mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 04:08:09 +03:00
2da16adcc2
Dropout can now be specified in the `Parser.update()` method via
the `drop` keyword argument, e.g.
nlp.entity.update(doc, gold, drop=0.4)
This will randomly drop 40% of features, and multiply the value of the
others by 1. / 0.4. This may be useful for generalising from small data
sets.
This commit also patches the examples/training/train_new_entity_type.py
example, to use dropout and fix the output (previously it did not output
the learned entity).
528 lines
19 KiB
Cython
528 lines
19 KiB
Cython
"""
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MALT-style dependency parser
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"""
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# coding: utf-8
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# cython: infer_types=True
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from __future__ import unicode_literals
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from collections import Counter
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import ujson
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cimport cython
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cimport cython.parallel
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import numpy.random
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from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
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from cpython.exc cimport PyErr_CheckSignals
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from libc.stdint cimport uint32_t, uint64_t
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from libc.string cimport memset, memcpy
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from libc.stdlib cimport malloc, calloc, free
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from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t
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from thinc.linear.avgtron cimport AveragedPerceptron
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from thinc.linalg cimport VecVec
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from thinc.structs cimport SparseArrayC, FeatureC, ExampleC
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from thinc.extra.eg cimport Example
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from cymem.cymem cimport Pool, Address
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from murmurhash.mrmr cimport hash64
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from preshed.maps cimport MapStruct
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from preshed.maps cimport map_get
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from . import _parse_features
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from ._parse_features cimport CONTEXT_SIZE
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from ._parse_features cimport fill_context
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from .stateclass cimport StateClass
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from ._state cimport StateC
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from .nonproj import PseudoProjectivity
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from .transition_system import OracleError
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from .transition_system cimport TransitionSystem, Transition
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from ..structs cimport TokenC
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from ..tokens.doc cimport Doc
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from ..strings cimport StringStore
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from ..gold cimport GoldParse
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USE_FTRL = True
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DEBUG = False
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def set_debug(val):
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global DEBUG
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DEBUG = val
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def get_templates(name):
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pf = _parse_features
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if name == 'ner':
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return pf.ner
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elif name == 'debug':
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return pf.unigrams
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elif name.startswith('embed'):
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return (pf.words, pf.tags, pf.labels)
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else:
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return (pf.unigrams + pf.s0_n0 + pf.s1_n0 + pf.s1_s0 + pf.s0_n1 + pf.n0_n1 + \
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pf.tree_shape + pf.trigrams)
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cdef class ParserModel(AveragedPerceptron):
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cdef int set_featuresC(self, atom_t* context, FeatureC* features,
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const StateC* state) nogil:
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fill_context(context, state)
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nr_feat = self.extracter.set_features(features, context)
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return nr_feat
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def update(self, Example eg, itn=0):
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"""
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Does regression on negative cost. Sort of cute?
