--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/qiime2-2020.8/qiime_longitudinal_feature-volatility.xml	Fri Sep 04 12:44:24 2020 +0000
@@ -0,0 +1,242 @@
+<?xml version="1.0" ?>
+<tool id="qiime_longitudinal_feature-volatility" name="qiime longitudinal feature-volatility"
+      version="2020.8">
+  <description>Feature volatility analysis</description>
+  <requirements>
+    <requirement type="package" version="2020.8">qiime2</requirement>
+  </requirements>
+  <command><![CDATA[
+qiime longitudinal feature-volatility
+
+--i-table=$itable
+# 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($input_files_mmetadatafile)
+# end if
+
+#if '__ob__' in str($pstatecolumn):
+  #set $pstatecolumn_temp = $pstatecolumn.replace('__ob__', '[')
+  #set $pstatecolumn = $pstatecolumn_temp
+#end if
+#if '__cb__' in str($pstatecolumn):
+  #set $pstatecolumn_temp = $pstatecolumn.replace('__cb__', ']')
+  #set $pstatecolumn = $pstatecolumn_temp
+#end if
+#if 'X' in str($pstatecolumn):
+  #set $pstatecolumn_temp = $pstatecolumn.replace('X', '\\')
+  #set $pstatecolumn = $pstatecolumn_temp
+#end if
+#if '__sq__' in str($pstatecolumn):
+  #set $pstatecolumn_temp = $pstatecolumn.replace('__sq__', "'")
+  #set $pstatecolumn = $pstatecolumn_temp
+#end if
+#if '__db__' in str($pstatecolumn):
+  #set $pstatecolumn_temp = $pstatecolumn.replace('__db__', '"')
+  #set $pstatecolumn = $pstatecolumn_temp
+#end if
+
+--p-state-column=$pstatecolumn
+
+
+#if '__ob__' in str($pindividualidcolumn):
+  #set $pindividualidcolumn_temp = $pindividualidcolumn.replace('__ob__', '[')
+  #set $pindividualidcolumn = $pindividualidcolumn_temp
+#end if
+#if '__cb__' in str($pindividualidcolumn):
+  #set $pindividualidcolumn_temp = $pindividualidcolumn.replace('__cb__', ']')
+  #set $pindividualidcolumn = $pindividualidcolumn_temp
+#end if
+#if 'X' in str($pindividualidcolumn):
+  #set $pindividualidcolumn_temp = $pindividualidcolumn.replace('X', '\\')
+  #set $pindividualidcolumn = $pindividualidcolumn_temp
+#end if
+#if '__sq__' in str($pindividualidcolumn):
+  #set $pindividualidcolumn_temp = $pindividualidcolumn.replace('__sq__', "'")
+  #set $pindividualidcolumn = $pindividualidcolumn_temp
+#end if
+#if '__db__' in str($pindividualidcolumn):
+  #set $pindividualidcolumn_temp = $pindividualidcolumn.replace('__db__', '"')
+  #set $pindividualidcolumn = $pindividualidcolumn_temp
+#end if
+
+#if str($pindividualidcolumn):
+  --p-individual-id-column=$pindividualidcolumn
+#end if
+
+--p-cv=$pcv
+
+#if str($prandomstate):
+  --p-random-state=$prandomstate
+#end if
+--p-n-jobs=$pnjobs
+
+--p-n-estimators=$pnestimators
+
+#if str($pestimator) != 'None':
+--p-estimator=$pestimator
+#end if
+
+#if $pparametertuning:
+ --p-parameter-tuning
+#end if
+
+#if str($pmissingsamples) != 'None':
+--p-missing-samples=$pmissingsamples
+#end if
+
+#if str($pimportancethreshold) != 'None':
+--p-importance-threshold=$pimportancethreshold
+#end if
+
+#if str($pfeaturecount) != 'None':
+--p-feature-count=$pfeaturecount
+#end if
+
+--o-filtered-table=ofilteredtable
+
+--o-feature-importance=ofeatureimportance
+
+--o-volatility-plot=ovolatilityplot
+
+--o-accuracy-results=oaccuracyresults
+
+--o-sample-estimator=osampleestimator
+
+#if str($examples) != 'None':
+--examples=$examples
+#end if
+
+;
+cp osampleestimator.qza $osampleestimator
+
+  ]]></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" />
+    <repeat name="input_files_mmetadatafile" optional="False" title="--m-metadata-file">
+      <param format="tabular,qza,no_unzip.zip" label="--m-metadata-file: METADATA... (multiple            Sample metadata file containing arguments will be   individual-id-column. merged)                                                        [required]" name="additional_input" optional="False" type="data" />
+    </repeat>
+    <param label="--p-state-column: TEXT  Metadata containing collection time (state) values for each sample. Must contain exclusively numeric values.                                    [required]" name="pstatecolumn" optional="False" type="text" />
+    <param label="--p-individual-id-column: TEXT Metadata column containing IDs for individual subjects.                                  [optional]" name="pindividualidcolumn" optional="False" type="text" />
