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Tidy up language, lemmatizer and scorer
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ines
parent
778212efea
commit
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@ -11,21 +11,18 @@ from collections import OrderedDict
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import itertools
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import weakref
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import functools
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import tqdm
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from .tokenizer import Tokenizer
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from .vocab import Vocab
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from .tagger import Tagger
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from .lemmatizer import Lemmatizer
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from .pipeline import DependencyParser, Tensorizer, Tagger
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from .pipeline import EntityRecognizer, SimilarityHook, TextCategorizer
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from .compat import json_dumps, izip, copy_reg
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from .compat import json_dumps, izip
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from .scorer import Scorer
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from ._ml import link_vectors_to_models
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from .attrs import IS_STOP
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from .lang.punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES, TOKENIZER_INFIXES
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from .lang.punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
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from .lang.punctuation import TOKENIZER_INFIXES
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from .lang.tokenizer_exceptions import TOKEN_MATCH
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from .lang.tag_map import TAG_MAP
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from .lang.lex_attrs import LEX_ATTRS, is_stop
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@ -57,16 +54,18 @@ class BaseDefaults(object):
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def create_tokenizer(cls, nlp=None):
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rules = cls.tokenizer_exceptions
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token_match = cls.token_match
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prefix_search = util.compile_prefix_regex(cls.prefixes).search \
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if cls.prefixes else None
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suffix_search = util.compile_suffix_regex(cls.suffixes).search \
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if cls.suffixes else None
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infix_finditer = util.compile_infix_regex(cls.infixes).finditer \
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if cls.infixes else None
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prefix_search = (util.compile_prefix_regex(cls.prefixes).search
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if cls.prefixes else None)
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suffix_search = (util.compile_suffix_regex(cls.suffixes).search
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if cls.suffixes else None)
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infix_finditer = (util.compile_infix_regex(cls.infixes).finditer
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if cls.infixes else None)
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vocab = nlp.vocab if nlp is not None else cls.create_vocab(nlp)
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return Tokenizer(vocab, rules=rules,
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prefix_search=prefix_search, suffix_search=suffix_search,
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infix_finditer=infix_finditer, token_match=token_match)
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prefix_search=prefix_search,
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suffix_search=suffix_search,
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infix_finditer=infix_finditer,
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token_match=token_match)
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pipe_names = ['tensorizer', 'tagger', 'parser', 'ner']
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token_match = TOKEN_MATCH
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@ -98,7 +97,7 @@ class Language(object):
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factories = {
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'tokenizer': lambda nlp: nlp.Defaults.create_tokenizer(nlp),
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'tensorizer': lambda nlp, **cfg: TokenVectorEncoder(nlp.vocab, **cfg),
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'tensorizer': lambda nlp, **cfg: Tensorizer(nlp.vocab, **cfg),
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'tagger': lambda nlp, **cfg: Tagger(nlp.vocab, **cfg),
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'parser': lambda nlp, **cfg: DependencyParser(nlp.vocab, **cfg),
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'ner': lambda nlp, **cfg: EntityRecognizer(nlp.vocab, **cfg),
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@ -218,14 +217,14 @@ class Language(object):
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def add_pipe(self, component, name=None, before=None, after=None,
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first=None, last=None):
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"""Add a component to the processing pipeline. Valid components are
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callables that take a `Doc` object, modify it and return it. Only one of
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before, after, first or last can be set. Default behaviour is "last".
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callables that take a `Doc` object, modify it and return it. Only one
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of before/after/first/last can be set. Default behaviour is "last".
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component (callable): The pipeline component.
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name (unicode): Name of pipeline component. Overwrites existing
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component.name attribute if available. If no name is set and
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the component exposes no name attribute, component.__name__ is
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used. An error is raised if the name already exists in the pipeline.
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used. An error is raised if a name already exists in the pipeline.
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before (unicode): Component name to insert component directly before.
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after (unicode): Component name to insert component directly after.
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first (bool): Insert component first / not first in the pipeline.
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@ -240,7 +239,8 @@ class Language(object):
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name = component.name
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elif hasattr(component, '__name__'):
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name = component.__name__
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elif hasattr(component, '__class__') and hasattr(component.__class__, '__name__'):
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elif (hasattr(component, '__class__') and
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hasattr(component.__class__, '__name__')):
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name = component.__class__.__name__
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else:
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name = repr(component)
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@ -269,7 +269,7 @@ class Language(object):
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`name in nlp.pipe_names`.
