rasahq--rasa/rasa/nlu/utils/bilou_utils.py

import logging
import operator
from collections import defaultdict, Counter
from typing import List, Tuple, Text, Optional, Dict, Any, TYPE_CHECKING

from rasa.nlu.constants import (
    TOKENS_NAMES,
    BILOU_ENTITIES,
    BILOU_ENTITIES_GROUP,
    BILOU_ENTITIES_ROLE,
)
from rasa.shared.nlu.constants import (
    TEXT,
    ENTITIES,
    ENTITY_ATTRIBUTE_START,
    ENTITY_ATTRIBUTE_END,
    ENTITY_ATTRIBUTE_TYPE,
    ENTITY_ATTRIBUTE_GROUP,
    ENTITY_ATTRIBUTE_ROLE,
    NO_ENTITY_TAG,
)

if TYPE_CHECKING:
    from rasa.nlu.tokenizers.tokenizer import Token
    from rasa.shared.nlu.training_data.training_data import TrainingData
    from rasa.shared.nlu.training_data.message import Message

logger = logging.getLogger(__name__)

BEGINNING = "B-"
INSIDE = "I-"
LAST = "L-"
UNIT = "U-"
BILOU_PREFIXES = [BEGINNING, INSIDE, LAST, UNIT]


def bilou_prefix_from_tag(tag: Text) -> Optional[Text]:
    """Returns the BILOU prefix from the given tag.

    Args:
        tag: the tag

    Returns: the BILOU prefix of the tag
    """
    if tag[:2] in BILOU_PREFIXES:
        return tag[:2]
    return None


def tag_without_prefix(tag: Text) -> Text:
    """Remove the BILOU prefix from the given tag.

    Args:
        tag: the tag

    Returns: the tag without the BILOU prefix
    """
    if tag[:2] in BILOU_PREFIXES:
        return tag[2:]
    return tag


def bilou_tags_to_ids(
    message: "Message",
    tag_id_dict: Dict[Text, int],
    tag_name: Text = ENTITY_ATTRIBUTE_TYPE,
) -> List[int]:
    """Maps the entity tags of the message to the ids of the provided dict.

    Args:
        message: the message
        tag_id_dict: mapping of tags to ids
        tag_name: tag name of interest

    Returns: a list of tag ids
    """
    bilou_key = get_bilou_key_for_tag(tag_name)

    if message.get(bilou_key):
        _tags = [
            tag_id_dict[_tag] if _tag in tag_id_dict else tag_id_dict[NO_ENTITY_TAG]
            for _tag in message.get(bilou_key)
        ]
    else:
        _tags = [tag_id_dict[NO_ENTITY_TAG] for _ in message.get(TOKENS_NAMES[TEXT])]

    return _tags


def get_bilou_key_for_tag(tag_name: Text) -> Text:
    """Get the message key for the BILOU tagging format of the provided tag name.

    Args:
        tag_name: the tag name

    Returns:
        the message key to store the BILOU tags
    """
    if tag_name == ENTITY_ATTRIBUTE_ROLE:
        return BILOU_ENTITIES_ROLE

    if tag_name == ENTITY_ATTRIBUTE_GROUP:
        return BILOU_ENTITIES_GROUP

    return BILOU_ENTITIES


def build_tag_id_dict(
    training_data: "TrainingData", tag_name: Text = ENTITY_ATTRIBUTE_TYPE
) -> Optional[Dict[Text, int]]:
    """Create a mapping of unique tags to ids.

    Args:
        training_data: the training data
        tag_name: tag name of interest

    Returns: a mapping of tags to ids
    """
    bilou_key = get_bilou_key_for_tag(tag_name)

    distinct_tags = set(
        [
            tag_without_prefix(e)
            for example in training_data.nlu_examples
            if example.get(bilou_key)
            for e in example.get(bilou_key)
        ]
    ) - {NO_ENTITY_TAG}

    if not distinct_tags:
        return None

    tag_id_dict = {
        f"{prefix}{tag}": idx_1 * len(BILOU_PREFIXES) + idx_2 + 1
        for idx_1, tag in enumerate(sorted(distinct_tags))
        for idx_2, prefix in enumerate(BILOU_PREFIXES)
    }
    # NO_ENTITY_TAG corresponds to non-entity which should correspond to 0 index
    # needed for correct prediction for padding
    tag_id_dict[NO_ENTITY_TAG] = 0

    return tag_id_dict


def apply_bilou_schema(training_data: "TrainingData") -> None:
    """Get a list of BILOU entity tags and set them on the given messages.

    Args:
        training_data: the training data
    """
    for message in training_data.nlu_examples:
        apply_bilou_schema_to_message(message)


def apply_bilou_schema_to_message(message: "Message") -> None:
    """Get a list of BILOU entity tags and set them on the given message.

