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annotate label_encoder.xml @ 0:03155260beb3 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
author bgruening
date Fri, 30 Apr 2021 23:36:38 +0000
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children b008b609205e
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1 <tool id="sklearn_label_encoder" name="Label encoder" version="@VERSION@">
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2 <description>Encode target labels with value between 0 and n_classes-1</description>
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3 <macros>
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4 <import>main_macros.xml</import>
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5 </macros>
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6 <expand macro="python_requirements"/>
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7 <expand macro="macro_stdio"/>
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8 <version_command>echo "@VERSION@"</version_command>
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9 <command detect_errors="exit_code"><![CDATA[
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10 python '$__tool_directory__/label_encoder.py'
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11 --inputs '$inputs'
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12 --infile '$infile'
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13 --outfile '$outfile'
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14 ]]>
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15 </command>
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16 <configfiles>
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17 <inputs name="inputs" />
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18 </configfiles>
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19 <inputs>
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20 <param name="infile" type="data" format="tabular" label="Input file"/>
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21 <param name="header0" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Does the dataset contain header?"/>
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22 </inputs>
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23 <outputs>
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24 <data name="outfile" format="tabular"/>
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25 </outputs>
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26 <tests>
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27 <test>
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28 <param name="infile" value="le_input_w_header.tabular" ftype="tabular"/>
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29 <param name="header0" value="true"/>
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30 <output name="outfile" file="le_output.tabular" ftype="tabular"/>
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31 </test>
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32 <test>
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33 <param name="infile" value="le_input_wo_header.tabular" ftype="tabular"/>
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34 <param name="header0" value="false"/>
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35 <output name="outfile" file="le_output.tabular" ftype="tabular"/>
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36 </test>
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37 </tests>
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38 <help><![CDATA[
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39 **What it does**
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40
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41 class sklearn.preprocessing.LabelEncoder
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42
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43 Encode target labels with value between 0 and n_classes-1.
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44
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45 This transformer should be used to encode target values, i.e. y, and not the input X.
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46
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47 Attributes: classes : ndarray of shape (n_classes,)
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48 Hold the label for each class.
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49 LabelEncoder can be used to normalize labels.
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50
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51 It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels.
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52
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53 Methods
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55 fit_transform(y)
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56
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57 Fit label encoder and return encoded labels.
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58
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59 Parameters: y: array-like of shape (n_samples,)
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60
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61 Returns: y: array-like of shape (n_samples,)
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62
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63 ]]></help>
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64 <expand macro="sklearn_citation"/>
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65 </tool>