mirror of
https://github.com/explosion/spaCy.git
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583 lines
23 KiB
Python
583 lines
23 KiB
Python
from typing import List, Sequence, Dict, Any, Tuple, Optional
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from pathlib import Path
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from collections import Counter
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import sys
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import srsly
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from wasabi import Printer, MESSAGES, msg
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import typer
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from ._util import app, Arg, Opt, show_validation_error, parse_config_overrides
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from ._util import import_code, debug_cli
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from ..training import Example
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from ..training.initialize import get_sourced_components
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from ..schemas import ConfigSchemaTraining
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from ..pipeline._parser_internals import nonproj
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from ..language import Language
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from ..util import registry, resolve_dot_names
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from .. import util
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# Minimum number of expected occurrences of NER label in data to train new label
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NEW_LABEL_THRESHOLD = 50
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# Minimum number of expected occurrences of dependency labels
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DEP_LABEL_THRESHOLD = 20
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# Minimum number of expected examples to train a new pipeline
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BLANK_MODEL_MIN_THRESHOLD = 100
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BLANK_MODEL_THRESHOLD = 2000
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@debug_cli.command(
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"data", context_settings={"allow_extra_args": True, "ignore_unknown_options": True}
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)
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@app.command(
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"debug-data",
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context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
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hidden=True, # hide this from main CLI help but still allow it to work with warning
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)
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def debug_data_cli(
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# fmt: off
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ctx: typer.Context, # This is only used to read additional arguments
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config_path: Path = Arg(..., help="Path to config file", exists=True),
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code_path: Optional[Path] = Opt(None, "--code-path", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
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ignore_warnings: bool = Opt(False, "--ignore-warnings", "-IW", help="Ignore warnings, only show stats and errors"),
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verbose: bool = Opt(False, "--verbose", "-V", help="Print additional information and explanations"),
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no_format: bool = Opt(False, "--no-format", "-NF", help="Don't pretty-print the results"),
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# fmt: on
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):
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"""
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Analyze, debug and validate your training and development data. Outputs
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useful stats, and can help you find problems like invalid entity annotations,
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cyclic dependencies, low data labels and more.
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DOCS: https://nightly.spacy.io/api/cli#debug-data
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"""
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if ctx.command.name == "debug-data":
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msg.warn(
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"The debug-data command is now available via the 'debug data' "
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"subcommand (without the hyphen). You can run python -m spacy debug "
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"--help for an overview of the other available debugging commands."
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)
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overrides = parse_config_overrides(ctx.args)
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import_code(code_path)
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debug_data(
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config_path,
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config_overrides=overrides,
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ignore_warnings=ignore_warnings,
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verbose=verbose,
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no_format=no_format,
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silent=False,
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)
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def debug_data(
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config_path: Path,
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*,
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config_overrides: Dict[str, Any] = {},
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ignore_warnings: bool = False,
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verbose: bool = False,
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no_format: bool = True,
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silent: bool = True,
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):
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msg = Printer(
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no_print=silent, pretty=not no_format, ignore_warnings=ignore_warnings
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)
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# Make sure all files and paths exists if they are needed
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with show_validation_error(config_path):
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cfg = util.load_config(config_path, overrides=config_overrides)
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nlp = util.load_model_from_config(cfg)
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config = nlp.config.interpolate()
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T = registry.resolve(config["training"], schema=ConfigSchemaTraining)
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# Use original config here, not resolved version
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sourced_components = get_sourced_components(cfg)
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frozen_components = T["frozen_components"]
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resume_components = [p for p in sourced_components if p not in frozen_components]
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pipeline = nlp.pipe_names
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factory_names = [nlp.get_pipe_meta(pipe).factory for pipe in nlp.pipe_names]
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msg.divider("Data file validation")
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# Create the gold corpus to be able to better analyze data
