349 lines
13 KiB
Cython
349 lines
13 KiB
Cython
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# cython: infer_types=True, binding=True
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from collections import defaultdict
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from typing import Callable, Optional
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from thinc.api import Config, Model
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from ._parser_internals.transition_system import TransitionSystem
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from ._parser_internals.arc_eager cimport ArcEager
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from .transition_parser cimport Parser
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from ..language import Language
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from ..scorer import Scorer
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from ..training import remove_bilu_prefix
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from ..util import registry
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from ._parser_internals import nonproj
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from ._parser_internals.nonproj import DELIMITER
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from .functions import merge_subtokens
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default_model_config = """
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[model]
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@architectures = "spacy.TransitionBasedParser.v2"
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state_type = "parser"
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extra_state_tokens = false
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hidden_width = 64
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maxout_pieces = 2
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use_upper = true
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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_PARSER_MODEL = Config().from_str(default_model_config)["model"]
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@Language.factory(
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"parser",
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assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
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default_config={
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"moves": None,
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"update_with_oracle_cut_size": 100,
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"learn_tokens": False,
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"min_action_freq": 30,
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"model": DEFAULT_PARSER_MODEL,
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"scorer": {"@scorers": "spacy.parser_scorer.v1"},
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},
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default_score_weights={
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"dep_uas": 0.5,
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"dep_las": 0.5,
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"dep_las_per_type": None,
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"sents_p": None,
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"sents_r": None,
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"sents_f": 0.0,
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},
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)
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def make_parser(
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nlp: Language,
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name: str,
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model: Model,
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moves: Optional[TransitionSystem],
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update_with_oracle_cut_size: int,
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learn_tokens: bool,
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min_action_freq: int,
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scorer: Optional[Callable],
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):
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"""Create a transition-based DependencyParser component. The dependency parser
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jointly learns sentence segmentation and labelled dependency parsing, and can
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optionally learn to merge tokens that had been over-segmented by the tokenizer.
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The parser uses a variant of the non-monotonic arc-eager transition-system
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described by Honnibal and Johnson (2014), with the addition of a "break"
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transition to perform the sentence segmentation. Nivre's pseudo-projective
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dependency transformation is used to allow the parser to predict
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non-projective parses.
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The parser is trained using an imitation learning objective. The parser follows
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the actions predicted by the current weights, and at each state, determines
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which actions are compatible with the optimal parse that could be reached
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from the current state. The weights such that the scores assigned to the
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set of optimal actions is increased, while scores assigned to other
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actions are decreased. Note that more than one action may be optimal for
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a given state.
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model (Model): The model for the transition-based parser. The model needs
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to have a specific substructure of named components --- see the
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spacy.ml.tb_framework.TransitionModel for details.
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moves (Optional[TransitionSystem]): This defines how the parse-state is created,
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updated and evaluated. If 'moves' is None, a new instance is
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created with `self.TransitionSystem()`. Defaults to `None`.
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update_with_oracle_cut_size (int): During training, cut long sequences into
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shorter segments by creating intermediate states based on the gold-standard
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history. The model is not very sensitive to this parameter, so you usually
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won't need to change it. 100 is a good default.
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learn_tokens (bool): Whether to learn to merge subtokens that are split
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relative to the gold standard. Experimental.
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min_action_freq (int): The minimum frequency of labelled actions to retain.
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Rarer labelled actions have their label backed-off to "dep". While this
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primarily affects the label accuracy, it can also affect the attachment
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structure, as the labels are used to represent the pseudo-projectivity
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transformation.
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scorer (Optional[Callable]): The scoring method.
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"""
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return DependencyParser(
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nlp.vocab,
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model,
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name,
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moves=moves,
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update_with_oracle_cut_size=update_with_oracle_cut_size,
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multitasks=[],
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learn_tokens=learn_tokens,
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min_action_freq=min_action_freq,
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beam_width=1,
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beam_density=0.0,
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beam_update_prob=0.0,
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# At some point in the future we can try to implement support for
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# partial annotations, perhaps only in the beam objective.
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incorrect_spans_key=None,
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scorer=scorer,
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)
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@Language.factory(
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"beam_parser",
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assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
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default_config={
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"beam_width": 8,
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"beam_density": 0.01,
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"beam_update_prob": 0.5,
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"moves": None,
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"update_with_oracle_cut_size": 100,
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"learn_tokens": False,
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"min_action_freq": 30,
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"model": DEFAULT_PARSER_MODEL,
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"scorer": {"@scorers": "spacy.parser_scorer.v1"},
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},
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default_score_weights={
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"dep_uas": 0.5,
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"dep_las": 0.5,
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"dep_las_per_type": None,
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"sents_p": None,
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"sents_r": None,
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"sents_f": 0.0,
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},
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)
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def make_beam_parser(
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nlp: Language,
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name: str,
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model: Model,
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moves: Optional[TransitionSystem],
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update_with_oracle_cut_size: int,
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learn_tokens: bool,
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min_action_freq: int,
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beam_width: int,
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beam_density: float,
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beam_update_prob: float,
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scorer: Optional[Callable],
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):
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"""Create a transition-based DependencyParser component that uses beam-search.
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The dependency parser jointly learns sentence segmentation and labelled
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dependency parsing, and can optionally learn to merge tokens that had been
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over-segmented by the tokenizer.
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The parser uses a variant of the non-monotonic arc-eager transition-system
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described by Honnibal and Johnson (2014), with the addition of a "break"
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transition to perform the sentence segmentation. Nivre's pseudo-projective
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dependency transformation is used to allow the parser to predict
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non-projective parses.
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The parser is trained using a global objective. That is, it learns to assign
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probabilities to whole parses.
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model (Model): The model for the transition-based parser. The model needs
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to have a specific substructure of named components --- see the
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spacy.ml.tb_framework.TransitionModel for details.
