mirror of https://github.com/explosion/spaCy.git
424 lines
15 KiB
Python
424 lines
15 KiB
Python
from collections import Counter
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from itertools import islice
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from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, cast
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import numpy as np
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import srsly
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from thinc.api import Config, Model, NumpyOps, SequenceCategoricalCrossentropy
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from thinc.types import Floats2d, Ints2d
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from .. import util
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from ..errors import Errors
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from ..language import Language
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from ..tokens import Doc
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from ..training import Example, validate_examples, validate_get_examples
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from ..vocab import Vocab
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from ._edit_tree_internals.edit_trees import EditTrees
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from ._edit_tree_internals.schemas import validate_edit_tree
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from .lemmatizer import lemmatizer_score
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from .trainable_pipe import TrainablePipe
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# The cutoff value of *top_k* above which an alternative method is used to process guesses.
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TOP_K_GUARDRAIL = 20
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default_model_config = """
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[model]
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@architectures = "spacy.Tagger.v2"
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[model.tok2vec]
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@architectures = "spacy.HashEmbedCNN.v2"
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pretrained_vectors = null
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width = 96
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depth = 4
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embed_size = 2000
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window_size = 1
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maxout_pieces = 3
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subword_features = true
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"""
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DEFAULT_EDIT_TREE_LEMMATIZER_MODEL = Config().from_str(default_model_config)["model"]
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@Language.factory(
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"trainable_lemmatizer",
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assigns=["token.lemma"],
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requires=[],
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default_config={
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"model": DEFAULT_EDIT_TREE_LEMMATIZER_MODEL,
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"backoff": "orth",
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"min_tree_freq": 3,
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"overwrite": False,
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"top_k": 1,
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"scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
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},
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default_score_weights={"lemma_acc": 1.0},
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)
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def make_edit_tree_lemmatizer(
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nlp: Language,
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name: str,
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model: Model,
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backoff: Optional[str],
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min_tree_freq: int,
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overwrite: bool,
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top_k: int,
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scorer: Optional[Callable],
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):
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"""Construct an EditTreeLemmatizer component."""
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return EditTreeLemmatizer(
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nlp.vocab,
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model,
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name,
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backoff=backoff,
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min_tree_freq=min_tree_freq,
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overwrite=overwrite,
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top_k=top_k,
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scorer=scorer,
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)
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class EditTreeLemmatizer(TrainablePipe):
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"""
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Lemmatizer that lemmatizes each word using a predicted edit tree.
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"""
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def __init__(
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self,
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vocab: Vocab,
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model: Model,
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name: str = "trainable_lemmatizer",
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*,
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backoff: Optional[str] = "orth",
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min_tree_freq: int = 3,
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overwrite: bool = False,
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top_k: int = 1,
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scorer: Optional[Callable] = lemmatizer_score,
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):
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"""
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Construct an edit tree lemmatizer.
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backoff (Optional[str]): backoff to use when the predicted edit trees
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are not applicable. Must be an attribute of Token or None (leave the
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lemma unset).
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min_tree_freq (int): prune trees that are applied less than this
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frequency in the training data.
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overwrite (bool): overwrite existing lemma annotations.
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top_k (int): try to apply at most the k most probable edit trees.
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"""
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self.vocab = vocab
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self.model = model
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self.name = name
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self.backoff = backoff
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self.min_tree_freq = min_tree_freq
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self.overwrite = overwrite
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self.top_k = top_k
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self.trees = EditTrees(self.vocab.strings)
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self.tree2label: Dict[int, int] = {}
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self.cfg: Dict[str, Any] = {"labels": []}
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self.scorer = scorer
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self.numpy_ops = NumpyOps()
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def get_loss(
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self, examples: Iterable[Example], scores: List[Floats2d]
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) -> Tuple[float, List[Floats2d]]:
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validate_examples(examples, "EditTreeLemmatizer.get_loss")
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loss_func = SequenceCategoricalCrossentropy(normalize=False, missing_value=-1)
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truths = []
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for eg in examples:
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eg_truths = []
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for (predicted, gold_lemma) in zip(
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eg.predicted, eg.get_aligned("LEMMA", as_string=True)
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):
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if gold_lemma is None or gold_lemma == "":
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label = -1
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else:
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tree_id = self.trees.add(predicted.text, gold_lemma)
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label = self.tree2label.get(tree_id, 0)
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eg_truths.append(label)
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truths.append(eg_truths)
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d_scores, loss = loss_func(scores, truths)
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if self.model.ops.xp.isnan(loss):
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raise ValueError(Errors.E910.format(name=self.name))
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return float(loss), d_scores
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def predict(self, docs: Iterable[Doc]) -> List[Ints2d]:
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if self.top_k == 1:
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scores2guesses = self._scores2guesses_top_k_equals_1
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elif self.top_k <= TOP_K_GUARDRAIL:
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scores2guesses = self._scores2guesses_top_k_greater_1
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else:
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scores2guesses = self._scores2guesses_top_k_guardrail
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# The behaviour of *_scores2guesses_top_k_greater_1()* is efficient for values
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# of *top_k>1* that are likely to be useful when the edit tree lemmatizer is used
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# for its principal purpose of lemmatizing tokens. However, the code could also
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# be used for other purposes, and with very large values of *top_k* the method
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# becomes inefficient. In such cases, *_scores2guesses_top_k_guardrail()* is used
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# instead.
