Fix non-projective label filtering

spacy/cli/ud_train.py

@@ -0,0 +1,371 @@
+'''Train for CONLL 2017 UD treebank evaluation. Takes .conllu files, writes
+.conllu format for development data, allowing the official scorer to be used.
+'''
+from __future__ import unicode_literals
+import plac
+import tqdm
+from pathlib import Path
+import re
+import sys
+import json
+
+import spacy
+import spacy.util
+from ..tokens import Token, Doc
+from ..gold import GoldParse
+from ..util import compounding, minibatch_by_words
+from ..syntax.nonproj import projectivize
+from ..matcher import Matcher
+from .. import displacy
+from collections import defaultdict, Counter
+from timeit import default_timer as timer
+
+import itertools
+import random
+import numpy.random
+import cytoolz
+
+from . import conll17_ud_eval
+
+from .. import lang
+from .. import lang
+from ..lang import zh
+from ..lang import ja
+
+
+################
+# Data reading #
+################
+
+space_re = re.compile('\s+')
+def split_text(text):
+    return [space_re.sub(' ', par.strip()) for par in text.split('\n\n')]
+ 
+
+def read_data(nlp, conllu_file, text_file, raw_text=True, oracle_segments=False,
+              max_doc_length=None, limit=None):
+    '''Read the CONLLU format into (Doc, GoldParse) tuples. If raw_text=True,
+    include Doc objects created using nlp.make_doc and then aligned against
+    the gold-standard sequences. If oracle_segments=True, include Doc objects
+    created from the gold-standard segments. At least one must be True.'''
+    if not raw_text and not oracle_segments:
+        raise ValueError("At least one of raw_text or oracle_segments must be True")
+    paragraphs = split_text(text_file.read())
+    conllu = read_conllu(conllu_file)
+    # sd is spacy doc; cd is conllu doc
+    # cs is conllu sent, ct is conllu token
+    docs = []
+    golds = []
+    for doc_id, (text, cd) in enumerate(zip(paragraphs, conllu)):
+        sent_annots = []
+        for cs in cd:
+            sent = defaultdict(list)
+            for id_, word, lemma, pos, tag, morph, head, dep, _, space_after in cs:
+                if '.' in id_:
+                    continue
+                if '-' in id_:
+                    continue
+                id_ = int(id_)-1
+                head = int(head)-1 if head != '0' else id_
+                sent['words'].append(word)
+                sent['tags'].append(tag)
+                sent['heads'].append(head)
+                sent['deps'].append('ROOT' if dep == 'root' else dep)
+                sent['spaces'].append(space_after == '_')
+            sent['entities'] = ['-'] * len(sent['words'])
+            sent['heads'], sent['deps'] = projectivize(sent['heads'],
+                                                       sent['deps'])
+            if oracle_segments:
+                docs.append(Doc(nlp.vocab, words=sent['words'], spaces=sent['spaces']))
+                golds.append(GoldParse(docs[-1], **sent))
+
+            sent_annots.append(sent)
+            if raw_text and max_doc_length and len(sent_annots) >= max_doc_length:
+                doc, gold = _make_gold(nlp, None, sent_annots)
+                sent_annots = []
+                docs.append(doc)
+                golds.append(gold)
+                if limit and len(docs) >= limit:
+                    return docs, golds
+
+        if raw_text and sent_annots:
+            doc, gold = _make_gold(nlp, None, sent_annots)
+            docs.append(doc)
+            golds.append(gold)
+        if limit and len(docs) >= limit:
+            return docs, golds
+    return docs, golds
+
+
+def read_conllu(file_):
+    docs = []
+    sent = []
+    doc = []
+    for line in file_:
+        if line.startswith('# newdoc'):
+            if doc:
+                docs.append(doc)
+            doc = []
+        elif line.startswith('#'):
+            continue
+        elif not line.strip():
+            if sent:
+                doc.append(sent)
+            sent = []
+        else:
+            sent.append(list(line.strip().split('\t')))
+            if len(sent[-1]) != 10:
+                print(repr(line))
+                raise ValueError
+    if sent:
+        doc.append(sent)
+    if doc:
+        docs.append(doc)
+    return docs
+
+
+def _make_gold(nlp, text, sent_annots):
+    # Flatten the conll annotations, and adjust the head indices
+    flat = defaultdict(list)
+    for sent in sent_annots:
+        flat['heads'].extend(len(flat['words'])+head for head in sent['heads'])
