comparison qiime_feature-classifier_classify-sklearn.xml @ 0:09b7bcb72fa7 draft

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0:09b7bcb72fa7

<?xml version="1.0" ?>
<tool id="qiime_feature-classifier_classify-sklearn" name="qiime feature-classifier classify-sklearn" version="2018.4">
	<description>- Pre-fitted sklearn-based taxonomy classifier</description>
	<requirements>
		<requirement type="package" version="2018.4">qiime2</requirement>
	</requirements>
	<command><![CDATA[
  qiime feature-classifier classify-sklearn

  #if str( $id_to_classifier_fp.selector ) == 'history'
  	#set $classifier = $id_to_classifier_fp.classifier_fp
  	--i-classifier '$classifier'
  #else:
  	#set $classifier = $id_to_classifier_fp.classifier.fields.path
  	--i-classifier '$classifier'
  #end if
  
  	--i-reads=$ireads
  
    #if str($pconfidence) != '':
      #if float($pconfidence) >= 0.0:
        --p-confidence=$pconfidence
      #end if
    #end if
  
  #if str($preadorientation) != 'None':
    --p-read-orientation=$preadorientation
  #end if
  
  #if str($cmdconfig) != 'None':
   --cmd-config=$cmdconfig
  #end if
  
  #set $pnjobs = '${GALAXY_SLOTS:-4}'
   --p-n-jobs="$pnjobs"
   --o-classification=oclassification;
   
  cp oclassification.qza $oclassification;
  ]]></command>
	<inputs>
		<param format="qza,no_unzip.zip" label="--i-reads: FeatureData[Sequence] The feature data to be classified. [required]" name="ireads" optional="False" type="data"/>

		<conditional name="id_to_classifier_fp" optional="True">
		   <param name="selector" type="select" label="Reference classifier to query">
			  <option value="cached">Public classifiers</option>
			  <option value="history">Classifiers from your history</option>
		   </param>
		   <when value="cached">
			  <param name="classifier_fp" label="Reference classifier" type="select" optional="True">
				 <options from_data_table="qiime_taxonomy" />
			  </param>
		   </when>
		   <when value="history">
			  <param name="classifier_fp" type="data" format="qza,no_unzip.zip" label="Reference classifier" optional="True" />
		   </when>
		</conditional>

		<param label="--p-confidence: Confidence threshold for limiting taxonomic depth. Provide -1 to disable confidence calculation, or 0 to calculate confidence but not apply it to limit the taxonomic depth of the assignments.  [default: 0.7]" name="pconfidence" optional="True" type="float" value="0.7"/>
		<param label="--p-read-orientation: [reverse-complement|same] 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. Default is to autodetect based on the confidence estimates for the first 100 reads.  [optional]" name="preadorientation" optional="True" type="select">
			<option selected="True" value="None">Selection is Optional</option>
			<option value="reverse-complement">reverse-complement</option>
			<option value="same">same</option>
		</param>
		<param label="--cmd-config: Use config file for command options" name="cmdconfig" optional="True" 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 0, this parameter is
    autoscaled to the number of query sequences / n_jobs.
pre_dispatch : Str, optional
    "all" or expression, as in "3*n_jobs". The number of batches (of tasks)
    to be pre-dispatched.
confidence : Float, optional
    Confidence threshold for limiting taxonomic depth. Provide -1 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({'reverse-complement', 'same'}), 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. Default is to
    autodetect based on the confidence estimates for the first 100 reads.

Returns
-------
classification : FeatureData[Taxonomy]
]]>
	</help>
</tool>