spaCy/spacy/tests/test_cli.py

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import pytest
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from click import NoSuchOption
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from spacy.training import docs_to_json, offsets_to_biluo_tags
from spacy.training.converters import iob_to_docs, conll_ner_to_docs, conllu_to_docs
from spacy.schemas import ProjectConfigSchema, RecommendationSchema, validate
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from spacy.lang.nl import Dutch
from spacy.util import ENV_VARS, load_model_from_config
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from spacy.cli import info
from spacy.cli.init_config import init_config, RECOMMENDATIONS
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from spacy.cli._util import validate_project_commands, parse_config_overrides
from spacy.cli._util import load_project_config, substitute_project_variables
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from spacy.cli._util import string_to_list
from spacy import about
from spacy.util import get_minor_version
from spacy.cli.validate import get_model_pkgs
from spacy.cli.download import get_compatibility, get_version
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from thinc.api import ConfigValidationError, Config
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import srsly
import os
from .util import make_tempdir
from ..cli.init_pipeline import _init_labels
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def test_cli_info():
nlp = Dutch()
nlp.add_pipe("textcat")
with make_tempdir() as tmp_dir:
nlp.to_disk(tmp_dir)
raw_data = info(tmp_dir, exclude=[""])
assert raw_data["lang"] == "nl"
assert raw_data["components"] == ["textcat"]
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def test_cli_converters_conllu_to_docs():
# 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_docs = list(conllu_to_docs(input_data, n_sents=1))
assert len(converted_docs) == 1
converted = [docs_to_json(converted_docs)]
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"]
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ent_offsets = [
(e[0], e[1], e[2]) for e in converted[0]["paragraphs"][0]["entities"]
]
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biluo_tags = offsets_to_biluo_tags(converted_docs[0], ent_offsets, missing="O")
assert biluo_tags == ["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",
),
],
)
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def test_cli_converters_conllu_to_docs_name_ner_map(lines):
input_data = "\n".join(lines)
converted_docs = list(
conllu_to_docs(input_data, n_sents=1, ner_map={"PER": "PERSON", "BAD": ""})
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)
assert len(converted_docs) == 1
converted = [docs_to_json(converted_docs)]
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"]
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ent_offsets = [
(e[0], e[1], e[2]) for e in converted[0]["paragraphs"][0]["entities"]
]
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biluo_tags = offsets_to_biluo_tags(converted_docs[0], ent_offsets, missing="O")
assert biluo_tags == ["O", "B-PERSON", "L-PERSON", "O", "O"]
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def test_cli_converters_conllu_to_docs_subtokens():
Add convert CLI option to merge CoNLL-U subtokens (#4722) * Add convert CLI option to merge CoNLL-U subtokens Add `-T` option to convert CLI that merges CoNLL-U subtokens into one token in the converted data. Each CoNLL-U sentence is read into a `Doc` and the `Retokenizer` is used to merge subtokens with features as follows: * `orth` is the merged token orth (should correspond to raw text and `# text`) * `tag` is all subtoken tags concatenated with `_`, e.g. `ADP_DET` * `pos` is the POS of the syntactic root of the span (as determined by the Retokenizer) * `morph` is all morphological features merged * `lemma` is all subtoken lemmas concatenated with ` `, e.g. `de o` * with `-m` all morphological features are combined with the tag using the separator `__`, e.g. `ADP_DET__Definite=Def|Gender=Masc|Number=Sing|PronType=Art` * `dep` is the dependency relation for the syntactic root of the span (as determined by the Retokenizer) Concatenated tags will be mapped to the UD POS of the syntactic root (e.g., `ADP`) and the morphological features will be the combined features. In many cases, the original UD subtokens can be reconstructed from the available features given a language-specific lookup table, e.g., Portuguese `do / ADP_DET / Definite=Def|Gender=Masc|Number=Sing|PronType=Art` is `de / ADP`, `o / DET / Definite=Def|Gender=Masc|Number=Sing|PronType=Art` or lookup rules for forms containing open class words like Spanish `hablarlo / VERB_PRON / Case=Acc|Gender=Masc|Number=Sing|Person=3|PrepCase=Npr|PronType=Prs|VerbForm=Inf`. * Clean up imports
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# 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_docs = list(
conllu_to_docs(
input_data, n_sents=1, merge_subtokens=True, append_morphology=True
)
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)
assert len(converted_docs) == 1
converted = [docs_to_json(converted_docs)]
Add convert CLI option to merge CoNLL-U subtokens (#4722) * Add convert CLI option to merge CoNLL-U subtokens Add `-T` option to convert CLI that merges CoNLL-U subtokens into one token in the converted data. Each CoNLL-U sentence is read into a `Doc` and the `Retokenizer` is used to merge subtokens with features as follows: * `orth` is the merged token orth (should correspond to raw text and `# text`) * `tag` is all subtoken tags concatenated with `_`, e.g. `ADP_DET` * `pos` is the POS of the syntactic root of the span (as determined by the Retokenizer) * `morph` is all morphological features merged * `lemma` is all subtoken lemmas concatenated with ` `, e.g. `de o` * with `-m` all morphological features are combined with the tag using the separator `__`, e.g. `ADP_DET__Definite=Def|Gender=Masc|Number=Sing|PronType=Art` * `dep` is the dependency relation for the syntactic root of the span (as determined by the Retokenizer) Concatenated tags will be mapped to the UD POS of the syntactic root (e.g., `ADP`) and the morphological features will be the combined features. In many cases, the original UD subtokens can be reconstructed from the available features given a language-specific lookup table, e.g., Portuguese `do / ADP_DET / Definite=Def|Gender=Masc|Number=Sing|PronType=Art` is `de / ADP`, `o / DET / Definite=Def|Gender=Masc|Number=Sing|PronType=Art` or lookup rules for forms containing open class words like Spanish `hablarlo / VERB_PRON / Case=Acc|Gender=Masc|Number=Sing|Person=3|PrepCase=Npr|PronType=Prs|VerbForm=Inf`. * Clean up imports
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assert converted[0]["id"] == 0
assert len(converted[0]["paragraphs"]) == 1
assert converted[0]["paragraphs"][0]["raw"] == "Dommer FE avstår. "
Add convert CLI option to merge CoNLL-U subtokens (#4722) * Add convert CLI option to merge CoNLL-U subtokens Add `-T` option to convert CLI that merges CoNLL-U subtokens into one token in the converted data. Each CoNLL-U sentence is read into a `Doc` and the `Retokenizer` is used to merge subtokens with features as follows: * `orth` is the merged token orth (should correspond to raw text and `# text`) * `tag` is all subtoken tags concatenated with `_`, e.g. `ADP_DET` * `pos` is the POS of the syntactic root of the span (as determined by the Retokenizer) * `morph` is all morphological features merged * `lemma` is all subtoken lemmas concatenated with ` `, e.g. `de o` * with `-m` all morphological features are combined with the tag using the separator `__`, e.g. `ADP_DET__Definite=Def|Gender=Masc|Number=Sing|PronType=Art` * `dep` is the dependency relation for the syntactic root of the span (as determined by the Retokenizer) Concatenated tags will be mapped to the UD POS of the syntactic root (e.g., `ADP`) and the morphological features will be the combined features. In many cases, the original UD subtokens can be reconstructed from the available features given a language-specific lookup table, e.g., Portuguese `do / ADP_DET / Definite=Def|Gender=Masc|Number=Sing|PronType=Art` is `de / ADP`, `o / DET / Definite=Def|Gender=Masc|Number=Sing|PronType=Art` or lookup rules for forms containing open class words like Spanish `hablarlo / VERB_PRON / Case=Acc|Gender=Masc|Number=Sing|Person=3|PrepCase=Npr|PronType=Prs|VerbForm=Inf`. * Clean up imports
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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",
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"PUNCT",
Add convert CLI option to merge CoNLL-U subtokens (#4722) * Add convert CLI option to merge CoNLL-U subtokens Add `-T` option to convert CLI that merges CoNLL-U subtokens into one token in the converted data. Each CoNLL-U sentence is read into a `Doc` and the `Retokenizer` is used to merge subtokens with features as follows: * `orth` is the merged token orth (should correspond to raw text and `# text`) * `tag` is all subtoken tags concatenated with `_`, e.g. `ADP_DET` * `pos` is the POS of the syntactic root of the span (as determined by the Retokenizer) * `morph` is all morphological features merged * `lemma` is all subtoken lemmas concatenated with ` `, e.g. `de o` * with `-m` all morphological features are combined with the tag using the separator `__`, e.g. `ADP_DET__Definite=Def|Gender=Masc|Number=Sing|PronType=Art` * `dep` is the dependency relation for the syntactic root of the span (as determined by the Retokenizer) Concatenated tags will be mapped to the UD POS of the syntactic root (e.g., `ADP`) and the morphological features will be the combined features. In many cases, the original UD subtokens can be reconstructed from the available features given a language-specific lookup table, e.g., Portuguese `do / ADP_DET / Definite=Def|Gender=Masc|Number=Sing|PronType=Art` is `de / ADP`, `o / DET / Definite=Def|Gender=Masc|Number=Sing|PronType=Art` or lookup rules for forms containing open class words like Spanish `hablarlo / VERB_PRON / Case=Acc|Gender=Masc|Number=Sing|Person=3|PrepCase=Npr|PronType=Prs|VerbForm=Inf`. * Clean up imports
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]
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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", "$."]
