sample-classifier fit-regressor
+
+--i-table=$itable
+--m-metadata-column="$mmetadatacolumn"
+
+#if $pstep:
+ --p-step=$pstep
+#end if
+
+#if $pcv:
+ --p-cv=$pcv
+#end if
+
+#if str($prandomstate):
+ --p-random-state="$prandomstate"
+#end if
+
+#set $pnjobs = '${GALAXY_SLOTS:-4}'
+#if str($pnjobs):
+ --p-n-jobs="$pnjobs"
+#end if
+
+
+#if $pnestimators:
+ --p-n-estimators=$pnestimators
+#end if
+
+#if str($pestimator) != 'None':
+ --p-estimator=$pestimator
+#end if
+
+#if $poptimizefeatureselection:
+ --p-optimize-feature-selection
+#end if
+
+#if $pparametertuning:
+ --p-parameter-tuning
+#end if
+
+#if str($pmissingsamples) != 'None':
+ --p-missing-samples=$pmissingsamples
+#end if
+
+
+#if $input_files_mmetadatafile:
+#def list_dict_to_string(list_dict):
+	#set $file_list = list_dict[0]['additional_input'].__getattr__('file_name')
+	#for d in list_dict[1:]:
+		#set $file_list = $file_list + ' --m-metadata-file=' + d['additional_input'].__getattr__('file_name')
+	#end for
+	#return $file_list
+#end def
+ --m-metadata-file=$list_dict_to_string($input_files_mmetadatafile)
+#end if
+
+
+--o-sample-estimator=osampleestimator
+--o-feature-importance=ofeatureimportance
+;
+cp osampleestimator.qza $osampleestimator;
+cp ofeatureimportance.qza $ofeatureimportance
+	]]></command>
+	<inputs>
+		<param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[Frequency] Feature table containing all features that should be used for target prediction.                  [required]" name="itable" optional="False" type="data"/>
+		<param label="--m-metadata-column: COLUMN  MetadataColumn[Numeric] Numeric metadata column to use as prediction target. [required]" name="mmetadatacolumn" optional="False" type="text"/>
+		<param label="--p-step: PROPORTION Range(0.0, 1.0, inclusive_start=False) If optimize-feature-selection is True, step is the percentage of features to remove at each iteration. [default: 0.05]" name="pstep" optional="True" type="float" value="0.05" min="0" max="1" exclusive_start="True"/>
+		<param label="--p-cv: INTEGER       Number of k-fold cross-validations to perform. Range(1, None)                                                [default: 5]" name="pcv" optional="True" type="integer" value="5" min="1"/>
+		<param label="--p-random-state: INTEGER Seed used by random number generator.        [optional]" name="prandomstate" optional="True" type="integer"/>
+		<param label="--p-n-estimators: INTEGER Range(1, None)     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.        [default: 100]" name="pnestimators" optional="True" type="integer" value="100" min="1"/>
+		<param label="--p-estimator: " name="pestimator" optional="True" type="select">
+			<option selected="True" value="None">Selection is Optional</option>
+			<option value="RandomForestRegressor">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 label="--p-optimize-feature-selection: --p-no-optimize-feature-selection Automatically optimize input feature selection using recursive feature elimination.         [default: False]" name="poptimizefeatureselection" selected="False" type="boolean"/>
+		<param label="--p-parameter-tuning: --p-no-parameter-tuning Automatically tune hyperparameters using random grid search.                                [default: False]" name="pparametertuning" selected="False" type="boolean"/>
+		<param label="--p-missing-samples: " name="pmissingsamples" optional="True" type="select">
+			<option selected="True" value="None">Selection is Optional</option>
+			<option value="error">error</option>
+			<option value="ignore">ignore</option>
+		</param>
+
+		<repeat name="input_files_mmetadatafile" optional="True" title="--m-metadata-file">
+			<param label="--m-metadata-file: Metadata file or artifact viewable as metadata. This option may be supplied multiple times to merge metadata. [optional]" name="additional_input" type="data" format="tabular,qza,no_unzip.zip" />
+		</repeat>
+
+	</inputs>
+	<outputs>
+		<data format="qza" label="${tool.name} on ${on_string}: sampleestimator.qza" name="osampleestimator"/>
+		<data format="qza" label="${tool.name} on ${on_string}: featureimportance.qza" name="ofeatureimportance"/>
+	</outputs>
+	<help><![CDATA[
+Fit a supervised learning regressor.
+####################################
+
+Fit a supervised learning regressor. Outputs the fit estimator (for
+prediction of test samples and/or unknown samples) and the relative
+importance of each feature for model accuracy. Optionally use k-fold cross-
+validation for automatic recursive feature elimination and hyperparameter
+tuning.
+
+Parameters
+----------
+table : FeatureTable[Frequency]
+    Feature table containing all features that should be used for target
+    prediction.
+metadata : MetadataColumn[Numeric]
+    Numeric metadata column to use as prediction target.
+step : Float % Range(0.0, 1.0, inclusive_start=False), optional
+    If optimize_feature_selection is True, step is the percentage of
+    features to remove at each iteration.
+cv : Int % Range(1, None), optional
+    Number of k-fold cross-validations to perform.
+random_state : Int, optional
+    Seed used by random number generator.
+n_estimators : Int % Range(1, None), optional
+    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.
+estimator : Str % Choices('RandomForestRegressor', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor', 'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR'), optional
+    Estimator method to use for sample prediction.
+optimize_feature_selection : Bool, optional
+    Automatically optimize input feature selection using recursive feature
+    elimination.
+parameter_tuning : Bool, optional
+    Automatically tune hyperparameters using random grid search.
+missing_samples : Str % Choices('error', 'ignore'), optional
+    How to handle missing samples in metadata. "error" will fail if missing
+    samples are detected. "ignore" will cause the feature table and
+    metadata to be filtered, so that only samples found in both files are
+    retained.
+
+Returns
+-------
+sample_estimator : SampleEstimator[Regressor]
+	\
+feature_importance : FeatureData[Importance]
+    Importance of each input feature to model accuracy.
+	]]></help>
+<macros>
+    <import>qiime_citation.xml</import>
+</macros>
+<expand macro="qiime_citation"/>
+</tool>