[python package] added get_ref_chain function to Dataset object, used…

… it in train() to… (#745)

* added get_ref_chain function to Dataset object, used it in train() to compare if training and validation have common reference ancestors

* moved check of common ancestor reference from train() in engine.py to set_reference() in Dataset

* moved check of common ancestor reference from train() in engine.py to set_reference() in Dataset

* removed check for handle = None

python-package/lightgbm/basic.py

@@ -1004,7 +1004,8 @@ def set_reference(self, reference):
         self.set_categorical_feature(reference.categorical_feature)
         self.set_feature_name(reference.feature_name)
         self._set_predictor(reference._predictor)
-        if self.reference is reference:
+        # we're done if self and reference share a common upstrem reference
+        if self.get_ref_chain().intersection(reference.get_ref_chain()):
             return
         if self.data is not None:
             self.reference = reference
@@ -1174,6 +1175,28 @@ def num_feature(self):
         else:
             raise LightGBMError("Cannot get num_feature before construct dataset")
 
+    def get_ref_chain(self, ref_limit=100):
+        '''
+        Gets a chain of Dataset objects, starting with r, then going to r.reference if exists,
+            then to r.reference.reference, etc. until we hit ref_limit or a reference loop
+
+        Returns
+        -------
+        chain of references of self : set of Dataset objects
+        '''
+        head = self
+        ref_chain = []
+        while len(ref_chain) < ref_limit:
+            if isinstance(head, Dataset):
+                ref_chain += [head]
+                if (head.reference is not None) and (head.reference not in ref_chain):
+                    head = head.reference
+                else:
+                    break
+            else:
+                break
+        return(set(ref_chain))
+
 
 class Booster(object):
     """"Booster in LightGBM."""