+    <param label="--p-cv: INTEGER         Number of k-fold cross-validations to perform. Range(1, None)                                                [default: 5]" min="1" name="pcv" optional="True" type="integer" value="5" />
+    <param label="--p-random-state: INTEGER Seed used by random number generator.      [optional]" name="prandomstate" optional="False" type="text" />
+    <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]" min="1" name="pnestimators" optional="True" type="integer" value="100" />
+    <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-parameter-tuning: --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>
+    <param label="--p-importance-threshold: " name="pimportancethreshold" optional="True" type="select">
+      <option selected="True" value="None">Selection is Optional</option>
+      <option value="Float % Range(0">Float % Range(0</option>
+      <option value="None">None</option>
+      <option value="inclusive_start=False">inclusive_start=False</option>
+    </param>
+    <param label="--p-feature-count: " name="pfeaturecount" optional="True" type="select">
+      <option selected="True" value="None">Selection is Optional</option>
+      <option value="Int % Range(1">Int % Range(1</option>
+      <option value="None">None</option>
+    </param>
+    <param label="--examples: Show usage examples and exit." name="examples" optional="False" type="data" />
+    
+  </inputs>
+
+  <outputs>
+    <data format="qza" label="${tool.name} on ${on_string}: filteredtable.qza" name="ofilteredtable" />
+    <data format="qza" label="${tool.name} on ${on_string}: featureimportance.qza" name="ofeatureimportance" />
+    <data format="html" label="${tool.name} on ${on_string}: volatilityplot.html" name="ovolatilityplot" />
+    <data format="html" label="${tool.name} on ${on_string}: accuracyresults.html" name="oaccuracyresults" />
+    <data format="qza" label="${tool.name} on ${on_string}: sampleestimator.qza" name="osampleestimator" />
+    
+  </outputs>
+
+  <help><![CDATA[
+Feature volatility analysis
+###############################################################
+
+Identify features that are predictive of a numeric metadata column,
+state_column (e.g., time), and plot their relative frequencies across
+states using interactive feature volatility plots. A supervised learning
+regressor is used to identify important features and assess their ability
+to predict sample states. state_column will typically be a measure of time,
+but any numeric metadata column can be used.
+
+Parameters
+----------
+table : FeatureTable[Frequency]
+    Feature table containing all features that should be used for target
+    prediction.
+metadata : Metadata
+    Sample metadata file containing individual_id_column.
+state_column : Str
+    Metadata containing collection time (state) values for each sample.
+    Must contain exclusively numeric values.
+individual_id_column : Str, optional
+    Metadata column containing IDs for individual subjects.
+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_jobs : Int, optional
+    Number of jobs to run in parallel.
+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.
+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.
+importance_threshold : Float % Range(0, None, inclusive_start=False) | Str % Choices('q1', 'q2', 'q3'), optional
+    Filter feature table to exclude any features with an importance score
+    less than this threshold. Set to "q1", "q2", or "q3" to select the
+    first, second, or third quartile of values. Set to "None" to disable
+    this filter.
+feature_count : Int % Range(1, None) | Str % Choices('all'), optional
+    Filter feature table to include top N most important features. Set to
+    "all" to include all features.
+
+Returns
+-------
+filtered_table : FeatureTable[RelativeFrequency]
+    Feature table containing only important features.
+feature_importance : FeatureData[Importance]
+    Importance of each input feature to model accuracy.
+volatility_plot : Visualization
+    Interactive volatility plot visualization.
+accuracy_results : Visualization
+    Accuracy results visualization.
+sample_estimator : SampleEstimator[Regressor]
+    Trained sample regressor.
+  ]]></help>
+  <macros>
+    <import>qiime_citation.xml</import>
+  </macros>
+  <expand macro="qiime_citation"/>
+</tool>
\ No newline at end of file