Add convert CLI option to merge CoNLL-U subtokens (#4722) * Add convert CLI option to merge CoNLL-U subtokens Add `-T` option to convert CLI that merges CoNLL-U subtokens into one token in the converted data. Each CoNLL-U sentence is read into a `Doc` and the `Retokenizer` is used to merge subtokens with features as follows: * `orth` is the merged token orth (should correspond to raw text and `# text`) * `tag` is all subtoken tags concatenated with `_`, e.g. `ADP_DET` * `pos` is the POS of the syntactic root of the span (as determined by the Retokenizer) * `morph` is all morphological features merged * `lemma` is all subtoken lemmas concatenated with ` `, e.g. `de o` * with `-m` all morphological features are combined with the tag using the separator `__`, e.g. `ADP_DET__Definite=Def|Gender=Masc|Number=Sing|PronType=Art` * `dep` is the dependency relation for the syntactic root of the span (as determined by the Retokenizer) Concatenated tags will be mapped to the UD POS of the syntactic root (e.g., `ADP`) and the morphological features will be the combined features. In many cases, the original UD subtokens can be reconstructed from the available features given a language-specific lookup table, e.g., Portuguese `do / ADP_DET / Definite=Def|Gender=Masc|Number=Sing|PronType=Art` is `de / ADP`, `o / DET / Definite=Def|Gender=Masc|Number=Sing|PronType=Art` or lookup rules for forms containing open class words like Spanish `hablarlo / VERB_PRON / Case=Acc|Gender=Masc|Number=Sing|Person=3|PrepCase=Npr|PronType=Prs|VerbForm=Inf`. * Clean up imports
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assert [t["head"] for t in tokens] == [1, 1, 0, -1]
assert [t["dep"] for t in tokens] == ["appos", "nsubj", "ROOT", "punct"]
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ent_offsets = [
(e[0], e[1], e[2]) for e in converted[0]["paragraphs"][0]["entities"]
]
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biluo_tags = offsets_to_biluo_tags(converted_docs[0], ent_offsets, missing="O")
assert biluo_tags == ["O", "U-PER", "O", "O"]
Add convert CLI option to merge CoNLL-U subtokens (#4722) * Add convert CLI option to merge CoNLL-U subtokens Add `-T` option to convert CLI that merges CoNLL-U subtokens into one token in the converted data. Each CoNLL-U sentence is read into a `Doc` and the `Retokenizer` is used to merge subtokens with features as follows: * `orth` is the merged token orth (should correspond to raw text and `# text`) * `tag` is all subtoken tags concatenated with `_`, e.g. `ADP_DET` * `pos` is the POS of the syntactic root of the span (as determined by the Retokenizer) * `morph` is all morphological features merged * `lemma` is all subtoken lemmas concatenated with ` `, e.g. `de o` * with `-m` all morphological features are combined with the tag using the separator `__`, e.g. `ADP_DET__Definite=Def|Gender=Masc|Number=Sing|PronType=Art` * `dep` is the dependency relation for the syntactic root of the span (as determined by the Retokenizer) Concatenated tags will be mapped to the UD POS of the syntactic root (e.g., `ADP`) and the morphological features will be the combined features. In many cases, the original UD subtokens can be reconstructed from the available features given a language-specific lookup table, e.g., Portuguese `do / ADP_DET / Definite=Def|Gender=Masc|Number=Sing|PronType=Art` is `de / ADP`, `o / DET / Definite=Def|Gender=Masc|Number=Sing|PronType=Art` or lookup rules for forms containing open class words like Spanish `hablarlo / VERB_PRON / Case=Acc|Gender=Masc|Number=Sing|Person=3|PrepCase=Npr|PronType=Prs|VerbForm=Inf`. * Clean up imports
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def test_cli_converters_iob_to_docs():
Updates/bugfixes for NER/IOB converters (#4186) * Updates/bugfixes for NER/IOB converters * Converter formats `ner` and `iob` use autodetect to choose a converter if possible * `iob2json` is reverted to handle sentence-per-line data like `word1|pos1|ent1 word2|pos2|ent2` * Fix bug in `merge_sentences()` so the second sentence in each batch isn't skipped * `conll_ner2json` is made more general so it can handle more formats with whitespace-separated columns * Supports all formats where the first column is the token and the final column is the IOB tag; if present, the second column is the POS tag * As in CoNLL 2003 NER, blank lines separate sentences, `-DOCSTART- -X- O O` separates documents * Add option for segmenting sentences (new flag `-s`) * Parser-based sentence segmentation with a provided model, otherwise with sentencizer (new option `-b` to specify model) * Can group sentences into documents with `n_sents` as long as sentence segmentation is available * Only applies automatic segmentation when there are no existing delimiters in the data * Provide info about settings applied during conversion with warnings and suggestions if settings conflict or might not be not optimal. * Add tests for common formats * Add '(default)' back to docs for -c auto * Add document count back to output * Revert changes to converter output message * Use explicit tabs in convert CLI test data * Adjust/add messages for n_sents=1 default * Add sample NER data to training examples * Update README * Add links in docs to example NER data * Define msg within converters
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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_docs = list(iob_to_docs(input_data, n_sents=10))
Improve spacy.gold (no GoldParse, no json format!) (#5555) * Update errors * Remove beam for now (maybe) Remove beam_utils Update setup.py Remove beam * Remove GoldParse WIP on removing goldparse Get ArcEager compiling after GoldParse excise Update setup.py Get spacy.syntax compiling after removing GoldParse Rename NewExample -> Example and clean up Clean html files Start updating tests Update Morphologizer * fix error numbers * fix merge conflict * informative error when calling to_array with wrong field * fix error catching * fixing language and scoring tests * start testing get_aligned * additional tests for new get_aligned function * Draft create_gold_state for arc_eager oracle * Fix import * Fix import * Remove TokenAnnotation code from nonproj * fixing NER one-to-many alignment * Fix many-to-one IOB codes * fix test for misaligned * attempt to fix cases with weird spaces * fix spaces * test_gold_biluo_different_tokenization works * allow None as BILUO annotation * fixed some tests + WIP roundtrip unit test * add spaces to json output format * minibatch utiltiy can deal with strings, docs or examples * fix augment (needs further testing) * various fixes in scripts - needs to be further tested * fix test_cli * cleanup * correct silly typo * add support for MORPH in to/from_array, fix morphologizer overfitting test * fix tagger * fix entity linker * ensure test keeps working with non-linked entities * pipe() takes docs, not examples * small bug fix * textcat bugfix * throw informative error when running the components with the wrong type of objects * fix parser tests to work with example (most still failing) * fix BiluoPushDown parsing entities * small fixes * bugfix tok2vec * fix renames and simple_ner labels * various small fixes * prevent writing dummy values like deps because that could interfer with sent_start values * fix the fix * implement split_sent with aligned SENT_START attribute * test for split sentences with various alignment issues, works * Return ArcEagerGoldParse from ArcEager * Update parser and NER gold stuff * Draft new GoldCorpus class * add links to to_dict * clean up * fix test checking for variants * Fix oracles * Start updating converters * Move converters under spacy.gold * Move things around * Fix naming * Fix name * Update converter to produce DocBin * Update converters * Allow DocBin to take list of Doc objects. * Make spacy convert output docbin * Fix import * Fix docbin * Fix compile in ArcEager * Fix import * Serialize all attrs by default * Update converter * Remove jsonl converter * Add json2docs converter * Draft Corpus class for DocBin * Work on train script * Update Corpus * Update DocBin * Allocate Doc before starting to add words * Make doc.from_array several times faster * Update train.py * Fix Corpus * Fix parser model * Start debugging arc_eager oracle * Update header * Fix parser declaration * Xfail some tests * Skip tests that cause crashes * Skip test causing segfault * Remove GoldCorpus * Update imports * Update after removing GoldCorpus * Fix module name of corpus * Fix mimport * Work on parser oracle * Update arc_eager oracle * Restore ArcEager.get_cost function * Update transition system * Update test_arc_eager_oracle * Remove beam test * Update test * Unskip * Unskip tests * add links to to_dict * clean up * fix test checking for variants * Allow DocBin to take list of Doc objects. * Fix compile in ArcEager * Serialize all attrs by default Move converters under spacy.gold Move things around Fix naming Fix name Update converter to produce DocBin Update converters Make spacy convert output docbin Fix import Fix docbin Fix import Update converter Remove jsonl converter Add json2docs converter * Allocate Doc before starting to add words * Make doc.from_array several times faster * Start updating converters * Work on train script * Draft Corpus class for DocBin Update Corpus Fix Corpus * Update DocBin Add missing strings when serializing * Update train.py * Fix parser model * Start debugging arc_eager oracle * Update header * Fix parser declaration * Xfail some tests Skip tests that cause crashes Skip test causing segfault * Remove GoldCorpus Update imports Update after removing GoldCorpus Fix module name of corpus Fix mimport * Work on parser oracle Update arc_eager oracle Restore ArcEager.get_cost function Update transition system * Update tests Remove beam test Update test Unskip Unskip tests * Add get_aligned_parse method in Example Fix Example.get_aligned_parse * Add kwargs to Corpus.dev_dataset to match train_dataset * Update nonproj * Use get_aligned_parse in ArcEager * Add another arc-eager oracle test * Remove Example.doc property Remove Example.doc Remove Example.doc Remove Example.doc Remove Example.doc * Update ArcEager oracle Fix Break oracle * Debugging * Fix Corpus * Fix eg.doc * Format * small fixes * limit arg for Corpus * fix test_roundtrip_docs_to_docbin * fix test_make_orth_variants * fix add_label test * Update tests * avoid writing temp dir in json2docs, fixing 4402 test * Update test * Add missing costs to NER oracle * Update test * Work on Example.get_aligned_ner method * Clean up debugging * Xfail tests * Remove prints * Remove print * Xfail some tests * Replace unseen labels for parser * Update test * Update test * Xfail test * Fix Corpus * fix imports * fix docs_to_json * various small fixes * cleanup * Support gold_preproc in Corpus * Support gold_preproc * Pass gold_preproc setting into corpus * Remove debugging * Fix gold_preproc * Fix json2docs converter * Fix convert command * Fix flake8 * Fix import * fix output_dir (converted to Path by typer) * fix var * bugfix: update states after creating golds to avoid out of bounds indexing * Improve efficiency of ArEager oracle * pull merge_sent into iob2docs to avoid Doc creation for each line * fix asserts * bugfix excl Span.end in iob2docs * Support max_length in Corpus * Fix arc_eager oracle * Filter out uannotated sentences in NER * Remove debugging in parser * Simplify NER alignment * Fix conversion of NER data * Fix NER init_gold_batch * Tweak efficiency of precomputable affine * Update onto-json default * Update gold test for NER * Fix parser test * Update test * Add NER data test * Fix convert for single file * Fix test * Hack scorer to avoid evaluating non-nered data * Fix handling of NER data in Example * Output unlabelled spans from O biluo tags in iob_utils * Fix unset variable * Return kept examples from init_gold_batch * Return examples from init_gold_batch * Dont return Example from init_gold_batch * Set spaces on gold doc after conversion * Add test * Fix spaces reading * Improve NER alignment * Improve handling of missing values in NER * Restore the 'cutting' in parser training * Add assertion * Print epochs * Restore random cuts in parser/ner training * Implement Doc.copy * Implement Example.copy * Copy examples at the start of Language.update * Don't unset example docs * Tweak parser model slightly * attempt to fix _guess_spaces * _add_entities_to_doc first, so that links don't get overwritten * fixing get_aligned_ner for one-to-many * fix indexing into x_text * small fix biluo_tags_from_offsets * Add onto-ner config * Simplify NER alignment * Fix NER scoring for partially annotated documents * fix indexing into x_text * fix test_cli failing tests by ignoring spans in doc.ents with empty label * Fix limit * Improve NER alignment * Fix count_train * Remove print statement * fix tests, we're not having nothing but None * fix clumsy fingers * Fix tests * Fix doc.ents * Remove empty docs in Corpus and improve limit * Update config Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
