actual commit with test for custom readers with ml_datasets >= 0.2

requirements.txt

@@ -3,7 +3,7 @@ cymem>=2.0.2,<2.1.0
 preshed>=3.0.2,<3.1.0
 thinc>=8.0.0a31,<8.0.0a40
 blis>=0.4.0,<0.5.0
-ml_datasets>=0.1.1
+ml_datasets>=0.2.0
 murmurhash>=0.28.0,<1.1.0
 wasabi>=0.8.0,<1.1.0
 srsly>=2.1.0,<3.0.0

spacy/tests/training/test_readers.py

@@ -0,0 +1,58 @@
+import pytest
+from thinc.api import Config
+from spacy.util import load_model_from_config
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize(
+    "reader,additional_config",
+    [
+        ("ml_datasets.imdb_sentiment.v1", {"train_limit": 10, "dev_limit": 2}),
+        ("ml_datasets.dbpedia.v1", {"train_limit": 10, "dev_limit": 2}),
+        ("ml_datasets.cmu_movies.v1", {"limit": 10, "freq_cutoff": 200, "split": 0.8}),
+    ],
+)
+def test_cat_readers(reader, additional_config):
+    nlp_config_string = """
+    [training]
+    
+    [training.corpus]
+    @readers = "PLACEHOLDER"
+
+    [nlp]
+    lang = "en"
+    pipeline = ["tok2vec", "textcat"]
+    
+    [components]
+    
+    [components.tok2vec]
+    factory = "tok2vec"
+    
+    [components.textcat]
+    factory = "textcat"
+    """
+    config = Config().from_str(nlp_config_string)
+    config["training"]["corpus"]["@readers"] = reader
+    config["training"]["corpus"].update(additional_config)
+    nlp, resolved = load_model_from_config(config, auto_fill=True)
+
+    train_corpus = resolved["training"]["corpus"]["train"]
+    optimizer = resolved["training"]["optimizer"]
+    # simulate a training loop
+    nlp.begin_training(lambda: train_corpus(nlp), sgd=optimizer)
+    for example in train_corpus(nlp):
+        assert example.y.cats
+        # this shouldn't fail if each training example has at least one positive label
+        assert sorted(list(set(example.y.cats.values()))) == [0.0, 1.0]
+        nlp.update([example], sgd=optimizer)
+    # simulate performance benchmark on dev corpus
+    dev_corpus = resolved["training"]["corpus"]["dev"]
+    dev_examples = list(dev_corpus(nlp))
+    for example in dev_examples:
+        # this shouldn't fail if each dev example has at least one positive label
+        assert sorted(list(set(example.y.cats.values()))) == [0.0, 1.0]
+    scores = nlp.evaluate(dev_examples)
+    assert scores["cats_score"]
+    # ensure the pipeline runs
+    doc = nlp("Quick test")
+    assert doc.cats