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Add spans in spacy benchmark (#12575)
* Add spans in spacy benchmark The current implementation of spaCy benchmark accuracy / spacy evaluate doesn't include the "spans" type, so calling the command doesn't render the HTML displaCy file needed. This PR attempts to fix that by creating a new parameter for "spans" and calling the appropriate displaCy value. * Reformat file with black * Add tests for evaluate * Fix spans -> span for displacy style * Update test to check render instead * Update source so mypy passes * Add parser information to avoid warnings
This commit is contained in:
Lj Miranda
GitHub
parent
6817e3d372
commit
298e6036b7
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@ -122,6 +122,8 @@ def evaluate(
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docs = list(nlp.pipe(ex.reference.text for ex in dev_dataset[:displacy_limit]))
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render_deps = "parser" in factory_names
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render_ents = "ner" in factory_names
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render_spans = "spancat" in factory_names
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render_parses(
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docs,
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displacy_path,
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@ -129,6 +131,7 @@ def evaluate(
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limit=displacy_limit,
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deps=render_deps,
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ents=render_ents,
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spans=render_spans,
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)
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msg.good(f"Generated {displacy_limit} parses as HTML", displacy_path)
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@ -182,6 +185,7 @@ def render_parses(
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limit: int = 250,
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deps: bool = True,
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ents: bool = True,
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spans: bool = True,
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):
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docs[0].user_data["title"] = model_name
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if ents:
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@ -195,6 +199,11 @@ def render_parses(
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with (output_path / "parses.html").open("w", encoding="utf8") as file_:
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file_.write(html)
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if spans:
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html = displacy.render(docs[:limit], style="span", page=True)
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with (output_path / "spans.html").open("w", encoding="utf8") as file_:
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file_.write(html)
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def print_prf_per_type(
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msg: Printer, scores: Dict[str, Dict[str, float]], name: str, type: str
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@ -12,6 +12,7 @@ import srsly
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from click import NoSuchOption
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from packaging.specifiers import SpecifierSet
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from thinc.api import Config, ConfigValidationError
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from spacy.tokens import DocBin
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from spacy import about
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from spacy.cli import info
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@ -27,6 +28,7 @@ from spacy.cli.debug_data import _get_span_characteristics
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from spacy.cli.debug_data import _print_span_characteristics
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from spacy.cli.debug_data import _get_spans_length_freq_dist
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from spacy.cli.download import get_compatibility, get_version
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from spacy.cli.evaluate import render_parses
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from spacy.cli.init_config import RECOMMENDATIONS, init_config, fill_config
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from spacy.cli.init_pipeline import _init_labels
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from spacy.cli.package import get_third_party_dependencies
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@ -144,6 +146,70 @@ def test_issue11235():
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assert cfg["commands"][0]["script"][0] == f"hello {lang_var}"
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@pytest.mark.issue(12566)
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@pytest.mark.parametrize(
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"factory,output_file",
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[("deps", "parses.html"), ("ents", "entities.html"), ("spans", "spans.html")],
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)
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def test_issue12566(factory: str, output_file: str):
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"""
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Test if all displaCy types (ents, dep, spans) produce an HTML file
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"""
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with make_tempdir() as tmp_dir:
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# Create sample spaCy file
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doc_json = {
