From 6aedd98d02b55672469556f4d61f2ad6254f3759 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?K=C3=A1d=C3=A1r=20=C3=81kos?= <akos@onyx.uvt.nl>
Date: Mon, 11 Apr 2022 16:10:14 +0200
Subject: [PATCH] fixing scorer

---
 spacy/pipeline/coref.py | 37 +++++++++++++++++--------------------
 1 file changed, 17 insertions(+), 20 deletions(-)

diff --git a/spacy/pipeline/coref.py b/spacy/pipeline/coref.py
index 02c93f712..fc04d1a3e 100644
--- a/spacy/pipeline/coref.py
+++ b/spacy/pipeline/coref.py
@@ -511,10 +511,13 @@ class SpanPredictor(TrainablePipe):
         total_loss = 0
         for eg in examples:
             span_scores, backprop = self.model.begin_update([eg.predicted])
-            loss, d_scores = self.get_loss([eg], span_scores)
-            total_loss += loss
-            # TODO check shape here
-            backprop((d_scores))
+            # FIXME, this only happens once in the first 1000 docs of OntoNotes
+            # and I'm not sure yet why.
+            if span_scores.size:
+                loss, d_scores = self.get_loss([eg], span_scores)
+                total_loss += loss
+                # TODO check shape here
+                backprop((d_scores))
 
         if sgd is not None:
             self.finish_update(sgd)
@@ -557,7 +560,6 @@ class SpanPredictor(TrainablePipe):
         assert len(examples) == 1, "Only fake batching is supported."
         # starts and ends are gold starts and ends (Ints1d)
         # span_scores is a Floats3d. What are the axes? mention x token x start/end
-
         for eg in examples:
             starts = []
             ends = []
@@ -610,8 +612,8 @@ class SpanPredictor(TrainablePipe):
         Evaluate on reconstructing the correct spans around
         gold heads.
         """
-        start_scores = []
-        end_scores = []
+        scores = []
+        xp = self.model.ops.xp
         for eg in examples:
             starts = []
             ends = []
@@ -628,16 +630,11 @@ class SpanPredictor(TrainablePipe):
                         pred_starts.append(pred_mention.start)
                         pred_ends.append(pred_mention.end)
 
-            starts = self.model.ops.xp.asarray(starts)
-            ends = self.model.ops.xp.asarray(ends)
-            pred_starts = self.model.ops.xp.asarray(pred_starts)
-            pred_ends = self.model.ops.xp.asarray(pred_ends)
-            start_accuracy = (starts == pred_starts).mean()
-            end_accuracy = (ends == pred_ends).mean()
-            start_scores.append(float(start_accuracy))
-            end_scores.append(float(end_accuracy))
-        out = {
-            "span_start_accuracy": mean(start_scores),
-            "span_end_accuracy": mean(end_scores)
-        }
-        return out
+            starts = xp.asarray(starts)
+            ends = xp.asarray(ends)
+            pred_starts = xp.asarray(pred_starts)
+            pred_ends = xp.asarray(pred_ends)
+            correct = (starts == pred_starts) * (ends == pred_ends)
+            accuracy = correct.mean()
+            scores.append(float(accuracy))
+        return {"span_accuracy": mean(scores)}