+        for field in ['words', 'tags', 'deps', 'entities', 'spaces']:
+            flat[field].extend(sent[field])
+    # Construct text if necessary
+    assert len(flat['words']) == len(flat['spaces'])
+    if text is None:
+        text = ''.join(word+' '*space for word, space in zip(flat['words'], flat['spaces'])) 
+    doc = nlp.make_doc(text)
+    flat.pop('spaces')
+    gold = GoldParse(doc, **flat)
+    return doc, gold
+
+#############################
+# Data transforms for spaCy #
+#############################
+
+def golds_to_gold_tuples(docs, golds):
+    '''Get out the annoying 'tuples' format used by begin_training, given the
+    GoldParse objects.'''
+    tuples = []
+    for doc, gold in zip(docs, golds):
+        text = doc.text
+        ids, words, tags, heads, labels, iob = zip(*gold.orig_annot)
+        sents = [((ids, words, tags, heads, labels, iob), [])]
+        tuples.append((text, sents))
+    return tuples
+
+
+##############
+# Evaluation #
+##############
+
+def evaluate(nlp, text_loc, gold_loc, sys_loc, limit=None):
+    with text_loc.open('r', encoding='utf8') as text_file:
+        texts = split_text(text_file.read())
+        docs = list(nlp.pipe(texts))
+    with sys_loc.open('w', encoding='utf8') as out_file:
+        write_conllu(docs, out_file)
+    with gold_loc.open('r', encoding='utf8') as gold_file:
+        gold_ud = conll17_ud_eval.load_conllu(gold_file)
+        with sys_loc.open('r', encoding='utf8') as sys_file:
+            sys_ud = conll17_ud_eval.load_conllu(sys_file)
+        scores = conll17_ud_eval.evaluate(gold_ud, sys_ud)
+    return docs, scores
+
+
+def write_conllu(docs, file_):
+    merger = Matcher(docs[0].vocab)
+    merger.add('SUBTOK', None, [{'DEP': 'subtok', 'op': '+'}])
+    for i, doc in enumerate(docs):
+        matches = merger(doc)
+        spans = [doc[start:end+1] for _, start, end in matches]
+        offsets = [(span.start_char, span.end_char) for span in spans]
+        for start_char, end_char in offsets:
+            doc.merge(start_char, end_char)
+        file_.write("# newdoc id = {i}\n".format(i=i))
+        for j, sent in enumerate(doc.sents):
+            file_.write("# sent_id = {i}.{j}\n".format(i=i, j=j))
+            file_.write("# text = {text}\n".format(text=sent.text))
+            for k, token in enumerate(sent):
+                file_.write(token._.get_conllu_lines(k) + '\n')
+            file_.write('\n')
+
+
+def print_progress(itn, losses, ud_scores):
+    fields = {
+        'dep_loss': losses.get('parser', 0.0),
+        'tag_loss': losses.get('tagger', 0.0),
+        'words': ud_scores['Words'].f1 * 100,
+        'sents': ud_scores['Sentences'].f1 * 100,
+        'tags': ud_scores['XPOS'].f1 * 100,
+        'uas': ud_scores['UAS'].f1 * 100,
+        'las': ud_scores['LAS'].f1 * 100,
+    }
+    header = ['Epoch', 'Loss', 'LAS', 'UAS', 'TAG', 'SENT', 'WORD']
+    if itn == 0:
+        print('\t'.join(header))
+    tpl = '\t'.join((
+        '{:d}',
+        '{dep_loss:.1f}',
+        '{las:.1f}',
+        '{uas:.1f}',
+        '{tags:.1f}',
+        '{sents:.1f}',
+        '{words:.1f}',
+    ))
+    print(tpl.format(itn, **fields))
+
+#def get_sent_conllu(sent, sent_id):
+#    lines = ["# sent_id = {sent_id}".format(sent_id=sent_id)]
+
+def get_token_conllu(token, i):
+    if token._.begins_fused:
+        n = 1
+        while token.nbor(n)._.inside_fused:
+            n += 1
+        id_ = '%d-%d' % (i, i+n)
+        lines = [id_, token.text, '_', '_', '_', '_', '_', '_', '_', '_']
+    else:
+        lines = []
+    if token.head.i == token.i:
+        head = 0
+    else:
+        head = i + (token.head.i - token.i) + 1
+    fields = [str(i+1), token.text, token.lemma_, token.pos_, token.tag_, '_',
+              str(head), token.dep_.lower(), '_', '_']
+    lines.append('\t'.join(fields))
+    return '\n'.join(lines)
+
+Token.set_extension('get_conllu_lines', method=get_token_conllu)
+Token.set_extension('begins_fused', default=False)
+Token.set_extension('inside_fused', default=False)
+
+
+##################
+# Initialization #
+##################
+
+
+def load_nlp(corpus, config):
+    lang = corpus.split('_')[0]
+    nlp = spacy.blank(lang)
+    if config.vectors:
+        nlp.vocab.from_disk(Path(config.vectors) / 'vocab')
+    return nlp
+
+def initialize_pipeline(nlp, docs, golds, config, device):
+    nlp.add_pipe(nlp.create_pipe('parser'))
+    if config.multitask_tag:
