Handles the case when no legal operations are possible [refs #1348]

pgmpy/estimators/HillClimbSearch.py

@@ -284,7 +284,8 @@ def estimate(
             iteration = range(int(max_iter))
 
         # Step 2: For each iteration, find the best scoring operation and
-        #         do that to the current model.
+        #         do that to the current model. If no legal operation is
+        #         possible, sets best_operation=None.
         for _ in iteration:
             best_operation, best_score_delta = max(
                 self._legal_operations(
@@ -297,6 +298,7 @@ def estimate(
                     fixed_edges,
                 ),
                 key=lambda t: t[1],
+                default=(None, None),
             )
 
             if best_operation is None or best_score_delta < epsilon:

pgmpy/tests/test_estimators/test_HillClimbSearch.py

@@ -206,6 +206,25 @@ def test_estimate_titanic(self):
             in self.est_titanic2.estimate(fixed_edges=[("Pclass", "Survived")]).edges()
         )
 
+    def test_no_legal_operation(self):
+        data = pd.DataFrame(
+            [
+                [1, 0, 0, 1, 0, 0, 1, 1, 0],
+                [1, 0, 1, 0, 0, 1, 0, 1, 0],
+                [1, 0, 0, 0, 0, 1, 0, 1, 1],
+                [1, 1, 0, 1, 0, 1, 1, 0, 0],
+                [0, 0, 1, 0, 0, 1, 1, 0, 0],
+            ],
+            columns=list("ABCDEFGHI"),
+        )
+        est = HillClimbSearch(data)
+        best_model = est.estimate(
+            fixed_edges=[("A", "B"), ("B", "C")], white_list=[("F", "C")]
+        )
+        self.assertEqual(
+            set(best_model.edges()), set([("A", "B"), ("B", "C"), ("F", "C")])
+        )
+
     def tearDown(self):
         del self.rand_data
         del self.est_rand