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n_docs = len(list(docs))
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if not any(len(doc) for doc in docs):
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# Handle cases where there are no tokens in any docs.
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n_labels = len(self.cfg["labels"])
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guesses: List[Ints2d] = [self.model.ops.alloc2i(0, n_labels) for _ in docs]
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assert len(guesses) == n_docs
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return guesses
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scores = self.model.predict(docs)
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assert len(scores) == n_docs
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guesses = scores2guesses(docs, scores)
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assert len(guesses) == n_docs
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return guesses
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def _scores2guesses_top_k_equals_1(self, docs, scores):
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guesses = []
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for doc, doc_scores in zip(docs, scores):
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doc_guesses = doc_scores.argmax(axis=1)
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doc_guesses = self.numpy_ops.asarray(doc_guesses)
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doc_compat_guesses = []
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for i, token in enumerate(doc):
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tree_id = self.cfg["labels"][doc_guesses[i]]
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if self.trees.apply(tree_id, token.text) is not None:
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doc_compat_guesses.append(tree_id)
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else:
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doc_compat_guesses.append(-1)
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guesses.append(np.array(doc_compat_guesses))
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return guesses
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def _scores2guesses_top_k_greater_1(self, docs, scores):
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guesses = []
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top_k = min(self.top_k, len(self.labels))
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for doc, doc_scores in zip(docs, scores):
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doc_scores = self.numpy_ops.asarray(doc_scores)
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doc_compat_guesses = []
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for i, token in enumerate(doc):
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for _ in range(top_k):
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candidate = int(doc_scores[i].argmax())
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candidate_tree_id = self.cfg["labels"][candidate]
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if self.trees.apply(candidate_tree_id, token.text) is not None:
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doc_compat_guesses.append(candidate_tree_id)
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break
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doc_scores[i, candidate] = np.finfo(np.float32).min
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else:
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doc_compat_guesses.append(-1)
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guesses.append(np.array(doc_compat_guesses))
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return guesses
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def _scores2guesses_top_k_guardrail(self, docs, scores):
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guesses = []
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for doc, doc_scores in zip(docs, scores):
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doc_guesses = np.argsort(doc_scores)[..., : -self.top_k - 1 : -1]
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doc_guesses = self.numpy_ops.asarray(doc_guesses)
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doc_compat_guesses = []
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for token, candidates in zip(doc, doc_guesses):
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tree_id = -1
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for candidate in candidates:
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candidate_tree_id = self.cfg["labels"][candidate]
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if self.trees.apply(candidate_tree_id, token.text) is not None:
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tree_id = candidate_tree_id
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break
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doc_compat_guesses.append(tree_id)
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guesses.append(np.array(doc_compat_guesses))
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return guesses
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def set_annotations(self, docs: Iterable[Doc], batch_tree_ids):
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for i, doc in enumerate(docs):
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doc_tree_ids = batch_tree_ids[i]
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if hasattr(doc_tree_ids, "get"):
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doc_tree_ids = doc_tree_ids.get()
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for j, tree_id in enumerate(doc_tree_ids):
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if self.overwrite or doc[j].lemma == 0:
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# If no applicable tree could be found during prediction,
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# the special identifier -1 is used. Otherwise the tree
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# is guaranteed to be applicable.
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if tree_id == -1:
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if self.backoff is not None:
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doc[j].lemma = getattr(doc[j], self.backoff)
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else:
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lemma = self.trees.apply(tree_id, doc[j].text)
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doc[j].lemma_ = lemma
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@property
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def labels(self) -> Tuple[int, ...]:
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"""Returns the labels currently added to the component."""
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return tuple(self.cfg["labels"])
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@property
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def hide_labels(self) -> bool:
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return True
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@property
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def label_data(self) -> Dict:
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trees = []
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for tree_id in range(len(self.trees)):
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tree = self.trees[tree_id]
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if "orig" in tree:
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tree["orig"] = self.vocab.strings[tree["orig"]]
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if "subst" in tree:
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tree["subst"] = self.vocab.strings[tree["subst"]]
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trees.append(tree)
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return dict(trees=trees, labels=tuple(self.cfg["labels"]))
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def initialize(
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self,
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get_examples: Callable[[], Iterable[Example]],
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*,
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nlp: Optional[Language] = None,
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labels: Optional[Dict] = None,
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):
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validate_get_examples(get_examples, "EditTreeLemmatizer.initialize")
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if labels is None:
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self._labels_from_data(get_examples)
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else:
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self._add_labels(labels)
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# Sample for the model.