+        nlp.parser.add_multitask_objective('tag')
+    if config.multitask_sent:
+        nlp.parser.add_multitask_objective('sent_start')
+    nlp.add_pipe(nlp.create_pipe('tagger'))
+    for gold in golds:
+        for tag in gold.tags:
+            if tag is not None:
+                nlp.tagger.add_label(tag)
+    return nlp.begin_training(lambda: golds_to_gold_tuples(docs, golds), device=device)
+
+
+########################
+# Command line helpers #
+########################
+
+class Config(object):
+    def __init__(self, vectors=None, max_doc_length=10, multitask_tag=True,
+            multitask_sent=True, nr_epoch=30, batch_size=1000, dropout=0.2):
+        for key, value in locals().items():
+            setattr(self, key, value)
+
+    @classmethod
+    def load(cls, loc):
+        with Path(loc).open('r', encoding='utf8') as file_:
+            cfg = json.load(file_)
+        return cls(**cfg)
+
+
+class Dataset(object):
+    def __init__(self, path, section):
+        self.path = path
+        self.section = section
+        self.conllu = None
+        self.text = None
+        for file_path in self.path.iterdir():
+            name = file_path.parts[-1]
+            if section in name and name.endswith('conllu'):
+                self.conllu = file_path
+            elif section in name and name.endswith('txt'):
+                self.text = file_path
+        if self.conllu is None:
+            msg = "Could not find .txt file in {path} for {section}"
+            raise IOError(msg.format(section=section, path=path))
+        if self.text is None:
+            msg = "Could not find .txt file in {path} for {section}"
+        self.lang = self.conllu.parts[-1].split('-')[0].split('_')[0]
+
+
+class TreebankPaths(object):
+    def __init__(self, ud_path, treebank, **cfg):
+        self.train = Dataset(ud_path / treebank, 'train')
+        self.dev = Dataset(ud_path / treebank, 'dev')
+        self.lang = self.train.lang
+
+
+@plac.annotations(
+    ud_dir=("Path to Universal Dependencies corpus", "positional", None, Path),
+    corpus=("UD corpus to train and evaluate on, e.g. en, es_ancora, etc",
+            "positional", None, str),
+    parses_dir=("Directory to write the development parses", "positional", None, Path),
+    config=("Path to json formatted config file", "positional"),
+    limit=("Size limit", "option", "n", int),
+    use_gpu=("Use GPU", "option", "g", int)
+)
+def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1):
+    spacy.util.fix_random_seed()
+    lang.zh.Chinese.Defaults.use_jieba = False
+    lang.ja.Japanese.Defaults.use_janome = False
+
+    config = Config.load(config)
+    paths = TreebankPaths(ud_dir, corpus)
+    if not (parses_dir / corpus).exists():
+        (parses_dir / corpus).mkdir()
+    print("Train and evaluate", corpus, "using lang", paths.lang)
+    nlp = load_nlp(paths.lang, config)
+
+    docs, golds = read_data(nlp, paths.train.conllu.open(), paths.train.text.open(),
+                            max_doc_length=config.max_doc_length, limit=limit)
+
+    optimizer = initialize_pipeline(nlp, docs, golds, config, use_gpu)
+
+    batch_sizes = compounding(config.batch_size//10, config.batch_size, 1.001)
+    for i in range(config.nr_epoch):
+        docs = [nlp.make_doc(doc.text) for doc in docs]
+        Xs = list(zip(docs, golds))
+        random.shuffle(Xs)
+        batches = minibatch_by_words(Xs, size=batch_sizes)
+        losses = {}
+        n_train_words = sum(len(doc) for doc in docs)
+        with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
+            for batch in batches:
+                batch_docs, batch_gold = zip(*batch)
+                pbar.update(sum(len(doc) for doc in batch_docs))
+                nlp.update(batch_docs, batch_gold, sgd=optimizer,
+                           drop=config.dropout, losses=losses)
+        
+        out_path = parses_dir / corpus / 'epoch-{i}.conllu'.format(i=i)
+        with nlp.use_params(optimizer.averages):
+            parsed_docs, scores = evaluate(nlp, paths.dev.text, paths.dev.conllu, out_path)
+            print_progress(i, losses, scores)
+            _render_parses(i, parsed_docs[:50]) 
+
+
+def _render_parses(i, to_render):
+    to_render[0].user_data['title'] = "Batch %d" % i
+    with Path('/tmp/parses.html').open('w') as file_:
+        html = displacy.render(to_render[:5], style='dep', page=True)
+        file_.write(html)
+
+
+if __name__ == '__main__':
+    plac.call(main)