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"""
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self.time += 1
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cdef int best = arg_max_if_gold(eg.c.scores, eg.c.costs, eg.c.nr_class)
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cdef int guess = eg.guess
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if guess == best or best == -1:
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return 0.0
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cdef FeatureC feat
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cdef int clas
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cdef weight_t gradient
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if USE_FTRL:
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for feat in eg.c.features[:eg.c.nr_feat]:
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for clas in range(eg.c.nr_class):
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if eg.c.is_valid[clas] and eg.c.scores[clas] >= eg.c.scores[best]:
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gradient = eg.c.scores[clas] + eg.c.costs[clas]
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self.update_weight_ftrl(feat.key, clas, feat.value * gradient)
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else:
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for feat in eg.c.features[:eg.c.nr_feat]:
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self.update_weight(feat.key, guess, feat.value * eg.c.costs[guess])
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self.update_weight(feat.key, best, -feat.value * eg.c.costs[guess])
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return eg.c.costs[guess]
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def update_from_histories(self, TransitionSystem moves, Doc doc, histories, weight_t min_grad=0.0):
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cdef Pool mem = Pool()
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features = <FeatureC*>mem.alloc(self.nr_feat, sizeof(FeatureC))
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cdef StateClass stcls
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cdef class_t clas
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self.time += 1
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cdef atom_t[CONTEXT_SIZE] atoms
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histories = [(grad, hist) for grad, hist in histories if abs(grad) >= min_grad and hist]
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if not histories:
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return None
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gradient = [Counter() for _ in range(max([max(h)+1 for _, h in histories]))]
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for d_loss, history in histories:
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stcls = StateClass.init(doc.c, doc.length)
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moves.initialize_state(stcls.c)
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for clas in history:
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nr_feat = self.set_featuresC(atoms, features, stcls.c)
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clas_grad = gradient[clas]
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for feat in features[:nr_feat]:
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clas_grad[feat.key] += d_loss * feat.value
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moves.c[clas].do(stcls.c, moves.c[clas].label)
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cdef feat_t key
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cdef weight_t d_feat
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for clas, clas_grad in enumerate(gradient):
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for key, d_feat in clas_grad.items():
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if d_feat != 0:
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self.update_weight_ftrl(key, clas, d_feat)
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cdef class Parser:
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"""
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Base class of the DependencyParser and EntityRecognizer.
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"""
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@classmethod
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def load(cls, path, Vocab vocab, TransitionSystem=None, require=False, **cfg):
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"""
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Load the statistical model from the supplied path.
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Arguments:
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path (Path):
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The path to load from.
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vocab (Vocab):
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The vocabulary. Must be shared by the documents to be processed.
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require (bool):
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Whether to raise an error if the files are not found.
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Returns (Parser):
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The newly constructed object.
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"""
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with (path / 'config.json').open() as file_:
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cfg = ujson.load(file_)
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# TODO: remove this shim when we don't have to support older data
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if 'labels' in cfg and 'actions' not in cfg:
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cfg['actions'] = cfg.pop('labels')
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# TODO: remove this shim when we don't have to support older data
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for action_name, labels in dict(cfg.get('actions', {})).items():
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# We need this to be sorted
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if isinstance(labels, dict):
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labels = list(sorted(labels.keys()))
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cfg['actions'][action_name] = labels
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self = cls(vocab, TransitionSystem=TransitionSystem, model=None, **cfg)
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if (path / 'model').exists():
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self.model.load(str(path / 'model'))
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elif require:
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raise IOError(
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"Required file %s/model not found when loading" % str(path))
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return self
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def __init__(self, Vocab vocab, TransitionSystem=None, ParserModel model=None, **cfg):
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"""
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Create a Parser.
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Arguments:
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vocab (Vocab):
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The vocabulary object. Must be shared with documents to be processed.
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model (thinc.linear.AveragedPerceptron):
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The statistical model.
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Returns (Parser):
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The newly constructed object.
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"""
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if TransitionSystem is None:
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TransitionSystem = self.TransitionSystem
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self.vocab = vocab
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cfg['actions'] = TransitionSystem.get_actions(**cfg)
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self.moves = TransitionSystem(vocab.strings, cfg['actions'])
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# TODO: Remove this when we no longer need to support old-style models
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if isinstance(cfg.get('features'), basestring):
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cfg['features'] = get_templates(cfg['features'])
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elif 'features' not in cfg:
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cfg['features'] = self.feature_templates
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self.model = ParserModel(cfg['features'])
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self.model.l1_penalty = cfg.get('L1', 0.0)
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self.model.learn_rate = cfg.get('learn_rate', 0.001)
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self.cfg = cfg
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# TODO: This is a pretty hacky fix to the problem of adding more
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# labels. The issue is they come in out of order, if labels are
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# added during training
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for label in cfg.get('extra_labels', []):
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self.add_label(label)
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def __reduce__(self):
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return (Parser, (self.vocab, self.moves, self.model), None, None)
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def __call__(self, Doc tokens):
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"""
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Apply the entity recognizer, setting the annotations onto the Doc object.
