Mercurial > repos > bgruening > sklearn_to_categorical
diff to_categorical.xml @ 0:59e8b4328c82 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
author | bgruening |
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date | Tue, 13 Apr 2021 22:40:10 +0000 |
parents | |
children | ec69cbe34b73 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/to_categorical.xml Tue Apr 13 22:40:10 2021 +0000 @@ -0,0 +1,93 @@ +<tool id="sklearn_to_categorical" name="To categorical" version="@VERSION@" profile="20.05"> + <description>Converts a class vector (integers) to binary class matrix</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 '$__tool_directory__/to_categorical.py' + --inputs '$inputs' + --infile '$infile' + #if $num_classes + --num_classes '$num_classes' + #end if + --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?" /> + <param name="num_classes" type="integer" optional="true" label="Total number of classes" /> + </inputs> + <outputs> + <data name="outfile" format="tabular" /> + </outputs> + <tests> + <test> + <param name="infile" value="ohe_in_w_header.tabular" ftype="tabular" /> + <param name="header0" value="true" /> + <output name="outfile" file="ohe_out_4.tabular" ftype="tabular" /> + </test> + <test> + <param name="infile" value="ohe_in_w_header.tabular" ftype="tabular" /> + <param name="header0" value="true" /> + <param name="num_classes" value="4" /> + <output name="outfile" file="ohe_out_4.tabular" ftype="tabular" /> + </test> + <test> + <param name="infile" value="ohe_in_w_header.tabular" ftype="tabular" /> + <param name="header0" value="true" /> + <param name="num_classes" value="5" /> + <output name="outfile" file="ohe_out_5.tabular" ftype="tabular" /> + </test> + <test> + <param name="infile" value="ohe_in_wo_header.tabular" ftype="tabular" /> + <param name="header0" value="false" /> + <output name="outfile" file="ohe_out_4.tabular" ftype="tabular" /> + </test> + <test> + <param name="infile" value="ohe_in_wo_header.tabular" ftype="tabular" /> + <param name="header0" value="false" /> + <param name="num_classes" value="4" /> + <output name="outfile" file="ohe_out_4.tabular" ftype="tabular" /> + </test> + <test> + <param name="infile" value="ohe_in_wo_header.tabular" ftype="tabular" /> + <param name="header0" value="false" /> + <param name="num_classes" value="5" /> + <output name="outfile" file="ohe_out_5.tabular" ftype="tabular" /> + </test> + </tests> + <help><![CDATA[ +**What it does** + +Converts a class vector (integers) to binary class matrix. + +tf.keras.utils.to_categorical( + y, num_classes=None, dtype='float32' +) + +E.g. for use with categorical_crossentropy. + +Arguments + +y: a vector of numbers to be converted into a matrix of one-hot encoded values. +num_classes: total number of classes. If None, this would be inferred as the (largest number in y) + 1. +dtype: The data type expected by the input. Default: 'float32'. + +Returns + +A binary matrix representation of the input. The classes axis is placed last. + +Raises + +Value Error: If input contains string value + + ]]> </help> + <expand macro="sklearn_citation" /> +</tool>