2020-06-26 17:34:12 +00:00
assert len(converted_docs) == 1
converted = docs_to_json(converted_docs)
assert converted["id"] == 0
assert len(converted["paragraphs"]) == 1
assert len(converted["paragraphs"][0]["sentences"]) == 4
Updates/bugfixes for NER/IOB converters (#4186) * Updates/bugfixes for NER/IOB converters * Converter formats `ner` and `iob` use autodetect to choose a converter if possible * `iob2json` is reverted to handle sentence-per-line data like `word1|pos1|ent1 word2|pos2|ent2` * Fix bug in `merge_sentences()` so the second sentence in each batch isn't skipped * `conll_ner2json` is made more general so it can handle more formats with whitespace-separated columns * Supports all formats where the first column is the token and the final column is the IOB tag; if present, the second column is the POS tag * As in CoNLL 2003 NER, blank lines separate sentences, `-DOCSTART- -X- O O` separates documents * Add option for segmenting sentences (new flag `-s`) * Parser-based sentence segmentation with a provided model, otherwise with sentencizer (new option `-b` to specify model) * Can group sentences into documents with `n_sents` as long as sentence segmentation is available * Only applies automatic segmentation when there are no existing delimiters in the data * Provide info about settings applied during conversion with warnings and suggestions if settings conflict or might not be not optimal. * Add tests for common formats * Add '(default)' back to docs for -c auto * Add document count back to output * Revert changes to converter output message * Use explicit tabs in convert CLI test data * Adjust/add messages for n_sents=1 default * Add sample NER data to training examples * Update README * Add links in docs to example NER data * Define msg within converters
2019-08-29 10:04:01 +00:00
for i in range(0, 4):
Improve spacy.gold (no GoldParse, no json format!) (#5555) * Update errors * Remove beam for now (maybe) Remove beam_utils Update setup.py Remove beam * Remove GoldParse WIP on removing goldparse Get ArcEager compiling after GoldParse excise Update setup.py Get spacy.syntax compiling after removing GoldParse Rename NewExample -> Example and clean up Clean html files Start updating tests Update Morphologizer * fix error numbers * fix merge conflict * informative error when calling to_array with wrong field * fix error catching * fixing language and scoring tests * start testing get_aligned * additional tests for new get_aligned function * Draft create_gold_state for arc_eager oracle * Fix import * Fix import * Remove TokenAnnotation code from nonproj * fixing NER one-to-many alignment * Fix many-to-one IOB codes * fix test for misaligned * attempt to fix cases with weird spaces * fix spaces * test_gold_biluo_different_tokenization works * allow None as BILUO annotation * fixed some tests + WIP roundtrip unit test * add spaces to json output format * minibatch utiltiy can deal with strings, docs or examples * fix augment (needs further testing) * various fixes in scripts - needs to be further tested * fix test_cli * cleanup * correct silly typo * add support for MORPH in to/from_array, fix morphologizer overfitting test * fix tagger * fix entity linker * ensure test keeps working with non-linked entities * pipe() takes docs, not examples * small bug fix * textcat bugfix * throw informative error when running the components with the wrong type of objects * fix parser tests to work with example (most still failing) * fix BiluoPushDown parsing entities * small fixes * bugfix tok2vec * fix renames and simple_ner labels * various small fixes * prevent writing dummy values like deps because that could interfer with sent_start values * fix the fix * implement split_sent with aligned SENT_START attribute * test for split sentences with various alignment issues, works * Return ArcEagerGoldParse from ArcEager * Update parser and NER gold stuff * Draft new GoldCorpus class * add links to to_dict * clean up * fix test checking for variants * Fix oracles * Start updating converters * Move converters under spacy.gold * Move things around * Fix naming * Fix name * Update converter to produce DocBin * Update converters * Allow DocBin to take list of Doc objects. * Make spacy convert output docbin * Fix import * Fix docbin * Fix compile in ArcEager * Fix import * Serialize all attrs by default * Update converter * Remove jsonl converter * Add json2docs converter * Draft Corpus class for DocBin * Work on train script * Update Corpus * Update DocBin * Allocate Doc before starting to add words * Make doc.from_array several times faster * Update train.py * Fix Corpus * Fix parser model * Start debugging arc_eager oracle * Update header * Fix parser declaration * Xfail some tests * Skip tests that cause crashes * Skip test causing segfault * Remove GoldCorpus * Update imports * Update after removing GoldCorpus * Fix module name of corpus * Fix mimport * Work on parser oracle * Update arc_eager oracle * Restore ArcEager.get_cost function * Update transition system * Update test_arc_eager_oracle * Remove beam test * Update test * Unskip * Unskip tests * add links to to_dict * clean up * fix test checking for variants * Allow DocBin to take list of Doc objects. * Fix compile in ArcEager * Serialize all attrs by default Move converters under spacy.gold Move things around Fix naming Fix name Update converter to produce DocBin Update converters Make spacy convert output docbin Fix import Fix docbin Fix import Update converter Remove jsonl converter Add json2docs converter * Allocate Doc before starting to add words * Make doc.from_array several times faster * Start updating converters * Work on train script * Draft Corpus class for DocBin Update Corpus Fix Corpus * Update DocBin Add missing strings when serializing * Update train.py * Fix parser model * Start debugging arc_eager oracle * Update header * Fix parser declaration * Xfail some tests Skip tests that cause crashes Skip test causing segfault * Remove GoldCorpus Update imports Update after removing GoldCorpus Fix module name of corpus Fix mimport * Work on parser oracle Update arc_eager oracle Restore ArcEager.get_cost function Update transition system * Update tests Remove beam test Update test Unskip Unskip tests * Add get_aligned_parse method in Example Fix Example.get_aligned_parse * Add kwargs to Corpus.dev_dataset to match train_dataset * Update nonproj * Use get_aligned_parse in ArcEager * Add another arc-eager oracle test * Remove Example.doc property Remove Example.doc Remove Example.doc Remove Example.doc Remove Example.doc * Update ArcEager oracle Fix Break oracle * Debugging * Fix Corpus * Fix eg.doc * Format * small fixes * limit arg for Corpus * fix test_roundtrip_docs_to_docbin * fix test_make_orth_variants * fix add_label test * Update tests * avoid writing temp dir in json2docs, fixing 4402 test * Update test * Add missing costs to NER oracle * Update test * Work on Example.get_aligned_ner method * Clean up debugging * Xfail tests * Remove prints * Remove print * Xfail some tests * Replace unseen labels for parser * Update test * Update test * Xfail test * Fix Corpus * fix imports * fix docs_to_json * various small fixes * cleanup * Support gold_preproc in Corpus * Support gold_preproc * Pass gold_preproc setting into corpus * Remove debugging * Fix gold_preproc * Fix json2docs converter * Fix convert command * Fix flake8 * Fix import * fix output_dir (converted to Path by typer) * fix var * bugfix: update states after creating golds to avoid out of bounds indexing * Improve efficiency of ArEager oracle * pull merge_sent into iob2docs to avoid Doc creation for each line * fix asserts * bugfix excl Span.end in iob2docs * Support max_length in Corpus * Fix arc_eager oracle * Filter out uannotated sentences in NER * Remove debugging in parser * Simplify NER alignment * Fix conversion of NER data * Fix NER init_gold_batch * Tweak efficiency of precomputable affine * Update onto-json default * Update gold test for NER * Fix parser test * Update test * Add NER data test * Fix convert for single file * Fix test * Hack scorer to avoid evaluating non-nered data * Fix handling of NER data in Example * Output unlabelled spans from O biluo tags in iob_utils * Fix unset variable * Return kept examples from init_gold_batch * Return examples from init_gold_batch * Dont return Example from init_gold_batch * Set spaces on gold doc after conversion * Add test * Fix spaces reading * Improve NER alignment * Improve handling of missing values in NER * Restore the 'cutting' in parser training * Add assertion * Print epochs * Restore random cuts in parser/ner training * Implement Doc.copy * Implement Example.copy * Copy examples at the start of Language.update * Don't unset example docs * Tweak parser model slightly * attempt to fix _guess_spaces * _add_entities_to_doc first, so that links don't get overwritten * fixing get_aligned_ner for one-to-many * fix indexing into x_text * small fix biluo_tags_from_offsets * Add onto-ner config * Simplify NER alignment * Fix NER scoring for partially annotated documents * fix indexing into x_text * fix test_cli failing tests by ignoring spans in doc.ents with empty label * Fix limit * Improve NER alignment * Fix count_train * Remove print statement * fix tests, we're not having nothing but None * fix clumsy fingers * Fix tests * Fix doc.ents * Remove empty docs in Corpus and improve limit * Update config Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
2020-06-26 17:34:12 +00:00
sent = converted["paragraphs"][0]["sentences"][i]
Updates/bugfixes for NER/IOB converters (#4186) * Updates/bugfixes for NER/IOB converters * Converter formats `ner` and `iob` use autodetect to choose a converter if possible * `iob2json` is reverted to handle sentence-per-line data like `word1|pos1|ent1 word2|pos2|ent2` * Fix bug in `merge_sentences()` so the second sentence in each batch isn't skipped * `conll_ner2json` is made more general so it can handle more formats with whitespace-separated columns * Supports all formats where the first column is the token and the final column is the IOB tag; if present, the second column is the POS tag * As in CoNLL 2003 NER, blank lines separate sentences, `-DOCSTART- -X- O O` separates documents * Add option for segmenting sentences (new flag `-s`) * Parser-based sentence segmentation with a provided model, otherwise with sentencizer (new option `-b` to specify model) * Can group sentences into documents with `n_sents` as long as sentence segmentation is available * Only applies automatic segmentation when there are no existing delimiters in the data * Provide info about settings applied during conversion with warnings and suggestions if settings conflict or might not be not optimal. * Add tests for common formats * Add '(default)' back to docs for -c auto * Add document count back to output * Revert changes to converter output message * Use explicit tabs in convert CLI test data * Adjust/add messages for n_sents=1 default * Add sample NER data to training examples * Update README * Add links in docs to example NER data * Define msg within converters
2019-08-29 10:04:01 +00:00
assert len(sent["tokens"]) == 8
tokens = sent["tokens"]
2020-07-04 14:25:34 +00:00
expected = ["I", "like", "London", "and", "New", "York", "City", "."]