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doc_sample = []
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label_sample = []
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for example in islice(get_examples(), 10):
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doc_sample.append(example.x)
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gold_labels: List[List[float]] = []
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for token in example.reference:
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if token.lemma == 0:
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gold_label = None
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else:
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gold_label = self._pair2label(token.text, token.lemma_)
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gold_labels.append(
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[
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1.0 if label == gold_label else 0.0
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for label in self.cfg["labels"]
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]
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)
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gold_labels = cast(Floats2d, gold_labels)
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label_sample.append(self.model.ops.asarray(gold_labels, dtype="float32"))
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self._require_labels()
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assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
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assert len(label_sample) > 0, Errors.E923.format(name=self.name)
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self.model.initialize(X=doc_sample, Y=label_sample)
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def from_bytes(self, bytes_data, *, exclude=tuple()):
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deserializers = {
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"cfg": lambda b: self.cfg.update(srsly.json_loads(b)),
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"model": lambda b: self.model.from_bytes(b),
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"vocab": lambda b: self.vocab.from_bytes(b, exclude=exclude),
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"trees": lambda b: self.trees.from_bytes(b),
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}
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util.from_bytes(bytes_data, deserializers, exclude)
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return self
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def to_bytes(self, *, exclude=tuple()):
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serializers = {
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"cfg": lambda: srsly.json_dumps(self.cfg),
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"model": lambda: self.model.to_bytes(),
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"vocab": lambda: self.vocab.to_bytes(exclude=exclude),
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"trees": lambda: self.trees.to_bytes(),
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}
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return util.to_bytes(serializers, exclude)
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def to_disk(self, path, exclude=tuple()):
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path = util.ensure_path(path)
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serializers = {
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"cfg": lambda p: srsly.write_json(p, self.cfg),
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"model": lambda p: self.model.to_disk(p),
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"vocab": lambda p: self.vocab.to_disk(p, exclude=exclude),
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"trees": lambda p: self.trees.to_disk(p),
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}
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util.to_disk(path, serializers, exclude)
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def from_disk(self, path, exclude=tuple()):
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def load_model(p):
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try:
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with open(p, "rb") as mfile:
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self.model.from_bytes(mfile.read())
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except AttributeError:
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raise ValueError(Errors.E149) from None
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deserializers = {
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"cfg": lambda p: self.cfg.update(srsly.read_json(p)),
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"model": load_model,
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"vocab": lambda p: self.vocab.from_disk(p, exclude=exclude),
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"trees": lambda p: self.trees.from_disk(p),
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}
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util.from_disk(path, deserializers, exclude)
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return self
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def _add_labels(self, labels: Dict):
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if "labels" not in labels:
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raise ValueError(Errors.E857.format(name="labels"))
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if "trees" not in labels:
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raise ValueError(Errors.E857.format(name="trees"))
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self.cfg["labels"] = list(labels["labels"])
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trees = []
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for tree in labels["trees"]:
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errors = validate_edit_tree(tree)
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if errors:
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raise ValueError(Errors.E1026.format(errors="\n".join(errors)))
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tree = dict(tree)
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if "orig" in tree:
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tree["orig"] = self.vocab.strings.add(tree["orig"])
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if "orig" in tree:
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tree["subst"] = self.vocab.strings.add(tree["subst"])
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trees.append(tree)
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self.trees.from_json(trees)
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for label, tree in enumerate(self.labels):
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self.tree2label[tree] = label
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def _labels_from_data(self, get_examples: Callable[[], Iterable[Example]]):
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# Count corpus tree frequencies in ad-hoc storage to avoid cluttering
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# the final pipe/string store.
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vocab = Vocab()
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trees = EditTrees(vocab.strings)
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tree_freqs: Counter = Counter()
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repr_pairs: Dict = {}
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for example in get_examples():
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for token in example.reference:
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if token.lemma != 0:
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tree_id = trees.add(token.text, token.lemma_)
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tree_freqs[tree_id] += 1
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repr_pairs[tree_id] = (token.text, token.lemma_)
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# Construct trees that make the frequency cut-off using representative
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# form - token pairs.
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for tree_id, freq in tree_freqs.items():
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if freq >= self.min_tree_freq:
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form, lemma = repr_pairs[tree_id]
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self._pair2label(form, lemma, add_label=True)
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def _pair2label(self, form, lemma, add_label=False):
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"""
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Look up the edit tree identifier for a form/label pair. If the edit
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tree is unknown and "add_label" is set, the edit tree will be added to
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the labels.
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"""
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tree_id = self.trees.add(form, lemma)
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if tree_id not in self.tree2label:
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if not add_label:
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return None
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self.tree2label[tree_id] = len(self.cfg["labels"])
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self.cfg["labels"].append(tree_id)
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return self.tree2label[tree_id]
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