diff simple_model_fit.xml @ 0:734c66aa945a 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/simple_model_fit.xml	Fri Nov 01 17:18:28 2019 -0400
@@ -0,0 +1,94 @@
+<tool id="sklearn_model_fit" name="Fit a Pipeline, Ensemble" version="@VERSION@">
+    <description>or other models using a labeled dataset</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 '$__tool_directory__/simple_model_fit.py'
+            --inputs '$inputs'
+            --infile_estimator '$infile_estimator'
+            --infile1 '$input_options.infile1'
+            --infile2 '$input_options.infile2'
+            --out_object '$out_object'
+            #if $is_deep_learning == 'booltrue'
+            --out_weights '$out_weights'
+            #end if
+        ]]>
+    </command>
+    <configfiles>
+        <inputs name="inputs" />
+    </configfiles>
+    <inputs>
+        <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator"/>
+        <param name="is_deep_learning" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Is the estimator a deep learning model?"/>
+        <conditional name="input_options">
+            <expand macro="data_input_options"/>
+            <when value="tabular">
+                <expand macro="samples_tabular" label1="Choose the training dataset containing features" multiple1="true" multiple2="false"/>
+            </when>
+            <when value="sparse">
+                <expand macro="sparse_target"/>
+            </when>
+        </conditional>
+    </inputs>
+    <outputs>
+        <data format="zip" name="out_object" label="Fitted model (skeleton) on $(on_string)"/>
+        <data format="h5" name="out_weights" label="Weights trained on ${on_string}">
+            <filter>is_deep_learning</filter>
+        </data>
+    </outputs>
+    <tests>
+        <test>
+            <param name="infile_estimator" value="pipeline05" ftype="zip"/>
+            <param name="infile1" value="regression_X.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.tabular" ftype="tabular"/>
+            <param name="header2" value="true"/>
+            <param name="col2" value="1"/>
+            <output name="out_object" file="model_fit01" compare="sim_size" delta="50"/>
+        </test>
+        <test>
+            <param name="infile_estimator" value="keras_model04"/>
+            <param name="is_deep_learning" value="true"/>
+            <param name="infile1" value="regression_X.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.tabular" ftype="tabular"/>
+            <param name="header2" value="true"/>
+            <param name="col2" value="1"/>
+            <output name="out_object" file="model_fit02" compare="sim_size" delta="10"/>
+            <output name="out_weights" file="model_fit02.h5" compare="sim_size" delta="10"/>
+        </test>
+    </tests>
+    <help>
+        <![CDATA[
+**What it does**
+
+This tools takes a pre-built model to simply fit on the provided labeled dataset by calling scikit-learn API `fit`. The model could be built in `build_pipeline` tool, `stacking_ensemble` tool or other places. Limited deep learning model is also supported.
+
+The supported labeled dataset are the following:
+
+- tabular
+
+- sparse
+
+
+**Output**
+
+- fitted model, a pickled python object in zip format.
+- optional hdf5 file containing weights for deep learning models.
+
+        ]]>
+    </help>
+    <expand macro="sklearn_citation">
+        <expand macro="keras_citation"/>
+        <expand macro="selene_citation"/>
+    </expand>
+</tool>