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moves (Optional[TransitionSystem]): This defines how the parse-state is created,
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updated and evaluated. If 'moves' is None, a new instance is
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created with `self.TransitionSystem()`. Defaults to `None`.
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update_with_oracle_cut_size (int): During training, cut long sequences into
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shorter segments by creating intermediate states based on the gold-standard
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history. The model is not very sensitive to this parameter, so you usually
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won't need to change it. 100 is a good default.
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beam_width (int): The number of candidate analyses to maintain.
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beam_density (float): The minimum ratio between the scores of the first and
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last candidates in the beam. This allows the parser to avoid exploring
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candidates that are too far behind. This is mostly intended to improve
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efficiency, but it can also improve accuracy as deeper search is not
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always better.
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beam_update_prob (float): The chance of making a beam update, instead of a
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greedy update. Greedy updates are an approximation for the beam updates,
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and are faster to compute.
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learn_tokens (bool): Whether to learn to merge subtokens that are split
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relative to the gold standard. Experimental.
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min_action_freq (int): The minimum frequency of labelled actions to retain.
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Rarer labelled actions have their label backed-off to "dep". While this
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primarily affects the label accuracy, it can also affect the attachment
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structure, as the labels are used to represent the pseudo-projectivity
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transformation.
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"""
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return DependencyParser(
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nlp.vocab,
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model,
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name,
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moves=moves,
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update_with_oracle_cut_size=update_with_oracle_cut_size,
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beam_width=beam_width,
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beam_density=beam_density,
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beam_update_prob=beam_update_prob,
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multitasks=[],
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learn_tokens=learn_tokens,
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min_action_freq=min_action_freq,
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# At some point in the future we can try to implement support for
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# partial annotations, perhaps only in the beam objective.
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incorrect_spans_key=None,
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scorer=scorer,
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)
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def parser_score(examples, **kwargs):
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"""Score a batch of examples.
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examples (Iterable[Example]): The examples to score.
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RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_spans
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and Scorer.score_deps.
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DOCS: https://spacy.io/api/dependencyparser#score
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"""
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def has_sents(doc):
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return doc.has_annotation("SENT_START")
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def dep_getter(token, attr):
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dep = getattr(token, attr)
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dep = token.vocab.strings.as_string(dep).lower()
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return dep
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results = {}
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results.update(Scorer.score_spans(examples, "sents", has_annotation=has_sents, **kwargs))
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kwargs.setdefault("getter", dep_getter)
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kwargs.setdefault("ignore_labels", ("p", "punct"))
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results.update(Scorer.score_deps(examples, "dep", **kwargs))
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del results["sents_per_type"]
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return results
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@registry.scorers("spacy.parser_scorer.v1")
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def make_parser_scorer():
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return parser_score
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cdef class DependencyParser(Parser):
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"""Pipeline component for dependency parsing.
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DOCS: https://spacy.io/api/dependencyparser
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"""
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TransitionSystem = ArcEager
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def __init__(
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self,
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vocab,
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model,
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name="parser",
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moves=None,
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*,
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update_with_oracle_cut_size=100,
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min_action_freq=30,
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learn_tokens=False,
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beam_width=1,
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beam_density=0.0,
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beam_update_prob=0.0,
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multitasks=tuple(),
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incorrect_spans_key=None,
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scorer=parser_score,
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):
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"""Create a DependencyParser.
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"""
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super().__init__(
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vocab,
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model,
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name,
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moves,
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update_with_oracle_cut_size=update_with_oracle_cut_size,
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min_action_freq=min_action_freq,
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learn_tokens=learn_tokens,
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beam_width=beam_width,
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beam_density=beam_density,
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beam_update_prob=beam_update_prob,
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multitasks=multitasks,
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incorrect_spans_key=incorrect_spans_key,
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scorer=scorer,
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)
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@property
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def postprocesses(self):
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output = [nonproj.deprojectivize]
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if self.cfg.get("learn_tokens") is True:
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output.append(merge_subtokens)
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return tuple(output)
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def add_multitask_objective(self, mt_component):
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self._multitasks.append(mt_component)
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def init_multitask_objectives(self, get_examples, nlp=None, **cfg):
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# TODO: transfer self.model.get_ref("tok2vec") to the multitask's model ?
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for labeller in self._multitasks:
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labeller.model.set_dim("nO", len(self.labels))
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if labeller.model.has_ref("output_layer"):
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labeller.model.get_ref("output_layer").set_dim("nO", len(self.labels))
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labeller.initialize(get_examples, nlp=nlp)
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@property
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def labels(self):
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labels = set()
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# Get the labels from the model by looking at the available moves
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for move in self.move_names:
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if "-" in move:
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label = remove_bilu_prefix(move)
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if DELIMITER in label:
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label = label.split(DELIMITER)[1]
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labels.add(label)
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return tuple(sorted(labels))
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def scored_parses(self, beams):
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"""Return two dictionaries with scores for each beam/doc that was processed:
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one containing (i, head) keys, and another containing (i, label) keys.
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"""
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head_scores = []
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label_scores = []
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for beam in beams:
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score_head_dict = defaultdict(float)
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score_label_dict = defaultdict(float)
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for score, parses in self.moves.get_beam_parses(beam):
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for head, i, label in parses:
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score_head_dict[(i, head)] += score
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score_label_dict[(i, label)] += score
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head_scores.append(score_head_dict)
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label_scores.append(score_label_dict)
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return head_scores, label_scores
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def _ensure_labels_are_added(self, docs):
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# This gives the parser a chance to add labels it's missing for a batch
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# of documents. However, this isn't desirable for the dependency parser,
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# because we instead have a label frequency cut-off and back off rare
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# labels to 'dep'.
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pass
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