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Arguments:
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doc (Doc): The document to be processed.
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Returns:
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None
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"""
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cdef int nr_feat = self.model.nr_feat
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with nogil:
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status = self.parseC(tokens.c, tokens.length, nr_feat)
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# Check for KeyboardInterrupt etc. Untested
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PyErr_CheckSignals()
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if status != 0:
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raise ParserStateError(tokens)
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self.moves.finalize_doc(tokens)
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def pipe(self, stream, int batch_size=1000, int n_threads=2):
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"""
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Process a stream of documents.
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Arguments:
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stream: The sequence of documents to process.
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batch_size (int):
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The number of documents to accumulate into a working set.
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n_threads (int):
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The number of threads with which to work on the buffer in parallel.
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Yields (Doc): Documents, in order.
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"""
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cdef Pool mem = Pool()
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cdef TokenC** doc_ptr = <TokenC**>mem.alloc(batch_size, sizeof(TokenC*))
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cdef int* lengths = <int*>mem.alloc(batch_size, sizeof(int))
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cdef Doc doc
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cdef int i
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cdef int nr_feat = self.model.nr_feat
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cdef int status
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queue = []
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for doc in stream:
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doc_ptr[len(queue)] = doc.c
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lengths[len(queue)] = doc.length
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queue.append(doc)
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if len(queue) == batch_size:
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with nogil:
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for i in cython.parallel.prange(batch_size, num_threads=n_threads):
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status = self.parseC(doc_ptr[i], lengths[i], nr_feat)
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if status != 0:
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with gil:
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raise ParserStateError(queue[i])
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PyErr_CheckSignals()
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for doc in queue:
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self.moves.finalize_doc(doc)
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yield doc
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queue = []
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batch_size = len(queue)
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with nogil:
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for i in cython.parallel.prange(batch_size, num_threads=n_threads):
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status = self.parseC(doc_ptr[i], lengths[i], nr_feat)
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if status != 0:
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with gil:
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raise ParserStateError(queue[i])
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PyErr_CheckSignals()
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for doc in queue:
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self.moves.finalize_doc(doc)
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yield doc
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cdef int parseC(self, TokenC* tokens, int length, int nr_feat) nogil:
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state = new StateC(tokens, length)
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# NB: This can change self.moves.n_moves!
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# I think this causes memory errors if called by .pipe()
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self.moves.initialize_state(state)
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nr_class = self.moves.n_moves
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cdef ExampleC eg
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eg.nr_feat = nr_feat
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eg.nr_atom = CONTEXT_SIZE
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eg.nr_class = nr_class
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eg.features = <FeatureC*>calloc(sizeof(FeatureC), nr_feat)
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eg.atoms = <atom_t*>calloc(sizeof(atom_t), CONTEXT_SIZE)
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eg.scores = <weight_t*>calloc(sizeof(weight_t), nr_class)
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eg.is_valid = <int*>calloc(sizeof(int), nr_class)
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cdef int i
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while not state.is_final():
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eg.nr_feat = self.model.set_featuresC(eg.atoms, eg.features, state)
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self.moves.set_valid(eg.is_valid, state)
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self.model.set_scoresC(eg.scores, eg.features, eg.nr_feat)
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guess = VecVec.arg_max_if_true(eg.scores, eg.is_valid, eg.nr_class)
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if guess < 0:
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return 1
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action = self.moves.c[guess]
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action.do(state, action.label)
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memset(eg.scores, 0, sizeof(eg.scores[0]) * eg.nr_class)
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for i in range(eg.nr_class):
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eg.is_valid[i] = 1
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self.moves.finalize_state(state)
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for i in range(length):
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tokens[i] = state._sent[i]
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del state
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free(eg.features)
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free(eg.atoms)
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free(eg.scores)
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free(eg.is_valid)
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return 0
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def update(self, Doc tokens, GoldParse gold, itn=0, double drop=0.0):
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"""
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Update the statistical model.
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Arguments:
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doc (Doc):
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The example document for the update.