assert [t["orth"] for t in tokens] == expected
Improve spacy.gold (no GoldParse, no json format!) (#5555) * Update errors * Remove beam for now (maybe) Remove beam_utils Update setup.py Remove beam * Remove GoldParse WIP on removing goldparse Get ArcEager compiling after GoldParse excise Update setup.py Get spacy.syntax compiling after removing GoldParse Rename NewExample -> Example and clean up Clean html files Start updating tests Update Morphologizer * fix error numbers * fix merge conflict * informative error when calling to_array with wrong field * fix error catching * fixing language and scoring tests * start testing get_aligned * additional tests for new get_aligned function * Draft create_gold_state for arc_eager oracle * Fix import * Fix import * Remove TokenAnnotation code from nonproj * fixing NER one-to-many alignment * Fix many-to-one IOB codes * fix test for misaligned * attempt to fix cases with weird spaces * fix spaces * test_gold_biluo_different_tokenization works * allow None as BILUO annotation * fixed some tests + WIP roundtrip unit test * add spaces to json output format * minibatch utiltiy can deal with strings, docs or examples * fix augment (needs further testing) * various fixes in scripts - needs to be further tested * fix test_cli * cleanup * correct silly typo * add support for MORPH in to/from_array, fix morphologizer overfitting test * fix tagger * fix entity linker * ensure test keeps working with non-linked entities * pipe() takes docs, not examples * small bug fix * textcat bugfix * throw informative error when running the components with the wrong type of objects * fix parser tests to work with example (most still failing) * fix BiluoPushDown parsing entities * small fixes * bugfix tok2vec * fix renames and simple_ner labels * various small fixes * prevent writing dummy values like deps because that could interfer with sent_start values * fix the fix * implement split_sent with aligned SENT_START attribute * test for split sentences with various alignment issues, works * Return ArcEagerGoldParse from ArcEager * Update parser and NER gold stuff * Draft new GoldCorpus class * add links to to_dict * clean up * fix test checking for variants * Fix oracles * Start updating converters * Move converters under spacy.gold * Move things around * Fix naming * Fix name * Update converter to produce DocBin * Update converters * Allow DocBin to take list of Doc objects. * Make spacy convert output docbin * Fix import * Fix docbin * Fix compile in ArcEager * Fix import * Serialize all attrs by default * Update converter * Remove jsonl converter * Add json2docs converter * Draft Corpus class for DocBin * Work on train script * Update Corpus * Update DocBin * Allocate Doc before starting to add words * Make doc.from_array several times faster * Update train.py * Fix Corpus * Fix parser model * Start debugging arc_eager oracle * Update header * Fix parser declaration * Xfail some tests * Skip tests that cause crashes * Skip test causing segfault * Remove GoldCorpus * Update imports * Update after removing GoldCorpus * Fix module name of corpus * Fix mimport * Work on parser oracle * Update arc_eager oracle * Restore ArcEager.get_cost function * Update transition system * Update test_arc_eager_oracle * Remove beam test * Update test * Unskip * Unskip tests * add links to to_dict * clean up * fix test checking for variants * Allow DocBin to take list of Doc objects. * Fix compile in ArcEager * Serialize all attrs by default Move converters under spacy.gold Move things around Fix naming Fix name Update converter to produce DocBin Update converters Make spacy convert output docbin Fix import Fix docbin Fix import Update converter Remove jsonl converter Add json2docs converter * Allocate Doc before starting to add words * Make doc.from_array several times faster * Start updating converters * Work on train script * Draft Corpus class for DocBin Update Corpus Fix Corpus * Update DocBin Add missing strings when serializing * Update train.py * Fix parser model * Start debugging arc_eager oracle * Update header * Fix parser declaration * Xfail some tests Skip tests that cause crashes Skip test causing segfault * Remove GoldCorpus Update imports Update after removing GoldCorpus Fix module name of corpus Fix mimport * Work on parser oracle Update arc_eager oracle Restore ArcEager.get_cost function Update transition system * Update tests Remove beam test Update test Unskip Unskip tests * Add get_aligned_parse method in Example Fix Example.get_aligned_parse * Add kwargs to Corpus.dev_dataset to match train_dataset * Update nonproj * Use get_aligned_parse in ArcEager * Add another arc-eager oracle test * Remove Example.doc property Remove Example.doc Remove Example.doc Remove Example.doc Remove Example.doc * Update ArcEager oracle Fix Break oracle * Debugging * Fix Corpus * Fix eg.doc * Format * small fixes * limit arg for Corpus * fix test_roundtrip_docs_to_docbin * fix test_make_orth_variants * fix add_label test * Update tests * avoid writing temp dir in json2docs, fixing 4402 test * Update test * Add missing costs to NER oracle * Update test * Work on Example.get_aligned_ner method * Clean up debugging * Xfail tests * Remove prints * Remove print * Xfail some tests * Replace unseen labels for parser * Update test * Update test * Xfail test * Fix Corpus * fix imports * fix docs_to_json * various small fixes * cleanup * Support gold_preproc in Corpus * Support gold_preproc * Pass gold_preproc setting into corpus * Remove debugging * Fix gold_preproc * Fix json2docs converter * Fix convert command * Fix flake8 * Fix import * fix output_dir (converted to Path by typer) * fix var * bugfix: update states after creating golds to avoid out of bounds indexing * Improve efficiency of ArEager oracle * pull merge_sent into iob2docs to avoid Doc creation for each line * fix asserts * bugfix excl Span.end in iob2docs * Support max_length in Corpus * Fix arc_eager oracle * Filter out uannotated sentences in NER * Remove debugging in parser * Simplify NER alignment * Fix conversion of NER data * Fix NER init_gold_batch * Tweak efficiency of precomputable affine * Update onto-json default * Update gold test for NER * Fix parser test * Update test * Add NER data test * Fix convert for single file * Fix test * Hack scorer to avoid evaluating non-nered data * Fix handling of NER data in Example * Output unlabelled spans from O biluo tags in iob_utils * Fix unset variable * Return kept examples from init_gold_batch * Return examples from init_gold_batch * Dont return Example from init_gold_batch * Set spaces on gold doc after conversion * Add test * Fix spaces reading * Improve NER alignment * Improve handling of missing values in NER * Restore the 'cutting' in parser training * Add assertion * Print epochs * Restore random cuts in parser/ner training * Implement Doc.copy * Implement Example.copy * Copy examples at the start of Language.update * Don't unset example docs * Tweak parser model slightly * attempt to fix _guess_spaces * _add_entities_to_doc first, so that links don't get overwritten * fixing get_aligned_ner for one-to-many * fix indexing into x_text * small fix biluo_tags_from_offsets * Add onto-ner config * Simplify NER alignment * Fix NER scoring for partially annotated documents * fix indexing into x_text * fix test_cli failing tests by ignoring spans in doc.ents with empty label * Fix limit * Improve NER alignment * Fix count_train * Remove print statement * fix tests, we're not having nothing but None * fix clumsy fingers * Fix tests * Fix doc.ents * Remove empty docs in Corpus and improve limit * Update config Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
2020-06-26 17:34:12 +00:00
assert len(converted_docs[0].ents) == 8
for ent in converted_docs[0].ents:
2020-07-04 14:25:34 +00:00
assert ent.text in ["New York City", "London"]
Updates/bugfixes for NER/IOB converters (#4186) * Updates/bugfixes for NER/IOB converters * Converter formats `ner` and `iob` use autodetect to choose a converter if possible * `iob2json` is reverted to handle sentence-per-line data like `word1|pos1|ent1 word2|pos2|ent2` * Fix bug in `merge_sentences()` so the second sentence in each batch isn't skipped * `conll_ner2json` is made more general so it can handle more formats with whitespace-separated columns * Supports all formats where the first column is the token and the final column is the IOB tag; if present, the second column is the POS tag * As in CoNLL 2003 NER, blank lines separate sentences, `-DOCSTART- -X- O O` separates documents * Add option for segmenting sentences (new flag `-s`) * Parser-based sentence segmentation with a provided model, otherwise with sentencizer (new option `-b` to specify model) * Can group sentences into documents with `n_sents` as long as sentence segmentation is available * Only applies automatic segmentation when there are no existing delimiters in the data * Provide info about settings applied during conversion with warnings and suggestions if settings conflict or might not be not optimal. * Add tests for common formats * Add '(default)' back to docs for -c auto * Add document count back to output * Revert changes to converter output message * Use explicit tabs in convert CLI test data * Adjust/add messages for n_sents=1 default * Add sample NER data to training examples * Update README * Add links in docs to example NER data * Define msg within converters
2019-08-29 10:04:01 +00:00
2020-09-22 09:50:19 +00:00
def test_cli_converters_conll_ner_to_docs():
Updates/bugfixes for NER/IOB converters (#4186) * Updates/bugfixes for NER/IOB converters * Converter formats `ner` and `iob` use autodetect to choose a converter if possible * `iob2json` is reverted to handle sentence-per-line data like `word1|pos1|ent1 word2|pos2|ent2` * Fix bug in `merge_sentences()` so the second sentence in each batch isn't skipped * `conll_ner2json` is made more general so it can handle more formats with whitespace-separated columns * Supports all formats where the first column is the token and the final column is the IOB tag; if present, the second column is the POS tag * As in CoNLL 2003 NER, blank lines separate sentences, `-DOCSTART- -X- O O` separates documents * Add option for segmenting sentences (new flag `-s`) * Parser-based sentence segmentation with a provided model, otherwise with sentencizer (new option `-b` to specify model) * Can group sentences into documents with `n_sents` as long as sentence segmentation is available * Only applies automatic segmentation when there are no existing delimiters in the data * Provide info about settings applied during conversion with warnings and suggestions if settings conflict or might not be not optimal. * Add tests for common formats * Add '(default)' back to docs for -c auto * Add document count back to output * Revert changes to converter output message * Use explicit tabs in convert CLI test data * Adjust/add messages for n_sents=1 default * Add sample NER data to training examples * Update README * Add links in docs to example NER data * Define msg within converters
