Mercurial > repos > bgruening > sklearn_label_encoder
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 80417bf0158a9b596e485dd66408f738f405145a
author | bgruening |
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date | Mon, 02 Oct 2023 09:40:02 +0000 |
parents | 108141350edb |
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<tool id="sklearn_label_encoder" name="Label encoder" version="@VERSION@" profile="@PROFILE@"> <description>Encode target labels with value between 0 and n_classes-1</description> <macros> <import>main_macros.xml</import> </macros> <expand macro="python_requirements"/> <expand macro="macro_stdio"/> <version_command>echo "@VERSION@"</version_command> <command detect_errors="exit_code"><![CDATA[ python '$__tool_directory__/label_encoder.py' --inputs '$inputs' --infile '$infile' --outfile '$outfile' ]]> </command> <configfiles> <inputs name="inputs" /> </configfiles> <inputs> <param name="infile" type="data" format="tabular" label="Input file"/> <param name="header0" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Does the dataset contain header?"/> </inputs> <outputs> <data name="outfile" format="tabular"/> </outputs> <tests> <test> <param name="infile" value="le_input_w_header.tabular" ftype="tabular"/> <param name="header0" value="true"/> <output name="outfile" file="le_output.tabular" ftype="tabular"/> </test> <test> <param name="infile" value="le_input_wo_header.tabular" ftype="tabular"/> <param name="header0" value="false"/> <output name="outfile" file="le_output.tabular" ftype="tabular"/> </test> </tests> <help><![CDATA[ **What it does** class sklearn.preprocessing.LabelEncoder Encode target labels with value between 0 and n_classes-1. This transformer should be used to encode target values, i.e. y, and not the input X. Attributes: classes : ndarray of shape (n_classes,) Hold the label for each class. LabelEncoder can be used to normalize labels. It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels. Methods fit_transform(y) Fit label encoder and return encoded labels. Parameters: y: array-like of shape (n_samples,) Returns: y: array-like of shape (n_samples,) ]]></help> <expand macro="sklearn_citation"/> </tool>