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name (unicode): Name of the component.
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RETURNS (bool): Whether a component of that name exists in the pipeline.
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RETURNS (bool): Whether a component of the name exists in the pipeline.
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"""
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return name in self.pipe_names
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@ -332,15 +332,12 @@ class Language(object):
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return doc
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def disable_pipes(self, *names):
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'''Disable one or more pipeline components.
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If used as a context manager, the pipeline will be restored to the initial
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state at the end of the block. Otherwise, a DisabledPipes object is
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returned, that has a `.restore()` method you can use to undo your
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changes.
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"""Disable one or more pipeline components. If used as a context
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manager, the pipeline will be restored to the initial state at the end
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of the block. Otherwise, a DisabledPipes object is returned, that has
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a `.restore()` method you can use to undo your changes.
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EXAMPLE:
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>>> nlp.add_pipe('parser')
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>>> nlp.add_pipe('tagger')
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>>> with nlp.disable_pipes('parser', 'tagger'):
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@ -351,7 +348,7 @@ class Language(object):
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>>> assert not nlp.has_pipe('parser')
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>>> disabled.restore()
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>>> assert nlp.has_pipe('parser')
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'''
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"""
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return DisabledPipes(self, *names)
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def make_doc(self, text):
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@ -367,14 +364,14 @@ class Language(object):
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RETURNS (dict): Results from the update.
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EXAMPLE:
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>>> with nlp.begin_training(gold, use_gpu=True) as (trainer, optimizer):
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>>> with nlp.begin_training(gold) as (trainer, optimizer):
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>>> for epoch in trainer.epochs(gold):
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>>> for docs, golds in epoch:
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>>> state = nlp.update(docs, golds, sgd=optimizer)
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"""
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if len(docs) != len(golds):
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raise IndexError("Update expects same number of docs and golds "
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"Got: %d, %d" % (len(docs), len(golds)))
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"Got: %d, %d" % (len(docs), len(golds)))
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if len(docs) == 0:
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return
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if sgd is None:
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@ -382,8 +379,10 @@ class Language(object):
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self._optimizer = Adam(Model.ops, 0.001)
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sgd = self._optimizer
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grads = {}
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def get_grads(W, dW, key=None):
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grads[key] = (W, dW)
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pipes = list(self.pipeline)
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random.shuffle(pipes)
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for name, proc in pipes:
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@ -421,7 +420,7 @@ class Language(object):
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L2 = util.env_opt('L2_penalty', 1e-6)
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max_grad_norm = util.env_opt('grad_norm_clip', 1.)
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self._optimizer = Adam(Model.ops, learn_rate, L2=L2, beta1=beta1,
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beta2=beta2, eps=eps)
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beta2=beta2, eps=eps)
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self._optimizer.max_grad_norm = max_grad_norm
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self._optimizer.device = device
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return self._optimizer
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@ -461,7 +460,7 @@ class Language(object):
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L2 = util.env_opt('L2_penalty', 1e-6)
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max_grad_norm = util.env_opt('grad_norm_clip', 1.)
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self._optimizer = Adam(Model.ops, learn_rate, L2=L2, beta1=beta1,
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beta2=beta2, eps=eps)
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beta2=beta2, eps=eps)
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self._optimizer.max_grad_norm = max_grad_norm
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self._optimizer.device = device
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return self._optimizer
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@ -512,17 +511,17 @@ class Language(object):
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pass
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def pipe(self, texts, as_tuples=False, n_threads=2, batch_size=1000,
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disable=[]):
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"""Process texts as a stream, and yield `Doc` objects in order. Supports
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GIL-free multi-threading.
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disable=[]):
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"""Process texts as a stream, and yield `Doc` objects in order.
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Supports GIL-free multi-threading.
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texts (iterator): A sequence of texts to process.
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as_tuples (bool):
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If set to True, inputs should be a sequence of
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(text, context) tuples. Output will then be a sequence of
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(doc, context) tuples. Defaults to False.
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n_threads (int): The number of worker threads to use. If -1, OpenMP will
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decide how many to use at run time. Default is 2.
