Mercurial > repos > bgruening > sklearn_label_encoder
comparison label_encoder.xml @ 0:3b6ee54eb7e2 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
author | bgruening |
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date | Sat, 01 May 2021 00:57:35 +0000 |
parents | |
children | 108141350edb |
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-1:000000000000 | 0:3b6ee54eb7e2 |
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1 <tool id="sklearn_label_encoder" name="Label encoder" version="@VERSION@"> | |
2 <description>Encode target labels with value between 0 and n_classes-1</description> | |
3 <macros> | |
4 <import>main_macros.xml</import> | |
5 </macros> | |
6 <expand macro="python_requirements"/> | |
7 <expand macro="macro_stdio"/> | |
8 <version_command>echo "@VERSION@"</version_command> | |
9 <command detect_errors="exit_code"><![CDATA[ | |
10 python '$__tool_directory__/label_encoder.py' | |
11 --inputs '$inputs' | |
12 --infile '$infile' | |
13 --outfile '$outfile' | |
14 ]]> | |
15 </command> | |
16 <configfiles> | |
17 <inputs name="inputs" /> | |
18 </configfiles> | |
19 <inputs> | |
20 <param name="infile" type="data" format="tabular" label="Input file"/> | |
21 <param name="header0" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Does the dataset contain header?"/> | |
22 </inputs> | |
23 <outputs> | |
24 <data name="outfile" format="tabular"/> | |
25 </outputs> | |
26 <tests> | |
27 <test> | |
28 <param name="infile" value="le_input_w_header.tabular" ftype="tabular"/> | |
29 <param name="header0" value="true"/> | |
30 <output name="outfile" file="le_output.tabular" ftype="tabular"/> | |
31 </test> | |
32 <test> | |
33 <param name="infile" value="le_input_wo_header.tabular" ftype="tabular"/> | |
34 <param name="header0" value="false"/> | |
35 <output name="outfile" file="le_output.tabular" ftype="tabular"/> | |
36 </test> | |
37 </tests> | |
38 <help><![CDATA[ | |
39 **What it does** | |
40 | |
41 class sklearn.preprocessing.LabelEncoder | |
42 | |
43 Encode target labels with value between 0 and n_classes-1. | |
44 | |
45 This transformer should be used to encode target values, i.e. y, and not the input X. | |
46 | |
47 Attributes: classes : ndarray of shape (n_classes,) | |
48 Hold the label for each class. | |
49 LabelEncoder can be used to normalize labels. | |
50 | |
51 It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels. | |
52 | |
53 Methods | |
54 | |
55 fit_transform(y) | |
56 | |
57 Fit label encoder and return encoded labels. | |
58 | |
59 Parameters: y: array-like of shape (n_samples,) | |
60 | |
61 Returns: y: array-like of shape (n_samples,) | |
62 | |
63 ]]></help> | |
64 <expand macro="sklearn_citation"/> | |
65 </tool> |