2019-08-29 10:04:01 +00:00
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_docs = list(conll_ner_to_docs(input_data, n_sents=10))
Improve spacy.gold (no GoldParse, no json format!) (#5555) * Update errors * Remove beam for now (maybe) Remove beam_utils Update setup.py Remove beam * Remove GoldParse WIP on removing goldparse Get ArcEager compiling after GoldParse excise Update setup.py Get spacy.syntax compiling after removing GoldParse Rename NewExample -> Example and clean up Clean html files Start updating tests Update Morphologizer * fix error numbers * fix merge conflict * informative error when calling to_array with wrong field * fix error catching * fixing language and scoring tests * start testing get_aligned * additional tests for new get_aligned function * Draft create_gold_state for arc_eager oracle * Fix import * Fix import * Remove TokenAnnotation code from nonproj * fixing NER one-to-many alignment * Fix many-to-one IOB codes * fix test for misaligned * attempt to fix cases with weird spaces * fix spaces * test_gold_biluo_different_tokenization works * allow None as BILUO annotation * fixed some tests + WIP roundtrip unit test * add spaces to json output format * minibatch utiltiy can deal with strings, docs or examples * fix augment (needs further testing) * various fixes in scripts - needs to be further tested * fix test_cli * cleanup * correct silly typo * add support for MORPH in to/from_array, fix morphologizer overfitting test * fix tagger * fix entity linker * ensure test keeps working with non-linked entities * pipe() takes docs, not examples * small bug fix * textcat bugfix * throw informative error when running the components with the wrong type of objects * fix parser tests to work with example (most still failing) * fix BiluoPushDown parsing entities * small fixes * bugfix tok2vec * fix renames and simple_ner labels * various small fixes * prevent writing dummy values like deps because that could interfer with sent_start values * fix the fix * implement split_sent with aligned SENT_START attribute * test for split sentences with various alignment issues, works * Return ArcEagerGoldParse from ArcEager * Update parser and NER gold stuff * Draft new GoldCorpus class * add links to to_dict * clean up * fix test checking for variants * Fix oracles * Start updating converters * Move converters under spacy.gold * Move things around * Fix naming * Fix name * Update converter to produce DocBin * Update converters * Allow DocBin to take list of Doc objects. * Make spacy convert output docbin * Fix import * Fix docbin * Fix compile in ArcEager * Fix import * Serialize all attrs by default * Update converter * Remove jsonl converter * Add json2docs converter * Draft Corpus class for DocBin * Work on train script * Update Corpus * Update DocBin * Allocate Doc before starting to add words * Make doc.from_array several times faster * Update train.py * Fix Corpus * Fix parser model * Start debugging arc_eager oracle * Update header * Fix parser declaration * Xfail some tests * Skip tests that cause crashes * Skip test causing segfault * Remove GoldCorpus * Update imports * Update after removing GoldCorpus * Fix module name of corpus * Fix mimport * Work on parser oracle * Update arc_eager oracle * Restore ArcEager.get_cost function * Update transition system * Update test_arc_eager_oracle * Remove beam test * Update test * Unskip * Unskip tests * add links to to_dict * clean up * fix test checking for variants * Allow DocBin to take list of Doc objects. * Fix compile in ArcEager * Serialize all attrs by default Move converters under spacy.gold Move things around Fix naming Fix name Update converter to produce DocBin Update converters Make spacy convert output docbin Fix import Fix docbin Fix import Update converter Remove jsonl converter Add json2docs converter * Allocate Doc before starting to add words * Make doc.from_array several times faster * Start updating converters * Work on train script * Draft Corpus class for DocBin Update Corpus Fix Corpus * Update DocBin Add missing strings when serializing * Update train.py * Fix parser model * Start debugging arc_eager oracle * Update header * Fix parser declaration * Xfail some tests Skip tests that cause crashes Skip test causing segfault * Remove GoldCorpus Update imports Update after removing GoldCorpus Fix module name of corpus Fix mimport * Work on parser oracle Update arc_eager oracle Restore ArcEager.get_cost function Update transition system * Update tests Remove beam test Update test Unskip Unskip tests * Add get_aligned_parse method in Example Fix Example.get_aligned_parse * Add kwargs to Corpus.dev_dataset to match train_dataset * Update nonproj * Use get_aligned_parse in ArcEager * Add another arc-eager oracle test * Remove Example.doc property Remove Example.doc Remove Example.doc Remove Example.doc Remove Example.doc * Update ArcEager oracle Fix Break oracle * Debugging * Fix Corpus * Fix eg.doc * Format * small fixes * limit arg for Corpus * fix test_roundtrip_docs_to_docbin * fix test_make_orth_variants * fix add_label test * Update tests * avoid writing temp dir in json2docs, fixing 4402 test * Update test * Add missing costs to NER oracle * Update test * Work on Example.get_aligned_ner method * Clean up debugging * Xfail tests * Remove prints * Remove print * Xfail some tests * Replace unseen labels for parser * Update test * Update test * Xfail test * Fix Corpus * fix imports * fix docs_to_json * various small fixes * cleanup * Support gold_preproc in Corpus * Support gold_preproc * Pass gold_preproc setting into corpus * Remove debugging * Fix gold_preproc * Fix json2docs converter * Fix convert command * Fix flake8 * Fix import * fix output_dir (converted to Path by typer) * fix var * bugfix: update states after creating golds to avoid out of bounds indexing * Improve efficiency of ArEager oracle * pull merge_sent into iob2docs to avoid Doc creation for each line * fix asserts * bugfix excl Span.end in iob2docs * Support max_length in Corpus * Fix arc_eager oracle * Filter out uannotated sentences in NER * Remove debugging in parser * Simplify NER alignment * Fix conversion of NER data * Fix NER init_gold_batch * Tweak efficiency of precomputable affine * Update onto-json default * Update gold test for NER * Fix parser test * Update test * Add NER data test * Fix convert for single file * Fix test * Hack scorer to avoid evaluating non-nered data * Fix handling of NER data in Example * Output unlabelled spans from O biluo tags in iob_utils * Fix unset variable * Return kept examples from init_gold_batch * Return examples from init_gold_batch * Dont return Example from init_gold_batch * Set spaces on gold doc after conversion * Add test * Fix spaces reading * Improve NER alignment * Improve handling of missing values in NER * Restore the 'cutting' in parser training * Add assertion * Print epochs * Restore random cuts in parser/ner training * Implement Doc.copy * Implement Example.copy * Copy examples at the start of Language.update * Don't unset example docs * Tweak parser model slightly * attempt to fix _guess_spaces * _add_entities_to_doc first, so that links don't get overwritten * fixing get_aligned_ner for one-to-many * fix indexing into x_text * small fix biluo_tags_from_offsets * Add onto-ner config * Simplify NER alignment * Fix NER scoring for partially annotated documents * fix indexing into x_text * fix test_cli failing tests by ignoring spans in doc.ents with empty label * Fix limit * Improve NER alignment * Fix count_train * Remove print statement * fix tests, we're not having nothing but None * fix clumsy fingers * Fix tests * Fix doc.ents * Remove empty docs in Corpus and improve limit * Update config Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
2020-06-26 17:34:12 +00:00
assert len(converted_docs) == 1
converted = docs_to_json(converted_docs)
assert converted["id"] == 0
assert len(converted["paragraphs"]) == 1
assert len(converted["paragraphs"][0]["sentences"]) == 5
Updates/bugfixes for NER/IOB converters (#4186) * Updates/bugfixes for NER/IOB converters * Converter formats `ner` and `iob` use autodetect to choose a converter if possible * `iob2json` is reverted to handle sentence-per-line data like `word1|pos1|ent1 word2|pos2|ent2` * Fix bug in `merge_sentences()` so the second sentence in each batch isn't skipped * `conll_ner2json` is made more general so it can handle more formats with whitespace-separated columns * Supports all formats where the first column is the token and the final column is the IOB tag; if present, the second column is the POS tag * As in CoNLL 2003 NER, blank lines separate sentences, `-DOCSTART- -X- O O` separates documents * Add option for segmenting sentences (new flag `-s`) * Parser-based sentence segmentation with a provided model, otherwise with sentencizer (new option `-b` to specify model) * Can group sentences into documents with `n_sents` as long as sentence segmentation is available * Only applies automatic segmentation when there are no existing delimiters in the data * Provide info about settings applied during conversion with warnings and suggestions if settings conflict or might not be not optimal. * Add tests for common formats * Add '(default)' back to docs for -c auto * Add document count back to output * Revert changes to converter output message * Use explicit tabs in convert CLI test data * Adjust/add messages for n_sents=1 default * Add sample NER data to training examples * Update README * Add links in docs to example NER data * Define msg within converters
2019-08-29 10:04:01 +00:00
for i in range(0, 5):
