annotate qiime2/qiime_feature-classifier_classify-sklearn.xml @ 15:276ec629f09a draft

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<?xml version="1.0" ?>
<tool id="qiime_feature-classifier_classify-sklearn" name="qiime feature-classifier classify-sklearn"
      version="2020.8">
  <description>Pre-fitted sklearn-based taxonomy classifier</description>
  <requirements>
    <requirement type="package" version="2020.8">qiime2</requirement>
  </requirements>
  <command><![CDATA[
qiime feature-classifier classify-sklearn

--i-reads=$ireads

--i-classifier=$iclassifier

--p-n-jobs=$pnjobs

#if str($pconfidence) != 'None':
--p-confidence=$pconfidence
#end if

#if str($preadorientation) != 'None':
--p-read-orientation=$preadorientation
#end if

--o-classification=oclassification

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

;
cp oclassification.qza $oclassification

  ]]></command>
  <inputs>
    <param format="qza,no_unzip.zip" label="--i-reads: ARTIFACT FeatureData[Sequence] The feature data to be classified.         [required]" name="ireads" optional="False" type="data" />
    <param format="qza,no_unzip.zip" label="--i-classifier: ARTIFACT TaxonomicClassifier  The taxonomic classifier for classifying the reads. [required]" name="iclassifier" optional="False" type="data" />
    <param label="--p-confidence: " name="pconfidence" optional="True" type="select">
      <option selected="True" value="None">Selection is Optional</option>
      <option value="Float % Range(0">Float % Range(0</option>
      <option value="1">1</option>
      <option value="inclusive_end=True">inclusive_end=True</option>
    </param>
    <param label="--p-read-orientation: " name="preadorientation" optional="True" type="select">
      <option selected="True" value="None">Selection is Optional</option>
      <option value="same">same</option>
      <option value="reverse-complement">reverse-complement</option>
      <option value="auto">auto</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}: classification.qza" name="oclassification" />
    
  </outputs>

  <help><![CDATA[
Pre-fitted sklearn-based taxonomy classifier
###############################################################

Classify reads by taxon using a fitted classifier.

Parameters
----------
reads : FeatureData[Sequence]
    The feature data to be classified.
classifier : TaxonomicClassifier
    The taxonomic classifier for classifying the reads.
reads_per_batch : Int % Range(0, None), optional
    Number of reads to process in each batch. If "auto", this parameter is
    autoscaled to min( number of query sequences / n_jobs, 20000).
n_jobs : Int, optional
    The maximum number of concurrently worker processes. If -1 all CPUs are
    used. If 1 is given, no parallel computing code is used at all, which
    is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are
    used. Thus for n_jobs = -2, all CPUs but one are used.
pre_dispatch : Str, optional
    "all" or expression, as in "3*n_jobs". The number of batches (of tasks)
    to be pre-dispatched.
confidence : Float % Range(0, 1, inclusive_end=True) | Str % Choices('disable'), optional
    Confidence threshold for limiting taxonomic depth. Set to "disable" to
    disable confidence calculation, or 0 to calculate confidence but not
    apply it to limit the taxonomic depth of the assignments.
read_orientation : Str % Choices('same', 'reverse-complement', 'auto'), optional
    Direction of reads with respect to reference sequences. same will cause
    reads to be classified unchanged; reverse-complement will cause reads
    to be reversed and complemented prior to classification. "auto" will
    autodetect orientation based on the confidence estimates for the first
    100 reads.

Returns
-------
classification : FeatureData[Taxonomy]
  ]]></help>
  <macros>
    <import>qiime_citation.xml</import>
  </macros>
  <expand macro="qiime_citation"/>
</tool>