spacy/language.py

@@ -462,7 +462,7 @@ class Language(object):
         self._optimizer = sgd
         for name, proc in self.pipeline:
             if hasattr(proc, 'begin_training'):
-                proc.begin_training(get_gold_tuples(),
+                proc.begin_training(get_gold_tuples,
                                     pipeline=self.pipeline,
                                     sgd=self._optimizer,
                                     **cfg)

spacy/pipeline.pyx

@@ -172,7 +172,7 @@ class Pipe(object):
         return create_default_optimizer(self.model.ops,
                                         **self.cfg.get('optimizer', {}))
 
-    def begin_training(self, gold_tuples=tuple(), pipeline=None, sgd=None,
+    def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, sgd=None,
                        **kwargs):
         """Initialize the pipe for training, using data exampes if available.
         If no model has been initialized yet, the model is added."""
@@ -374,7 +374,7 @@ class Tensorizer(Pipe):
         loss = (d_scores**2).sum()
         return loss, d_scores
 
-    def begin_training(self, gold_tuples=tuple(), pipeline=None, sgd=None,
+    def begin_training(self, gold_tuples=lambda: [], pipeline=None, sgd=None,
                         **kwargs):
         """Allocate models, pre-process training data and acquire an
         optimizer.
@@ -498,11 +498,11 @@ class Tagger(Pipe):
         d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs])
         return float(loss), d_scores
 
-    def begin_training(self, gold_tuples=tuple(), pipeline=None, sgd=None,
+    def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, sgd=None,
                        **kwargs):
         orig_tag_map = dict(self.vocab.morphology.tag_map)
         new_tag_map = OrderedDict()
-        for raw_text, annots_brackets in gold_tuples:
+        for raw_text, annots_brackets in get_gold_tuples():
             for annots, brackets in annots_brackets:
                 ids, words, tags, heads, deps, ents = annots
                 for tag in tags:
@@ -673,9 +673,9 @@ class MultitaskObjective(Tagger):
     def set_annotations(self, docs, dep_ids, tensors=None):
         pass
 
-    def begin_training(self, gold_tuples=tuple(), pipeline=None, tok2vec=None,
+    def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, tok2vec=None,
                        sgd=None, **kwargs):
-        gold_tuples = nonproj.preprocess_training_data(gold_tuples)
+        gold_tuples = nonproj.preprocess_training_data(get_gold_tuples())
         for raw_text, annots_brackets in gold_tuples:
             for annots, brackets in annots_brackets:
                 ids, words, tags, heads, deps, ents = annots
@@ -898,7 +898,7 @@ class TextCategorizer(Pipe):
         self.labels.append(label)
         return 1
 
-    def begin_training(self, gold_tuples=tuple(), pipeline=None, sgd=None):
+    def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, sgd=None):
         if pipeline and getattr(pipeline[0], 'name', None) == 'tensorizer':
             token_vector_width = pipeline[0].model.nO
         else:
@@ -925,10 +925,10 @@ cdef class DependencyParser(Parser):
         labeller = MultitaskObjective(self.vocab, target=target)
         self._multitasks.append(labeller)
 
