diff label_encoder.xml @ 0:3b6ee54eb7e2 draft

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

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/label_encoder.xml	Sat May 01 00:57:35 2021 +0000
@@ -0,0 +1,65 @@
+<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>