dodal relieff

workflows/cforange/db/package_data.json

@@ -1641,6 +1641,116 @@
       "name": "Feature Subset Selection"
     }
   },
+  {
+    "pk": 72,
+    "model": "workflows.abstractwidget",
+    "fields": {
+      "category": 14,
+      "treeview_image": "",
+      "name": "Filter ReliefF",
+      "is_streaming": false,
+      "uid": "7547eb6d-e83d-4033-b0cb-1e8319c2508f",
+      "interaction_view": "",
+      "image": "",
+      "package": "cforange",
+      "static_image": "",
+      "post_interact_action": "",
+      "user": null,
+      "visualization_view": "",
+      "action": "cforange_filter_relieff",
+      "wsdl_method": "",
+      "wsdl": "",
+      "interactive": false,
+      "has_progress_bar": false,
+      "order": 1,
+      "description": "Takes the data set data and a measure for score of attributes measure. Repeats the process of estimating attributes and removing the worst attribute if its measure is lower than margin. Stops when no attribute score is below this margin. The default for measure is relief(k=20, m=50), and margin defaults to 0.0"
+    }
+  },
+  {
+    "pk": 134,
+    "model": "workflows.abstractinput",
+    "fields": {
+      "widget": 72,
+      "name": "Dataset",
+      "short_name": "odt",
+      "uid": "2dc5a9e7-06c8-494f-9f4b-80b3c5eb31ff",
+      "default": "",
+      "required": false,
+      "multi": false,
+      "parameter_type": null,
+      "variable": "dataset",
+      "parameter": false,
+      "order": 1,
+      "description": ""
+    }
+  },
+  {
+    "pk": 135,
+    "model": "workflows.abstractinput",
+    "fields": {
+      "widget": 72,
+      "name": "k",
+      "short_name": "k",
+      "uid": "3ef628d4-03af-4d46-b90b-6c4475054d31",
+      "default": "20",
+      "required": false,
+      "multi": false,
+      "parameter_type": "text",
+      "variable": "k",
+      "parameter": true,
+      "order": 1,
+      "description": ""
+    }
+  },
+  {
+    "pk": 136,
+    "model": "workflows.abstractinput",
+    "fields": {
+      "widget": 72,
+      "name": "m",
+      "short_name": "m",
+      "uid": "9b3082b4-5903-41cb-a4c0-63d471f80f57",
+      "default": "50",
+      "required": false,
+      "multi": false,
+      "parameter_type": "text",
+      "variable": "m",
+      "parameter": true,
+      "order": 1,
+      "description": ""
+    }
+  },
+  {
+    "pk": 137,
+    "model": "workflows.abstractinput",
+    "fields": {
+      "widget": 72,
+      "name": "margin",
+      "short_name": "mrg",
+      "uid": "1cce36a4-167d-4a23-83ee-eae626a3b492",
+      "default": "0.0",
+      "required": false,
+      "multi": false,
+      "parameter_type": "text",
+      "variable": "margin",
+      "parameter": true,
+      "order": 1,
+      "description": ""
+    }
+  },
+  {
+    "pk": 74,
+    "model": "workflows.abstractoutput",
+    "fields": {
+      "widget": 72,
+      "name": "New Dataset",
+      "short_name": "odt",
+      "variable": "new_dataset",
+      "uid": "60efae56-9748-471d-b85d-9c9a3bc84f52",
+      "order": 1,
+      "description": ""
+    }
+  },
   {
     "pk": 69,
     "model": "workflows.abstractwidget",

workflows/cforange/library.py

@@ -8,3 +8,46 @@ def cforange_split_dataset(input_dict):
     output_dict['train_data']=train_data
     output_dict['test_data']=test_data
     return output_dict
+
+def cforange_score_estimation(input_dict):
+    import orange
+    import orngFSS
+    data = input_dict['dataset']
+    ma = orngFSS.attMeasure(data,orange.MeasureAttribute_relief(k=int(input_dict['k']), m=int(input_dict['m'])))
+    output_string = ""
+    output_dict = {}
+    output_dict['results'] = ma
+    return output_dict
+
+def cforange_best_natts(input_dict):
+    import orange
+    import orngFSS
+    data = input_dict['dataset']
+    scores = input_dict['scores']
+    n = int(input_dict['n'])
+    new_dataset = orngFSS.selectBestNAtts(data,scores,n)
+    output_dict={}
+    output_dict['new_dataset'] = new_dataset
+    return output_dict
+
+def cforange_atts_above_thresh(input_dict):
+    import orange
+    import orngFSS
+    data = input_dict['dataset']
+    scores = input_dict['scores']
+    thresh = float(input_dict['thresh'])
+    new_dataset = orngFSS.selectAttsAboveThresh(data,scores,thresh)
+    output_dict={}
+    output_dict['new_dataset'] = new_dataset
+    return output_dict
+
+def cforange_filter_relieff(input_dict):
+    import orange
+    import orngFSS
+    data = input_dict['dataset']
+    measure = orange.MeasureAttribute_relief(k=int(input_dict['k']), m=int(input_dict['m']))
+    margin = float(input_dict['margin'])
+    new_dataset = orngFSS.filterRelieff(data,measure,margin)
+    output_dict = {}
+    output_dict['new_dataset'] = new_dataset
+    return output_dict    
\ No newline at end of file

workflows/library.py

@@ -21,39 +21,6 @@ def add_multiple(input_dict):
         output_dict['sum'] = int(i)+output_dict['sum']
     return output_dict
 
-def cforange_score_estimation(input_dict):
-    import orange
-    import orngFSS
-    data = input_dict['dataset']
-    ma = orngFSS.attMeasure(data,orange.MeasureAttribute_relief(k=int(input_dict['k']), m=int(input_dict['m'])))
-    output_string = ""
-    output_dict = {}
-    output_dict['results'] = ma
-    return output_dict
-
-def cforange_best_natts(input_dict):
-    import orange
-    import orngFSS
-    data = input_dict['dataset']
-    scores = input_dict['scores']
-    n = int(input_dict['n'])
-    new_dataset = orngFSS.selectBestNAtts(data,scores,n)
-    output_dict={}
-    output_dict['new_dataset'] = new_dataset
-    return output_dict
-
-def cforange_atts_above_thresh(input_dict):
-    import orange
-    import orngFSS
-    data = input_dict['dataset']
-    scores = input_dict['scores']
-    thresh = float(input_dict['thresh'])
-    new_dataset = orngFSS.selectAttsAboveThresh(data,scores,thresh)
-    output_dict={}
-    output_dict['new_dataset'] = new_dataset
-    return output_dict
-
-
 def delay(input_dict,widget):
     widget.progress=0
     widget.save()