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dot_names = [T["train_corpus"], T["dev_corpus"]]
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train_corpus, dev_corpus = resolve_dot_names(config, dot_names)
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train_dataset = list(train_corpus(nlp))
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dev_dataset = list(dev_corpus(nlp))
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msg.good("Corpus is loadable")
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nlp.initialize(lambda: train_dataset)
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msg.good("Pipeline can be initialized with data")
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# Create all gold data here to avoid iterating over the train_dataset constantly
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gold_train_data = _compile_gold(train_dataset, factory_names, nlp, make_proj=True)
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gold_train_unpreprocessed_data = _compile_gold(
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train_dataset, factory_names, nlp, make_proj=False
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)
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gold_dev_data = _compile_gold(dev_dataset, factory_names, nlp, make_proj=True)
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train_texts = gold_train_data["texts"]
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dev_texts = gold_dev_data["texts"]
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frozen_components = T["frozen_components"]
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msg.divider("Training stats")
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msg.text(f"Language: {nlp.lang}")
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msg.text(f"Training pipeline: {', '.join(pipeline)}")
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if resume_components:
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msg.text(f"Components from other pipelines: {', '.join(resume_components)}")
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if frozen_components:
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msg.text(f"Frozen components: {', '.join(frozen_components)}")
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msg.text(f"{len(train_dataset)} training docs")
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msg.text(f"{len(dev_dataset)} evaluation docs")
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if not len(gold_dev_data):
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msg.fail("No evaluation docs")
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overlap = len(train_texts.intersection(dev_texts))
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if overlap:
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msg.warn(f"{overlap} training examples also in evaluation data")
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else:
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msg.good("No overlap between training and evaluation data")
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# TODO: make this feedback more fine-grained and report on updated
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# components vs. blank components
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if not resume_components and len(train_dataset) < BLANK_MODEL_THRESHOLD:
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text = f"Low number of examples to train a new pipeline ({len(train_dataset)})"
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if len(train_dataset) < BLANK_MODEL_MIN_THRESHOLD:
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msg.fail(text)
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else:
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msg.warn(text)
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msg.text(
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f"It's recommended to use at least {BLANK_MODEL_THRESHOLD} examples "
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f"(minimum {BLANK_MODEL_MIN_THRESHOLD})",
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show=verbose,
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)
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msg.divider("Vocab & Vectors")
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n_words = gold_train_data["n_words"]
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msg.info(
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f"{n_words} total word(s) in the data ({len(gold_train_data['words'])} unique)"
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)
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if gold_train_data["n_misaligned_words"] > 0:
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n_misaligned = gold_train_data["n_misaligned_words"]
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msg.warn(f"{n_misaligned} misaligned tokens in the training data")
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if gold_dev_data["n_misaligned_words"] > 0:
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n_misaligned = gold_dev_data["n_misaligned_words"]
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msg.warn(f"{n_misaligned} misaligned tokens in the dev data")
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most_common_words = gold_train_data["words"].most_common(10)
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msg.text(
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f"10 most common words: {_format_labels(most_common_words, counts=True)}",
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show=verbose,
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)
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if len(nlp.vocab.vectors):
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msg.info(
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f"{len(nlp.vocab.vectors)} vectors ({nlp.vocab.vectors.n_keys} "
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f"unique keys, {nlp.vocab.vectors_length} dimensions)"
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)
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n_missing_vectors = sum(gold_train_data["words_missing_vectors"].values())
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msg.warn(
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"{} words in training data without vectors ({:0.2f}%)".format(
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n_missing_vectors, n_missing_vectors / gold_train_data["n_words"]
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),
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)
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msg.text(
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"10 most common words without vectors: {}".format(
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_format_labels(
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gold_train_data["words_missing_vectors"].most_common(10),
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counts=True,
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)
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),
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show=verbose,
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)
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else:
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msg.info("No word vectors present in the package")
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if "ner" in factory_names:
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# Get all unique NER labels present in the data
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labels = set(
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label for label in gold_train_data["ner"] if label not in ("O", "-", None)
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)
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label_counts = gold_train_data["ner"]
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model_labels = _get_labels_from_model(nlp, "ner")
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new_labels = [l for l in labels if l not in model_labels]
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existing_labels = [l for l in labels if l in model_labels]
