spaCy/spacy/cli/debug_data.py

from typing import List, Sequence, Dict, Any, Tuple, Optional, Set
from pathlib import Path
from collections import Counter
import sys
import srsly
from wasabi import Printer, MESSAGES, msg
import typer

from ._util import app, Arg, Opt, show_validation_error, parse_config_overrides
from ._util import import_code, debug_cli
from ..training import Example
from ..training.initialize import get_sourced_components
from ..schemas import ConfigSchemaTraining
from ..pipeline._parser_internals import nonproj
from ..pipeline._parser_internals.nonproj import DELIMITER
from ..pipeline import Morphologizer
from ..morphology import Morphology
from ..language import Language
from ..util import registry, resolve_dot_names
from .. import util


# Minimum number of expected occurrences of NER label in data to train new label
NEW_LABEL_THRESHOLD = 50
# Minimum number of expected occurrences of dependency labels
DEP_LABEL_THRESHOLD = 20
# Minimum number of expected examples to train a new pipeline
BLANK_MODEL_MIN_THRESHOLD = 100
BLANK_MODEL_THRESHOLD = 2000


@debug_cli.command(
    "data", context_settings={"allow_extra_args": True, "ignore_unknown_options": True}
)
@app.command(
    "debug-data",
    context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
    hidden=True,  # hide this from main CLI help but still allow it to work with warning
)
def debug_data_cli(
    # fmt: off
    ctx: typer.Context,  # This is only used to read additional arguments
    config_path: Path = Arg(..., help="Path to config file", exists=True, allow_dash=True),
    code_path: Optional[Path] = Opt(None, "--code-path", "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
    ignore_warnings: bool = Opt(False, "--ignore-warnings", "-IW", help="Ignore warnings, only show stats and errors"),
    verbose: bool = Opt(False, "--verbose", "-V", help="Print additional information and explanations"),
    no_format: bool = Opt(False, "--no-format", "-NF", help="Don't pretty-print the results"),
    # fmt: on
):
    """
    Analyze, debug and validate your training and development data. Outputs
    useful stats, and can help you find problems like invalid entity annotations,
    cyclic dependencies, low data labels and more.

    DOCS: https://spacy.io/api/cli#debug-data
    """
    if ctx.command.name == "debug-data":
        msg.warn(
            "The debug-data command is now available via the 'debug data' "
            "subcommand (without the hyphen). You can run python -m spacy debug "
            "--help for an overview of the other available debugging commands."
        )
    overrides = parse_config_overrides(ctx.args)
    import_code(code_path)
    debug_data(
        config_path,
        config_overrides=overrides,
        ignore_warnings=ignore_warnings,
        verbose=verbose,
        no_format=no_format,
        silent=False,
    )


def debug_data(
    config_path: Path,
    *,
    config_overrides: Dict[str, Any] = {},
    ignore_warnings: bool = False,
    verbose: bool = False,
    no_format: bool = True,
    silent: bool = True,
):
    msg = Printer(
        no_print=silent, pretty=not no_format, ignore_warnings=ignore_warnings
    )
    # Make sure all files and paths exists if they are needed
    with show_validation_error(config_path):
        cfg = util.load_config(config_path, overrides=config_overrides)
        nlp = util.load_model_from_config(cfg)
        config = nlp.config.interpolate()
        T = registry.resolve(config["training"], schema=ConfigSchemaTraining)
    # Use original config here, not resolved version
    sourced_components = get_sourced_components(cfg)
    frozen_components = T["frozen_components"]
    resume_components = [p for p in sourced_components if p not in frozen_components]
    pipeline = nlp.pipe_names
    factory_names = [nlp.get_pipe_meta(pipe).factory for pipe in nlp.pipe_names]
    msg.divider("Data file validation")

    # Create the gold corpus to be able to better analyze data
    dot_names = [T["train_corpus"], T["dev_corpus"]]
    train_corpus, dev_corpus = resolve_dot_names(config, dot_names)
    train_dataset = list(train_corpus(nlp))
    dev_dataset = list(dev_corpus(nlp))
    msg.good("Corpus is loadable")

    nlp.initialize(lambda: train_dataset)
    msg.good("Pipeline can be initialized with data")

