--- a/model_validation.xml	Thu Oct 11 03:38:11 2018 -0400
+++ b/model_validation.xml	Sun Dec 30 02:02:32 2018 -0500
@@ -23,24 +23,26 @@
 from sklearn import preprocessing, model_selection, svm, linear_model, ensemble, naive_bayes, tree, neighbors
 from sklearn.pipeline import Pipeline
 
-exec(open("$__tool_directory__/utils.py").read(), globals())
+exec(open('$__tool_directory__/utils.py').read(), globals())
+
+warnings.filterwarnings('ignore')
 
 safe_eval = SafeEval()
 
 input_json_path = sys.argv[1]
-with open(input_json_path, "r") as param_handler:
+with open(input_json_path, 'r') as param_handler:
     params = json.load(param_handler)
 
-input_type = params["input_options"]["selected_input"]
-if input_type=="tabular":
-    header = 'infer' if params["input_options"]["header1"] else None
-    column_option = params["input_options"]["column_selector_options_1"]["selected_column_selector_option"]
-    if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
-        c = params["input_options"]["column_selector_options_1"]["col1"]
+input_type = params['input_options']['selected_input']
+if input_type == 'tabular':
+    header = 'infer' if params['input_options']['header1'] else None
+    column_option = params['input_options']['column_selector_options_1']['selected_column_selector_option']
+    if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']:
+        c = params['input_options']['column_selector_options_1']['col1']
     else:
         c = None
     X = read_columns(
-            "$input_options.infile1",
+            '$input_options.infile1',
             c = c,
             c_option = column_option,
             sep='\t',
@@ -48,16 +50,16 @@
             parse_dates=True
     )
 else:
-    X = mmread("$input_options.infile1")
+    X = mmread('$input_options.infile1')
 
-header = 'infer' if params["input_options"]["header2"] else None
-column_option = params["input_options"]["column_selector_options_2"]["selected_column_selector_option2"]
-if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
-    c = params["input_options"]["column_selector_options_2"]["col2"]
+header = 'infer' if params['input_options']['header2'] else None
+column_option = params['input_options']['column_selector_options_2']['selected_column_selector_option2']
+if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']:
+    c = params['input_options']['column_selector_options_2']['col2']
 else:
     c = None
 y = read_columns(
-        "$input_options.infile2",
+        '$input_options.infile2',
         c = c,
         c_option = column_option,
         sep='\t',
@@ -66,8 +68,14 @@
 )
 y=y.ravel()
 
-options = params["model_validation_functions"]["options"]
-options['cv'] = get_cv( options['cv'] )
+options = params['model_validation_functions']['options']
+splitter, groups = get_cv( options.pop('cv_selector') )
+if groups is None:
+    options['cv'] = splitter
+elif groups == '':
+    options['cv'] = list( splitter.split(X, y, groups=None) )
+else:
+    options['cv'] = list( splitter.split(X, y, groups=groups) )
 options['n_jobs'] = N_JOBS
 if 'scoring' in options:
     options['scoring'] = get_scoring(options['scoring'])
@@ -78,8 +86,8 @@
 
 ## Set up pre_processor and add to pipeline steps.
 if params['pre_processing']['do_pre_processing'] == 'Yes':
-    preprocessor = params["pre_processing"]["pre_processors"]["selected_pre_processor"]
-    pre_processor_options = params["pre_processing"]["pre_processors"]["options"]
+    preprocessor = params['pre_processing']['pre_processors']['selected_pre_processor']
+    pre_processor_options = params['pre_processing']['pre_processors']['options']
     my_class = getattr(preprocessing, preprocessor)
     pipeline_steps.append( ('pre_processor', my_class(**pre_processor_options)) )
 
@@ -89,7 +97,7 @@
     pipeline_steps.append( ('feature_selector', feature_selector) )
 
 ## Set up estimator and add to pipeline.
-estimator_json = params["model_validation_functions"]['estimator_selector']
+estimator_json = params['model_validation_functions']['estimator_selector']
 estimator = get_estimator(estimator_json)
 
 pipeline_steps.append( ('estimator', estimator) )
@@ -98,11 +106,11 @@
 
 ## Set up validator, run pipeline through validator and return results.
 
-validator = params["model_validation_functions"]["selected_function"]
+validator = params['model_validation_functions']['selected_function']
 validator = getattr(model_selection, validator)
 
-selected_function = params["model_validation_functions"]["selected_function"]
-rval_type = params["model_validation_functions"].get("return_type", None)
+selected_function = params['model_validation_functions']['selected_function']
+rval_type = params['model_validation_functions'].get('return_type', None)
 
 if selected_function == 'cross_validate':
     res = validator(pipeline, X, y, **options)
@@ -114,7 +122,7 @@
 elif selected_function == 'permutation_test_score':
     score, permutation_scores, pvalue = validator(pipeline, X, y, **options)
     rval = eval(rval_type)
-    if rval_type in ["score", "pvalue"]:
+    if rval_type in ['score', 'pvalue']:
         rval = [rval]
 elif selected_function == 'validation_curve':
     options['param_name'] = 'estimator__' + options['param_name']
@@ -125,7 +133,7 @@
     rval = validator(pipeline, X, y, **options)
 
 rval = pandas.DataFrame(rval)
-rval.to_csv(path_or_buf="$outfile", sep='\t', header=False, index=False)
+rval.to_csv(path_or_buf='$outfile', sep='\t', header=False, index=False)
 
             ]]>
         </configfile>
@@ -151,9 +159,7 @@
             </param>
             <when value="No"/>
             <when value="Yes">
-                <expand macro="feature_selection_all">
-                    <expand macro="fs_selectfrommodel_no_prefitted"/>
-                </expand>
+                <expand macro="feature_selection_pipeline"/>
             </when>
         </conditional>
         <conditional name="model_validation_functions">