remove artificially duplicated test [ci skip]
@@ -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)