    # Create all gold data here to avoid iterating over the train_dataset constantly
    gold_train_data = _compile_gold(train_dataset, factory_names, nlp, make_proj=True)
    gold_train_unpreprocessed_data = _compile_gold(
        train_dataset, factory_names, nlp, make_proj=False
    )
    gold_dev_data = _compile_gold(dev_dataset, factory_names, nlp, make_proj=True)

    train_texts = gold_train_data["texts"]
    dev_texts = gold_dev_data["texts"]
    frozen_components = T["frozen_components"]

    msg.divider("Training stats")
    msg.text(f"Language: {nlp.lang}")
    msg.text(f"Training pipeline: {', '.join(pipeline)}")
    if resume_components:
        msg.text(f"Components from other pipelines: {', '.join(resume_components)}")
    if frozen_components:
        msg.text(f"Frozen components: {', '.join(frozen_components)}")
    msg.text(f"{len(train_dataset)} training docs")
    msg.text(f"{len(dev_dataset)} evaluation docs")

    if not len(gold_dev_data):
        msg.fail("No evaluation docs")
    overlap = len(train_texts.intersection(dev_texts))
    if overlap:
        msg.warn(f"{overlap} training examples also in evaluation data")
    else:
        msg.good("No overlap between training and evaluation data")
    # TODO: make this feedback more fine-grained and report on updated
    # components vs. blank components
    if not resume_components and len(train_dataset) < BLANK_MODEL_THRESHOLD:
        text = f"Low number of examples to train a new pipeline ({len(train_dataset)})"
        if len(train_dataset) < BLANK_MODEL_MIN_THRESHOLD:
            msg.fail(text)
        else:
            msg.warn(text)
        msg.text(
            f"It's recommended to use at least {BLANK_MODEL_THRESHOLD} examples "
            f"(minimum {BLANK_MODEL_MIN_THRESHOLD})",
            show=verbose,
        )

    msg.divider("Vocab & Vectors")
    n_words = gold_train_data["n_words"]
    msg.info(
        f"{n_words} total word(s) in the data ({len(gold_train_data['words'])} unique)"
    )
    if gold_train_data["n_misaligned_words"] > 0:
        n_misaligned = gold_train_data["n_misaligned_words"]
        msg.warn(f"{n_misaligned} misaligned tokens in the training data")
    if gold_dev_data["n_misaligned_words"] > 0:
        n_misaligned = gold_dev_data["n_misaligned_words"]
        msg.warn(f"{n_misaligned} misaligned tokens in the dev data")
    most_common_words = gold_train_data["words"].most_common(10)
    msg.text(
        f"10 most common words: {_format_labels(most_common_words, counts=True)}",
        show=verbose,
    )
    if len(nlp.vocab.vectors):
        msg.info(
            f"{len(nlp.vocab.vectors)} vectors ({nlp.vocab.vectors.n_keys} "
            f"unique keys, {nlp.vocab.vectors_length} dimensions)"
        )
        n_missing_vectors = sum(gold_train_data["words_missing_vectors"].values())
        msg.warn(
            "{} words in training data without vectors ({:.0f}%)".format(
                n_missing_vectors,
                100 * (n_missing_vectors / gold_train_data["n_words"]),
            ),
        )
        msg.text(
            "10 most common words without vectors: {}".format(
                _format_labels(
                    gold_train_data["words_missing_vectors"].most_common(10),
                    counts=True,
                )
            ),
            show=verbose,
        )
    else:
        msg.info("No word vectors present in the package")

    if "ner" in factory_names:
        # Get all unique NER labels present in the data
        labels = set(
            label for label in gold_train_data["ner"] if label not in ("O", "-", None)
        )
        label_counts = gold_train_data["ner"]
        model_labels = _get_labels_from_model(nlp, "ner")
        has_low_data_warning = False
        has_no_neg_warning = False
        has_ws_ents_error = False
        has_punct_ents_warning = False

        msg.divider("Named Entity Recognition")
        msg.info(f"{len(model_labels)} label(s)")
        missing_values = label_counts["-"]
        msg.text(f"{missing_values} missing value(s) (tokens with '-' label)")
        for label in labels:
            if len(label) == 0:
                msg.fail("Empty label found in train data")
        labels_with_counts = [
            (label, count)
            for label, count in label_counts.most_common()
            if label != "-"
        ]
        labels_with_counts = _format_labels(labels_with_counts, counts=True)
        msg.text(f"Labels in train data: {_format_labels(labels)}", show=verbose)
        missing_labels = model_labels - labels
        if missing_labels:
            msg.warn(
                "Some model labels are not present in the train data. The "
                "model performance may be degraded for these labels after "
                f"training: {_format_labels(missing_labels)}."
            )
        if gold_train_data["ws_ents"]:
            msg.fail(f"{gold_train_data['ws_ents']} invalid whitespace entity spans")
            has_ws_ents_error = True

