remove artificially duplicated test [ci skip]

svlandeg · web-flow · commit d93cd3b7c0d5 · 2021-01-21T10:53:16.000+01:00
diff --git a/spacy/tests/pipeline/test_textcat.py b/spacy/tests/pipeline/test_textcat.py
@@ -226,54 +226,6 @@ def test_overfitting_IO():
     assert_equal(batch_cats_1, no_batch_cats)
 
 
-@pytest.mark.skip(reason="TODO: Can this be removed?")
-def test_overfitting_IO_multi_old():
-    # Simple test to try and quickly overfit the multi-label textcat component - ensuring the ML models work correctly
-    fix_random_seed(0)
-    nlp = English()
-    # Set exclusive labels to False
-    config = {"model": {"linear_model": {"exclusive_classes": False}}}
-    textcat = nlp.add_pipe("textcat", config=config)
-    train_examples = []
-    for text, annotations in TRAIN_DATA_MULTI_LABEL:
-        train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
-    optimizer = nlp.initialize(get_examples=lambda: train_examples)
-    assert textcat.model.get_dim("nO") == 2
-
-    for i in range(50):
-        losses = {}
-        nlp.update(train_examples, sgd=optimizer, losses=losses)
-    assert losses["textcat"] < 0.01
-
-    # test the trained model
-    test_text = "I am happy."
-    doc = nlp(test_text)
-    cats = doc.cats
-    assert cats["POSITIVE"] > 0.9
-
-    # Also test the results are still the same after IO
-    with make_tempdir() as tmp_dir:
-        nlp.to_disk(tmp_dir)
-        nlp2 = util.load_model_from_path(tmp_dir)
-        doc2 = nlp2(test_text)
-        cats2 = doc2.cats
-        assert cats2["POSITIVE"] > 0.9
-
-    # Test scoring
-    scores = nlp.evaluate(train_examples)
-    assert scores["cats_micro_f"] == 1.0
-    assert scores["cats_score"] == 1.0
-    assert "cats_score_desc" in scores
-
-    # Make sure that running pipe twice, or comparing to call, always amounts to the same predictions
-    texts = ["Just a sentence.", "I like green eggs.", "I am happy.", "I eat ham."]
-    batch_cats_1 = [doc.cats for doc in nlp.pipe(texts)]
-    batch_cats_2 = [doc.cats for doc in nlp.pipe(texts)]
-    no_batch_cats = [doc.cats for doc in [nlp(text) for text in texts]]
-    assert_equal(batch_cats_1, batch_cats_2)
-    assert_equal(batch_cats_1, no_batch_cats)
-
-
 def test_overfitting_IO_multi():
     # Simple test to try and quickly overfit the multi-label textcat component - ensuring the ML models work correctly
     fix_random_seed(0)