Improve spacy.gold (no GoldParse, no json format!) (#5555) * Update errors * Remove beam for now (maybe) Remove beam_utils Update setup.py Remove beam * Remove GoldParse WIP on removing goldparse Get ArcEager compiling after GoldParse excise Update setup.py Get spacy.syntax compiling after removing GoldParse Rename NewExample -> Example and clean up Clean html files Start updating tests Update Morphologizer * fix error numbers * fix merge conflict * informative error when calling to_array with wrong field * fix error catching * fixing language and scoring tests * start testing get_aligned * additional tests for new get_aligned function * Draft create_gold_state for arc_eager oracle * Fix import * Fix import * Remove TokenAnnotation code from nonproj * fixing NER one-to-many alignment * Fix many-to-one IOB codes * fix test for misaligned * attempt to fix cases with weird spaces * fix spaces * test_gold_biluo_different_tokenization works * allow None as BILUO annotation * fixed some tests + WIP roundtrip unit test * add spaces to json output format * minibatch utiltiy can deal with strings, docs or examples * fix augment (needs further testing) * various fixes in scripts - needs to be further tested * fix test_cli * cleanup * correct silly typo * add support for MORPH in to/from_array, fix morphologizer overfitting test * fix tagger * fix entity linker * ensure test keeps working with non-linked entities * pipe() takes docs, not examples * small bug fix * textcat bugfix * throw informative error when running the components with the wrong type of objects * fix parser tests to work with example (most still failing) * fix BiluoPushDown parsing entities * small fixes * bugfix tok2vec * fix renames and simple_ner labels * various small fixes * prevent writing dummy values like deps because that could interfer with sent_start values * fix the fix * implement split_sent with aligned SENT_START attribute * test for split sentences with various alignment issues, works * Return ArcEagerGoldParse from ArcEager * Update parser and NER gold stuff * Draft new GoldCorpus class * add links to to_dict * clean up * fix test checking for variants * Fix oracles * Start updating converters * Move converters under spacy.gold * Move things around * Fix naming * Fix name * Update converter to produce DocBin * Update converters * Allow DocBin to take list of Doc objects. * Make spacy convert output docbin * Fix import * Fix docbin * Fix compile in ArcEager * Fix import * Serialize all attrs by default * Update converter * Remove jsonl converter * Add json2docs converter * Draft Corpus class for DocBin * Work on train script * Update Corpus * Update DocBin * Allocate Doc before starting to add words * Make doc.from_array several times faster * Update train.py * Fix Corpus * Fix parser model * Start debugging arc_eager oracle * Update header * Fix parser declaration * Xfail some tests * Skip tests that cause crashes * Skip test causing segfault * Remove GoldCorpus * Update imports * Update after removing GoldCorpus * Fix module name of corpus * Fix mimport * Work on parser oracle * Update arc_eager oracle * Restore ArcEager.get_cost function * Update transition system * Update test_arc_eager_oracle * Remove beam test * Update test * Unskip * Unskip tests * add links to to_dict * clean up * fix test checking for variants * Allow DocBin to take list of Doc objects. * Fix compile in ArcEager * Serialize all attrs by default Move converters under spacy.gold Move things around Fix naming Fix name Update converter to produce DocBin Update converters Make spacy convert output docbin Fix import Fix docbin Fix import Update converter Remove jsonl converter Add json2docs converter * Allocate Doc before starting to add words * Make doc.from_array several times faster * Start updating converters * Work on train script * Draft Corpus class for DocBin Update Corpus Fix Corpus * Update DocBin Add missing strings when serializing * Update train.py * Fix parser model * Start debugging arc_eager oracle * Update header * Fix parser declaration * Xfail some tests Skip tests that cause crashes Skip test causing segfault * Remove GoldCorpus Update imports Update after removing GoldCorpus Fix module name of corpus Fix mimport * Work on parser oracle Update arc_eager oracle Restore ArcEager.get_cost function Update transition system * Update tests Remove beam test Update test Unskip Unskip tests * Add get_aligned_parse method in Example Fix Example.get_aligned_parse * Add kwargs to Corpus.dev_dataset to match train_dataset * Update nonproj * Use get_aligned_parse in ArcEager * Add another arc-eager oracle test * Remove Example.doc property Remove Example.doc Remove Example.doc Remove Example.doc Remove Example.doc * Update ArcEager oracle Fix Break oracle * Debugging * Fix Corpus * Fix eg.doc * Format * small fixes * limit arg for Corpus * fix test_roundtrip_docs_to_docbin * fix test_make_orth_variants * fix add_label test * Update tests * avoid writing temp dir in json2docs, fixing 4402 test * Update test * Add missing costs to NER oracle * Update test * Work on Example.get_aligned_ner method * Clean up debugging * Xfail tests * Remove prints * Remove print * Xfail some tests * Replace unseen labels for parser * Update test * Update test * Xfail test * Fix Corpus * fix imports * fix docs_to_json * various small fixes * cleanup * Support gold_preproc in Corpus * Support gold_preproc * Pass gold_preproc setting into corpus * Remove debugging * Fix gold_preproc * Fix json2docs converter * Fix convert command * Fix flake8 * Fix import * fix output_dir (converted to Path by typer) * fix var * bugfix: update states after creating golds to avoid out of bounds indexing * Improve efficiency of ArEager oracle * pull merge_sent into iob2docs to avoid Doc creation for each line * fix asserts * bugfix excl Span.end in iob2docs * Support max_length in Corpus * Fix arc_eager oracle * Filter out uannotated sentences in NER * Remove debugging in parser * Simplify NER alignment * Fix conversion of NER data * Fix NER init_gold_batch * Tweak efficiency of precomputable affine * Update onto-json default * Update gold test for NER * Fix parser test * Update test * Add NER data test * Fix convert for single file * Fix test * Hack scorer to avoid evaluating non-nered data * Fix handling of NER data in Example * Output unlabelled spans from O biluo tags in iob_utils * Fix unset variable * Return kept examples from init_gold_batch * Return examples from init_gold_batch * Dont return Example from init_gold_batch * Set spaces on gold doc after conversion * Add test * Fix spaces reading * Improve NER alignment * Improve handling of missing values in NER * Restore the 'cutting' in parser training * Add assertion * Print epochs * Restore random cuts in parser/ner training * Implement Doc.copy * Implement Example.copy * Copy examples at the start of Language.update * Don't unset example docs * Tweak parser model slightly * attempt to fix _guess_spaces * _add_entities_to_doc first, so that links don't get overwritten * fixing get_aligned_ner for one-to-many * fix indexing into x_text * small fix biluo_tags_from_offsets * Add onto-ner config * Simplify NER alignment * Fix NER scoring for partially annotated documents * fix indexing into x_text * fix test_cli failing tests by ignoring spans in doc.ents with empty label * Fix limit * Improve NER alignment * Fix count_train * Remove print statement * fix tests, we're not having nothing but None * fix clumsy fingers * Fix tests * Fix doc.ents * Remove empty docs in Corpus and improve limit * Update config Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
2020-06-26 17:34:12 +00:00
sent = converted["paragraphs"][0]["sentences"][i]
Updates/bugfixes for NER/IOB converters (#4186) * Updates/bugfixes for NER/IOB converters * Converter formats `ner` and `iob` use autodetect to choose a converter if possible * `iob2json` is reverted to handle sentence-per-line data like `word1|pos1|ent1 word2|pos2|ent2` * Fix bug in `merge_sentences()` so the second sentence in each batch isn't skipped * `conll_ner2json` is made more general so it can handle more formats with whitespace-separated columns * Supports all formats where the first column is the token and the final column is the IOB tag; if present, the second column is the POS tag * As in CoNLL 2003 NER, blank lines separate sentences, `-DOCSTART- -X- O O` separates documents * Add option for segmenting sentences (new flag `-s`) * Parser-based sentence segmentation with a provided model, otherwise with sentencizer (new option `-b` to specify model) * Can group sentences into documents with `n_sents` as long as sentence segmentation is available * Only applies automatic segmentation when there are no existing delimiters in the data * Provide info about settings applied during conversion with warnings and suggestions if settings conflict or might not be not optimal. * Add tests for common formats * Add '(default)' back to docs for -c auto * Add document count back to output * Revert changes to converter output message * Use explicit tabs in convert CLI test data * Adjust/add messages for n_sents=1 default * Add sample NER data to training examples * Update README * Add links in docs to example NER data * Define msg within converters
2019-08-29 10:04:01 +00:00
assert len(sent["tokens"]) == 8
tokens = sent["tokens"]
2019-08-31 11:39:06 +00:00
# fmt: off
Updates/bugfixes for NER/IOB converters (#4186) * Updates/bugfixes for NER/IOB converters * Converter formats `ner` and `iob` use autodetect to choose a converter if possible * `iob2json` is reverted to handle sentence-per-line data like `word1|pos1|ent1 word2|pos2|ent2` * Fix bug in `merge_sentences()` so the second sentence in each batch isn't skipped * `conll_ner2json` is made more general so it can handle more formats with whitespace-separated columns * Supports all formats where the first column is the token and the final column is the IOB tag; if present, the second column is the POS tag * As in CoNLL 2003 NER, blank lines separate sentences, `-DOCSTART- -X- O O` separates documents * Add option for segmenting sentences (new flag `-s`) * Parser-based sentence segmentation with a provided model, otherwise with sentencizer (new option `-b` to specify model) * Can group sentences into documents with `n_sents` as long as sentence segmentation is available * Only applies automatic segmentation when there are no existing delimiters in the data * Provide info about settings applied during conversion with warnings and suggestions if settings conflict or might not be not optimal. * Add tests for common formats * Add '(default)' back to docs for -c auto * Add document count back to output * Revert changes to converter output message * Use explicit tabs in convert CLI test data * Adjust/add messages for n_sents=1 default * Add sample NER data to training examples * Update README * Add links in docs to example NER data * Define msg within converters
2019-08-29 10:04:01 +00:00
assert [t["orth"] for t in tokens] == ["I", "like", "London", "and", "New", "York", "City", "."]
