From 4bbe41f017ffc6334a35f2a682804cf6365dfd9e Mon Sep 17 00:00:00 2001
From: Ines Montani <ines@ines.io>
Date: Thu, 24 Sep 2020 10:42:47 +0200
Subject: [PATCH] Fix combined scores and update test

---
 spacy/language.py                           |  7 ++-----
 spacy/tests/pipeline/test_pipe_factories.py |  4 ++--
 spacy/util.py                               | 10 ++++++++--
 3 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/spacy/language.py b/spacy/language.py
index 0b7deacad..a52391419 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -251,11 +251,8 @@ class Language:
         # We're merging the existing score weights back into the combined
         # weights to make sure we're preserving custom settings in the config
         # but also reflect updates (e.g. new components added)
-        prev_score_weights = self._config["training"].get("score_weights", {})
-        combined_score_weights = combine_score_weights(score_weights)
-        combined_score_weights.update(prev_score_weights)
-        # Combine the scores a second time to normalize them
-        combined_score_weights = combine_score_weights([combined_score_weights])
+        prev_weights = self._config["training"].get("score_weights", {})
+        combined_score_weights = combine_score_weights(score_weights, prev_weights)
         self._config["training"]["score_weights"] = combined_score_weights
         if not srsly.is_json_serializable(self._config):
             raise ValueError(Errors.E961.format(config=self._config))
diff --git a/spacy/tests/pipeline/test_pipe_factories.py b/spacy/tests/pipeline/test_pipe_factories.py
index 4ab1c4248..4c197005e 100644
--- a/spacy/tests/pipeline/test_pipe_factories.py
+++ b/spacy/tests/pipeline/test_pipe_factories.py
@@ -378,14 +378,14 @@ def test_language_factories_scores():
     config["training"]["score_weights"]["b3"] = 1.0
     nlp = English.from_config(config)
     score_weights = nlp.config["training"]["score_weights"]
-    expected = {"a1": 0.0, "a2": 0.15, "b1": 0.06, "b2": 0.21, "b3": 0.59}
+    expected = {"a1": 0.0, "a2": 0.5, "b1": 0.03, "b2": 0.12, "b3": 0.34}
     assert score_weights == expected
     # Test with null values
     config = nlp.config.copy()
     config["training"]["score_weights"]["a1"] = None
     nlp = English.from_config(config)
     score_weights = nlp.config["training"]["score_weights"]
-    expected = {"a1": None, "a2": 0.15, "b1": 0.06, "b2": 0.21, "b3": 0.58}  # rounding :(
+    expected = {"a1": None, "a2": 0.5, "b1": 0.03, "b2": 0.12, "b3": 0.35}
     assert score_weights == expected
 
 
diff --git a/spacy/util.py b/spacy/util.py
index f7c5cff59..709da8d29 100644
--- a/spacy/util.py
+++ b/spacy/util.py
@@ -1202,11 +1202,16 @@ def get_arg_names(func: Callable) -> List[str]:
     return list(set([*argspec.args, *argspec.kwonlyargs]))
 
 
-def combine_score_weights(weights: List[Dict[str, float]]) -> Dict[str, float]:
+def combine_score_weights(
+    weights: List[Dict[str, float]],
+    overrides: Dict[str, Optional[Union[float, int]]] = SimpleFrozenDict(),
+) -> Dict[str, float]:
     """Combine and normalize score weights defined by components, e.g.
     {"ents_r": 0.2, "ents_p": 0.3, "ents_f": 0.5} and {"some_other_score": 1.0}.
 
     weights (List[dict]): The weights defined by the components.
+    overrides (Dict[str, Optional[Union[float, int]]]): Existing scores that
+        should be preserved.
     RETURNS (Dict[str, float]): The combined and normalized weights.
     """
     # We first need to extract all None/null values for score weights that
@@ -1216,6 +1221,7 @@ def combine_score_weights(weights: List[Dict[str, float]]) -> Dict[str, float]:
     for w_dict in weights:
         filtered_weights = {}
         for key, value in w_dict.items():
+            value = overrides.get(key, value)
             if value is None:
                 result[key] = None
             else:
@@ -1227,7 +1233,7 @@ def combine_score_weights(weights: List[Dict[str, float]]) -> Dict[str, float]:
         # components.
         total = sum(w_dict.values())
         for key, value in w_dict.items():
-            weight = round(value / total / len(weights), 2)
+            weight = round(value / total / len(all_weights), 2)
             result[key] = result.get(key, 0.0) + weight
     return result