diff nn_classifier.xml @ 7:5072ac474cd5 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2a058459e6daf0486871f93845f00fdb4a4eaca1

--- a/nn_classifier.xml	Thu Aug 23 16:15:30 2018 -0400
+++ b/nn_classifier.xml	Sat Sep 29 07:29:02 2018 -0400
@@ -20,8 +20,9 @@
 import sklearn.neighbors
 import pandas
 
-execfile("$__tool_directory__/sk_whitelist.py")
-execfile("$__tool_directory__/utils.py", globals())
+with open("$__tool_directory__/sk_whitelist.json", "r") as f:
+    sk_whitelist = json.load(f)
+exec(open("$__tool_directory__/utils.py").read(), globals())
 
 input_json_path = sys.argv[1]
 with open(input_json_path, "r") as param_handler:
@@ -30,7 +31,7 @@
 #if $selected_tasks.selected_task == "load":
 
 with open("$infile_model", 'rb') as model_handler:
-    classifier_object = SafePickler.load(model_handler)
+    classifier_object = load_model(model_handler)
 
 header = 'infer' if params["selected_tasks"]["header"] else None
 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=header, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)
@@ -118,7 +119,7 @@
             <param name="selected_algorithm" value="nneighbors"/>
             <param name="sampling_method" value="KNeighborsClassifier" />
             <param name="algorithm" value="brute" />
-            <output name="outfile_fit" file="nn_model01.txt"/>
+            <output name="outfile_fit" file="nn_model01"/>
         </test>
         <test>
             <param name="infile1" value="train_set.tabular" ftype="tabular"/>
@@ -128,10 +129,9 @@
             <param name="col1" value="1,2,3,4"/>
             <param name="col2" value="5"/>
             <param name="selected_task" value="train"/>
-            <param name="selected_algorithm" value=""/>
             <param name="selected_algorithm" value="nneighbors"/>
             <param name="sampling_method" value="RadiusNeighborsClassifier" />
-            <output name="outfile_fit" file="nn_model02.txt"/>
+            <output name="outfile_fit" file="nn_model02"/>
         </test>
         <test>
             <param name="infile1" value="train_set.tabular" ftype="tabular"/>
@@ -142,24 +142,24 @@
             <param name="col2" value="5"/>
             <param name="selected_task" value="train"/>
             <param name="selected_algorithm" value="ncentroid"/>
-            <output name="outfile_fit" file="nn_model03.txt"/>
+            <output name="outfile_fit" file="nn_model03"/>
         </test>
         <test>
-            <param name="infile_model" value="nn_model01.txt" ftype="txt"/>
+            <param name="infile_model" value="nn_model01" ftype="zip"/>
             <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
             <param name="header" value="True"/>
             <param name="selected_task" value="load"/>
             <output name="outfile_predict" file="nn_prediction_result01.tabular"/>
         </test>
         <test>
-            <param name="infile_model" value="nn_model02.txt" ftype="txt"/>
+            <param name="infile_model" value="nn_model02" ftype="zip"/>
             <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
             <param name="header" value="True"/>
             <param name="selected_task" value="load"/>
             <output name="outfile_predict" file="nn_prediction_result02.tabular"/>
         </test>
         <test>
-            <param name="infile_model" value="nn_model03.txt" ftype="txt"/>
+            <param name="infile_model" value="nn_model03" ftype="zip"/>
             <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
             <param name="header" value="True"/>
             <param name="selected_task" value="load"/>