<?xml version="1.0" ?>
<tool id="qiime_sample-classifier_predict-classification" name="qiime sample-classifier predict-classification"
      version="2020.8">
  <description>Use trained classifier to predict target values for new samples.</description>
  <requirements>
    <requirement type="package" version="2020.8">qiime2</requirement>
  </requirements>
  <command><![CDATA[
qiime sample-classifier predict-classification

--i-table=$itable

--i-sample-estimator=$isampleestimator

--p-n-jobs=$pnjobs

--o-predictions=opredictions

--o-probabilities=oprobabilities

#if str($examples) != 'None':
--examples=$examples
#end if

;
cp oprobabilities.qza $oprobabilities

  ]]></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 format="qza,no_unzip.zip" label="--i-sample-estimator: ARTIFACT SampleEstimator[Classifier] Sample classifier trained with fit_classifier. [required]" name="isampleestimator" optional="False" type="data" />
    <param label="--examples: Show usage examples and exit." name="examples" optional="False" type="data" />
    
  </inputs>

  <outputs>
    <data format="qza" label="${tool.name} on ${on_string}: predictions.qza" name="opredictions" />
    <data format="qza" label="${tool.name} on ${on_string}: probabilities.qza" name="oprobabilities" />
    
  </outputs>

  <help><![CDATA[
Use trained classifier to predict target values for new samples.
###############################################################

Use trained estimator to predict target values for new samples. These will
typically be unseen samples, e.g., test data (derived manually or from
split_table) or samples with unknown values, but can theoretically be any
samples present in a feature table that contain overlapping features with
the feature table used to train the estimator.

Parameters
----------
table : FeatureTable[Frequency]
    Feature table containing all features that should be used for target
    prediction.
sample_estimator : SampleEstimator[Classifier]
    Sample classifier trained with fit_classifier.
n_jobs : Int, optional
    Number of jobs to run in parallel.

Returns
-------
predictions : SampleData[ClassifierPredictions]
    Predicted target values for each input sample.
probabilities : SampleData[Probabilities]
    Predicted class probabilities for each input sample.
  ]]></help>
  <macros>
    <import>qiime_citation.xml</import>
  </macros>
  <expand macro="qiime_citation"/>
</tool>