comment print functions in examples

shogun-toolbox · Mar 19, 2012 · aba8078 · aba8078
1 parent f081ae0
commit aba8078
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Showing 4 changed files with 24 additions and 24 deletions.
diff --git a/examples/undocumented/python_modular/evaluation_cross_validation_classification.py b/examples/undocumented/python_modular/evaluation_cross_validation_classification.py
@@ -56,9 +56,9 @@ def evaluation_cross_validation_classification(traindat=traindat, label_traindat
 
     # perform cross-validation and print results
     result=cross_validation.evaluate()
-    print "mean:", result.mean
-    if result.has_conf_int:
-        print "[", result.conf_int_low, ",", result.conf_int_up, "] with alpha=", result.conf_int_alpha
+    #print "mean:", result.mean
+    #if result.has_conf_int:
+    #    print "[", result.conf_int_low, ",", result.conf_int_up, "] with alpha=", result.conf_int_alpha
 
 if __name__=='__main__':
 	print 'Evaluation CrossValidationClassification'

diff --git a/examples/undocumented/python_modular/evaluation_cross_validation_regression.py b/examples/undocumented/python_modular/evaluation_cross_validation_regression.py
@@ -58,9 +58,9 @@ def evaluation_cross_validation_regression(fm_train=traindat,fm_test=testdat,lab
 
     # perform cross-validation and print results
     result=cross_validation.evaluate()
-    print "mean:", result.mean
-    if result.has_conf_int:
-        print "[", result.conf_int_low, ",", result.conf_int_up, "] with alpha=", result.conf_int_alpha
+    #print "mean:", result.mean
+    #if result.has_conf_int:
+    #    print "[", result.conf_int_low, ",", result.conf_int_up, "] with alpha=", result.conf_int_alpha
 
 if __name__=='__main__':
 	print 'Evaluation CrossValidationClassification'

diff --git a/examples/undocumented/python_modular/modelselection_grid_search_libsvr.py b/examples/undocumented/python_modular/modelselection_grid_search_libsvr.py
@@ -21,7 +21,7 @@
 parameter_list = [[traindat,testdat,label_traindat,2.1,1,1e-5,1e-2], \
                  [traindat,testdat,label_traindat,2.1,1,1e-5,1e-2]]
 
-def evaluation_cross_validation_classification(fm_train=traindat,fm_test=testdat,label_train=label_traindat,\
+def evaluation_cross_validation_regression(fm_train=traindat,fm_test=testdat,label_train=label_traindat,\
 				       width=2.1,C=1,epsilon=1e-5,tube_epsilon=1e-2):
     from shogun.Evaluation import CrossValidation, CrossValidationResult
     from shogun.Evaluation import MeanSquaredError
@@ -43,7 +43,7 @@ def evaluation_cross_validation_classification(fm_train=traindat,fm_test=testdat
 
     # print all parameter available for modelselection
     # Dont worry if yours is not included but, write to the mailing list
-    kernel.print_modsel_params()
+    #kernel.print_modsel_params()
 
     labels=Labels(label_train)
 
@@ -72,7 +72,7 @@ def evaluation_cross_validation_classification(fm_train=traindat,fm_test=testdat
 
     # print all parameter available for modelselection
     # Dont worry if yours is not included but, write to the mailing list
-    predictor.print_modsel_params()
+    #predictor.print_modsel_params()
 
     # build parameter tree to select C1 and C2 
     param_tree_root=ModelSelectionParameters()
@@ -90,29 +90,29 @@ def evaluation_cross_validation_classification(fm_train=traindat,fm_test=testdat
 
     # perform model selection with selected methods
     #print "performing model selection of"
-    print "parameter tree"
-    param_tree_root.print_tree()
+    #print "parameter tree"
+    #param_tree_root.print_tree()
 
-    print "starting model selection"
+    #print "starting model selection"
     # print the current parameter combination, if no parameter nothing is printed
-    print_state=True
+    print_state=False
     # lock data before since model selection will not change the kernel matrix
     # (use with care) This avoids that the kernel matrix is recomputed in every
     # iteration of the model search
     predictor.data_lock(labels, features_train)
     best_parameters=model_selection.select_model(print_state)
 
     # print best parameters
-    print "best parameters:"
-    best_parameters.print_tree()
+    #print "best parameters:"
+    #best_parameters.print_tree()
 
     # apply them and print result
     best_parameters.apply_to_machine(predictor)
     result=cross_validation.evaluate()
-    print "mean:", result.mean
-    if result.has_conf_int:
-        print "[", result.conf_int_low, ",", result.conf_int_up, "] with alpha=", result.conf_int_alpha
+    #print "mean:", result.mean
+    #if result.has_conf_int:
+    #    print "[", result.conf_int_low, ",", result.conf_int_up, "] with alpha=", result.conf_int_alpha
 
 if __name__=='__main__':
-	print 'Evaluation CrossValidationClassification'
-	evaluation_cross_validation_classification(*parameter_list[0])
+	print 'Modelselection Grid Search LibSVR'
+	evaluation_cross_validation_regression(*parameter_list[0])
diff --git a/examples/undocumented/python_modular/modelselection_parameter_tree_modular.py b/examples/undocumented/python_modular/modelselection_parameter_tree_modular.py
@@ -32,7 +32,7 @@ def modelselection_parameter_tree_modular(dummy):
 
     # print all parameter available for modelselection
     # Dont worry if yours is not included but, write to the mailing list
-    power_kernel.print_modsel_params()
+    #power_kernel.print_modsel_params()
 
     param_power_kernel=ModelSelectionParameters('kernel', power_kernel)
     root.append_child(param_power_kernel)
@@ -45,7 +45,7 @@ def modelselection_parameter_tree_modular(dummy):
 
     # print all parameter available for modelselection
     # Dont worry if yours is not included but, write to the mailing list
-    metric1.print_modsel_params()
+    #metric1.print_modsel_params()
 
     param_power_kernel_metric1=ModelSelectionParameters('distance', metric1)
 
@@ -59,7 +59,7 @@ def modelselection_parameter_tree_modular(dummy):
 
     # print all parameter available for modelselection
     # Dont worry if yours is not included but, write to the mailing list
-    gaussian_kernel.print_modsel_params()
+    #gaussian_kernel.print_modsel_params()
 
     param_gaussian_kernel=ModelSelectionParameters('kernel', gaussian_kernel)
 
@@ -73,7 +73,7 @@ def modelselection_parameter_tree_modular(dummy):
 
     # print all parameter available for modelselection
     # Dont worry if yours is not included but, write to the mailing list
-    ds_kernel.print_modsel_params()
+    #ds_kernel.print_modsel_params()
 
     param_ds_kernel=ModelSelectionParameters('kernel', ds_kernel)