Added second param set for the estimators in _proximity_forest.py

sktime · Oct 2, 2022 · b970962 · b970962
1 parent 395346c
commit b970962
Showing 1 changed file with 26 additions and 5 deletions.
diff --git a/sktime/classification/distance_based/_proximity_forest.py b/sktime/classification/distance_based/_proximity_forest.py
@@ -973,7 +973,7 @@ def _predict_proba(self, X) -> np.ndarray:
         return distributions
 
     @classmethod
-    def get_test_params(cls):
+    def get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Returns
@@ -984,10 +984,14 @@ def get_test_params(cls):
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
             `create_test_instance` uses the first (or only) dictionary in `params`
         """
-        params = {
+        params1 = {
             "random_state": 0,
         }
-        return params
+        params2 = {
+            "random_state": 42,
+            "distance_measure": "dtw"
+        }
+        return [params1, params2]
 
 
 class ProximityTree(BaseClassifier):
@@ -1226,7 +1230,16 @@ def get_test_params(cls, parameter_set="default"):
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
             `create_test_instance` uses the first (or only) dictionary in `params`.
         """
-        return {"max_depth": 1, "n_stump_evaluations": 1}
+        params1 = {
+            "max_depth": 1,
+            "n_stump_evaluations": 1
+        }
+        params2 = {
+            "max_depth": 1,
+            "n_stump_evaluations": 1,
+            "distance_measure": "dtw"
+        }
+        return [params1, params2]
 
 
 class ProximityForest(BaseClassifier):
@@ -1504,7 +1517,15 @@ def get_test_params(cls, parameter_set="default"):
         if parameter_set == "results_comparison":
             return {"n_estimators": 3, "max_depth": 2, "n_stump_evaluations": 2}
         else:
-            return {"n_estimators": 2, "max_depth": 1, "n_stump_evaluations": 1}
+            param1 = {
+                "n_estimators": 2,
+                "max_depth": 1,
+                "n_stump_evaluations": 1}
+            param2 = {"n_estimators": 2,
+                      "max_depth": 1,
+                      "n_stump_evaluations": 1,
+                      "distance_measure": "dtw"}
+            return [param1, param2]
 
 
 # start of util functions