mirror of
https://github.com/explosion/spaCy.git
synced 2025-01-26 01:04:34 +03:00
b5d999e510
* Add doc.cats to spacy.gold at the paragraph level
Support `doc.cats` as `"cats": [{"label": string, "value": number}]` in
the spacy JSON training format at the paragraph level.
* `spacy.gold.docs_to_json()` writes `docs.cats`
* `GoldCorpus` reads in cats in each `GoldParse`
* Update instances of gold_tuples to handle cats
Update iteration over gold_tuples / gold_parses to handle addition of
cats at the paragraph level.
* Add textcat to train CLI
* Add textcat options to train CLI
* Add textcat labels in `TextCategorizer.begin_training()`
* Add textcat evaluation to `Scorer`:
* For binary exclusive classes with provided label: F1 for label
* For 2+ exclusive classes: F1 macro average
* For multilabel (not exclusive): ROC AUC macro average (currently
relying on sklearn)
* Provide user info on textcat evaluation settings, potential
incompatibilities
* Provide pipeline to Scorer in `Language.evaluate` for textcat config
* Customize train CLI output to include only metrics relevant to current
pipeline
* Add textcat evaluation to evaluate CLI
* Fix handling of unset arguments and config params
Fix handling of unset arguments and model confiug parameters in Scorer
initialization.
* Temporarily add sklearn requirement
* Remove sklearn version number
* Improve Scorer handling of models without textcats
* Fixing Scorer handling of models without textcats
* Update Scorer output for python 2.7
* Modify inf in Scorer for python 2.7
* Auto-format
Also make small adjustments to make auto-formatting with black easier and produce nicer results
* Move error message to Errors
* Update documentation
* Add cats to annotation JSON format [ci skip]
* Fix tpl flag and docs [ci skip]
* Switch to internal roc_auc_score
Switch to internal `roc_auc_score()` adapted from scikit-learn.
* Add AUCROCScore tests and improve errors/warnings
* Add tests for AUCROCScore and roc_auc_score
* Add missing error for only positive/negative values
* Remove unnecessary warnings and errors
* Make reduced roc_auc_score functions private
Because most of the checks and warnings have been stripped for the
internal functions and access is only intended through `ROCAUCScore`,
make the functions for roc_auc_score adapted from scikit-learn private.
* Check that data corresponds with multilabel flag
Check that the training instances correspond with the multilabel flag,
adding the multilabel flag if required.
* Add textcat score to early stopping check
* Add more checks to debug-data for textcat
* Add example training data for textcat
* Add more checks to textcat train CLI
* Check configuration when extending base model
* Fix typos
* Update textcat example data
* Provide licensing details and licenses for data
* Remove two labels with no positive instances from jigsaw-toxic-comment
data.
Co-authored-by: Ines Montani <ines@ines.io>
599 lines
22 KiB
Python
599 lines
22 KiB
Python
# coding: utf8
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from __future__ import unicode_literals, print_function
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from pathlib import Path
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from collections import Counter
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import plac
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import sys
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import srsly
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from wasabi import Printer, MESSAGES
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from ..gold import GoldCorpus
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from ..syntax import nonproj
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from ..util import load_model, get_lang_class
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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 blank model
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BLANK_MODEL_MIN_THRESHOLD = 100
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BLANK_MODEL_THRESHOLD = 2000
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@plac.annotations(
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lang=("model language", "positional", None, str),
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train_path=("location of JSON-formatted training data", "positional", None, Path),
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dev_path=("location of JSON-formatted development data", "positional", None, Path),
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base_model=("name of model to update (optional)", "option", "b", str),
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pipeline=(
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"Comma-separated names of pipeline components to train",
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"option",
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"p",
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str,
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),
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ignore_warnings=("Ignore warnings, only show stats and errors", "flag", "IW", bool),
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verbose=("Print additional information and explanations", "flag", "V", bool),
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no_format=("Don't pretty-print the results", "flag", "NF", bool),
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)
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def debug_data(
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lang,
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train_path,
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dev_path,
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base_model=None,
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pipeline="tagger,parser,ner",
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ignore_warnings=False,
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verbose=False,
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no_format=False,
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):
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"""
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Analyze, debug and validate your training and development data, get useful
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stats, and find problems like invalid entity annotations, cyclic
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dependencies, low data labels and more.
