view label_encoder.xml @ 0:03155260beb3 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"

+
−<tool id="sklearn_label_encoder" name="Label encoder" version="@VERSION@">
+
−    <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>