comparison qiime2/qiime_sample-classifier_split-table.xml @ 14:a0a8d77a991c draft

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13:887cd4ad8e16

<?xml version="1.0" ?>
<tool id="qiime_sample-classifier_split-table" name="qiime sample-classifier split-table" version="2019.7">
	<description> - Split a feature table into training and testing sets.</description>
	<requirements>
		<requirement type="package" version="2019.7">qiime2</requirement>
	</requirements>
	<command><![CDATA[
qiime sample-classifier split-table

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



#if $metadatafile:
 --m-metadata-file=$metadatafile
#end if




#if str($ptestsize):
 --p-test-size=$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>

		<param label="--m-metadata-file METADATA" name="metadatafile" type="data" format="tabular,qza,no_unzip.zip" />

	</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

    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>

14:a0a8d77a991c

<?xml version="1.0" ?>
<tool id="qiime_sample-classifier_split-table" name="qiime sample-classifier split-table"
      version="2020.8">
  <description>Split a feature table into training and testing sets.</description>
  <requirements>
    <requirement type="package" version="2020.8">qiime2</requirement>
  </requirements>
  <command><![CDATA[
qiime sample-classifier split-table

--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($mmetadatacolumn):
  #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('__ob__', '[')
  #set $mmetadatacolumn = $mmetadatacolumn_temp
#end if
#if '__cb__' in str($mmetadatacolumn):
  #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('__cb__', ']')
  #set $mmetadatacolumn = $mmetadatacolumn_temp
#end if
#if 'X' in str($mmetadatacolumn):
  #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('X', '\\')
  #set $mmetadatacolumn = $mmetadatacolumn_temp
#end if
#if '__sq__' in str($mmetadatacolumn):
  #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('__sq__', "'")
  #set $mmetadatacolumn = $mmetadatacolumn_temp
#end if
#if '__db__' in str($mmetadatacolumn):
  #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('__db__', '"')
  #set $mmetadatacolumn = $mmetadatacolumn_temp
#end if

--m-metadata-column=$mmetadatacolumn


--p-test-size=$ptestsize

#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

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

;
cp otesttable.qza $otesttable

  ]]></command>
  <inputs>
    <param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[Frequency¹ | RelativeFrequency² | PresenceAbsence³ | Balance⁴ | PercentileNormalized⁵ | Design⁶] 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="True" title="--m-metadata-file">
      <param format="tabular,qza,no_unzip.zip" label="--m-metadata-file: METADATA" name="additional_input" optional="True" type="data" />
    </repeat>
    <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 exclude_min="True" 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]" max="1.0" min="0.0" name="ptestsize" optional="True" type="float" value="0.2" />
    <param label="--p-random-state: INTEGER Seed used by random number generator.        [optional]" name="prandomstate" optional="False" type="text" />
    <param label="--p-no-stratify: Do not evenly stratify training and test data among metadata categories. If True, all values in column must match at least two samples.                      [default: True]" 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>
    <param label="--examples: Show usage examples and exit." name="examples" optional="False" type="data" />
    
  </inputs>

  <outputs>
    <data format="qza" label="${tool.name} on ${on_string}: trainingtable.qza" name="otrainingtable" />
    <data format="qza" label="${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[Frequency¹ | RelativeFrequency² | PresenceAbsence³ | Balance⁴ | PercentileNormalized⁵ | Design⁶]
    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

    metadata to be filtered, so that only samples found in both files are
    retained.

Returns
-------
training_table : FeatureTable[Frequency¹ | RelativeFrequency² | PresenceAbsence³ | Balance⁴ | PercentileNormalized⁵ | Design⁶]
    Feature table containing training samples
test_table : FeatureTable[Frequency¹ | RelativeFrequency² | PresenceAbsence³ | Balance⁴ | PercentileNormalized⁵ | Design⁶]
    Feature table containing test samples
  ]]></help>
  <macros>
    <import>qiime_citation.xml</import>
  </macros>
  <expand macro="qiime_citation"/>
</tool>