annotate qiime2-2020.8/qiime_sample-classifier_confusion-matrix.xml @ 25:499916460746 draft

Deleted selected files

<?xml version="1.0" ?>
<tool id="qiime_sample-classifier_confusion-matrix" name="qiime sample-classifier confusion-matrix"
      version="2020.8">
  <description>Make a confusion matrix from sample classifier predictions.</description>
  <requirements>
    <requirement type="package" version="2020.8">qiime2</requirement>
  </requirements>
  <command><![CDATA[
qiime sample-classifier confusion-matrix

--i-predictions=$ipredictions

#if str($iprobabilities) != 'None':
--i-probabilities=$iprobabilities
#end if

#if str($mtruthfile) != 'None':
--m-truth-file=$mtruthfile
#end if

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

--m-truth-column=$mtruthcolumn


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

#if str($pvmin) != 'None':
--p-vmin=$pvmin
#end if

#if str($pvmax) != 'None':
--p-vmax=$pvmax
#end if

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

--o-visualization=ovisualization

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

;
cp oprobabilities.qza $oprobabilities

;
qiime tools export  ovisualization.qzv --output-path out
&& mkdir -p '$ovisualization.files_path'
&& cp -r out/* '$ovisualization.files_path'
&& mv '$ovisualization.files_path/index.html' '$ovisualization'

  ]]></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 format="qza,no_unzip.zip" label="--i-probabilities: ARTIFACT SampleData[Probabilities] Predicted class probabilities for each input sample. [optional]" name="iprobabilities" optional="False" type="data" />
    <param label="--m-truth-file: METADATA" name="mtruthfile" 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-vmin: " name="pvmin" optional="True" type="select">
      <option selected="True" value="None">Selection is Optional</option>
      <option value="auto">auto</option>
    </param>
    <param label="--p-vmax: " name="pvmax" optional="True" type="select">
      <option selected="True" value="None">Selection is Optional</option>
      <option value="auto">auto</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="cividis">cividis</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>
    <param label="--examples: Show usage examples and exit." name="examples" optional="False" type="data" />
    
  </inputs>

  <outputs>
    <data format="html" label="${tool.name} on ${on_string}: visualization.html" name="ovisualization" />
    
  </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. If per-sample
class probabilities are provided, will also generate Receiver Operating
Characteristic curves and calculate area under the curve for each class.

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.
probabilities : SampleData[Probabilities], optional
    Predicted class probabilities for each input sample.
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.
vmin : Float | Str % Choices('auto'), optional
    The minimum value to use for anchoring the colormap. If "auto", vmin is
    set to the minimum value in the data.
vmax : Float | Str % Choices('auto'), optional
    The maximum value to use for anchoring the colormap. If "auto", vmax is
    set to the maximum value in the data.
palette : Str % Choices('YellowOrangeBrown', 'YellowOrangeRed', 'OrangeRed', 'PurpleRed', 'RedPurple', 'BluePurple', 'GreenBlue', 'PurpleBlue', 'YellowGreen', 'summer', 'copper', 'viridis', 'cividis', '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>