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spaCy/spacy/tests/test_cli.py
Adriane Boyd 4b229bfc22 Improve handling of NER in CoNLL-U MISC
2020-05-20 18:48:51 +02:00

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import pytest
from spacy.lang.en import English
from spacy.cli.converters import conllu2json, iob2json, conll_ner2json
from spacy.cli.pretrain import make_docs
def test_cli_converters_conllu2json():
# from NorNE: https://github.com/ltgoslo/norne/blob/3d23274965f513f23aa48455b28b1878dad23c05/ud/nob/no_bokmaal-ud-dev.conllu
lines = [
"1\tDommer\tdommer\tNOUN\t_\tDefinite=Ind|Gender=Masc|Number=Sing\t2\tappos\t_\tO",
"2\tFinn\tFinn\tPROPN\t_\tGender=Masc\t4\tnsubj\t_\tB-PER",
"3\tEilertsen\tEilertsen\tPROPN\t_\t_\t2\tname\t_\tI-PER",
"4\tavstår\tavstå\tVERB\t_\tMood=Ind|Tense=Pres|VerbForm=Fin\t0\troot\t_\tO",
]
input_data = "\n".join(lines)
converted = conllu2json(input_data, n_sents=1)
assert len(converted) == 1
assert converted[0]["id"] == 0
assert len(converted[0]["paragraphs"]) == 1
assert len(converted[0]["paragraphs"][0]["sentences"]) == 1
sent = converted[0]["paragraphs"][0]["sentences"][0]
assert len(sent["tokens"]) == 4
tokens = sent["tokens"]
assert [t["orth"] for t in tokens] == ["Dommer", "Finn", "Eilertsen", "avstår"]
assert [t["tag"] for t in tokens] == ["NOUN", "PROPN", "PROPN", "VERB"]
assert [t["head"] for t in tokens] == [1, 2, -1, 0]
assert [t["dep"] for t in tokens] == ["appos", "nsubj", "name", "ROOT"]
assert [t["ner"] for t in tokens] == ["O", "B-PER", "L-PER", "O"]
@pytest.mark.parametrize(
"lines",
[
(
"1\tDommer\tdommer\tNOUN\t_\tDefinite=Ind|Gender=Masc|Number=Sing\t2\tappos\t_\tname=O",
"2\tFinn\tFinn\tPROPN\t_\tGender=Masc\t4\tnsubj\t_\tSpaceAfter=No|name=B-PER",
"3\tEilertsen\tEilertsen\tPROPN\t_\t_\t2\tname\t_\tname=I-PER",
"4\tavstår\tavstå\tVERB\t_\tMood=Ind|Tense=Pres|VerbForm=Fin\t0\troot\t_\tSpaceAfter=No|name=O",
"5\t.\t$.\tPUNCT\t_\t_\t4\tpunct\t_\tname=B-BAD",
),
(
"1\tDommer\tdommer\tNOUN\t_\tDefinite=Ind|Gender=Masc|Number=Sing\t2\tappos\t_\t_",
"2\tFinn\tFinn\tPROPN\t_\tGender=Masc\t4\tnsubj\t_\tSpaceAfter=No|NE=B-PER",
"3\tEilertsen\tEilertsen\tPROPN\t_\t_\t2\tname\t_\tNE=L-PER",
"4\tavstår\tavstå\tVERB\t_\tMood=Ind|Tense=Pres|VerbForm=Fin\t0\troot\t_\tSpaceAfter=No",
"5\t.\t$.\tPUNCT\t_\t_\t4\tpunct\t_\tNE=B-BAD",
),
],
)
def test_cli_converters_conllu2json_name_ner_map(lines):
input_data = "\n".join(lines)
converted = conllu2json(input_data, n_sents=1, ner_map={"PER": "PERSON", "BAD": ""})
assert len(converted) == 1
assert converted[0]["id"] == 0
assert len(converted[0]["paragraphs"]) == 1
assert converted[0]["paragraphs"][0]["raw"] == "Dommer FinnEilertsen avstår."
