diff fitted_model_eval.xml @ 0:4fc9e02801f9 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/fitted_model_eval.xml	Fri Nov 01 16:39:35 2019 -0400
@@ -0,0 +1,87 @@
+<tool id="sklearn_fitted_model_eval" name="Evaluate a Fitted Model" version="@VERSION@">
+    <description>using a new batch of labeled data</description>
+    <macros>
+        <import>main_macros.xml</import>
+        <import>keras_macros.xml</import>
+    </macros>
+    <expand macro="python_requirements"/>
+    <expand macro="macro_stdio"/>
+    <version_command>echo "@VERSION@"</version_command>
+    <command>
+        <![CDATA[
+        export HDF5_USE_FILE_LOCKING='FALSE';
+        python '$__tool_directory__/fitted_model_eval.py'
+            --inputs '$inputs'
+            --infile_estimator '$infile_estimator'
+            --outfile_eval '$outfile_eval'
+            --infile_weights '$infile_weights'
+            --infile1 '$input_options.infile1'
+            --infile2 '$input_options.infile2'
+        ]]>
+    </command>
+    <configfiles>
+        <inputs name="inputs" />
+    </configfiles>
+    <inputs>
+        <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/>
+        <param name="infile_weights" type="data" format="h5" optional="true" label="Choose the dataset containing weights for the estimator above" help="Optional. For deep learning only."/>
+        <expand macro="scoring_selection"/>
+        <conditional name="input_options">
+            <expand macro="data_input_options"/>
+            <when value="tabular">
+                <expand macro="samples_tabular" label1="Dataset containing features:" multiple1="true" multiple2="false"/>
+            </when>
+            <when value="sparse">
+                <expand macro="sparse_target"/>
+            </when>
+    </conditional>
+    </inputs>
+    <outputs>
+        <data format="tabular" name="outfile_eval"/>
+    </outputs>
+    <tests>
+        <test>
+            <param name="infile_estimator" value="searchCV01" ftype="zip"/>
+            <conditional name="scoring">
+                <param name="primary_scoring" value="r2"/>
+            </conditional>
+            <param name="infile1" value="train_test_split_test01.tabular" ftype="tabular"/>
+            <param name="header1" value="true" />
+            <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
+            <param name="infile2" value="regression_y_split_test01.tabular" ftype="tabular"/>
+            <param name="header2" value="true"/>
+            <param name="col2" value="1"/>
+            <output name="outfile_eval" file="fitted_model_eval01.tabular"/>
+        </test>
+    </tests>
+    <help>
+        <![CDATA[
+**What it does**
+
+Given a fitted estimator and a labeled dataset, this tool outputs the performances of the fitted estimator on the labeled dataset with selected scorers.
+
+For the estimator, this tool supports fitted sklearn estimators (pickled) and trained deep learning models (model skeleton + weights). For input datasets, it supports the following:
+
+- tabular
+
+- sparse
+
+
+**Output**
+
+A tabular file containing performance scores,
+e.g.:
+
+======== ======== =========
+accuracy f1_macro precision
+======== ======== =========
+ 0.8613   0.6759   0.7928
+======== ======== =========
+
+        ]]>
+    </help>
+    <expand macro="sklearn_citation">
+        <expand macro="keras_citation"/>
+        <expand macro="selene_citation"/>
+    </expand>
+</tool>