    Args:
        message: the message
    """
    entities = message.get(ENTITIES)

    if not entities:
        return

    tokens = message.get(TOKENS_NAMES[TEXT])

    for attribute, message_key in [
        (ENTITY_ATTRIBUTE_TYPE, BILOU_ENTITIES),
        (ENTITY_ATTRIBUTE_ROLE, BILOU_ENTITIES_ROLE),
        (ENTITY_ATTRIBUTE_GROUP, BILOU_ENTITIES_GROUP),
    ]:
        entities = map_message_entities(message, attribute)
        output = bilou_tags_from_offsets(tokens, entities)
        message.set(message_key, output)


def map_message_entities(
    message: "Message", attribute_key: Text = ENTITY_ATTRIBUTE_TYPE
) -> List[Tuple[int, int, Text]]:
    """Maps the entities of the given message to their start, end, and tag values.

    Args:
        message: the message
        attribute_key: key of tag value to use

    Returns: a list of start, end, and tag value tuples
    """

    def convert_entity(entity: Dict[Text, Any]) -> Tuple[int, int, Text]:
        return (
            entity[ENTITY_ATTRIBUTE_START],
            entity[ENTITY_ATTRIBUTE_END],
            entity.get(attribute_key) or NO_ENTITY_TAG,
        )

    entities = [convert_entity(entity) for entity in message.get(ENTITIES, [])]

    # entities is a list of tuples (start, end, tag value).
    # filter out all entities with tag value == NO_ENTITY_TAG.
    tag_value_idx = 2
    return [entity for entity in entities if entity[tag_value_idx] != NO_ENTITY_TAG]


def bilou_tags_from_offsets(
    tokens: List["Token"], entities: List[Tuple[int, int, Text]]
) -> List[Text]:
    """Creates BILOU tags for the given tokens and entities.

    Args:
        message: The message object.
        tokens: The list of tokens.
        entities: The list of start, end, and tag tuples.
        missing: The tag for missing entities.

    Returns:
        BILOU tags.
    """
    start_pos_to_token_idx = {token.start: i for i, token in enumerate(tokens)}
    end_pos_to_token_idx = {token.end: i for i, token in enumerate(tokens)}

    bilou = [NO_ENTITY_TAG for _ in tokens]

    _add_bilou_tags_to_entities(
        bilou, entities, end_pos_to_token_idx, start_pos_to_token_idx
    )

    return bilou


def _add_bilou_tags_to_entities(
    bilou: List[Text],
    entities: List[Tuple[int, int, Text]],
    end_pos_to_token_idx: Dict[int, int],
    start_pos_to_token_idx: Dict[int, int],
) -> None:
    for start_pos, end_pos, label in entities:
        start_token_idx = start_pos_to_token_idx.get(start_pos)
        end_token_idx = end_pos_to_token_idx.get(end_pos)

        # Only interested if the tokenization is correct
        if start_token_idx is not None and end_token_idx is not None:
            if start_token_idx == end_token_idx:
                bilou[start_token_idx] = f"{UNIT}{label}"
            else:
                bilou[start_token_idx] = f"{BEGINNING}{label}"
                for i in range(start_token_idx + 1, end_token_idx):
                    bilou[i] = f"{INSIDE}{label}"
                bilou[end_token_idx] = f"{LAST}{label}"


def ensure_consistent_bilou_tagging(
    predicted_tags: List[Text], predicted_confidences: List[float]
) -> Tuple[List[Text], List[float]]:
    """
    Ensure predicted tags follow the BILOU tagging schema.

    We assume that starting B- tags are correct. Followed tags that belong to start
    tag but have a different entity type are updated considering also the confidence
    values of those tags.
    For example, B-a I-b L-a is updated to B-a I-a L-a and B-a I-a O is changed to
    B-a L-a.

    Args:
        predicted_tags: predicted tags
        predicted_confidences: predicted confidences

    Return:
        List of tags.
        List of confidences.
    """

    for idx, predicted_tag in enumerate(predicted_tags):
        prefix = bilou_prefix_from_tag(predicted_tag)
        tag = tag_without_prefix(predicted_tag)

        if prefix == BEGINNING:
            last_idx = _find_bilou_end(idx, predicted_tags)

            relevant_confidences = predicted_confidences[idx : last_idx + 1]
            relevant_tags = [
                tag_without_prefix(tag) for tag in predicted_tags[idx : last_idx + 1]
            ]

            # if not all tags are the same, for example, B-person I-person L-location
            # we need to check what tag we should use depending on the confidence
            # values and update the tags and confidences accordingly
            if not all(relevant_tags[0] == tag for tag in relevant_tags):
                # decide which tag this entity should use
                tag, tag_score = _tag_to_use(relevant_tags, relevant_confidences)

                logger.debug(
                    f"Using tag '{tag}' for entity with mixed tag labels "
                    f"(original tags: {predicted_tags[idx : last_idx + 1]}, "
                    f"(original confidences: "
                    f"{predicted_confidences[idx : last_idx + 1]})."
                )

                # all tags that change get the score of that tag assigned
                predicted_confidences = _update_confidences(
                    predicted_confidences, predicted_tags, tag, tag_score, idx, last_idx
                )