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has_low_data_warning = False
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has_no_neg_warning = False
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has_ws_ents_error = False
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has_punct_ents_warning = False
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msg.divider("Named Entity Recognition")
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msg.info(
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f"{len(new_labels)} new label(s), {len(existing_labels)} existing label(s)"
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)
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missing_values = label_counts["-"]
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msg.text(f"{missing_values} missing value(s) (tokens with '-' label)")
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for label in new_labels:
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if len(label) == 0:
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msg.fail("Empty label found in new labels")
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if new_labels:
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labels_with_counts = [
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(label, count)
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for label, count in label_counts.most_common()
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if label != "-"
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]
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labels_with_counts = _format_labels(labels_with_counts, counts=True)
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msg.text(f"New: {labels_with_counts}", show=verbose)
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if existing_labels:
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msg.text(f"Existing: {_format_labels(existing_labels)}", show=verbose)
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if gold_train_data["ws_ents"]:
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msg.fail(f"{gold_train_data['ws_ents']} invalid whitespace entity spans")
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has_ws_ents_error = True
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if gold_train_data["punct_ents"]:
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msg.warn(f"{gold_train_data['punct_ents']} entity span(s) with punctuation")
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has_punct_ents_warning = True
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for label in new_labels:
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if label_counts[label] <= NEW_LABEL_THRESHOLD:
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msg.warn(
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f"Low number of examples for new label '{label}' ({label_counts[label]})"
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)
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has_low_data_warning = True
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with msg.loading("Analyzing label distribution..."):
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neg_docs = _get_examples_without_label(train_dataset, label)
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if neg_docs == 0:
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msg.warn(f"No examples for texts WITHOUT new label '{label}'")
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has_no_neg_warning = True
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if not has_low_data_warning:
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msg.good("Good amount of examples for all labels")
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if not has_no_neg_warning:
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msg.good("Examples without occurrences available for all labels")
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if not has_ws_ents_error:
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msg.good("No entities consisting of or starting/ending with whitespace")
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if not has_punct_ents_warning:
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msg.good("No entities consisting of or starting/ending with punctuation")
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if has_low_data_warning:
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msg.text(
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f"To train a new entity type, your data should include at "
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f"least {NEW_LABEL_THRESHOLD} instances of the new label",
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show=verbose,
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)
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if has_no_neg_warning:
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msg.text(
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"Training data should always include examples of entities "
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"in context, as well as examples without a given entity "
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"type.",
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show=verbose,
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)
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if has_ws_ents_error:
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msg.text(
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"As of spaCy v2.1.0, entity spans consisting of or starting/ending "
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"with whitespace characters are considered invalid."
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)
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if has_punct_ents_warning:
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msg.text(
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"Entity spans consisting of or starting/ending "
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"with punctuation can not be trained with a noise level > 0."
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)
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if "textcat" in factory_names:
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msg.divider("Text Classification")
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labels = [label for label in gold_train_data["cats"]]
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model_labels = _get_labels_from_model(nlp, "textcat")
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new_labels = [l for l in labels if l not in model_labels]
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existing_labels = [l for l in labels if l in model_labels]
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msg.info(
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f"Text Classification: {len(new_labels)} new label(s), "
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f"{len(existing_labels)} existing label(s)"
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)
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if new_labels:
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labels_with_counts = _format_labels(
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gold_train_data["cats"].most_common(), counts=True
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)
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msg.text(f"New: {labels_with_counts}", show=verbose)
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if existing_labels:
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msg.text(f"Existing: {_format_labels(existing_labels)}", show=verbose)
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if set(gold_train_data["cats"]) != set(gold_dev_data["cats"]):
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msg.fail(
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f"The train and dev labels are not the same. "
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f"Train labels: {_format_labels(gold_train_data['cats'])}. "
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f"Dev labels: {_format_labels(gold_dev_data['cats'])}."
