Merge branch 'master' into spacy.io

spacy/pipeline/pipes.pyx

@@ -942,9 +942,16 @@ class TextCategorizer(Pipe):
 
     def predict(self, docs):
         self.require_model()
+        tensors = [doc.tensor for doc in docs]
+
+        if not any(len(doc) for doc in docs):
+            # Handle cases where there are no tokens in any docs.
+            xp = get_array_module(tensors)
+            scores = xp.zeros((len(docs), len(self.labels)))
+            return scores, tensors
+
         scores = self.model(docs)
         scores = self.model.ops.asarray(scores)
-        tensors = [doc.tensor for doc in docs]
         return scores, tensors
 
     def set_annotations(self, docs, scores, tensors=None):

spacy/tests/regression/test_issue4030.py

@@ -0,0 +1,57 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+import spacy
+from spacy.util import minibatch, compounding
+
+
+def test_issue4030():
+    """ Test whether textcat works fine with empty doc """
+    unique_classes = ["offensive", "inoffensive"]
+    x_train = [
+        "This is an offensive text",
+        "This is the second offensive text",
+        "inoff",
+    ]
+    y_train = ["offensive", "offensive", "inoffensive"]
+
+    # preparing the data
+    pos_cats = list()
+    for train_instance in y_train:
+        pos_cats.append({label: label == train_instance for label in unique_classes})
+    train_data = list(zip(x_train, [{"cats": cats} for cats in pos_cats]))
+
+    # set up the spacy model with a text categorizer component
+    nlp = spacy.blank("en")
+
+    textcat = nlp.create_pipe(
+        "textcat",
+        config={"exclusive_classes": True, "architecture": "bow", "ngram_size": 2},
+    )
+
+    for label in unique_classes:
+        textcat.add_label(label)
+    nlp.add_pipe(textcat, last=True)
+
+    # training the network
+    other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "textcat"]
+    with nlp.disable_pipes(*other_pipes):
+        optimizer = nlp.begin_training()
+        for i in range(3):
+            losses = {}
+            batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
+
+            for batch in batches:
+                texts, annotations = zip(*batch)
+                nlp.update(
+                    docs=texts,
+                    golds=annotations,
+                    sgd=optimizer,
+                    drop=0.1,
+                    losses=losses,
+                )
+
+    # processing of an empty doc should result in 0.0 for all categories
+    doc = nlp("")
+    assert doc.cats["offensive"] == 0.0
+    assert doc.cats["inoffensive"] == 0.0