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Fixes in test suite (#6457)

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* fix slow test for textcat readers

* cleanup test_issue5551

* add explicit score weight

* cleanup
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Sofie Van Landeghem 2020-12-02 12:57:08 +01:00 committed by GitHub
parent 31ec9a906e
commit d6c616a125
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2 changed files with 24 additions and 19 deletions
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35
spacy/tests/regression/test_issue5501-6000.py
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@ -1,35 +1,38 @@
from thinc.api import fix_random_seed import pytest
from thinc.api import Config, fix_random_seed
from spacy.lang.en import English from spacy.lang.en import English
from spacy.pipeline.textcat import default_model_config, bow_model_config
from spacy.pipeline.textcat import cnn_model_config
from spacy.tokens import Span from spacy.tokens import Span
from spacy import displacy from spacy import displacy
from spacy.pipeline import merge_entities from spacy.pipeline import merge_entities
from spacy.training import Example
def test_issue5551(): @pytest.mark.parametrize(
"textcat_config", [default_model_config, bow_model_config, cnn_model_config]
)
def test_issue5551(textcat_config):
"""Test that after fixing the random seed, the results of the pipeline are truly identical""" """Test that after fixing the random seed, the results of the pipeline are truly identical"""
component = "textcat" component = "textcat"
pipe_cfg = {
"model": { pipe_cfg = Config().from_str(textcat_config)
"@architectures": "spacy.TextCatBOW.v1",
"exclusive_classes": True,
"ngram_size": 2,
"no_output_layer": False,
}
}
results = [] results = []
for i in range(3): for i in range(3):
fix_random_seed(0) fix_random_seed(0)
nlp = English() nlp = English()
example = ( text = "Once hot, form ping-pong-ball-sized balls of the mixture, each weighing roughly 25 g."
"Once hot, form ping-pong-ball-sized balls of the mixture, each weighing roughly 25 g.", annots = {"cats": {"Labe1": 1.0, "Label2": 0.0, "Label3": 0.0}}
{"cats": {"Labe1": 1.0, "Label2": 0.0, "Label3": 0.0}},
)
pipe = nlp.add_pipe(component, config=pipe_cfg, last=True) pipe = nlp.add_pipe(component, config=pipe_cfg, last=True)
for label in set(example[1]["cats"]): for label in set(annots["cats"]):
pipe.add_label(label) pipe.add_label(label)
# Train
nlp.initialize() nlp.initialize()
doc = nlp.make_doc(text)
nlp.update([Example.from_dict(doc, annots)])
# Store the result of each iteration # Store the result of each iteration
result = pipe.model.predict([nlp.make_doc(example[0])]) result = pipe.model.predict([doc])
results.append(list(result[0])) results.append(list(result[0]))
# All results should be the same because of the fixed seed # All results should be the same because of the fixed seed
assert len(results) == 3 assert len(results) == 3

8
spacy/tests/training/test_readers.py
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@ -72,6 +72,10 @@ def test_readers():
def test_cat_readers(reader, additional_config): def test_cat_readers(reader, additional_config):
nlp_config_string = """ nlp_config_string = """
[training] [training]
seed = 0
[training.score_weights]
cats_macro_auc = 1.0
[corpora] [corpora]
@readers = "PLACEHOLDER" @readers = "PLACEHOLDER"
@ -92,9 +96,7 @@ def test_cat_readers(reader, additional_config):
config["corpora"]["@readers"] = reader config["corpora"]["@readers"] = reader
config["corpora"].update(additional_config) config["corpora"].update(additional_config)
nlp = load_model_from_config(config, auto_fill=True) nlp = load_model_from_config(config, auto_fill=True)
T = registry.resolve( T = registry.resolve(nlp.config["training"], schema=ConfigSchemaTraining)
nlp.config["training"].interpolate(), schema=ConfigSchemaTraining
)
dot_names = [T["train_corpus"], T["dev_corpus"]] dot_names = [T["train_corpus"], T["dev_corpus"]]
train_corpus, dev_corpus = resolve_dot_names(nlp.config, dot_names) train_corpus, dev_corpus = resolve_dot_names(nlp.config, dot_names)
optimizer = T["optimizer"] optimizer = T["optimizer"]