fixes

predicators/approaches/active_sampler_learning_approach.py

@@ -275,7 +275,10 @@ def _learn_nsrt_sampler(self, nsrt_data: _OptionSamplerDataset,
                 weight_init="default")
         else:
             assert CFG.active_sampler_learning_model.endswith("knn")
-            classifier = KNeighborsClassifier(seed=CFG.seed)
+            n_neighbors = min(len(X_arr_classifier),
+                              CFG.active_sampler_learning_knn_neighbors)
+            classifier = KNeighborsClassifier(seed=CFG.seed,
+                                              n_neighbors=n_neighbors)
         classifier.fit(X_arr_classifier, y_arr_classifier)
 
         # Save the sampler classifier for external analysis.

predicators/ml_models.py

@@ -278,6 +278,9 @@ def classify(self, x: Array) -> bool:
 
     def predict_proba(self, x: Array) -> float:
         probs = self._model.predict_proba([x])[0]
+        # Special case: only one class.
+        if probs.shape == (1, ):
+            return float(self.classify(x))
         assert probs.shape == (2, )  # [P(x is class 0), P(x is class 1)]
         return probs[1]  # return the second element of probs
 

predicators/settings.py

@@ -520,6 +520,7 @@ class GlobalSettings:
     # active sampler learning parameters
     active_sampler_learning_model = "myopic_classifier_mlp"
     active_sampler_learning_feature_selection = "all"
+    active_sampler_learning_knn_neighbors = 3
     active_sampler_learning_use_teacher = True
     active_sampler_learning_num_samples = 100
     active_sampler_learning_score_gamma = 0.5

tests/approaches/test_active_sampler_learning_approach.py

@@ -16,18 +16,22 @@
 from predicators.teacher import Teacher
 
 
-@pytest.mark.parametrize("model_name,right_targets,num_demo",
-                         [("myopic_classifier_mlp", False, 0),
-                          ("myopic_classifier_mlp", True, 1),
-                          ("myopic_classifier_ensemble", False, 0),
-                          ("myopic_classifier_ensemble", False, 1),
-                          ("fitted_q", False, 0), ("fitted_q", True, 0)])
-def test_active_sampler_learning_approach(model_name, right_targets, num_demo):
+@pytest.mark.parametrize("model_name,right_targets,num_demo,feat_type",
+                         [("myopic_classifier_mlp", False, 0, "all"),
+                          ("myopic_classifier_mlp", True, 1, "all"),
+                          ("myopic_classifier_ensemble", False, 0, "all"),
+                          ("myopic_classifier_ensemble", False, 1, "all"),
+                          ("fitted_q", False, 0, "all"),
+                          ("fitted_q", True, 0, "all"),
+                          ("myopic_classifier_knn", False, 0, "oracle")])
+def test_active_sampler_learning_approach(model_name, right_targets, num_demo,
+                                          feat_type):
     """Test for ActiveSamplerLearningApproach class, entire pipeline."""
     utils.reset_config({
         "env": "bumpy_cover",
         "approach": "active_sampler_learning",
         "active_sampler_learning_model": model_name,
+        "active_sampler_learning_feature_selection": feat_type,
         "timeout": 10,
         "strips_learner": "oracle",
         "sampler_learner": "oracle",