assert len(converted[0]["paragraphs"][0]["sentences"]) == 1
sent = converted[0]["paragraphs"][0]["sentences"][0]
assert len(sent["tokens"]) == 5
tokens = sent["tokens"]
assert [t["orth"] for t in tokens] == ["Dommer", "Finn", "Eilertsen", "avstår", "."]
assert [t["tag"] for t in tokens] == ["NOUN", "PROPN", "PROPN", "VERB", "PUNCT"]
assert [t["head"] for t in tokens] == [1, 2, -1, 0, -1]
assert [t["dep"] for t in tokens] == ["appos", "nsubj", "name", "ROOT", "punct"]
assert [t["ner"] for t in tokens] == ["O", "B-PERSON", "L-PERSON", "O", "O"]
def test_cli_converters_conllu2json_subtokens():
# https://raw.githubusercontent.com/ohenrik/nb_news_ud_sm/master/original_data/no-ud-dev-ner.conllu
lines = [
"1\tDommer\tdommer\tNOUN\t_\tDefinite=Ind|Gender=Masc|Number=Sing\t2\tappos\t_\tname=O",
"2-3\tFE\t_\t_\t_\t_\t_\t_\t_\t_",
"2\tFinn\tFinn\tPROPN\t_\tGender=Masc\t4\tnsubj\t_\tname=B-PER",
"3\tEilertsen\tEilertsen\tX\t_\tGender=Fem|Tense=past\t2\tname\t_\tname=I-PER",
"4\tavstår\tavstå\tVERB\t_\tMood=Ind|Tense=Pres|VerbForm=Fin\t0\troot\t_\tSpaceAfter=No|name=O",
"5\t.\t$.\tPUNCT\t_\t_\t4\tpunct\t_\tname=O",
]
input_data = "\n".join(lines)
converted = conllu2json(
input_data, n_sents=1, merge_subtokens=True, append_morphology=True
)
assert len(converted) == 1
assert converted[0]["id"] == 0
assert len(converted[0]["paragraphs"]) == 1
assert converted[0]["paragraphs"][0]["raw"] == "Dommer FE avstår."
assert len(converted[0]["paragraphs"][0]["sentences"]) == 1
sent = converted[0]["paragraphs"][0]["sentences"][0]
assert len(sent["tokens"]) == 4
tokens = sent["tokens"]
print(tokens)
assert [t["orth"] for t in tokens] == ["Dommer", "FE", "avstår", "."]
assert [t["tag"] for t in tokens] == [
"NOUN__Definite=Ind|Gender=Masc|Number=Sing",
"PROPN_X__Gender=Fem,Masc|Tense=past",
"VERB__Mood=Ind|Tense=Pres|VerbForm=Fin",
"PUNCT",
]
assert [t["pos"] for t in tokens] == ["NOUN", "PROPN", "VERB", "PUNCT"]
assert [t["morph"] for t in tokens] == [
"Definite=Ind|Gender=Masc|Number=Sing",
"Gender=Fem,Masc|Tense=past",
"Mood=Ind|Tense=Pres|VerbForm=Fin",
"",
]
assert [t["lemma"] for t in tokens] == ["dommer", "Finn Eilertsen", "avstå", "$."]
assert [t["head"] for t in tokens] == [1, 1, 0, -1]
assert [t["dep"] for t in tokens] == ["appos", "nsubj", "ROOT", "punct"]
assert [t["ner"] for t in tokens] == ["O", "U-PER", "O", "O"]
def test_cli_converters_iob2json():
lines = [
"I|O like|O London|I-GPE and|O New|B-GPE York|I-GPE City|I-GPE .|O",
"I|O like|O London|B-GPE and|O New|B-GPE York|I-GPE City|I-GPE .|O",
"I|PRP|O like|VBP|O London|NNP|I-GPE and|CC|O New|NNP|B-GPE York|NNP|I-GPE City|NNP|I-GPE .|.|O",
"I|PRP|O like|VBP|O London|NNP|B-GPE and|CC|O New|NNP|B-GPE York|NNP|I-GPE City|NNP|I-GPE .|.|O",
]
input_data = "\n".join(lines)
converted = iob2json(input_data, n_sents=10)
assert len(converted) == 1
assert converted[0]["id"] == 0
assert len(converted[0]["paragraphs"]) == 1
assert len(converted[0]["paragraphs"][0]["sentences"]) == 4
for i in range(0, 4):
sent = converted[0]["paragraphs"][0]["sentences"][i]
assert len(sent["tokens"]) == 8
tokens = sent["tokens"]
# fmt: off
assert [t["orth"] for t in tokens] == ["I", "like", "London", "and", "New", "York", "City", "."]