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"""
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msg = Printer(pretty=not no_format, ignore_warnings=ignore_warnings)
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# Make sure all files and paths exists if they are needed
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if not train_path.exists():
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msg.fail("Training data not found", train_path, exits=1)
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if not dev_path.exists():
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msg.fail("Development data not found", dev_path, exits=1)
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# Initialize the model and pipeline
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pipeline = [p.strip() for p in pipeline.split(",")]
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if base_model:
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nlp = load_model(base_model)
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else:
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lang_cls = get_lang_class(lang)
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nlp = lang_cls()
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msg.divider("Data format validation")
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# TODO: Validate data format using the JSON schema
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# TODO: update once the new format is ready
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# TODO: move validation to GoldCorpus in order to be able to load from dir
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# Create the gold corpus to be able to better analyze data
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loading_train_error_message = ""
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loading_dev_error_message = ""
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with msg.loading("Loading corpus..."):
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corpus = GoldCorpus(train_path, dev_path)
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try:
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train_docs = list(corpus.train_docs(nlp))
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train_docs_unpreprocessed = list(
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corpus.train_docs_without_preprocessing(nlp)
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)
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except ValueError as e:
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loading_train_error_message = "Training data cannot be loaded: {}".format(
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str(e)
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)
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try:
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dev_docs = list(corpus.dev_docs(nlp))
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except ValueError as e:
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loading_dev_error_message = "Development data cannot be loaded: {}".format(
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str(e)
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)
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if loading_train_error_message or loading_dev_error_message:
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if loading_train_error_message:
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msg.fail(loading_train_error_message)
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if loading_dev_error_message:
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msg.fail(loading_dev_error_message)
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sys.exit(1)
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msg.good("Corpus is loadable")
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# Create all gold data here to avoid iterating over the train_docs constantly
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gold_train_data = _compile_gold(train_docs, pipeline)
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gold_train_unpreprocessed_data = _compile_gold(train_docs_unpreprocessed, pipeline)
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gold_dev_data = _compile_gold(dev_docs, pipeline)
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train_texts = gold_train_data["texts"]
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dev_texts = gold_dev_data["texts"]
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msg.divider("Training stats")
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msg.text("Training pipeline: {}".format(", ".join(pipeline)))
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for pipe in [p for p in pipeline if p not in nlp.factories]:
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msg.fail("Pipeline component '{}' not available in factories".format(pipe))
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if base_model:
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msg.text("Starting with base model '{}'".format(base_model))
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else:
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msg.text("Starting with blank model '{}'".format(lang))
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msg.text("{} training docs".format(len(train_docs)))
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msg.text("{} evaluation docs".format(len(dev_docs)))
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overlap = len(train_texts.intersection(dev_texts))
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if overlap:
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msg.warn("{} training examples also in evaluation data".format(overlap))
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else:
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msg.good("No overlap between training and evaluation data")
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if not base_model and len(train_docs) < BLANK_MODEL_THRESHOLD:
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text = "Low number of examples to train from a blank model ({})".format(
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len(train_docs)
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)
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if len(train_docs) < 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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"It's recommended to use at least {} examples (minimum {})".format(
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BLANK_MODEL_THRESHOLD, BLANK_MODEL_MIN_THRESHOLD
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),
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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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"{} total {} in the data ({} unique)".format(
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n_words, "word" if n_words == 1 else "words", len(gold_train_data["words"])
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)
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)
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if gold_train_data["n_misaligned_words"] > 0:
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msg.warn(
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"{} misaligned tokens in the training data".format(
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gold_train_data["n_misaligned_words"]
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)
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)
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if gold_dev_data["n_misaligned_words"] > 0:
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msg.warn(
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"{} misaligned tokens in the dev data".format(
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gold_dev_data["n_misaligned_words"]
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)
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)
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most_common_words = gold_train_data["words"].most_common(10)
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msg.text(
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"10 most common words: {}".format(
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_format_labels(most_common_words, counts=True)
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),
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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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"{} vectors ({} unique keys, {} dimensions)".format(
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len(nlp.vocab.vectors),
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nlp.vocab.vectors.n_keys,
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nlp.vocab.vectors_length,
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)
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)
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else:
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msg.info("No word vectors present in the model")
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if "ner" in pipeline:
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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", "-")
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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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msg.divider("Named Entity Recognition")
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msg.info(
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"{} new {}, {} existing {}".format(
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len(new_labels),
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"label" if len(new_labels) == 1 else "labels",
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len(existing_labels),
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"label" if len(existing_labels) == 1 else "labels",
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)
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)
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missing_values = label_counts["-"]
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msg.text(
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"{} missing {} (tokens with '-' label)".format(
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missing_values, "value" if missing_values == 1 else "values"
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)
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)
