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Move pipeline definitions into tests
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@ -58,29 +58,6 @@ def nlp():
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return nlp
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@pytest.fixture
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def nlp_tcm():
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nlp = Language(Vocab())
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textcat_multilabel = nlp.add_pipe("textcat_multilabel")
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for label in ("FEATURE", "REQUEST", "BUG", "QUESTION"):
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textcat_multilabel.add_label(label)
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nlp.initialize()
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return nlp
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@pytest.fixture
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def nlp_tc_tcm():
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nlp = Language(Vocab())
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textcat = nlp.add_pipe("textcat")
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for label in ("POSITIVE", "NEGATIVE"):
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textcat.add_label(label)
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textcat_multilabel = nlp.add_pipe("textcat_multilabel")
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for label in ("FEATURE", "REQUEST", "BUG", "QUESTION"):
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textcat_multilabel.add_label(label)
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nlp.initialize()
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return nlp
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def test_language_update(nlp):
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text = "hello world"
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annots = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}
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@ -149,14 +126,19 @@ def test_evaluate_no_pipe(nlp):
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nlp.evaluate([Example.from_dict(doc, annots)])
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def test_evaluate_textcat_multilabel(nlp_tcm):
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def test_evaluate_textcat_multilabel(en_vocab):
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"""Test that evaluate works with a multilabel textcat pipe."""
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text = "hello world"
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nlp = Language(en_vocab)
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textcat_multilabel = nlp.add_pipe("textcat_multilabel")
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for label in ("FEATURE", "REQUEST", "BUG", "QUESTION"):
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textcat_multilabel.add_label(label)
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nlp.initialize()
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annots = {"cats": {"FEATURE": 1.0, "QUESTION": 1.0}}
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doc = Doc(nlp_tcm.vocab, words=text.split(" "))
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doc = nlp.make_doc("hello world")
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example = Example.from_dict(doc, annots)
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scores = nlp_tcm.evaluate([example])
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labels = nlp_tcm.get_pipe("textcat_multilabel").labels
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scores = nlp.evaluate([example])
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labels = nlp.get_pipe("textcat_multilabel").labels
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for label in labels:
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assert scores["cats_f_per_type"].get(label) is not None
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for key in example.reference.cats.keys():
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@ -164,10 +146,18 @@ def test_evaluate_textcat_multilabel(nlp_tcm):
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assert scores["cats_f_per_type"].get(key) is None
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def test_evaluate_multiple_textcat(nlp_tc_tcm):
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def test_evaluate_multiple_textcat_final(en_vocab):
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"""Test that evaluate evaluates the final textcat component in a pipeline
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with more than one textcat or textcat_multilabel."""
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text = "hello world"
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nlp = Language(en_vocab)
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textcat = nlp.add_pipe("textcat")
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for label in ("POSITIVE", "NEGATIVE"):
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textcat.add_label(label)
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textcat_multilabel = nlp.add_pipe("textcat_multilabel")
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for label in ("FEATURE", "REQUEST", "BUG", "QUESTION"):
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textcat_multilabel.add_label(label)
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nlp.initialize()
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annots = {
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"cats": {
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"POSITIVE": 1.0,
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@ -178,11 +168,11 @@ def test_evaluate_multiple_textcat(nlp_tc_tcm):
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"NEGATIVE": 0.0,
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}
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}
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doc = Doc(nlp_tc_tcm.vocab, words=text.split(" "))
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doc = nlp.make_doc("hello world")
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example = Example.from_dict(doc, annots)
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scores = nlp_tc_tcm.evaluate([example])
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scores = nlp.evaluate([example])
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# get the labels from the final pipe
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labels = nlp_tc_tcm.get_pipe(nlp_tc_tcm.pipe_names[-1]).labels
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labels = nlp.get_pipe(nlp.pipe_names[-1]).labels
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for label in labels:
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assert scores["cats_f_per_type"].get(label) is not None
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for key in example.reference.cats.keys():
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