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)
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if gold_train_data["n_cats_multilabel"] > 0:
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msg.info(
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"The train data contains instances without "
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"mutually-exclusive classes. Use '--textcat-multilabel' "
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"when training."
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)
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if gold_dev_data["n_cats_multilabel"] == 0:
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msg.warn(
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"Potential train/dev mismatch: the train data contains "
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"instances without mutually-exclusive classes while the "
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"dev data does not."
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)
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else:
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msg.info(
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"The train data contains only instances with "
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"mutually-exclusive classes."
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)
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if gold_dev_data["n_cats_multilabel"] > 0:
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msg.fail(
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"Train/dev mismatch: the dev data contains instances "
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"without mutually-exclusive classes while the train data "
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"contains only instances with mutually-exclusive classes."
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)
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if "tagger" in factory_names:
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msg.divider("Part-of-speech Tagging")
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labels = [label for label in gold_train_data["tags"]]
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# TODO: does this need to be updated?
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msg.info(f"{len(labels)} label(s) in data")
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labels_with_counts = _format_labels(
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gold_train_data["tags"].most_common(), counts=True
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)
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msg.text(labels_with_counts, show=verbose)
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if "parser" in factory_names:
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has_low_data_warning = False
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msg.divider("Dependency Parsing")
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# profile sentence length
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msg.info(
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f"Found {gold_train_data['n_sents']} sentence(s) with an average "
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f"length of {gold_train_data['n_words'] / gold_train_data['n_sents']:.1f} words."
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)
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# check for documents with multiple sentences
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sents_per_doc = gold_train_data["n_sents"] / len(gold_train_data["texts"])
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if sents_per_doc < 1.1:
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msg.warn(
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f"The training data contains {sents_per_doc:.2f} sentences per "
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f"document. When there are very few documents containing more "
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f"than one sentence, the parser will not learn how to segment "
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f"longer texts into sentences."
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)
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# profile labels
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labels_train = [label for label in gold_train_data["deps"]]
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labels_train_unpreprocessed = [
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label for label in gold_train_unpreprocessed_data["deps"]
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]
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labels_dev = [label for label in gold_dev_data["deps"]]
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if gold_train_unpreprocessed_data["n_nonproj"] > 0:
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n_nonproj = gold_train_unpreprocessed_data["n_nonproj"]
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msg.info(f"Found {n_nonproj} nonprojective train sentence(s)")
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if gold_dev_data["n_nonproj"] > 0:
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n_nonproj = gold_dev_data["n_nonproj"]
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msg.info(f"Found {n_nonproj} nonprojective dev sentence(s)")
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msg.info(f"{len(labels_train_unpreprocessed)} label(s) in train data")
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msg.info(f"{len(labels_train)} label(s) in projectivized train data")
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labels_with_counts = _format_labels(
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gold_train_unpreprocessed_data["deps"].most_common(), counts=True
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)
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msg.text(labels_with_counts, show=verbose)
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# rare labels in train
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for label in gold_train_unpreprocessed_data["deps"]:
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if gold_train_unpreprocessed_data["deps"][label] <= DEP_LABEL_THRESHOLD:
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msg.warn(
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f"Low number of examples for label '{label}' "
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f"({gold_train_unpreprocessed_data['deps'][label]})"
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)
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has_low_data_warning = True
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# rare labels in projectivized train
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rare_projectivized_labels = []
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for label in gold_train_data["deps"]:
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if gold_train_data["deps"][label] <= DEP_LABEL_THRESHOLD and "||" in label:
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rare_projectivized_labels.append(
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f"{label}: {gold_train_data['deps'][label]}"
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)
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if len(rare_projectivized_labels) > 0:
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msg.warn(
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f"Low number of examples for {len(rare_projectivized_labels)} "
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"label(s) in the projectivized dependency trees used for "
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"training. You may want to projectivize labels such as punct "
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"before training in order to improve parser performance."