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"ents": [
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{"end": 54, "label": "nam_adj_country", "start": 44},
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{"end": 83, "label": "nam_liv_person", "start": 69},
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{"end": 100, "label": "nam_pro_title_book", "start": 86},
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],
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"spans": {
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"sc": [
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{"end": 54, "kb_id": "", "label": "nam_adj_country", "start": 44},
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{"end": 83, "kb_id": "", "label": "nam_liv_person", "start": 69},
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{
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"end": 100,
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"kb_id": "",
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"label": "nam_pro_title_book",
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"start": 86,
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},
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]
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},
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"text": "Niedawno czytał em nową książkę znakomitego szkockiego medioznawcy , "
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"Briana McNaira - Cultural Chaos .",
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"tokens": [
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# fmt: off
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{"id": 0, "start": 0, "end": 8, "tag": "ADV", "pos": "ADV", "morph": "Degree=Pos", "lemma": "niedawno", "dep": "advmod", "head": 1, },
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{"id": 1, "start": 9, "end": 15, "tag": "PRAET", "pos": "VERB", "morph": "Animacy=Hum|Aspect=Imp|Gender=Masc|Mood=Ind|Number=Sing|Tense=Past|VerbForm=Fin|Voice=Act", "lemma": "czytać", "dep": "ROOT", "head": 1, },
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{"id": 2, "start": 16, "end": 18, "tag": "AGLT", "pos": "NOUN", "morph": "Animacy=Inan|Case=Ins|Gender=Masc|Number=Sing", "lemma": "em", "dep": "iobj", "head": 1, },
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{"id": 3, "start": 19, "end": 23, "tag": "ADJ", "pos": "ADJ", "morph": "Case=Acc|Degree=Pos|Gender=Fem|Number=Sing", "lemma": "nowy", "dep": "amod", "head": 4, },
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{"id": 4, "start": 24, "end": 31, "tag": "SUBST", "pos": "NOUN", "morph": "Case=Acc|Gender=Fem|Number=Sing", "lemma": "książka", "dep": "obj", "head": 1, },
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{"id": 5, "start": 32, "end": 43, "tag": "ADJ", "pos": "ADJ", "morph": "Animacy=Nhum|Case=Gen|Degree=Pos|Gender=Masc|Number=Sing", "lemma": "znakomit", "dep": "acl", "head": 4, },
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{"id": 6, "start": 44, "end": 54, "tag": "ADJ", "pos": "ADJ", "morph": "Animacy=Hum|Case=Gen|Degree=Pos|Gender=Masc|Number=Sing", "lemma": "szkockiy", "dep": "amod", "head": 7, },
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{"id": 7, "start": 55, "end": 66, "tag": "SUBST", "pos": "NOUN", "morph": "Animacy=Hum|Case=Gen|Gender=Masc|Number=Sing", "lemma": "medioznawca", "dep": "iobj", "head": 5, },
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{"id": 8, "start": 67, "end": 68, "tag": "INTERP", "pos": "PUNCT", "morph": "PunctType=Comm", "lemma": ",", "dep": "punct", "head": 9, },
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{"id": 9, "start": 69, "end": 75, "tag": "SUBST", "pos": "PROPN", "morph": "Animacy=Hum|Case=Gen|Gender=Masc|Number=Sing", "lemma": "Brian", "dep": "nmod", "head": 4, },
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{"id": 10, "start": 76, "end": 83, "tag": "SUBST", "pos": "PROPN", "morph": "Animacy=Hum|Case=Gen|Gender=Masc|Number=Sing", "lemma": "McNair", "dep": "flat", "head": 9, },
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{"id": 11, "start": 84, "end": 85, "tag": "INTERP", "pos": "PUNCT", "morph": "PunctType=Dash", "lemma": "-", "dep": "punct", "head": 12, },
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{"id": 12, "start": 86, "end": 94, "tag": "SUBST", "pos": "PROPN", "morph": "Animacy=Inan|Case=Nom|Gender=Masc|Number=Sing", "lemma": "Cultural", "dep": "conj", "head": 4, },
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{"id": 13, "start": 95, "end": 100, "tag": "SUBST", "pos": "NOUN", "morph": "Animacy=Inan|Case=Nom|Gender=Masc|Number=Sing", "lemma": "Chaos", "dep": "flat", "head": 12, },
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{"id": 14, "start": 101, "end": 102, "tag": "INTERP", "pos": "PUNCT", "morph": "PunctType=Peri", "lemma": ".", "dep": "punct", "head": 1, },
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# fmt: on
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],
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}
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# Create a .spacy file
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nlp = spacy.blank("pl")
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doc = Doc(nlp.vocab).from_json(doc_json)
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# Run the evaluate command and check if the html files exist
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render_parses(
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docs=[doc], output_path=tmp_dir, model_name="", limit=1, **{factory: True}
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)
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assert (tmp_dir / output_file).is_file()
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def test_cli_info():
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nlp = Dutch()
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nlp.add_pipe("textcat")
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