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n_threads (int): The number of worker threads to use. If -1, OpenMP
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will decide how many to use at run time. Default is 2.
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batch_size (int): The number of texts to buffer.
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disable (list): Names of the pipeline components to disable.
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YIELDS (Doc): Documents in the order of the original text.
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@ -546,7 +545,8 @@ class Language(object):
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if name in disable:
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continue
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if hasattr(proc, 'pipe'):
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docs = proc.pipe(docs, n_threads=n_threads, batch_size=batch_size)
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docs = proc.pipe(docs, n_threads=n_threads,
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batch_size=batch_size)
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else:
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# Apply the function, but yield the doc
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docs = _pipe(proc, docs)
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@ -583,7 +583,7 @@ class Language(object):
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will include the model.
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path (unicode or Path): A path to a directory, which will be created if
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it doesn't exist. Paths may be either strings or `Path`-like objects.
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it doesn't exist. Paths may be strings or `Path`-like objects.
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disable (list): Names of pipeline components to disable and prevent
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from being saved.
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@ -682,7 +682,7 @@ class Language(object):
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class DisabledPipes(list):
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'''Manager for temporary pipeline disabling.'''
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"""Manager for temporary pipeline disabling."""
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def __init__(self, nlp, *names):
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self.nlp = nlp
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self.names = names
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@ -702,7 +702,8 @@ class DisabledPipes(list):
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def restore(self):
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'''Restore the pipeline to its state when DisabledPipes was created.'''
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current, self.nlp.pipeline = self.nlp.pipeline, self.original_pipeline
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unexpected = [name for name, pipe in current if not self.nlp.has_pipe(name)]
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unexpected = [name for name, pipe in current
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if not self.nlp.has_pipe(name)]
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if unexpected:
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# Don't change the pipeline if we're raising an error.
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self.nlp.pipeline = current
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@ -43,16 +43,15 @@ class Lemmatizer(object):
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morphology = {} if morphology is None else morphology
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others = [key for key in morphology
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if key not in (POS, 'Number', 'POS', 'VerbForm', 'Tense')]
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true_morph_key = morphology.get('morph', 0)
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if univ_pos == 'noun' and morphology.get('Number') == 'sing':
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return True
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elif univ_pos == 'verb' and morphology.get('VerbForm') == 'inf':
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return True
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# This maps 'VBP' to base form -- probably just need 'IS_BASE'
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# morphology
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elif univ_pos == 'verb' and (morphology.get('VerbForm') == 'fin' and \
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morphology.get('Tense') == 'pres' and \
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morphology.get('Number') is None and \
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elif univ_pos == 'verb' and (morphology.get('VerbForm') == 'fin' and
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morphology.get('Tense') == 'pres' and
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morphology.get('Number') is None and
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not others):
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return True
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elif univ_pos == 'adj' and morphology.get('Degree') == 'pos':
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@ -89,9 +88,6 @@ class Lemmatizer(object):
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def lemmatize(string, index, exceptions, rules):
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string = string.lower()
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forms = []
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# TODO: Is this correct? See discussion in Issue #435.
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#if string in index:
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# forms.append(string)
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forms.extend(exceptions.get(string, []))
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oov_forms = []
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if not forms:
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@ -74,8 +74,11 @@ class Scorer(object):
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@property
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def scores(self):
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return {
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'uas': self.uas, 'las': self.las,
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'ents_p': self.ents_p, 'ents_r': self.ents_r, 'ents_f': self.ents_f,
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'uas': self.uas,
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'las': self.las,
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'ents_p': self.ents_p,
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'ents_r': self.ents_r,
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'ents_f': self.ents_f,
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'tags_acc': self.tags_acc,
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'token_acc': self.token_acc
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}
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@ -85,7 +88,8 @@ class Scorer(object):
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gold_deps = set()
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gold_tags = set()
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gold_ents = set(tags_to_entities([annot[-1] for annot in gold.orig_annot]))
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gold_ents = set(tags_to_entities([annot[-1]
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for annot in gold.orig_annot]))
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for id_, word, tag, head, dep, ner in gold.orig_annot:
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gold_tags.add((id_, tag))
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if dep not in (None, "") and dep.lower() not in punct_labels:
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