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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("New: {}".format(labels_with_counts), show=verbose)
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if existing_labels:
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msg.text(
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"Existing: {}".format(_format_labels(existing_labels)), show=verbose
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)
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if gold_train_data["ws_ents"]:
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msg.fail(
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"{} invalid whitespace entity spans".format(gold_train_data["ws_ents"])
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)
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has_ws_ents_error = 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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"Low number of examples for new label '{}' ({})".format(
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label, label_counts[label]
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)
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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_docs, label)
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if neg_docs == 0:
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msg.warn(
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"No examples for texts WITHOUT new label '{}'".format(label)
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)
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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 has_low_data_warning:
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msg.text(
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"To train a new entity type, your data should include at "
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"least {} instances of the new label".format(NEW_LABEL_THRESHOLD),
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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 "textcat" in pipeline:
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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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"Text Classification: {} new label(s), {} existing label(s)".format(
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len(new_labels), len(existing_labels)
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)
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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("New: {}".format(labels_with_counts), show=verbose)
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if existing_labels:
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msg.text(
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"Existing: {}".format(_format_labels(existing_labels)), show=verbose
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)
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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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"The train and dev labels are not the same. "
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"Train labels: {}. "
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"Dev labels: {}.".format(
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_format_labels(gold_train_data["cats"]),
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_format_labels(gold_dev_data["cats"]),
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)
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)
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if gold_train_data["n_cats_multilabel"] > 0:
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msg.info("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 pipeline:
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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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tag_map = nlp.Defaults.tag_map
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msg.info(
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"{} {} in data ({} {} in tag map)".format(
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len(labels),
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"label" if len(labels) == 1 else "labels",
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len(tag_map),
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"label" if len(tag_map) == 1 else "labels",
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)
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)
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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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non_tagmap = [l for l in labels if l not in tag_map]
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if not non_tagmap:
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msg.good("All labels present in tag map for language '{}'".format(nlp.lang))
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for label in non_tagmap:
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msg.fail(
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"Label '{}' not found in tag map for language '{}'".format(
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label, nlp.lang
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)
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)
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if "parser" in pipeline:
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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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"Found {} sentence{} with an average length of {:.1f} words.".format(
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gold_train_data["n_sents"],
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"s" if len(train_docs) > 1 else "",
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gold_train_data["n_words"] / gold_train_data["n_sents"],
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)
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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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msg.info(
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"Found {} nonprojective train sentence{}".format(
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gold_train_unpreprocessed_data["n_nonproj"],
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"s" if gold_train_unpreprocessed_data["n_nonproj"] > 1 else "",
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)
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)
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if gold_dev_data["n_nonproj"] > 0:
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msg.info(
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"Found {} nonprojective dev sentence{}".format(
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gold_dev_data["n_nonproj"],
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"s" if gold_dev_data["n_nonproj"] > 1 else "",
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)
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)
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msg.info(
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"{} {} in train data".format(
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len(labels_train_unpreprocessed),
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"label" if len(labels_train) == 1 else "labels",
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)
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)
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msg.info(
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"{} {} in projectivized train data".format(
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len(labels_train), "label" if len(labels_train) == 1 else "labels"
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)
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)
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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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"Low number of examples for label '{}' ({})".format(
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label, gold_train_unpreprocessed_data["deps"][label]
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)
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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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"{}: {}".format(label, str(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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"Low number of examples for {} label{} in the "
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"projectivized dependency trees used for training. You may "
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"want to projectivize labels such as punct before "
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"training in order to improve parser performance.".format(
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len(rare_projectivized_labels),
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"s" if len(rare_projectivized_labels) > 1 else "",
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)
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)
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msg.warn(
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"Projectivized labels with low numbers of examples: "
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"{}".format("\n".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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"{}".format(", ".join(set(labels_train) - set(labels_dev))),
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show=verbose,
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|
)
|
|
|
|
# labels only in dev
|
|
if set(labels_dev) - set(labels_train):
|
|
msg.warn(
|
|
"The following labels were found only in the dev data: "
|
|
+ ", ".join(set(labels_dev) - set(labels_train)),
|
|
show=verbose,
|
|
)
|
|
|
|
if has_low_data_warning:
|
|
msg.text(
|
|
"To train a parser, your data should include at "
|
|
"least {} instances of each label.".format(DEP_LABEL_THRESHOLD),
|
|
show=verbose,
|
|
)
|
|
|
|
# multiple root labels
|
|
if len(gold_train_unpreprocessed_data["roots"]) > 1:
|
|
msg.warn(
|
|
"Multiple root labels ({}) ".format(
|
|
", ".join(gold_train_unpreprocessed_data["roots"])
|
|
)
|
|
+ "found in training data. spaCy's parser uses a single root "
|
|
"label ROOT so this distinction will not be available."
