fix ser/strut options and parameters

Author: atiqm

tests/test_transfertree.py

@@ -108,15 +108,13 @@ def test_transfer_tree():
             transferred_rf = TransferForestClassifier(estimator=clf_transfer_rf,algo="ser")
             transferred_rf._ser_rf(Xt, yt,original_ser=False,no_red_on_cl=True,cl_no_red=[0])
 
-        # WARNING! Error Raised with this test
         if method == 'ser_no_ext':
-            pass
             #decision tree
-            #transferred_dt = TransferTreeClassifier(estimator=clf_transfer_dt,algo="ser")
-            #transferred_dt._ser(Xt, yt,node=0,original_ser=False,no_ext_on_cl=True,cl_no_red=[0],ext_cond=True)
+            transferred_dt = TransferTreeClassifier(estimator=clf_transfer_dt,algo="ser")
+            transferred_dt._ser(Xt, yt,node=0,original_ser=False,no_ext_on_cl=True,cl_no_ext=[0],ext_cond=True)
             #random forest
-            #transferred_rf = TransferForestClassifier(estimator=clf_transfer_rf,algo="ser")
-            #transferred_rf._ser_rf(Xt, yt,original_ser=False,no_ext_on_cl=True,cl_no_ext=[0],ext_cond=True)
+            transferred_rf = TransferForestClassifier(estimator=clf_transfer_rf,algo="ser")
+            transferred_rf._ser_rf(Xt, yt,original_ser=False,no_ext_on_cl=True,cl_no_ext=[0],ext_cond=True)
         if method == 'ser_nr_lambda':
             #decision tree
             transferred_dt = TransferTreeClassifier(estimator=clf_transfer_dt,algo="ser")
@@ -159,7 +157,7 @@ def test_transfer_tree():
                                 root_source_values=root_source_values,Nkmin=Nkmin,coeffs=coeffs)
             #random forest
             transferred_rf = TransferForestClassifier(estimator=clf_transfer_rf,algo="strut")
-            transferred_rf._strut(Xt, yt,adapt_prop=False,no_prune_on_cl=True,cl_no_prune=[0],
+            transferred_rf._strut_rf(Xt, yt,adapt_prop=False,no_prune_on_cl=True,cl_no_prune=[0],
                                 leaf_loss_quantify=False,leaf_loss_threshold=0.5,no_prune_with_translation=False,
                                 root_source_values=root_source_values,Nkmin=Nkmin,coeffs=coeffs)
         if method == 'strut_lambda_np':
@@ -170,7 +168,7 @@ def test_transfer_tree():
                                 root_source_values=root_source_values,Nkmin=Nkmin,coeffs=coeffs)
             #random forest
             transferred_rf = TransferForestClassifier(estimator=clf_transfer_rf,algo="strut")
-            transferred_rf._strut(Xt, yt,adapt_prop=True,no_prune_on_cl=True,cl_no_prune=[0],
+            transferred_rf._strut_rf(Xt, yt,adapt_prop=True,no_prune_on_cl=True,cl_no_prune=[0],
                                 leaf_loss_quantify=True,leaf_loss_threshold=0.5,no_prune_with_translation=False,
                                 root_source_values=root_source_values,Nkmin=Nkmin,coeffs=coeffs)
         if method == 'strut_lambda_np2':
@@ -179,12 +177,11 @@ def test_transfer_tree():
             transferred_dt._strut(Xt, yt,node=0,adapt_prop=False,no_prune_on_cl=True,cl_no_prune=[0],
                                 leaf_loss_quantify=False,leaf_loss_threshold=0.5,no_prune_with_translation=False,
                                 root_source_values=root_source_values,Nkmin=Nkmin,coeffs=coeffs)
-            # Warning! Error Raised because `strut` not in TransferForest
             #random forest
-            #transferred_rf = TransferForestClassifier(estimator=clf_transfer_rf,algo="strut")
-            #transferred_rf._strut(Xt, yt,adapt_prop=True,no_prune_on_cl=True,cl_no_prune=[0],
-            #                    leaf_loss_quantify=True,leaf_loss_threshold=0.5,no_prune_with_translation=True,
-            #                    root_source_values=root_source_values,Nkmin=Nkmin,coeffs=coeffs) 
+            transferred_rf = TransferForestClassifier(estimator=clf_transfer_rf,algo="strut")
+            transferred_rf._strut_rf(Xt, yt,adapt_prop=True,no_prune_on_cl=True,cl_no_prune=[0],
+                                leaf_loss_quantify=True,leaf_loss_threshold=0.5,no_prune_with_translation=True,
+                                root_source_values=root_source_values,Nkmin=Nkmin,coeffs=coeffs) 
 
         score = transferred_dt.estimator.score(Xt_test, yt_test)
         #score = clf_transfer.score(Xt_test, yt_test)