fix grid search and small fixes

shogun-toolbox · Jul 18, 2014 · 4bba4e8 · 4bba4e8
1 parent d914473
commit 4bba4e8
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Showing 3 changed files with 11 additions and 8 deletions.
diff --git a/demos/classifier/gp.py b/demos/classifier/gp.py
@@ -44,7 +44,7 @@ def handler(request):
             'argument_type': 'decimal',
             'argument_name': 'sigma',
             'argument_label': 'Sigma',
-            'argument_default' : '0.1',
+            'argument_default' : '1',
             'argument_explain': 'The sigma to use in the GaussianKernel'},
         {
             'argument_type': 'integer',
@@ -56,7 +56,7 @@ def handler(request):
             'argument_type': 'decimal',
             'argument_name': 'scale',
             'argument_label': 'Kernel scaling',
-            'argument_default' : '0.1',
+            'argument_default' : '1',
             'argument_explain': 'The scale for kernel'},
 
         {

diff --git a/demos/classifier/svm.py b/demos/classifier/svm.py
@@ -6,16 +6,16 @@ def classify_svm(classifier, features, labels, kernel, domain, learn, value, C=1
         root = sg.ModelSelectionParameters()
         c1 = sg.ModelSelectionParameters("C1")
         root.append_child(c1)
-        c1.build_values(-1.0, 5.0, sg.R_EXP)
+        c1.build_values(1.0, 10.0, sg.R_LINEAR, 2)
 
         c2 = sg.ModelSelectionParameters("C2")
         root.append_child(c2)
-        c2.build_values(-1.0, 5.0, sg.R_EXP)
+        c2.build_values(1.0, 10.0, sg.R_LINEAR, 2)
 
         if kernel.get_name() == 'GaussianKernel':
             param_kernel = sg.ModelSelectionParameters("kernel", kernel)
             width = sg.ModelSelectionParameters("width")
-            width.build_values(-1.0, 1.0, sg.R_EXP, 0.05, 2.0)
+            width.build_values(0.0, 10.0, sg.R_LINEAR, 0.5)
             param_kernel.append_child(width)
             root.append_child(param_kernel)
 
@@ -29,6 +29,10 @@ def classify_svm(classifier, features, labels, kernel, domain, learn, value, C=1
             param_kernel.append_child(degree)
             root.append_child(param_kernel)
 
+        elif kernel.get_name() == 'LinearKernel':
+            param_kernel = sg.ModelSelectionParameters("kernel", kernel)
+            root.append_child(param_kernel)
+
         pos=0
         neg=0
         for i in range(0, labels.get_num_labels()):
@@ -46,8 +50,7 @@ class LabelsError(Exception):
             splitting_strategy = sg.StratifiedCrossValidationSplitting(labels, 3)
         evaluation_criterium = sg.ContingencyTableEvaluation(sg.ACCURACY)
         cross = sg.CrossValidation(svm, features, labels, splitting_strategy, evaluation_criterium)
-        cross.set_num_runs(10)
-        cross.set_conf_int_alpha(0.01)
+        cross.set_num_runs(2)
         grid_search = sg.GridSearchModelSelection(cross, root)
         best_combination = grid_search.select_model()
         best_combination.apply_to_machine(svm)

diff --git a/demos/clustering/kmeans.py b/demos/clustering/kmeans.py
@@ -60,7 +60,7 @@
                         'panel_name': 'toy_data',
                         'panel_label': 'Toy Data',
                         'panel_property': toy_data_arguments}],
-                   'data_sets' : ['australian']}
+                   'data_sets' : ['diabetes']}
 
 def entrance(request):
     return render_to_response("clustering/kmeans.html", properties, context_instance=RequestContext(request))