        if gold_train_data["punct_ents"]:
            msg.warn(f"{gold_train_data['punct_ents']} entity span(s) with punctuation")
            has_punct_ents_warning = True

        for label in labels:
            if label_counts[label] <= NEW_LABEL_THRESHOLD:
                msg.warn(
                    f"Low number of examples for label '{label}' ({label_counts[label]})"
                )
                has_low_data_warning = True

                with msg.loading("Analyzing label distribution..."):
                    neg_docs = _get_examples_without_label(train_dataset, label)
                if neg_docs == 0:
                    msg.warn(f"No examples for texts WITHOUT new label '{label}'")
                    has_no_neg_warning = True

        if not has_low_data_warning:
            msg.good("Good amount of examples for all labels")
        if not has_no_neg_warning:
            msg.good("Examples without occurrences available for all labels")
        if not has_ws_ents_error:
            msg.good("No entities consisting of or starting/ending with whitespace")
        if not has_punct_ents_warning:
            msg.good("No entities consisting of or starting/ending with punctuation")

        if has_low_data_warning:
            msg.text(
                f"To train a new entity type, your data should include at "
                f"least {NEW_LABEL_THRESHOLD} instances of the new label",
                show=verbose,
            )
        if has_no_neg_warning:
            msg.text(
                "Training data should always include examples of entities "
                "in context, as well as examples without a given entity "
                "type.",
                show=verbose,
            )
        if has_ws_ents_error:
            msg.text(
                "As of spaCy v2.1.0, entity spans consisting of or starting/ending "
                "with whitespace characters are considered invalid."
            )

        if has_punct_ents_warning:
            msg.text(
                "Entity spans consisting of or starting/ending "
                "with punctuation can not be trained with a noise level > 0."
            )

    if "textcat" in factory_names:
        msg.divider("Text Classification (Exclusive Classes)")
        labels = _get_labels_from_model(nlp, "textcat")
        msg.info(f"Text Classification: {len(labels)} label(s)")
        msg.text(f"Labels: {_format_labels(labels)}", show=verbose)
        missing_labels = labels - set(gold_train_data["cats"])
        if missing_labels:
            msg.warn(
                "Some model labels are not present in the train data. The "
                "model performance may be degraded for these labels after "
                f"training: {_format_labels(missing_labels)}."
            )
        if set(gold_train_data["cats"]) != set(gold_dev_data["cats"]):
            msg.warn(
                "Potential train/dev mismatch: the train and dev labels are "
                "not the same. "
                f"Train labels: {_format_labels(gold_train_data['cats'])}. "
                f"Dev labels: {_format_labels(gold_dev_data['cats'])}."
            )
        if len(labels) < 2:
            msg.fail(
                "The model does not have enough labels. 'textcat' requires at "
                "least two labels due to mutually-exclusive classes, e.g. "
                "LABEL/NOT_LABEL or POSITIVE/NEGATIVE for a binary "
                "classification task."
            )
        if (
            gold_train_data["n_cats_bad_values"] > 0
            or gold_dev_data["n_cats_bad_values"] > 0
        ):
            msg.fail(
                "Unsupported values for cats: the supported values are "
                "1.0/True and 0.0/False."
            )
        if gold_train_data["n_cats_multilabel"] > 0:
            # Note: you should never get here because you run into E895 on
            # initialization first.
            msg.fail(
                "The train data contains instances without mutually-exclusive "
                "classes. Use the component 'textcat_multilabel' instead of "
                "'textcat'."
            )
        if gold_dev_data["n_cats_multilabel"] > 0:
            msg.fail(
                "The dev data contains instances without mutually-exclusive "
                "classes. Use the component 'textcat_multilabel' instead of "
                "'textcat'."
            )