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gold (GoldParse):
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The gold-standard annotations, to calculate the loss.
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Returns (float):
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The loss on this example.
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"""
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self.moves.preprocess_gold(gold)
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cdef StateClass stcls = StateClass.init(tokens.c, tokens.length)
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self.moves.initialize_state(stcls.c)
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cdef Pool mem = Pool()
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cdef Example eg = Example(
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nr_class=self.moves.n_moves,
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nr_atom=CONTEXT_SIZE,
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nr_feat=self.model.nr_feat)
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cdef weight_t loss = 0
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cdef Transition action
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cdef double dropout_rate = self.cfg.get('dropout', drop)
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while not stcls.is_final():
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eg.c.nr_feat = self.model.set_featuresC(eg.c.atoms, eg.c.features,
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stcls.c)
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dropout(eg.c.features, eg.c.nr_feat, dropout_rate)
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self.moves.set_costs(eg.c.is_valid, eg.c.costs, stcls, gold)
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self.model.set_scoresC(eg.c.scores, eg.c.features, eg.c.nr_feat)
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guess = VecVec.arg_max_if_true(eg.c.scores, eg.c.is_valid, eg.c.nr_class)
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self.model.update(eg)
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action = self.moves.c[guess]
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action.do(stcls.c, action.label)
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loss += eg.costs[guess]
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eg.fill_scores(0, eg.c.nr_class)
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eg.fill_costs(0, eg.c.nr_class)
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eg.fill_is_valid(1, eg.c.nr_class)
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self.moves.finalize_state(stcls.c)
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return loss
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def step_through(self, Doc doc, GoldParse gold=None):
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"""
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Set up a stepwise state, to introspect and control the transition sequence.
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Arguments:
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doc (Doc): The document to step through.
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gold (GoldParse): Optional gold parse
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Returns (StepwiseState):
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A state object, to step through the annotation process.
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"""
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return StepwiseState(self, doc, gold=gold)
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def from_transition_sequence(self, Doc doc, sequence):
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"""Control the annotations on a document by specifying a transition sequence
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to follow.
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Arguments:
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doc (Doc): The document to annotate.
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sequence: A sequence of action names, as unicode strings.
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Returns: None
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"""
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with self.step_through(doc) as stepwise:
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for transition in sequence:
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stepwise.transition(transition)
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def add_label(self, label):
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# Doesn't set label into serializer -- subclasses override it to do that.
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for action in self.moves.action_types:
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added = self.moves.add_action(action, label)
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if added:
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# Important that the labels be stored as a list! We need the
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# order, or the model goes out of synch
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self.cfg.setdefault('extra_labels', []).append(label)
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cdef int dropout(FeatureC* feats, int nr_feat, float prob) except -1:
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if prob <= 0 or prob >= 1.:
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return 0
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cdef double[::1] py_probs = numpy.random.uniform(0., 1., nr_feat)
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cdef double* probs = &py_probs[0]
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for i in range(nr_feat):
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if probs[i] >= prob:
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feats[i].value /= prob
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else:
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feats[i].value = 0.
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cdef class StepwiseState:
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cdef readonly StateClass stcls
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cdef readonly Example eg
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cdef readonly Doc doc
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cdef readonly GoldParse gold
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cdef readonly Parser parser
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def __init__(self, Parser parser, Doc doc, GoldParse gold=None):
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self.parser = parser
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self.doc = doc
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if gold is not None:
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self.gold = gold
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self.parser.moves.preprocess_gold(self.gold)
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else:
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self.gold = GoldParse(doc)
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self.stcls = StateClass.init(doc.c, doc.length)
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self.parser.moves.initialize_state(self.stcls.c)
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self.eg = Example(
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nr_class=self.parser.moves.n_moves,
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nr_atom=CONTEXT_SIZE,
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nr_feat=self.parser.model.nr_feat)
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def __enter__(self):
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return self
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def __exit__(self, type, value, traceback):
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self.finish()
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@property
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def is_final(self):
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return self.stcls.is_final()
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@property
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def stack(self):
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return self.stcls.stack
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@property
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def queue(self):
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return self.stcls.queue
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@property
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def heads(self):
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return [self.stcls.H(i) for i in range(self.stcls.c.length)]
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@property
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def deps(self):
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return [self.doc.vocab.strings[self.stcls.c._sent[i].dep]
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for i in range(self.stcls.c.length)]
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@property
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def costs(self):
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"""
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Find the action-costs for the current state.