2019-08-31 11:39:06 +00:00
# fmt: on
Improve spacy.gold (no GoldParse, no json format!) (#5555) * Update errors * Remove beam for now (maybe) Remove beam_utils Update setup.py Remove beam * Remove GoldParse WIP on removing goldparse Get ArcEager compiling after GoldParse excise Update setup.py Get spacy.syntax compiling after removing GoldParse Rename NewExample -> Example and clean up Clean html files Start updating tests Update Morphologizer * fix error numbers * fix merge conflict * informative error when calling to_array with wrong field * fix error catching * fixing language and scoring tests * start testing get_aligned * additional tests for new get_aligned function * Draft create_gold_state for arc_eager oracle * Fix import * Fix import * Remove TokenAnnotation code from nonproj * fixing NER one-to-many alignment * Fix many-to-one IOB codes * fix test for misaligned * attempt to fix cases with weird spaces * fix spaces * test_gold_biluo_different_tokenization works * allow None as BILUO annotation * fixed some tests + WIP roundtrip unit test * add spaces to json output format * minibatch utiltiy can deal with strings, docs or examples * fix augment (needs further testing) * various fixes in scripts - needs to be further tested * fix test_cli * cleanup * correct silly typo * add support for MORPH in to/from_array, fix morphologizer overfitting test * fix tagger * fix entity linker * ensure test keeps working with non-linked entities * pipe() takes docs, not examples * small bug fix * textcat bugfix * throw informative error when running the components with the wrong type of objects * fix parser tests to work with example (most still failing) * fix BiluoPushDown parsing entities * small fixes * bugfix tok2vec * fix renames and simple_ner labels * various small fixes * prevent writing dummy values like deps because that could interfer with sent_start values * fix the fix * implement split_sent with aligned SENT_START attribute * test for split sentences with various alignment issues, works * Return ArcEagerGoldParse from ArcEager * Update parser and NER gold stuff * Draft new GoldCorpus class * add links to to_dict * clean up * fix test checking for variants * Fix oracles * Start updating converters * Move converters under spacy.gold * Move things around * Fix naming * Fix name * Update converter to produce DocBin * Update converters * Allow DocBin to take list of Doc objects. * Make spacy convert output docbin * Fix import * Fix docbin * Fix compile in ArcEager * Fix import * Serialize all attrs by default * Update converter * Remove jsonl converter * Add json2docs converter * Draft Corpus class for DocBin * Work on train script * Update Corpus * Update DocBin * Allocate Doc before starting to add words * Make doc.from_array several times faster * Update train.py * Fix Corpus * Fix parser model * Start debugging arc_eager oracle * Update header * Fix parser declaration * Xfail some tests * Skip tests that cause crashes * Skip test causing segfault * Remove GoldCorpus * Update imports * Update after removing GoldCorpus * Fix module name of corpus * Fix mimport * Work on parser oracle * Update arc_eager oracle * Restore ArcEager.get_cost function * Update transition system * Update test_arc_eager_oracle * Remove beam test * Update test * Unskip * Unskip tests * add links to to_dict * clean up * fix test checking for variants * Allow DocBin to take list of Doc objects. * Fix compile in ArcEager * Serialize all attrs by default Move converters under spacy.gold Move things around Fix naming Fix name Update converter to produce DocBin Update converters Make spacy convert output docbin Fix import Fix docbin Fix import Update converter Remove jsonl converter Add json2docs converter * Allocate Doc before starting to add words * Make doc.from_array several times faster * Start updating converters * Work on train script * Draft Corpus class for DocBin Update Corpus Fix Corpus * Update DocBin Add missing strings when serializing * Update train.py * Fix parser model * Start debugging arc_eager oracle * Update header * Fix parser declaration * Xfail some tests Skip tests that cause crashes Skip test causing segfault * Remove GoldCorpus Update imports Update after removing GoldCorpus Fix module name of corpus Fix mimport * Work on parser oracle Update arc_eager oracle Restore ArcEager.get_cost function Update transition system * Update tests Remove beam test Update test Unskip Unskip tests * Add get_aligned_parse method in Example Fix Example.get_aligned_parse * Add kwargs to Corpus.dev_dataset to match train_dataset * Update nonproj * Use get_aligned_parse in ArcEager * Add another arc-eager oracle test * Remove Example.doc property Remove Example.doc Remove Example.doc Remove Example.doc Remove Example.doc * Update ArcEager oracle Fix Break oracle * Debugging * Fix Corpus * Fix eg.doc * Format * small fixes * limit arg for Corpus * fix test_roundtrip_docs_to_docbin * fix test_make_orth_variants * fix add_label test * Update tests * avoid writing temp dir in json2docs, fixing 4402 test * Update test * Add missing costs to NER oracle * Update test * Work on Example.get_aligned_ner method * Clean up debugging * Xfail tests * Remove prints * Remove print * Xfail some tests * Replace unseen labels for parser * Update test * Update test * Xfail test * Fix Corpus * fix imports * fix docs_to_json * various small fixes * cleanup * Support gold_preproc in Corpus * Support gold_preproc * Pass gold_preproc setting into corpus * Remove debugging * Fix gold_preproc * Fix json2docs converter * Fix convert command * Fix flake8 * Fix import * fix output_dir (converted to Path by typer) * fix var * bugfix: update states after creating golds to avoid out of bounds indexing * Improve efficiency of ArEager oracle * pull merge_sent into iob2docs to avoid Doc creation for each line * fix asserts * bugfix excl Span.end in iob2docs * Support max_length in Corpus * Fix arc_eager oracle * Filter out uannotated sentences in NER * Remove debugging in parser * Simplify NER alignment * Fix conversion of NER data * Fix NER init_gold_batch * Tweak efficiency of precomputable affine * Update onto-json default * Update gold test for NER * Fix parser test * Update test * Add NER data test * Fix convert for single file * Fix test * Hack scorer to avoid evaluating non-nered data * Fix handling of NER data in Example * Output unlabelled spans from O biluo tags in iob_utils * Fix unset variable * Return kept examples from init_gold_batch * Return examples from init_gold_batch * Dont return Example from init_gold_batch * Set spaces on gold doc after conversion * Add test * Fix spaces reading * Improve NER alignment * Improve handling of missing values in NER * Restore the 'cutting' in parser training * Add assertion * Print epochs * Restore random cuts in parser/ner training * Implement Doc.copy * Implement Example.copy * Copy examples at the start of Language.update * Don't unset example docs * Tweak parser model slightly * attempt to fix _guess_spaces * _add_entities_to_doc first, so that links don't get overwritten * fixing get_aligned_ner for one-to-many * fix indexing into x_text * small fix biluo_tags_from_offsets * Add onto-ner config * Simplify NER alignment * Fix NER scoring for partially annotated documents * fix indexing into x_text * fix test_cli failing tests by ignoring spans in doc.ents with empty label * Fix limit * Improve NER alignment * Fix count_train * Remove print statement * fix tests, we're not having nothing but None * fix clumsy fingers * Fix tests * Fix doc.ents * Remove empty docs in Corpus and improve limit * Update config Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
2020-06-26 17:34:12 +00:00
assert len(converted_docs[0].ents) == 10
for ent in converted_docs[0].ents:
2020-07-04 14:25:34 +00:00
assert ent.text in ["New York City", "London"]
Updates/bugfixes for NER/IOB converters (#4186) * Updates/bugfixes for NER/IOB converters * Converter formats `ner` and `iob` use autodetect to choose a converter if possible * `iob2json` is reverted to handle sentence-per-line data like `word1|pos1|ent1 word2|pos2|ent2` * Fix bug in `merge_sentences()` so the second sentence in each batch isn't skipped * `conll_ner2json` is made more general so it can handle more formats with whitespace-separated columns * Supports all formats where the first column is the token and the final column is the IOB tag; if present, the second column is the POS tag * As in CoNLL 2003 NER, blank lines separate sentences, `-DOCSTART- -X- O O` separates documents * Add option for segmenting sentences (new flag `-s`) * Parser-based sentence segmentation with a provided model, otherwise with sentencizer (new option `-b` to specify model) * Can group sentences into documents with `n_sents` as long as sentence segmentation is available * Only applies automatic segmentation when there are no existing delimiters in the data * Provide info about settings applied during conversion with warnings and suggestions if settings conflict or might not be not optimal. * Add tests for common formats * Add '(default)' back to docs for -c auto * Add document count back to output * Revert changes to converter output message * Use explicit tabs in convert CLI test data * Adjust/add messages for n_sents=1 default * Add sample NER data to training examples * Update README * Add links in docs to example NER data * Define msg within converters
