mirror of
https://github.com/explosion/spaCy.git
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93 lines
3.4 KiB
Python
93 lines
3.4 KiB
Python
from timeit import default_timer as timer
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from wasabi import msg
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from ..gold import GoldCorpus
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from .. import util
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from .. import displacy
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def evaluate(
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# fmt: off
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model: ("Model name or path", "positional", None, str),
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data_path: ("Location of JSON-formatted evaluation data", "positional", None, str),
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gpu_id: ("Use GPU", "option", "g", int) = -1,
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gold_preproc: ("Use gold preprocessing", "flag", "G", bool) = False,
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displacy_path: ("Directory to output rendered parses as HTML", "option", "dp", str) = None,
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displacy_limit: ("Limit of parses to render as HTML", "option", "dl", int) = 25,
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return_scores: ("Return dict containing model scores", "flag", "R", bool) = False,
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# fmt: on
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):
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"""
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Evaluate a model. To render a sample of parses in a HTML file, set an
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output directory as the displacy_path argument.
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"""
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util.fix_random_seed()
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if gpu_id >= 0:
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util.use_gpu(gpu_id)
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util.set_env_log(False)
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data_path = util.ensure_path(data_path)
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displacy_path = util.ensure_path(displacy_path)
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if not data_path.exists():
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msg.fail("Evaluation data not found", data_path, exits=1)
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if displacy_path and not displacy_path.exists():
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msg.fail("Visualization output directory not found", displacy_path, exits=1)
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corpus = GoldCorpus(data_path, data_path)
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if model.startswith("blank:"):
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nlp = util.get_lang_class(model.replace("blank:", ""))()
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else:
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nlp = util.load_model(model)
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dev_dataset = list(corpus.dev_dataset(nlp, gold_preproc=gold_preproc))
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begin = timer()
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scorer = nlp.evaluate(dev_dataset, verbose=False)
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end = timer()
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nwords = sum(len(ex.doc) for ex in dev_dataset)
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results = {
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"Time": f"{end - begin:.2f} s",
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"Words": nwords,
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"Words/s": f"{nwords / (end - begin):.0f}",
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"TOK": f"{scorer.token_acc:.2f}",
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"TAG": f"{scorer.tags_acc:.2f}",
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"POS": f"{scorer.pos_acc:.2f}",
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"MORPH": f"{scorer.morphs_acc:.2f}",
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"UAS": f"{scorer.uas:.2f}",
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"LAS": f"{scorer.las:.2f}",
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"NER P": f"{scorer.ents_p:.2f}",
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"NER R": f"{scorer.ents_r:.2f}",
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"NER F": f"{scorer.ents_f:.2f}",
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"Textcat": f"{scorer.textcat_score:.2f}",
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"Sent P": f"{scorer.sent_p:.2f}",
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"Sent R": f"{scorer.sent_r:.2f}",
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"Sent F": f"{scorer.sent_f:.2f}",
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}
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msg.table(results, title="Results")
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if displacy_path:
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docs = [ex.doc for ex in dev_dataset]
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render_deps = "parser" in nlp.meta.get("pipeline", [])
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render_ents = "ner" in nlp.meta.get("pipeline", [])
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render_parses(
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docs,
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displacy_path,
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model_name=model,
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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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)
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msg.good(f"Generated {displacy_limit} parses as HTML", displacy_path)
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if return_scores:
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return scorer.scores
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def render_parses(docs, output_path, model_name="", limit=250, deps=True, ents=True):
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docs[0].user_data["title"] = model_name
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if ents:
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html = displacy.render(docs[:limit], style="ent", page=True)
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with (output_path / "entities.html").open("w", encoding="utf8") as file_:
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file_.write(html)
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if deps:
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html = displacy.render(
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docs[:limit], style="dep", page=True, options={"compact": True}
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)
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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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