            # ensure correct BILOU annotations
            if last_idx == idx:
                predicted_tags[idx] = f"{UNIT}{tag}"
            elif last_idx - idx == 1:
                predicted_tags[idx] = f"{BEGINNING}{tag}"
                predicted_tags[last_idx] = f"{LAST}{tag}"
            else:
                predicted_tags[idx] = f"{BEGINNING}{tag}"
                predicted_tags[last_idx] = f"{LAST}{tag}"
                for i in range(idx + 1, last_idx):
                    predicted_tags[i] = f"{INSIDE}{tag}"

    return predicted_tags, predicted_confidences


def _tag_to_use(
    relevant_tags: List[Text], relevant_confidences: List[float]
) -> Tuple[Text, float]:
    """Decide what tag to use according to the following metric:

    Calculate the average confidence per tag.
    Calculate the percentage of tokens assigned to a tag within the entity per tag.
    The harmonic mean of those two metrics is the score for the tag.
    The tag with the highest score is taken as the tag for the entity.

    Args:
        relevant_tags: The tags of the entity.
        relevant_confidences: The confidence values.

    Returns:
        The tag to use. The score of that tag.
    """
    # Calculate the average confidence per tag.
    avg_confidence_per_tag = _avg_confidence_per_tag(
        relevant_tags, relevant_confidences
    )
    # Calculate the percentage of tokens assigned to a tag per tag.
    tag_counts = Counter(relevant_tags)
    token_percentage_per_tag: Dict[Text, float] = {}
    for tag, count in tag_counts.items():
        token_percentage_per_tag[tag] = round(count / len(relevant_tags), 2)

    # Calculate the harmonic mean between the two metrics per tag.
    score_per_tag = {}
    for tag, token_percentage in token_percentage_per_tag.items():
        avg_confidence = avg_confidence_per_tag[tag]
        score_per_tag[tag] = (
            2
            * (avg_confidence * token_percentage)
            / (avg_confidence + token_percentage)
        )

    # Take the tag with the highest score as the tag for the entity
    tag, score = max(score_per_tag.items(), key=operator.itemgetter(1))

    return tag, score


def _update_confidences(
    predicted_confidences: List[float],
    predicted_tags: List[Text],
    tag: Text,
    score: float,
    idx: int,
    last_idx: int,
) -> List[float]:
    """Update the confidence values.

    Set the confidence value of a tag to score value if the predicated
    tag changed.

    Args:
        predicted_confidences: The list of predicted confidences.
        predicted_tags: The list of predicted tags.
        tag: The tag of the entity.
        score: The score value of that tag.
        idx: The start index of the entity.
        last_idx: The end index of the entity.

    Returns:
        The updated list of confidences.
    """
    for i in range(idx, last_idx + 1):
        predicted_confidences[i] = (
            round(score, 2)
            if tag_without_prefix(predicted_tags[i]) != tag
            else predicted_confidences[i]
        )
    return predicted_confidences


def _avg_confidence_per_tag(
    relevant_tags: List[Text], relevant_confidences: List[float]
) -> Dict[Text, float]:
    confidences_per_tag = defaultdict(list)

    for tag, confidence in zip(relevant_tags, relevant_confidences):
        confidences_per_tag[tag].append(confidence)

    avg_confidence_per_tag = {}
    for tag, confidences in confidences_per_tag.items():
        avg_confidence_per_tag[tag] = round(sum(confidences) / len(confidences), 2)

    return avg_confidence_per_tag


def _find_bilou_end(start_idx: int, predicted_tags: List[Text]) -> int:
    """Find the last index of the entity.

    The start index is pointing to a B- tag. The entity is closed as soon as we find
    a L- tag or a O tag.

    Args:
        start_idx: The start index of the entity
        predicted_tags: The list of predicted tags

    Returns:
        The end index of the entity
    """
    current_idx = start_idx + 1
    finished = False
    start_tag = tag_without_prefix(predicted_tags[start_idx])

    while not finished:
        if current_idx >= len(predicted_tags):
            logger.debug(
                "Inconsistent BILOU tagging found, B- tag not closed by L- tag, "
                "i.e [B-a, I-a, O] instead of [B-a, L-a, O].\n"
                "Assuming last tag is L- instead of I-."
            )
            current_idx -= 1
            break

        current_label = predicted_tags[current_idx]
        prefix = bilou_prefix_from_tag(current_label)
        tag = tag_without_prefix(current_label)

        if tag != start_tag:
            # words are not tagged the same entity class
            logger.debug(
                "Inconsistent BILOU tagging found, B- tag, L- tag pair encloses "
                "multiple entity classes.i.e. [B-a, I-b, L-a] instead of "
                "[B-a, I-a, L-a].\nAssuming B- class is correct."
            )

        if prefix == LAST:
            finished = True
        elif prefix == INSIDE:
            # middle part of the entity
            current_idx += 1
        else:
            # entity not closed by an L- tag
            finished = True
            current_idx -= 1
            logger.debug(
                "Inconsistent BILOU tagging found, B- tag not closed by L- tag, "
                "i.e [B-a, I-a, O] instead of [B-a, L-a, O].\n"
                "Assuming last tag is L- instead of I-."
            )

    return current_idx