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)
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msg.warn(
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f"Projectivized labels with low numbers of examples: ",
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", ".join(rare_projectivized_labels),
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show=verbose,
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)
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has_low_data_warning = True
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# labels only in train
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if set(labels_train) - set(labels_dev):
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msg.warn(
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"The following labels were found only in the train data:",
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", ".join(set(labels_train) - set(labels_dev)),
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show=verbose,
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)
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# labels only in dev
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if set(labels_dev) - set(labels_train):
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msg.warn(
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"The following labels were found only in the dev data:",
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", ".join(set(labels_dev) - set(labels_train)),
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show=verbose,
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)
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if has_low_data_warning:
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msg.text(
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f"To train a parser, your data should include at "
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f"least {DEP_LABEL_THRESHOLD} instances of each label.",
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show=verbose,
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)
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# multiple root labels
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if len(gold_train_unpreprocessed_data["roots"]) > 1:
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msg.warn(
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f"Multiple root labels "
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f"({', '.join(gold_train_unpreprocessed_data['roots'])}) "
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f"found in training data. spaCy's parser uses a single root "
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f"label ROOT so this distinction will not be available."
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)
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# these should not happen, but just in case
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if gold_train_data["n_nonproj"] > 0:
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msg.fail(
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f"Found {gold_train_data['n_nonproj']} nonprojective "
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f"projectivized train sentence(s)"
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)
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if gold_train_data["n_cycles"] > 0:
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msg.fail(
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f"Found {gold_train_data['n_cycles']} projectivized train sentence(s) with cycles"
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)
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msg.divider("Summary")
|
|
good_counts = msg.counts[MESSAGES.GOOD]
|
|
warn_counts = msg.counts[MESSAGES.WARN]
|
|
fail_counts = msg.counts[MESSAGES.FAIL]
|
|
if good_counts:
|
|
msg.good(f"{good_counts} {'check' if good_counts == 1 else 'checks'} passed")
|
|