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"""
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if not self.gold:
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raise ValueError("Can't set costs: No GoldParse provided")
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self.parser.moves.set_costs(self.eg.c.is_valid, self.eg.c.costs,
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self.stcls, self.gold)
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costs = {}
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for i in range(self.parser.moves.n_moves):
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if not self.eg.c.is_valid[i]:
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continue
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transition = self.parser.moves.c[i]
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name = self.parser.moves.move_name(transition.move, transition.label)
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costs[name] = self.eg.c.costs[i]
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return costs
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def predict(self):
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self.eg.reset()
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self.eg.c.nr_feat = self.parser.model.set_featuresC(self.eg.c.atoms, self.eg.c.features,
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self.stcls.c)
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self.parser.moves.set_valid(self.eg.c.is_valid, self.stcls.c)
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self.parser.model.set_scoresC(self.eg.c.scores,
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self.eg.c.features, self.eg.c.nr_feat)
|
|
|
|
cdef Transition action = self.parser.moves.c[self.eg.guess]
|
|
return self.parser.moves.move_name(action.move, action.label)
|
|
|
|
def transition(self, action_name=None):
|
|
if action_name is None:
|
|
action_name = self.predict()
|
|
moves = {'S': 0, 'D': 1, 'L': 2, 'R': 3}
|
|
if action_name == '_':
|
|
action_name = self.predict()
|
|
action = self.parser.moves.lookup_transition(action_name)
|
|
elif action_name == 'L' or action_name == 'R':
|
|
self.predict()
|
|
move = moves[action_name]
|
|
clas = _arg_max_clas(self.eg.c.scores, move, self.parser.moves.c,
|
|
self.eg.c.nr_class)
|
|
action = self.parser.moves.c[clas]
|
|
else:
|
|
action = self.parser.moves.lookup_transition(action_name)
|
|
action.do(self.stcls.c, action.label)
|
|
|
|
def finish(self):
|
|
if self.stcls.is_final():
|
|
self.parser.moves.finalize_state(self.stcls.c)
|
|
self.doc.set_parse(self.stcls.c._sent)
|
|
self.parser.moves.finalize_doc(self.doc)
|
|
|
|
|
|
class ParserStateError(ValueError):
|
|
def __init__(self, doc):
|
|
ValueError.__init__(self,
|
|
"Error analysing doc -- no valid actions available. This should "
|
|
"never happen, so please report the error on the issue tracker. "
|
|
"Here's the thread to do so --- reopen it if it's closed:\n"
|
|
"https://github.com/spacy-io/spaCy/issues/429\n"
|
|
"Please include the text that the parser failed on, which is:\n"
|
|
"%s" % repr(doc.text))
|
|
|
|
cdef int arg_max_if_gold(const weight_t* scores, const weight_t* costs, int n) nogil:
|
|
cdef int best = -1
|
|
for i in range(n):
|
|
if costs[i] <= 0:
|
|
if best == -1 or scores[i] > scores[best]:
|
|
best = i
|
|
return best
|
|
|
|
|
|
cdef int _arg_max_clas(const weight_t* scores, int move, const Transition* actions,
|
|
int nr_class) except -1:
|
|
cdef weight_t score = 0
|
|
cdef int mode = -1
|
|
cdef int i
|
|
for i in range(nr_class):
|
|
if actions[i].move == move and (mode == -1 or scores[i] >= score):
|
|
mode = i
|
|
score = scores[i]
|
|
return mode
|