assert [t["ner"] for t in tokens] == ["O", "O", "U-GPE", "O", "B-GPE", "I-GPE", "L-GPE", "O"]
# fmt: on
def test_cli_converters_conll_ner2json():
lines = [
"-DOCSTART- -X- O O",
"",
"I\tO",
"like\tO",
"London\tB-GPE",
"and\tO",
"New\tB-GPE",
"York\tI-GPE",
"City\tI-GPE",
".\tO",
"",
"I O",
"like O",
"London B-GPE",
"and O",
"New B-GPE",
"York I-GPE",
"City I-GPE",
". O",
"",
"I PRP O",
"like VBP O",
"London NNP B-GPE",
"and CC O",
"New NNP B-GPE",
"York NNP I-GPE",
"City NNP I-GPE",
". . O",
"",
"I PRP _ O",
"like VBP _ O",
"London NNP _ B-GPE",
"and CC _ O",
"New NNP _ B-GPE",
"York NNP _ I-GPE",
"City NNP _ I-GPE",
". . _ O",
"",
"I\tPRP\t_\tO",
"like\tVBP\t_\tO",
"London\tNNP\t_\tB-GPE",
"and\tCC\t_\tO",
"New\tNNP\t_\tB-GPE",
"York\tNNP\t_\tI-GPE",
"City\tNNP\t_\tI-GPE",
".\t.\t_\tO",
]
input_data = "\n".join(lines)
converted = conll_ner2json(input_data, n_sents=10)
assert len(converted) == 1
assert converted[0]["id"] == 0
assert len(converted[0]["paragraphs"]) == 1
assert len(converted[0]["paragraphs"][0]["sentences"]) == 5
for i in range(0, 5):
sent = converted[0]["paragraphs"][0]["sentences"][i]
assert len(sent["tokens"]) == 8
tokens = sent["tokens"]
# fmt: off
assert [t["orth"] for t in tokens] == ["I", "like", "London", "and", "New", "York", "City", "."]
assert [t["ner"] for t in tokens] == ["O", "O", "U-GPE", "O", "B-GPE", "I-GPE", "L-GPE", "O"]
# fmt: on
def test_pretrain_make_docs():
nlp = English()
valid_jsonl_text = {"text": "Some text"}
docs, skip_count = make_docs(nlp, [valid_jsonl_text], 1, 10)
assert len(docs) == 1
assert skip_count == 0
valid_jsonl_tokens = {"tokens": ["Some", "tokens"]}
docs, skip_count = make_docs(nlp, [valid_jsonl_tokens], 1, 10)
assert len(docs) == 1
assert skip_count == 0
invalid_jsonl_type = 0
with pytest.raises(TypeError):
make_docs(nlp, [invalid_jsonl_type], 1, 100)
invalid_jsonl_key = {"invalid": "Does not matter"}
with pytest.raises(ValueError):
make_docs(nlp, [invalid_jsonl_key], 1, 100)
empty_jsonl_text = {"text": ""}
docs, skip_count = make_docs(nlp, [empty_jsonl_text], 1, 10)
assert len(docs) == 0
assert skip_count == 1
empty_jsonl_tokens = {"tokens": []}
docs, skip_count = make_docs(nlp, [empty_jsonl_tokens], 1, 10)
assert len(docs) == 0
assert skip_count == 1
too_short_jsonl = {"text": "This text is not long enough"}
docs, skip_count = make_docs(nlp, [too_short_jsonl], 10, 15)
assert len(docs) == 0
assert skip_count == 0
too_long_jsonl = {"text": "This text contains way too much tokens for this test"}
docs, skip_count = make_docs(nlp, [too_long_jsonl], 1, 5)
assert len(docs) == 0
assert skip_count == 0
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