2019-08-29 10:04:01 +00:00
def test_project_config_validation_full():
config = {
"vars": {"some_var": 20},
"directories": ["assets", "configs", "corpus", "scripts", "training"],
"assets": [
{
"dest": "x",
"url": "https://example.com",
"checksum": "63373dd656daa1fd3043ce166a59474c",
},
{
"dest": "y",
"git": {
"repo": "https://github.com/example/repo",
"branch": "develop",
"path": "y",
},
},
],
"commands": [
{
"name": "train",
"help": "Train a model",
"script": ["python -m spacy train config.cfg -o training"],
"deps": ["config.cfg", "corpus/training.spcy"],
"outputs": ["training/model-best"],
},
{"name": "test", "script": ["pytest", "custom.py"], "no_skip": True},
],
"workflows": {"all": ["train", "test"], "train": ["train"]},
}
errors = validate(ProjectConfigSchema, config)
assert not errors
2020-07-10 16:21:01 +00:00
@pytest.mark.parametrize(
"config",
[
{"commands": [{"name": "a"}, {"name": "a"}]},
{"commands": [{"name": "a"}], "workflows": {"a": []}},
{"commands": [{"name": "a"}], "workflows": {"b": ["c"]}},
],
)
def test_project_config_validation1(config):
with pytest.raises(SystemExit):
validate_project_commands(config)
@pytest.mark.parametrize(
"config,n_errors",
[
({"commands": {"a": []}}, 1),
({"commands": [{"help": "..."}]}, 1),
({"commands": [{"name": "a", "extra": "b"}]}, 1),
({"commands": [{"extra": "b"}]}, 2),
({"commands": [{"name": "a", "deps": [123]}]}, 1),
],
)
def test_project_config_validation2(config, n_errors):
errors = validate(ProjectConfigSchema, config)
assert len(errors) == n_errors
@pytest.mark.parametrize(
"int_value",
[10, pytest.param("10", marks=pytest.mark.xfail)],
)
def test_project_config_interpolation(int_value):
variables = {"a": int_value, "b": {"c": "foo", "d": True}}
commands = [
{"name": "x", "script": ["hello ${vars.a} ${vars.b.c}"]},
{"name": "y", "script": ["${vars.b.c} ${vars.b.d}"]},
]
project = {"commands": commands, "vars": variables}
with make_tempdir() as d:
srsly.write_yaml(d / "project.yml", project)
cfg = load_project_config(d)
assert type(cfg) == dict
assert type(cfg["commands"]) == list
assert cfg["commands"][0]["script"][0] == "hello 10 foo"
assert cfg["commands"][1]["script"][0] == "foo true"
commands = [{"name": "x", "script": ["hello ${vars.a} ${vars.b.e}"]}]
project = {"commands": commands, "vars": variables}
with pytest.raises(ConfigValidationError):
substitute_project_variables(project)
@pytest.mark.parametrize(
"greeting",
[342, "everyone", "tout le monde", pytest.param("42", marks=pytest.mark.xfail)],
)
def test_project_config_interpolation_override(greeting):
variables = {"a": "world"}
commands = [
{"name": "x", "script": ["hello ${vars.a}"]},
]
overrides = {"vars.a": greeting}
project = {"commands": commands, "vars": variables}
with make_tempdir() as d:
srsly.write_yaml(d / "project.yml", project)
cfg = load_project_config(d, overrides=overrides)
assert type(cfg) == dict
assert type(cfg["commands"]) == list
assert cfg["commands"][0]["script"][0] == f"hello {greeting}"
def test_project_config_interpolation_env():
variables = {"a": 10}
env_var = "SPACY_TEST_FOO"
env_vars = {"foo": env_var}
commands = [{"name": "x", "script": ["hello ${vars.a} ${env.foo}"]}]
project = {"commands": commands, "vars": variables, "env": env_vars}
with make_tempdir() as d:
srsly.write_yaml(d / "project.yml", project)
cfg = load_project_config(d)
assert cfg["commands"][0]["script"][0] == "hello 10 "
os.environ[env_var] = "123"
with make_tempdir() as d:
srsly.write_yaml(d / "project.yml", project)
cfg = load_project_config(d)
assert cfg["commands"][0]["script"][0] == "hello 10 123"
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@pytest.mark.parametrize(
"args,expected",
[
# fmt: off
(["--x.foo", "10"], {"x.foo": 10}),
(["--x.foo=10"], {"x.foo": 10}),
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(["--x.foo", "bar"], {"x.foo": "bar"}),
(["--x.foo=bar"], {"x.foo": "bar"}),
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(["--x.foo", "--x.bar", "baz"], {"x.foo": True, "x.bar": "baz"}),
(["--x.foo", "--x.bar=baz"], {"x.foo": True, "x.bar": "baz"}),
(["--x.foo", "10.1", "--x.bar", "--x.baz", "false"], {"x.foo": 10.1, "x.bar": True, "x.baz": False}),
(["--x.foo", "10.1", "--x.bar", "--x.baz=false"], {"x.foo": 10.1, "x.bar": True, "x.baz": False})
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# fmt: on
],
)
def test_parse_config_overrides(args, expected):
assert parse_config_overrides(args) == expected
@pytest.mark.parametrize("args", [["--foo"], ["--x.foo", "bar", "--baz"]])
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def test_parse_config_overrides_invalid(args):
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with pytest.raises(NoSuchOption):
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parse_config_overrides(args)
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@pytest.mark.parametrize("args", [["--x.foo", "bar", "baz"], ["x.foo"]])
def test_parse_config_overrides_invalid_2(args):
with pytest.raises(SystemExit):
parse_config_overrides(args)
def test_parse_cli_overrides():
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overrides = "--x.foo bar --x.bar=12 --x.baz false --y.foo=hello"
os.environ[ENV_VARS.CONFIG_OVERRIDES] = overrides
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result = parse_config_overrides([])
assert len(result) == 4
assert result["x.foo"] == "bar"
assert result["x.bar"] == 12
assert result["x.baz"] is False
assert result["y.foo"] == "hello"
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os.environ[ENV_VARS.CONFIG_OVERRIDES] = "--x"
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assert parse_config_overrides([], env_var=None) == {}
with pytest.raises(SystemExit):
parse_config_overrides([])
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os.environ[ENV_VARS.CONFIG_OVERRIDES] = "hello world"
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with pytest.raises(SystemExit):
parse_config_overrides([])
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del os.environ[ENV_VARS.CONFIG_OVERRIDES]
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@pytest.mark.parametrize("lang", ["en", "nl"])
@pytest.mark.parametrize(
"pipeline", [["tagger", "parser", "ner"], [], ["ner", "textcat", "sentencizer"]]
)
@pytest.mark.parametrize("optimize", ["efficiency", "accuracy"])
@pytest.mark.parametrize("pretraining", [True, False])
def test_init_config(lang, pipeline, optimize, pretraining):
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# TODO: add more tests and also check for GPU with transformers
config = init_config(
lang=lang,
pipeline=pipeline,
optimize=optimize,
pretraining=pretraining,
gpu=False,
)
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assert isinstance(config, Config)
if pretraining:
config["paths"]["raw_text"] = "my_data.jsonl"
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load_model_from_config(config, auto_fill=True)
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def test_model_recommendations():
for lang, data in RECOMMENDATIONS.items():
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assert RecommendationSchema(**data)
@pytest.mark.parametrize(
"value",
[
# fmt: off
"parser,textcat,tagger",
" parser, textcat ,tagger ",
'parser,textcat,tagger',
' parser, textcat ,tagger ',
' "parser"," textcat " ,"tagger "',
" 'parser',' textcat ' ,'tagger '",
'[parser,textcat,tagger]',
'["parser","textcat","tagger"]',
'[" parser" ,"textcat ", " tagger " ]',
"[parser,textcat,tagger]",
"[ parser, textcat , tagger]",
"['parser','textcat','tagger']",
"[' parser' , 'textcat', ' tagger ' ]",
# fmt: on
],
)
def test_string_to_list(value):
assert string_to_list(value, intify=False) == ["parser", "textcat", "tagger"]
@pytest.mark.parametrize(
"value",
[
# fmt: off
"1,2,3",
'[1,2,3]',
'["1","2","3"]',
'[" 1" ,"2 ", " 3 " ]',
"[' 1' , '2', ' 3 ' ]",
# fmt: on
],
)
def test_string_to_list_intify(value):
assert string_to_list(value, intify=False) == ["1", "2", "3"]
assert string_to_list(value, intify=True) == [1, 2, 3]
def test_download_compatibility():
model_name = "en_core_web_sm"
compatibility = get_compatibility()
version = get_version(model_name, compatibility)
assert get_minor_version(about.__version__) == get_minor_version(version)
def test_validate_compatibility_table():
model_pkgs, compat = get_model_pkgs()
spacy_version = get_minor_version(about.__version__)
current_compat = compat.get(spacy_version, {})
assert len(current_compat) > 0
assert "en_core_web_sm" in current_compat
@pytest.mark.parametrize("component_name", ["ner", "textcat", "spancat", "tagger"])
def test_init_labels(component_name):
nlp = Dutch()
component = nlp.add_pipe(component_name)
for label in ["T1", "T2", "T3", "T4"]:
component.add_label(label)
assert len(nlp.get_pipe(component_name).labels) == 4
with make_tempdir() as tmp_dir:
_init_labels(nlp, tmp_dir)
config = init_config(
lang="nl",
pipeline=[component_name],
optimize="efficiency",
gpu=False,
)
config["initialize"]["components"][component_name] = {
"labels": {
"@readers": "spacy.read_labels.v1",
"path": f"{tmp_dir}/{component_name}.json",
}
}
nlp2 = load_model_from_config(config, auto_fill=True)
assert len(nlp2.get_pipe(component_name).labels) == 0
nlp2.initialize()
assert len(nlp2.get_pipe(component_name).labels) == 4