    if "textcat_multilabel" in factory_names:
        msg.divider("Text Classification (Multilabel)")
        labels = _get_labels_from_model(nlp, "textcat_multilabel")
        msg.info(f"Text Classification: {len(labels)} label(s)")
        msg.text(f"Labels: {_format_labels(labels)}", show=verbose)
        missing_labels = labels - set(gold_train_data["cats"])
        if missing_labels:
            msg.warn(
                "Some model labels are not present in the train data. The "
                "model performance may be degraded for these labels after "
                f"training: {_format_labels(missing_labels)}."
            )
        if set(gold_train_data["cats"]) != set(gold_dev_data["cats"]):
            msg.warn(
                "Potential train/dev mismatch: the train and dev labels are "
                "not the same. "
                f"Train labels: {_format_labels(gold_train_data['cats'])}. "
                f"Dev labels: {_format_labels(gold_dev_data['cats'])}."
            )
        if (
            gold_train_data["n_cats_bad_values"] > 0
            or gold_dev_data["n_cats_bad_values"] > 0
        ):
            msg.fail(
                "Unsupported values for cats: the supported values are "
                "1.0/True and 0.0/False."
            )
        if gold_train_data["n_cats_multilabel"] > 0:
            if gold_dev_data["n_cats_multilabel"] == 0:
                msg.warn(
                    "Potential train/dev mismatch: the train data contains "
                    "instances without mutually-exclusive classes while the "
                    "dev data contains only instances with mutually-exclusive "
                    "classes."
                )
        else:
            msg.warn(
                "The train data contains only instances with "
                "mutually-exclusive classes. You can potentially use the "
                "component 'textcat' instead of 'textcat_multilabel'."
            )
            if gold_dev_data["n_cats_multilabel"] > 0:
                msg.fail(
                    "Train/dev mismatch: the dev data contains instances "
                    "without mutually-exclusive classes while the train data "
                    "contains only instances with mutually-exclusive classes."
                )

    if "tagger" in factory_names:
        msg.divider("Part-of-speech Tagging")
        labels = [label for label in gold_train_data["tags"]]
        model_labels = _get_labels_from_model(nlp, "tagger")
        msg.info(f"{len(labels)} label(s) in train data")
        missing_labels = model_labels - set(labels)
        if missing_labels:
            msg.warn(
                "Some model labels are not present in the train data. The "
                "model performance may be degraded for these labels after "
                f"training: {_format_labels(missing_labels)}."
            )
        labels_with_counts = _format_labels(
            gold_train_data["tags"].most_common(), counts=True
        )
        msg.text(labels_with_counts, show=verbose)

    if "morphologizer" in factory_names:
        msg.divider("Morphologizer (POS+Morph)")
        labels = [label for label in gold_train_data["morphs"]]
        model_labels = _get_labels_from_model(nlp, "morphologizer")
        msg.info(f"{len(labels)} label(s) in train data")
        missing_labels = model_labels - set(labels)
        if missing_labels:
            msg.warn(
                "Some model labels are not present in the train data. The "
                "model performance may be degraded for these labels after "
                f"training: {_format_labels(missing_labels)}."
            )
        labels_with_counts = _format_labels(
            gold_train_data["morphs"].most_common(), counts=True
        )
        msg.text(labels_with_counts, show=verbose)

    if "parser" in factory_names:
        has_low_data_warning = False
        msg.divider("Dependency Parsing")

        # profile sentence length
        msg.info(
            f"Found {gold_train_data['n_sents']} sentence(s) with an average "
            f"length of {gold_train_data['n_words'] / gold_train_data['n_sents']:.1f} words."
        )

        # check for documents with multiple sentences
        sents_per_doc = gold_train_data["n_sents"] / len(gold_train_data["texts"])
        if sents_per_doc < 1.1:
            msg.warn(
                f"The training data contains {sents_per_doc:.2f} sentences per "
                f"document. When there are very few documents containing more "
                f"than one sentence, the parser will not learn how to segment "
                f"longer texts into sentences."
            )

        # profile labels
        labels_train = [label for label in gold_train_data["deps"]]
        labels_train_unpreprocessed = [
            label for label in gold_train_unpreprocessed_data["deps"]
        ]
        labels_dev = [label for label in gold_dev_data["deps"]]

        if gold_train_unpreprocessed_data["n_nonproj"] > 0:
            n_nonproj = gold_train_unpreprocessed_data["n_nonproj"]
            msg.info(f"Found {n_nonproj} nonprojective train sentence(s)")
        if gold_dev_data["n_nonproj"] > 0:
            n_nonproj = gold_dev_data["n_nonproj"]
            msg.info(f"Found {n_nonproj} nonprojective dev sentence(s)")
        msg.info(f"{len(labels_train_unpreprocessed)} label(s) in train data")
        msg.info(f"{len(labels_train)} label(s) in projectivized train data")
        labels_with_counts = _format_labels(
            gold_train_unpreprocessed_data["deps"].most_common(), counts=True
        )
        msg.text(labels_with_counts, show=verbose)

