<?xml version="1.0" ?>
<tool id="qiime_sample-classifier_confusion-matrix" name="qiime sample-classifier confusion-matrix" version="2019.4">
	<description> - Make a confusion matrix from sample classifier predictions.</description>
	<requirements>
		<requirement type="package" version="2019.4">qiime2</requirement>
	</requirements>
	<command><![CDATA[
qiime sample-classifier confusion-matrix

--i-predictions=$ipredictions
--m-truth-column="$mtruthcolumn"
--m-truth-file=mtruthfile

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

#if str($ppalette) != 'None':
 --p-palette=$ppalette
#end if

--o-visualization=ovisualization
;
qiime tools export --input-path ovisualization.qzv --output-path out   && mkdir -p '$ovisualization.files_path'
&& cp -r out/* '$ovisualization.files_path'
&& mv '$ovisualization.files_path/index.html' '$ovisualization';
cp mtruthfile.qza $mtruthfile
	]]></command>
	<inputs>
		<param format="qza,no_unzip.zip" label="--i-predictions: ARTIFACT SampleData[ClassifierPredictions] Predicted values to plot on x axis. Should be predictions of categorical data produced by a sample classifier.                                  [required]" name="ipredictions" optional="False" type="data"/>
		<param label="--m-truth-column: COLUMN  MetadataColumn[Categorical] Metadata column (true values) to plot on y axis. [required]" name="mtruthcolumn" optional="False" type="text"/>
		<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="--p-palette: " name="ppalette" optional="True" type="select">
			<option selected="True" value="None">Selection is Optional</option>
			<option value="YellowOrangeBrown">YellowOrangeBrown</option>
			<option value="YellowOrangeRed">YellowOrangeRed</option>
			<option value="OrangeRed">OrangeRed</option>
			<option value="PurpleRed">PurpleRed</option>
			<option value="RedPurple">RedPurple</option>
			<option value="BluePurple">BluePurple</option>
			<option value="GreenBlue">GreenBlue</option>
			<option value="PurpleBlue">PurpleBlue</option>
			<option value="YellowGreen">YellowGreen</option>
			<option value="summer">summer</option>
			<option value="copper">copper</option>
			<option value="viridis">viridis</option>
			<option value="plasma">plasma</option>
			<option value="inferno">inferno</option>
			<option value="magma">magma</option>
			<option value="sirocco">sirocco</option>
			<option value="drifting">drifting</option>
			<option value="melancholy">melancholy</option>
			<option value="enigma">enigma</option>
			<option value="eros">eros</option>
			<option value="spectre">spectre</option>
			<option value="ambition">ambition</option>
			<option value="mysteriousstains">mysteriousstains</option>
			<option value="daydream">daydream</option>
			<option value="solano">solano</option>
			<option value="navarro">navarro</option>
			<option value="dandelions">dandelions</option>
			<option value="deepblue">deepblue</option>
			<option value="verve">verve</option>
			<option value="greyscale">greyscale</option>
		</param>
	</inputs>
	<outputs>
		<data format="html" label="${tool.name} on ${on_string}: visualization.qzv" name="ovisualization"/>
		<data format="qza" label="${tool.name} on ${on_string}: truthfile.qza" name="mtruthfile"/>
	</outputs>
	<help><![CDATA[
Make a confusion matrix from sample classifier predictions.
###########################################################

Make a confusion matrix and calculate accuracy of predicted vs. true values
for a set of samples classified using a sample classifier.

Parameters
----------
predictions : SampleData[ClassifierPredictions]
    Predicted values to plot on x axis. Should be predictions of
    categorical data produced by a sample classifier.
truth : MetadataColumn[Categorical]
    Metadata column (true values) to plot on y axis.
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.
palette : Str % Choices('YellowOrangeBrown', 'YellowOrangeRed', 'OrangeRed', 'PurpleRed', 'RedPurple', 'BluePurple', 'GreenBlue', 'PurpleBlue', 'YellowGreen', 'summer', 'copper', 'viridis', 'plasma', 'inferno', 'magma', 'sirocco', 'drifting', 'melancholy', 'enigma', 'eros', 'spectre', 'ambition', 'mysteriousstains', 'daydream', 'solano', 'navarro', 'dandelions', 'deepblue', 'verve', 'greyscale'), optional
    The color palette to use for plotting.

Returns
-------
visualization : Visualization
	]]></help>
<macros>
    <import>qiime_citation.xml</import>
</macros>
<expand macro="qiime_citation"/>
</tool>