Implemented PR review suggestions

coupled_biased_random_walks/detect.py

@@ -94,9 +94,7 @@ def _score(self, observation):
         Computes the weighted anomaly score (object_score in the paper) for an observation
         :param observation: dict of the form {feature_name: feature_value, ...}
         """
-        return sum(self._get_feature_relevance(item) * \
-                   self._stationary_prob.get(item, self._unknown_feature_score)
-                    for item in iteritems(observation))
+        return sum((self._value_scores(observation)).values())
 
     def value_scores(self, observation_iterable):
         """
@@ -111,16 +109,17 @@ def value_scores(self, observation_iterable):
             raise CBRWScoreError()
         if isinstance(observation_iterable, dict):
             observation_iterable = [observation_iterable]
-        return np.array([self._value_scores(obs) for obs in observation_iterable])
+        return [self._value_scores(obs) for obs in observation_iterable]
 
     def _value_scores(self, observation):
         """
         Computes the weighted value scores for each feature value of an observation
+        :param observation: dict of the form {feature_name: feature_value, ...}
         """
         score_keys = []
         value_scores = []
         for item in iteritems(observation):
-            score_keys.append(item[0])
+            score_keys.append(get_feature_name(item))
             value_scores.append(self._get_feature_relevance(item) * \
                                self._stationary_prob.get(item, self._unknown_feature_score))
         return dict(zip(score_keys, value_scores))

example.py

@@ -3,7 +3,7 @@
 from six.moves import zip
 
 #from coupled_biased_random_walks import CBRW
-from coupled_biased_random_walks.detect import CBRW
+from coupled_biased_random_walks import CBRW
 from data.loading import load_from_csv
 
 
@@ -23,14 +23,13 @@
 
     # fit and score data
     detector.fit()
-    print('Feature weights: ', detector.feature_weights)
-    print()
+    print('Feature weights:\n{}\n'.format(detector.feature_weights))
+
     scores = detector.score(data)
     # print scores and observations
     for score, datum in zip(scores, data):
         print('Score: {} | Data: {}'.format(round(score, 4), datum))
 
-    print()
-    print('Scores: ', scores)
-    print()
-    print('Value scores per attribute: ', detector.value_scores(data))
+    print('\nValue scores per attribute:')
+    for i, value_score in enumerate(detector.value_scores(data)):
+        print('Observation {}: {}'.format(i, value_score))