|
|
)
|
|
|
|
# these should not happen, but just in case
|
|
if gold_train_data["n_nonproj"] > 0:
|
|
msg.fail(
|
|
"Found {} nonprojective projectivized train sentence{}".format(
|
|
gold_train_data["n_nonproj"],
|
|
"s" if gold_train_data["n_nonproj"] > 1 else "",
|
|
)
|
|
)
|
|
if gold_train_data["n_cycles"] > 0:
|
|
msg.fail(
|
|
"Found {} projectivized train sentence{} with cycles".format(
|
|
gold_train_data["n_cycles"],
|
|
"s" if gold_train_data["n_cycles"] > 1 else "",
|
|
)
|
|
)
|
|
|
|
|
|
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(
|
|
"{} {} passed".format(
|
|
good_counts, "check" if good_counts == 1 else "checks"
|
|
)
|
|
)
|
|
if warn_counts:
|
|
msg.warn(
|
|
"{} {}".format(warn_counts, "warning" if warn_counts == 1 else "warnings")
|
|
)
|
|
if fail_counts:
|
|
msg.fail("{} {}".format(fail_counts, "error" if fail_counts == 1 else "errors"))
|
|
|
|
if fail_counts:
|
|
sys.exit(1)
|
|
|
|
|
|
def _load_file(file_path, msg):
|
|
file_name = file_path.parts[-1]
|
|
if file_path.suffix == ".json":
|
|
with msg.loading("Loading {}...".format(file_name)):
|
|
data = srsly.read_json(file_path)
|
|
msg.good("Loaded {}".format(file_name))
|
|
return data
|
|
elif file_path.suffix == ".jsonl":
|
|
with msg.loading("Loading {}...".format(file_name)):
|
|
data = srsly.read_jsonl(file_path)
|
|
msg.good("Loaded {}".format(file_name))
|
|
return data
|
|
msg.fail(
|
|
"Can't load file extension {}".format(file_path.suffix),
|
|
"Expected .json or .jsonl",
|
|
exits=1,
|
|
)
|
|
|
|
|
|
def _compile_gold(train_docs, pipeline):
|
|
data = {
|
|
"ner": Counter(),
|
|
"cats": Counter(),
|
|
"tags": Counter(),
|
|
"deps": Counter(),
|
|
"words": Counter(),
|
|
"roots": Counter(),
|
|
"ws_ents": 0,
|
|
"n_words": 0,
|
|
"n_misaligned_words": 0,
|
|
"n_sents": 0,
|
|
"n_nonproj": 0,
|
|
"n_cycles": 0,
|
|
"n_cats_multilabel": 0,
|
|
"texts": set(),
|
|
}
|
|
for doc, gold in train_docs:
|
|
valid_words = [x for x in gold.words if x is not None]
|
|
data["words"].update(valid_words)
|
|
data["n_words"] += len(valid_words)
|
|
data["n_misaligned_words"] += len(gold.words) - len(valid_words)
|
|
data["texts"].add(doc.text)
|
|
if "ner" in pipeline:
|
|
for i, label in enumerate(gold.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-")):
|
|
combined_label = label.split("-")[1]
|
|
data["ner"][combined_label] += 1
|
|
elif label == "-":
|
|
data["ner"]["-"] += 1
|
|
if "textcat" in pipeline:
|
|
data["cats"].update(gold.cats)
|
|
if list(gold.cats.values()).count(1.0) != 1:
|
|
data["n_cats_multilabel"] += 1
|
|
if "tagger" in pipeline:
|
|
data["tags"].update([x for x in gold.tags if x is not None])
|
|
if "parser" in pipeline:
|
|
data["deps"].update([x for x in gold.labels if x is not None])
|
|
for i, (dep, head) in enumerate(zip(gold.labels, gold.heads)):
|
|
if head == i:
|
|
data["roots"].update([dep])
|
|
data["n_sents"] += 1
|
|
if nonproj.is_nonproj_tree(gold.heads):
|
|
data["n_nonproj"] += 1
|
|
if nonproj.contains_cycle(gold.heads):
|
|
data["n_cycles"] += 1
|
|
return data
|
|
|
|
|
|
def _format_labels(labels, counts=False):
|
|
if counts:
|
|
return ", ".join(["'{}' ({})".format(l, c) for l, c in labels])
|
|
return ", ".join(["'{}'".format(l) for l in labels])
|
|
|
|
|
|
def _get_examples_without_label(data, label):
|
|
count = 0
|
|
for doc, gold in data:
|
|
labels = [label.split("-")[1] for label in gold.ner if label not in ("O", "-")]
|
|
if label not in labels:
|
|
count += 1
|
|
return count
|
|
|
|
|
|
def _get_labels_from_model(nlp, pipe_name):
|
|
if pipe_name not in nlp.pipe_names:
|
|
return set()
|
|
pipe = nlp.get_pipe(pipe_name)
|
|
return pipe.labels
|