spaCy/spacy/ml/models/coref_util.py

from thinc.types import Ints2d
from spacy.tokens import Doc
from typing import List, Tuple, Callable, Any, Set, Dict
from ...util import registry
import torch

# type alias to make writing this less tedious
MentionClusters = List[List[Tuple[int, int]]]

DEFAULT_CLUSTER_PREFIX = "coref_clusters"

EPSILON = 1e-7

class GraphNode:
    def __init__(self, node_id: int):
        self.id = node_id
        self.links: Set[GraphNode] = set()
        self.visited = False

    def link(self, another: "GraphNode"):
        self.links.add(another)
        another.links.add(self)

    def __repr__(self) -> str:
        return str(self.id)


def add_dummy(tensor: torch.Tensor, eps: bool = False):
    """ Prepends zeros (or a very small value if eps is True)
    to the first (not zeroth) dimension of tensor.
    """
    kwargs = dict(device=tensor.device, dtype=tensor.dtype)
    shape: List[int] = list(tensor.shape)
    shape[1] = 1
    if not eps:
        dummy = torch.zeros(shape, **kwargs)          # type: ignore
    else:
        dummy = torch.full(shape, EPSILON, **kwargs)  # type: ignore
    output = torch.cat((dummy, tensor), dim=1)
    return output

def get_sentence_ids(doc):
    out = []
    sent_id = -1
    for tok in doc:
        if tok.is_sent_start:
            sent_id += 1
        out.append(sent_id)
    return out

def doc2clusters(doc: Doc, prefix=DEFAULT_CLUSTER_PREFIX) -> MentionClusters:
    """Given a doc, give the mention clusters.

    This is useful for scoring.
    """
    out = []
    for name, val in doc.spans.items():
        if not name.startswith(prefix):
            continue

        cluster = []
        for mention in val:
            cluster.append((mention.start, mention.end))
        out.append(cluster)
    return out


# from model.py, refactored to be non-member
def get_predicted_antecedents(xp, antecedent_idx, antecedent_scores):
    """Get the ID of the antecedent for each span. -1 if no antecedent."""
    predicted_antecedents = []
    for i, idx in enumerate(xp.argmax(antecedent_scores, axis=1) - 1):
        if idx < 0:
            predicted_antecedents.append(-1)
        else:
            predicted_antecedents.append(antecedent_idx[i][idx])
    return predicted_antecedents


# from model.py, refactored to be non-member
def get_predicted_clusters(
    xp, span_starts, span_ends, antecedent_idx, antecedent_scores
):
    """Convert predictions to usable cluster data.

    return values:

    clusters: a list of spans (i, j) that are a cluster

    Note that not all spans will be in the final output; spans with no
    antecedent or referrent are omitted from clusters and mention2cluster.
    """
    # Get predicted antecedents
    predicted_antecedents = get_predicted_antecedents(
        xp, antecedent_idx, antecedent_scores
    )

    # Get predicted clusters
    mention_to_cluster_id = {}
    predicted_clusters = []
    for i, predicted_idx in enumerate(predicted_antecedents):
        if predicted_idx < 0:
            continue
        assert i > predicted_idx, f"span idx: {i}; antecedent idx: {predicted_idx}"
        # Check antecedent's cluster
        antecedent = (int(span_starts[predicted_idx]), int(span_ends[predicted_idx]))
        antecedent_cluster_id = mention_to_cluster_id.get(antecedent, -1)
        if antecedent_cluster_id == -1:
            antecedent_cluster_id = len(predicted_clusters)
            predicted_clusters.append([antecedent])
            mention_to_cluster_id[antecedent] = antecedent_cluster_id
        # Add mention to cluster
        mention = (int(span_starts[i]), int(span_ends[i]))
        predicted_clusters[antecedent_cluster_id].append(mention)
        mention_to_cluster_id[mention] = antecedent_cluster_id

    predicted_clusters = [tuple(c) for c in predicted_clusters]
    return predicted_clusters



def select_non_crossing_spans(
    idxs: List[int], starts: List[int], ends: List[int], limit: int
) -> List[int]:
    """Given a list of spans sorted in descending order, return the indexes of
    spans to keep, discarding spans that cross.

    Nested spans are allowed.
    """
    # ported from Model._extract_top_spans
    selected = []
    start_to_max_end = {}
    end_to_min_start = {}

    for idx in idxs:
        if len(selected) >= limit or idx > len(starts):
            break

        start, end = starts[idx], ends[idx]
        cross = False

        for ti in range(start, end):
            max_end = start_to_max_end.get(ti, -1)
            if ti > start and max_end > end:
                cross = True
                break

            min_start = end_to_min_start.get(ti, -1)
            if ti < end and 0 <= min_start < start:
                cross = True
                break

        if not cross:
            # this index will be kept
            # record it so we can exclude anything that crosses it
            selected.append(idx)
            max_end = start_to_max_end.get(start, -1)
            if end > max_end:
                start_to_max_end[start] = end
            min_start = end_to_min_start.get(end, -1)
            if min_start == -1 or start < min_start:
                end_to_min_start[end] = start

    # sort idxs by order in doc
    selected = sorted(selected, key=lambda idx: (starts[idx], ends[idx]))
    # This was causing many repetitive entities in the output - removed for now
    # while len(selected) < limit:
    #     selected.append(selected[0])  # this seems a bit weird?
    return selected

def create_head_span_idxs(ops, doclen: int):
    """Helper function to create single-token span indices."""
    aa = ops.xp.arange(0, doclen)
    bb = ops.xp.arange(0, doclen) + 1
    return ops.asarray2i([aa, bb]).T

def get_clusters_from_doc(doc) -> List[List[Tuple[int, int]]]:
    """Given a Doc, convert the cluster spans to simple int tuple lists."""
    out = []
    for key, val in doc.spans.items():
        cluster = []
        for span in val:
            # TODO check that there isn't an off-by-one error here
            #cluster.append((span.start, span.end))
            # TODO This conversion should be happening earlier in processing
            head_i = span.root.i
            cluster.append( (head_i, head_i + 1) )

        # don't want duplicates
        cluster = list(set(cluster))
        out.append(cluster)
    return out


def create_gold_scores(
    ments: Ints2d, clusters: List[List[Tuple[int, int]]]
) -> List[List[bool]]:
    """Given mentions considered for antecedents and gold clusters,
    construct a gold score matrix. This does not include the placeholder.

    In the gold matrix, the value of a true antecedent is True, and otherwise
    it is False. These will be converted to 1/0 values later.
    """
    # make a mapping of mentions to cluster id
    # id is not important but equality will be
    ment2cid = {}
    for cid, cluster in enumerate(clusters):
        for ment in cluster:
            ment2cid[ment] = cid

    ll = len(ments)
    out = []
    # The .tolist() call is necessary with cupy but not numpy
    mentuples = [tuple(mm.tolist()) for mm in ments]
    for ii, ment in enumerate(mentuples):
        if ment not in ment2cid:
            # this is not in a cluster so it has no antecedent
            out.append([False] * ll)
            continue

        # this might change if no real antecedent is a candidate
        row = []
        cid = ment2cid[ment]
        for jj, ante in enumerate(mentuples):
            # antecedents must come first
            if jj >= ii:
                row.append(False)
                continue

            row.append(cid == ment2cid.get(ante, -1))

        out.append(row)

    # caller needs to convert to array, and add placeholder
    return out