explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2025-08-07 13:44:55 +03:00
Code Issues Packages Projects Releases Wiki Activity

Move pipeline definitions into tests

Browse Source
This commit is contained in:
Adriane Boyd 2022-12-13 14:37:13 +01:00
parent 38abc802a6
commit ae919e9907
1 changed files with 23 additions and 33 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

56
spacy/tests/test_language.py
View File

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