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https://github.com/explosion/spaCy.git
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Update debug data further for v3 (#7602)
* Update debug data further for v3 * Remove new/existing label distinction (new labels are not immediately distinguishable because the pipeline is already initialized) * Warn on missing labels in training data for all components except parser * Separate textcat and textcat_multilabel sections * Add section for morphologizer * Reword missing label warnings
This commit is contained in:
Adriane Boyd
GitHub
parent
2516896849
commit
73a8c0f992
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@ -1,4 +1,4 @@
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from typing import List, Sequence, Dict, Any, Tuple, Optional
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from typing import List, Sequence, Dict, Any, Tuple, Optional, Set
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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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@ -13,6 +13,8 @@ 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 ..pipeline._parser_internals.nonproj import DELIMITER
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from ..pipeline import Morphologizer
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from ..morphology import Morphology
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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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@ -194,32 +196,32 @@ def debug_data(
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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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msg.info(f"{len(model_labels)} label(s)")
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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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for label in 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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msg.fail("Empty label found in train data")
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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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msg.text(f"Labels in train data: {_format_labels(labels)}", show=verbose)
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missing_labels = model_labels - labels
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if missing_labels:
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msg.warn(
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"Some model labels are not present in the train data. The "
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"model performance may be degraded for these labels after "
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f"training: {_format_labels(missing_labels)}."
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)
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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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@ -228,10 +230,10 @@ def debug_data(
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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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for label in 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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f"Low number of examples for label '{label}' ({label_counts[label]})"
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)
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has_low_data_warning = True
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@ -276,22 +278,52 @@ def debug_data(
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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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msg.divider("Text Classification (Exclusive Classes)")
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labels = _get_labels_from_model(nlp, "textcat")
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msg.info(f"Text Classification: {len(labels)} label(s)")
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msg.text(f"Labels: {_format_labels(labels)}", show=verbose)
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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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msg.text(f"Labels in train data: {labels_with_counts}", show=verbose)
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missing_labels = labels - set(gold_train_data["cats"].keys())
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if missing_labels:
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msg.warn(
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"Some model labels are not present in the train data. The "
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"model performance may be degraded for these labels after "
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f"training: {_format_labels(missing_labels)}."
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)
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if gold_train_data["n_cats_multilabel"] > 0:
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# Note: you should never get here because you run into E895 on
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# initialization first.
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msg.warn(
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"The train data contains instances without "
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"mutually-exclusive classes. Use the component "
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"'textcat_multilabel' instead of 'textcat'."
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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 "textcat_multilabel" in factory_names:
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msg.divider("Text Classification (Multilabel)")
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labels = _get_labels_from_model(nlp, "textcat_multilabel")
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msg.info(f"Text Classification: {len(labels)} label(s)")
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msg.text(f"Labels: {_format_labels(labels)}", show=verbose)
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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"Labels in train data: {labels_with_counts}", show=verbose)
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missing_labels = labels - set(gold_train_data["cats"].keys())
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if missing_labels:
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msg.warn(
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"Some model labels are not present in the train data. The "
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"model performance may be degraded for these labels after "
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f"training: {_format_labels(missing_labels)}."
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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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f"The train and dev labels are not the same. "
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@ -299,11 +331,6 @@ def debug_data(
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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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@ -311,9 +338,10 @@ def debug_data(
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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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msg.warn(
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"The train data contains only instances with "
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"mutually-exclusive classes."
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"mutually-exclusive classes. You can potentially use the "
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"component 'textcat' instead of 'textcat_multilabel'."
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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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@ -325,13 +353,37 @@ def debug_data(
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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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model_labels = _get_labels_from_model(nlp, "tagger")
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msg.info(f"{len(labels)} label(s) in train data")
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missing_labels = model_labels - set(labels)
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if missing_labels:
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msg.warn(
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"Some model labels are not present in the train data. The "
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"model performance may be degraded for these labels after "
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f"training: {_format_labels(missing_labels)}."
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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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if "morphologizer" in factory_names:
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msg.divider("Morphologizer (POS+Morph)")
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labels = [label for label in gold_train_data["morphs"]]
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model_labels = _get_labels_from_model(nlp, "morphologizer")
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msg.info(f"{len(labels)} label(s) in train data")
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missing_labels = model_labels - set(labels)
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if missing_labels:
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msg.warn(
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"Some model labels are not present in the train data. The "
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"model performance may be degraded for these labels after "
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f"training: {_format_labels(missing_labels)}."
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)
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labels_with_counts = _format_labels(
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gold_train_data["morphs"].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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@ -491,6 +543,7 @@ def _compile_gold(
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"ner": Counter(),
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"cats": Counter(),
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"tags": Counter(),
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"morphs": Counter(),
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"deps": Counter(),
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"words": Counter(),
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"roots": Counter(),
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@ -544,13 +597,36 @@ def _compile_gold(
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data["ner"][combined_label] += 1
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elif label == "-":
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data["ner"]["-"] += 1
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if "textcat" in factory_names:
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if "textcat" in factory_names or "textcat_multilabel" in factory_names:
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data["cats"].update(gold.cats)
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if list(gold.cats.values()).count(1.0) != 1:
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data["n_cats_multilabel"] += 1
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if "tagger" in factory_names:
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tags = eg.get_aligned("TAG", as_string=True)
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data["tags"].update([x for x in tags if x is not None])
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if "morphologizer" in factory_names:
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pos_tags = eg.get_aligned("POS", as_string=True)
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morphs = eg.get_aligned("MORPH", as_string=True)
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for pos, morph in zip(pos_tags, morphs):
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# POS may align (same value for multiple tokens) when morph
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# doesn't, so if either is misaligned (None), treat the
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# annotation as missing so that truths doesn't end up with an
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# unknown morph+POS combination
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if pos is None or morph is None:
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pass
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# If both are unset, the annotation is missing (empty morph
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# converted from int is "_" rather than "")
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elif pos == "" and morph == "":
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pass
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# Otherwise, generate the combined label
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else:
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label_dict = Morphology.feats_to_dict(morph)
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if pos:
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label_dict[Morphologizer.POS_FEAT] = pos
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label = eg.reference.vocab.strings[
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eg.reference.vocab.morphology.add(label_dict)
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]
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data["morphs"].update([label])
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if "parser" in factory_names:
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aligned_heads, aligned_deps = eg.get_aligned_parse(projectivize=make_proj)
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data["deps"].update([x for x in aligned_deps if x is not None])
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@ -584,8 +660,8 @@ def _get_examples_without_label(data: Sequence[Example], label: str) -> int:
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return count
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def _get_labels_from_model(nlp: Language, pipe_name: str) -> Sequence[str]:
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def _get_labels_from_model(nlp: Language, pipe_name: str) -> Set[str]:
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if pipe_name not in nlp.pipe_names:
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return set()
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pipe = nlp.get_pipe(pipe_name)
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return pipe.labels
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return set(pipe.labels)
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