-    def init_multitask_objectives(self, gold_tuples, pipeline, sgd=None, **cfg):
+    def init_multitask_objectives(self, get_gold_tuples, pipeline, sgd=None, **cfg):
         for labeller in self._multitasks:
             tok2vec = self.model[0]
-            labeller.begin_training(gold_tuples, pipeline=pipeline,
+            labeller.begin_training(get_gold_tuples, pipeline=pipeline,
                                     tok2vec=tok2vec, sgd=sgd)
 
     def __reduce__(self):
@@ -946,10 +946,10 @@ cdef class EntityRecognizer(Parser):
         labeller = MultitaskObjective(self.vocab, target=target)
         self._multitasks.append(labeller)
 
-    def init_multitask_objectives(self, gold_tuples, pipeline, sgd=None, **cfg):
+    def init_multitask_objectives(self, get_gold_tuples, pipeline, sgd=None, **cfg):
         for labeller in self._multitasks:
             tok2vec = self.model[0]
-            labeller.begin_training(gold_tuples, pipeline=pipeline,
+            labeller.begin_training(get_gold_tuples, pipeline=pipeline,
                                     tok2vec=tok2vec)
 
     def __reduce__(self):

spacy/syntax/nn_parser.pyx

@@ -164,16 +164,17 @@ cdef void sum_state_features(float* output,
     cdef const float* feature
     padding = cached
     cached += F * O
+    cdef int id_stride = F*O
+    cdef float one = 1.
     for b in range(B):
         for f in range(F):
             if token_ids[f] < 0:
                 feature = &padding[f*O]
             else:
-                idx = token_ids[f] * F * O + f*O
+                idx = token_ids[f] * id_stride + f*O
                 feature = &cached[idx]
-            for i in range(O):
-                output[i] += feature[i]
-        output += O
+            openblas.simple_axpy(&output[b*O], O,
+                feature, one)
         token_ids += F
 
 
@@ -726,7 +727,7 @@ cdef class Parser:
                                        lower, stream, drop=0.0)
         return (tokvecs, bp_tokvecs), state2vec, upper
 
-    nr_feature = 13
+    nr_feature = 8
 
     def get_token_ids(self, states):
         cdef StateClass state
@@ -821,15 +822,13 @@ cdef class Parser:
             copy_array(larger.b[:smaller.nO], smaller.b)
             self.model[-1]._layers[-1] = larger
 
-    def begin_training(self, gold_tuples, pipeline=None, sgd=None, **cfg):
+    def begin_training(self, get_gold_tuples, pipeline=None, sgd=None, **cfg):
         if 'model' in cfg:
             self.model = cfg['model']
-        gold_tuples = nonproj.preprocess_training_data(gold_tuples,
-                                                       label_freq_cutoff=100)
-        actions = self.moves.get_actions(gold_parses=gold_tuples)
-        for action, labels in actions.items():
-            for label in labels:
-                self.moves.add_action(action, label)
+        cfg.setdefault('min_action_freq', 30)
+        actions = self.moves.get_actions(gold_parses=get_gold_tuples(),
+                                         min_freq=cfg.get('min_action_freq', 30))
+        self.moves.initialize_actions(actions)
         cfg.setdefault('token_vector_width', 128)
         if self.model is True:
             cfg['pretrained_dims'] = self.vocab.vectors_length
@@ -837,9 +836,9 @@ cdef class Parser:
             if sgd is None:
                 sgd = self.create_optimizer()
             self.model[1].begin_training(
-                    self.model[1].ops.allocate((5, cfg['token_vector_width'])))
+                self.model[1].ops.allocate((5, cfg['token_vector_width'])))
             if pipeline is not None:
-                self.init_multitask_objectives(gold_tuples, pipeline, sgd=sgd, **cfg)
+                self.init_multitask_objectives(get_gold_tuples, pipeline, sgd=sgd, **cfg)
             link_vectors_to_models(self.vocab)
         else:
             if sgd is None:
@@ -853,7 +852,7 @@ cdef class Parser:
         # Defined in subclasses, to avoid circular import
         raise NotImplementedError
     
-    def init_multitask_objectives(self, gold_tuples, pipeline, **cfg):
+    def init_multitask_objectives(self, get_gold_tuples, pipeline, **cfg):
         '''Setup models for secondary objectives, to benefit from multi-task
         learning. This method is intended to be overridden by subclasses.