        # rare labels in train
        for label in gold_train_unpreprocessed_data["deps"]:
            if gold_train_unpreprocessed_data["deps"][label] <= DEP_LABEL_THRESHOLD:
                msg.warn(
                    f"Low number of examples for label '{label}' "
                    f"({gold_train_unpreprocessed_data['deps'][label]})"
                )
                has_low_data_warning = True

        # rare labels in projectivized train
        rare_projectivized_labels = []
        for label in gold_train_data["deps"]:
            if (
                gold_train_data["deps"][label] <= DEP_LABEL_THRESHOLD
                and DELIMITER in label
            ):
                rare_projectivized_labels.append(
                    f"{label}: {gold_train_data['deps'][label]}"
                )

        if len(rare_projectivized_labels) > 0:
            msg.warn(
                f"Low number of examples for {len(rare_projectivized_labels)} "
                "label(s) in the projectivized dependency trees used for "
                "training. You may want to projectivize labels such as punct "
                "before training in order to improve parser performance."
            )
            msg.warn(
                f"Projectivized labels with low numbers of examples: ",
                ", ".join(rare_projectivized_labels),
                show=verbose,
            )
            has_low_data_warning = True

        # labels only in train
        if set(labels_train) - set(labels_dev):
            msg.warn(
                "The following labels were found only in the train data:",
                ", ".join(set(labels_train) - set(labels_dev)),
                show=verbose,
            )

        # 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(
                f"To train a parser, your data should include at "
                f"least {DEP_LABEL_THRESHOLD} instances of each label.",
                show=verbose,
            )

        # multiple root labels
        if len(gold_train_unpreprocessed_data["roots"]) > 1:
            msg.warn(
                f"Multiple root labels "
                f"({', '.join(gold_train_unpreprocessed_data['roots'])}) "
                f"found in training data. spaCy's parser uses a single root "
                f"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(
                f"Found {gold_train_data['n_nonproj']} nonprojective "
                f"projectivized train sentence(s)"
            )
        if gold_train_data["n_cycles"] > 0:
            msg.fail(
                f"Found {gold_train_data['n_cycles']} projectivized train sentence(s) with cycles"
            )

    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(),
        "morphs": 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,
        "n_cats_bad_values": 0,
        "texts": set(),
    }
    for eg in examples:
        gold = eg.reference
        doc = eg.predicted
        valid_words = [x.text for x in gold]
        data["words"].update(valid_words)
        data["n_words"] += len(valid_words)
        align = eg.alignment
        for token in doc:
            if token.orth_.isspace():
                continue
            if align.x2y.lengths[token.i] != 1:
                data["n_misaligned_words"] += 1
        data["texts"].add(doc.text)
        if len(nlp.vocab.vectors):
            for word in [t.text for t in doc]:
                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 or "textcat_multilabel" in factory_names:
            data["cats"].update(gold.cats)
            if any(val not in (0, 1) for val in gold.cats.values()):
                data["n_cats_bad_values"] += 1
            if list(gold.cats.values()).count(1) != 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 "morphologizer" in factory_names:
            pos_tags = eg.get_aligned("POS", as_string=True)
            morphs = eg.get_aligned("MORPH", as_string=True)
            for pos, morph in zip(pos_tags, morphs):
                # POS may align (same value for multiple tokens) when morph
                # doesn't, so if either is misaligned (None), treat the
                # annotation as missing so that truths doesn't end up with an
                # unknown morph+POS combination
                if pos is None or morph is None:
                    pass
                # If both are unset, the annotation is missing (empty morph
                # converted from int is "_" rather than "")
                elif pos == "" and morph == "":
                    pass
                # Otherwise, generate the combined label
                else:
                    label_dict = Morphology.feats_to_dict(morph)
                    if pos:
                        label_dict[Morphologizer.POS_FEAT] = pos
                    label = eg.reference.vocab.strings[
                        eg.reference.vocab.morphology.add(label_dict)
                    ]
                    data["morphs"].update([label])
        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) -> Set[str]:
    if pipe_name not in nlp.pipe_names:
        return set()
    pipe = nlp.get_pipe(pipe_name)
    return set(pipe.labels)