annotate qiime2/qiime_sample-classifier_split-table.xml @ 4:914fa4daf16a draft

Fixes

<?xml version="1.0" ?>
<tool id="qiime_sample-classifier_split-table" name="qiime sample-classifier split-table" version="2019.4">
	<description> - Split a feature table into training and testing sets.</description>
	<requirements>
		<requirement type="package" version="2019.4">qiime2</requirement>
	</requirements>
	<command><![CDATA[
qiime sample-classifier split-table

--i-table=$itable
--m-metadata-column="$mmetadatacolumn"



#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 $ptestsize:
 --p-test-size=$ptestsize
#end if

#if str($prandomstate):
 --p-random-state="$prandomstate"
#end if

#if $pnostratify:
 --p-no-stratify
#end if

#if str($pmissingsamples) != 'None':
 --p-missing-samples=$pmissingsamples
#end if

--o-training-table=otrainingtable
--o-test-table=otesttable
;
cp otrainingtable.qza $otrainingtable;
cp otesttable.qza $otesttable;
	]]></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 | Categorical] Numeric metadata column to use as prediction target. [required]" name="mmetadatacolumn" optional="False" type="text"/>
		<param label="--p-test-size: PROPORTION Range(0.0, 1.0, inclusive_start=False) Fraction of input samples to exclude from training set and use for classifier testing.          [default: 0.2]" name="ptestsize" optional="True" type="float" value="0.2" min="0" max="1" exclusive_end="True"/>
		<param label="--p-random-state: INTEGER Seed used by random number generator.        [optional]" name="prandomstate" optional="True" type="integer"/>
		<param label="--p-no-stratify: Evenly stratify training and test data among metadata categories. If True, all values in column must match at least two samples.                      [default: False]" name="pnostratify" 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 ${on_string}: trainingtable.qza" name="otrainingtable"/>
		<data format="qza" label="${tool.name} on ${on_string}: testtable.qza" name="otesttable"/>
	</outputs>
	<help><![CDATA[
Split a feature table into training and testing sets.
#####################################################

Split a feature table into training and testing sets. By default stratifies
training and test sets on a metadata column, such that values in that
column are evenly represented across training and test sets.

Parameters
----------
table : FeatureTable[Frequency]
    Feature table containing all features that should be used for target
    prediction.
metadata : MetadataColumn[Numeric | Categorical]
    Numeric metadata column to use as prediction target.
test_size : Float % Range(0.0, 1.0, inclusive_start=False), optional
    Fraction of input samples to exclude from training set and use for
    classifier testing.
random_state : Int, optional
    Seed used by random number generator.
stratify : Bool, optional
    Evenly stratify training and test data among metadata categories. If
    True, all values in column must match at least two samples.
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
-------
training_table : FeatureTable[Frequency]
    Feature table containing training samples
test_table : FeatureTable[Frequency]
    Feature table containing test samples
	]]></help>
<macros>
    <import>qiime_citation.xml</import>
</macros>
<expand macro="qiime_citation"/>
</tool>