if warn_counts:
|
|
msg.warn(f"{warn_counts} {'warning' if warn_counts == 1 else 'warnings'}")
|
|
if fail_counts:
|
|
msg.fail(f"{fail_counts} {'error' if fail_counts == 1 else 'errors'}")
|
|
sys.exit(1)
|
|
|
|
|
|
def _load_file(file_path: Path, msg: Printer) -> None:
|
|
file_name = file_path.parts[-1]
|
|
if file_path.suffix == ".json":
|
|
with msg.loading(f"Loading {file_name}..."):
|
|
data = srsly.read_json(file_path)
|
|
msg.good(f"Loaded {file_name}")
|
|
return data
|
|
elif file_path.suffix == ".jsonl":
|
|
with msg.loading(f"Loading {file_name}..."):
|
|
data = srsly.read_jsonl(file_path)
|
|
msg.good(f"Loaded {file_name}")
|
|
return data
|
|
msg.fail(
|
|
f"Can't load file extension {file_path.suffix}",
|
|
"Expected .json or .jsonl",
|
|
exits=1,
|
|
)
|
|
|
|
|
|
def _compile_gold(
|
|
examples: Sequence[Example],
|
|
factory_names: List[str],
|
|
nlp: Language,
|
|
make_proj: bool,
|
|
) -> Dict[str, Any]:
|
|
data = {
|
|
"ner": Counter(),
|
|
"cats": Counter(),
|
|
"tags": Counter(),
|
|
"deps": Counter(),
|
|
"words": Counter(),
|
|
"roots": Counter(),
|
|
"ws_ents": 0,
|
|
"punct_ents": 0,
|
|
"n_words": 0,
|
|
"n_misaligned_words": 0,
|
|
"words_missing_vectors": Counter(),
|
|
"n_sents": 0,
|
|
"n_nonproj": 0,
|
|
"n_cycles": 0,
|
|
"n_cats_multilabel": 0,
|
|
"texts": set(),
|
|
}
|
|
for eg in examples:
|
|
gold = eg.reference
|
|
doc = eg.predicted
|
|
valid_words = [x for x in gold if x is not None]
|
|
data["words"].update(valid_words)
|
|
data["n_words"] += len(valid_words)
|
|
data["n_misaligned_words"] += len(gold) - len(valid_words)
|
|
data["texts"].add(doc.text)
|
|
if len(nlp.vocab.vectors):
|
|
for word in valid_words:
|
|
if nlp.vocab.strings[word] not in nlp.vocab.vectors:
|
|
data["words_missing_vectors"].update([word])
|
|
if "ner" in factory_names:
|
|
for i, label in enumerate(eg.get_aligned_ner()):
|
|
if label is None:
|
|
continue
|
|
if label.startswith(("B-", "U-", "L-")) and doc[i].is_space:
|
|
# "Illegal" whitespace entity
|
|
data["ws_ents"] += 1
|
|
if label.startswith(("B-", "U-", "L-")) and doc[i].text in [
|
|
".",
|
|
"'",
|
|
"!",
|
|
"?",
|
|
",",
|
|
]:
|
|
# punctuation entity: could be replaced by whitespace when training with noise,
|
|
# so add a warning to alert the user to this unexpected side effect.
|
|
data["punct_ents"] += 1
|
|
if label.startswith(("B-", "U-")):
|
|
combined_label = label.split("-")[1]
|
|
data["ner"][combined_label] += 1
|
|
elif label == "-":
|
|
data["ner"]["-"] += 1
|
|
if "textcat" in factory_names:
|
|
data["cats"].update(gold.cats)
|
|
if list(gold.cats.values()).count(1.0) != 1:
|
|
data["n_cats_multilabel"] += 1
|
|
if "tagger" in factory_names:
|
|
tags = eg.get_aligned("TAG", as_string=True)
|
|
data["tags"].update([x for x in tags if x is not None])
|
|
if "parser" in factory_names:
|
|
aligned_heads, aligned_deps = eg.get_aligned_parse(projectivize=make_proj)
|
|
data["deps"].update([x for x in aligned_deps if x is not None])
|
|
for i, (dep, head) in enumerate(zip(aligned_deps, aligned_heads)):
|
|
if head == i:
|
|
data["roots"].update([dep])
|
|
data["n_sents"] += 1
|
|
if nonproj.is_nonproj_tree(aligned_heads):
|
|
data["n_nonproj"] += 1
|
|
if nonproj.contains_cycle(aligned_heads):
|
|
data["n_cycles"] += 1
|
|
return data
|
|
|
|
|
|
def _format_labels(labels: List[Tuple[str, int]], counts: bool = False) -> str:
|
|
if counts:
|
|
return ", ".join([f"'{l}' ({c})" for l, c in labels])
|
|
return ", ".join([f"'{l}'" for l in labels])
|
|
|
|
|
|
def _get_examples_without_label(data: Sequence[Example], label: str) -> int:
|
|
count = 0
|
|
for eg in data:
|
|
labels = [
|
|
label.split("-")[1]
|
|
for label in eg.get_aligned_ner()
|
|
if label not in ("O", "-", None)
|
|
]
|
|
if label not in labels:
|
|
count += 1
|
|
return count
|
|
|
|
|
|
def _get_labels_from_model(nlp: Language, pipe_name: str) -> Sequence[str]:
|
|
if pipe_name not in nlp.pipe_names:
|
|
return set()
|
|
pipe = nlp.get_pipe(pipe_name)
|
|
return pipe.labels
|